Diffusion Decision Model: Current Issues and History

Limitation of switching sensory information flow in flexible perceptual decision making

Humans can flexibly change rules to categorize sensory stimuli, but their performance degrades immediately after a task switch. This switch cost is believed to reflect a limitation in cognitive control, although the bottlenecks remain controversial. Here, we show that humans exhibit a brief reduction in the efficiency of using sensory inputs to form a decision after a rule change. Participants classified face stimuli based on one of two rules, switching every few trials. Psychophysical reverse correlation and computational modeling reveal a reduction in sensory weighting, which recovers within a few hundred milliseconds after stimulus presentation. This reduction depends on the sensory features being switched, suggesting a constraint in routing the sensory information flow. We propose that decision-making circuits cannot fully adjust their sensory readout based on a context cue alone, but require the presence of an actual stimulus to tune it, leading to a limitation in flexible perceptual decision making.

How can we apply decision-making theories to wild animal behavior? Predictions arising from dual process theory and Bayesian decision theory

Our understanding of decision-making processes and cognitive biases is ever increasing, thanks to an accumulation of testable models and a large body of research over the last several decades. The vast majority of this work has been done in humans and laboratory animals because these study subjects and situations allow for tightly controlled experiments. However, it raises questions about how this knowledge can be applied to wild animals in their complex environments. Here, we review two prominent decision-making theories, dual process theory and Bayesian decision theory, to assess the similarities in these approaches and consider how they may apply to wild animals living in heterogenous environments within complicated social groupings. In particular, we wanted to assess when wild animals are likely to respond to a situation with a quick heuristic decision and when they are likely to spend more time and energy on the decision-making process. Based on the literature and evidence from our multi-destination routing experiments on primates, we find that individuals are likely to make quick, heuristic decisions when they encounter routine situations, or signals/cues that accurately predict a certain outcome, or easy problems that experience or evolutionary history has prepared them for. Conversely, effortful decision-making is likely in novel or surprising situations, when signals and cues have unpredictable or uncertain relationships to an outcome, and when problems are computationally complex. Though if problems are overly complex, satisficing via heuristics is likely, to avoid costly mental effort. We present hypotheses for how animals with different socio-ecologies may have to distribute their cognitive effort. Finally, we examine the conservation implications and potential cognitive overload for animals experiencing increasingly novel situations caused by current human-induced rapid environmental change.

Computational modeling of selective attention differentiates subtypes of amnestic mild cognitive impairment

Individuals with amnestic mild cognitive impairment (aMCI), a prodromal stage of Alzheimer's disease and other dementias, show inhibition deficits in addition to episodic memory. How the latent processes of selective attention (i.e., from perception to motor response) contribute to these inhibition deficits remains unclear. Therefore, the present study examined contributions of selective attention to aMCI-related inhibition deficits using computational modeling of attentional dynamics. Two models of selective attention - the dual-stage two-phase model and the shrinking spotlight model - were fitted to individual participant data from a flanker task completed by 34 individuals with single-domain aMCI (sdaMCI, 66-86 years), 20 individuals with multiple-domain aMCI (mdaMCI, 68-88 years), and 52 healthy controls (64-88 years). Findings showed greater commission errors in the mdaMCI group compared to controls. Final-fitting model parameters indicated inhibitory and early perceptual deficits in mdaMCI , and impaired spatial allocation of attention in both MCI groups. Model parameters differentiated mdaMCI from sdaMCI and controls with moderate-to-high sensitivity and specificity. Impairments in perception and selective attention may contribute to inhibition deficits in both aMCI subtypes.

Affective integration in experience, judgment, and decision-making

The role of affect in value-based judgment and decision-making has attracted increasing interest in recent decades. Most previous approaches neglect the temporal dependence of mental states leading to mapping a relatively well-defined, but largely static, feeling state to a behavioral tendency. In contrast, we posit that expected and experienced consequences of actions are integrated over time into a unified overall affective experience reflecting current resources under current demands. This affective integration is shaped by context and continually modulates judgments and decisions. Changes in affective states modulate evaluation of new information (affect-as-information), signal changes in the environment (affect-as-a-spotlight) and influence behavioral tendencies in relation to goals (affect-as-motivation). We advocate for an approach that integrates affective dynamics into decision-making paradigms. This dynamical account identifies the key variables explaining how changes in affect influence information processing may provide us with new insights into the role of affect in value-based judgment and decision-making.

Using hierarchical drift diffusion models to elucidate computational mechanisms of reduced reward sensitivity in adolescent major depressive disorder

BACKGROUND

Anhedonia-a core symptom of major depressive disorder (MDD)-is closely related to diminished reward sensitivity. Nonetheless, the psychopathological and computational mechanism underlying anhedonia in young patients with MDD remains unclear. Therefore, this study aims to investigate reward sensitivity in adolescents and young adults with MDD using computational modelling.

METHODS

Overall, 70 patients with MDD and 54 age- and sex-matched healthy controls (HC) completed a probabilistic reward task (PRT) to assess their general behavioral inclination towards more frequently reinforced stimuli (i.e., "response bias"). Bayesian hierarchical drift diffusion modeling (HDDM) was employed to determine changes in reward sensitivity and computational process during decision-making.

RESULTS

Adolescents with depression showed a trend toward reduced response bias compared to those in HC. HDDM analysis revealed wider decision thresholds in both adolescents and young adults with MDD group. Adolescents with MDD exhibited significantly lower drift rates and reduced starting point bias compared to those in HC. Higher anhedonia levels were linked to lower drift rates and wider decision thresholds. Additionally, increased discriminability correlated with higher drift rates, while higher response bias was linked to larger starting points.

CONCLUSIONS

Our findings suggest that reduced reward sensitivity and slower evidence accumulation during reward learning may serve as potential indicators of anhedonia in adolescents with MDD. These findings provided crucial insights into the dysregulated positive affect model, underscoring a dysfunctional reward system as a key factor in anhedonia developmental psychopathology in depression.

Disentangling the perceptual underpinnings of autism: Evidence from a face aftereffects experiment

Existing literature has documented diminished norm-based adaptation (aftereffects) across several perceptual domains in autism. However, the exact underlying mechanisms, such as sensory dominance possibly caused by imprecise priors and/or increased sensory precision, remain elusive. The "Bayesian brain" framework offers refined methods to investigate these mechanisms. This study utilized both model-free (frequentist statistics) and model-based (hierarchical Drift Diffusion Modeling) analytical approaches to compare gender face aftereffects in male adolescents with autism (n = 29) to neurotypical controls (n = 39) using a behavioral choice experiment. Contrary to our initial hypotheses, our analyses did not find support for imprecise priors or increased sensory precision within the autistic group. Instead, we observed generally decreased drift rates towards male but not female stimuli in the autistic group. Thus, our findings suggest a lack of own-gender bias in face processing among the autistic participants. These findings align with more recent behavioral and neurophysiological research observing intact priors in individuals with autism, suggesting that other mechanisms may better explain the perceptual challenges in autism. Our study contributes to the ongoing discourse on perceptual processing in autism, emphasizing the necessity for more nuanced analytical approaches in order to unravel the complexity of this condition.

