In this study, we utilized a novel fMRI paradigm to examine the behavioral and neural correlates of parenting self- evaluation in a sample of mothers with at least one child under the age of 4 (N¼ 37). Prior self-report, behavioral and obser- vational research document the implications of parenting self-evaluations for parent well-being and caregiving behavior; however, relatively little is known about the neural circuitry underlying these self-referential processes and to what extent they are inﬂuenced by caregiving experience. Although neuroimaging paradigms indexing other aspects of parental func- tion exist, this is the ﬁrst to use functional neuroimaging to study parenting self-evaluation in a controlled laboratory set- ting. We found parenting self-evaluations elicited signiﬁcantly greater activity across most cortical midline structures, including the medial prefrontal cortex compared to control evaluations; these ﬁndings converge with previous work on the neural underpinning of general trait self-evaluation. Notable differences by parity were observed in exploratory analyses: speciﬁcally, primiparous mothers endorsed a higher number of developmentally supportive traits, exhibited faster reaction times, and showed a greater difference in mPFC activity when making self-evaluations of developmentally supportive traits than of developmentally unsupportive traits, compared to multiparous mothers. Implications of these ﬁndings and study limitations are discussed. Key words: parenting self-evaluation; parental self-efﬁcacy; parity; medial prefrontal cortex; functional magnetic resonance imaging laboratory settings. In this paper, we present a novel experi- Introduction mental task designed to fill this gap in the literature, and initial For many individuals, the period spent parenting a young child behavioral and neuroimaging data documenting the correlates represents a time of rapid, complex identity development. of parenting self-evaluation in mothers of young children. Within this context, parents’ implicit and explicit judgments of themselves as caregivers (i.e. parenting self-evaluations) across A neuroscience-based approach to parenting self- such dimensions as competence, consistency, stress and evaluation warmth represent important variables of interest, as these may have a significant impact on parental well-being and caregiving Despite the robust behavioral literature documenting links be- behavior. However, apart from self-report questionnaires, few tween parenting self-evaluations and parental function measures examine parenting self-evaluation in controlled (Coleman and Karraker, 1997; Jones and Prinz, 2005), relatively Received: 26 July 2017; Revised: 8 April 2018; Accepted: 23 April 2018 V C The Author(s) (2018). Published by Oxford University Press. This is an Open Access article distributed under the terms of the Creative Commons Attribution-NonCommercial-NoDerivs licence (http://creativecom- mons.org/licenses/by-nc-nd/4.0/), which permits non-commercial reproduction and distribution of the work, in any medium, provided the original work is not altered or transformed in any way, and that the work is properly cited. For commercial re-use, please firstname.lastname@example.org Downloaded from https://academic.oup.com/scan/article-abstract/13/5/535/4990260 by Ed 'DeepDyve' Gillespie user on 21 June 2018 536 | Social Cognitive and Affective Neuroscience, 2018, Vol. 13, No. 5 little is known about the neural circuitry underlying these proc- However, no research has examined the impact of parity on esses. Historically, there has been an extensive focus on paren- parenting self-evaluation in vivo. Since positive parenting tal self-efficacy as a domain of parenting self-evaluation in thoughts increase across the perinatal period in healthy indi- parenting studies; however, there is a paucity of parenting self- viduals (Kim et al., 2013), previous caregiving experience may evaluation research using paradigms that assess global/categor- impact parenting self-evaluation. ical judgments parents make about themselves across self- To understand the underlying mechanisms of such changes, evaluation domains or across multiple levels of analysis. new multimodal experimental paradigms characterizing the Neuroimaging is a promising methodology to deepen our neural correlates of parenting self-evaluations, alongside other understanding of parenting self-evaluation, as it allows for ex- salient factors (e.g. parents’ history of childhood adversity, plication of mechanisms underlying links between parents’ demographic risk, parity) are needed. Additional research is self-referential processing and the neural correlates of parental needed to examine the relationship between domain-specific function. Notably, although extensive neuroimaging research components of parenting self-evaluation (e.g. parental self- documents the neural correlates of general self-evaluation and efficacy, parenting stress) and the neural underpinnings of glo- self-reflection, no functional magnetic resonance imaging bal parenting self-evaluations; such investigations may repre- (fMRI) work has investigated the specific neural correlates of sent a precursor to further elucidating self-other processing (as parenting self-evaluation. with parental mentalization and reflective function). Reviews and meta-analyses identify robust correlations be- tween self-referential processes (including those with import- The current study ant implications for parenting, such as mentalizing) and activation in cortical midline structures (CMS). Particularly The goal of this study was to examine the behavioral and neural strong associations have been found within the medial pre- correlates of parenting self-evaluation via a new experimental frontal cortex (mPFC) and adjacent regions of the anterior cingu- paradigm. This study builds on Pfeifer et al.’s research on self- late cortex (Northoff et al., 2006; Qin and Northoff, 2011; Denny referential processing across the lifespan, and self- and et al., 2012; Wagner et al., 2012). It remains unknown, however, malleability-evaluations among emerging adults (Pfeifer et al.’s, to what extent parenting self-evaluations, specifically, recruit 2007, 2009, 2013; Jankowski et al., 2014). To the best of our know- these same circuits. ledge, this is the first study to examine the behavioral and neur- al correlates of self-evaluation in the context of one’s own Links between parenting self-evaluations and parental parenting. However, consistent with meta-analyses document- function ing robust CMS activity during self-referential processes (Denny et al., 2012; Wagner et al., 2012), we hypothesized that self- With respect to parenting self-evaluations of ability, consist- evaluation of parenting qualities would engage these structures. ency, and warmth, a large body of empirical work using self- We further hypothesized that the mPFC would be specifically report questionnaires suggests that differences in parental self- sensitive to individual differences in these evaluations due to efficacy (i.e. the extent to which a parent evaluates themselves its known role in personal evaluation, self-referential process- as competent in their parenting role) are associated with ing and social evaluative judgments (Mitchell et al., 2006; Denny aspects of parent well-being, parenting behavior and child de- et al., 2012; Nicolle et al., 