Cognitive, Affective, & Behavioral Neuroscience

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During childhood and adolescence, exploring the unknown is important to build a better model of the world. This means that youths have to regularly solve the exploration-exploitation trade-off, a dilemma in which adults are known to deploy a mixture of computationally light and heavy exploration strategies. In this developmental study, we investigated how youths (aged 8 to 17) performed an exploration task that allows us to dissociate these different exploration strategies. Using computational modelling, we demonstrate that tabula-rasa exploration, a computationally light exploration heuristic, is used to a higher degree in children and younger adolescents compared to older adolescents. Additionally, we show that this tabula-rasa exploration is more extensively used by youths with high attention-deficit/hyperactivity disorder (ADHD) traits. In the light of ongoing brain development, our findings show that children and younger adolescents use computationally less burdensome strategies, but that an excessive use thereof might be a risk for mental health conditions.

Healthy individuals typically attribute successes to internal causes, such as their abilities, and failures to external factors, like bad luck. In contrast, individuals with depression and low self-esteem are more likely to attribute failures to internal causes and successes to external causes. At the same time, depression and low self-esteem are associated with negatively biased self-related learning and self-beliefs. Although causal attributions have been shown to influence belief formation and updating, the dynamic interaction between real-time attributions and self-related learning remains poorly understood. In this study, we used a validated self-related learning task to investigate how internal versus external attributions of performance feedback affect the formation of self-beliefs and how these processes relate to depressive symptoms and self-esteem. Drawing on a computational model that incorporates prediction error valence and causal attributions, we found that participants updated their self-beliefs less when feedback was attributed to external causes. Furthermore, individuals with higher levels of depression and lower self-esteem showed a stronger negativity bias in learning. Lower self-esteem was also linked to a reduced self-serving bias in attributions. These findings provide insight into the cognitive mechanisms that may contribute to the development and maintenance of negative self-beliefs commonly observed in depression.

Choice selection strategies and decision making are typically investigated using multiple-choice gambling paradigms that require participants to maximize reward payoff. However, research shows that performance in such paradigms suffers from individual biases towards the frequency of gains to choose smaller local gains over larger longer term gain, also referred to as melioration. Here, we developed a simple two-choice reward task, implemented in 186 healthy human adult subjects across the adult lifespan to understand the behavioral, computational, and neural bases of payoff maximization versus melioration. The observed reward choice behavior on this task was best explained by a reinforcement learning model of differential future reward prediction. Simultaneously recorded and source-localized electroencephalography (EEG) showed that diminished theta-band activations in the right rostral anterior cingulate cortex (rACC) correspond to greater reward payoff maximization, specifically during the presentation of cumulative reward information at the end of each task trial. Notably, these activations (greater rACC theta) predicted depressed mood symptoms, thereby showcasing a reward processing marker of potential clinical utility. Significance StatementThis study presents cognitive, computational and neural (EEG-based) analyses of a rapid reward-based decision-making task. The research has the following three highlights. 1) It teases apart two core aspects of reward processing, i.e. long term expected value maximization versus immediate gain frequency melioration based choice behavior. 2) It models reinforcement learning based behavioral differences between individuals showing that observed performance is best explained by differential extents of reward prediction. 3) It investigates neural correlates in 186 healthy human subjects across the adult lifespan, revealing specific theta band cortical source activations in right rostral anterior cingulate as correlates for maximization that further predict depressed mood across subjects.

The catecholamine precursor tyrosine has been linked to improved cognitive performance, but investigations into decision-making and reinforcement learning processes known to be under catecholamine control are sparse. We examined the impact of a single dose of Tyrosine (2g) on reinforcement learning and exploration in a large (n=63) gender-balanced sample in a within-subjects preregistered study. Reinforcement learning performance was improved under Tyrosine, and computational modeling revealed that this performance increase was due to a stabilization of choice behavior reflected in increased value-driven exploitation. Further non-preregistered modeling analyses confirmed that accounting for higher-order perseveration substantially improved model fit, and substantiated the observation of increased value-driven exploitation under Tyrosine. Furthermore, it revealed a more fine-grained computational impact of Tyrosine, showing attenuated effects of directed exploration and value-independent perseveration. Supplementation with Tyrosine therefore improved reinforcement learning performance by stabilizing choice patterns in the service of optimizing reward accumulation. Results confirm that Tyrosine supplementation modulates specific computational mechanisms thought to be under catecholamine control.

