Don't Tell Us How Strong It Feels! Converging and Discriminant Validity of an Indirect Measure of Emotional Evidence Accumulation Efficiency.

Childhood material hardship, including insecurity in housing, utilities, food, and medical care, is a critical factor influencing cognitive functioning and mental health outcomes. However, past research on material hardship is limited by cross-sectional data and reliance on conventional behavioral measures of cognitive functioning that suffer from poor reliability and lack of specificity. To address these gaps, this study used a well-validated computational model of cognition to examine individuals' drift rate, which is hypothesized to be driven by the efficiency of evidence accumulation (EEA) for decision-relevant information, a key process that supports higher-order cognitive functioning. Here, we examined how material hardship during childhood was associated with adolescent drift rate, and whether drift rate was linked with attention difficulties. 187 adolescents recruited from the Future of Families and Child Wellbeing Study were included in the analyses. Adolescents exposed to greater material hardship showed lower drift rate, suggesting less effective processing. Growth curve modeling revealed that initial exposure to material hardship, but not changes across childhood, was associated with drift rate in adolescence. Lower drift rate was also associated with concurrent attentional problems and served as a significant indirect pathway linking material hardship to adolescent attention. This is the first longitudinal study that examined the associations among childhood material hardship exposure, adolescent drift rate, and attentional difficulties. Our results suggest that challenges to essential living conditions in childhood may impact cognitive processes underlying goal-directed behavior in adolescents. These findings highlight the application of computational models to reveal specific cognitive processes impacted by adversity. SUMMARY: Existing research on material hardship and cognitive functioning is limited by reliance on cross-sectional task-based performance, resulting in inconsistent findings. We applied the diffusion-decision model to decompose trial-wise performance into underlying cognitive processes, including drift rate, a key evidence accumulation process underlying goal-directed behavior. Greater cumulative childhood material hardship, beyond exposure to other adversity, is associated with lower drift rate, which is linked with greater attentional problems in adolescents. Findings were specific to drift rate, not average response time or accuracy, emphasizing the value of computational methods in capturing cognitive changes associated with material hardship.

BackgroundRecent advances in cognitive digital assessment methodology, including high-frequency, ambulatory assessments, promise to improve the detection of subtle cognitive changes. Computational modeling approaches may further improve the sensitivity of digital cognitive assessments to detect subtle cognitive changes by capturing features that map onto core cognitive processes.ObjectiveWe explored the validity of a brief smartphone-based adaptation of a visual working memory task that has shown sensitivity for detecting preclinical Alzheimer disease risk. We aimed to optimize properties of the task for computational cognitive feature extraction with drift diffusion modeling.MethodsWe analyzed data from 68 participants (n=47, 69% women; n=55, 81% White; mean age 49, SD 14; range 24-80 years) who completed 60 trials for each of 16 variations of a visual working memory binding task (the Color Shapes task) on smartphones, over an 8-day period. A drift diffusion model was fit to the response time and accuracy data from the task. We experimentally manipulated 3 properties of the Color Shapes task (study time, probability of change, and choice urgency) to test how they yielded differences in key drift diffusion model parameters (drift rate, initial bias toward a response option, and caution in decision-making). We also evaluated how an additional task property, the test array size, impacted responses across all conditions. For array size, we tested a whole display of 3 shapes against a single probe of 1 shape only.ResultsThe 3 task property manipulations yielded the following results: (1) increasing the ratio of different responses was credibly associated with higher initial bias toward the different response (mean 0.06, SD 0.02 for the whole display; mean 0.15, SD 0.02, for the single probe condition); (2) increasing the choice urgency during the test phase was credibly associated with decreased caution in decision-making in the single probe condition (mean −0.04, SD 0.02) but not in the whole display (mean −0.01, SD 0.02); and (3) contrary to expectation, longer study times did not yield a credibly faster drift rate but produced credibly slower ones for the whole display condition (mean −0.28, SD 0.05) and a null effect for the single probe condition (mean 0.01, SD 0.05). In addition, as expected, we found that individual differences in drift rate were associated with age in both array sizes (r=−0.45 with Bayes factor=191), with older participants having a slower drift rate. Older participants also showed higher caution (r=0.42 with Bayes factor=80.76) in the single probe condition.ConclusionsWe identified a version of the Color Shapes task optimized for smartphone-based cognitive assessments in real-world settings, with data designed for analysis through computational cognitive modeling. Our proposed approach can advance the development of tools for efficient and effective early detection and monitoring of risk for Alzheimer disease.