2012). Furthermore, the mPFC is central velopment (for reviews see Coleman and Karraker, 1997; Jones to the neural circuit involved in affective processing and and Prinz, 2005). For example, parents with low parenting self- efficacy are more likely to be depressed (Kohlhoff and Barnett, responding, and thus may be particularly sensitive to individual 2013; Michl et al., 2015) and less likely to report satisfaction with differences in stress and affect (Callaghan and Tottenham, parenting (Coleman and Karraker, 2000). In contrast, robust 2016). associations have been reported between endorsements of high We also hypothesized that behavioral and neural correlates parenting self-efficacy and developmentally supportive parent- of parenting self-evaluation would relate to both self-reported ing behavior (Bohlin and Hagekull, 1987; Teti and Gelfand, 1991). parenting stress and self-efficacy, as seen in previous theoretic- Recent longitudinal work suggests that poor parenting self- al and empirical work (Jones and Prinz, 2005). Given the docu- efficacy may represent a risk factor for negative dyadic transac- mented associations between early childhood adversity and tions across early childhood (Verhage et al., 2013) and both dir- adult outcomes including reduced parental self-efficacy (e.g. ect and indirect effects have been observed between parental Michl et al., 2015; Kunseler et al., 2016), we posited that individ- self-efficacy and children’s academic and social competence ual differences in mothers’ history of early childhood adversity (Bogenschneider et al., 1997; Ardelt and Eccles, 2001; Junttila would be negatively associated with parenting self-evaluation et al., 2007). Taken together, these findings suggest parents’ and related neural activity. Finally, we explored differences in evaluations of their self-efficacy, including developmentally brain activity and its associations with self-reported parental supportive and unsupportive qualities, represent important fac- self-efficacy and history of childhood adversity in first-time tors in the family system that may have a significant impact on mothers (primiparous) compared to those who have more than child development. one child (multiparous). Notably, the small sample size made these parity examinations preliminary in nature. Parity as a predictor of parental function Given documented associations between negative affect and clinical disorders characterized by poor self-evaluation (Lonigan Growing evidence from human and non-human animal studies et al., 2003; Crawford and Henry, 2004), it is possible that state suggests that significant, long-lasting changes in maternal cir- affect could confound the hypothesized associations between cuitry occur with caregiving experience, and may underlie parenting self-evaluation and brain activity. As such, we observed enhancements in maternal responsiveness (Pereira included negative and positive state affect, alongside key demo- and Ferreira, 2016) and other aspects of parental function, such as neural responses to infant cues (Maupin et al., 2018). graphics (income, maternal age) as covariates in our analyses. Downloaded from https://academic.oup.com/scan/article-abstract/13/5/535/4990260 by Ed 'DeepDyve' Gillespie user on 21 June 2018 L. K. Noll et al. | 537 The PSET paradigm is a mixed block/event-related design, Materials and methods consisting of two block types representing evaluation perspec- Participants tive and two event types representing trait valence (Figure 1). This produces four conditions, with 26 trials per condition. For Thirty-seven mothers aged 20–43 (M¼ 31.16 years, s.d.¼ 5.78 years) each trial, participants answer the prompt via a left or right but- with at least one child under the age of 4 were recruited via fliers ton press indicating a ‘yes’ or ‘no’ response. Behavioral perform- and targeted advertising on social media as part of a larger study ance is calculated as percent of qualities endorsed in each investigating the impact of a video-coaching program for care- condition and the corresponding average reaction time. givers of young children. The ethnic composition of participants was representative of the region: 86.5% Caucasian, 8.1% Hispanic fMRI data collection and analysis. Data were acquired using a 3.0- and 5.4% Asian/Pacific Islander. Maternal education ranged from T Siemens Skyra scanner at the LCNI. Blood oxygen-level de- GED to doctoral diploma, and family gross income ranged from $0 pendent echo planar images (BOLD-EPI) were acquired with a to $200 000 per year (M¼ $56 610, s.d.¼ $40 653). Of the 37 mothers, T2*-weighted gradient echo sequence (TR¼ 2000 ms, TE¼ 25 ms, 16 were primiparous (number of children: range¼ 1–6, M¼ 1.89, flip angle¼ 90 , matrix size¼ 104104, 72 contiguous axial slices s.d.¼ 1.20). Interested participants were screened by phone for eli- with interleaved acquisition, field of view¼ 200 mm, slice thick- gibility (i.e. right-handedness, absence of neurological disorders ness¼ 2 mm; total time¼ 5 min 50 s per run 2 runs). For each and MRI contraindications) and scheduled for an initial MRI ses- participant, a high-resolution structural T1-weighted 3D sion at the University of Oregon Robert and Beverly Lewis Center MPRAGE pulse sequence (TR¼ 2300 ms, TE¼ 2.1 ms, matrix for Neuroimaging (LCNI). size¼ 192 192, 160 contiguous axial slices, voxel size¼ 1 mm, slice thickness¼ 1 mm; total time¼ 5 min 59 s) was acquired co- Procedure planar with the functional images, as well as a pair of opposite phase encoded images (SE-EPI) to be used to account for inho- All procedures used in this study were approved and monitored mogeneities in the magnetic field within the functional by the university’s Office for the Protection of Human Subjects images (TR¼ 6390 ms, TE¼ 47.8 ms, flip angle¼ 90 , matrix and written informed consent was obtained from all partici- size¼ 104104, 72 slices, field of view¼ 200 mm, slice thick- pants at the beginning of their first study visit. ness¼ 2 mm, total time¼ 1 min 8 s per run2 runs). Additional functional runs that assessed inhibitory motor control and at- Measures tentional control were collected that are not reported here, and run order was counterbalanced across subject. Therefore, we do Self-report measures. Parenting stress was measured using the not expect that the addition of these tasks to the PSET signifi- Parenting Stress Index, Third Edition Short Form (PSI-3-SF; cantly affected either PSET behavior or associated brain activity. Abidin, 1995), which contains subscales assessing parental dis- Before preprocessing, all DICOM images were converted to tress, parent–child dysfunctional interaction, and difficult child. NIfTI format via MRI-Convert (http://lcni.uoregon.edu/jolinda/ Parental self-efficacy was measured using a modified version of MRIConvert/), and non-brain tissue was removed from MPRAGE the Parenting Sense of Competence Scale (PSOC; Johnston and images using robust skull stripping with the Brain Extraction Mash, 1989). State affect was measured using the Positive and Tool in FMRIB’s Software Library (FSL; http://www.fmrib.ox.ac. Negative Affect Schedule (PANAS; Watson et al., 1988). uk/fsl/). All further analyses were conducted using SPM12 Participants’ history of childhood adversity was measured using (Wellcome Department of Cognitive Neurology, London, UK; an abbreviated version of the Adverse Childhood Experiences