Category, Letter, and Emotional Verbal Fluency in Spanish–English Bilingual Speakers: A Preliminary Report

Category, Letter, and Emotional Verbal Fluency in Spanish–English Bilingual Speakers: A... Abstract Objective The purpose of this study was to compare the performance of bilingual speakers on an emotional verbal fluency task to category and letter verbal fluency tasks. A second purpose was to compare performances on these tasks to language proficiency ratings. Method Twelve verbal fluency tasks were administered to 21 Spanish–English bilingual speakers. Results were analyzed for differences between fluency types (category, letter, and emotional) and languages (English and Spanish). Results Participants generated the most items in category fluency tasks and the least items in emotional fluency tasks. The number of items generated for letter and emotional fluency tasks were not significantly different, but both were significantly lower than the number of items generated in category fluency. More items were generated for positive emotions than for negative emotions. Differences between languages for category and letter fluency tasks were significantly correlated with differences in language proficiency ratings, but this finding was not found for emotional fluency tasks. Conclusions Self-ratings of proficiency and language dominance correlated significantly with performance on category and letter fluency tasks and may be useful predictors of differences between languages on these tasks. Emotional fluency was not significantly correlated with language proficiency ratings, suggesting that performance on emotional fluency may be more significantly affected by emotional processing ability. The emotional verbal fluency task has potential as a component of neuropsychological evaluations to screen easily and quickly for emotional processing deficits, including those associated with traumatic brain injury and depression. Additionally, results support a positivity bias in language and cognition processes. Verbal fluency, Language, Emotional processing, Cross-cultural, Assessment Introduction Verbal fluency tasks commonly are used in neuropsychological assessments to screen for impairments in language ability and executive functioning; however, no fluency task has been adopted to examine the relationship between executive functioning, language, and emotional content. Recent investigations (Ashby, Isen, & Turken, 1999; Sass et al., 2011) have suggested that emotion influences performance on language and cognition tasks (Ashby et al., 1999; Sass et al., 2011). Several clinical groups including those with depression (Klumpp & Deldin, 2010), right hemisphere brain damage (Borod, Bloom, Brickman, Nakhutina, & Curko, 2002) and traumatic brain injury (TBI) (Croker & McDonald, 2005) may present with emotional processing impairments. For individuals with emotional processing deficits, a task of emotional verbal fluency could contribute meaningful information to neuropsychological assessment (Klumpp & Deldin, 2010). The following study examined the performance of bilingual individuals on three types of verbal fluency tasks (category, letter, and emotional) to compare output in emotional generative naming, a new task for assessing emotional expressive vocabulary, with output in category and letter generative naming tasks. Bilingual individuals comprise a clinical population which is underrepresented in the research literature. Though individuals are often tested in only one language based on patient preference or evaluator ability, completion of testing in both languages is needed, especially for those with a history of a neurological event or onset of a neurological condition that may affect language systems (Lorenzen & Murray, 2008; Marrero, Golden, & Espe-Pfeifer, 2002). Neurological events and conditions can result in divergent outcomes for different languages. A better understanding of (1) the relationship between task performances in each language and (2) the relationship between patient perception of language ability and task performance in each language is critically needed. A second purpose was to examine the relationship of performance on these tasks with language proficiency ratings for bilingual speakers. Verbal Fluency Verbal fluency tasks (i.e., generative naming) measure the ability to retrieve related lexical items. A fixed time is provided (usually 60 s) to name as many words as possible based on the given criteria. Category (or semantic) fluency measures the ability to retrieve words based on a semantic category (e.g., animals, fruits, and vehicles) (Newcombe, 1969). Letter (or phonemic) fluency measures the ability to retrieve words based on the initial letter of the word (often F, A, or S) (Benton, 1968). Both tasks require a combination of language ability (verbal retrieval, integrity of semantic lexical networks) and executive functioning ability (effortful initiation, organization of the lexical search, inhibition of noncategorical words, self-monitoring) (Henry & Crawford, 2004). The two tasks differ with respect to the demands placed on the retrieval process. Category fluency resembles how words are generated in every-day situations. For example, when planning a grocery shopping trip, a list of foods is generated. Letter fluency, in contrast, is a task that does not resemble every-day demands for lexical retrieval. Additional inhibition is required to suppress semantically related words, and novel retrieval strategies must be used (Shao, Janse, Visser, & Meyer, 2014). In terms of cognitive demands, category fluency is associated more significantly with language abilities such as size of vocabulary and integrity of semantic lexical networks. Letter fluency is associated more with executive functioning abilities, especially self-monitoring, inhibition and working memory (Luo, Luk, & Bialystok, 2010; Shao et al., 2014). Performance on categorical and letter fluency tasks differs. In general, more exemplars are generated on category fluency tasks than on letter fluency tasks; however, Brickman and colleagues (2005) showed that the number of exemplars for category fluency declines significantly across the normal age span, while the number of exemplars for letter fluency declines at a much slower rate. As age increases, the difference between category and letter fluency scores decreases. Deficits in category fluency are associated with compromised integrity of semantic knowledge structures, and deficits in letter fluency are associated with impairments in executive functioning (Henry & Crawford, 2004; Henry, Crawford, & Phillips, 2004). Analysis of deficits in verbal fluency tasks is used in the assessment of several clinical conditions, including attention deficit hyperactivity disorder (Andreou & Trott, 2013), Alzheimer's disease (Zhao, Guo, & Hong, 2013) and Parkinson's disease (Pettit, McCarthy, Davenport, & Abrahams, 2013). Additionally, generative naming tasks are used in standardized language assessments for aphasia (“The Western Aphasia Battery”: Kertesz, 1982), cognitive impairment (“The Cognitive Linguistic Quick Test”: Helm-Estabrooks, 2001), and traumatic brain injury (“Scales of Cognitive Ability in Traumatic Brain Injury”: Adamovich & Henderson, 1992). Each condition demonstrates characteristic patterns that may be used in assessment. For example, individuals with Alzheimer's disease and psychosis perform consistently poorer on category fluency tasks than on letter fluency tasks (Laws, Duncan, & Gale, 2010; Magaud et al., 2010). Individuals with amyotrophic lateral sclerosis have demonstrated greater impairment in letter fluency relative to category fluency (Quinn et al., 2012). Differences in performance on the different tasks of verbal fluency can add valuable information to a diagnostic or evaluative decision in neuropsychological assessment. Verbal Fluency in Bilingual Individuals No universal definition for bilingualism exists. Broadly defined, individuals who are bilingual have the ability to use at least two languages in their every-day lives (Grosjean, 1998). Bilinguals may speak each language for different purposes. For example, the first language (L1) may be used in the home and the second language (L2) at work. As a result, bilingual individuals rarely are equally proficient in both languages (“balanced”). The relationship between the two languages may change over time as demands in daily life require more use of one language than the other (Grosjean, 1998). This changing relationship means that either L1 or L2 can be the dominant language and that both languages can be the dominant language at different points during the lifespan. Several factors influence language proficiency. Age of acquisition is known to influence language proficiency (Johnson & Newport, 1989), but is not a certain predictor. Other factors include which language is used in different environments (e.g., home, work, and school), personal and social attitudes toward each language, cultural identity, socioeconomic level, and geographic location (Ardila, 1998; Muñoz & Marquardt, 2003). Assessment of cognitive–linguistic skills in bilingual individuals often is conducted with the erroneous assumption that bilingual individuals can be directly compared to monolingual norms or that bilingual individuals possess similar abilities in each language (Paradis, 2001; Roberts, Garcia, Desrochers, & Hernandez, 2002). Neuropsychological assessment of bilingual individuals should take into account the different profile of linguistic and cognitive strengths and weaknesses these individuals demonstrate as compared to monolingual individuals. Performance on linguistic tasks should consider history, use and proficiency for both languages (Muñoz & Marquardt, 2003). Failure to do so can result in misinterpretations of test results and inappropriate treatment recommendations. Differences in performance on verbal fluency tasks may exist between age-matched monolingual and bilingual speakers. Gollan, Montoya, and Werner (2002) compared performance of English monolinguals and Spanish–English bilinguals on 12 category, 10 letter, and two proper name fluency tasks. Participants were tested in English, and the bilingual group also completed trials in which they could name words in either English or Spanish. Results indicated that the bilingual group produced significantly fewer exemplars than the monolingual group on all tasks. The difference was much larger for category fluency tasks than for letter fluency or proper name fluency tasks. Rosselli and colleagues (2002) found similar results when comparing verbal fluency in older English monolingual, Spanish monolingual, and Spanish–English bilingual groups. The bilingual group performed similarly to both monolingual groups on letter fluency tasks, but produced significantly fewer exemplars than both monolingual groups for the semantic category (animals). Portocarrero, Burright, and Donovick (2007) replicated these results with college-aged participants. Thus, bilingual individuals appear to experience a disadvantage in performance on category fluency tasks. Assessments of verbal fluency should take this difference into account. Bilingual speaker decreased performance on categorical fluency tasks may reflect a number of linguistic factors. Bilingual speakers, for example, have smaller vocabularies in each language compared to the vocabularies of monolingual speakers (Perani et al., 1998; Portocarrero et al., 2007). They have reduced picture naming accuracy (Roberts et al., 2002; Sheppard, Kousaie, Monetta, & Taler, 2016) and more “tip of the tongue” experiences (Gollan & Acenas, 2004). Some studies have suggested that cognitive load during category fluency is increased for bilinguals because they must actively inhibit cross-language interference when words from both languages are activated (Gollan et al., 2002). Lower average vocabulary size appears to be the most likely explanation. Luo and colleagues (2010) found that a group of bilinguals with vocabulary scores equivalent to those of a monolingual group performed equivalently on category fluency tasks and produced significantly more exemplars on letter fluency tasks. These results suggest that if vocabulary size is controlled, the bilingual disadvantage disappears. A bilingual advantage in letter fluency suggests that bilingual individuals have enhanced executive functioning abilities. Bilingual speakers have demonstrated enhanced inhibition abilities through superior performance on Stroop tasks (Bialystok, Craik, & Luk, 2008) and greater attention through superior performance on an attentional network task (Costa, Hernández, & Sebastián-Gallés, 2008). Performance on letter fluency tasks draws more heavily on executive functioning than performance on category fluency tasks. Enhanced executive functioning abilities in bilingual speakers should result in enhanced performance on letter fluency tasks. Emotional Verbal Fluency Emotion interacts with language and cognition. Pessoa (2008) argued that, based on neuroimaging studies, emotion and cognition do not embody separate neurological systems. Instead, brain regions viewed as “affective” (e.g., the amygdala, ventral striatum and hypothalamus) are also involved in cognition, and brain regions viewed as “cognitive” (e.g., the prefrontal and parietal cortices) also are involved in emotion. He concluded that cognition and emotion are integrated in the brain and jointly contribute to behavior. Emotional information and semantic association processes also are intertwined. Words are stored not only in semantic networks of literal meanings, but also in emotional or connotative networks (Kuchinke et al., 2005; Skrandies, 2011). Positive and negative emotional information may affect semantic processing differently. For example, Sass and colleagues (2011) found that priming participants with positively associated words increased the speed with which participants identified another positively associated word (as compared to unrelated word pairs). Conversely, priming participants with negatively associated words did not increase the speed with which participants identified another negatively associated word. The study concluded that positively associated words appear to be easily processed whereas negatively associated words may be suppressed or inhibited (Sass et al., 2011). Other studies also support a bias in language processing networks for positively affected words and information (Dodds et al., 2015; Kuchinke et al., 2005). Valence of emotional content has been demonstrated to affect cognitive processing. Positive affect increases dopamine levels in the brain, which in turn may improve executive functioning skills such as problem solving (Ashby et al., 1999). Positive emotional information, then, may aid both semantic and cognitive processing. Emotional verbal fluency may be used to explore the interaction between emotion, language and cognition. Sass, Heim, Fetz, Oetken, and Habel (2013) investigated the performance of 21 healthy individuals on an emotional verbal fluency task (i.e., generating items that represent emotions such as “fear” and “joy”) as compared to a category verbal fluency task (i.e., generating items that belong to a semantic category such as “animals” or “vehicles”) to examine how individuals without emotional processing deficits performed on the task. For the emotional naming, participants were instructed to generate objects or circumstances that represented or elicited the given emotion. Because the emotional categories elicited multi-word phrases, scores were adjusted for the difference in number of syllables between the experimental tasks. The results did not yield significant differences in the number of items generated in the neutral semantic and emotional categories, suggesting that the tasks were comparable in difficulty. Presumably, if output in the two tasks is comparable for neurotypical individuals, decreased performance on the emotional tasks may indicate impaired emotional processing. The best performance was observed in the category “joy,” which supports the positivity bias reported in previous studies (Dodds et al., 2015; Kuchinke et al., 2005; Sass et al., 2011). Sass and colleagues (2013) concluded that emotional verbal fluency may be an effective task for investigating emotional components of executive functioning. In summary, category verbal fluency and letter verbal fluency tasks have been used extensively in neuropsychological assessment batteries. Performance on these tasks may differ between clinical populations and between monolingual and bilingual individuals. Emotional verbal fluency, a recently introduced task by Sass and colleagues (2013), may provide additional useful information for the neuropsychological evaluation of individuals with possible emotional processing deficits. Purpose The current study investigated category, letter, and emotional verbal fluency in young bilingual speakers. Performance on the three tasks was used to explore the effectiveness of the emotional verbal fluency task for cognitive-communicative assessment in bilingual individuals. Participants were hypothesized to perform comparatively on category fluency and emotional fluency tasks Sass and colleagues (2013). The difference in the number of items generated for joy versus anger was compared to examine whether