Every day, we make choices about how much effort we are willing and able to use to achieve the outcomes we desire against the backdrop of constantly shifting effort demands and available rewards. While factors like visual short-term memory and chronic stress levels can predict responses to stable cognitive effort demands, we do not yet know whether they constrain ones choices of higher effort trials for larger rewards when task demands and potential outcomes shift over time. Here, we examined whether these factors predicted the choice to deploy cognitive effort given increasing effort demands and the tendency to deploy effort given shifting reward availability. Undergraduate participants first performed an online visual short-term memory task to assess capacity for visuospatial short-term memory. They then completed a series of choice trials where they could choose between high-effort, high-reward or low-effort, low-reward trials. In two blocks, we varied either the effort required on high-effort trials or the reward offered on both trial types. We found that visual short-term memory predicted the likelihood of choosing high-effort trials given shifting rewards, while chronic stress and everyday preferences for cognitively effortful strategies predicted the tendency to deploy increasing amounts of effort for a stable reward. Furthermore, participants subjective reports show a strong focus on attentional processes, and balancing rewards and losses, when making decisions about how much effort to deploy. These findings shed light on distinct trait-level factors associated with cognitive effort choices given shifting demands and outcomes. Significance statementWe must often choose how much work to put in to complete everyday tasks. However, we do not know what behavioural factors drive these choices in humans when the effort required to complete a task - or potential rewards - shifts over time. In a visual short-term memory task adapted from rodent work, we found that those with higher visual short-term memory ability chose more high effort trials as effort demands increased, while chronic stress and everyday preferences for effortful strategies predicted more effort for a reward. Furthermore, participants described prioritizing sustaining attention in order to successfully complete the task. These findings shed light on distinct trait-level factors associated with cognitive effort choices given shifting demands and outcomes.

To behave adaptively, people need to integrate information about probabilistic outcomes and balance drives to approach positive outcomes and avoid negative outcomes. However, questions remain about how uncertainty in positive and negative outcomes influence approach-avoid decision-making dynamics. To fill this gap, we developed a novel Probabilistic Approach Avoidance Task (PAAT) and characterized behavior in this task using sequential sampling models In this task, participants (Study 1: blinded mixed clinical sample N=34; Study 2: online nonpsychiatric sample N = 58) made a series of choices between pairs of options, each consisting of variable probabilities of reaching a positive outcome (monetary reward) and of reaching a negative outcome (aversive image). Participants tended to choose options that maximized the likelihood of reward and minimized the likelihood of aversive outcomes. Moreover, the weights they placed on each of these differed for choices where these likelihoods were in opposition (i.e., the riskier option was also more rewarding; incongruent trials) relative to when these were aligned (congruent trials). Computational modeling revealed that the relative influence of rewarding and aversive outcomes on choice was captured by differences in the rate of decision-relevant information accumulation. These modeling results were validated with a series of model comparisons and posterior predictive checks, demonstrating that our sequential sampling models reliably captured our behavioral data. Together, these findings improve our understanding of the influence of motivational conflict, outcome type, and levels of uncertainty on approach-avoid decision-making.