A Gentle Introduction and Application of Feature-Based Clustering with Psychological Time Series

Psychological researchers and practitioners collect increasingly complex time series data aimed at identifying differences between the developments of participants or patients. Past research has proposed a number of dynamic measures that describe meaningful developmental patterns for psychological data (e.g., instability, inertia, linear trend). Yet, commonly used clustering approaches are often not able to include these meaningful measures (e.g., due to model assumptions). We propose feature-based time series clustering as a flexible, transparent, and well-grounded approach that clusters participants based on the dynamic measures directly using common clustering algorithms. We introduce the approach and illustrate the utility of the method with real-world empirical data that highlight common ESM challenges of multivariate conceptualizations, structural missingness, and non-stationary trends. We use the data to showcase the main steps of input selection, feature extraction, feature reduction, feature clustering, and cluster evaluation. We also provide practical algorithm overviews and readily available code for data preparation, analysis, and interpretation.

Detailed analysis of drift diffusion model parameters estimated for the ultimatum game

Bargaining is fundamental in human social interactions and often studied using the ultimatum game, where a proposer offers a division of resources, and the responder decides whether to accept or reject it. If accepted, the resources are divided as proposed, but neither party receives anything otherwise. While previous research has typically focused on either the choice or response time, a computational approach that integrates both can provide deeper insights into the cognitive and neural processes involved. Although the drift diffusion model (DDM) has been used for this purpose, few studies have tested it in the context of the ultimatum game. Here, we collected participants' behaviors as a responder during the ultimatum game (n = 71) and analyzed them using a Bayesian version of DDM. The best (estimated) model included parameters for non-decision time, boundary separation, bias, and drift, with drift expressed as a linear combination of self-reward, advantageous inequity, and disadvantageous inequity. This model accurately replicated participants' choices and response times. Our analysis revealed that the drift parameter represents trial-by-trial choices and response times, while other parameters represent average rejection rates and response times. We also found that boundary separation and bias exhibited a more complex interaction than previously recognized. Thus, this study provides important insights into the application of DDM to studies on neural analysis during human bargaining behavior.

High-intensity physiological activation disrupts the neural signatures of conflict processing

Physiological activation fluctuates throughout the day. Previous studies have shown that during periods of reduced activation, cognitive control remains resilient due to neural compensatory mechanisms. In this study, we investigate the effects of high physiological activation on both behavioural and neural markers of cognitive control. We hypothesize that while behavioural measures of cognitive control would remain intact during periods of high activation, there would be observable changes in neural correlates. In our electroencephalography study, we manipulate levels of physiological activation through physical exercise. Although we observe no significant impact on behavioural measures of cognitive conflict, both univariate and multivariate time-frequency markers prove unreliable under conditions of high activation. Moreover, we observe no modulation of whole-brain connectivity measures by physiological activation. We suggest that this dissociation between behavioural and neural measures indicates that the human cognitive control system remains resilient even at high activation, possibly due to underlying neural compensatory mechanisms.

Combining hierarchical drift-diffusion model and event-related potentials to reveal how do natural sounds nudge green product purchases

Sound is one of the important environmental factors that influence individuals' decision-making. However, it is still unclear whether and how natural sounds nudge green product purchases. This study proposes an extension of the Stimulus-Organism-Response (S-O-R) framework, suggesting that natural sounds increase early attentional congruency associated with green products, thereby promoting individuals' green product purchases. To test our theory, we conducted an experiment employing a hierarchical drift-diffusion model (HDDM) and utilized an event-related potentials (ERP) method. Results showed that natural sounds not only increased the purchase rate for green products but also enhanced drift rate in favor of purchasing green products. Additionally, consumers also exhibited a reduced frontal early P2 wave (150-230 ms) in response to green products under natural sounds, indicating that natural sounds increased the early attentional congruency associated with green products. More importantly, neural correlates of early attentional congruency meditated the nudge effect of natural sounds on purchase rate and drift rate for green products. This study contributes to the neural understanding of how natural sounds influence green product purchases and provides actionable implications for market managers to design the green products sales environments.

Neural and Computational Mechanisms of Motivation and Decision-making

Motivation is often thought to enhance adaptive decision-making by biasing actions toward rewards and away from punishment. Emerging evidence, however, points to a more nuanced view whereby motivation can both enhance and impair different aspects of decision-making. Model-based approaches have gained prominence over the past decade for developing more precise mechanistic explanations for how incentives impact goal-directed behavior. In this Special Focus, we highlight three studies that demonstrate how computational frameworks help decompose decision processes into constituent cognitive components, as well as formalize when and how motivational factors (e.g., monetary rewards) influence specific cognitive processes, decision-making strategies, and self-report measures. Finally, I conclude with a provocative suggestion based on recent advances in the field: that organisms do not merely seek to maximize the expected value of extrinsic incentives. Instead, they may be optimizing decision-making to achieve a desired internal state (e.g., homeostasis, effort, affect). Future investigation into such internal processes will be a fruitful endeavor for unlocking the cognitive, computational, and neural mechanisms of motivated decision-making.

Emotion expression salience and racially biased weapon identification: A diffusion modeling approach

Racial stereotypes are commonly activated by informational cues that are detectable in people's faces. Here, we used a sequential priming task to examine whether and how the salience of emotion (angry/scowling vs. happy/smiling expressions) or apparent race (Black vs. White) information in male face primes shapes racially biased weapon identification (gun vs. tool) decisions. In two experiments (Ntotal = 546) using two different manipulations of facial information salience, racial bias in weapon identification was weaker when the salience of emotion expression versus race was heightened. Using diffusion decision modeling, we tested competing accounts of the cognitive mechanism by which the salience of facial information moderates this behavioral effect. Consistent support emerged for an initial bias account, whereby the decision process began closer to the "gun" response upon seeing faces of Black versus White men, and this racially biased shift in the starting position was weaker when emotion versus race information was salient. We discuss these results vis-à-vis prior empirical and theoretical work on how facial information salience moderates racial bias in decision-making.

The neural implausibility of the diffusion decision model doesn't matter for cognitive psychometrics, but the Ornstein-Uhlenbeck model is better

In cognitive psychometrics, the parameters of cognitive models are used as measurements of the processes underlying observed behavior. In decision making, the diffusion decision model (DDM) is by far the most commonly used cognitive psychometric tool. One concern when using this model is that more recent theoretical accounts of decision-making place more emphasis on neural plausibility, and thus incorporate many assumptions not found in the DDM. One such model is the Ising Decision Maker (IDM), which builds from the assumption that two pools of neurons with self-excitation and mutual inhibition receive perceptual input from external excitatory fields. In this study, we investigate whether the lack of such mechanisms in the DDM compromises its ability to measure the processes it does purport to measure. We cross-fit the DDM and IDM, and find that the conclusions of DDM would be mostly consistent with those from an analysis using a more neurally plausible model. We also show that the Ornstein-Uhlenbeck Model (OUM) model, a variant of the DDM that includes the potential for leakage (or self-excitation), reaches similar conclusions to the DDM regarding the assumptions they share, while also sharing an interpretation with the IDM in terms of self-excitation (but not leakage). Since the OUM is relatively easy to fit to data, while being able to capture more neurally plausible mechanisms, we propose that it be considered an alternative cognitive psychometric tool to the DDM.