participants demonstrated a positivity bias. Data also were analyzed to determine patterns of performance within and between languages. Gollan and colleagues (2002) suggested that bilingual individuals may produce a comparable numbers of items in letter and category fluency; however, this pattern may have resulted from the use of narrow categories used for the category fluency tasks (i.e., musical instruments, occupations, college majors, etc.). Other studies that used broader semantic categories (Portocarrero et al., 2007; Rosselli et al., 2002) suggested that a greater number of items would be generated in category fluency, consistent with patterns observed in monolingual populations. Based on these findings more items were expected to be generated on the category fluency tasks relative to letter fluency tasks. Differences between numbers of items generated in each language were examined to determine if participants demonstrated greater output in one language as compared to the other. Participants were hypothesized to produce more items on all three task types in the dominant language. Language proficiency ratings were expected to significantly correlate with the number of items generated in each language. Materials and Methods Participants Twenty-one individuals (five male,16 female) aged 22–41 participated in the study (see Table 1). Participants were recruited through advertisements at the University of Texas at Austin and referrals from other participants. Participants demonstrated adequate hearing and no history of psychiatric illnesses, neurological disorders, learning disorders, attention disorders, alcoholism, drug addiction, stroke, or brain injury based on an interview. Participants reported conversational fluency in both Spanish and English. They also demonstrated adequate ability to converse with the examiner in both languages and to complete the experimental tasks in both languages. Informed consent was obtained for all participants. Participants were tested at a quiet location of their choosing. Testing took place over two sessions for a total of 40 min. Sessions were separated by a minimum of 48 hr. All procedures were conducted in compliance with relevant laws and guidelines and were approved by The University of Texas Institutional Review Board. Table 1. Demographic and language proficiency data for participants ID Gender Age Years of education English AoAa Spanish AoAa English proficiency Spanish proficiency Dominance 1 F 29 18.5 4 0 97.5 92.26 3.33 2 F 25 16.5 0 0 100 49.76 26.67 3 F 23 17 0 11 100 59.4 8.33 4 F 28 20.5 5 0 100 97.61 18.3 5 F 22 16 0 11 100 69.17 17.5 6 F 24 17 0 0 100 58.93 23.33 7 F 24 17 0 0 100 59.4 25 8 F 30 17 0 14 100 52.43 33.33 9 M 30 17 8 0 72.25 99.64 3.33 10 F 24 18 2 0 97.25 62.73 30.83 11 F 24 17 0 14 97.26 69.76 4.16 12 M 25 18 0 0 96.3 80.35 23.33 13 M 37 17 5 0 95.36 100 0 14 F 29 19 0 14 100 61.55 26.67 15 F 24 18 0 17 100 78.87 16.66 16 F 29 21 0 12 100 77.49 20 17 F 25 17 0 12 100 63.57 20 18 F 24 18 8 0 100 96.55 1.67 19 F 42 18 0 15 100 78.93 18.33 20 M 40 16 7 0 83.1 100 -3.33 21 M 26 14 6 0 82.37 41.66 33.33 MN — 28 17.5 2.14 5.71 96.26 73.81 16.7 ID Gender Age Years of education English AoAa Spanish AoAa English proficiency Spanish proficiency Dominance 1 F 29 18.5 4 0 97.5 92.26 3.33 2 F 25 16.5 0 0 100 49.76 26.67 3 F 23 17 0 11 100 59.4 8.33 4 F 28 20.5 5 0 100 97.61 18.3 5 F 22 16 0 11 100 69.17 17.5 6 F 24 17 0 0 100 58.93 23.33 7 F 24 17 0 0 100 59.4 25 8 F 30 17 0 14 100 52.43 33.33 9 M 30 17 8 0 72.25 99.64 3.33 10 F 24 18 2 0 97.25 62.73 30.83 11 F 24 17 0 14 97.26 69.76 4.16 12 M 25 18 0 0 96.3 80.35 23.33 13 M 37 17 5 0 95.36 100 0 14 F 29 19 0 14 100 61.55 26.67 15 F 24 18 0 17 100 78.87 16.66 16 F 29 21 0 12 100 77.49 20 17 F 25 17 0 12 100 63.57 20 18 F 24 18 8 0 100 96.55 1.67 19 F 42 18 0 15 100 78.93 18.33 20 M 40 16 7 0 83.1 100 -3.33 21 M 26 14 6 0 82.37 41.66 33.33 MN — 28 17.5 2.14 5.71 96.26 73.81 16.7 aAoA = age of acquisition. Table 1. Demographic and language proficiency data for participants ID Gender Age Years of education English AoAa Spanish AoAa English proficiency Spanish proficiency Dominance 1 F 29 18.5 4 0 97.5 92.26 3.33 2 F 25 16.5 0 0 100 49.76 26.67 3 F 23 17 0 11 100 59.4 8.33 4 F 28 20.5 5 0 100 97.61 18.3 5 F 22 16 0 11 100 69.17 17.5 6 F 24 17 0 0 100 58.93 23.33 7 F 24 17 0 0 100 59.4 25 8 F 30 17 0 14 100 52.43 33.33 9 M 30 17 8 0 72.25 99.64 3.33 10 F 24 18 2 0 97.25 62.73 30.83 11 F 24 17 0 14 97.26 69.76 4.16 12 M 25 18 0 0 96.3 80.35 23.33 13 M 37 17 5 0 95.36 100 0 14 F 29 19 0 14 100 61.55 26.67 15 F 24 18 0 17 100 78.87 16.66 16 F 29 21 0 12 100 77.49 20 17 F 25 17 0 12 100 63.57 20 18 F 24 18 8 0 100 96.55 1.67 19 F 42 18 0 15 100 78.93 18.33 20 M 40 16 7 0 83.1 100 -3.33 21 M 26 14 6 0 82.37 41.66 33.33 MN — 28 17.5 2.14 5.71 96.26 73.81 16.7 ID Gender Age Years of education English AoAa Spanish AoAa English proficiency Spanish proficiency Dominance 1 F 29 18.5 4 0 97.5 92.26 3.33 2 F 25 16.5 0 0 100 49.76 26.67 3 F 23 17 0 11 100 59.4 8.33 4 F 28 20.5 5 0 100 97.61 18.3 5 F 22 16 0 11 100 69.17 17.5 6 F 24 17 0 0 100 58.93 23.33 7 F 24 17 0 0 100 59.4 25 8 F 30 17 0 14 100 52.43 33.33 9 M 30 17 8 0 72.25 99.64 3.33 10 F 24 18 2 0 97.25 62.73 30.83 11 F 24 17 0 14 97.26 69.76 4.16 12 M 25 18 0 0 96.3 80.35 23.33 13 M 37 17 5 0 95.36 100 0 14 F 29 19 0 14 100 61.55 26.67 15 F 24 18 0 17 100 78.87 16.66 16 F 29 21 0 12 100 77.49 20 17 F 25 17 0 12 100 63.57 20 18 F 24 18 8 0 100 96.55 1.67 19 F 42 18 0 15 100 78.93 18.33 20 M 40 16 7 0 83.1 100 -3.33 21 M 26 14 6 0 82.37 41.66 33.33 MN — 28 17.5 2.14 5.71 96.26 73.81 16.7 aAoA = age of acquisition. Measures of Bilingualism Participants completed the Language Use Survey (LUS: Muñoz, Marquardt, & Copeland, 1999), a questionnaire that gathers information about language use across the lifespan and self-assessments of proficiency in each language. Proficiency in each language was assessed with 12 cm visual analog scales (the left anchor representing no proficiency and the right anchor representing native-like proficiency) for seven language domains (e.g., overall ability, speaking and listening in casual situations, speaking and listening in formal situations, reading, and writing). A visual analog scale also was used to obtain a language dominance rating. Spanish was represented on the left anchor of the scale, and English was represented on the right anchor of the scale. Language proficiency scores were calculated by measuring the placement of marks in millimeters on the visual analog scales and converting them to a scale of 100. Ratings from the seven language domains were averaged to obtain an overall proficiency score. Dominance ratings were calculated by measuring how many millimeters from the midpoint of the line was bisected. Ratings were converted to a scale of 50. The midpoint was considered to be a rating of 0. The left anchor point was considered to be a rating of −50 and the right anchor point was considered to be a rating of +50. Thus, negative ratings represented Spanish dominance and positive ratings represented English dominance. Dominance ratings between −8 and +8 were considered relatively balanced bilingualism. Table 1 shows the demographic information and language proficiency ratings for each participant. No objective measures of language proficiency were administered in this study. Self-ratings of language proficiency in bilingual individuals correlate significantly with objective measures of proficiency (Luk & Bialystok, 2013). Ratings of language dominance also are significantly correlated with objective measures of confrontation naming (Gollan, Weissberger, Runnqvist, Montoya, & Cera, 2011). Ideally, a combination of self-reports and objective measures is recommended (Gollan et al., 2011); however, the present study acknowledges the limitation that no objective measures of language proficiency were administered. Experimental Task: Generative Naming Measures of verbal fluency were administered as experimental tasks. Participants were asked to generate as many words as possible in 1 min based on broad semantic categories (animals, foods), word-initial letters (F, S), and emotional categories (joy, anger). Each generation task was administered in both languages for a total of 12 generative naming samples from each participant. To minimize priming effects, an individual generation task was not administered in both languages during the same session. For example, the examiners elicited samples for “animals” in English during one session and in Spanish during the other. Order of administration was pseudo-randomized, and one of two different administration sequences was randomly assigned to each participant in order to minimize sequence effects. Prior to beginning the tasks, the examiners read instructions in English and provided examples of naming in each type of generative task (two in English, one in Spanish). Participants were requested to retrieve single words and to avoid repeating the same word. After confirming that the participants understood the tasks, the examiners provided instructions for each experimental task in the language of the target sample. For example, when eliciting the category of animals in English, the examiner said, “Tell me all the animals you know. You have one minute. Are you ready? Start now.” When eliciting the category of alegría in Spanish, the examiner said, “Dígame todas las palabras que sabe que están relacionadas con el sentimiento de alegría. Tiene uno minuto. Esta listo? Ahora empiece.” For all task conditions, examiners told participants to stop naming after 60 s. Responses were audio recorded for scoring. Six participants expressed unfamiliarity with the word ira as a term for anger. Dialectical or proficiency differences may have accounted for the unfamiliarity. If the participant did not understand the word, the examiner provided enojo as a closely related synonymn. Enojo and ira both have English translations of anger. All participants understood enojo. Data Analysis Responses were transcribed and scored by the researcher and a trained undergraduate research assistant. Each generated item was counted as 1 if it was spoken in the language of the instructions, represented the target category and was not a repetition of a previous word in the sample. Errors were categorized into three groups: language choice errors, categorical errors, and repetition errors (see Table 2). Responses in the emotional categories were more specific to an individual's experiences than responses in the other two categories. As a result, items were accepted as representative of an emotional category if they were not definitively in contradiction with it (e.g., “laughter” in the category of “sadness”). In the category fluency and emotional fluency tasks, if a participant produced a superordinate category (such as “birds”) in conjunction with specific exemplars (such as “toucan” and “parrot”), only the specific exemplars were counted as unique items. Proper nouns were not included in the total number of correct responses. Table 2. Response codes for verbal fluency transcripts Description Example Error codes Language choice error (L) An item produced in the non-target language. Some items from the non-target language were accepted if speakers had generally adopted the word into the language. “Possole” in the English foods task is a language choice error. “Tacos” in the English foods task is not a language choice error because the word has been generally adopted as a food term in American English. Repetition error (R) An item that was already produced in the same sample. “Cat, dog, horse, cat.” The repetition of the word “cat” is a repetition error. “Walk, walked, walking.” The repetitions of the word “walk” with different regular conjugations are repetition errors. Category error (C) An item in the target language that does not belong in the target category. “Restaurant” in the English foods condition is a category error. “Cistern” in the English letter S condition is a category error. Other exclusion codes Proper noun (P) An item which refers to one specific person, place, object or day. “Christmas,” “Gloria,” and “Friday,” are proper nouns. Superordinate category (SC) A broad label that could be applied to several exemplars. An item was only coded as SC if exemplars of the category were also generated. “Birds, eagle, bluebird, parrot.” Birds is the superordinate category for eagle, bluebird and parrot. Unintelligible words and unrecognized words (X) An item that could not be accurately transcribed due to the speaker's delivery or an item that the scorer did not recognize as a word. These items were excluded but not considered errors because (a) some dialects of Spanish may include words that the Mexican-American dialect does not and (b) at times, audio recordings may be unreliable representations of speech productions. “Forugo” in the Spanish letter F condition was not recognized by the scorer. A few items for which delivery was unclear (i.e., mumbled, very softly spoken) could not be accurately transcribed. Description Example Error codes Language choice error (L) An item produced in the non-target language. Some items from the non-target language were accepted if speakers had generally adopted the word into the language. “Possole” in the English foods task is a language choice error. “Tacos” in the English foods task is not a language choice error because the word has been generally adopted as a food term in American English. Repetition error (R) An item that was already produced in the same sample. “Cat, dog, horse, cat.” The repetition of the word “cat” is a repetition error. “Walk, walked, walking.” The repetitions of the word “walk” with different regular conjugations are repetition errors. Category error (C) An item in the target language that does not belong in the target category. “Restaurant” in the English foods condition is a category error. “Cistern” in the English letter S condition is a category error. Other exclusion codes Proper noun (P) An item which refers to one specific person, place, object or day. “Christmas,” “Gloria,” and “Friday,” are proper nouns. Superordinate category (SC) A broad label that could be applied to several exemplars. An item was only coded as SC if exemplars of the category were also generated. “Birds, eagle, bluebird, parrot.” Birds is the superordinate category for eagle, bluebird and parrot. Unintelligible words and unrecognized words (X) An item that could not be accurately transcribed due to the speaker's delivery or an item that the scorer did not recognize as a word. These items were excluded but not considered errors because (a) some dialects of Spanish may include words that the Mexican-American dialect does not and (b) at times, audio recordings may be unreliable representations of speech productions. “Forugo” in the Spanish letter F condition was not recognized by the scorer. A few items for which delivery was unclear (i.e., mumbled, very softly spoken) could not be accurately transcribed. Table 2. Response codes for verbal fluency transcripts Description Example Error codes Language choice error (L) An item produced in the non-target language. Some items from the non-target language were accepted if speakers had generally adopted the word into the language. “Possole” in the English foods task is a language choice error. “Tacos” in the English foods task is not a language choice error because the word has been generally adopted as a food term in American English. Repetition error (R) An item that was already produced in the same sample. “Cat, dog, horse, cat.” The repetition of the word “cat” is a repetition error. “Walk, walked, walking.” The repetitions of the word “walk” with different regular conjugations are repetition errors. Category error (C) An item in the target language that does not belong in the target category. “Restaurant” in the English foods condition is a category error. “Cistern” in the English letter S condition is a category error. Other exclusion codes Proper noun (P) An item which refers to one specific person, place, object or day. “Christmas,” “Gloria,” and “Friday,” are proper nouns. Superordinate category (SC) A broad label that could be applied to several exemplars. An item was only coded as SC if exemplars of the category were also generated. “Birds, eagle, bluebird, parrot.” Birds is the superordinate category for eagle, bluebird and parrot. Unintelligible words and unrecognized words (X) An item that could not be accurately transcribed due to the speaker's delivery or an item that the scorer did not recognize as a word. These items were excluded but not considered errors because (a) some dialects of Spanish may include words that the Mexican-American dialect does not and (b) at times, audio recordings may be unreliable representations of speech productions. “Forugo” in the Spanish letter F condition was not recognized by the scorer. A few items for which delivery was unclear (i.e., mumbled, very softly spoken) could not be accurately transcribed. Description Example Error codes Language choice error (L) An item produced in the non-target language. Some items from the non-target language were accepted if speakers had generally adopted the word into the language. “Possole” in the English foods task is a language choice error. “Tacos” in the English foods task is not