Abstract Objectives: Developing efficient tools for assessing general cognitive functions in older adults is essential. Previous studies found that inhibition of return (IOR) occurred later in the older adults than in the younger. However, little is known about the relationship between the onset time of IOR (IOR-OT) and cognitive functions in the aging population. The present study examined this issue and investigated the potential of using IOR-OT as an index of cognitive functioning in older adults. Methods In two studies, the IOR-OT of healthy younger and older adults was measured by a modified Posner peripheral cueing task, and cognitive functions of the older adults were evaluated with the Addenbrooke’s Cognitive Examination Revised (ACE-R). Results Both studies showed a significant correlation (r = ~.5) between IOR-OT and cognitive functions as assessed by ACE-R in older individuals: later IOR-OT was accompanied by a lower ACE-R score. Discussion To our knowledge, the present studies are the first to discover a relatively strong correlation between IOR-OT and cognitive functions in older adults. These findings provide new evidence supporting the inhibition deficit theory of aging and lay the foundation of using IOR-OT as an objective measure of cognitive functions in the aging population. ACE-R, Cognitive aging, Inhibition ability, Visual attention With population aging, the cognitive wellbeing of older adults has become an important issue for society. Cognitive aging refers to the phenomenon that individuals’ cognitive functions decline with age in late adulthood. Many cognitive functions decline with age, such as processing speed (e.g., Cerrella, 1985; Salthouse, 1996), attention (e.g., Verhaeghen & Cerella, 2002), and memory (e.g., Kramer et al., 2006). In the process of cognitive aging, some diseases, such as Alzheimer’s disease (AD) (Amieva, Phillips, Della Sala, & Henry, 2004), could lead to an abnormal cognitive decline—a major challenge for healthy aging. The screening of abnormal cognitive decline requires objective and efficient tools for assessing cognitive functions in older adults. The Mini-Mental State Examination (MMSE) (Folstein, Robins, & Helzer, 1983; Tombaugh & McIntyre, 1992) and Montreal Cognitive Assessment (MoCA) (Nasreddine et al., 2005) are the most widely used cognitive assessment tools in clinical practice. These tests, however, are insensitive in differentiating levels of cognitive functioning (Luis, Keegan, & Mullan, 2009; Mitchell, 2009), and are not suitable for evaluating cognition comprehensively. To address these limitations, researchers have developed a more sensitive and comprehensive cognitive test battery, known as the Addenbrooke’s Cognitive Examination Revised (ACE-R) (Mathuranath, Nestor, Berrios, Rakowicz, & Hodges, 2000). ACE-R extends the content of MMSE, measuring five major cognitive functions: attention and orientation function, episodic and semantic memory, verbal fluency, language function, and visuo-spatial ability. With good validity and cross-culture usability (Fang et al., 2014; Mioshi, Dawson, Mitchell, Arnold, & Hodges, 2006), the ACE-R is recommended as a suitable and efficient tool for evaluating cognitive functions in older adults (Larner & Mitchell, 2014), including those with normal cognitive functions or unnoticeable cognitive declines. However, there are two major shortcomings for these neuropsychological tests in practical application. First, performance on neuropsychological tests may be influenced by the language capacity or education level of the participants (Gamaldo et al., 2018; Velayudhan et al., 2014). Specifically, participants with limitations in language ability or low education may have difficulty completing the assessment. Second, using these neuropsychological tests also requires professional assessment skills and/or language skills of interviewers, which could consume social resources in personnel training. Therefore, it is necessary to develop more objective and efficient assessment tools. A novel approach to this issue is to identify key cognitive processes impacted by cognitive aging and then to examine whether these processes could account for individual differences in general cognitive ability in the aging population. Among the many theories addressing the causes of cognitive aging (see Anderson & Craik, 2017 for a review), the inhibitory deficit theory (Hasher, Stoltzfus, Zack, & Rypma, 1991; Hasher & Zack, 1988) is arguably the most influential. This theory proposes that older adults generally would not effectively inhibit irrelevant information or stop prepotent responses, leading to a decline of cognitive and behavioral functions (e.g., slowdown of response, reduced memory, and poor reading comprehension, Weeks & Hasher, 2014). Based on this theory, an older adult with a weaker inhibitory ability would have poorer general cognitive functions. In the study of inhibitory processes, inhibition of return (IOR) is widely