Cognitive, Affective, & Behavioral Neuroscience

High intra-individual reaction time variability (RTV) is traditionally viewed through a deficit perspective and interpreted as a maladaptive signature of attentional lapses, cognitive inefficiency, and systemic noise. However, theories from motor learning and the competitive neurocognitive networks framework suggest that behavioral variability and reduced top-down control might actually facilitate certain forms of implicit skill acquisition. The present study addresses the apparent conflict between these perspectives by investigating whether elevated RTV serves as an adaptive, functional precursor to implicit statistical learning. Across two independent studies, participants completed the Alternating Serial Reaction Time (ASRT) task. We quantified early RTV during the initial task phase using multiple metrics -- coefficient of variation, inter-trial RTV, and ex-Gaussian parameters Sigma and Tau-- to predict subsequent statistical learning. Analyses controlled for baseline response speed and early learning artifacts, and test-retest reliability measures were also evaluated. Our results show that early RTV predicted later statistical learning measured via reaction times. This predictive relationship was most consistent for metrics capturing dynamic, moment-to-moment fluctuations (inter-trial RTV and Sigma) rather than extreme attentional lapses (Tau). While the effect size was relatively small, the association remained significant after controlling for potential statistical confounds. Furthermore, early RTV demonstrated strong test-retest stability. These findings challenge the exclusively deficit-oriented perspective on behavioral noise. Instead, we propose that elevated RTV may reflect an adaptive, exploratory processing tendency, analogous to kinematic exploration in motor learning, that could support the brains ability to implicitly extract and model probabilistic environmental regularities.

The willingness to exert cognitive effort is essential but is constrained by the subjective cost of effort. Although effortful tasks are often avoided, positive bias about ones own performance may help sustain engagement with cognitive demands. Here, participants completed an effort-based decision-making task and reported trial-by-trial predictions of their own performance, allowing us to quantify performance prediction error (PPE) as the discrepancy between subjective and objective accuracy. The results showed that PPE was predominantly positive and increased with effort level, indicating greater overestimation under higher cognitive demands. Using a computational model, we show that choices were best explained by a learning model in which rewarded trials accompanied by positive PPE decreased subsequent sensitivity to effort. A confidence-based control model did not provide a better account of choices, suggesting that this effect was better captured by positive performance bias than by confidence alone. Our findings provide a computational account of how biased self-evaluation may attenuate the subjective cost of cognitive effort and extend the positive bias literature to the task need for cognitive effort.

Goal-directed behavior often requires sustained effort across a sequence of interdependent decisions, yet the determinants of persistence in such contexts remain poorly understood. Here, we investigated how individuals regulate persistence in a novel sequential effort-based task in which they controlled an avatar through successive checkpoints to reach a final goal and could make repeated attempts following failure. At each attempt, participants could choose either to persist in the same task or to disengage toward an easier but less rewarding alternative. We found that decisions to persist or disengage were jointly shaped by multiple interacting factors. Disengagement increased with task difficulty and lower skill level. It also increased with repeated attempts and time-on-task, indexing fatigue, and with accumulated errors, indexing lack of progress. Conversely, proximity to the goal promoted persistence and shaped decision dynamics by reducing choice conflict during persistence decisions and increasing hesitation during disengagement near the goal. Notably, clearing the first checkpoint produced a sharp increase in persistence, suggesting that early success plays a pivotal role. Furthermore, persistence reflected both retrospective and prospective evaluations of effort, with prior investment promoting commitment and anticipated effort reducing it. Finally, disengagement was preceded by short-term performance decline but not by gradual increases in decision conflict, suggesting relatively abrupt strategy shifts following repeated failures. Together, these findings provide a comprehensive account of persistence in sequential effortful tasks, showing that decisions to persist or disengage are jointly shaped by multiple factors related to fatigue, (lack of) progress, goal proximity, and early success.