a language choice error because the word has been generally adopted as a food term in American English. Repetition error (R) An item that was already produced in the same sample. “Cat, dog, horse, cat.” The repetition of the word “cat” is a repetition error. “Walk, walked, walking.” The repetitions of the word “walk” with different regular conjugations are repetition errors. Category error (C) An item in the target language that does not belong in the target category. “Restaurant” in the English foods condition is a category error. “Cistern” in the English letter S condition is a category error. Other exclusion codes Proper noun (P) An item which refers to one specific person, place, object or day. “Christmas,” “Gloria,” and “Friday,” are proper nouns. Superordinate category (SC) A broad label that could be applied to several exemplars. An item was only coded as SC if exemplars of the category were also generated. “Birds, eagle, bluebird, parrot.” Birds is the superordinate category for eagle, bluebird and parrot. Unintelligible words and unrecognized words (X) An item that could not be accurately transcribed due to the speaker's delivery or an item that the scorer did not recognize as a word. These items were excluded but not considered errors because (a) some dialects of Spanish may include words that the Mexican-American dialect does not and (b) at times, audio recordings may be unreliable representations of speech productions. “Forugo” in the Spanish letter F condition was not recognized by the scorer. A few items for which delivery was unclear (i.e., mumbled, very softly spoken) could not be accurately transcribed. Based on the scoring criteria, the number of correct responses produced on each task for each participant was tabulated. Group means were calculated for each task and task type within and across languages. A two-way repeated measures analysis of variance (ANOVA) was completed to compare differences in the number of items produced in the category, emotional and letter fluency tasks for the two languages. Paired sample t-tests were used to examine differences in the number of items produced for the English joy and anger conditions and the Spanish alegría and ira conditions. Pearson product moment correlation coefficients were calculated to determine the relationship between language proficiency and number of items produced in each language. Pearson coefficients also were calculated to examine correlations between language proficiency differences and the differences in the number of items produced between languages, as well as dominance ratings and differences in the number of items produced between languages. Reliability Interjudge reliability between two trained undergraduate research assistants was obtained for the transcriptions and response coding of three participants. Interjudge agreement was calculated by dividing the number of agreements between judges by the total number of agreements and disagreements. Interjudge reliability for the transcription of responses was .96. Interjudge reliability for the assignment of scoring codes to responses also was .96. Results The participants generated a total of 4,369 items on the generative naming tasks. Of these items, 258 (5.9%) were coded as errors or exclusions and were not included in the total number of correct responses. Table 3 summarizes the types of responses that were excluded. The most common type of exclusion was repeated items (n = 95). Table 3. Number of exclusions divided by response code R C L SC P X Total Number 95 18 20 60 57 8 258 Percentagea 2.2 <1 <1 1.4 1.3 <1 5.9 R C L SC P X Total Number 95 18 20 60 57 8 258 Percentagea 2.2 <1 <1 1.4 1.3 <1 5.9 Note: R = repetition errors; C = category errors; L = language choice errors; SC = subordinate category; P = proper noun; X = unintelligible or unrecognized response aPercentage of total responses (4,369). Table 3. Number of exclusions divided by response code R C L SC P X Total Number 95 18 20 60 57 8 258 Percentagea 2.2 <1 <1 1.4 1.3 <1 5.9 R C L SC P X Total Number 95 18 20 60 57 8 258 Percentagea 2.2 <1 <1 1.4 1.3 <1 5.9 Note: R = repetition errors; C = category errors; L = language choice errors; SC = subordinate category; P = proper noun; X = unintelligible or unrecognized response aPercentage of total responses (4,369). The total number of correct items produced for each task and language, as well as group means for each task, are shown in Table 4. The mean number of items produced in English was 112.48; the mean number in Spanish was 84.43. The greatest number of items was produced for the English foods condition (M = 25.57; SD = 7.05). The least number of items was produced for the Spanish ira condition (M = 9.52; SD = 4.21). Group means for each task in each language are shown in Fig. 1. Table 4. Total number of correct items produced by participants by task condition and language ID English Spanish Animals Foods Letter S Letter F Joy Anger Total Animales Comidas Letra S Letra F Alegría Ira Total 1 19 18 8 3 23 10 81 15 19 3 12 11 8 68 2 37 36 27 17 19 13 149 17 25 15 7 12 7 83 3 17 25 14 19 15 10 100 13 16 11 11 12 7 70 4 29 24 17 15 22 14 121 23 25 12 6 19 13 98 5 32 28 14 16 12 10 112 12 23 15 13 12 11 86 6 21 18 21 14 12 13 99 15 16 8 9 3 3 54 7 20 34 21 20 15 13 123 18 22 13 9 12 9 83 8 25 27 21 14 7 7 101 11 18 11 11 7 5 63 9 22 15 17 17 11 10 92 26 19 23 19 11 11 109 10 22 20 16 17 21 17 113 24 21 15 16 22 10 108 11 36 35 18 11 12 8 120 20 23 11 10 14 10 88 12 21 24 15 13 12 9 94 16 18 5 6 8 5 58 13 24 27 18 16 18 7 110 27 30 24 17 12 7 117 14 26 23 21 16 18 9 113 24 15 11 13 12 7 82 15 29 36 22 15 17 18 137 16 19 12 10 19 16 92 16 27 32 30 24 26 20 159 19 21 17 11 10 14 92 17 25 36 34 24 20 16 155 15 25 14 12 16 12 94 18 23 18 13 10 14 7 85 18 19 13 11 7 12 80 19 21 26 12 12 10 13 94 18 17 3 9 15 10 72 20 29 18 19 26 12 18 122 29 25 25 22 16 20 137 21 22 17 14 12 11 6 82 12 12 4 3 5 3 39 MN 25.1 25.57 18.67 15.76 15.57 11.81 112.48 18.48 20.38 12.62 11.29 12.14 9.52 84.43 SD 5.35 7.05 6.12 5.18 4.97 4.15 22.9 5.22 4.26 6.24 4.46 4.71 4.21 22.58 ID English Spanish Animals Foods Letter S Letter F Joy Anger Total Animales Comidas Letra S Letra F Alegría Ira Total 1 19 18 8 3 23 10 81 15 19 3 12 11 8 68 2 37 36 27 17 19 13 149 17 25 15 7 12 7 83 3 17 25 14 19 15 10 100 13 16 11 11 12 7 70 4 29 24 17 15 22 14 121 23 25 12 6 19 13 98 5 32 28 14 16 12 10 112 12 23 15 13 12 11 86 6 21 18 21 14 12 13 99 15 16 8 9 3 3 54 7 20 34 21 20 15 13 123 18 22 13 9 12 9 83 8 25 27 21 14 7 7 101 11 18 11 11 7 5 63 9 22 15 17 17 11 10 92 26 19 23 19 11 11 109 10 22 20 16 17 21 17 113 24 21 15 16 22 10 108 11 36 35 18 11 12 8 120 20 23 11 10 14 10 88 12 21 24 15 13 12 9 94 16 18 5 6 8 5 58 13 24 27 18 16 18 7 110 27 30 24 17 12 7 117 14 26 23 21 16 18 9 113 24 15 11 13 12 7 82 15 29 36 22 15 17 18 137 16 19 12 10 19 16 92 16 27 32 30 24 26 20 159 19 21 17 11 10 14 92 17 25 36 34 24 20 16 155 15 25 14 12 16 12 94 18 23 18 13 10 14 7 85 18 19 13 11 7 12 80 19 21 26 12 12 10 13 94 18 17 3 9 15 10 72 20 29 18 19 26 12 18 122 29 25 25 22 16 20 137 21 22 17 14 12 11 6 82 12 12 4 3 5 3 39 MN 25.1 25.57 18.67 15.76 15.57 11.81 112.48 18.48 20.38 12.62 11.29 12.14 9.52 84.43 SD 5.35 7.05 6.12 5.18 4.97 4.15 22.9 5.22 4.26 6.24 4.46 4.71 4.21 22.58 Table 4. Total number of correct items produced by participants by task condition and language ID English Spanish Animals Foods Letter S Letter F Joy Anger Total Animales Comidas Letra S Letra F Alegría Ira Total 1 19 18 8 3 23 10 81 15 19 3 12 11 8 68 2 37 36 27 17 19 13 149 17 25 15 7 12 7 83 3 17 25 14 19 15 10 100 13 16 11 11 12 7 70 4 29 24 17 15 22 14 121 23 25 12 6 19 13 98 5 32 28 14 16 12 10 112 12 23 15 13 12 11 86 6 21 18 21 14 12 13 99 15 16 8 9 3 3 54 7 20 34 21 20 15 13 123 18 22 13 9 12 9 83 8 25 27 21 14 7 7 101 11 18 11 11 7 5 63 9 22 15 17 17 11 10 92 26 19 23 19 11 11 109 10 22 20 16 17 21 17 113 24 21 15 16 22 10 108 11 36 35 18 11 12 8 120 20 23 11 10 14 10 88 12 21 24 15 13 12 9 94 16 18 5 6 8 5 58 13 24 27 18 16 18 7 110 27 30 24 17 12 7 117 14 26 23 21 16 18 9 113 24 15 11 13 12 7 82 15 29 36 22 15 17 18 137 16 19 12 10 19 16 92 16 27 32 30 24 26 20 159 19 21 17 11 10 14 92 17 25 36 34 24 20 16 155 15 25 14 12 16 12 94 18 23 18 13 10 14 7 85 18 19 13 11 7 12 80 19 21 26 12 12 10 13 94 18 17 3 9 15 10 72 20 29 18 19 26 12 18 122 29 25 25 22 16 20 137 21 22 17 14 12 11 6 82 12 12 4 3 5 3 39 MN 25.1 25.57 18.67 15.76 15.57 11.81 112.48 18.48 20.38 12.62 11.29 12.14 9.52 84.43 SD 5.35 7.05 6.12 5.18 4.97 4.15 22.9 5.22 4.26 6.24 4.46 4.71 4.21 22.58 ID English Spanish Animals Foods Letter S Letter F Joy Anger Total Animales Comidas Letra S Letra F Alegría Ira Total 1 19 18 8 3 23 10 81 15 19 3 12 11 8 68 2 37 36 27 17 19 13 149 17 25 15 7 12 7 83 3 17 25 14 19 15 10 100 13 16 11 11 12 7 70 4 29 24 17 15 22 14 121 23 25 12 6 19 13 98 5 32 28 14 16 12 10 112 12 23 15 13 12 11 86 6 21 18 21 14 12 13 99 15 16 8 9 3 3 54 7 20 34 21 20 15 13 123 18 22 13 9 12 9 83 8 25 27 21 14 7 7 101 11 18 11 11 7 5 63 9 22 15 17 17 11 10 92 26 19 23 19 11 11 109 10 22 20 16 17 21 17 113 24 21 15 16 22 10 108 11 36 35 18 11 12 8 120 20 23 11 10 14 10 88 12 21 24 15 13 12 9 94 16 18 5 6 8 5 58 13 24 27 18 16 18 7 110 27 30 24 17 12 7 117 14 26 23 21 16 18 9 113 24 15 11 13 12 7 82 15 29 36 22 15 17 18 137 16 19 12 10 19 16 92 16 27 32 30 24 26 20 159 19 21 17 11 10 14 92 17 25 36 34 24 20 16 155 15 25 14 12 16 12 94 18 23 18 13 10 14 7 85 18 19 13 11 7 12 80 19 21 26 12 12 10 13 94 18 17 3 9 15 10 72 20 29 18 19 26 12 18 122 29 25 25 22 16 20 137 21 22 17 14 12 11 6 82 12 12 4 3 5 3 39 MN 25.1 25.57 18.67 15.76 15.57 11.81 112.48 18.48 20.38 12.62 11.29 12.14 9.52 84.43 SD 5.35 7.05 6.12 5.18 4.97 4.15 22.9 5.22 4.26 6.24 4.46 4.71 4.21 22.58 Fig. 1. View largeDownload slide Mean number of items generated in English and Spanish for each task. Fig. 1. View largeDownload slide Mean number of items generated in English and Spanish for each task. The group means for items generated divided by task type and language are presented in Table 5 and Fig. 2. Category fluency had the highest mean (M = 22.38; SD = 6.26); emotional fluency had the lowest mean (M = 12.26; SD = 4.95). Letter fluency had a mean slightly greater than emotional fluency (M = 14.58; SD = 6.16). The average number of items produced was 18.75 for English and 14.07 for Spanish. Table 5. Group means and standard deviations by task type and language Category Letter Emotional Overall Mean SD Mean SD Mean SD Mean SD English 25.33 6.19 17.21 5.79 13.69 4.91 18.75 7.44 Spanish 19.43 4.8 11.95 5.4 10.83 4.61 14.07 6.23 Both 22.38 6.26 14.58 6.16 12.26 4.95 16.41 7.24 Category Letter Emotional Overall Mean SD Mean SD Mean SD Mean SD English 25.33 6.19 17.21 5.79 13.69 4.91 18.75 7.44 Spanish 19.43 4.8 11.95 5.4 10.83 4.61 14.07 6.23 Both 22.38 6.26 14.58 6.16 12.26 4.95 16.41 7.24 Table 5. Group means and standard deviations by task type and language Category Letter Emotional Overall Mean SD Mean SD Mean SD Mean SD English 25.33 6.19 17.21 5.79 13.69 4.91 18.75 7.44 Spanish 19.43 4.8 11.95 5.4 10.83 4.61 14.07 6.23 Both 22.38 6.26 14.58 6.16 12.26 4.95 16.41 7.24 Category Letter Emotional Overall Mean SD Mean SD Mean SD Mean SD English 25.33 6.19 17.21 5.79 13.69 4.91 18.75 7.44 Spanish 19.43 4.8 11.95 5.4 10.83 4.61 14.07 6.23 Both 22.38 6.26 14.58 6.16 12.26 4.95 16.41 7.24 Fig. 2. View largeDownload slide Group means and standard deviations by task type and language. Fig. 2. View largeDownload slide Group means and standard deviations by task type and language. Number of Items Produced in English and Spanish on Verbal Fluency Tasks A two-way repeated measures ANOVA revealed significant differences between language (F = 28.38, p < .01) and task type (F = 95.20, p < .01) for the number of items produced. The interaction between language and task type was not significant (F = 2.62, p > .05). Significantly more items were produced in English than in Spanish, consistent with higher ratings of English proficiency (M = 96.26; SD = 7.51) than Spanish proficiency (M = 73.81; SD = 18.29). A post-hoc paired samples t-test with Bonferroni correction examined differences in the number of items produced between task types (see Table 6). There was a significant difference in the number of items produced in category fluency (M = 22.38, SD = 6.26) compared to letter fluency (M = 14.58, SD = 6.16); t(20) = 9.55, p < .05. The difference between category fluency and emotional fluency (M = 12.26, SD = 4.95) also was significant; t(20) = 13.26, p < .05. The difference between letter fluency and emotional fluency was not significant. Table 6. Post-hoc paired t-test results for category fluency, letter fluency and emotional fluency in Spanish and English 95% CI for mean difference t-Value df p-Value (two-tailed) Category–Letter 5.67, 9.93 9.55 20 .000* Category–Emotional 8.13, 12.11 13.26 20 .000* Letter–Emotional 0.01, 4.64 2.62 20 .016 95% CI for mean difference t-Value df p-Value (two-tailed) Category–Letter 5.67, 9.93 9.55 20 .000* Category–Emotional 8.13, 12.11 13.26 20 .000* Letter–Emotional 0.01, 4.64 2.62 20 .016 *Significant at α = .05 with Bonferroni correction. Table 6. Post-hoc paired t-test results for category fluency, letter fluency and emotional fluency in Spanish and English 95% CI for mean difference t-Value df p-Value (two-tailed) Category–Letter 5.67, 9.93 9.55 20 .000* Category–Emotional 8.13, 12.11 13.26 20 .000* Letter–Emotional 0.01, 4.64 2.62 20 .016 95% CI for mean difference t-Value df p-Value (two-tailed) Category–Letter 5.67, 9.93 9.55 20 .000* Category–Emotional 8.13, 12.11 13.26 20 .000* Letter–Emotional 0.01, 4.64 2.62 20 .016 *Significant at α = .05 with Bonferroni correction. Post-hoc paired samples t-tests with Bonferroni correction revealed a significant difference in number of items produced in English category fluency (M = 25.33, SD = 6.19) compared to English letter fluency (M = 17.21, SD = 5.79), English category fluency compared to English emotional fluency (M = 13.69, SD = 4.91), and English letter fluency compared to English emotional fluency (t(20) = 6.95, 9.18, 3.40, p < .05) (see Table 7). Table 7. Post-hoc paired t-test results for category fluency, letter fluency, and emotional fluency in English 95% CI for mean difference t-Value df p-Value (two-tailed) Category–Letter 5.68, 10.56 6.95 20 .000* Category–Emotional 8.99, 14.29 9.18 20 .000* Letter–Emotional 1.36, 5.69 3.40 20 .003* 95% CI for mean difference t-Value df p-Value (two-tailed) Category–Letter 5.68, 10.56 6.95 20 .000* Category–Emotional 8.99, 14.29 9.18 20 .000* Letter–Emotional 1.36, 5.69 3.40 20 .003* *Significant at α = .05 with Bonferroni correction. Table 7. Post-hoc paired t-test results for category fluency, letter fluency, and emotional fluency in English 95% CI for mean difference t-Value df p-Value (two-tailed) Category–Letter 5.68, 10.56 6.95 20 .000* Category–Emotional 8.99, 14.29 9.18 20 .000* Letter–Emotional 1.36, 5.69 3.40 20 .003* 95% CI for mean difference t-Value df p-Value (two-tailed) Category–Letter 5.68, 10.56 6.95 20 .000* Category–Emotional 8.99, 14.29 9.18 20 .000* Letter–Emotional 1.36, 5.69 3.40 20 .003* *Significant at α = .05 with Bonferroni correction. A post-hoc paired samples t-test with Bonferroni correction revealed a significant difference in number of items produced in Spanish category fluency (M = 19.43, SD = 4.80) compared to Spanish letter fluency (M = 11.95, SD = 5.40) and in Spanish category fluency compared to Spanish emotional fluency (M = 10.83, SD = 4.61); t(20) = 10.35, 10.76, p < .05. No significant difference was found between Spanish letter fluency and Spanish emotional fluency (see Table 8). Table 8. Post-hoc paired t-test results for category fluency, letter fluency and emotional fluency in Spanish 95% CI for mean difference t-Value df p-Value (two-tailed) Category–Letter 5.97, 8.98 10.35 20 .000* Category–Emotional 6.93, 10.26 10.76 20 .000* Letter–Emotional −1.04, 3.28 1.08 20 .292 95% CI for mean difference t-Value df p-Value (two-tailed) Category–Letter 5.97, 8.98 10.35 20 .000* Category–Emotional 6.93, 10.26 10.76 20 .000* Letter–Emotional −1.04, 3.28 1.08 20 .292 *Significant at α = .05 with Bonferroni correction. Table 8. Post-hoc paired t-test results for category fluency, letter fluency and emotional fluency in Spanish 95% CI for mean difference t-Value df p-Value (two-tailed) Category–Letter 5.97, 8.98 10.35 20 .000* Category–Emotional 6.93, 10.26 10.76 20 .000* Letter–Emotional −1.04, 3.28 1.08 20 .292 95% CI for mean difference t-Value df p-Value (two-tailed) Category–Letter 5.97, 8.98 10.35 20 .000* Category–Emotional 6.93, 10.26 10.76 20 .000* Letter–Emotional −1.04, 3.28 1.08 20 .292 *Significant at α = .05 with Bonferroni correction. Relationship Between Language Proficiency and Number of Items Produced in Each Language Pearson product moment correlation coefficients were calculated for English proficiency ratings and number of English items produced in each task as well as overall (see Table 9). Self-reported English proficiency ratings were weakly correlated with number of items produced in letter fluency, emotional fluency, and overall (r = 0.05, 0.28, and 0.31). English proficiency ratings were moderately correlated with number of English items produced in category fluency (r = 0.41). This relationship did not reach significance. Correlations were likely reduced because of