In the basal ganglia, different dopamine subtypes have opposing dynamics at post-synaptic receptors, with the ratio of D1 to D2 receptors determining the relative sensitivity to gains and losses, respectively, during value-based learning. This effective sensitivity to reward feedback interacts with phasic dopamine levels to determine the effectiveness of learning, particularly in dynamic feedback situations where frequency and magnitude of rewards need to be integrated over time to make optimal decisions. Using both simulations and behavioral data in humans, we evaluated how reduced sensitivity to losses, relative to gains, leads to suboptimal learning in the Iowa Gambling Task (IGT), a complex value-learning task. In the behavioral data, we tested individuals with a variant of the human dopamine receptor D2 (DRD2; -141C Ins/Del and Del/Del) gene that associates with lower levels of D2 receptor expression (N=119) and compared their performance to non-carrier controls (N=319). The magnitude of the reward response was measured by looking at ventral striatal (VS) reactivity to rewards in the Cards task using fMRI. DRD2 variant carriers had generally lower performance in the IGT than non-carriers, consistent with reduced sensitivity to losses. There was also a positive association between VS reactivity and performance in the IGT, however, we found no statistically significant difference in this effect between DRD2 carriers and non-carriers. Thus, while reduced D2 receptor expression was associated with less efficient learning in the IGT, we did not find evidence for the moderation of this effect by the magnitude of the reward response.

Reinforcement learning is a core facet of motivation and alterations have been associated with various mental disorders. To build better models of individual learning, repeated measurement of value-based decision-making is crucial. However, the focus on lab-based assessment of reward learning has limited the number of measurements and the test-retest reliability of many decision-related parameters is therefore unknown. Here, we developed an open-source cross-platform application Influenca that provides a novel reward learning task complemented by ecological momentary assessment (EMA) for repeated assessment over weeks. In this task, players have to identify the most effective medication by selecting the best option after integrating offered points with changing probabilities (according to random Gaussian walks). Participants can complete up to 31 levels with 150 trials each. To encourage replay on their preferred device, in-game screens provide feedback on the progress. Using an initial validation sample of 127 players (2904 runs), we found that reinforcement learning parameters such as the learning rate and reward sensitivity show low to medium intra-class correlations (ICC: 0.22-0.52), indicating substantial within- and between-subject variance. Notably, state items showed comparable ICCs as reinforcement learning parameters. To conclude, our innovative and openly customizable app framework provides a gamified task that optimizes repeated assessments of reward learning to better quantify intra- and inter-individual differences in value-based decision-making over time.

Cognitively demanding tasks are often perceived as costly due to the cognitive control resources they require, leading to effort avoidance, particularly in psychiatric populations with motivational impairments. Research on anxiety and cognitive effort are mixed: some studies suggest anxiety increases the perceived effort cost and avoidance, while others indicate that cognitive effort engagement can serve as an adaptive coping strategy. To reconcile these perspectives, we examined the interaction between state and trait anxiety on cognitive effort evaluation and engagement in two experiments. We hypothesized that state anxiety enhances task engagement as difficulty increases, and that this effect is diminished in individuals with high trait anxiety. Experiment 1 assessed self-reported anxiety in an online sample, while Experiment 2 manipulated state anxiety through autobiographical recall. Both experiments employed flow induction and effort discounting paradigms. Across both studies, the effect of state anxiety on task engagement depended on trait anxiety, but the direction of the state anxiety effect was opposite to the effect we predicted. In Experiment 1, participants with low trait anxiety reported reduced task engagement, as indexed by lower flow scores, when state anxiety was higher, but only in easy tasks. This effect was attenuated in participants with higher trait anxiety. The same pattern was observed in Experiment 2, but this time the interaction between trait and state anxiety was present regardless of task difficulty. These findings suggest that trait anxiety may reflect reduced impact of state anxiety on task disengagement. Public significance statementThis study demonstrated that effects of state anxiety on task disengagement depend on individual differences in trait anxiety. People with higher trait anxiety reported reduced effects of state anxiety on task disengagement.