When humans learn under conditions of uncertainty, they dynamically adjust how they prepare for and respond to feedback. In navigating uncertain environments, the brain minimizes error by continuously refining internal models via memory updating (MU). Feedback is critical for MU, and anticipatory neural mechanisms shape how feedback is processed, likely reflecting learned environmental certainty. However, the literature has largely focused on post-feedback activity, leaving pre-feedback certainty-related mechanisms less understood. The present study aims to address this gap by examining how certainty modulates anticipatory states, preceding feedback and subsequent MU. We examined oscillatory activity prior to performance feedback in a reanalysis of EEG data previously published by Hassall and colleagues (2023). Twenty-one participants (16 female, Mage = 25.81 years) predicted the strength of cartoon characters with varying predictability levels which were learned through exposure. Feedback on prediction accuracy was presented via an animated rising bar. Results revealed that theta power is modulated by accumulative feedback. Linear mixed-effects models revealed an interaction between predictability-related certainty and learning stage: in late learning, higher performance was associated with increased pre-feedback alpha and beta power for low-certainty trials, whereas in early learning, higher performance was associated with decreased beta power. These learning-related modulations in alpha and beta power suggest that initial learning is marked by adaptable exploratory processing. Subsequent learning exhibited increased alpha-mediated inhibition and beta-related anticipatory activity for lower certainty trials, indicative of dynamic strategy refinement and selective engagement of task-relevant information. These results demonstrate that certainty shapes preparatory oscillatory activity associated with MU.

Summary paragraphA hallmark of learning is the need for sensory stimuli (Ginns, 2015; McGraw et al., 2009; Reinwein, 2012; Spence, 1950) so that learning is fundamentally based on sensory input signals affecting behaviour, physiology, and neurology. If behavioural measures of learning can be causally linked to physiological and neurological variables, a broader understanding of the mechanisms related to learning in schools, learning disabilities, and learning and health issues may emerge (McGraw et al., 2009). Despite decades of research on the physiological/neurological variable of sympathetic activation, learning, and achievement (Horvers et al., 2021), any causal relation remains unclear (Cowley et al., 2014; Mason et al., 2020; Pijeira-Diaz et al., 2016; Sung et al., 2023; Yu et al., 2024) and issues with instrument validation remain (Costantini et al., 2023; Hu et al., 2024; Milstein & Gordon, 2020; Van Der Mee et al., 2021). Here we investigate the effect of sensory input on sympathetic activation by using validated instruments for skin conductance measurement (Batista et al., 2019) and whether sympathetic activation is connected to learning in a cognitive laboratory context and an ecologically valid classroom context. In both contexts, we found a physiological variable which correlated with learning and that sensory input affected this variable while student movement did not. These sensory inputs varied depending on the different instructional activities the students participated in. Together, these findings bring us one step closer to a model linking sensory input to behavioural, physiological, and neurological variables.

Autistic people can find social interactions difficult to navigate, traditionally attributed to difficulties in taking others perspectives. However, we have a limited understanding of how autistic people integrate self and other information efficiently during social decision-making. We conducted four highly powered experiments (total N = 1,621) to determine whether autistic traits affect two aspects of self-other integration during social decision making: self-bias and social basis function use. Using Bayesian analyses, we found strong support for the absence of a relationship between autistic traits and either aspect of social decision making, even after controlling for potential confounds (BF01 = 32.13 for self-bias, BF01 = 7.04 for social basis function use). Our results indicate that variations along autistic traits do not impact how people prioritise self-relevant information (self-bias) or utilize compressed social patterns of interaction (social basis function use) to guide their decisions about oneself and other people. These findings nuance the conceptualisation of social-cognitive processes across autistic traits while highlighting the need for large samples to validate null effects.