the ceiling effect in English. Most participants rated English proficiency at or near 100%. Table 9. Pearson product moment correlation coefficients for language proficiency and the number of items produced in each language Category fluency Letter fluency Emotional fluency Total English proficiency 0.41 0.05 0.28 0.31 Spanish proficiency 0.61* 0.48 0.45 0.59* Category fluency Letter fluency Emotional fluency Total English proficiency 0.41 0.05 0.28 0.31 Spanish proficiency 0.61* 0.48 0.45 0.59* *Significant at α = .05 with Bonferroni correction. Table 9. Pearson product moment correlation coefficients for language proficiency and the number of items produced in each language Category fluency Letter fluency Emotional fluency Total English proficiency 0.41 0.05 0.28 0.31 Spanish proficiency 0.61* 0.48 0.45 0.59* Category fluency Letter fluency Emotional fluency Total English proficiency 0.41 0.05 0.28 0.31 Spanish proficiency 0.61* 0.48 0.45 0.59* *Significant at α = .05 with Bonferroni correction. Pearson coefficients also were calculated between Spanish proficiency ratings and number of Spanish items produced in each task type as well as overall (see Table 9). Self-reported Spanish proficiency ratings were moderately correlated with number of Spanish items produced in letter fluency tasks and emotional fluency tasks (r = 0.48, and 0.45). These relationships did not reach significance after Bonferroni correction. Spanish proficiency ratings were strongly correlated with number of Spanish items produced in category fluency tasks and overall (r = 0.61, 0.59). These correlations were significant with Bonferroni correction (p < .0125). Self-Reported Dominance Ratings and Differences in Language Proficiency Ratings The difference score between the Spanish and English overall proficiencies was calculated for each participant by subtracting the Spanish overall proficiency score from the English overall proficiency score. Differences were positive or negative, depending on whether the participants rated themselves higher in English or Spanish. A larger difference indicated a less balanced bilingualism, and a smaller difference indicated a more balanced bilingualism. A negative value indicated higher proficiency in Spanish than English and a positive value indicated higher proficiency in English. Dominance ratings obtained from the visual analog scale for dominance on the LUS were represented by a negative score (as low as −50) for Spanish dominance or a positive score (as high as +50) for English dominance. Balanced bilingualism was represented by a 0. A Pearson correlation coefficient was calculated between difference scores and dominance ratings. The two measures were strongly correlated (r = 0.77), and this relationship was significant after Bonferroni correction (p < .005). Relationship Between Dominance Ratings and Verbal Fluency Pearson correlation coefficients were calculated for dominance ratings and the difference in number of productions between languages for each task type and overall (see Table 10). Differences in the number of productions for each task type were calculated by subtracting the number of Spanish productions for a task type from the number of English productions. A resulting negative difference indicated that more items were produced in Spanish. A resulting positive difference indicated that more items were produced in English. Dominance ratings were moderately correlated with the language production difference in category fluency and emotional fluency (r = 0.44, 0.38). These relationships were not significant after Bonferroni correction. Dominance ratings were strongly correlated with language production difference in letter fluency and overalli (r = 0.66, 0.63). These relationships reached significance after Bonferroni correction (p < .005). Table 10. Pearson product moment correlation coefficients for language proficiency difference scores, language dominance ratings, and language production differences CF difference LF difference EF difference Total difference Language proficiency difference score 0.69* 0.68* 0.38 0.77* Language dominance rating 0.44 0.66* 0.38 0.63* CF difference LF difference EF difference Total difference Language proficiency difference score 0.69* 0.68* 0.38 0.77* Language dominance rating 0.44 0.66* 0.38 0.63* Note: CF = category fluency; LF = letter fluency; EF = emotional fluency. *Significant at α = .05 with Bonferroni correction. Table 10. Pearson product moment correlation coefficients for language proficiency difference scores, language dominance ratings, and language production differences CF difference LF difference EF difference Total difference Language proficiency difference score 0.69* 0.68* 0.38 0.77* Language dominance rating 0.44 0.66* 0.38 0.63* CF difference LF difference EF difference Total difference Language proficiency difference score 0.69* 0.68* 0.38 0.77* Language dominance rating 0.44 0.66* 0.38 0.63* Note: CF = category fluency; LF = letter fluency; EF = emotional fluency. *Significant at α = .05 with Bonferroni correction. Relationships Between Proficiency Difference Scores and Language Production Differences Pearson correlation coefficients were calculated between proficiency difference scores and production differences (see Table 10). Proficiency difference scores were strongly correlated with language production differences in category fluency, letter fluency and overall (r = 0.69, 0.68, and 0.77). These relationships reached significance after Bonferroni correction (p < .005). Proficiency difference scores were moderately correlated with language production difference in emotional fluency (r = 0.38). This relationship did not reach significance. Differences Between Positive and Negative Emotional Verbal Fluency Tasks Paired t-tests corrected with the Bonferroni procedure examined the difference between the joy and anger conditions in English and Spanish (see Table 11). Results indicated that the mean group difference was significant in both English (t = 3.75, p < .05) and Spanish (t = 3.13, p < .05). Table 11. Paired t-test results for joy and anger comparisons in English and Spanish 95% CI for mean difference t-Value df p-Value (two-tailed) Joy–Anger 1.67–5.85 3.75 20 .001* Alegría–Ira 0.87–4.37 3.13 20 .005* 95% CI for mean difference t-Value df p-Value (two-tailed) Joy–Anger 1.67–5.85 3.75 20 .001* Alegría–Ira 0.87–4.37 3.13 20 .005* *Significant at α = .05 with Bonferroni correction. Table 11. Paired t-test results for joy and anger comparisons in English and Spanish 95% CI for mean difference t-Value df p-Value (two-tailed) Joy–Anger 1.67–5.85 3.75 20 .001* Alegría–Ira 0.87–4.37 3.13 20 .005* 95% CI for mean difference t-Value df p-Value (two-tailed) Joy–Anger 1.67–5.85 3.75 20 .001* Alegría–Ira 0.87–4.37 3.13 20 .005* *Significant at α = .05 with Bonferroni correction. Discussion The participants generated the greatest number of items on the category fluency tasks. The group generated the least number of items in the emotional fluency tasks. ANOVA results revealed significant differences between the mean number of items generated for category fluency versus letter fluency and category fluency versus emotional fluency, but not for letter fluency versus emotional fluency. This pattern remained the same in a comparison of the three task types in Spanish, but a comparison of the three task types in English revealed a significant difference between letter fluency and emotional fluency. Most of the participants reported formal education in English and lower Spanish proficiency levels. Performance in letter fluency is related to level of literacy, so lack of formal education in Spanish may have contributed to lower letter fluency scores, which led to comparable performance between letter and emotional fluency in Spanish. This pattern is an important consideration for bilingual individuals who may be completing testing in their second or less literate language. The results of the study do not support results from Sass and colleagues (2013) who found that performance on category fluency tasks was comparable to performance on emotional fluency tasks. One explanation for this discrepancy may be the different designs of the emotional verbal fluency tasks. Participants in Sass and colleagues (2013) produced single words and short phrases during the emotional fluency task. To adjust for the difference between average length of responses between the category fluency task and the emotional fluency task the examiners adjusted verbal fluency scores by dividing the total number of correct items by the mean number of syllables. The adjustment may have artificially inflated emotional verbal fluency scores relative to category fluency scores. Participants in the present study were asked to respond using single words, which eliminated the need for this data analysis adjustment. Performance on emotional verbal fluency tasks depends upon utilization of lexical–conceptual connections and the integrity of semantic networks as well as emotional association networks. When generating a list of words that are related to an emotion, cognitive processing load is increased relative to a neutral category fluency task. This increased cognitive processing load is one possible explanation for decreased output in emotional verbal fluency tasks. Furthermore, results suggested that emotional verbal fluency is less dependent upon language proficiency than category fluency or letter fluency. The calculation of Pearson correlation coefficients between language proficiency scores (English proficiency score – Spanish proficiency score) and production difference scores (items produced in English – Items produced in Spanish for each task type) revealed that language proficiency difference was significantly correlated with production difference for category fluency (0.69), letter fluency (0.68) and overall production (0.77), but not for emotional fluency (0.38). Emotional fluency also showed the weakest correlation between language dominance ratings and production differences. The significantly weaker relationship of emotional verbal fluency to language proficiencies as compared to category fluency and letter fluency suggests that task performance is strongly affected by a domain other than language ability, most likely emotional processing. If emotional verbal fluency is more indicative of emotional processing ability than language ability, it may be a useful tool for assessing the emotional processing deficits observed in depression, TBI, right hemisphere disorders and other clinical populations. For example, individuals with traumatic brain damage present with a variety of deficits along a broad continuum of severity. Impairment profiles often vary widely between individuals. Individuals with TBI often demonstrate emotional processing deficits post-injury (Dimoska, Mcdonald, Pell, Tate, & James, 2010), though every individual requires a tailored evaluation and treatment plan. Emotional verbal fluency may prove to be an inexpensive and quick way to assess whether an individual experiences a deficit in emotional processing. Differences between numbers of items generated for joy versus anger were compared to examine whether participants demonstrated a positivity bias within the task. Significantly more items were produced in English joy as compared to English anger, and significantly more items were produced in Spanish alegría as compared to Spanish ira. These results support findings from Sass and colleagues (2013) who found that participants produced the greatest number of items for the emotional category of joy. Furthermore, these results support a positivity bias in language processing (Kuchinke et al., 2005; Sass et al., 2011) as well as an advantage for cognitive processing of positively associated information (Ashby et al., 1999). Increased performance on positive emotional fluency tasks as compared to negative emotional fluency tasks may be influenced by the beneficial effects of positive associations on semantic processing and cognitive processing. The positivity bias demonstrated in emotional verbal fluency suggests another potential clinical application. The assessment may be effective as one component of screening for depression in returning war veterans, as well as other at-risk individuals. Neurotypical individuals experience increased performance on positive emotional fluency tasks as compared to negative emotional fluency tasks, likely due to a positivity bias in language and cognitive processing. Studies have suggested that individuals with depression often demonstrate a negativity bias in language processing (Gotlib, Krasnoperova, Yue, & Joormann, 2004; Rude, Wenzlaff, Gibbs, Vane, & Whitney, 2002), which may affect their pattern of performance. This clinical application can be explored in future research that focuses on the performance of individuals with diagnosed depression on emotional verbal fluency tasks. Significantly more items were generated in English than in Spanish, which is consistent with self-reports of English dominance. Group participants reported English dominance (n = 15) or balanced bilingualism (n = 6). No participants reported Spanish dominance. Pearson correlation coefficients between language proficiency ratings and number of items generated for each task type in each language were moderately positive. Only the relationships of Spanish proficiency with Spanish category fluency and Spanish proficiency with overall Spanish productions reached significance. English proficiency levels may have been too homogenous (majority near 100) to provide a meaningful scale for correlations. A clear pattern emerged in correlations between the differences in language proficiency ratings and the differences between productions in each language. The difference between language proficiency ratings was significantly correlated with the production difference in category fluency, letter fluency and overall performance. This measure was not correlated with production difference in emotional fluency. This pattern suggests that for category and letter fluency, obtained self-ratings of proficiency in each language may be a reliable way to interpret observed differences between performances in each language. Dominance ratings correlated significantly with production differences in letter fluency and overall, but not with category fluency or emotional fluency. Calculation of a language proficiency difference score may be a more accurate measure for identifying differences between languages than a rating for language dominance. There were several limitations to the study. First, the bilingual group consisted of a limited sample size (n = 21) and exhibited a high level of homogeneity in regards to language dominance, gender and education levels. The study relied solely on self-reports that participants did not have a history of psychiatric illnesses, neurological disorders, learning disorders, attention disorders, alcoholism, drug addiction, stroke or brain injury. The failure of six participants to initially recognize the term ira for anger is also a limitation of the study. Future studies should take care to choose stimuli that are easily understood across dialects and proficiency levels. Also, the study relied on self-report to determine language proficiencies and dominance because objective measures of language proficiency were not obtained. Conclusions In summary, the results from the present study provided preliminary data on the emotional verbal fluency task in Spanish–English bilingual individuals and demonstrated the value of the emotional verbal fluency task as a component of neuropsychological assessment. The greatest number of items was generated in category fluency tasks. The number of items generated for letter fluency tasks and emotional fluency tasks were not significantly different. The group produced significantly more items in English than in Spanish, reflecting self-ratings of English dominance. Language proficiency difference scores were significantly correlated with the differences in productions between languages, which highlights the usefulness of self-ratings in the estimation of expected performance in both languages. The emotional verbal fluency tasks appeared to be more difficult than category verbal fluency tasks and comparable in difficulty to letter verbal fluency tasks. The emotional tasks appear to rely less on language ability than category fluency or letter fluency; they may be a valid tool for assessing emotional processing. Future studies should assess performance of individuals with and without emotional processing deficits on these tasks in relation to other measures of emotional processing to further validate this assessment tool. Funding This research received financial support from the Ben F. Love Regents Professorship at the University of Texas. Conflict of Interest None declared. References Adamovich , B. B. , & Henderson , J. A. ( 1992 ). 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Clustering and switching during a semantic verbal fluency test contribute to differential diagnosis of cognitive impairment . Neuroscience Bulletin , 29 , 75 – 82 . Google Scholar CrossRef Search ADS PubMed © The Author 2017. Published by Oxford University Press. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com. This article is published and distributed under the terms of the Oxford University Press, Standard Journals Publication Model (https://academic.oup.com/journals/pages/about_us/legal/notices) http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Archives of Clinical Neuropsychology Oxford University Press