regarded as a reliable indicator of inhibitory attentional control. IOR is measured by the Posner peripheral cueing task developed by Posner and Cohen (1984). In this task, participants are asked to detect the presence of the target and respond as quickly as possible. Before the display of the target, an uninformative peripheral cue that does not predict the location of the target is presented. When the cue-target stimulus onset asynchrony (SOA) is short (e.g., < 200 ms), the reaction time (RT) at the cued location (valid trial) is faster than that at the uncued location (invalid trial). This is called a facilitatory effect, indicating a reflexive shift of attention to the cued location. In long SOA conditions (e.g., > 500 ms), the response pattern reverses (i.e., responses to targets occurring at the cued location are slowed). This phenomenon is called IOR. Although the specific processes of information processing affected by IOR remain controversial (e.g., response processes, Pastötter, Hanslmayr, & Bäuml, 2008; or sensory processing, Hopfinger & Mangun, 2001), the prevailing view holds that IOR, as an inhibitory aftereffect, reflects inhibition of the previously attended location (Dukewich & Klein, 2015; Gazzaniga, Ivry, & Mangun, 2014; Klein, 2000). The inhibition may begin when attention moves away from the cued location, or when the cue is presented (but the early, attentionally-mediated facilitation obscures the inhibition; Klein, 2000). The IOR effect may depend on the relative strength of two opponent processes (i.e., facilitation vs inhibition). The IOR effect appears when the inhibition dominates the facilitation, therefore any mechanism that decreases inhibitory control will delay the appearance of IOR. Previous studies found an age-related difference of IOR effects. For instance, with 11 cue-target SOAs that ranged between 50 and 3,000 ms, Castel, Chasteen, Scialfa, and Pratt (2003) compared the time course of IOR in younger and older adults. When researchers plotted cueing effects as a function of cue-target SOAs they found that, compared with younger adults, IOR effects appeared in longer cue-target SOA conditions in older adults, meaning that the onset time of IOR (IOR-OT) in older adults was delayed. This delay in IOR-OT has been replicated in a number of subsequent studies (e.g., Bao, Zhou, & Fu, 2004; Langley, Friesen, Saville, & Ciernia, 2011; Muiños, Palmero, & Ballesteros, 2016; Wascher, Falkenstein, & Wildwall, 2011). The inhibitory deficit theory may account for the delay of IOR-OT in older adults. Inhibitory processes are considered to act in the service of goals to (a) prevent irrelevant information from gaining access to the focus of attention, (b) delete irrelevant information from the focus of attention, and (c) restrain prepotent responses (Hasher, Lustig, & Zack, 2007). Thus, due to their deficits in inhibitory ability, older adults might not efficiently prohibit and/or recover from attentional capture by a distracting cue, resulting in a longer dwell time and hence a later onset of IOR. It remains unclear, however, whether the delay of IOR-OT in the aging population is associated with their decline in general cognitive functions. Previous cross-sectional studies have identified an age-related decline in both onset of IOR and general cognitive functioning. These results suggested that IOR-OT and cognitive functioning are related. However, in many conditions, if we merely compare the data of different age groups and neglect the confounding effect of age, we may find an unreliable “correlation” between factors. For example, there are differences in both foot size and intelligence between children and adults, which leads to an apparent “correlation” that those with bigger feet have higher intelligence. But when controlling for age, the correlation between foot size and intelligence would disappear. The correlation between IOR-OT and general cognitive functions among a group of older adults would support the use of IOR-OT in assessing cognitive functions in older individuals. To the best of our knowledge, however, no research has been conducted to explore their relationship. The main purpose of this research was to directly explore the relationship between IOR-OT and the general cognitive functions in older individuals. Specifically, we used the Posner peripheral cueing task to measure individual’s IOR-OT and the ACE-R to evaluate cognitive functions of older adults. Based on the inhibitory deficit theory, we expect to find a negative association between IOR-OT and general cognitive functioning in older adults: the later one’s IOR-OT is, the worse are his/her general cognitive functions. In addition, this research focuses on the assessment of cognitive function in healthy older adults. The data from cognitively normal older adults allow us to better understand what is “normal” in cognitive aging and can help define what is “abnormal” in future studies (e.g., Petersen, 2004; Sperling