Dissociable Effects of Urgency and Evidence Accumulation during Reaching Revealed by Dynamic Multisensory Integration

When making perceptual decisions, humans combine information across sensory modalities dependent on their respective uncertainties. However, it remains unknown how the brain integrates multisensory feedback during movement and which factors besides sensory uncertainty influence sensory contributions. We performed two reaching experiments on healthy adults to investigate whether movement corrections to combined visual and mechanical perturbations scale with visual uncertainty. To describe the dynamics of multimodal feedback responses, we further varied movement time and visual feedback duration during the movement. The results of our first experiment show that the contribution of visual feedback decreased with uncertainty. Additionally, we observed a transient phase during which visual feedback responses were stronger during faster movements. In a follow-up experiment, we found that the contribution of vision increased more quickly during slow movements when we presented the visual feedback for a longer time. Muscle activity corresponding to these visual responses exhibited modulations with sensory uncertainty and movement speed ca. 100 ms following the onset of the visual feedback. Using an optimal feedback control model, we show that the increased response to visual feedback during fast movements can be explained by an urgency-dependent increase in control gains. Further, the fact that a longer viewing duration increased the visual contributions suggests that the brain accumulates sensory information over time to estimate the state of the arm during reaching. Our results provide additional evidence concerning the link between reaching control and decision-making, both of which appear to be influenced by sensory evidence accumulation and response urgency.

Task goals shape the relationship between decision and movement speed

The speed at which we move is linked to the speed at which we decide to make these movements. Yet the principles guiding such relationship remain unclear: whereas some studies point toward a shared invigoration process boosting decision and movement speed jointly, others rather indicate a trade-off between both levels of control, with slower movements accompanying faster decisions. Here, we aimed 1) at further investigating the existence of a shared invigoration process linking decision and movement and 2) at testing the hypothesis that such a link is masked when detrimental to the reward rate. To this aim, we tested 62 subjects who performed the Tokens task in two experiments (separate sessions): experiment 1 evaluated how changing decision speed affects movement speed, whereas experiment 2 assessed how changing movement speed affects decision speed. In the latter experiment, subjects were encouraged to favor either decision speed (fast decision group) or decision accuracy (slow decision group). Various mixed model analyses revealed a coregulation of decision (urgency) and movement speed in experiment 1 and in the fast decision group of experiment 2 but not in the slow decision group, despite the fact that these same subjects displayed a coregulation effect in experiment 1. Altogether, our findings support the idea that coregulation occurs as a default mode but that this form of control is diminished or supplanted by a trade-off relationship, contingent on reward rate maximization. Drawing from these behavioral observations, we propose that multiple processes contribute to shaping the speed of decisions and movements.NEW & NOTEWORTHY The principles guiding the relationship between decision and movement speed are still unclear. In the present behavioral study involving two experiments conducted with 62 human subjects, we report findings indicating a relationship that varies as a function of the task goals. Coregulation emerges as a default mode of control that fades when detrimental to the reward rate, possibly because of the influence of other processes that can selectively shape the speed of our decisions or movements.

Integrated Ising Model with global inhibition for decision making

Humans and other organisms make decisions choosing between different options, with the aim to maximize the reward and minimize the cost. The main theoretical framework for modeling the decision-making process has been based on the highly successful drift-diffusion model, which is a simple tool for explaining many aspects of this process. However, new observations challenge this model. Recently, it was found that inhibitory tone increases during high cognitive load and situations of uncertainty, but the origin of this phenomenon is not understood. Motivated by this observation, we extend a recently developed model for decision making while animals move towards targets in real space. We introduce an integrated Ising-type model, that includes global inhibition, and use it to explore its role in decision-making. This model can explain how the brain may utilize inhibition to improve its decision-making accuracy. Compared to experimental results, this model suggests that the regime of the brain's decision-making activity is in proximity to a critical transition line between the ordered and disordered. Within the model, the critical region near the transition line has the advantageous property of enabling a significant decrease in error with a small increase in inhibition and also exhibits unique properties with respect to learning and memory decay.

The Role of the Rat Prefrontal Cortex and Sex Differences in Decision-Making

The prefrontal cortex is critical for decision-making across species, with its activity linked to choosing between options. Drift diffusion models (DDMs) are commonly employed to understand the neural computations underlying this behavior. Studies exploring the specific roles of regions of the rodent prefrontal cortex in controlling the decision process are limited. This study explored the role of the prelimbic cortex (PLC) in decision-making using a two-alternative forced-choice task. Rats first learned to report the location of a lateralized visual stimulus. The brightness of the stimulus indicated its reward value. Then, the rats learned to make choices between pairs of stimuli. Sex differences in learning were observed, with females responding faster and more selectively to high-value stimuli than males. DDM analysis found that males had decreased decision thresholds during initial learning, whereas females maintained a consistently higher drift rate. Pharmacological manipulations revealed that PLC inactivation reduced the decision threshold for all rats, indicating that less information was needed to make a choice in the absence of normal PLC processing. μ-Opioid receptor stimulation of the PLC had the opposite effect, raising the decision threshold and reducing bias in the decision process toward high-value stimuli. These effects were observed without any impact on the rats' choice preferences. Our findings suggest that PLC has an inhibitory role in the decision process and regulates the amount of evidence that is required to make a choice. That is, PLC activity controls "when," but not "how," to act.

Neural reward system reflects individual value comparison strategy in cost-benefit decisions

A core assumption in decision neuroscience is that individuals decide between options by comparing option-specific subjective reward values. Psychological accounts challenge this view and suggest that decisions are better explained by comparisons between choice attributes than by comparisons between option-specific values, casting doubts on the interpretation of activation in the neural reward system as subjective value signals. Here, we provide neuroimaging and pharmacological evidence that value-related neural activity follows the value comparison strategy employed by an individual on the psychological level. Neural model comparisons reveal that activation in the striatum, rather than generally reflecting attribute-wise or option-wise value comparisons, reflects the value comparison strategy that provides the best explanation for an individual's choice behavior. Strikingly, manipulating activation in the dopaminergic reward system reveals that dopamine antagonism counteracts the engagement in an individual's dominant value comparison strategy. Together, our findings provide evidence for the biological plausibility of psychological accounts of decision making and emphasize the importance of neural model comparisons to prevent misinterpretations of brain activation.

Common neural choice signals can emerge artefactually amid multiple distinct value signals

Previous work has identified characteristic neural signatures of value-based decision-making, including neural dynamics that closely resemble the ramping evidence accumulation process believed to underpin choice. Here we test whether these signatures of the choice process can be temporally dissociated from additional, choice-'independent' value signals. Indeed, EEG activity during value-based choice revealed distinct spatiotemporal clusters, with a stimulus-locked cluster reflecting affective reactions to choice sets and a response-locked cluster reflecting choice difficulty. Surprisingly, 'neither' of these clusters met the criteria for an evidence accumulation signal. Instead, we found that stimulus-locked activity can 'mimic' an evidence accumulation process when aligned to the response. Re-analysing four previous studies, including three perceptual decision-making studies, we show that response-locked signatures of evidence accumulation disappear when stimulus-locked and response-locked activity are modelled jointly. Collectively, our findings show that neural signatures of value can reflect choice-independent processes and look deceptively like evidence accumulation.