Category, Letter, and Emotional Verbal Fluency in Spanish–English Bilingual Speakers: A Preliminary Report

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Abstract

Abstract Objective The purpose of this study was to compare the performance of bilingual speakers on an emotional verbal fluency task to category and letter verbal fluency tasks. A second purpose was to compare performances on these tasks to language proficiency ratings. Method Twelve verbal fluency tasks were administered to 21 Spanish–English bilingual speakers. Results were analyzed for differences between fluency types (category, letter, and emotional) and languages (English and Spanish). Results Participants generated the most items in category fluency tasks and the least items in emotional fluency tasks. The number of items generated for letter and emotional fluency tasks were not significantly different, but both were significantly lower than the number of items generated in category fluency. More items were generated for positive emotions than for negative emotions. Differences between languages for category and letter fluency tasks were significantly correlated with differences in language proficiency ratings, but this finding was not found for emotional fluency tasks. Conclusions Self-ratings of proficiency and language dominance correlated significantly with performance on category and letter fluency tasks and may be useful predictors of differences between languages on these tasks. Emotional fluency was not significantly correlated with language proficiency ratings, suggesting that performance on emotional fluency may be more significantly affected by emotional processing ability. The emotional verbal fluency task has potential as a component of neuropsychological evaluations to screen easily and quickly for emotional processing deficits, including those associated with traumatic brain injury and depression. Additionally, results support a positivity bias in language and cognition processes. Verbal fluency, Language, Emotional processing, Cross-cultural, Assessment Introduction Verbal fluency tasks commonly are used in neuropsychological assessments to screen for impairments in language ability and executive functioning; however, no fluency task has been adopted to examine the relationship between executive functioning, language, and emotional content. Recent investigations (Ashby, Isen, & Turken, 1999; Sass et al., 2011) have suggested that emotion influences performance on language and cognition tasks (Ashby et al., 1999; Sass et al., 2011). Several clinical groups including those with depression (Klumpp & Deldin, 2010), right hemisphere brain damage (Borod, Bloom, Brickman, Nakhutina, & Curko, 2002) and traumatic brain injury (TBI) (Croker & McDonald, 2005) may present with emotional processing impairments. For individuals with emotional processing deficits, a task of emotional verbal fluency could contribute meaningful information to neuropsychological assessment (Klumpp & Deldin, 2010). The following study examined the performance of bilingual individuals on three types of verbal fluency tasks (category, letter, and emotional) to compare output in emotional generative naming, a new task for assessing emotional expressive vocabulary, with output in category and letter generative naming tasks. Bilingual individuals comprise a clinical population which is underrepresented in the research literature. Though individuals are often tested in only one language based on patient preference or evaluator ability, completion of testing in both languages is needed, especially for those with a history of a neurological event or onset of a neurological condition that may affect language systems (Lorenzen & Murray, 2008; Marrero, Golden, & Espe-Pfeifer, 2002). Neurological events and conditions can result in divergent outcomes for different languages. A better understanding of (1) the relationship between task performances in each language and (2) the relationship between patient perception of language ability and task performance in each language is critically needed. A second purpose was to examine the relationship of performance on these tasks with language proficiency ratings for bilingual speakers. Verbal Fluency Verbal fluency tasks (i.e., generative naming) measure the ability to retrieve related lexical items. A fixed time is provided (usually 60 s) to name as many words as possible based on the given criteria. Category (or semantic) fluency measures the ability to retrieve words based on a semantic category (e.g., animals, fruits, and vehicles) (Newcombe, 1969). Letter (or phonemic) fluency measures the ability to retrieve words based on the initial letter of the word (often F, A, or S) (Benton, 1968). Both tasks require a combination of language ability (verbal retrieval, integrity of semantic lexical networks) and executive functioning ability (effortful initiation, organization of the lexical search, inhibition of noncategorical words, self-monitoring) (Henry & Crawford, 2004). The two tasks differ with respect to the demands placed on the retrieval process. Category fluency resembles how words are generated in every-day situations. For example, when planning a grocery shopping trip, a list of foods is generated. Letter fluency, in contrast, is a task that does not resemble every-day demands for lexical retrieval. Additional inhibition is required to suppress semantically related words, and novel retrieval strategies must be used (Shao, Janse, Visser, & Meyer, 2014). In terms of cognitive demands, category fluency is associated more significantly with language abilities such as size of vocabulary and integrity of semantic lexical networks. Letter fluency is associated more with executive functioning abilities, especially self-monitoring, inhibition and working memory (Luo, Luk, & Bialystok, 2010; Shao et al., 2014). Performance on categorical and letter fluency tasks differs. In general, more exemplars are generated on category fluency tasks than on letter fluency tasks; however, Brickman and colleagues (2005) showed that the number of exemplars for category fluency declines significantly across the normal age span, while the number of exemplars for letter fluency declines at a much slower rate. As age increases, the difference between category and letter fluency scores decreases. Deficits in category fluency are associated with compromised integrity of semantic knowledge structures, and deficits in letter fluency are associated with impairments in executive functioning (Henry & Crawford, 2004; Henry, Crawford, & Phillips, 2004). Analysis of deficits in verbal fluency tasks is used in the assessment of several clinical conditions, including attention deficit hyperactivity disorder (Andreou & Trott, 2013), Alzheimer's disease (Zhao, Guo, & Hong, 2013) and Parkinson's disease (Pettit, McCarthy, Davenport, & Abrahams, 2013). Additionally, generative naming tasks are used in standardized language assessments for aphasia (“The Western Aphasia Battery”: Kertesz, 1982), cognitive impairment (“The Cognitive Linguistic Quick Test”: Helm-Estabrooks, 2001), and traumatic brain injury (“Scales of Cognitive Ability in Traumatic Brain Injury”: Adamovich & Henderson, 1992). Each condition demonstrates characteristic patterns that may be used in assessment. For example, individuals with Alzheimer's disease and psychosis perform consistently poorer on category fluency tasks than on letter fluency tasks (Laws, Duncan, & Gale, 2010; Magaud et al., 2010). Individuals with amyotrophic lateral sclerosis have demonstrated greater impairment in letter fluency relative to category fluency (Quinn et al., 2012). Differences in performance on the different tasks of verbal fluency can add valuable information to a diagnostic or evaluative decision in neuropsychological assessment. Verbal Fluency in Bilingual Individuals No universal definition for bilingualism exists. Broadly defined, individuals who are bilingual have the ability to use at least two languages in their every-day lives (Grosjean, 1998). Bilinguals may speak each language for different purposes. For example, the first language (L1) may be used in the home and the second language (L2) at work. As a result, bilingual individuals rarely are equally proficient in both languages (“balanced”). The relationship between the two languages may change over time as demands in daily life require more use of one language than the other (Grosjean, 1998). This changing relationship means that either L1 or L2 can be the dominant language and that both languages can be the dominant language at different points during the lifespan. Several factors influence language proficiency. Age of acquisition is known to influence language proficiency (Johnson & Newport, 1989), but is not a certain predictor. Other factors include which language is used in different environments (e.g., home, work, and school), personal and social attitudes toward each language, cultural identity, socioeconomic level, and geographic location (Ardila, 1998; Muñoz & Marquardt, 2003). Assessment of cognitive–linguistic skills in bilingual individuals often is conducted with the erroneous assumption that bilingual individuals can be directly compared to monolingual norms or that bilingual individuals possess similar abilities in each language (Paradis, 2001; Roberts, Garcia, Desrochers, & Hernandez, 2002). Neuropsychological assessment of bilingual individuals should take into account the different profile of linguistic and cognitive strengths and weaknesses these individuals demonstrate as compared to monolingual individuals. Performance on linguistic tasks should consider history, use and proficiency for both languages (Muñoz & Marquardt, 2003). Failure to do so can result in misinterpretations of test results and inappropriate treatment recommendations. Differences in performance on verbal fluency tasks may exist between age-matched monolingual and bilingual speakers. Gollan, Montoya, and Werner (2002) compared performance of English monolinguals and Spanish–English bilinguals on 12 category, 10 letter, and two proper name fluency tasks. Participants were tested in English, and the bilingual group also completed trials in which they could name words in either English or Spanish. Results indicated that the bilingual group produced significantly fewer exemplars than the monolingual group on all tasks. The difference was much larger for category fluency tasks than for letter fluency or proper name fluency tasks. Rosselli and colleagues (2002) found similar results when comparing verbal fluency in older English monolingual, Spanish monolingual, and Spanish–English bilingual groups. The bilingual group performed similarly to both monolingual groups on letter fluency tasks, but produced significantly fewer exemplars than both monolingual groups for the semantic category (animals). Portocarrero, Burright, and Donovick (2007) replicated these results with college-aged participants. Thus, bilingual individuals appear to experience a disadvantage in performance on category fluency tasks. Assessments of verbal fluency should take this difference into account. Bilingual speaker decreased performance on categorical fluency tasks may reflect a number of linguistic factors. Bilingual speakers, for example, have smaller vocabularies in each language compared to the vocabularies of monolingual speakers (Perani et al., 1998; Portocarrero et al., 2007). They have reduced picture naming accuracy (Roberts et al., 2002; Sheppard, Kousaie, Monetta, & Taler, 2016) and more “tip of the tongue” experiences (Gollan & Acenas, 2004). Some studies have suggested that cognitive load during category fluency is increased for bilinguals because they must actively inhibit cross-language interference when words from both languages are activated (Gollan et al., 2002). Lower average vocabulary size appears to be the most likely explanation. Luo and colleagues (2010) found that a group of bilinguals with vocabulary scores equivalent to those of a monolingual group performed equivalently on category fluency tasks and produced significantly more exemplars on letter fluency tasks. These results suggest that if vocabulary size is controlled, the bilingual disadvantage disappears. A bilingual advantage in letter fluency suggests that bilingual individuals have enhanced executive functioning abilities. Bilingual speakers have demonstrated enhanced inhibition abilities through superior performance on Stroop tasks (Bialystok, Craik, & Luk, 2008) and greater attention through superior performance on an attentional network task (Costa, Hernández, & Sebastián-Gallés, 2008). Performance on letter fluency tasks draws more heavily on executive functioning than performance on category fluency tasks. Enhanced executive functioning abilities in bilingual speakers should result in enhanced performance on letter fluency tasks. Emotional Verbal Fluency Emotion interacts with language and cognition. Pessoa (2008) argued that, based on neuroimaging studies, emotion and cognition do not embody separate neurological systems. Instead, brain regions viewed as “affective” (e.g., the amygdala, ventral striatum and hypothalamus) are also involved in cognition, and brain regions viewed as “cognitive” (e.g., the prefrontal and parietal cortices) also are involved in emotion. He concluded that cognition and emotion are integrated in the brain and jointly contribute to behavior. Emotional information and semantic association processes also are intertwined. Words are stored not only in semantic networks of literal meanings, but also in emotional or connotative networks (Kuchinke et al., 2005; Skrandies, 2011). Positive and negative emotional information may affect semantic processing differently. For example, Sass and colleagues (2011) found that priming participants with positively associated words increased the speed with which participants identified another positively associated word (as compared to unrelated word pairs). Conversely, priming participants with negatively associated words did not increase the speed with which participants identified another negatively associated word. The study concluded that positively associated words appear to be easily processed whereas negatively associated words may be suppressed or inhibited (Sass et al., 2011). Other studies also support a bias in language processing networks for positively affected words and information (Dodds et al., 2015; Kuchinke et al., 2005). Valence of emotional content has been demonstrated to affect cognitive processing. Positive affect increases dopamine levels in the brain, which in turn may improve executive functioning skills such as problem solving (Ashby et al., 1999). Positive emotional information, then, may aid both semantic and cognitive processing. Emotional verbal fluency may be used to explore the interaction between emotion, language and cognition. Sass, Heim, Fetz, Oetken, and Habel (2013) investigated the performance of 21 healthy individuals on an emotional verbal fluency task (i.e., generating items that represent emotions such as “fear” and “joy”) as compared to a category verbal fluency task (i.e., generating items that belong to a semantic category such as “animals” or “vehicles”) to examine how individuals without emotional processing deficits performed on the task. For the emotional naming, participants were instructed to generate objects or circumstances that represented or elicited the given emotion. Because the emotional categories elicited multi-word phrases, scores were adjusted for the difference in number of syllables between the experimental tasks. The results did not yield significant differences in the number of items generated in the neutral semantic and emotional categories, suggesting that the tasks were comparable in difficulty. Presumably, if output in the two tasks is comparable for neurotypical individuals, decreased performance on the emotional tasks may indicate impaired emotional processing. The best performance was observed in the category “joy,” which supports the positivity bias reported in previous studies (Dodds et al., 2015; Kuchinke et al., 2005; Sass et al., 2011). Sass and colleagues (2013) concluded that emotional verbal fluency may be an effective task for investigating emotional components of executive functioning. In summary, category verbal fluency and letter verbal fluency tasks have been used extensively in neuropsychological assessment batteries. Performance on these tasks may differ between clinical populations and between monolingual and bilingual individuals. Emotional verbal fluency, a recently introduced task by Sass and colleagues (2013), may provide additional useful information for the neuropsychological evaluation of individuals with possible emotional processing deficits. Purpose The current study investigated category, letter, and emotional verbal fluency in young bilingual speakers. Performance on the three tasks was used to explore the effectiveness of the emotional verbal fluency task for cognitive-communicative assessment in bilingual