Computational modelling can be used to precisely characterize the cognitive processes involved in attentional biases towards threat, yet so far has only been applied in the context of adult anxiety. Furthermore, studies investigating attentional biases in childhood anxiety have largely used tasks that conflate automatic and controlled attentional processes. By using a perceptual load paradigm, we separately investigate contributions from automatic and controlled processes to attentional biases towards negative stimuli and their association with paediatric anxiety. We also use computational modelling to investigate these mechanisms in children for the first time. In a sample of 60 children (aged 5‐11 years) we used a perceptual load task specifically adapted for children, in order to investigate attentional biases towards fearful (compared with happy and neutral) faces. Outcome measures were reaction time and percentage accuracy. We applied a drift diffusion model to investigate the precise cognitive mechanisms involved. The load effect was associated with significant differences in response time, accuracy and the diffusion modelling parameters drift rate and extra‐decisional time. Greater anxiety was associated with greater accuracy and the diffusion modelling parameter ‘drift rate’ on the fearful face trials. This was specific to the high load condition. These findings suggest that attentional biases towards fearful faces in childhood anxiety are driven by increased perceptual sensitivity towards fear in automatic attentional systems. Our findings from computational modelling suggest that current attention bias modification treatments should target perceptual encoding directly rather than processes occurring afterwards.

Perceptual decision making (PDM) has been studied using two approaches. Threshold measurement is predominant used in psychophysics, while reaction times (RT) with associated models have been used to estimate components of PDM (i.e., drift rate). To test if these two approaches reflect overlapping mechanisms, we conducted 3 experiments: a motion, a static orientation, and a dynamic orientation task. DT is the shortest stimulus presentation time sufficient to make accurate perceptual decisions. RTs and choices were fitted by a drift diffusion model (DDM). We expected a close relationship between DTs and drift rates, allowing us to accurately predict DTs from RT. In the motion task, we found a close relation between the empirical DTs and the DTs predicted by the DDM. Surprisingly, in the static task, there was little correlation between the two; DTs, improved monotonically with higher contrast, but drift rates saturated at 6%. We hypothesize that this mismatch is due to the information being available immediately in the static task, without needing to accumulate new evidence. Thus, we developed a novel dynamic orientation task that mimics the dynamic nature of the motion task and found a similar relation between DTs and drift rates. In summary, we show a close link between DTs and drift rate for the two dynamic tasks. This result supports the conceptualization of drift rate as a proxy for perceptual sensitivity but only for task where new information becomes available over time.

The flanker task is a widely used measure of cognitive control abilities. Drift-diffusion modeling of flanker task behavior can yield separable parameters of cognitive control-related subprocesses, but the parameters' psychometrics are not well-established. We examined the reliability and validity of four behavioral measures: (1) raw accuracy, (2) reaction time (RT) interference, (3) NIH Toolbox flanker score, and (4) two drift-diffusion model (DDM) parameters-drift rate and boundary separation-capturing evidence accumulation efficiency and speed-accuracy trade-off, respectively. Participants from two independent studies - one cross-sectional (N = 381) and one with three timepoints (N = 83) - completed the flanker task while electroencephalography data were collected. Across both studies, drift rate and boundary separation demonstrated comparable split-half and test-retest reliability to accuracy, RT interference, and NIH Toolbox flanker score, but better incremental convergent validity with psychophysiological measures (i.e., the error-related negativity; ERN) and neuropsychological measures of cognitive control than the other behavioral indices. Greater drift rate (i.e., faster and more accurate responses) to congruent and incongruent stimuli, and smaller boundary separation to incongruent stimuli were related to 1) larger ERN amplitudes (in both studies) and 2) faster and more accurate inhibition and set-shifting over and above raw accuracy, reaction time, and NIH Toolbox flanker scores (in Study 1). Computational models, such as DDM, can parse behavioral performance into subprocesses that exhibit comparable reliability to other scoring approaches, but more meaningful relationships with other measures of cognitive control. The application of these computational models may be applied to existing data and enhance the identification of cognitive control deficits in psychiatric disorders.