Staying in control: Characterizing the mechanisms underlying cognitive control in high and low arousal states

Throughout the day, humans show natural fluctuations in arousal that impact cognitive function. To study the behavioural dynamics of cognitive control during high and low arousal states, healthy participants performed an auditory conflict task during high-intensity physical exercise (N = 39) or drowsiness (N = 33). In line with the pre-registered hypotheses, conflict and conflict adaptation effects were preserved during both altered arousal states. Overall task performance was markedly poorer during low arousal, but not for high arousal. Modelling behavioural dynamics with drift diffusion analysis revealed evidence accumulation and non-decision time decelerated, and decisional boundaries became wider during low arousal, whereas high arousal was unexpectedly associated with a decrease in the interference of task-irrelevant information processing. These findings show how arousal differentially modulates cognitive control at both sides of normal alertness, and further validate drowsiness and physical exercise as key experimental models to disentangle the interaction between physiological fluctuations on cognitive dynamics.

Brief mindfulness-based meditation enhances the speed of learning following positive prediction errors

Recent research has demonstrated that mindfulness-based meditation facilitates basic aspects of cognition, including memory and attention. Further developing this line of inquiry, here we considered the possibility that similar effects may extend to another core psychological process-instrumental learning. To explore this matter, in combination with a probabilistic selection task, computational modelling (i.e., reinforcement drift diffusion model analysis) was adopted to establish whether and how brief mindfulness-based meditation influences learning under conditions of uncertainty (i.e., choices based on the perceived likelihood of positive and negative outcomes). Three effects were observed. Compared with performance in the control condition (i.e., no meditation), mindfulness-based meditation (1) accelerated the rate of learning following positive prediction errors; (2) elicited a preference for the exploration (vs. exploitation) of choice selections; and (3) increased response caution. Collectively, these findings elucidate the pathways through which brief meditative experiences impact learning and decision-making, with implications for interventions designed to debias aspects of social-cognitive functioning using mindfulness-based meditation.

Spatiotemporal integration of contextual and sensory information within the cortical hierarchy in human pain experience

Pain is not a mere reflection of noxious input. Rather, it is constructed through the dynamic integration of current predictions with incoming sensory input. However, the temporal dynamics of the behavioral and neural processes underpinning this integration remain elusive. In the current study involving 59 human participants, we identified a series of brain mediators that integrated cue-induced expectations with noxious inputs into ongoing pain predictions using a semicircular scale designed to capture rating trajectories. Temporal mediation analysis revealed that during the early-to-mid stages of integration, the frontoparietal and dorsal attention network regions, such as the lateral prefrontal, premotor, and parietal cortex, mediated the cue effects. Conversely, during the mid-to-late stages of integration, the somatomotor network regions mediated the effects of stimulus intensity, suggesting that the integration occurs along the cortical hierarchy from the association to sensorimotor brain systems. Our findings advance the understanding of how the brain integrates contextual and sensory information into pain experience over time.

Dissociable Contributions of Goal-Relevant Evidence and Goal-Irrelevant Familiarity to Individual and Developmental Differences in Conflict Recognition

Recent studies using the diffusion decision model find that performance across many cognitive control tasks can be largely attributed to a task-general efficiency of evidence accumulation (EEA) factor that reflects individuals' ability to selectively gather evidence relevant to task goals. However, estimates of EEA from an n-back "conflict recognition" paradigm in the Adolescent Brain Cognitive DevelopmentSM (ABCD) Study, a large, diverse sample of youth, appear to contradict these findings. EEA estimates from "lure" trials-which present stimuli that are familiar (i.e., presented previously) but do not meet formal criteria for being a target-show inconsistent relations with EEA estimates from other trials and display atypical v-shaped bivariate distributions, suggesting many individuals are responding based largely on stimulus familiarity rather than goal-relevant stimulus features. We present a new formal model of evidence integration in conflict recognition tasks that distinguishes individuals' EEA for goal-relevant evidence from their use of goal-irrelevant familiarity. We then investigate developmental, cognitive, and clinical correlates of these novel parameters. Parameters for EEA and goal-irrelevant familiarity-based processing showed strong correlations across levels of n-back load, suggesting they are task-general dimensions that influence individuals' performance regardless of working memory demands. Only EEA showed large, robust developmental differences in the ABCD sample and an independent age-diverse sample. EEA also exhibited higher test-retest reliability and uniquely meaningful associations with clinically relevant dimensions. These findings establish a principled modeling framework for characterizing conflict recognition mechanisms and have several broader implications for research on individual and developmental differences in cognitive control.

Impulsive adolescents exhibit inefficient processing and a low decision threshold when decoding facial expressions of emotions

Background

Borderline personality disorder (BPD) is a debilitating psychiatric illness whose symptoms frequently emerge during adolescence. Initial studies in adults suggest that the interpersonal difficulties common in BPD may emerge from disrupted processing of social and emotional stimuli. Less is known about these processes in adolescents with BPD symptoms, despite substantial changes in socioemotional processing during this developmental period.

Methods

86 adolescents and young adults with and without BPD symptoms completed an emotional interference task involving the identification of a facial emotion expression in the presence of a conflicting or congruent emotion word. We used hierarchical drift diffusion modeling to index speed of processing and decision boundary. Using Bayesian multilevel regression, we characterized age-related differences in facial emotion processing. We then examined whether BPD symptom dimensions were associated with facial emotion processing on this task. To determine the specificity of our effects, we analyzed behavioral data from a corresponding nonemotional interference task.

Results

Impulsivity, but not negative affectivity or interpersonal dysfunction, predicted inefficient processing when presented with conflicting negative emotional stimuli. Across both tasks, impulsivity in adolescents was further associated with a lower decision boundary. Impulsive adolescents were especially likely to make fast, but inaccurate decisions about another person's emotional state.

Conclusion

Impulsive adolescents with BPD symptoms are prone to making errors when appraising facial expressions of emotions, which may potentiate or worsen interpersonal conflicts. Our findings highlight the role of lower-level social cognitive processes in interpersonal difficulties among vulnerable youth during a sensitive developmental window.

Psychometrics of drift-diffusion model parameters derived from the Eriksen flanker task: Reliability and validity in two independent samples

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.

Using precision approaches to improve brain-behavior prediction

Predicting individual behavioral traits from brain idiosyncrasies has broad practical implications, yet predictions vary widely. This constraint may be driven by a combination of signal and noise in both brain and behavioral variables. Here, we expand on this idea, highlighting the potential of extended sampling 'precision' studies. First, we discuss their relevance to improving the reliability of individualized estimates by minimizing measurement noise. Second, we review how targeted within-subject experiments, when combined with individualized analysis or modeling frameworks, can maximize signal. These improvements in signal-to-noise facilitated by precision designs can help boost prediction studies. We close by discussing the integration of precision approaches with large-sample consortia studies to leverage the advantages of both.