et al., 2011). Study 1 The main purpose of Study 1 is to directly explore the relationship between IOR-OT and general cognitive functions in older adults. We adopted a Posner peripheral cueing task with six SOAs to characterize the time course of IOR. Unlike previous studies (e.g., Castel et al., 2003), which defined individual IOR-OT as the first SOA where each observer showed an IOR effect of at least 10 ms, the present study innovatively evaluates IOR-OT for each individual using a quadratic polynomial fitting approach. Method Participant Thirty-three older adults (56–85 years, mean age = 70.39, 11 males) participated in Study 1. Older adults were recruited from the community in Zhuhai, China. They had an average 12.79 (SD = 2.88; range: 6–16) years of education. For replicating the age difference in the time course of IOR reported in previous studies (i.e., Castel et al., 2003), 22 younger adults (18–23 years, mean age = 20.59, 8 males) were also recruited. The younger adults were undergraduate students from Sun Yat-Sen University and had an average 14.32 (SD = 1.39) years of education. All older and younger participants were recruited as paid volunteers. Informed consent was obtained from all participants before the study. All participants were fluent in Chinese. All participants reported normal or corrected-to-normal vision. None of the participants suffered any known general psychiatric or neurological disease. All older participants self-reported no obvious behavioral impairment and had an ACE-R score not less than 85, which means they were functionally normal using the screening criteria of Fang et al. (2014). To ensure reliable assessment of ACE-R, older participants were required to have at least 6 years of education (i.e., primary school education). The study was approved by the Ethics Committee of the Department of Psychology, Sun Yat-Sen University. Apparatus and procedure The participants performed the tasks in a sound attenuated room with bright illumination. All older participants were given a Posner peripheral cueing task and an ACE-R assessment within half a day. The order of the ACE-R and Posner peripheral cueing task administration was counterbalanced across participants. The measurement of ACE-R was in strict accordance with the standards (Hodges, 2007; and its Chinese translation version Hodges (2013), translated by Xiong, Liu & Yang and edited by Zhou). The Posner peripheral cueing task was similar to that of Castel et al. (2003). Participants were seated 60 cm in front of a 14-inch color monitor of an IBM Thinkpad laptop, with a 1,024 × 768 resolution (32 cm × 19 cm). Responses were made on an external numeric keypad. Participants were asked to stare at a fixation cross at the center of the screen. The sequence of each trial is shown in Figure 1. Full contrast stimuli (white or green) were presented on a black background. On each trial, a white cross fixation (0.5° × 0.5°) and two white boxes (1° × 1°) served as an initial display and these remained on the screen until a response was made. The boxes were centered 5° from the fixation cross and located horizontally to the left and right of the fixation. About 1,000–1,200 ms after the presence of the initial display, a bigger and concentric white box (the cue, 1.4° × 1.4°) appeared at the outer of one of the two horizontal boxes for 50 ms. In 80% of the trials, a green circle (the target, 0.7°) was presented at the center of one of the two boxes for 2,000 ms or until response. The cue-target SOA could be 50, 100, 250, 500, 750, or 1,000 ms; these SOAs were randomly selected with equal probabilities within blocks of trials. The location of the target was either same as the cued location (valid trial) or different from the cued location (invalid trial), randomized with equal probabilities. The remaining 20% of trials served as catch trials in which no target was presented. The participants were asked to respond as accurately and quickly as possible if the target was presented. A 500 ms blank display was presented after a response. The entire session consisted of 360 trials (including 72 catch trials) with five short breaks. Twelve practice trials were given before the formal study. Figure 1. View largeDownload slide The sequence of trial events in the Posner peripheral cueing task in Study 1 and 2. In this task, participants pressed a key in response to a target (a green circle) that was preceded by a non-predictive cue (a bigger white square). Study 1 used 6 cue-target SOAs, whereas Study 2 used 58 cue-target SOAs. The target was not present on 20% of trials (catch trials) to discourage anticipatory responses. SOA = Stimulus onset asynchrony. Figure 1. View largeDownload slide The sequence of trial events in the Posner peripheral cueing task in Study 1 and 2. In this task, participants pressed a key in response to a target (a green circle) that was preceded by a non-predictive cue (a bigger white square). Study 1 used 6 cue-target SOAs, whereas