http://www.fil.ion.ucl.ac.uk/spm/). Briefly, MPRAGE images Scale (ACES; Felitti et al., 1998). Basic demographic information were coregistered to the MNI template and segmented into gray was collected via a questionnaire created by the researchers, matter, white matter and cerebrospinal fluid, and combined to and all surveys were administered after the experimental task. create a study-specific template using the DARTEL toolbox for SPM12. Field inhomogeneities were corrected by using a field- The parenting self-evaluation task (PSET). The PSET is an adapta- map to unwarp functional images. Images were motion- tion of an experimental task described in Jankowski et al. (2014), corrected using realignment, and the mean of all functional which we modified to focus on qualities associated with parent- images was co-registered to each subject’s own structural ing. Participants are presented with positively and negatively MPRAGE using a six-parameter rigid body transformation valenced terms that are widely regarded to correspond to devel- model. All images were then spatially normalized into MNI tem- opmentally supportive (“DS”) or developmentally unsupportive plate space using the study-specific template, and smoothed (“DU”) caregiving behavior, respectively (Table 1). Blocks vary by 3 using a 4-mm full-width at half-maximum Gaussian kernel. instruction, asking participants to evaluate whether these Statistical analyses were conducted in SPM12. For each sub- words described them as a parent (Self) or whether they believe ject, event-related condition effects were estimated according these qualities can change for parents in general (Change). In to the general linear model, using a canonical hemodynamic re- contrast to low-level control conditions used in previous studies sponse function, high-pass filtering (128 s) and a first-order of positive and negative trait evaluations (e.g. counting vowels, autoregressive error structure. At the subject level, BOLD signal capital letters or syllables) and consistent with Jankowski et al. was modeled in a fixed effects analysis with separate regressors (2014) paradigm, the latter malleability-evaluation was selected modeling each condition of interest (Self DS, Self DU, Change as a high-level control condition with similar semantic and DS, Change DU) for the 4 s after the time of onset. No subject evaluative demands as self-evaluation. Indeed, it could be moved more than one voxel in any direction over the course of argued that some self-evaluation may be involved in the control each run. To control for the effect of the motion that did occur, (Change) condition, rendering this a very conservative contrast. five-parameter motion regressors were calculated as deviations Mothers with more than one child were instructed to think of from the origin (Euclidean translation, Euclidean rotation, de- parenting their youngest child (study child age for full sample rivative of Euclidean translation, derivative of Euclidean rota- ranged 7 weeks–4 years, M¼ 1.72 years, s.d.¼ 1.35). tion, and trash), and entered into single-subject models as Downloaded from https://academic.oup.com/scan/article-abstract/13/5/535/4990260 by Ed 'DeepDyve' Gillespie user on 21 June 2018 538 | Social Cognitive and Affective Neuroscience, 2018, Vol. 13, No. 5 Table 1. Neuroimaging task stimuli by quality valence PSET stimuli Positive/developmentally supportive (DS) qualities Negative/developmentally unsupportive (DU) qualities At ease Helpful At my wit’s end Irritable Attentive Interested Bad parent Lazy Aware Nurturing Burdened Lonely Calm Patient Cannot handle it Nervous Capable Present Distracted Overwhelmed Comforting Relaxed Exhausted Stressed Committed Reliable Frustrated Tense Competent Responsive Inadequate Too busy Consistent Sensitive Inattentive Unpredictable Effective Skilled Incapable Unsatisﬁed Encouraging Supportive Incompetent Unsure of myself Flexible Understanding Inconsistent Whined at Good parent Warm Ineffective Worried Note. Candidate PSET stimuli were ﬁrst extracted from commonly used parenting self-report questionnaires and observational scales, then evaluated for inclusion by a panel of experienced clinicians with expertise in early childhood development. Words or phrases with high demand characteristics (e.g. “abusive,” “neglecting”) were eliminated. The most highly rated 26 words from each valence category were selected for inclusion in the PSET task. Fig. 1. Example Self (top) and Change (bottom) blocks from the PSET. The task included two runs, with 10 blocks per run. Each block began with a 4.7-s cue instructing participants how to respond to the following trials, followed by ﬁve to six trials of 4.7 s each separated by a jittered inter-stimulus-interval (ISI) averaging 277 ms. Blocks were separated by a jittered rest period averaging 4.98 s. A total of 26 trials were conducted in each of 4 conditions: instruction block (self, change) by trial type (developmentally supportive, developmentally unsupportive). Each trial (see Table 1 for stimuli) was seen under each instruction, and traits were mixed within blocks. General code can be found at gitlab.com/dsnlab/svc. covariates of non-interest. Button press (left/right index finger) model) to generate probability estimates (using Monte-Carlo and reaction time were also included as covariates of non- simulations) of a random field of noise producing a cluster of interest in the single-subject models. Linear contrasts were cre- voxels of a given size for a set of voxels passing a given voxel- ated for each condition vs implicit resting baseline (i.e., DS wise P-value threshold. In our data, these simulations deter- Self> Rest, DU Self> Rest, DS Change> Rest, DU Change> Rest) mined that a voxel-wise threshold of P< 0.001 combined with a for each participant, which were then imported to group-level spatial extent threshold of 46 voxels corresponded to a family- analyses. A 2 (instruction block: Self vs Change)2 (trial type: DS wise error (FWE) corrected false-positive probability of P< 0.05 vs DU) whole-brain, repeated measures ANOVA was conducted across the whole brain. to examine the main effect of instruction block, the main effect Because several of our hypotheses were specific to mPFC ac- of trial type and their interaction [(DS self> DS change)> (DU tivity associated with self-evaluation, we built an anatomical self> DU change)]. region of interest (ROI) based on the WFU Pickatlas anterior cin- Since the brain regions previously identified in self- gulate volume (Maldjian et al., 2003), which overlaps with clus- evaluation encompass several large CMS, we investigated the ters found in previous investigations of self-evaluation (see neural correlates of parenting self-evaluation using whole-brain Jankowski et al., 2014). The use of an anatomical ROI is naturally analyses. For these analyses, we applied a combined voxel more conservative than a cluster-based ROI, as it is independent height and cluster-extent correction for multiple comparisons from the hypotheses, and is naturally biased toward the null to guard against Type I error derived from AFNI’s 3dClustSim hypothesis by virtue of containing more voxels than would be software with the –acf (spatial autocorrelation function) option in a cluster-based ROI (see Poldrack and Mumford, 2009). (Cox, 1996). 