Precision and temporal dynamics in heading perception assessed by continuous psychophysics

It is a well-established finding that more informative optic flow (e.g., faster, denser, or presented over a larger portion of the visual field) yields decreased variability in heading judgements. Current models of heading perception further predict faster processing under such circumstances, which has, however, not been supported empirically so far. In this study, we validate a novel continuous psychophysics paradigm by replicating the effect of the speed and density of optic flow on variability in performance, and we investigate how these manipulations affect the temporal dynamics. To this end, we tested 30 participants in a continuous psychophysics paradigm administered in Virtual Reality. We immersed them in a simple virtual environment where they experienced four 90-second blocks of optic flow where their linear heading direction (no simulated rotation) at any given moment was determined by a random walk. We asked them to continuously indicate with a joystick the direction in which they perceived themselves to be moving. In each of the four blocks they experienced a different combination of simulated self-motion speeds (SLOW and FAST) and density of optic flow (SPARSE and DENSE). Using a Cross-Correlogram Analysis, we determined that participants reacted faster and displayed lower variability in their performance in the FAST and DENSE conditions than in the SLOW and SPARSE conditions, respectively. Using a Kalman Filter-based analysis approach, we found a similar pattern, where the fitted perceptual noise parameters were higher for SLOW and SPARSE. While replicating previous results on variability, we show that more informative optic flow can speed up heading judgements, while at the same time validating a continuous psychophysics as an efficient method for studying heading perception.

Cardiac-Sympathetic Contractility and Neural Alpha-Band Power: Cross-Modal Collaboration during Approach-Avoidance Conflict

As evidence mounts that the cardiac-sympathetic nervous system reacts to challenging cognitive settings, we ask if these responses are epiphenomenal companions or if there is evidence suggesting a more intertwined role of this system with cognitive function. Healthy male and female human participants performed an approach-avoidance paradigm, trading off monetary reward for painful electric shock, while we recorded simultaneous electroencephalographic and cardiac-sympathetic signals. Participants were reward sensitive but also experienced approach-avoidance "conflict" when the subjective appeal of the reward was near equivalent to the revulsion of the cost. Drift-diffusion model parameters suggested that participants managed conflict in part by integrating larger volumes of evidence into choices (wider decision boundaries). Late alpha-band (neural) dynamics were consistent with widening decision boundaries serving to combat reward sensitivity and spread attention more fairly to all dimensions of available information. Independently, wider boundaries were also associated with cardiac "contractility" (an index of sympathetically mediated positive inotropy). We also saw evidence of conflict-specific "collaboration" between the neural and cardiac-sympathetic signals. In states of high conflict, the alignment (i.e., product) of alpha dynamics and contractility were associated with a further widening of the boundary, independent of either signal's singular association. Cross-trial coherence analyses provided additional evidence that the autonomic systems controlling cardiac-sympathetics might influence the assessment of information streams during conflict by disrupting or overriding reward processing. We conclude that cardiac-sympathetic control might play a critical role, in collaboration with cognitive processes, during the approach-avoidance conflict in humans.

Emotion-Driven Moral Evaluation: A Mechanistic Study Based on the Drift-Diffusion Model

BACKGROUND

Moral evaluation is identified as the first stage in the theory of moral judgment, and academics believe that it may align with the social intuitionist model. This study aims to prove that the model's emotional dominance hypothesis applies to moral evaluation by presenting a computational decision-making model that mathematically formalizes this emotional dominance decision-making process. We also compared different types of valence evaluation tasks to test the emotional priority hypothesis.

METHODS

We used a convenience sampling method to randomly recruit 30 enrolled college students. The drift-diffusion model was employed to analyze reaction times for words with various emotional and moral valences Additionally, we designed different valence evaluation tasks based on the response relevance hypothesis and evaluated the processing order through reaction time comparisons.

RESULTS

The analysis revealed that the emotional mechanism of immoral evaluation differs from moral evaluation. An increase in emotional valence accelerates the speed of evidence accumulation (v) for moral evaluation (M = 1.21, 0.2% < 0 < 99.8%) but lowers decision caution (a) in immoral evaluation (M = -0.64, 96.1% < 0 < 3.9%). In contrast, moral valence does not have a significant influence on evaluation processes (v, M = -0.28, 72.1% < 0 < 27.9%; a, M = -0.32, 79.3% < 0 < 20.7%). Furthermore, We found no significant difference in reaction times between moral and immoral words in the emotional evaluation task (F(1,29) = 0.55, p = 0.464, partial η2 = 0.02), but a significant difference existed in the moral evaluation task (F(1,29) = 17.99, p < 0.001, partial η2 = 0.38), indicating that the tendency of relatively fast immoral evaluation in emotional evaluation tasks may be caused by emotional priority.

CONCLUSIONS

Our findings support the intuitive model's emotional dominance hypothesis and introduce a new emotional mechanism into moral evaluation. This study clarifies the distinct emotional processes in moral and immoral evaluations, fills a gap in the research on moral evaluation, and offers insights into human decision-making in moral contexts.

EZ-CDM: Fast, simple, robust, and accurate estimation of circular diffusion model parameters

The investigation of cognitive processes that form the basis of decision-making in paradigms involving continuous outcomes has gained the interest of modeling researchers who aim to develop a dynamic decision theory that accounts for both speed and accuracy. One of the most important of these continuous models is the circular diffusion model (CDM, Smith. Psychological Review, 123(4), 425. 2016), which posits a noisy accumulation process mathematically described as a stochastic two-dimensional Wiener process inside a disk. Despite the considerable benefits of this model, its mathematical intricacy has limited its utilization among scholars. Here, we propose a straightforward and user-friendly method for estimating the CDM parameters and fitting the model to continuous-scale data using simple formulas that can be readily computed and do not require theoretical knowledge of model fitting or extensive programming. Notwithstanding its simplicity, we demonstrate that the aforementioned method performs with a level of accuracy that is comparable to that of the maximum likelihood estimation method. Furthermore, a robust version of the method is presented, which maintains its simplicity while exhibiting a high degree of resistance to contaminant responses. Additionally, we show that the approach is capable of reliably measuring the key parameters of the CDM, even when these values are subject to across-trial variability. Finally, we demonstrate the practical application of the method on experimental data. Specifically, an illustrative example is presented wherein the method is employed along with estimating the probability of guessing. It is hoped that the straightforward methodology presented here will, on the one hand, help narrow the divide between theoretical constructs and empirical observations on continuous response tasks and, on the other hand, inspire cognitive psychology researchers to shift their laboratory investigations towards continuous response paradigms.

A computational account of conflict processing during mental imagery

Previous studies examining conflict processing within the context of a color-word Stroop task have focused on both stimulus and response conflicts. However, it has been unclear whether conflict can emerge independently of stimulus conflict. In this study, a novel arrow-gaze mental-rotation Stroop task was introduced to explore the interplay between conflict processing and mental rotation. A modelling approach was utilized to provide a process-level account of the findings. The results of our Stroop task indicate that conflict can emerge from mental rotation in the absence of stimulus conflict. The strength of this imagery conflict effect decreases and even reverses as mental rotation angles increase. Additionally, it was observed that participants responded more quickly and with greater accuracy to small rather than large face orientations. A comparison of three conflict diffusion models-the diffusion model for conflict tasks (DMC), the dual-stage two-phase model (DSTP), and the shrinking spotlight model (SSP)-yielded consistent support for the DSTP over the DMC and SSP in the majority of instances. The DSTP account of the experimental results revealed an increased nondecision time with increasing mental rotation, a reduction in interference from incompatible stimuli, and an improved drift rate in response selection phase, which suggests enhanced cognitive control. The findings from the model-based analysis provide evidence for a novel interaction between cognitive control and mental rotation.