Study 2 used 58 cue-target SOAs. The target was not present on 20% of trials (catch trials) to discourage anticipatory responses. SOA = Stimulus onset asynchrony. Data preprocessing For the ACE-R evaluation, the total score of each participant was calculated. In the Posner peripheral cueing task, the RTs of noncatch trials were computed and analyzed. RTs beyond 3 SD above or below a given participant’s mean were removed from further analysis. On average, 2.0% and 2.2% of noncatch trials were removed for the older and the younger adults, respectively. Response accuracies, including hit rates of noncatch trials and false-alarm rates of catch trials, were also analyzed. Cueing effects (RTs in invalid trials minus RTs in valid trials) in the six SOAs for each participant were fitted with quadratic polynomial function to derive the IOR-OT, which is defined as the SOA at which the cueing effect is zero (typical cases are shown in Figure 2A). IOR-OT reflects the time point that the cueing effect begins to change from a facilitatory effect (positive value) to an IOR effect (negative value). We also computed the onset time of group average IOR effect for each age group (see Figure 2B). For each group, we averaged the cueing effects across all participants in each SOA condition and then used the quadratic polynomial curve fitting to compute the group IOR-OT. Figure 2. View largeDownload slide Time courses of cueing effects in Study 1 and 2. Each point (red circle for the old and blue triangle for the young) represents a cueing effect (reaction time [RT] for invalid trials minus RT for valid trials) as a function of stimulus onset asynchrony. The curved line represents the quadratic polynomial curve indicating the time course of cueing effects (red = older adults; blue = younger adults). The abscissa where the fitted line crosses the horizontal axis represents the IOR-OT. Panel A and C show the time course of the cueing effects of a typical participant in each age group in Studies 1 and 2, respectively. Panel B and D show the time course of the cueing effects derived from the group means for each age group in Studies 1 and 2, respectively. Figure 2. View largeDownload slide Time courses of cueing effects in Study 1 and 2. Each point (red circle for the old and blue triangle for the young) represents a cueing effect (reaction time [RT] for invalid trials minus RT for valid trials) as a function of stimulus onset asynchrony. The curved line represents the quadratic polynomial curve indicating the time course of cueing effects (red = older adults; blue = younger adults). The abscissa where the fitted line crosses the horizontal axis represents the IOR-OT. Panel A and C show the time course of the cueing effects of a typical participant in each age group in Studies 1 and 2, respectively. Panel B and D show the time course of the cueing effects derived from the group means for each age group in Studies 1 and 2, respectively. Quadratic polynomial curve-fit was chosen to compute IOR-OT for the following reasons. First, from the graphic perspective, the time course function of IOR effects looks like a quadratic polynomial curve both in our results (see Figure 2) and in previous studies (e.g., Castel et al., 2003). Second, from the data-driven perspective, the time course of IOR effects in the present study fits well with the quadratic polynomial curve. The mean fitting degree R2s using quadratic polynomial curve fitting are .706 (0.041, SE) and .731 (0.032) for the older group and the younger group, respectively. These R2s are much better than the fitting degree R2s of linear fitting (ps < .001, Cohen’s ds > 0.47), which are .579 (0.053) and .529 (0.046) for the older group and the younger group, respectively. Result In the ACE-R assessment, the older participants obtained a mean score of 92.67 (SE = 0.753; range: 85–100). In the Posner peripheral cueing task, the hit rates were high and the false-alarm (FA) rates were low for both the older (hit: .996 ± 0.002, Mean ± SE; FA: .007 ± 0.002) and the younger participants (hit: .993 ± 0.004; FA: .008 ± 0.002), with no significant differences between groups (ps > .5). A three-way mixed-design analysis of variance (ANOVA) with Cue validity (valid, invalid) and SOA (six cue-target SOAs) as within-subject factors and Age (old, young) as a between-subject factor was conducted on RTs. This revealed significant main effects of Cue validity (F(1, 53) = 4.658, p = .035, ηp2 = .081), SOA (F(5, 256) = 6.835, p < .001, ηp2 = .114), and Age (F(1, 53) = 42.134, p < .001, ηp2 = .443). The main effect of Age illustrated that the younger participants (344.8 ± 6.1 ms) responded faster than the older adults (463.8 ± 14.3 ms). The analysis also showed significant 2-way interactions of Cue validity × Age (F(1, 53) = 29.891, p < .001, ηp2 = .361), and Cue validity × SOA (F(5, 265) = 40.749, p < .001, ηp2 = .435), as well as a significant 3-way interaction of Cue validity × SOA × Age (F(5, 265) = 