3dClustSim takes into account the size of the search Parameter estimates of individual subjects’ activity in this ROI space and the estimated smoothness of the data (calculated were extracted using MarsBar (MRC Cognition and Brain using individual subject residuals derived from the group-level Sciences Unit, Cambridge, UK; marsbar.sourceforge.net/) for the Downloaded from https://academic.oup.com/scan/article-abstract/13/5/535/4990260 by Ed 'DeepDyve' Gillespie user on 21 June 2018 L. K. Noll et al. | 539 Table 2. Means and standard deviations among major study variables across full sample and by parity Parity Full sample (N¼ 37) Primiparous (n¼ 16) Multiparous (n¼ 21) M s.d. M s.d. M s.d. Age 31.16 5.78 30.63 6.93 31.57 4.87 Income 56.61 40.65 56.57 45.38 55.62 34.14 PSET DS %-S 90.65 11.68 95.08 6.00 87.28 13.95 PSET DU %-S 22.38 14.49 15.01 9.59 28.00 15.24 PSET DS %-C 84.94 19.99 81.60 21.70 87.48 18.72 PSET DU %-C 81.39 17.85 81.00 17.81 81.83 18.31 PSET DS RT-S 1.18 0.22 1.08 0.14 1.27 0.24 PSET DU RT-S 1.55 0.30 1.37 0.20 1.68 0.28 PSET DS RT-C 1.46 0.35 1.41 0.30 1.50 0.38 PSET DU RT-C 1.63 0.35 1.55 0.30 1.70 0.39 mPFC ME instruction 0.315 0.263 0.328 0.298 0.306 0.241 mPFC ME trait 0.084 0.169 0.103 0.193 0.071 0.152 mPFC interaction 0.16 0.391 0.015 0.345 0.294 0.378 PSI – TOT 74.43 15.74 70.31 12.58 77.57 17.42 – PD 28.08 7.55 25.63 6.60 29.95 7.84 – PCDI 20.09 5.51 19.50 4.49 20.53 6.25 – DC 26.31 5.53 25.19 4.55 27.16 6.14 PSOC 52.43 5.59 54.94 5.88 50.52 4.63 PA 36.14 7.58 38.06 8.27 34.67 6.84 NA 20.05 6.75 18.25 6.22 21.43 6.96 ACES 2.97 2.85 2.81 2.71 3.10 3.02 Note. Age¼ Maternal age (years); Income¼ annual household gross income (thousands of dollars/year); PSET¼ Parenting Self-Evaluation Task; DS¼ developmentally supportive; DU¼ developmentally unsupportive; PSET DS %—S¼ percentage of developmentally supportive parenting qualities endorsed during the PSET in the ‘Self’ condition; PSET DU %—S¼ percentage of developmentally unsupportive parenting qualities endorsed during the PSET in the ‘Self’ condition; PSET DS %— C¼ percentage of developmentally supportive parenting qualities endorsed during the PSET in the ‘Change’ condition; PSET DU %—C¼ percentage of developmentally unsupportive parenting qualities endorsed during the PSET in the ‘Change’ condition; mPFC ME instruction¼ neural activity in the mPFC ROI for the main effect of in- struction (Self> Change, arbitrary units); mPFC ME trait¼ neural activity in the mPFC ROI for the main effect of trait (DS> DU; arbitrary units); mPFC interaction¼ neu- ral activity in the mPFC ROI for the interaction of instruction (self, change)trait (DS, DU; arbitrary units); PSI–TOT¼ Parenting Stress Index Total Score (possible range 36–180); PD¼ Parental Distress Subscale (possible range 12–60); PCDI¼ Parent-Child Dysfunctional Interaction Subscale (possible range 12–60); DC¼ Difﬁcult Child Subscale (possible range 12–60); PSOC¼ Parenting Sense of Competence Total Score (possible range 18–72); PA¼ Positive Affect Subscale Score from the PANAS (possible range 10–50); NA¼ Negative Affect Subscale Score from the PANAS (possible range 10–50); ACES¼ Adverse Childhood Experiences Survey Total Score (possible range 0–10). One participant chose not to report their income. three contrasts of interest (the main effect of instruction type, analyses. We end with a consideration of the effects of parity on the main effect of trial type, and the interaction). these findings. Analytic approach Behavioral Individual averages of BOLD signal in the mPFC ROI for all the Descriptive statistics for study variables are shown in Table 2. three contrasts of interest were exported to SPSS (version 24.0, Across all participants, an average of 90.65% of DS qualities IBM) for further analyses. We computed descriptive statistics (range¼ 54–100%, s.d.¼ 11.68%) and 22.38% of DU qualities (Table 2) and correlations (Tables 3 and 4) for PSET performance (range 0–67%, s.d.¼ 14.49%) were endorsed as self-descriptive. (percent endorsed, reaction time) across all qualities and by Average reaction time for these conditions was 1.18 s (range quality valence, as well as self-reported childhood adversity, 0.82–1.82 s, s.d.¼ .22) and 1.55 s (range 1.08–2.42 s, s.d.¼ 0.30), re- parenting stress, parental self-efficacy and positive and nega- spectively. For the change condition, an average of 84.94% tive state affect. We further interrogated significant effects (range 25–100%, s.d.¼ 19.99%) of the DS and 81.39% (range 35– among these variables using multiple regressions. For all varia- 100%, s.d.¼ 17.85%) of the DU qualities were endorsed as malle- bles, outliers were winsorized at three standard deviations from able, with average reaction times of 1.46 s (range 0.92–2.37 s, the mean, and checked for normality. Gross income and num- s.d.¼ 0.35) and 1.63 s (range 1–2.59 s, s.d.¼ 0.35), respectively. ber of ACES were transformed (square root) to improve the Total self-reported parenting stress on the PSI averaged distribution. 74.43 (range 50–124, s.d.¼ 15.74), and self-efficacy as reported on the PSOC averaged 52.43 (range 42–67, s.d.¼ 5.59). State posi- tive affect averaged 36.14 (range 21–50, s.d.¼ 7.58) and negative Results affect averaged 20.05 (range 10–39, s.d.¼ 6.75). Number of self- Here, we present main effects and associations with other vari- reported Adverse Childhood Experiences (ACEs) averaged 2.97 ables of interest for both the behavioral and neuroimaging (range 0–9, s.d.¼ 2.85). Downloaded from https://academic.oup.com/scan/article-abstract/13/5/535/4990260 by Ed 'DeepDyve' Gillespie user on 21 June 2018 540 | Social Cognitive and Affective Neuroscience, 2018, Vol. 13, No. 5 Table 3. Intercorrelations for variables of interest in the full sample (n¼ 37) Variables 1 2 3 4 5 6 7 8 9 10 11 12 1. Age – 2. Income 0.386* – 3. PSET DS % 0.035 0.051 – 4. PSET DU % 0.007 0.204 0.486** – 5. mPFC ME instruction 0.384* 0.093 0.073 0.058 – 6. mPFC ME trait 0.136 0.063 0.014 0.106 0.177 – 7. mPFC interaction 0.165 0.053 0.156 0.008 0.142 0.045 – 8. PSI 0.058 0.284 0.447** 0.709*** 0.167 0.043 0.087 – 9. PSOC 0.089 0.090 0.500** 0.636*** 0.156 0.085 0.238 0.741*** – 10. PA 0.069 0.142 0.482** 0.493** 0.196 0.029 0.142 0.600*** 0.718*** – 11. NA 0.049 0.275 0.519** 0.661*** 0.075 0.001 0.125 0.753*** 0.697*** 0.670*** – a þ þ 12. ACES 0.309 0.282 0.275 .304 .258 0.189 .194 .360* 0.093 0.042 .261 – Note. Age ¼ Maternal age (years); Income¼ annual household income; PSET¼ Parenting Self-Evaluation Task; DS¼ developmentally supportive; DU¼ developmentally unsupportive; PSET DS %¼ percentage of developmentally supportive parenting qualities endorsed during the PSET in the ‘Self’ condition; PSET DU %¼ percentage of developmentally unsupportive parenting qualities endorsed during the PSET in the ‘Self’ condition; mPFC ME instruction¼ neural activity in the mPFC ROI for the main effect of instruction (Self> Change, arbitrary units); mPFC ME trait¼ neural activity in the mPFC ROI for the main effect of trait (DS> DU; arbitrary units); mPFC interaction¼ neural activity in the mPFC ROI for the interaction of instruction (self, change)trait (DS, DU; arbitrary units); PSI¼ Parenting Stress Index Total Score; PSOC¼ Parenting Sense of Competence Total Score; PA¼ Positive Affect Subscale Score from the PANAS; NA¼ Negative Affect Subscale Score from the PANAS; ACES¼ Adverse Childhood Experiences Survey Total Score. P¼ 0.05–0.07. *P< 0.05. **P< 0.01. ***P< 0.001. Square root transformation to improve normality. Table 