individuals. Participants were hypothesized to perform comparatively on category fluency and emotional fluency tasks Sass and colleagues (2013). The difference in the number of items generated for joy versus anger was compared to examine whether participants demonstrated a positivity bias. Data also were analyzed to determine patterns of performance within and between languages. Gollan and colleagues (2002) suggested that bilingual individuals may produce a comparable numbers of items in letter and category fluency; however, this pattern may have resulted from the use of narrow categories used for the category fluency tasks (i.e., musical instruments, occupations, college majors, etc.). Other studies that used broader semantic categories (Portocarrero et al., 2007; Rosselli et al., 2002) suggested that a greater number of items would be generated in category fluency, consistent with patterns observed in monolingual populations. Based on these findings more items were expected to be generated on the category fluency tasks relative to letter fluency tasks. Differences between numbers of items generated in each language were examined to determine if participants demonstrated greater output in one language as compared to the other. Participants were hypothesized to produce more items on all three task types in the dominant language. Language proficiency ratings were expected to significantly correlate with the number of items generated in each language. Materials and Methods Participants Twenty-one individuals (five male,16 female) aged 22–41 participated in the study (see Table 1). Participants were recruited through advertisements at the University of Texas at Austin and referrals from other participants. Participants demonstrated adequate hearing and no history of psychiatric illnesses, neurological disorders, learning disorders, attention disorders, alcoholism, drug addiction, stroke, or brain injury based on an interview. Participants reported conversational fluency in both Spanish and English. They also demonstrated adequate ability to converse with the examiner in both languages and to complete the experimental tasks in both languages. Informed consent was obtained for all participants. Participants were tested at a quiet location of their choosing. Testing took place over two sessions for a total of 40 min. Sessions were separated by a minimum of 48 hr. All procedures were conducted in compliance with relevant laws and guidelines and were approved by The University of Texas Institutional Review Board. Table 1. Demographic and language proficiency data for participants ID Gender Age Years of education English AoAa Spanish AoAa English proficiency Spanish proficiency Dominance 1 F 29 18.5 4 0 97.5 92.26 3.33 2 F 25 16.5 0 0 100 49.76 26.67 3 F 23 17 0 11 100 59.4 8.33 4 F 28 20.5 5 0 100 97.61 18.3 5 F 22 16 0 11 100 69.17 17.5 6 F 24 17 0 0 100 58.93 23.33 7 F 24 17 0 0 100 59.4 25 8 F 30 17 0 14 100 52.43 33.33 9 M 30 17 8 0 72.25 99.64 3.33 10 F 24 18 2 0 97.25 62.73 30.83 11 F 24 17 0 14 97.26 69.76 4.16 12 M 25 18 0 0 96.3 80.35 23.33 13 M 37 17 5 0 95.36 100 0 14 F 29 19 0 14 100 61.55 26.67 15 F 24 18 0 17 100 78.87 16.66 16 F 29 21 0 12 100 77.49 20 17 F 25 17 0 12 100 63.57 20 18 F 24 18 8 0 100 96.55 1.67 19 F 42 18 0 15 100 78.93 18.33 20 M 40 16 7 0 83.1 100 -3.33 21 M 26 14 6 0 82.37 41.66 33.33 MN — 28 17.5 2.14 5.71 96.26 73.81 16.7 ID Gender Age Years of education English AoAa Spanish AoAa English proficiency Spanish proficiency Dominance 1 F 29 18.5 4 0 97.5 92.26 3.33 2 F 25 16.5 0 0 100 49.76 26.67 3 F 23 17 0 11 100 59.4 8.33 4 F 28 20.5 5 0 100 97.61 18.3 5 F 22 16 0 11 100 69.17 17.5 6 F 24 17 0 0 100 58.93 23.33 7 F 24 17 0 0 100 59.4 25 8 F 30 17 0 14 100 52.43 33.33 9 M 30 17 8 0 72.25 99.64 3.33 10 F 24 18 2 0 97.25 62.73 30.83 11 F 24 17 0 14 97.26 69.76 4.16 12 M 25 18 0 0 96.3 80.35 23.33 13 M 37 17 5 0 95.36 100 0 14 F 29 19 0 14 100 61.55 26.67 15 F 24 18 0 17 100 78.87 16.66 16 F 29 21 0 12 100 77.49 20 17 F 25 17 0 12 100 63.57 20 18 F 24 18 8 0 100 96.55 1.67 19 F 42 18 0 15 100 78.93 18.33 20 M 40 16 7 0 83.1 100 -3.33 21 M 26 14 6 0 82.37 41.66 33.33 MN — 28 17.5 2.14 5.71 96.26 73.81 16.7 aAoA = age of acquisition. Table 1. Demographic and language proficiency data for participants ID Gender Age Years of education English AoAa Spanish AoAa English proficiency Spanish proficiency Dominance 1 F 29 18.5 4 0 97.5 92.26 3.33 2 F 25 16.5 0 0 100 49.76 26.67 3 F 23 17 0 11 100 59.4 8.33 4 F 28 20.5 5 0 100 97.61 18.3 5 F 22 16 0 11 100 69.17 17.5 6 F 24 17 0 0 100 58.93 23.33 7 F 24 17 0 0 100 59.4 25 8 F 30 17 0 14 100 52.43 33.33 9 M 30 17 8 0 72.25 99.64 3.33 10 F 24 18 2 0 97.25 62.73 30.83 11 F 24 17 0 14 97.26 69.76 4.16 12 M 25 18 0 0 96.3 80.35 23.33 13 M 37 17 5 0 95.36 100 0 14 F 29 19 0 14 100 61.55 26.67 15 F 24 18 0 17 100 78.87 16.66 16 F 29 21 0 12 100 77.49 20 17 F 25 17 0 12 100 63.57 20 18 F 24 18 8 0 100 96.55 1.67 19 F 42 18 0 15 100 78.93 18.33 20 M 40 16 7 0 83.1 100 -3.33 21 M 26 14 6 0 82.37 41.66 33.33 MN — 28 17.5 2.14 5.71 96.26 73.81 16.7 ID Gender Age Years of education English AoAa Spanish AoAa English proficiency Spanish proficiency Dominance 1 F 29 18.5 4 0 97.5 92.26 3.33 2 F 25 16.5 0 0 100 49.76 26.67 3 F 23 17 0 11 100 59.4 8.33 4 F 28 20.5 5 0 100 97.61 18.3 5 F 22 16 0 11 100 69.17 17.5 6 F 24 17 0 0 100 58.93 23.33 7 F 24 17 0 0 100 59.4 25 8 F 30 17 0 14 100 52.43 33.33 9 M 30 17 8 0 72.25 99.64 3.33 10 F 24 18 2 0 97.25 62.73 30.83 11 F 24 17 0 14 97.26 69.76 4.16 12 M 25 18 0 0 96.3 80.35 23.33 13 M 37 17 5 0 95.36 100 0 14 F 29 19 0 14 100 61.55 26.67 15 F 24 18 0 17 100 78.87 16.66 16 F 29 21 0 12 100 77.49 20 17 F 25 17 0 12 100 63.57 20 18 F 24 18 8 0 100 96.55 1.67 19 F 42 18 0 15 100 78.93 18.33 20 M 40 16 7 0 83.1 100 -3.33 21 M 26 14 6 0 82.37 41.66 33.33 MN — 28 17.5 2.14 5.71 96.26 73.81 16.7 aAoA = age of acquisition. Measures of Bilingualism Participants completed the Language Use Survey (LUS: Muñoz, Marquardt, & Copeland, 1999), a questionnaire that gathers information about language use across the lifespan and self-assessments of proficiency in each language. Proficiency in each language was assessed with 12 cm visual analog scales (the left anchor representing no proficiency and the right anchor representing native-like proficiency) for seven language domains (e.g., overall ability, speaking and listening in casual situations, speaking and listening in formal situations, reading, and writing). A visual analog scale also was used to obtain a language dominance rating. Spanish was represented on the left anchor of the scale, and English was represented on the right anchor of the scale. Language proficiency scores were calculated by measuring the placement of marks in millimeters on the visual analog scales and converting them to a scale of 100. Ratings from the seven language domains were averaged to obtain an overall proficiency score. Dominance ratings were calculated by measuring how many millimeters from the midpoint of the line was bisected. Ratings were converted to a scale of 50. The midpoint was considered to be a rating of 0. The left anchor point was considered to be a rating of −50 and the right anchor point was considered to be a rating of +50. Thus, negative ratings represented Spanish dominance and positive ratings represented English dominance. Dominance ratings between −8 and +8 were considered relatively balanced bilingualism. Table 1 shows the demographic information and language proficiency ratings for each participant. No objective measures of language proficiency were administered in this study. Self-ratings of language proficiency in bilingual individuals correlate significantly with objective measures of proficiency (Luk & Bialystok, 2013). Ratings of language dominance also are significantly correlated with objective measures of confrontation naming (Gollan, Weissberger, Runnqvist, Montoya, & Cera, 2011). Ideally, a combination of self-reports and objective measures is recommended (Gollan et al., 2011); however, the present study acknowledges the limitation that no objective measures of language proficiency were administered. Experimental Task: Generative Naming Measures of verbal fluency were administered as experimental tasks. Participants were asked to generate as many words as possible in 1 min based on broad semantic categories (animals, foods), word-initial letters (F, S), and emotional categories (joy, anger). Each generation task was administered in both languages for a total of 12 generative naming samples from each participant. To minimize priming effects, an individual generation task was not administered in both languages during the same session. For example, the examiners elicited samples for “animals” in English during one session and in Spanish during the other. Order of administration was pseudo-randomized, and one of two different administration sequences was randomly assigned to each participant in order to minimize sequence effects. Prior to beginning the tasks, the examiners read instructions in English and provided examples of naming in each type of generative task (two in English, one in Spanish). Participants were requested to retrieve single words and to avoid repeating the same word. After confirming that the participants understood the tasks, the examiners provided instructions for each experimental task in the language of the target sample. For example, when eliciting the category of animals in English, the examiner said, “Tell me all the animals you know. You have one minute. Are you ready? Start now.” When eliciting the category of alegría in Spanish, the examiner said, “Dígame todas las palabras que sabe que están relacionadas con el sentimiento de alegría. Tiene uno minuto. Esta listo? Ahora empiece.” For all task conditions, examiners told participants to stop naming after 60 s. Responses were audio recorded for scoring. Six participants expressed unfamiliarity with the word ira as a term for anger. Dialectical or proficiency differences may have accounted for the unfamiliarity. If the participant did not understand the word, the examiner provided enojo as a closely related synonymn. Enojo and ira both have English translations of anger. All participants understood enojo. Data Analysis Responses were transcribed and scored by the researcher and a trained undergraduate research assistant. Each generated item was counted as 1 if it was spoken in the language of the instructions, represented the target category and was not a repetition of a previous word in the sample. Errors were categorized into three groups: language choice errors, categorical errors, and repetition errors (see Table 2). Responses in the emotional categories were more specific to an individual's experiences than responses in the other two categories. As a result, items were accepted as representative of an emotional category if they were not definitively in contradiction with it (e.g., “laughter” in the category of “sadness”). In the category fluency and emotional fluency tasks, if a participant produced a superordinate category (such as “birds”) in conjunction with specific exemplars (such as “toucan” and “parrot”), only the specific exemplars were counted as unique items. Proper nouns were not included in the total number of correct responses. Table 2. Response codes for verbal fluency transcripts Description Example Error codes Language choice error (L) An item produced in the non-target language. Some items from the non-target language were accepted if speakers had generally adopted the word into the language. “Possole” in the English foods task is a language choice error. “Tacos” in the English foods task is not a language choice error because the word has been generally adopted as a food term in American English. Repetition error (R) An item that was already produced in the same sample. “Cat, dog, horse, cat.” The repetition of the word “cat” is a repetition error. “Walk, walked, walking.” The repetitions of the word “walk” with different regular conjugations are repetition errors. Category error (C) An item in the target language that does not belong in the target category. “Restaurant” in the English foods condition is a category error. “Cistern” in the English letter S condition is a category error. Other exclusion codes Proper noun (P) An item which refers to one specific person, place, object or day. “Christmas,” “Gloria,” and “Friday,” are proper nouns. Superordinate category (SC) A broad label that could be applied to several exemplars. An item was only coded as SC if exemplars of the category were also generated. “Birds, eagle, bluebird, parrot.” Birds is the superordinate category for eagle, bluebird and parrot. Unintelligible words and unrecognized words (X) An item that could not be accurately transcribed due to the speaker's delivery or an item that the scorer did not recognize as a word. These items were excluded but not considered errors because (a) some dialects of Spanish may include words that the Mexican-American dialect does not and (b) at times, audio recordings may be unreliable representations of speech productions. “Forugo” in the Spanish letter F condition was not recognized by the scorer. A few items for which delivery was unclear (i.e., mumbled, very softly spoken) could not be accurately transcribed. Description Example Error codes Language choice error (L) An item produced in the non-target language. Some items from the non-target language were accepted if speakers had generally adopted the word into the language. “Possole” in the English foods task is a language choice error. “Tacos” in the English foods task is not a language choice error because the word has been generally adopted as a food term in American English. Repetition error (R) An item that was already produced in the same sample. “Cat, dog, horse, cat.” The repetition of the word “cat” is a repetition error. “Walk, walked, walking.” The repetitions of the word “walk” with different regular conjugations are repetition errors. Category error (C) An item in the target language that does not belong in the target category. “Restaurant” in the English foods condition is a category error. “Cistern” in the English letter S condition is a category error. Other exclusion codes Proper noun (P) An item which refers to one specific person, place, object or day. “Christmas,” “Gloria,” and “Friday,” are proper nouns. Superordinate category (SC) A broad label that could be applied to several exemplars. An item was only coded as SC if exemplars of the category were also generated. “Birds, eagle, bluebird, parrot.” Birds is the superordinate category for eagle, bluebird and parrot. Unintelligible words and unrecognized words (X) An item that could not be accurately transcribed due to the speaker's delivery or an item that the scorer did not recognize as a word. These items were excluded but not considered errors because (a) some dialects of Spanish may include words that the Mexican-American dialect does not and (b) at times, audio recordings may be unreliable representations of speech productions. “Forugo” in the Spanish letter F condition was not recognized by the scorer. A few items for which delivery was unclear (i.e., mumbled, very softly spoken) could not be accurately transcribed. Table 2. Response codes for verbal fluency transcripts Description Example Error codes Language choice error (L) An item produced in the non-target language. Some items from the non-target language were accepted if speakers had generally adopted the word into the language. “Possole” in the English foods task is a language choice error. “Tacos” in the English foods task is not a language choice error because the word has been generally adopted as a food term in American English. Repetition error (R) An item that was already produced in the same sample. “Cat, dog, horse, cat.” The repetition of the word “cat” is a repetition error. “Walk, walked, walking.” The repetitions of the word “walk” with different regular conjugations are repetition errors. Category error (C) An item in the target language that does not belong in the target category. “Restaurant” in the English foods condition is a category error. “Cistern” in the English letter S condition is a category error. Other exclusion codes Proper noun (P) An item which refers to one specific person, place, object or day. “Christmas,” “Gloria,” and “Friday,” are proper nouns. Superordinate category (SC) A broad label that could be applied to several exemplars. An item was only coded as SC if exemplars of the category were also generated. “Birds, eagle, bluebird, parrot.” Birds is the superordinate category for eagle, bluebird and parrot. Unintelligible words and unrecognized words (X) An item that could not be accurately transcribed due to the speaker's delivery or an item that the scorer did not recognize as a word. These items were excluded but not considered errors because (a) some dialects of Spanish may include words that the Mexican-American dialect does not and (b) at times, audio recordings may be unreliable representations of speech productions. “Forugo” in the Spanish letter F condition was not recognized by the scorer. A few items for which delivery was unclear (i.e., mumbled, very softly spoken) could not be accurately transcribed. Description Example Error codes Language choice error (L) An item produced in the non-target