4.713, p < .001, ηp2 = .082). The Cue validity × SOA interaction reflected a temporal dynamic of cueing effects across SOAs, from facilitatory effects at shorter SOAs to IOR effects at longer SOAs (see Figure 2B). The Cue validity × SOA × Age interaction indicated that the temporal dynamic pattern of cueing effects was different between older adults and younger adults (see Figure 2B). Further analyses showed that the cueing effects were significantly different between younger and older adults at SOA 50, 100, 250, and 500 ms (ps < .05), but not at SOA 750 and 1,000 ms (ps > .43). Consistent with the significant three-way interaction described above, the IOR-OT estimated by quadratic polynomial fitting was much later in the older participants (597.2 ± 45.9 ms) than in the younger adults (252.8 ± 33.5 ms; t(53) = 6.055, p < .001, Cohen’s d = 1.663; see Supplementary Figure 1A). The distribution pattern of IOR-OT of the older group was different from that of the younger group (see Supplementary Figure 1B). Specifically, IOR-OTs of the older group were mostly distributed after 300 ms, while the majority of IOR-OTs in the younger group were distributed prior to 300 ms. To explore the relationship between IOR-OT and general cognitive functions of the older adults, Pearson’s correlation analyses were carried out. As shown in Figure 3A, a significant negative correlation between the individual IOR-OT and ACE-R scores in the older group was found (Pearson’s r(31) = −.524, p = .002). This means that, for older adults, worse performance on the ACE-R assessment was accompanied by a later onset of IOR. Figure 3. View largeDownload slide The correlation between IOR-OT and ACE-R score in the older group for Study 1 (A), Study 2 (B), and Studies 1 and 2 combined (C). Figure 3. View largeDownload slide The correlation between IOR-OT and ACE-R score in the older group for Study 1 (A), Study 2 (B), and Studies 1 and 2 combined (C). In order to rule out the confounding effect of age, as well as the possible impacts of general processing speed (Cerella, 1985; Muiños et al., 2016; Salthouse, 1996), partial correlation analyses were used to further verify the correlation between IOR-OT and ACE-R score in older adults. As shown in Table 1, taking age, mean RT, or inverse efficiency (RT/accuracy) of the Posner cueing task as controlled factors, correlations between IOR-OT and ACE-R score remained significant (ps < .01), and these partial correlations did not significantly differ from the simple correlation between IOR-OT and ACE-R (ps > .73). These results indicated that the correlation could not be simply explained by decline of general processing speed or an increase of age. Table 1. Partial Correlations Between IOR-OT and ACE-R Performance in Older Adults After Controlling for Processing Speed (RT or RT/ACC of the Posner peripheral cueing task), and Age, Respectively Control factor RT RT/ACC Age Study 1 r = −.517** r = −.516** r = −.524** p = .002 p = .002 p = .002 Study 2 r = −.504** r = −.507** r = −.485* p = .009 p = .008 p = .012 Studies 1 and 2 Combined r = .516*** r = −.515*** r = .516*** p < .001 p < .001 p < .001 Control factor RT RT/ACC Age Study 1 r = −.517** r = −.516** r = −.524** p = .002 p = .002 p = .002 Study 2 r = −.504** r = −.507** r = −.485* p = .009 p = .008 p = .012 Studies 1 and 2 Combined r = .516*** r = −.515*** r = .516*** p < .001 p < .001 p < .001 Notes: RT = Reaction time; ACC = Accuracy. *Correlation is significant at the .05 level (two-tailed). **Correlation is significant at the .01 level (two-tailed). ***Correlation is significant at the .001 level (two-tailed). View Large Table 1. Partial Correlations Between IOR-OT and ACE-R Performance in Older Adults After Controlling for Processing Speed (RT or RT/ACC of the Posner peripheral cueing task), and Age, Respectively Control factor RT RT/ACC Age Study 1 r = −.517** r = −.516** r = −.524** p = .002 p = .002 p = .002 Study 2 r = −.504** r = −.507** r = −.485* p = .009 p = .008 p = .012 Studies 1 and 2 Combined r = .516*** r = −.515*** r = .516*** p < .001 p < .001 p < .001 Control factor RT RT/ACC Age Study 1 r = −.517** r = −.516** r = −.524** p = .002 p = .002 p = .002 Study 2 r = −.504** r = −.507** r = −.485* p = .009 p = .008 p = .012 Studies 1 and 2 Combined r = .516*** r = −.515*** r = .516*** p < .001 p < .001 p < .001 Notes: RT = Reaction time; ACC = Accuracy. *Correlation is significant at the .05 level (two-tailed). **Correlation is significant at the .01 level (two-tailed). ***Correlation is significant at the .001 level (two-tailed). View Large Study 2 In Study 1, a significant negative correlation between IOR-OT and ACE-R performance in older individuals was found, and this correlation was invulnerable to confounding factors, such as general processing speed and age. Is such a correlation limited to the specific experimental parameters used in Study 1, or reproducible with different experimental parameters? To address this question, in Study 