Historically, neural variability observed during task was interpreted as "noise," assumed to obscure meaningful signal and thus something to be minimized both analytically by researchers and functionally by the brain. Changes to this signal-to-noise ratio have been proposed as a possible neural mechanism behind the increased reaction-time variability (RTV) in attention deficit hyperactivity disorder (ADHD). However, not all variability is the same - in some cases, variability can have some underlying "statistical structure" that can be beneficial to information processing. The challenge lies in distinguishing meaningful variability from random noise. The edge-of-synchrony critical point, which describes a system poised between synchronous and asynchronous regimes, could be a good theoretical framework to study these different types of neural variability. In this study, we investigate whether changes in criticality and oscillatory dynamics preceded slower behavioral responses during a bimodal continuous performance task in ADHD. We find evidence that, prior to slower responses, neural dynamics shift toward criticality in both ADHD and control groups, suggesting that increase variability in ADHD and during attention lapses are related to structured variability and not necessarily random noise. Notably, these findings run counter predictions based on the proposed model and previous literature on neural noise in this population, challenging predictions of edge-of-synchrony criticality as a unifying account of neural variability and behavioral performance. Furthermore, this effect did not emerge at the between-subject level, underscoring the limitations of relying on between-subject correlations to infer neural mechanisms. Impact StatementOur findings add new perspective to the hypothesis that links neural variability to reaction time variability in adults with and without ADHD. We found that neural dynamics shift towards criticality prior to slow reaction times in adults with and without ADHD, but in ADHD, dynamics lie closer to criticality regardless of response type, suggesting a different "attractor" state.

Choice consistency is a fundamental aspect of rational decision-making, reflecting the stability and reliability of an individuals preferences. However, real-world decision-making often deviates from this ideal, as individuals frequently make irrational or inconsistent choices in value-based decision-making. This study combined computational modeling, neuroimaging, and behavioral assessments to elucidate the mechanisms by which stress and memory affect choice consistency. Remembered items exhibited higher choice consistency compared to forgotten items. Computational modeling further indicated that the drift rate was higher, and the decision threshold lower, for remembered food items compared to forgotten ones. Stress was found to impair both choice consistency and memory retrieval, with stress-induced declines in memory accuracy positively correlating with reductions in choice reaction times. Activation of the dorsolateral prefrontal cortex (DLPFC) during the pre-choice anticipation period was positively associated with choice consistency. Similarly, activation of the orbitofrontal cortex (OFC) during the memory retrieval of food stimuli correlated with improved memory accuracy. These findings suggest that stress may impair choice consistency by disrupting memory retrieval processes. Overall, our study provides novel insights into the role of stress and memory in decision-making, offering a more nuanced understanding of the neural and cognitive processes that govern choice behavior.

Understanding the ordinal relationships between items requires constructing a rank order supporting decision-making between options. This process depends on the ability to learn reciprocal relationships and to select the best option available when making a choice. In such forms of decision-making, the prefrontal cortex (PFC) plays a crucial role in encoding the relative value of alternatives as a decision is formed. Higher-order cognitive abilities are influenced by genetic factors that affect dopamine availability in the PFC, potentially contributing to individual differences. Here, we examined the performance of 83 participants in a transitive inference task (TI), grouped by genotype based on the Val158Met single-nucleotide polymorphism in the Catechol-O-Methyltransferase (COMT) gene. The task included a learning phase in which participants acquired the reciprocal relationships among a set of hierarchically ranked items (A>B>C>D>E>F), followed by a test phase in which they were required to compare all possible item pairs and select the higher-ranked one. While genotype did not significantly influence test-phase performance, it did affect learning efficiency. Specifically, Val homozygotes took a longer learning procedure than both heterozygotes and Met homozygotes during the learning phase. Drift diffusion modelling (DDM) revealed that task performance was explained by the efficiency of evidence accumulation, which was lower in Val homozygotes, accounting for their poorer performance not only during initial learning but also when required to switch to a reversed hierarchical structure (A<B<C<D<E<F). These findings suggest that individual differences in inferential decision-making and cognitive flexibility may be partially driven by genetically determined variations in prefrontal dopamine availability.