The Tweedledum and Tweedledee of dynamic decisions: Discriminating between diffusion decision and accumulator models

Theories of dynamic decision-making are typically built on evidence accumulation, which is modeled using racing accumulators or diffusion models that track a shifting balance of support over time. However, these two types of models are only two special cases of a more general evidence accumulation process where options correspond to directions in an accumulation space. Using this generalized evidence accumulation approach as a starting point, I identify four ways to discriminate between absolute-evidence and relative-evidence models. First, an experimenter can look at the information that decision-makers considered to identify whether there is a filtering of near-zero evidence samples, which is characteristic of a relative-evidence decision rule (e.g., diffusion decision model). Second, an experimenter can disentangle different components of drift rates by manipulating the discriminability of the two response options relative to the stimulus to delineate the balance of evidence from the total amount of evidence. Third, a modeler can use machine learning to classify a set of data according to its generative model. Finally, machine learning can also be used to directly estimate the geometric relationships between choice options. I illustrate these different approaches by applying them to data from an orientation-discrimination task, showing converging conclusions across all four methods in favor of accumulator-based representations of evidence during choice. These tools can clearly delineate absolute-evidence and relative-evidence models, and should be useful for comparing many other types of decision theories.

Timing decisions as the next frontier for collective intelligence

The past decade has witnessed a growing interest in collective decision making, particularly the idea that groups can make more accurate decisions compared with individuals. However, nearly all research to date has focused on spatial decisions (e.g., food patches). Here, we highlight the equally important, but severely understudied, realm of temporal collective decision making (i.e., decisions about when to perform an action). We illustrate differences between temporal and spatial decisions, including the irreversibility of time, cost asymmetries, the speed-accuracy tradeoff, and game theoretic dynamics. Given these fundamental differences, temporal collective decision making likely requires different mechanisms to generate collective intelligence. Research focused on temporal decisions should lead to an expanded understanding of the adaptiveness and constraints of living in groups.

Non-spatial inhibition of return attenuates audiovisual integration owing to modality disparities

Although previous studies have investigated the relationship between inhibition of return (IOR) and multisensory integration, the influence of non-spatial has not been explored. The present study aimed to investigate the influence of non-spatial IOR on audiovisual integration by using a "prime-neutral cue-target" paradigm. In Experiment 1, which manipulated prime validity and target modality, the targets were positioned centrally, revealing significant non-spatial IOR effects in the visual, auditory, and audiovisual modalities. Analysis of relative multisensory response enhancement (rMRE) indicated substantial audiovisual integration enhancement in both valid and invalid target conditions. Furthermore, the enhancement was weaker for valid targets than for invalid targets. In Experiment 2, the targets were positioned above and below to rule out repetition blindness (RB); this experiment successfully replicated the results observed in Experiment 1. Notably, Experiments 1 and 2 consistently found that the correlation between modality differences and rMRE for valid targets indicated that differences in signal strength between visual and auditory modalities contributed to a reduction in audiovisual integration. However, the absence of correlation with the invalid target suggests that attention, as a key factor, may play a significant role in this process. The present study highlights how non-spatial IOR reduces audiovisual integration and sheds light on the complex interaction between attention and multisensory integration.

The temporal profile of self-prioritization

Personal relevance exerts a powerful influence on decisional processing, such that arbitrary stimuli associated with the self are classified more rapidly than identical material linked with other people. Notwithstanding numerous demonstrations of this facilitatory effect, it remains unclear whether self-prioritization is a temporally stable outcome of decision-making. Accordingly, using a shape-label matching task in combination with computational modeling, the current experiment investigated this matter. The results were informative. First, regardless of the target of comparison (i.e., friend or stranger), self-prioritization was a persistent product of decision-making across the testing session. Second, a variant of the standard drift diffusion model in which decisional boundaries collapsed gradually over the course of the task best fit the observed data. Third, whereas the efficiency of stimulus processing increased for other-related stimuli during the task, it decreased for self-related material. Collectively, these findings advance understanding of the temporal profile of self-prioritization.

Pre-supplementary motor area strengthens reward sensitivity in intertemporal choice

Previous investigations on the causal neural mechanisms underlying intertemporal decision making focused on the dorsolateral prefrontal cortex as neural substrate of cognitive control. However, little is known, about the causal contributions of further parts of the frontoparietal control network to delaying gratification, including the pre-supplementary motor area (pre-SMA) and posterior parietal cortex (PPC). Conflicting previous evidence related pre-SMA and PPC either to evidence accumulation processes, choice biases, or response caution. To disentangle between these alternatives, we combined drift diffusion models of decision making with online transcranial magnetic stimulation (TMS) over pre-SMA and PPC during an intertemporal decision task. While we observed no robust effects of PPC TMS, perturbation of pre-SMA activity reduced preferences for larger over smaller rewards. A drift diffusion model of decision making suggests that pre-SMA increases the weight assigned to reward magnitudes during the evidence accumulation process without affecting choice biases or response caution. Taken together, the current findings reveal the computational role of the pre-SMA in value-based decision making, showing that pre-SMA promotes choices of larger, costly rewards by strengthening the sensitivity to reward magnitudes.

Auditory decision-making deficits after permanent noise-induced hearing loss

Loud noise exposure is one of the leading causes of permanent hearing loss. Individuals with noise-induced hearing loss (NIHL) suffer from speech comprehension deficits and experience impairments to cognitive functions such as attention and decision-making. Here, we tested whether a specific sensory deficit, NIHL, can directly impair auditory cognitive function. Gerbils were trained to perform an auditory decision-making task that involves discriminating between slow and fast presentation rates of amplitude-modulated (AM) noise. Decision-making task performance was assessed across pre-versus post-NIHL sessions within the same gerbils. A single exposure session (2 hours) to loud broadband noise (120 dB SPL) produced permanent NIHL with elevated threshold shifts in auditory brainstem responses (ABRs). Following NIHL, decision-making task performance was tested at sensation levels comparable to those prior to noise exposure in all animals. Our findings demonstrate NIHL diminished perceptual acuity, reduced attentional focus, altered choice bias, and slowed down evidence accumulation speed. Finally, video-tracking analysis of motor behavior during task performance demonstrates that NIHL can impact sensory-guided decision-based motor execution. Together, these results suggest that NIHL impairs the sensory, cognitive, and motor factors that support auditory decision-making.

Callous-unemotional traits moderate the association between inhibitory control and disruptive behavior problems

The presence of callous-unemotional (CU) traits may not be unique to conduct disorder (CD) but also extend to oppositional defiant disorder (ODD). While a distinct neurocognitive profile characterizes CU traits, it remains unclear whether this CU-related neurocognitive profile differs between youth with CD and ODD. This study investigated whether CU traits moderate the relationship between inhibitory control and CD or ODD symptoms. We leveraged computational modeling to decompose task-based inhibitory control in a sample of 200 children (59.5% boys, 86.5% Caucasian), aged 8 to 15 years (M = 10.10, SD = 1.88), referred to an outpatient child diagnostic clinic focused on externalizing problems. Analyses examined whether CU traits moderated the relationship between inhibitory control and CD or ODD symptoms while controlling for ADHD symptoms and child demographics. The results indicated that the strength of the relationship between inhibitory control and CD and ODD symptoms varies as a function of CU traits. Specifically, CD was linked to a more cautious decision-making style when elevated CU traits were present, whereas ODD was associated with more efficient decision making. These findings suggest distinct neurocognitive profiles based on CU traits, which vary between CD and ODD. Clinically, this underscores the importance of tailoring interventions for CD-CU and ODD-CU, focusing on decision making processes rather than merely addressing impulsivity. This research contributes to a more nuanced understanding of the interaction between neurocognitive processes and disruptive behavior, with significant implications for both theoretical models and treatment approaches.