4. Intercorrelations for variables of interest as a function of parity Variables 1 2 3 4 5 6 7 8 9 10 11 12 1. Age – 0.451 0.306 0.195 0.507* 0.132 0.288 0.073 0.022 0.016 0.084 0.276 2. Income 0.294 – 0.240 0.089 0.141 0.217 0.231 0.138 0.076 0.080 0.135 0.436 3. PSET DS % 0.118 0.228 – 0.393 0.018 0.030 0.099 0.570* 0.623* 0.551* 0.492 0.033 þ þ 4. PSET DU % 0.071 0.407 0.405 – 0.427 0.418 0.284 .742** 0.625* 0.567* 0.446 0.560* 5. mPFC ME instruction 0.219 0.023 0.105 0.322 – 0.119 0.099 0.410 0.287 0.404 0.112 0.457 6. mPFC ME trait 0.163 0.333 0.042 0.032 0.257 – 0.063 0.140 0.052 0.120 0.066 0.269 7. mPFC interaction .158 0.076 0.062 0.181 0.238 0.118 – 0.214 0.100 0.020 0.001 0.119 8. PSI 0.204 0.474* 0.379 0.682** 0.040 0.022 0.097 – 0.853*** 0.825*** 0.811*** 0.283 9. PSOC 0.116 0.178 0.431 0.569** 0.070 0.177 0.093 0.687** – 0.836*** 0.732** 0.156 10. PA 0.140 0.258 0.478* 0.420 0.006 0.122 0.091 0.447* 0.550* – 0.756** 0.053 11. NA 0.019 0.476* 0.511* 0.733*** 0.217 0.011 0.056 0.709*** 0.648** 0.580** – 0.054 a þ 12. ACES 0.360 0.159 0.377 0.252 0.103 0.130 0.269 0.412 0.055 0.035 0.393 – Note. Intercorrelations for primiparous mothers (n¼ 16) are presented above the diagonal and intercorrelations for multiparous mothers (n¼ 21) are presented below the diagonal. Age ¼ Maternal age (years); Income¼ annual household income; PSET¼ Parenting Self-Evaluation Task; DS¼ developmentally supportive; DU¼ developmentally unsupportive; PSET DS %¼ percentage of developmentally supportive parenting qualities endorsed during the PSET in the ‘Self’ condition; PSET DU %¼ percentage of developmentally unsupportive parenting qualities endorsed during the PSET in the ‘Self’ condition; mPFC ME instruction¼ neural activity in the mPFC ROI for the main effect of instruction (Self> Change, arbitrary units); mPFC ME trait¼ neural activity in the mPFC ROI for the main effect of trait (DS> DU; arbi- trary units); mPFC interaction¼ neural activity in the mPFC ROI for the interaction of instruction (self, change)trait (DS, DU; arbitrary units); PSI¼ Parenting Stress Index Total Score; PSOC¼ Parenting Sense of Competence Total Score; PA¼ Positive Affect Subscale Score from the PANAS; NA¼ Negative Affect Subscale Score from the PANAS; ACES¼ Adverse Childhood Experiences Survey Total Score. P¼ 0.05–0.07. *P< 0.05. **P< 0.01. ***P< 0.001. Square root transformation to improve normality. Associations. Bivariate correlations for self-evaluations in the full with DU (r¼0.636, P< 0.001) qualities. State positive affect (PA) sample are listed in Table 3. As shown, total parenting stress positively correlated with percent of self-endorsed DS (r¼ 0.482, negatively correlated with percent of self-endorsed DS (r¼0.447, P¼ 0.003) and negatively correlated with DU (r¼0.493, P¼ 0.002) P¼ 0.006) and positively correlated with DU (r¼ 0.709, P< 0.001) qualities, and state negative affect (NA) negatively correlated with qualities. All of the PSI subscales were in thesamedirectionsand percent of self-endorsed DS (r¼0.519, P¼ 0.001) and positively significant. Parental self-efficacy positively correlated with percent correlated with DU (r¼ 0.661, P< 0.001) qualities. Due to the inter- of self-endorsed DS (r¼ 0.5, P¼ 0.002) and negatively correlated relations between many of these items, we interrogated the Downloaded from https://academic.oup.com/scan/article-abstract/13/5/535/4990260 by Ed 'DeepDyve' Gillespie user on 21 June 2018 L. K. Noll et al. | 541 Fig. 2. Main effect of parenting self-evaluation vs malleability evaluation. Across all 37 subjects, the contrast of Self> Change was calculated across both types of parenting qualities (voxel-wise threshold of P<0.001 combined with a spatial threshold k¼ 46 corresponds to an FWE-corrected false-probability of P<0.05 across the whole brain). Illustrated here are the network of CMS involved in self-evaluation. The mPFC ROI is outlined in white. unique effects of total PSI, PSOC, PA, and NA on percent of self- (Self> Change) and trait (DS> DU), and their interaction. Activity endorsed DS and DU qualities using multiple regression. in the mPFC ROI for the main effect of instruction significantly Considered together, none of these individually significantly pre- negatively correlated with mother’s age (r¼0.384, P¼ 0.019), as dicted percent of self-endorsed DS qualities (Ps> 0.26), but total shown in Table 3. In other words, younger mothers showed a PSI remained a trend-level predictor of percent of self-endorsed greater difference in mPFC activity when performing self vs mal- DU qualities (t¼ 1.98, P¼ 0.056). leability evaluations compared to older mothers. This remained Age of mother and income did not significantly relate to task significant in a multiple regression model controlling for individ- performance. Considered alone, mothers’ number of ACEs ual differences in PSI, PSOC, PA and NA (t¼2.49, P¼ 0.018). None showed a trend-level positive association with the percent of of the other individual difference variables included in our DU qualities endorsed (r¼ 0.304, P¼ 0.067), but this did not re- hypotheses (income, ACES, PSI, PSOC, PA, NA) related to brain ac- main significant in a multiple regression model with other vari- tivity in the mPFC for our three contrasts of interest. ables significantly correlated with task performance (PSI, PSOC, PA, NA). Parity With regard to reaction time, self-reported parental self- To explore differences in results with regard to parity, we efficacy significantly negatively correlated with DS self RT looked at behavior and brain differences in primiparous vs mul- (r¼0.393, P¼ 0.016) and negatively correlated with DU self RT tiparous mothers, as well as correlations within each group at the trend level (r¼0.309, P¼ 0.063). Similarly, PA significant- (Table 4). ly negatively correlated with DS self RT (r¼0.418, P¼ 0.01) and Compared to multiparous mothers (n¼ 21), primiparous negatively correlated with DU self RT at the trend level mothers (n¼ 16) showed a higher percentage of self-endorsed DS (r¼0.302, P¼ 0.069). Reaction time for the change conditions qualities (F ¼ 4.428, P¼ 0.043), a lower percentage of self- (1, 35) did not significantly correlate with any of the self-reported endorsed DU qualities (F ¼ 8.9, P¼ 0.005), and overall faster (1, 35) measures. Neither of these associations remained significant reaction times for both DS (F ¼ 7.596, P¼ 0.009) and DU (F (1, 35) (1, when considered together in a multiple regression model pre- ¼ 14.79, P< 0.001) qualities. Furthermore, there was a signifi- 35) dicting RT. cant difference between multiparous and primiparous mothers in mPFC ROI activity for the interaction of instruction and trait Neuroimaging (F ¼ 6.586, P¼ 0.015), such that primiparous mothers showed (1, 35) a greater difference in mPFC activity when making self- As shown in Figure 2, the main effect of instruction (i.e. Self vs Change) across both types of stimuli (thresholded at P< 0.001 evaluations of DS traits than for DU traits compared to multipar- k¼ 46) produced a large cluster of voxels encompassing most ous mothers. This did not survive multiple-comparison correc- CMS, including the mPFC and orbitofrontal cortex, and anterior tion when modeled in a 222(parityinstructiontrait) RMANOVA across the whole brain. Lastly, primiparous mothers and posterior cingulate cortex. Other clusters with significant main effects for this contrast included the