language. Some items from the non-target language were accepted if speakers had generally adopted the word into the language. “Possole” in the English foods task is a language choice error. “Tacos” in the English foods task is not a language choice error because the word has been generally adopted as a food term in American English. Repetition error (R) An item that was already produced in the same sample. “Cat, dog, horse, cat.” The repetition of the word “cat” is a repetition error. “Walk, walked, walking.” The repetitions of the word “walk” with different regular conjugations are repetition errors. Category error (C) An item in the target language that does not belong in the target category. “Restaurant” in the English foods condition is a category error. “Cistern” in the English letter S condition is a category error. Other exclusion codes Proper noun (P) An item which refers to one specific person, place, object or day. “Christmas,” “Gloria,” and “Friday,” are proper nouns. Superordinate category (SC) A broad label that could be applied to several exemplars. An item was only coded as SC if exemplars of the category were also generated. “Birds, eagle, bluebird, parrot.” Birds is the superordinate category for eagle, bluebird and parrot. Unintelligible words and unrecognized words (X) An item that could not be accurately transcribed due to the speaker's delivery or an item that the scorer did not recognize as a word. These items were excluded but not considered errors because (a) some dialects of Spanish may include words that the Mexican-American dialect does not and (b) at times, audio recordings may be unreliable representations of speech productions. “Forugo” in the Spanish letter F condition was not recognized by the scorer. A few items for which delivery was unclear (i.e., mumbled, very softly spoken) could not be accurately transcribed. Based on the scoring criteria, the number of correct responses produced on each task for each participant was tabulated. Group means were calculated for each task and task type within and across languages. A two-way repeated measures analysis of variance (ANOVA) was completed to compare differences in the number of items produced in the category, emotional and letter fluency tasks for the two languages. Paired sample t-tests were used to examine differences in the number of items produced for the English joy and anger conditions and the Spanish alegría and ira conditions. Pearson product moment correlation coefficients were calculated to determine the relationship between language proficiency and number of items produced in each language. Pearson coefficients also were calculated to examine correlations between language proficiency differences and the differences in the number of items produced between languages, as well as dominance ratings and differences in the number of items produced between languages. Reliability Interjudge reliability between two trained undergraduate research assistants was obtained for the transcriptions and response coding of three participants. Interjudge agreement was calculated by dividing the number of agreements between judges by the total number of agreements and disagreements. Interjudge reliability for the transcription of responses was .96. Interjudge reliability for the assignment of scoring codes to responses also was .96. Results The participants generated a total of 4,369 items on the generative naming tasks. Of these items, 258 (5.9%) were coded as errors or exclusions and were not included in the total number of correct responses. Table 3 summarizes the types of responses that were excluded. The most common type of exclusion was repeated items (n = 95). Table 3. Number of exclusions divided by response code R C L SC P X Total Number 95 18 20 60 57 8 258 Percentagea 2.2 <1 <1 1.4 1.3 <1 5.9 R C L SC P X Total Number 95 18 20 60 57 8 258 Percentagea 2.2 <1 <1 1.4 1.3 <1 5.9 Note: R = repetition errors; C = category errors; L = language choice errors; SC = subordinate category; P = proper noun; X = unintelligible or unrecognized response aPercentage of total responses (4,369). Table 3. Number of exclusions divided by response code R C L SC P X Total Number 95 18 20 60 57 8 258 Percentagea 2.2 <1 <1 1.4 1.3 <1 5.9 R C L SC P X Total Number 95 18 20 60 57 8 258 Percentagea 2.2 <1 <1 1.4 1.3 <1 5.9 Note: R = repetition errors; C = category errors; L = language choice errors; SC = subordinate category; P = proper noun; X = unintelligible or unrecognized response aPercentage of total responses (4,369). The total number of correct items produced for each task and language, as well as group means for each task, are shown in Table 4. The mean number of items produced in English was 112.48; the mean number in Spanish was 84.43. The greatest number of items was produced for the English foods condition (M = 25.57; SD = 7.05). The least number of items was produced for the Spanish ira condition (M = 9.52; SD = 4.21). Group means for each task in each language are shown in Fig. 1. Table 4. Total number of correct items produced by participants by task condition and language ID English Spanish Animals Foods Letter S Letter F Joy Anger Total Animales Comidas Letra S Letra F Alegría Ira Total 1 19 18 8 3 23 10 81 15 19 3 12 11 8 68 2 37 36 27 17 19 13 149 17 25 15 7 12 7 83 3 17 25 14 19 15 10 100 13 16 11 11 12 7 70 4 29 24 17 15 22 14 121 23 25 12 6 19 13 98 5 32 28 14 16 12 10 112 12 23 15 13 12 11 86 6 21 18 21 14 12 13 99 15 16 8 9 3 3 54 7 20 34 21 20 15 13 123 18 22 13 9 12 9 83 8 25 27 21 14 7 7 101 11 18 11 11 7 5 63 9 22 15 17 17 11 10 92 26 19 23 19 11 11 109 10 22 20 16 17 21 17 113 24 21 15 16 22 10 108 11 36 35 18 11 12 8 120 20 23 11 10 14 10 88 12 21 24 15 13 12 9 94 16 18 5 6 8 5 58 13 24 27 18 16 18 7 110 27 30 24 17 12 7 117 14 26 23 21 16 18 9 113 24 15 11 13 12 7 82 15 29 36 22 15 17 18 137 16 19 12 10 19 16 92 16 27 32 30 24 26 20 159 19 21 17 11 10 14 92 17 25 36 34 24 20 16 155 15 25 14 12 16 12 94 18 23 18 13 10 14 7 85 18 19 13 11 7 12 80 19 21 26 12 12 10 13 94 18 17 3 9 15 10 72 20 29 18 19 26 12 18 122 29 25 25 22 16 20 137 21 22 17 14 12 11 6 82 12 12 4 3 5 3 39 MN 25.1 25.57 18.67 15.76 15.57 11.81 112.48 18.48 20.38 12.62 11.29 12.14 9.52 84.43 SD 5.35 7.05 6.12 5.18 4.97 4.15 22.9 5.22 4.26 6.24 4.46 4.71 4.21 22.58 ID English Spanish Animals Foods Letter S Letter F Joy Anger Total Animales Comidas Letra S Letra F Alegría Ira Total 1 19 18 8 3 23 10 81 15 19 3 12 11 8 68 2 37 36 27 17 19 13 149 17 25 15 7 12 7 83 3 17 25 14 19 15 10 100 13 16 11 11 12 7 70 4 29 24 17 15 22 14 121 23 25 12 6 19 13 98 5 32 28 14 16 12 10 112 12 23 15 13 12 11 86 6 21 18 21 14 12 13 99 15 16 8 9 3 3 54 7 20 34 21 20 15 13 123 18 22 13 9 12 9 83 8 25 27 21 14 7 7 101 11 18 11 11 7 5 63 9 22 15 17 17 11 10 92 26 19 23 19 11 11 109 10 22 20 16 17 21 17 113 24 21 15 16 22 10 108 11 36 35 18 11 12 8 120 20 23 11 10 14 10 88 12 21 24 15 13 12 9 94 16 18 5 6 8 5 58 13 24 27 18 16 18 7 110 27 30 24 17 12 7 117 14 26 23 21 16 18 9 113 24 15 11 13 12 7 82 15 29 36 22 15 17 18 137 16 19 12 10 19 16 92 16 27 32 30 24 26 20 159 19 21 17 11 10 14 92 17 25 36 34 24 20 16 155 15 25 14 12 16 12 94 18 23 18 13 10 14 7 85 18 19 13 11 7 12 80 19 21 26 12 12 10 13 94 18 17 3 9 15 10 72 20 29 18 19 26 12 18 122 29 25 25 22 16 20 137 21 22 17 14 12 11 6 82 12 12 4 3 5 3 39 MN 25.1 25.57 18.67 15.76 15.57 11.81 112.48 18.48 20.38 12.62 11.29 12.14 9.52 84.43 SD 5.35 7.05 6.12 5.18 4.97 4.15 22.9 5.22 4.26 6.24 4.46 4.71 4.21 22.58 Table 4. Total number of correct items produced by participants by task condition and language ID English Spanish Animals Foods Letter S Letter F Joy Anger Total Animales Comidas Letra S Letra F Alegría Ira Total 1 19 18 8 3 23 10 81 15 19 3 12 11 8 68 2 37 36 27 17 19 13 149 17 25 15 7 12 7 83 3 17 25 14 19 15 10 100 13 16 11 11 12 7 70 4 29 24 17 15 22 14 121 23 25 12 6 19 13 98 5 32 28 14 16 12 10 112 12 23 15 13 12 11 86 6 21 18 21 14 12 13 99 15 16 8 9 3 3 54 7 20 34 21 20 15 13 123 18 22 13 9 12 9 83 8 25 27 21 14 7 7 101 11 18 11 11 7 5 63 9 22 15 17 17 11 10 92 26 19 23 19 11 11 109 10 22 20 16 17 21 17 113 24 21 15 16 22 10 108 11 36 35 18 11 12 8 120 20 23 11 10 14 10 88 12 21 24 15 13 12 9 94 16 18 5 6 8 5 58 13 24 27 18 16 18 7 110 27 30 24 17 12 7 117 14 26 23 21 16 18 9 113 24 15 11 13 12 7 82 15 29 36 22 15 17 18 137 16 19 12 10 19 16 92 16 27 32 30 24 26 20 159 19 21 17 11 10 14 92 17 25 36 34 24 20 16 155 15 25 14 12 16 12 94 18 23 18 13 10 14 7 85 18 19 13 11 7 12 80 19 21 26 12 12 10 13 94 18 17 3 9 15 10 72 20 29 18 19 26 12 18 122 29 25 25 22 16 20 137 21 22 17 14 12 11 6 82 12 12 4 3 5 3 39 MN 25.1 25.57 18.67 15.76 15.57 11.81 112.48 18.48 20.38 12.62 11.29 12.14 9.52 84.43 SD 5.35 7.05 6.12 5.18 4.97 4.15 22.9 5.22 4.26 6.24 4.46 4.71 4.21 22.58 ID English Spanish Animals Foods Letter S Letter F Joy Anger Total Animales Comidas Letra S Letra F Alegría Ira Total 1 19 18 8 3 23 10 81 15 19 3 12 11 8 68 2 37 36 27 17 19 13 149 17 25 15 7 12 7 83 3 17 25 14 19 15 10 100 13 16 11 11 12 7 70 4 29 24 17 15 22 14 121 23 25 12 6 19 13 98 5 32 28 14 16 12 10 112 12 23 15 13 12 11 86 6 21 18 21 14 12 13 99 15 16 8 9 3 3 54 7 20 34 21 20 15 13 123 18 22 13 9 12 9 83 8 25 27 21 14 7 7 101 11 18 11 11 7 5 63 9 22 15 17 17 11 10 92 26 19 23 19 11 11 109 10 22 20 16 17 21 17 113 24 21 15 16 22 10 108 11 36 35 18 11 12 8 120 20 23 11 10 14 10 88 12 21 24 15 13 12 9 94 16 18 5 6 8 5 58 13 24 27 18 16 18 7 110 27 30 24 17 12 7 117 14 26 23 21 16 18 9 113 24 15 11 13 12 7 82 15 29 36 22 15 17 18 137 16 19 12 10 19 16 92 16 27 32 30 24 26 20 159 19 21 17 11 10 14 92 17 25 36 34 24 20 16 155 15 25 14 12 16 12 94 18 23 18 13 10 14 7 85 18 19 13 11 7 12 80 19 21 26 12 12 10 13 94 18 17 3 9 15 10 72 20 29 18 19 26 12 18 122 29 25 25 22 16 20 137 21 22 17 14 12 11 6 82 12 12 4 3 5 3 39 MN 25.1 25.57 18.67 15.76 15.57 11.81 112.48 18.48 20.38 12.62 11.29 12.14 9.52 84.43 SD 5.35 7.05 6.12 5.18 4.97 4.15 22.9 5.22 4.26 6.24 4.46 4.71 4.21 22.58 Fig. 1. View largeDownload slide Mean number of items generated in English and Spanish for each task. Fig. 1. View largeDownload slide Mean number of items generated in English and Spanish for each task. The group means for items generated divided by task type and language are presented in Table 5 and Fig. 2. Category fluency had the highest mean (M = 22.38; SD = 6.26); emotional fluency had the lowest mean (M = 12.26; SD = 4.95). Letter fluency had a mean slightly greater than emotional fluency (M = 14.58; SD = 6.16). The average number of items produced was 18.75 for English and 14.07 for Spanish. Table 5. Group means and standard deviations by task type and language Category Letter Emotional Overall Mean SD Mean SD Mean SD Mean SD English 25.33 6.19 17.21 5.79 13.69 4.91 18.75 7.44 Spanish 19.43 4.8 11.95 5.4 10.83 4.61 14.07 6.23 Both 22.38 6.26 14.58 6.16 12.26 4.95 16.41 7.24 Category Letter Emotional Overall Mean SD Mean SD Mean SD Mean SD English 25.33 6.19 17.21 5.79 13.69 4.91 18.75 7.44 Spanish 19.43 4.8 11.95 5.4 10.83 4.61 14.07 6.23 Both 22.38 6.26 14.58 6.16 12.26 4.95 16.41 7.24 Table 5. Group means and standard deviations by task type and language Category Letter Emotional Overall Mean SD Mean SD Mean SD Mean SD English 25.33 6.19 17.21 5.79 13.69 4.91 18.75 7.44 Spanish 19.43 4.8 11.95 5.4 10.83 4.61 14.07 6.23 Both 22.38 6.26 14.58 6.16 12.26 4.95 16.41 7.24 Category Letter Emotional Overall Mean SD Mean SD Mean SD Mean SD English 25.33 6.19 17.21 5.79 13.69 4.91 18.75 7.44 Spanish 19.43 4.8 11.95 5.4 10.83 4.61 14.07 6.23 Both 22.38 6.26 14.58 6.16 12.26 4.95 16.41 7.24 Fig. 2. View largeDownload slide Group means and standard deviations by task type and language. Fig. 2. View largeDownload slide Group means and standard deviations by task type and language. Number of Items Produced in English and Spanish on Verbal Fluency Tasks A two-way repeated measures ANOVA revealed significant differences between language (F = 28.38, p < .01) and task type (F = 95.20, p < .01) for the number of items produced. The interaction between language and task type was not significant (F = 2.62, p > .05). Significantly more items were produced in English than in Spanish, consistent with higher ratings of English proficiency (M = 96.26; SD = 7.51) than Spanish proficiency (M = 73.81; SD = 18.29). A post-hoc paired samples t-test with Bonferroni correction examined differences in the number of items produced between task types (see Table 6). There was a significant difference in the number of items produced in category fluency (M = 22.38, SD = 6.26) compared to letter fluency (M = 14.58, SD = 6.16); t(20) = 9.55, p < .05. The difference between category fluency and emotional fluency (M = 12.26, SD = 4.95) also was significant; t(20) = 13.26, p < .05. The difference between letter fluency and emotional fluency was not significant. Table 6. Post-hoc paired t-test results for category fluency, letter fluency and emotional fluency in Spanish and English 95% CI for mean difference t-Value df p-Value (two-tailed) Category–Letter 5.67, 9.93 9.55 20 .000* Category–Emotional 8.13, 12.11 13.26 20 .000* Letter–Emotional 0.01, 4.64 2.62 20 .016 95% CI for mean difference t-Value df p-Value (two-tailed) Category–Letter 5.67, 9.93 9.55 20 .000* Category–Emotional 8.13, 12.11 13.26 20 .000* Letter–Emotional 0.01, 4.64 2.62 20 .016 *Significant at α = .05 with Bonferroni correction. Table 6. Post-hoc paired t-test results for category fluency, letter fluency and emotional fluency in Spanish and English 95% CI for mean difference t-Value df p-Value (two-tailed) Category–Letter 5.67, 9.93 9.55 20 .000* Category–Emotional 8.13, 12.11 13.26 20 .000* Letter–Emotional 0.01, 4.64 2.62 20 .016 95% CI for mean difference t-Value df p-Value (two-tailed) Category–Letter 5.67, 9.93 9.55 20 .000* Category–Emotional 8.13, 12.11 13.26 20 .000* Letter–Emotional 0.01, 4.64 2.62 20 .016 *Significant at α = .05 with Bonferroni correction. Post-hoc paired samples t-tests with Bonferroni correction revealed a significant difference in number of items produced in English category fluency (M = 25.33, SD = 6.19) compared to English letter fluency (M = 17.21, SD = 5.79), English category fluency compared to English emotional fluency (M = 13.69, SD = 4.91), and English letter fluency compared to English emotional fluency (t(20) = 6.95, 9.18, 3.40, p < .05) (see Table 7). Table 7. Post-hoc paired t-test results for category fluency, letter fluency, and emotional fluency in English 95% CI for mean difference t-Value df p-Value (two-tailed) Category–Letter 5.68, 10.56 6.95 20 .000* Category–Emotional 8.99, 14.29 9.18 20 .000* Letter–Emotional 1.36, 5.69 3.40 20 .003* 95% CI for mean difference t-Value df p-Value (two-tailed) Category–Letter 5.68, 10.56 6.95 20 .000* Category–Emotional 8.99, 14.29 9.18 20 .000* Letter–Emotional 1.36, 5.69 3.40 20 .003* *Significant at α = .05 with Bonferroni correction. Table 7. Post-hoc paired t-test results for category fluency, letter fluency, and emotional fluency in English 95% CI for mean difference t-Value df p-Value (two-tailed) Category–Letter 5.68, 10.56 6.95 20 .000* Category–Emotional 8.99, 14.29 9.18 20 .000* Letter–Emotional 1.36, 5.69 3.40 20 .003* 95% CI for mean difference t-Value df p-Value (two-tailed) Category–Letter 5.68, 10.56 6.95 20 .000* Category–Emotional 8.99, 14.29 9.18 20 .000* Letter–Emotional 1.36, 5.69 3.40 20 .003* *Significant at α = .05 with Bonferroni correction. A post-hoc paired samples t-test with Bonferroni correction revealed a significant difference in number of items produced in Spanish category fluency (M = 19.43, SD = 4.80) compared to Spanish letter fluency (M = 11.95, SD = 5.40) and in Spanish category fluency compared to Spanish emotional fluency (M = 10.83, SD = 4.61); t(20) = 10.35, 10.76, p < .05. No significant difference was found between Spanish letter fluency and Spanish emotional fluency (see Table 8). Table 8. Post-hoc paired t-test results for category fluency, letter fluency and emotional fluency in Spanish 95% CI for mean difference t-Value df p-Value (two-tailed) Category–Letter 5.97, 8.98 10.35 20 .000* Category–Emotional 6.93, 10.26 10.76 20 .000* Letter–Emotional −1.04, 3.28 1.08 20 .292 95% CI for mean difference t-Value df p-Value (two-tailed) Category–Letter 5.97, 8.98 10.35 20 .000* Category–Emotional 6.93, 10.26 10.76 20 .000* Letter–Emotional −1.04, 3.28 1.08 20 .292 *Significant at α = .05 with Bonferroni correction. Table 8. Post-hoc paired t-test results for category fluency, letter fluency and emotional fluency in Spanish 95% CI for mean difference t-Value df p-Value (two-tailed) Category–Letter 5.97, 8.98 10.35 20 .000* Category–Emotional 6.93, 10.26 10.76 20 .000* Letter–Emotional −1.04, 3.28 1.08 20 .292 95% CI for mean difference t-Value df p-Value (two-tailed) Category–Letter 5.97, 8.98 10.35 20 .000* Category–Emotional 6.93, 10.26 10.76 20 .000* Letter–Emotional −1.04, 3.28 1.08 20 .292 *Significant at α = .05 with Bonferroni correction. Relationship Between Language Proficiency and Number of Items Produced in Each Language Pearson product moment correlation coefficients were calculated for English proficiency ratings and number of English items produced in each task as well as overall (see Table 9). Self-reported English proficiency ratings were weakly correlated with number of items produced in letter fluency, emotional fluency, and overall (r = 0.05, 