Cognitive Mechanisms of Aberrant Self-Referential Social Perception in Psychosis and Bipolar Disorder: Insights From Computational Modeling

BACKGROUND AND HYPOTHESIS

Individuals with schizophrenia (SZ) and bipolar disorder (BD) show disruptions in self-referential gaze perception-a social perceptual process related to symptoms and functioning. However, our current mechanistic understanding of these dysfunctions and relationships is imprecise.

STUDY DESIGN

The present study used mathematical modeling to uncover cognitive processes driving gaze perception abnormalities in SZ and BD, and how they relate to cognition, symptoms, and social functioning. We modeled the behavior of 28 SZ, 38 BD, and 34 controls (HC) in a self-referential gaze perception task using drift-diffusion models parameterized to index key cognitive components: drift rate (evidence accumulation efficiency), drift bias (perceptual bias), start point (expectation bias), threshold separation (response caution), and nondecision time (encoding/motor processes).

STUDY RESULTS

Results revealed that aberrant gaze perception in SZ and BD was driven by less efficient evidence accumulation, perceptual biases predisposing self-referential responses, and greater caution (SZ only). Across SZ and HC, poorer social functioning was related to greater expectation biases. Within SZ, perceptual and expectancy biases were associated with hallucination and delusion severity, respectively.

CONCLUSIONS

These findings indicate that diminished evidence accumulation and perceptual biases may underlie altered gaze perception in patients and that SZ may engage in compensatory cautiousness, sacrificing response speed to preserve accuracy. Moreover, biases at the belief and perceptual levels may relate to symptoms and functioning. Computational modeling can, therefore, be used to achieve a more nuanced, cognitive process-level understanding of the mechanisms of social cognitive difficulties, including gaze perception, in individuals with SZ and BD.

Clinically established early Parkinson's disease patients do not show impaired use of priors in conditions of perceptual uncertainty

The ability to use past learned experiences to guide decisions is an important component of adaptive behavior, especially when decision-making is performed under time pressure or when perceptual information is unreliable. Previous studies using visual discrimination tasks have shown that this prior-informed decision-making ability is impaired in Parkinson's disease (PD), but the mechanisms underlying this deficit and the precise impact of dopaminergic denervation within cortico-basal circuits remain unclear. To shed light on this problem, we evaluated prior-informed decision-making under various conditions of perceptual uncertainty in a sample of 13 clinically established early PD patients, and compared behavioral performance with healthy control (HC) subjects matched in age, sex and education. PD patients and HC subjects performed a random dot motion task in which they had to decide the net direction (leftward vs. rightward) of a field of moving dots and communicate their choices through manual button presses. We manipulated prior knowledge by modulating the probability of occurrence of leftward vs. rightward motion stimuli between blocks of trials, and by explicitly giving these probabilities to subjects at the beginning of each block. We further manipulated stimulus discriminability by varying the proportion of dots moving coherently in the signal direction and speed-accuracy instructions. PD patients used choice probabilities to guide perceptual decisions in both speed and accuracy conditions, and their performance did not significantly differ from that of HC subjects. An additional analysis of the data with the diffusion decision model confirmed this conclusion. These results suggest that the impaired use of priors during visual discrimination observed at more advanced stages of PD is independent of dopaminergic denervation, though additional studies with larger sample sizes are needed to more firmly establish this conclusion.

Sensory choices as logistic classification

Logistic classification is a simple way to make choices based on a set of factors: give each factor a weight, sum the results, and use the sum to set the log odds of a random draw. This operation is known to describe human and animal choices based on value (economic decisions). There is increasing evidence that it also describes choices based on sensory inputs (perceptual decisions), presented across sensory modalities (multisensory integration) and combined with non-sensory factors such as prior probability, expected value, overall motivation, and recent actions. Logistic classification can also capture the effects of brain manipulations such as local inactivations. The brain may implement it by thresholding stochastic inputs (as in signal detection theory) acquired over time (as in the drift diffusion model). It is the optimal strategy under certain conditions, and the brain appears to use it as a heuristic in a wider set of conditions.

Stimulus valence moderates self-learning

Self-relevance has been demonstrated to impair instrumental learning. Compared to unfamiliar symbols associated with a friend, analogous stimuli linked with the self are learned more slowly. What is not yet understood, however, is whether this effect extends beyond arbitrary stimuli to material with intrinsically meaningful properties. Take, for example, stimulus valence an established moderator of self-bias. Does the desirability of to-be-learned material influence self-learning? Here, in conjunction with computational modelling (i.e. Reinforcement Learning Drift Diffusion Model analysis), a probabilistic selection task was used to establish if and how stimulus valence (i.e. desirable/undesirable posters) impacts the acquisition of knowledge relating to object-ownership (i.e. owned-by-self vs. owned-by-friend). Several interesting results were observed. First, undesirable posters were learned more rapidly for self compared to friend, an effect that was reversed for desirable posters. Second, learning rates were accompanied by associated differences in reward sensitivity toward desirable and undesirable choice selections as a function of ownership. Third, decisional caution was greater for self-relevant (vs. friend relevant) responses. Collectively, these findings inform understanding of self-function and how valence and stimulus relevance mutually influence probabilistic learning.

Beyond discrete-choice options

While decision theories have evolved over the past five decades, their focus has largely been on choices among a limited number of discrete options, even though many real-world situations have a continuous-option space. Recently, theories have attempted to address decisions with continuous-option spaces, and several computational models have been proposed within the sequential sampling framework to explain how we make a decision in continuous-option space. This article aims to review the main attempts to understand decisions on continuous-option spaces, give an overview of applications of these types of decisions, and present puzzles to be addressed by future developments.

A tutorial on fitting joint models of M/EEG and behavior to understand cognition

We present motivation and practical steps necessary to find parameter estimates of joint models of behavior and neural electrophysiological data. This tutorial is written for researchers wishing to build joint models of human behavior and scalp and intracranial electroencephalographic (EEG) or magnetoencephalographic (MEG) data, and more specifically those researchers who seek to understand human cognition. Although these techniques could easily be applied to animal models, the focus of this tutorial is on human participants. Joint modeling of M/EEG and behavior requires some knowledge of existing computational and cognitive theories, M/EEG artifact correction, M/EEG analysis techniques, cognitive modeling, and programming for statistical modeling implementation. This paper seeks to give an introduction to these techniques as they apply to estimating parameters from neurocognitive models of M/EEG and human behavior, and to evaluate model results and compare models. Due to our research and knowledge on the subject matter, our examples in this paper will focus on testing specific hypotheses in human decision-making theory. However, most of the motivation and discussion of this paper applies across many modeling procedures and applications. We provide Python (and linked R) code examples in the tutorial and appendix. Readers are encouraged to try the exercises at the end of the document.