thalamus, left angu- reported a higher sense of parental self-efficacy via the PSOC lar gyrus, cerebellum, and right superior frontal gyrus (Table 5). compared to multiparous mothers (F ¼ 6.53, P¼ 0.015). There (1, 35) For the main effect of trait (i.e. DS vs DU) across both instruc- was no difference between the groups of mothers with regard to age, income, ACES, parenting stress, or state affect (Ps> 0.15). tions (thresholded at P< 0.001 k¼ 46), clusters of voxels emerged in the left anterior premotor cortex, right primary visual cortex, After investigating self-report and behavioral differences in and right intraparietal sulcus (Table 5). For the interaction of in- parental self-efficacy by parity, we examined parity group effects on the associations among brain activity, task perform- struction and trait at the same threshold, no significant clusters ance, and self-report measures. In terms of brain–behavior survived. associations by group, there was a significant effect of group on Associations. Individual participants’ activity within the anatom- the association between Self> Change activity in the mPFC ROI ical mPFC ROI was calculated for the main effects of instruction and percent of self-endorsed DU qualities (F ¼ 5.54, (1, 33) Downloaded from https://academic.oup.com/scan/article-abstract/13/5/535/4990260 by Ed 'DeepDyve' Gillespie user on 21 June 2018 542 | Social Cognitive and Affective Neuroscience, 2018, Vol. 13, No. 5 Table 5. Peak voxel and maximum Z-values for PSET main effect results Region Cluster size FZ-score Side MNI coordinates xy z Main effect of instruction (Self > Change) Mid Orbital Gyrus 2620 111.44 >10 Midline 0 34 6 52.91 6.60 Left 434 2 50.86 6.48 Midline 0 50 0 L Parahippocampal gyrus 1655 83.73 >10 Left 10 62 18 43.78 6.06 Right 4 62 26 42.73 5.99 Right 6 52 26 L Angular gyrus 430 34.36 5.42 Left 46 56 50 22.92 4.46 Left 42 54 42 21.16 4.28 Left 34 68 52 R Superior frontal gyrus 89 29.05 5.00 Right 42 14 46 Anterior cingulate 98 28.49 4.96 Midline 0 16 40 Thalamus 86 28.07 4.92 Midline 0 66 18.74 4.03 Left 2 18 10 L Orbital frontal cortex 73 24.86 4.64 Left 44 48 2 Posterior cingulate 50 20.05 4.17 Left 8 28 50 Cerebellum 74 19.33 4.09 Right 34 46 28 15.39 3.64 Right 22 50 26 R Orbital frontal cortex 49 18.40 3.99 Right 34 54 0 R Orbital frontal cortex 107 17.34 3.87 Right 46 60 48 14.93 3.59 Right 46 52 40 Main effect of trait (DS > DU) Left anterior premotor cortex 242 4.56 Left 42 0 34 4.23 Left 46 6 26 4.13 Left 52 12 28 Right primary visual cortex 123 4.17 Right 14 76 10 4.12 Right 10 90 4 Intraparietal sulcus 80 4.17 Left 28 56 48 3.79 Left 28 60 58 Interaction of instruction and trait No signiﬁcant clusters (r¼0.322, P¼ 0.155). There were no significant differences in the associations between self-report measures and brain activ- ity in the mPFC ROI by parity group. Discussion The purpose of this study was to examine the behavioral and neural correlates of parenting self-evaluation. Given the paucity of experimental paradigms for evaluating parenting self- evaluation in vivo, we created and employed a new task that represents an integration of behavioral research on parental self-experience and neuroimaging work documenting the neur- al underpinning of general self-evaluation. We focused on mothers of young children because early childhood is character- ized by dramatic developmental change and represents a poten- tial inflection point where parenting self-evaluations likely exert maximal influence on downstream outcomes. Behavioral correlates of parenting self-evaluation Fig. 3. Illustration of the signiﬁcant effect of parity group on the relationship be- tween percent of self-endorsed DU qualities and Self> Change activity in the While completing the PSET in the MRI scanner, mothers mPFC ROI (F ¼ 5.54, P¼0.024). (1, 33) endorsed significantly more positive (developmentally support- ive) qualities than negative (developmentally unsupportive) qualities in the self-evaluation trials than during the malleabil- P¼ 0.024; Figure 3) such that primiparous mothers showed a ity trials, indicating they believed negative qualities could non-significant positive association between percent DU qual- change more than they self-identified with those same qual- ities endorsed and mPFC activity (r¼ 0.427, P¼ 0.099) while mul- tiparous mothers had a non-significant negative association ities. These initial results suggest the presence of a positivity Downloaded from https://academic.oup.com/scan/article-abstract/13/5/535/4990260 by Ed 'DeepDyve' Gillespie user on 21 June 2018 L. K. Noll et al. | 543 bias in self-evaluation of parenting qualities similar to that typ- A secondary goal of this study was to explore the relation- ically seen in other forms of self-evaluation (Cunningham and ships between PSET performance, brain activation and self- Turk, 2017). The absence of differences between positive and report measures as a function of parity. Results of our analyses indicated that primiparous mothers reported higher endorse- negative qualities in the change condition suggests that malle- ment of positive parenting self-evaluations, lower endorsement ability evaluations represent an adequate high-level control of negative self-evaluations, faster reaction times and higher condition for parenting self-evaluations. Mothers were quicker parental self-efficacy compared to multiparous mothers. In to endorse positive qualities than negative qualities for both addition, higher self-reported self-efficacy related to less mPFC self-evaluation and change; and quicker to make self- activity during self-evaluations of DU qualities compared to evaluations than malleability evaluations—possibly indicating malleability evaluations in first-time mothers, but greater mPFC mothers gave comparatively greater consideration to the latter. activity in mothers of more than one child. Although these As expected, positive parenting self-evaluations were associ- results are exploratory and preliminary in nature, the greater ated with greater self-reported parental self-efficacy, lower lev- mPFC activity seen in multiparous mothers may index els of caregiving-related stress and higher levels of positive increased self-knowledge as a parent, greater self-differenti- state affect. Conversely, negative parenting self-evaluations ation and/or different neural circuitry than in mothers of only were associated with poorer parental self-efficacy, higher levels one child. of caregiving-related stress, more adverse childhood experien- ces and higher levels of negative state affect. These bivariate Study limitations and future directions associations provide preliminary evidence that the PSET may serve as a useful index of parenting self-evaluation with conver- Several limitations are important to acknowledge when consid- gent validity with respect to self-report measures that index ering these results. In designing the PSET, we attempted to bal- overlapping but conceptually distinct constructs. Additionally, ance the need for a high-level evaluative control condition with negative parenting self-evaluations may represent a risk factor the need for a contrast that would be simple enough to allow for for developmentally unsupportive caregiving, as previous work clear interpretation. Using Jankowski et al. (2014) paradigm as a indicates non-abusive mothers report higher parental self- model, we selected trait malleability evaluations