Cognitive Signatures of Depressive and Anhedonic Symptoms and Affective States Using Computational Modeling and Neurocognitive Testing

Primates’ ability to recognize objects in natural scenes is remarkable. As exemplified by rapid stimulus presentation paradigms, the visual system is both fast and accurate. Computational models have been proposed that predict the level of performance of human participants and how recognition may be affected by visual properties of images. However, these computational models do not make any predictions about reaction times, and cannot explain the time course of information accumulation in the visual cortex. Here we present an initial attempt to fill this gap using a decision model that allows for analysis of both behavioral responses and decision times within a unified framework. Participants performed an object recognition task in natural scenes using a backward masking paradigm with varying stimulus onset asynchrony (SOA) conditions. We estimated decision-related parameters for the task using a hierarchical drift diffusion model, an extension of the most widely used decision model. We examined how the drift rate, a parameter associated with task difficulty, can be used to explain the results, and show that changes in the drift rate alone does not seem to account for the distribution of reaction times under different masking conditions. Interestingly we find that both the SOA and image properties affect the variance of the drift rate, and that this change does not seem to simply reflect variability in the stimulus properties across conditions. We speculate that it may reflect multiple processing strategies employed by the visual system to process information. Our results suggest that decision models may constitute a promising tool for understanding the brain mechanisms underlying object recognition. Meeting abstract presented at VSS 2012

Gambling disorder is a behavioral addiction associated with impairments in value-based decision-making and cognitive control. These functions are thought to be regulated by dopamine within fronto-striatal circuits, but the role of altered dopamine neurotransmission in the etiology of gambling disorder remains controversial. Preliminary evidence suggests that increasing frontal dopamine tone might improve cognitive functioning in gambling disorder. We therefore examined whether increasing frontal dopamine tone via a single dose of the catechol-O-methyltransferase (COMT) inhibitor tolcapone would reduce risky choice in human gamblers (n = 14) in a randomized double-blind placebo-controlled crossover study. Data were analyzed using hierarchical Bayesian parameter estimation and a combined risky choice drift diffusion model (DDM). Model comparison revealed a nonlinear mapping from value differences to trial-wise drift rates, confirming recent findings. An increase in risk-taking under tolcapone versus placebo was about five times more likely, given the data, than a decrease [Bayes factor (BF) = 0.2]. Examination of drug effects on diffusion model parameters revealed that an increase in the value dependency of the drift rate under tolcapone was about thirteen times more likely than a decrease (BF = 0.073). In contrast, a reduction in the maximum drift rate under tolcapone was about seven times more likely than an increase (BF = 7.51). Results add to previous work on COMT inhibitors in behavioral addictions and to mounting evidence for the applicability of diffusion models in value-based decision-making. Future work should focus on individual genetic, clinical and cognitive factors that might account for heterogeneity in the effects of COMT inhibition.