2, we adopted a modified Posner peripheral cueing task with 58 SOAs (similar to the task used in Song, Meng, Chen, Zhou, & Luo, 2014), and explored the relationship between IOR-OT and ACE-R performance in an older group again. Method Participant Twenty-eight older adults participated in Study 2. Older adults were recruited from the community in Zhuhai, China. One of the older participants was excluded after the ACE-R assessment, because he had failed to meet the criteria of normal cognitive function, with a score below 85. The remaining 27 participants (59–83 years, mean age = 65.74, 5 males) were included in further analyses, with an average 11.37 (SD = 3.28; range: 6–16) years of education. Forty-five young adults (16–23 years, mean age = 19.89, 11 males) also participated in Study 2. The younger adults were undergraduate students from Sun Yat-Sen University and had an average 13.84 (SD = 0.67) years of education. All older and younger participants were recruited as paid volunteers. Informed consent was obtained from all participants before the study. All participants were fluent in Chinese. All participants reported normal or corrected-to-normal vision and had at least 6 years of education. None of the participants suffered any known general psychiatric or neurological disease. The study was approved by the Ethics Committee of the Department of Psychology, Sun Yat-Sen University. Apparatus and procedure The procedure in Study 2 was similar to Study 1, except that in Study 2, the Posner peripheral cueing task contained 58 levels of SOAs instead of six levels. The 58 cue-target SOAs were variable from 50 to 1,000 ms with every 16.67 ms interval. The entire Posner cueing task consisted of 435 trials (including 87 catch trials) with five short breaks. Data preprocessing The data preprocessing procedure in Study 2 was similar to that in Study 1. For the ACE-R evaluation, the total score of each participant was calculated. In the Posner peripheral cueing task, RTs beyond 3 SD above or below a given participant’s mean were excluded from analysis. This resulted in removal of 2.1% and 3.0% of noncatch trials for the older participants and the younger participants, respectively. The individual IOR-OT was computed according to the cueing effects in 58 SOAs for each participant (typical cases are presented in Figure 2C). The onset time of group average IOR effects for each group is shown in Figure 2D. Result In the ACE-R assessment, the older participants obtained a mean score of 92.48 (SE = 0.711; range: 85–99). In the Posner peripheral cueing task, the hit rates and the FA rates were comparable between the older (hit: .997 ± 0.002, Mean ± SE; FA: .003 ± 0.001) and the younger participants (hit: .994 ± 0.001; FA: .005 ± 0.001), ps > .14. A three-way mixed-design ANOVA with Cue validity (valid, invalid) and SOA (58 cue-target SOAs) as within-subject factors and Age (old, young) as a between-subject factor was conducted on RTs. This showed significant main effects of SOA (F(57, 3990) = 4.377, p < .001, ηp2 = .059), and Age (F(1, 70) = 118.026, p < .001, ηp2 = .628), the latter describing a significant difference in response time between the older group (464.8 ± 12.2 ms) and the younger group (347.4 ± 4.9 ms). The analysis also revealed significant two-way interactions of Cue validity × Age (F(1, 70) = 58.118, p < .001, ηp2 = .454), and Cue validity × SOA (F(57, 3990) = 7.42, p < .001, ηp2 = .096), as well as a significant three-way interaction of Cue validity × SOA × Age (F(57, 3990) = 1.95, p = .005, ηp2 = .027). Consistent with Study 1, the Cue validity × SOA interaction reflected a temporal dynamic of cueing effects across SOAs, and the Cue validity × SOA × Age interaction indicated that older and younger adults had different temporal dynamic patterns of cueing effects (see Figure 2D). Consistent with Study 1, the IOR-OT of older participants (633.4 ± 46.5 ms) was significant later than that of younger participants (357.7 ± 39.5 ms; t(70) = 4.412, p < .001,Cohen’s d = 1.055; see Supplementary Figure 2A). Again, the IOR-OTs of the older group were mostly distributed after 300 ms, while the majority of the IOR-OTs in the younger group were distributed before 400 ms (see Supplementary Figure 2B). Pearson’s correlation analyses were carried out to measure the strength of the relationship between IOR-OT and ACE-R scores in the older group. Consistent with Study 1, a significant and comparable negative correlation between these two variables was found (Pearson’s r(25) = −.506, p = .007; see Figure 3B). That is, an older individual with worse performance on the ACE-R test showed a later onset of IOR. Similar to Study 1, this relationship was not influenced by factors such as general response speed (mean RTs or RT/accuracy) or age: the partial correlations (as shown in Table 1) did not significantly differ from the simple correlation between IOR-OT and ACE-R (ps > .55). When we merged two sets of older group samples across