as our control efficacy than abusive mothers (e.g. Mash et al., 1983). This risk for this study, reasoning that the evaluation of malleability of factor may be particularly salient for parents who have experi- qualities requires comparable cognitive engagement to self- enced maltreatment in childhood, especially in the context of evaluations without necessitating self-evaluation per se. Despite challenging child behavior (e.g. Michl et al., 2015; Kunseler et al., the aforementioned strengths of this contrast, it is possible that 2016). Conversely, positive parenting self-evaluations may rep- malleability evaluations may activate some degree of self- resent an important intervention target and protective factor referential processing. Moreover, the forced choice nature of the that buffers parents from the cumulative impact of environ- task’s binary response options may have accentuated overlap in mental adversity (Peterson et al., 2003; Fisher et al., 2016) and dif- the latter and reduced the ecological validity of the task. Hence, ficulties that emerge during the transition to parenthood the neuroimaging contrasts presented here must be interpreted (Mihelic et al., 2016). with caution; future work, with additional contrasts and con- In this study, parenting stress independently predicted tinuous response options, is needed to further delineate the negative parenting self-evaluation in regression analyses at the neural circuitry involved in different types of self-evaluation in- trend level. This finding suggests the link between caregiving dependent of and specifically relating to change beliefs. For ex- stress and global parenting self-evaluation may be more salient ample, inclusion of additional contrasts that combine self- than the rest of the bivariate associations. It also highlights the evaluation with a change evaluation (e.g. to what extent can importance of differentiating between general state affect and this quality change for you as a parent?) might be of particular more specific self-referential trait endorsement in future work utility for intervention work that seeks to characterize parents’ openness to change. with greater statistical power. Due to the scope of this study, we excluded caregivers who were not biological mothers (e.g. fathers, foster parents, child Neural correlates of parenting self-evaluation care providers) and caregivers of older children from participa- As in previous neuroimaging work examining other types of tion. Within these constraints, the sample was representative self-evaluation in other populations (Denny et al., 2012; Wagner of our region (socioeconomically but not racially diverse). One et al., 2012) and consistent with our predictions, parenting self- consequence of this is that the sample did not contain a suffi- evaluations in this sample elicited greater activity in most CMS cient number of ethnic minority participants to make infer- of interest compared to control evaluations. The consistency of ences about non-white parents. All of these populations our results with previous non-parenting-related self-evaluation, warrant investigation with future experimental studies of coupled with the behavioral results reported above, provides parenting self-evaluation. Furthermore, due to the cross- initial proof-of-concept evidence of the PSET as an integrative sectional nature of this study, it remains unknown whether the task to index parenting self-evaluations and associated behav- parenting self-evaluations elicited by the PSET are stable across ior and neural processes. time, or conversely, sensitive to change with intervention. As It is noteworthy that our self-report measures of parenting such, future work should examine the PSET’s sensitivity to stress and parental self-efficacy did not significantly relate to change and ability to predict observed and self-reported parent- mPFC activity, particularly given the behavioral associations be- ing behavior. Despite these limitations, this study presents a tween these measures and positive and negative self- novel experimental task that can be used to investigate evaluations. This may indicate that mPFC activity specific to parenting-specific self-evaluation behavior and brain activity. parenting self-evaluations compared to malleability evaluations The exploratory findings presented here indicate that individual index a different aspect of parental experience than those differences in parenting self-evaluation may vary meaningfully assessed by self-report questionnaires. by parity and warrant future attention. Downloaded from https://academic.oup.com/scan/article-abstract/13/5/535/4990260 by Ed 'DeepDyve' Gillespie user on 21 June 2018 544 | Social Cognitive and Affective Neuroscience, 2018, Vol. 13, No. 5 Jankowski, K.F., Moore, W.E., Merchant, J.S., Kahn, L.E., Pfeifer, Acknowledgements J.H. (2014). But do you think I’m cool?: developmental differen- The authors are grateful to Melanie Berry and Kyndal ces in striatal recruitment during direct and reﬂected social Howell for their clinical expertise and collaboration with self-evaluations. Developmental Cognitive Neuroscience, 8, 40–54. stimuli selection; to Danielle Cosme and John Flournoy for Johnston, C., Mash, E.J. (1989). A measure of parenting satisfaction fMRI task support; and to the University of Oregon LCNI. We and efﬁcacy. Journal of Clinical Child Psychology, 18(2), 167–75. are especially grateful to the families who participated in Jones, T.L., Prinz, R.J. (2005). Potential roles of parental the study and shared their experiences with us. self-efﬁcacy in parent and child adjustment: a review. Clinical Psychology Review, 25(3), 341–63. Junttila, N., Vauras, M., Laakkonen, E. (2007). The role of parent- Funding ing self-efﬁcacy in children’s social and academic behavior. This study was supported by funding from the Hemera European Journal of Psychology of Education, 22(1), 41–61. Foundation and University of Oregon to PF. Kim, P., Mayes, L., Feldman, R., Leckman, J.F., Swain, J.E. (2013). Early postpartum parental preoccupation and positive parent- Conﬂict of interest. None declared. ing thoughts: relationship with parent–infant interaction. Infant Mental Health Journal, 34(2), 104–16. References Kohlhoff, J., Barnett, B. (2013). Parenting self-efﬁcacy: links with maternal depression, infant behaviour and adult attachment. Abidin, R.R. (1995). Parenting Stress Index, Third Edition: Professional Early Human Development, 89(4), 249–56. Manual. Odessa, FL: Psychological Assessment Resources, Inc. Kunseler, F.C., Oosterman, M., de Moor, M.H., Verhage, M.L., Ardelt, M., Eccles, J.S. (2001). Effects of mothers’ parental efﬁcacy Schuengel, C. (2016). Weakened resilience in parenting self-ef- beliefs and promotive parenting strategies on inner-city ﬁcacy in pregnant women who were abused in childhood: An youth. Journal of Family Issues, 22(8), 944–72. experimental test. PloS One, 11(2), e0141801. Bogenschneider, K., Small, S.A., Tsay, J.C. (1997). Child, parent, Lonigan, C.J., Phillips, B.M., Hooe, E.S. (2003). Relations of positive and contextual inﬂuences on perceived parenting competence and negative affectivity to anxiety and depression in children: among parents of adolescents. Journal of Marriage and the evidence from a latent variable longitudinal study. Journal of Family, 59(2), 345–62. Consulting and Clinical Psychology, 71(3), 465. Bohlin, G., Hagekull, B. (1987). “Good