The convergent and discriminant validity of three assessment methods were investigated in relation to the construct of childhood depression for a sample of elementary, intermediate, and secondary students. The three assessment methods of self-report, teacher, and peer rating scales were used to assess the response classes of depression, social withdrawal, social skills, and aggression. The self-report, teacher, and peer rating scales were subjected to a series of analyses to determine their psychometric properties. Test-retest and internal consistency reliabilities were generally acceptable, although the stability and item homogeneity of individual factors varied from the low-to-high range within each of the rating scales. Four factors (i.e., Depression, Social Skills, Aggression, and Social Withdrawal) were extracted from the self-report, teacher, and peer rating scales following a confirmatory factor analysis. Evidence for the criterion-related validity of the three rating scales was obtained using a self-report rating scale of depression, and two teacher rating scales of social skills, externalizing behavior problems, and internalizing behavior problems. The main purpose of the present study was to examine the convergent and discriminant validity of childhood depression across three sources and three grade levels through four multitrait-multimethod (MTMM) matrices. Evidence for convergent validity was found for the full MTMM matrix of grades 3 through 12 and for the three grade levels (i.e., 3-6, 7-9, and 10-12) using Campbell and Fiske's criteria. Of the 12 validity coefficients in each matrix, 10 attained statistical significance in the full matrix, 7 in the elementary grade matrix, 6 in the intermediate grade matrix, and 8 in the secondary grade matrix. Although the Aggression factor and the self/peer method showed the highest convergent validity, some evidence was obtained for the Depression factor, the Social Skills factor, and the other methods. Minimal evidence was gathered for discriminant validity using Campbell and Fiske's criteria. Results of an analysis of variance model for MTMM matrices showed significant convergent and discriminant validity for each of the four matrices, but also significant method variance. Findings were discussed in relation to the methodological problems in the assessment of childhood depression and to suggested areas of future research.

Population-based samples with valid, quantitative and genetically informative trait measures of psychopathology could be a powerful complement to case/control genetic designs. We report the convergent and predictive validity of the parent- and self-report versions of the Strengths and Weaknesses of ADHD Symptoms and Normal Behavior Rating Scale (SWAN). We tested if SWAN scores were associated with ADHD diagnosis, ADHD polygenic risk, as well as traits and polygenic risk for disorders that co-occur with ADHD: anxiety and obsessive-compulsive disorder (OCD). We collected parent- and self-report SWAN scores in a sample of 15,560 children and adolescents (6-17years) recruited at a science museum (Spit for Science sample). We established age and sex norms for the SWAN. Sensitivity-specificity analyses determined SWAN cut-points that discriminated those with and without a reported ADHD diagnosis. These cut-points were validated in a clinic sample (266 ADHD cases; 36 controls). Convergent validity was established using the Conners' parent- and self-report scales. Using Spit for Science participants with genome-wide data (n=5,154), we tested if low, medium and high SWAN scores were associated with polygenic risk for ADHD, OCD and anxiety disorders. Parent- and self-report SWAN scores showed high convergent validity with Conners' scales and distinguished ADHD participants with high sensitivity and specificity in the Spit for Science sample. In a clinic sample, the Spit for Science cut-points discriminated ADHD cases from controls with a sensitivity of 84% and specificity of 92%. High SWAN scores and scores above the Spit for Science cut-points were significantly associated with polygenic risk for ADHD. SWAN scores were not associated with polygenic risk for OCD or anxiety disorders. Our study supports the validity of the parent- and self-report SWAN scales and their potential in ADHD population-based genetic research.

The neuronal mechanisms that underlie deficits in effort cost computation in schizophrenia (SZ) are poorly understood. Given the role of frontostriatal circuits in valence-oriented motivation, we hypothesized that these circuits are either dysfunctional in SZ or do not appropriately predict behavior in SZ when task conditions are difficult and good performance is rewarded. A total of 52 people with recent onset SZ-spectrum disorders and 48 healthy controls (HCs) performed a 3T fMRI task with 2 valence conditions (rewarded vs neutral) and 2 difficulty conditions. Frontostriatal connectivity was extracted during the cue (anticipatory) phase. Individual behavior was fit using a drift-diffusion model, allowing the performance parameter, drift rate (DR), to vary between task conditions. Three models were examined: A group × condition model of DR, a group × condition model of connectivity, and a regression model of connectivity predicting DR depending on group and condition. DRs showed the expected positive correlation with accuracy and a negative association with reaction time. The SZ group showed a deficit in DR but did not differ in overall connectivity or show a valence-specific deficit in connectivity. Significant group × valence × difficulty interactions, however, were observed on the relationship between right dorsolateral prefrontal (DLPFC)-striatal connectivity and DR (DLPFC-Caudate: F = 10.92, PFDR = .004; DLPFC-Putamen: F = 5.14, PFDR = .048) driven by more positive relationships between DR and connectivity during cues for the difficult-rewarded condition in HCs compared to SZ. These findings suggest that frontostriatal connectivity is less predictive of performance in SZ when task difficulty is increased and a reward incentive is applied.