Risky decisions involve choosing between options where the outcomes are uncertain. Cognitive tasks such as the CANTAB Cambridge Gamble Task (CGT) have revealed that patients with depression make more conservative decisions, but the mechanisms of choice evaluation underlying such decisions, and how they lead to the observed differences in depression, remain unknown. To test this, we used a computational modelling approach in a broad general-population sample (N = 753) who performed the CANTAB CGT and completed questionnaires assessing symptoms of mental illness, including depression. We fit five different computational models to the data, including two novel ones, and found that a novel model that uses an inverse power function in the loss domain (contrary to standard Prospect Theory accounts), and is influenced by the probabilities but not the magnitudes of different outcomes, captures the characteristics of our dataset very well. Surprisingly, model parameters were not significantly associated with any mental health questionnaire scores, including depression scales; but they were related to demographic variables, particularly age, with stronger associations than typical model-agnostic task measures. This study showcases a new methodology to analyse data from CANTAB CGT, describes a noteworthy null finding with respect to mental health symptoms, and demonstrates the added precision that a computational approach can offer.

Theoretical models of Obsessive-Compulsive Disorder (OCD) emphasize that symptoms may arise from an imbalance between habitual and goal-directed processes, characterized by increased reliance on habitual behavior and reduced efficiency of goal-directed control. However, it remains unclear whether similar alterations appear at a more general functional level, beyond reward-driven mechanisms. The present study, therefore, investigated the relationship between statistical learning (SL), an implicit, reward-independent mechanism that supports the detection of environmental regularities, and cognitive flexibility, defined as the capacity to adapt behavior and cognitive strategies to changing environmental demands. By adopting a dimensional approach to obsessive-compulsive (OC) tendencies in a non-clinical sample, we aimed to clarify how continuous symptom variability relates to the interaction of these neurocognitive processes. A total of 404 participants completed an online probabilistic sequence learning task assessing SL and a card-sorting task measuring cognitive flexibility. Results revealed an antagonistic relationship between SL and cognitive flexibility. Importantly, this inverse association weakened as OC tendencies increased, suggesting that OC tendencies may alter the typical balance between automatic and goal-directed functions even at undiagnosed levels. Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=174 HEIGHT=200 SRC="FIGDIR/small/669948v3_ufig1.gif" ALT="Figure 1"> View larger version (41K): org.highwire.dtl.DTLVardef@14be655org.highwire.dtl.DTLVardef@1ff0a67org.highwire.dtl.DTLVardef@1041e59org.highwire.dtl.DTLVardef@164a21a_HPS_FORMAT_FIGEXP M_FIG C_FIG HighlightsO_LIOCD theories propose an imbalance between automatic and goal-directed control systems. C_LIO_LIWe tested whether this imbalance emerges at a functional level, beyond reward-based learning. C_LIO_LIStatistical learning and cognitive flexibility showed an antagonistic relationship in a non-clinical sample. C_LIO_LIThis inverse association weakened as obsessive-compulsive tendencies increased. C_LIO_LIOC tendencies alter the interaction between automatic learning and executive control even at subclinical levels. C_LI

The present study examines the neural mechanisms of value-based multiattribute decision making through the lens of choice overload during consumer choice. Two behavioral experiments established that choice sets of a moderate size with nine options were perceived as most optimal compared to choice sets with three and 24 options, and options chosen from the largest set size were judged to be the least satisfying. A functional MRI study then examined how the brain responds to choice sets varying in size and complexity by manipulating the number of options presented as well as the presence of asymmetrically dominated decoy alternatives during a simulated online shopping task. Results showed that the dorsolateral prefrontal cortex (DLPFC) activity followed an inverse U-shape as a function of choice set size, peaking for moderately sized choice sets. The anterior cingulate cortex (ACC) exhibited a linear trend with choice set size. Trials containing decoy options elicited greater activation of the anterior insula (AIns) and DLPFC. Computational modeling of choice behavior revealed a greater tendency to utilize a simplifying lexicographic decision strategy as decision difficulty increased, and individual differences in decision strategies were reflected in activity of the ACC and AIns. These findings advance understanding of how the brain integrates effort, control, and strategy during complex value-based decisions.