mothering”: maternal atti- Maldjian, J.A., Laurienti, P.J., Kraft, R.A. & Burdette, J.H. (2003). An tudes and mother-infant interaction. Infant Mental Health automated method for neuroanatomic and cytoarchitectonic Journal, 8(4), 352–63. atlas-based interrogation of fMRI data sets. NeuroImage, 19(3), Callaghan, B.L., Tottenham, N. (2016). The stress acceleration hy- 1233–9. pothesis: effects of early-life adversity on emotion circuits and Maupin, A.N., Rutherford, H.J.V., Landi, N., Potenza, M.N., Mayes, behavior. Current Opinions in Behavioral Sciences, 7, 76–81. L.C. (2018). Investigating the association between parity and Coleman, P.K., Karraker, K.H. (1997). Self-efﬁcacy and parenting maternal neural response to infant cues. Social Neuroscience, 8, quality: Findings and future applications. Developmental 1–12. Review, 18(1), 47–85. Mash, E.J., Johnston, C., Kovitz, K. (1983). A comparison of the Coleman, P.K., Karraker, K.H. (2000). Parenting self-efﬁcacy mother-child interactions of physically abused and non-a- among mothers of school-age children: conceptualization, bused children during play and task situations. Journal of measurement, and correlates. Family Relations, 49(1), 13–24. Clinical Child & Adolescent Psychology, 12(3), 337–46. Cox, R.W. (1996). AFNI: software for analysis and visualization of Michl, L.C., Handley, E.D., Rogosch, F., Cicchetti, D., Toth, S.L. functional magnetic resonance neuroimages. Computers and (2015). Self-criticism as a mechanism linking childhood mal- Biomedical Research, 29(3), 162–73. treatment and maternal efﬁcacy beliefs in low-income moth- Crawford, J.R., Henry, J.D. (2004). The Positive and Negative ers with and without depression. Child Maltreatment, 20(4), Affect Schedule (PANAS): construct validity, measurement 291–300. properties and normative data in a large non-clinical sample. Mihelic, M., Filus, A., Morawaska, A. (2016). Correlates of prenatal British Journal of Clinical Psychology, 43(3), 245–65. parenting expectations in new mothers: is better self-efﬁcacy Cunningham, S.J., Turk, D.J. (2017). Editorial: a review of a potential target for preventing postnatal adjustment difﬁcul- self-processing biases in cognition. The Quarterly Journal of ties? Prevention Science, 17(8), 949–59. Experimental Psychology, 70(6), 987–95. Mitchell, J.P., Macrae, C.N., Banaji, M.R. (2006). Dissociable medial Denny, B.T., Kober, H., Wager, T.D., Ochsner, K.N. (2012). A prefrontal contributions to judgments of similar and dissimi- meta-analysis of functional neuroimaging studies of self-and lar others. Neuron, 50(4), 655–63. other judgments reveals a spatial gradient for mentalizing in Nicolle, A., Klein-Flu ¨ gge, M.C., Hunt, L.T., Vlaev, I., Dolan, R.J., medial prefrontal cortex. Journal of Cognitive Neuroscience, 24(8), Behrens, T.E. (2012). An agent independent axis for executed 1742–52. and modeled choice in medial prefrontal cortex. Neuron, 75(6), Felitti, V.J., Anda, R.F., Nordenberg, D., et al. (1998). Relationship of childhood abuse and household dysfunction to many of the 1114–21. Northoff, G., Heinzel, A., De Greck, M., Bermpohl, F., Dobrowolny, leading causes of death in adults: the Adverse Childhood H., Panksepp, J. (2006). Self-referential processing in our Experiences (ACE) study. American Journal of Preventive Medicine, 14(4), 245–58. brain—a meta-analysis of imaging studies on the self. Fisher, P.A., Frenkel, T.I., Noll, L.K., Berry, M., Yockelson, M. Neuroimage, 31(1), 440–57. (2016). Promoting healthy child development via a two-gener- Pereira, M., Ferreira, A. (2016). Neuroanatomical and neurochem- ation translational neuroscience framework: the Filming ical basis of parenting: dynamic coordination of motivational, Interactions to Nurture Development video coaching program. affective and cognitive processes. Hormones and Behavior, 77, Child Development Perspectives, 10(4), 251–6. 72–85. Downloaded from https://academic.oup.com/scan/article-abstract/13/5/535/4990260 by Ed 'DeepDyve' Gillespie user on 21 June 2018 L. K. Noll et al. | 545 Qin, P., Northoff, G. (2011). How is our self related to midline regions Peterson, L., Tremblay, G., Ewigman, B., Saldana, L. (2003). Multilevel selected primary prevention of child maltreatment. and the default-mode network? Neuroimage, 57(3), 1221–33. Journal of Consulting and Clinical Psychology, 71(3), 601–11. Teti, D.M., Gelfand, D.M. (1991). Behavioral competence Pfeifer, J.H., Lieberman, M.D., Dapretto, M. (2007). “I know you are but among mothers of infants in the ﬁrst year: the mediational what am I?!”: neural bases of self-and social knowledge retrieval in role of maternal self-efﬁcacy. Child Development, 62(5), children and adults. Journal of Cognitive Neuroscience, 19(8), 1323–37. 918–29. Pfeifer, J.H., Masten, C.L., Borofsky, L.A., Dapretto, M., Fuligni, Verhage, M.L., Oosterman, M., Schuengel, C. (2013). Parenting A.J., Lieberman, M.D. (2009). Neural correlates of direct and self-efﬁcacy predicts perceptions of infant negative tempera- reﬂected self-appraisals in adolescents and adults: when so- ment characteristics, not vice versa. Journal of Family cial perspective-taking informs self-perception. Child Psychology, 27(5), 844. Development, 80(4), 1016–38. Wagner, D.D., Haxby, J.V., Heatherton, T.F. (2012). The represen- Pfeifer, J.H., Kahn, L.E., Merchant, J.S., et al. (2013). Longitudinal tation of self and person knowledge in the medial prefrontal change in the neural bases of adolescent social cortex. Wiley Interdisciplinary Reviews: Cognitive Science, 3(4), self-evaluations: effects of age and pubertal development. 451–70. Watson, D., Clark, L.A., Tellegen, A. (1988). Development and val- Journal of Neuroscience, 33(17), 7415–9. Poldrack, R.A., Mumford, J.A. (2009). Independence in ROI ana- idation of brief measures of positive and negative affect: the lysis: where is the voodoo?. Social Cognitive and Affective PANAS scales. Journal of Personality and Social Psychology, 54(6), Neuroscience, 4(2), 208–13. 1063. Downloaded from https://academic.oup.com/scan/article-abstract/13/5/535/4990260 by Ed 'DeepDyve' Gillespie user on 21 June 2018
Social Cognitive and Affective Neuroscience – Oxford University Press
Published: Apr 28, 2018
It’s your single place to instantly
discover and read the research
that matters to you.
Enjoy affordable access to
over 18 million articles from more than
15,000 peer-reviewed journals.
All for just $49/month
Query the DeepDyve database, plus search all of PubMed and Google Scholar seamlessly
Save any article or search result from DeepDyve, PubMed, and Google Scholar... all in one place.
Get unlimited, online access to over 18 million full-text articles from more than 15,000 scientific journals.
Read from thousands of the leading scholarly journals from SpringerNature, Wiley-Blackwell, Oxford University Press and more.
All the latest content is available, no embargo periods.
“Hi guys, I cannot tell you how much I love this resource. Incredible. I really believe you've hit the nail on the head with this site in regards to solving the research-purchase issue.”Daniel C.
“Whoa! It’s like Spotify but for academic articles.”@Phil_Robichaud
“I must say, @deepdyve is a fabulous solution to the independent researcher's problem of #access to #information.”@deepthiw
“My last article couldn't be possible without the platform @deepdyve that makes journal papers cheaper.”@JoseServera