studies (Study 1, N = 33; Study 2, N = 27), there was still a significant negative correlation between IOR-OT and ACE-R performance (Pearson’s r(58) = −.517, p < .001, shown in Figure 3C). This correlation remained significant after controlling factors such as age, mean RT or inverse efficiency (RT/accuracy) of the Posner cueing task (see partial correlation results in Table 1). The correlations between IOR-OT and ACE-R performance in older adults were comparable in each study, indicating a reliable and robust link between IOR-OT and general cognitive functions in older individuals. One may argue that the correlation between IOR-OT and ACE-R performance resulted from overlapping processes captured by IOR-OT and the attention/orientation subscale in ACE-R, since both are related to participants’ attentional orienting abilities. To address this concern, we defined a partial ACE-R as the sum of the other four subscales, excluding the attention/orientation subscale. To better understand how one’s IOR-OT relates with his/her performance in ACE-R, we explored the correlation of IOR-OT with the partial ACE-R and further with the five subscales respectively. The results (see Supplementary Table 1) showed that the negative correlation between IOR-OT and the partial ACE-R score remained significant (r(58) = −.455, p < .001), even though the attention/orientation subscale was excluded. Significant correlations between IOR-OT and the subscale scores of ACE-R were not only found in attention/orientation function (r(58) = −.429, p = .001), but in verbal fluency (r(58) = −.401, p = .001), language function (r(58) = −.392, p = .002), and episodic and semantic memory (r(58) = −.241, p = .064, marginally significant) as well. These results suggest that the connection between IOR-OT and cognitive functions in older adults was not limited to attentional orienting, but reflected general associations. Discussion The present study compared the temporal dynamic patterns of cueing effects in older and younger adults, and further examined the relationship between IOR-OT and general cognitive functions in older individuals. Consistent with previous studies (e.g., Castel et al., 2003; Muiños et al., 2016; Wascher et al., 2011), a group difference in IOR-OT between older and younger groups was found; that is, IOR-OT in the older adults was much later than that of the younger adults. More importantly, the present study revealed a significant and reliable correlation between IOR-OT and cognitive functions in older adults: the later the IOR-OT was, the worse the ACE-R score an older participant exhibited. This correlation was not caused by confounding factors such as general processing speed or age of the older adults. To the best of our knowledge, the present study was the first to reveal a significant association between the onset of IOR and cognitive functions in older adults. The present findings broaden our understanding of the role of inhibition in cognitive aging. The inhibitory deficit theory (Hasher et al., 1991; Hasher & Zack, 1988) proposed that an inhibition deficit could account for numerous age-related cognitive changes. Many studies have found that there are differences between young and old people in various tasks involving inhibition mechanisms, such as the Stroop task (e.g., Davidson, Zacks, & Williams, 2003), go/no go task (e.g., Rodríguez-Villagra, Göthe, Oberauer, & Kliegl, 2013), and reading comprehension (e.g., McGinnis, 2012). The present study found that, compared to young adults, there was a delay of IOR-OT in older adults. According to dominant theories of IOR, this reflects a deficit in inhibitory ability in aging people. Moreover, our results revealed a reliable correlation between IOR-OT and general cognitive functions in the older adult samples. This correlation remained significant even when factors of age and general processing speed were controlled. Our finding is well aligned with the prediction of the inhibitory deficit theory, specifically that an individual with a poorer inhibitory function (which was reflected by a later IOR-OT) would have a worse general cognitive function. Instead of merely comparing the difference in inhibition function between different age groups, the present study provides new evidence that further supports the inhibition deficit theory: there is not only a decline in attentional inhibition ability in the older adults, but also a close link between this inhibition decline and the decline of general cognitive function. Interestingly, the inhibitory ability measured by a simple Posner cueing task was associated with performance in broader and more complex cognitive tasks in ACE-R, not only in the attention/orientation subscale, but also in the verbal fluency, language function, and (at the trend level) episodic and semantic memory subscales. One possible explanation is that, although the task requirements and measurement processes were quite different between the Posner cueing