Executive functions are a set of cognitive processes essential for cognitive control and coordination, enabling the achievement of objectives. These functions include mental exploration of ideas, reasoning, logical conclusion drawing, discipline, decision making, thoughtful consideration before action, tackling unforeseen challenges, resisting temptations, and maintaining focus. Numerous neuropsychological tests assess executive functions in relation to different autonomic control pathways that regulate involuntary physiological processes. The complexity of the autonomic nervous system and the challenges in measuring it alongside cognitive assessments are significant. Issues such as subject movement, environmental changes, and time-consuming protocols further complicate this measurement. There is a notable lack of research on suitable neuropsychological tests for assessing executive functions across diverse autonomic regulatory states. This paper reviews the most frequently used neuropsychological instruments in this context, aiming to guide the research community towards optimal tasks and administration protocols for concurrent autonomic nervous system measurement. The diversity of current executive function tests presents both opportunities and challenges. Some tests are better suited for simultaneous neurophysiological measurements due to their design, duration, and cognitive load, while others may interfere with or be influenced by such monitoring. This variability can lead to inconsistencies in findings and complicate result interpretation and comparison across studies of brain disorders. A significant drawback of using different tasks is the difficulty in comparing outcomes and conducting meta-analyses. Standardizing a smaller selection of tasks with consistent protocols would enhance research reliability, facilitate robust comparisons, and improve the overall quality of meta-analyses. To achieve this standardization, it is essential to first survey and describe the current landscape of executive function assessments in conjunction with autonomic nervous system measurements. ObjectiveThis paper aims to comprehensively analyze current instruments utilized in the assessment of executive functions, elucidating their advantages, limitations, and implications for future standardization initiatives. By systematically examining the most prevalent tools for evaluating executive functions in conjunction with autonomic nervous system measurements within clinical and experimental research settings, this review seeks to provide valuable insights for enhancing methodological consistency and advancing research in this area. MethodsWe searched for articles published using the PubMed database with the following terms: (neuropsychological test OR neuropsychological evaluation OR neuropsychological measure*) AND (executive functions OR EF OR executive function) AND (autonomic OR ANS OR parasympathetic OR PNS OR vagal OR "heart rate variability" OR HRV OR sympathetic OR HPA OR electrodermal OR RSA) Only the healthy population was chosen. There was no language restriction. Results62 articles fulfilled all the inclusion criteria. The 5 neuropsychological tests most frequently used to evaluate executive functions in relation to autonomic regulation were: O_LITrail Making Test (TMT) B C_LIO_LIThe n-back Task including 2-back Task C_LIO_LIWisconsin Card Sorting Test C_LIO_LIStroop Test and its variants C_LIO_LIWechsler Adult Intelligence Scale (WAIS)-Working Memory Composite C_LI The domains of executive functions most frequently assessed are: cognitive flexibility, working memory, and inhibitory control/selective attention. ConclusionThese findings offer valuable insights for future research directions and the development of standardized assessment protocols for executive functions, tailored to diverse socio-demographic profiles.