When 2 become 1: Autistic simultaneity judgements about asynchronous audiovisual speech

It has been proposed that autistic people experience a temporal distortion whereby the temporal binding window of multisensory integration is extended. Research to date has focused on autistic children so whether these differences persist into adulthood remains unknown. In addition, the possibility that the previous observations have arisen from between-group differences in response bias, rather than perceptual differences, has not been addressed. Participants completed simultaneity judgements of audiovisual speech stimuli across a range of stimulus-onset asynchronies. Response times and accuracy data were fitted to a drift-diffusion model so that the drift rate (a measure of processing efficiency) and starting point (response bias) could be estimated. In Experiment 1, we tested a sample of non-autistic adults who completed the Autism Quotient questionnaire. Autism Quotient score was not correlated with either drift rate or response bias, nor were there between-group differences when splitting based on the first and third quantiles of scores. In Experiment 2, we compared the performance of autistic with a group of non-autistic adults. There were no between-group differences in either drift rate or starting point. The results of this study do not support the previous suggestion that autistic people have an extended temporal binding window for audiovisual speech. In addition, exploratory analysis revealed that operationalising the temporal binding window in different ways influenced whether a group difference was observed, which is an important consideration for future work.

Age-related differences in resting-state, task-related, and structural brain connectivity: graph theoretical analyses and visual search performance

Previous magnetic resonance imaging (MRI) research suggests that aging is associated with a decrease in the functional interconnections within and between groups of locally organized brain regions (modules). Further, this age-related decrease in the segregation of modules appears to be more pronounced for a task, relative to a resting state, reflecting the integration of functional modules and attentional allocation necessary to support task performance. Here, using graph-theoretical analyses, we investigated age-related differences in a whole-brain measure of module connectivity, system segregation, for 68 healthy, community-dwelling individuals 18-78 years of age. We obtained resting-state, task-related (visual search), and structural (diffusion-weighted) MRI data. Using a parcellation of modules derived from the participants' resting-state functional MRI data, we demonstrated that the decrease in system segregation from rest to task (i.e., reconfiguration) increased with age, suggesting an age-related increase in the integration of modules required by the attentional demands of visual search. Structural system segregation increased with age, reflecting weaker connectivity both within and between modules. Functional and structural system segregation had qualitatively different influences on age-related decline in visual search performance. Functional system segregation (and reconfiguration) influenced age-related decline in the rate of visual evidence accumulation (drift rate), whereas structural system segregation contributed to age-related slowing of encoding and response processes (nondecision time). The age-related differences in the functional system segregation measures, however, were relatively independent of those associated with structural connectivity.

Rhythm Facilitates Auditory Working Memory via Beta-Band Encoding and Theta-Band Maintenance

Rhythm, as a prominent characteristic of auditory experiences such as speech and music, is known to facilitate attention, yet its contribution to working memory (WM) remains unclear. Here, human participants temporarily retained a 12-tone sequence presented rhythmically or arrhythmically in WM and performed a pitch change-detection task. Behaviorally, while having comparable accuracy, rhythmic tone sequences showed a faster response time and lower response boundaries in decision-making. Electroencephalographic recordings revealed that rhythmic sequences elicited enhanced non-phase-locked beta-band (16 Hz-33 Hz) and theta-band (3 Hz-5 Hz) neural oscillations during sensory encoding and WM retention periods, respectively. Importantly, the two-stage neural signatures were correlated with each other and contributed to behavior. As beta-band and theta-band oscillations denote the engagement of motor systems and WM maintenance, respectively, our findings imply that rhythm facilitates auditory WM through intricate oscillation-based interactions between the motor and auditory systems that facilitate predictive attention to auditory sequences.

Facial misfits accelerate stereotype-based associative learning

Counterstereotypes challenge the deleterious effects that gender-typed beliefs exert on people's occupational aspirations and lifestyle choices. Surprisingly, however, the critical issue of how readily unexpected person-related knowledge can be acquired remains poorly understood. Accordingly, in two experiments in which the facial appearance of targets was varied to manipulate goodness-of-stereotype-fit (i.e., high vs. low femininity/masculinity), here we used a probabilistic selection task to probe the rate at which counter-stereotypic and stereotypic individuals can be learned. Whether occupational (Expt. 1) or trait-related (Expt. 2) gender stereotypes were explored, a computational analysis yielded consistent results. Underscoring the potency of surprising information (i.e., facial misfits), knowledge acquisition was accelerated for unexpected compared to expected persons, both in counter-stereotypic and stereotypic learning contexts. These findings affirm predictive accounts of social perception and speak to the optimal characteristics of interventions designed to reduce stereotyping outside the laboratory.

Population Representation of the Confidence in a Decision in the Lateral Intraparietal Area of the Macaque

Confidence in a decision is the belief, prior to feedback, that one's choice is correct. In the brain, many decisions are implemented as a race between competing evidence-accumulation processes. We ask whether the neurons that represent evidence accumulation also carry information about whether the choice is correct (i.e., confidence). Monkeys performed a reaction time version of the random dot motion task. Neuropixels probes were used to record from neurons in the lateral intraparietal (LIP) area. LIP neurons with response fields that overlap the choice-target contralateral to the recording site (Tin neurons) represent the accumulation of evidence in favor of contralateral target selection. We demonstrate that shortly before a contralateral choice is reported, the population of Tin neurons contains information about the accuracy of the choice (i.e., whether the choice is correct or incorrect). This finding is unexpected because, on average, Tin neurons exhibit a level of activity before the report that is independent of reaction time and evidence strength-both strong predictors of accuracy. This apparent contradiction is resolved by examining the variability in neuronal responses across the population of Tin neurons. While on average, Tin neurons exhibit a stereotyped level of activity before a contralateral choice, many neurons depart from this average in a consistent manner. From these neurons, the accuracy of the choice can be predicted using a simple logistic decoder. The accuracy of the choice predicted from neural activity reproduces the hallmarks of confidence identified in human behavioral experiments. Therefore, neurons that represent evidence accumulation can also inform the monkey's confidence.

Two fundamentally different mechanisms by which unconscious information impairs behavioral performance: Evidence from fMRI and computational modeling

It is increasingly clear that unconscious information impairs the performance of the corresponding action when the instruction to act is delayed. However, whether this impairment occurs at the response level or at the perceptual level remains controversial. This study used fMRI and a computational model with a pre-post design to address this elusive issue. The fMRI results showed that when the unconscious information containing strong stimulus-response associations was irrelevant to subsequent stimuli, the precuneus in the parietal lobe, which is thought to be involved in sensorimotor processing, was activated. In contrast, when the unconscious information was relevant to subsequent stimuli, regardless of the strength of the stimulus-response associations, some regions in the occipital and temporal cortices, which are thought to be involved in visual perceptual processing, were activated. In addition, the percent signal change in the regions of interest associated with motor inhibition was modulated by compatibility in the irrelevant but not in the relevant stimuli conditions. Modeling of behavioral data further supported that the irrelevant and relevant stimuli conditions involved fundamentally different mechanisms. Our finding reconciles the debate about the mechanism by which unconscious information impairs action performance and has important implications for understanding of unconscious cognition.