task and various tasks in ACE-R, both might involve a common inhibitory control system. The activity of this inhibitory control system changes with age (e.g., the frontoparietal control network; Campbell, Grady, Ng, & Hasher, 2012; Grady, Sarraf, Saverino, & Campbell, 2016; Li et al., 2015), influencing performance of various tasks that involving inhibitory control (e.g., Mayer, Dorflinger, Rao, & Seidenberg, 2004). In other words, inhibitory deficits might be one common cause of age-dependent declines in multiple cognitive functions. The present study supported the idea that IOR-OT is a promising and objective index for assessing the cognitive functions of older adults. The correlation between IOR-OT and general cognitive functions in older adults is reliable and robust. This correlation was replicated in different task settings, and was invulnerable to confounding factors, such as age or general response speed. In addition, the Posner peripheral cueing task is simple enough for most participants (even those with limitations in language ability or low education) to understand and complete. The evaluation of IOR/ IOR-OT can be conducted by computers, greatly reducing the manual operation during the assessment process and thus improving the evaluation efficiency. Using a Posner peripheral cueing task, a recent study (Bayer et al., 2014) compared cueing effects at various SOAs across three groups of older adults: people with amnestic mild cognitive impairment (aMCI), patients with dementia, and healthy controls. The IOR effect in aMCI persons occurred later than that in healthy controls, but earlier than that in dementia patients. A similar pattern of group differences in inhibitory ability among MCI, AD, and healthy older adults was also found in the Stroop task (Bélanger, Belleville, & Gauthier, 2010): the resistance to interference in MCI persons was worse than that in healthy older adults, but better than that in AD patients. These findings indicate that deficits of inhibition are closely linked to the development of cognitive impairment in older adults. The onset time of IOR thus seems sensitive to both large differences in cognitive wellbeing, as Bayer et al.’s comparisons across diagnostic category indicated, and smaller differences in cognitive functioning within healthy older adult samples, as the current results show. Together, these results suggest that the IOR onset time may serve as a sensitive indicator of cognitive decline, which potentially can be used to effectively identify older adults who are prone to cognitive impairment. This requires future validation in clinical studies. As initial research on the relationship between IOR-OT and individual general cognitive functions, our studies had relatively small sample sizes. Future studies need to adopt a larger sample size to establish the norm and distribution of IOR-OT in the aging population. In the present studies, IOR-OT accounted for approximately 25% of the variance in ACE-R scores. Although this is a pretty sizeable amount, it indicates that cognitive functioning in older adults is accounted for by other independent factors as well. Future studies are needed to explore other indicators and to develop an integrated tool set for assessing general cognitive functions of older individuals more comprehensively and efficiently. In conclusion, this study provides direct evidence for a robust and meaningful correlation between IOR-OT and general cognitive functions in older adults. Our findings indicate that attentional inhibition ability is a key factor influencing healthy cognitive aging, and IOR-OT may have great potential as a reliable and objective index for assessing cognitive functions of older individuals. Supplementary Material Supplementary data is available at The Journals of Gerontology, Series B: Psychological Sciences and Social Sciences online. Funding This work was supported by grants from National Nature Science Foundation of China (No. 31471070 and 31271190) to Y. Ding and Z. Qu, and Leading Talents in BaiQianWan Project of Guangdong Special Support Program (No. 201626026) to Y. Ding. Author Contributions Y. Ding conceived and designed the studies. L. Wang, T. Li, W. Huang, and C. Zhong performed the studies and analyzed the data under the supervision of Z. Qu and Y. Ding. L. Wang and Y. Zhen provided research materials; T. Li, Z. Qu, and Y. Ding wrote the manuscript. T. Li and L. Wang contributed equally to the present study and should be considered as co-first authors. Conflict of Interest The authors declare no conflict of interest. Acknowledgments The authors thank Cheng Li for his assistance in data collection and the Editor and Reviewers for their helpful comments. Reference Amieva , H. , Phillips , L. H. , Della Sala , S. , & Henry , J. D . ( 2004 ). Inhibitory functioning in Alzheimer’s disease . 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Published: Jun 6, 2018
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