0.28, and 0.31). English proficiency ratings were moderately correlated with number of English items produced in category fluency (r = 0.41). This relationship did not reach significance. Correlations were likely reduced because of the ceiling effect in English. Most participants rated English proficiency at or near 100%. Table 9. Pearson product moment correlation coefficients for language proficiency and the number of items produced in each language Category fluency Letter fluency Emotional fluency Total English proficiency 0.41 0.05 0.28 0.31 Spanish proficiency 0.61* 0.48 0.45 0.59* Category fluency Letter fluency Emotional fluency Total English proficiency 0.41 0.05 0.28 0.31 Spanish proficiency 0.61* 0.48 0.45 0.59* *Significant at α = .05 with Bonferroni correction. Table 9. Pearson product moment correlation coefficients for language proficiency and the number of items produced in each language Category fluency Letter fluency Emotional fluency Total English proficiency 0.41 0.05 0.28 0.31 Spanish proficiency 0.61* 0.48 0.45 0.59* Category fluency Letter fluency Emotional fluency Total English proficiency 0.41 0.05 0.28 0.31 Spanish proficiency 0.61* 0.48 0.45 0.59* *Significant at α = .05 with Bonferroni correction. Pearson coefficients also were calculated between Spanish proficiency ratings and number of Spanish items produced in each task type as well as overall (see Table 9). Self-reported Spanish proficiency ratings were moderately correlated with number of Spanish items produced in letter fluency tasks and emotional fluency tasks (r = 0.48, and 0.45). These relationships did not reach significance after Bonferroni correction. Spanish proficiency ratings were strongly correlated with number of Spanish items produced in category fluency tasks and overall (r = 0.61, 0.59). These correlations were significant with Bonferroni correction (p < .0125). Self-Reported Dominance Ratings and Differences in Language Proficiency Ratings The difference score between the Spanish and English overall proficiencies was calculated for each participant by subtracting the Spanish overall proficiency score from the English overall proficiency score. Differences were positive or negative, depending on whether the participants rated themselves higher in English or Spanish. A larger difference indicated a less balanced bilingualism, and a smaller difference indicated a more balanced bilingualism. A negative value indicated higher proficiency in Spanish than English and a positive value indicated higher proficiency in English. Dominance ratings obtained from the visual analog scale for dominance on the LUS were represented by a negative score (as low as −50) for Spanish dominance or a positive score (as high as +50) for English dominance. Balanced bilingualism was represented by a 0. A Pearson correlation coefficient was calculated between difference scores and dominance ratings. The two measures were strongly correlated (r = 0.77), and this relationship was significant after Bonferroni correction (p < .005). Relationship Between Dominance Ratings and Verbal Fluency Pearson correlation coefficients were calculated for dominance ratings and the difference in number of productions between languages for each task type and overall (see Table 10). Differences in the number of productions for each task type were calculated by subtracting the number of Spanish productions for a task type from the number of English productions. A resulting negative difference indicated that more items were produced in Spanish. A resulting positive difference indicated that more items were produced in English. Dominance ratings were moderately correlated with the language production difference in category fluency and emotional fluency (r = 0.44, 0.38). These relationships were not significant after Bonferroni correction. Dominance ratings were strongly correlated with language production difference in letter fluency and overalli (r = 0.66, 0.63). These relationships reached significance after Bonferroni correction (p < .005). Table 10. Pearson product moment correlation coefficients for language proficiency difference scores, language dominance ratings, and language production differences CF difference LF difference EF difference Total difference Language proficiency difference score 0.69* 0.68* 0.38 0.77* Language dominance rating 0.44 0.66* 0.38 0.63* CF difference LF difference EF difference Total difference Language proficiency difference score 0.69* 0.68* 0.38 0.77* Language dominance rating 0.44 0.66* 0.38 0.63* Note: CF = category fluency; LF = letter fluency; EF = emotional fluency. *Significant at α = .05 with Bonferroni correction. Table 10. Pearson product moment correlation coefficients for language proficiency difference scores, language dominance ratings, and language production differences CF difference LF difference EF difference Total difference Language proficiency difference score 0.69* 0.68* 0.38 0.77* Language dominance rating 0.44 0.66* 0.38 0.63* CF difference LF difference EF difference Total difference Language proficiency difference score 0.69* 0.68* 0.38 0.77* Language dominance rating 0.44 0.66* 0.38 0.63* Note: CF = category fluency; LF = letter fluency; EF = emotional fluency. *Significant at α = .05 with Bonferroni correction. Relationships Between Proficiency Difference Scores and Language Production Differences Pearson correlation coefficients were calculated between proficiency difference scores and production differences (see Table 10). Proficiency difference scores were strongly correlated with language production differences in category fluency, letter fluency and overall (r = 0.69, 0.68, and 0.77). These relationships reached significance after Bonferroni correction (p < .005). Proficiency difference scores were moderately correlated with language production difference in emotional fluency (r = 0.38). This relationship did not reach significance. Differences Between Positive and Negative Emotional Verbal Fluency Tasks Paired t-tests corrected with the Bonferroni procedure examined the difference between the joy and anger conditions in English and Spanish (see Table 11). Results indicated that the mean group difference was significant in both English (t = 3.75, p < .05) and Spanish (t = 3.13, p < .05). Table 11. Paired t-test results for joy and anger comparisons in English and Spanish 95% CI for mean difference t-Value df p-Value (two-tailed) Joy–Anger 1.67–5.85 3.75 20 .001* Alegría–Ira 0.87–4.37 3.13 20 .005* 95% CI for mean difference t-Value df p-Value (two-tailed) Joy–Anger 1.67–5.85 3.75 20 .001* Alegría–Ira 0.87–4.37 3.13 20 .005* *Significant at α = .05 with Bonferroni correction. Table 11. Paired t-test results for joy and anger comparisons in English and Spanish 95% CI for mean difference t-Value df p-Value (two-tailed) Joy–Anger 1.67–5.85 3.75 20 .001* Alegría–Ira 0.87–4.37 3.13 20 .005* 95% CI for mean difference t-Value df p-Value (two-tailed) Joy–Anger 1.67–5.85 3.75 20 .001* Alegría–Ira 0.87–4.37 3.13 20 .005* *Significant at α = .05 with Bonferroni correction. Discussion The participants generated the greatest number of items on the category fluency tasks. The group generated the least number of items in the emotional fluency tasks. ANOVA results revealed significant differences between the mean number of items generated for category fluency versus letter fluency and category fluency versus emotional fluency, but not for letter fluency versus emotional fluency. This pattern remained the same in a comparison of the three task types in Spanish, but a comparison of the three task types in English revealed a significant difference between letter fluency and emotional fluency. Most of the participants reported formal education in English and lower Spanish proficiency levels. Performance in letter fluency is related to level of literacy, so lack of formal education in Spanish may have contributed to lower letter fluency scores, which led to comparable performance between letter and emotional fluency in Spanish. This pattern is an important consideration for bilingual individuals who may be completing testing in their second or less literate language. The results of the study do not support results from Sass and colleagues (2013) who found that performance on category fluency tasks was comparable to performance on emotional fluency tasks. One explanation for this discrepancy may be the different designs of the emotional verbal fluency tasks. Participants in Sass and colleagues (2013) produced single words and short phrases during the emotional fluency task. To adjust for the difference between average length of responses between the category fluency task and the emotional fluency task the examiners adjusted verbal fluency scores by dividing the total number of correct items by the mean number of syllables. The adjustment may have artificially inflated emotional verbal fluency scores relative to category fluency scores. Participants in the present study were asked to respond using single words, which eliminated the need for this data analysis adjustment. Performance on emotional verbal fluency tasks depends upon utilization of lexical–conceptual connections and the integrity of semantic networks as well as emotional association networks. When generating a list of words that are related to an emotion, cognitive processing load is increased relative to a neutral category fluency task. This increased cognitive processing load is one possible explanation for decreased output in emotional verbal fluency tasks. Furthermore, results suggested that emotional verbal fluency is less dependent upon language proficiency than category fluency or letter fluency. The calculation of Pearson correlation coefficients between language proficiency scores (English proficiency score – Spanish proficiency score) and production difference scores (items produced in English – Items produced in Spanish for each task type) revealed that language proficiency difference was significantly correlated with production difference for category fluency (0.69), letter fluency (0.68) and overall production (0.77), but not for emotional fluency (0.38). Emotional fluency also showed the weakest correlation between language dominance ratings and production differences. The significantly weaker relationship of emotional verbal fluency to language proficiencies as compared to category fluency and letter fluency suggests that task performance is strongly affected by a domain other than language ability, most likely emotional processing. If emotional verbal fluency is more indicative of emotional processing ability than language ability, it may be a useful tool for assessing the emotional processing deficits observed in depression, TBI, right hemisphere disorders and other clinical populations. For example, individuals with traumatic brain damage present with a variety of deficits along a broad continuum of severity. Impairment profiles often vary widely between individuals. Individuals with TBI often demonstrate emotional processing deficits post-injury (Dimoska, Mcdonald, Pell, Tate, & James, 2010), though every individual requires a tailored evaluation and treatment plan. Emotional verbal fluency may prove to be an inexpensive and quick way to assess whether an individual experiences a deficit in emotional processing. Differences between numbers of items generated for joy versus anger were compared to examine whether participants demonstrated a positivity bias within the task. Significantly more items were produced in English joy as compared to English anger, and significantly more items were produced in Spanish alegría as compared to Spanish ira. These results support findings from Sass and colleagues (2013) who found that participants produced the greatest number of items for the emotional category of joy. Furthermore, these results support a positivity bias in language processing (Kuchinke et al., 2005; Sass et al., 2011) as well as an advantage for cognitive processing of positively associated information (Ashby et al., 1999). Increased performance on positive emotional fluency tasks as compared to negative emotional fluency tasks may be influenced by the beneficial effects of positive associations on semantic processing and cognitive processing. The positivity bias demonstrated in emotional verbal fluency suggests another potential clinical application. The assessment may be effective as one component of screening for depression in returning war veterans, as well as other at-risk individuals. Neurotypical individuals experience increased performance on positive emotional fluency tasks as compared to negative emotional fluency tasks, likely due to a positivity bias in language and cognitive processing. Studies have suggested that individuals with depression often demonstrate a negativity bias in language processing (Gotlib, Krasnoperova, Yue, & Joormann, 2004; Rude, Wenzlaff, Gibbs, Vane, & Whitney, 2002), which may affect their pattern of performance. This clinical application can be explored in future research that focuses on the performance of individuals with diagnosed depression on emotional verbal fluency tasks. Significantly more items were generated in English than in Spanish, which is consistent with self-reports of English dominance. Group participants reported English dominance (n = 15) or balanced bilingualism (n = 6). No participants reported Spanish dominance. Pearson correlation coefficients between language proficiency ratings and number of items generated for each task type in each language were moderately positive. Only the relationships of Spanish proficiency with Spanish category fluency and Spanish proficiency with overall Spanish productions reached significance. English proficiency levels may have been too homogenous (majority near 100) to provide a meaningful scale for correlations. A clear pattern emerged in correlations between the differences in language proficiency ratings and the differences between productions in each language. The difference between language proficiency ratings was significantly correlated with the production difference in category fluency, letter fluency and overall performance. This measure was not correlated with production difference in emotional fluency. This pattern suggests that for category and letter fluency, obtained self-ratings of proficiency in each language may be a reliable way to interpret observed differences between performances in each language. Dominance ratings correlated significantly with production differences in letter fluency and overall, but not with category fluency or emotional fluency. Calculation of a language proficiency difference score may be a more accurate measure for identifying differences between languages than a rating for language dominance. There were several limitations to the study. First, the bilingual group consisted of a limited sample size (n = 21) and exhibited a high level of homogeneity in regards to language dominance, gender and education levels. The study relied solely on self-reports that participants did not have a history of psychiatric illnesses, neurological disorders, learning disorders, attention disorders, alcoholism, drug addiction, stroke or brain injury. The failure of six participants to initially recognize the term ira for anger is also a limitation of the study. Future studies should take care to choose stimuli that are easily understood across dialects and proficiency levels. Also, the study relied on self-report to determine language proficiencies and dominance because objective measures of language proficiency were not obtained. Conclusions In summary, the results from the present study provided preliminary data on the emotional verbal fluency task in Spanish–English bilingual individuals and demonstrated the value of the emotional verbal fluency task as a component of neuropsychological assessment. The greatest number of items was generated in category fluency tasks. The number of items generated for letter fluency tasks and emotional fluency tasks were not significantly different. The group produced significantly more items in English than in Spanish, reflecting self-ratings of English dominance. Language proficiency difference scores were significantly correlated with the differences in productions between languages, which highlights the usefulness of self-ratings in the estimation of expected performance in both languages. The emotional verbal fluency tasks appeared to be more difficult than category verbal fluency tasks and comparable in difficulty to letter verbal fluency tasks. The emotional tasks appear to rely less on language ability than category fluency or letter fluency; they may be a valid tool for assessing emotional processing. Future studies should assess performance of individuals with and without emotional processing deficits on these tasks in relation to other measures of emotional processing to further validate this assessment tool. Funding This research received financial support from the Ben F. Love Regents Professorship at the University of Texas. Conflict of Interest None declared. References Adamovich , B. B. , & Henderson , J. A. ( 1992 ). 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Archives of Clinical NeuropsychologyOxford University Press

Published: Aug 28, 2017

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