Therapists' oxytocin response mediates the association between patients' negative emotions and psychotherapy outcomes

Emotional clarity has often been assessed with self-report measures, but efforts have also been made to measure it passively, which has advantages such as avoiding potential inaccuracy in responses stemming from social desirability bias or poor insight into emotional clarity. Response times (RTs) to emotion items administered in ecological momentary assessments (EMAs) may be an indirect indicator of emotional clarity. Another proposed indicator is the drift rate parameter, which assumes that, aside from how fast a person responds to emotion items, the measurement of emotional clarity also requires the consideration of how careful participants were in providing responses. This paper aims to examine the reliability and validity of RTs and drift rate parameters from EMA emotion items as indicators of individual differences in emotional clarity. Secondary data analysis was conducted on data from 196 adults with type 1 diabetes who completed a 2-week EMA study involving the completion of 5 to 6 surveys daily. If lower RTs and higher drift rates (from EMA emotion items) were indicators of emotional clarity, we hypothesized that greater levels (ie, higher clarity) should be associated with greater life satisfaction; lower levels of neuroticism, depression, anxiety, and diabetes distress; and fewer difficulties with emotion regulation. Because prior literature suggested emotional clarity could be valence specific, EMA items for negative affect (NA) and positive affect were examined separately. Reliability of the proposed indicators of emotional clarity was acceptable with a small number of EMA prompts (ie, 4 to 7 prompts in total or 1 to 2 days of EMA surveys). Consistent with expectations, the average drift rate of NA items across multiple EMAs had expected associations with other measures, such as correlations of r=-0.27 (P<.001) with depression symptoms, r=-0.27 (P=.001) with anxiety symptoms, r=-0.15 (P=.03) with emotion regulation difficulties, and r=0.63 (P<.001) with RTs to NA items. People with a higher NA drift rate responded faster to NA emotion items, had greater subjective well-being (eg, fewer depression symptoms), and had fewer difficulties with overall emotion regulation, which are all aligned with the expectation for an emotional clarity measure. Contrary to expectations, the validities of average RTs to NA items, the drift rate of positive affect items, and RTs to positive affect items were not strongly supported by our results. Study findings provided initial support for the validity of NA drift rate as an indicator of emotional clarity but not for that of other RT-based clarity measures. Evidence was preliminary because the sample size was not sufficient to detect small but potentially meaningful correlations, as the sample size of the diabetes EMA study was chosen for other more primary research questions. Further research on passive emotional clarity measures is needed.

Reaction time variability (RTV) has been estimated using Gaussian, ex-Gaussian, and diffusion model (DM) indices. Rarely have studies examined interrelationships among these performance indices in childhood, and the use of reaction time (RT) computational models has been slow to take hold in the developmental psychopathology literature. Here, we extend prior work in adults by examining the interrelationships among different model parameters in the ABCD sample and demonstrate how computational models of RT can clarify mechanisms of time-on-task effects and sex differences in RTs. This study utilized trial-level data from the stop signal task from 8916 children (9-10 years old) to examine Gaussian, ex-Gaussian, and DM indicators of RTV. In addition to describing RTV patterns, we examined interrelations among these indicators, temporal patterns, and sex differences. There was no one-to-one correspondence between DM and ex-Gaussian parameters. Nonetheless, drift rate was most strongly associated with standard deviation of RT and tau, while nondecisional processes were most strongly associated with RT, mu, and sigma. Performance worsened across time with changes driven primarily by decreasing drift rate. Boys were faster and less variable than girls, likely attributable to girls' wide boundary separation. Intercorrelations among model parameters are similar in children as has been observed in adults. Computational approaches play a crucial role in understanding performance changes over time and can also clarify mechanisms of group differences. For example, standard RT models may incorrectly suggest slowed processing speed in girls that is actually attributable to other factors.

ObjectiveTo determine the efficacy of psilocybin as an antidepressant compared with placebo or non-psychoactive drugs.DesignSystematic review and meta-analysis.Data sourcesFive electronic databases of published literature (Cochrane Central Register of Controlled Trials,...