Does favoring less valuable options that deliver more frequent rewards reflect flawed decision-making or an adaptive strategy under complex environments? Frequency effects, defined as a bias toward more frequently rewarded but less valuable options, have traditionally been viewed as maladaptive decision-making deficits. In the present study, we used a within-subject design in which participants completed a four-option reinforcement learning task twice, once under a baseline condition and once with a reward frequency manipulation, to test whether better baseline learning predicts greater or lesser susceptibility to frequency-based biases. Participants were first trained on two fixed option pairs and then transferred their knowledge to novel pairings in a testing phase. Across conditions, higher training accuracy generally predicted higher test accuracy, with one critical exception: on trials where a more valuable option was pitted against a more frequently rewarded but less valuable alternative, participants with higher training accuracy exhibited a stronger bias toward the more frequent option. Moreover, baseline optimal choice rates in these specific trials were unrelated to--and even slightly negatively correlated with--optimal choice rates under the frequency condition. Computational modeling further showed that participants with better baseline learning performance were better fit by frequency-sensitive models in the frequency condition and they weighed frequency-based processing more heavily than value-based processing. Overall, these findings suggest that frequency effects, rather than signaling flawed learning, manifest more strongly in individuals with better baseline learning performance. This seemingly irrational bias may, under conditions of uncertainty, reflect a flexible, adaptive strategy that emerges among the best learners when value-based approaches are costly or unreliable. Author SummaryIn daily life, people often face choices between familiar, frequently encountered options and unfamiliar alternatives that may be more valuable. For example, we may keep visiting a local restaurant we know well instead of trying a new one with better reviews. This tendency, known as the frequency effect, reflects a bias toward options that yield more frequent rewards, even when those rewards are smaller and suboptimal overall. Traditionally, such behavior has been interpreted as a sign of neuropsychological impairments or flawed learning, while our study found the opposite. We asked 495 participants to complete a reinforcement learning task under two conditions: one with balanced reward frequencies and another in which one option was rewarded more frequently despite being less valuable than its alternative. Surprisingly, we found that better learners in the balanced condition were more likely to show frequency effects when reward frequencies were manipulated and uneven. Computational modeling confirmed that these individuals shifted from value-based strategies to frequency-based ones when the environment made value-based decisions more difficult. These findings suggest that frequency effects are not simply errors. Instead, they may represent an adaptive shortcut that emerges more strongly in better decision-makers as a flexible strategy for navigating uncertain environments when value-based calculations are costly or unreliable

Attentional control theory (ACT) posits that elevated anxiety increases the probability of re-allocating cognitive resources needed to complete a task to processing anxiety-related stimuli. This process impairs processing efficiency and can lead to reduced performance effectiveness. Science, technology, engineering, and math (STEM) students frequently experience STEM-related anxiety, which can interfere with learning and performance and negatively impact student retention and graduation rates. The objective of this study was to extend the ACT framework to investigate the neurobiological associations between STEM-related anxiety and cognitive performance among 123 physics undergraduate students. Latent profile analysis (LPA) identified four profiles of student STEM-related anxiety, including two profiles that represented the majority of the sample (Low STEM Anxiety; 59.3% and High Math Anxiety; 21.9%) and two additional profiles that were not well represented (High STEM Anxiety; 6.5% and High Science Anxiety; 4.1%). Students underwent a functional magnetic resonance imaging (fMRI) session in which they performed two tasks involving physics cognition: the Force Concept Inventory (FCI) task and the Physics Knowledge (PK) task. No significant differences were observed in FCI or PK task performance between High Math Anxiety and Low STEM Anxiety students. During the three phases of the FCI task, we found no significant brain connectivity differences during scenario and question presentation, yet we observed significant differences during answer selection within and between the dorsal attention network (DAN), ventral attention network (VAN), and default mode network (DMN). Further, we found significant group differences during the PK task were limited to the DAN, including DAN-VAN and within-DAN connectivity. These results highlight the different cognitive processes required for physics conceptual reasoning compared to physics knowledge retrieval, provide new insight into the underlying brain dynamics associated with anxiety and physics cognition, and confirm the relevance of ACT theory for STEM-related anxiety.

An extensive body of literature has shown that humans tend to avoid expending cognitive effort, just like for physical effort or financial resources. How then, do we decide whether to put this effort in? Decision-making not only involves choosing our actions, but also the meta-decision of how much cognitive effort to invest in making this choice, weighing the costs of cognitive effort against potential rewards. Popular recent theories, grounded in the field of reinforcement learning, suggest that this cost-benefit trade-off can be informed by the opportunity costs of effort investment, which the brain may approximate by the estimated average reward rate per unit time. It follows from intuition that in a low reward environment, investing cognitive resources in the task at hand will less likely lead to missed opportunities. Recent studies provided support for this idea, showing that people exert more cognitive effort when reward rate is low. Here, we replicate one of the key previous findings but provide an important nuance to this result. Cognitive effort allocation was better explained by participants recent performance history (i.e. accuracy rate) than average reward rate. In combination with the observation that participants were insensitive to the reward currently at stake, this invites a reinterpretation of these previous findings and suggests the need for further studies to assess whether environmental richness may indeed serve as a heuristic to modulate cognitive effort allocation.