Working memory (WM) is a core cognitive function that supports goal-directed behavior by temporarily maintaining and manipulating information. One of the most widely used paradigms for investigating WM function is the N-back task, and numerous neuroimaging studies have examined load-dependent neural responses using a variety of analytical approaches. However, most previous studies have focused on low-to-moderate load ranges (primarily 0-3-back), and it remains unclear how whole-brain activity patterns reconfigure across a broader range of WM demands, including conditions approaching capacity limits. In the present study, we investigated behavioral performance and whole-brain activity patterns across an extended N-back task ranging from 0-back to 7-back. Behavioral analyses revealed that discrimination sensitivity (d') decreased nonlinearly with increasing WM load, whereas reaction time (RT) exhibited an inverted-U pattern, peaking at intermediate load conditions. To characterize load-dependent whole-brain activity patterns, we computed relative activation maps by subtracting the participant-wise mean activation map across all conditions from each condition-specific activation map. Spatial similarity analyses with the Yeo 7-network templates revealed that low-load conditions showed relatively high similarity to default mode network (DMN)-related patterns. Similarity to the dorsal attention network (DAN) and frontoparietal network (FPN) was maximal at intermediate load levels, indicating load-dependent changes in network similarity profiles. High-load conditions were characterized by partial re-emergence of DMN-related patterns, accompanied by reduced DAN/FPN similarity. In addition, semantic similarity analysis using Neurosynth-derived semantic maps revealed relatively high similarity to default mode-related and self-referential representations under low-load conditions. Intermediate-load conditions showed strong correspondence with working memory- and executive control-related representations, whereas high-load conditions exhibited increased similarity to salience-, aversive/interoceptive-, and inhibitory-control-related representations. Together, these findings suggest that increasing WM load is associated not merely with stronger activation, but with changes in whole-brain activity patterns accompanied by nonlinear changes in network similarity profiles across levels of cognitive demand. Furthermore, the relative activation map-based whole-brain pattern analysis used in this study may provide a useful approach for evaluating changes in whole-brain state representations associated with cognitive load.

Bayesian accounts of autism propose that perception is less influenced by prior expectations and more strongly driven by incoming sensory information in autistic than non-autistic individuals, with this altered balance cascading through the cognitive hierarchy to also influence higher cognitive functions. However, empirical support for these accounts remains mixed. Previous work has mostly tested these ideas in the context of objective environmental statistics, but recent work suggests that it may be subjective experience of structure, rather than structure itself, that shapes perceptual processing. Characterising these subjective experiences in autistic individuals is therefore crucial for understanding predictive processing in autism. In the present study, we thus examined subjective experience of statistical structure in autistic and non-autistic adults and tested how this experience relates to perceptual decisions. Participants were exposed to statistical regularities between action cues and visual stimuli (shapes), and we measured their speed and accuracy in reporting which shape they had seen. At the end of the study, participants were asked to estimate the probability and rate their surprise for each action-shape combination. Autistic and non-autistic participants showed similar subjective probability and surprise ratings and a comparable relationship between these ratings and perceptual decisions. Across participants, subjective ratings explained perceptual decisions better than objective structure. Together, these findings show that autistic and non-autistic adults experience statistical structure similarly, with these experiences exerting a similar influence on perceptual decisions - therefore suggesting that subjective experience plays a comparable role in predictive processing in autistic and non-autistic adults.

Disordered eating attitudes exist on a continuum that extends well below clinical diagnostic thresholds, yet the cognitive correlates of this non-clinical variation remain incompletely understood. Previous research linking executive functioning to disordered eating in non-clinical samples has relied almost exclusively on self-report questionnaire measures of executive function, which show weak correspondence with performance-based assessments. This methodological reliance leaves open the question of how objective executive performance relates to eating behavior across the spectrum. The present study addressed this gap by examining the association between performance-based measures of executive function and disordered eating attitudes in a non-clinical sample of 243 university students via an online experiment, using a dimensional approach consistent with the Research Domain Criteria framework. Participants completed established neurocognitive tasks covering three executive function domains: working memory was assessed with Digit Span and an N-back task, inhibitory control with a Go/No-Go task, and cognitive flexibility with the Card Sorting Task. Disordered eating attitudes were indexed using the EAT-26 total score and its subscales. A notable correlation was identified between cognitive flexibility and disordered eating attitudes, while working memory and inhibitory control exhibited no such association. Overall, our findings provide evidence for associations between executive functioning and disordered eating attitudes in a non-clinical sample.

Cognitive flexibility is an executive function that allows individuals to adjust behaviour in response to changing environmental demands. We assessed volitional switching under uncertainty, without rule-based learning, in the Change Your Mind task. Nineteen patients with obsessive-compulsive disorder (OCD), 19 patients with attention-deficit hyperactivity disorder (ADHD) and matched control participants (20 per group) completed the task whilst undergoing a functional MRI scan. The task was a two-alternative forced choice paradigm where each stimulus was presented twice successively, with spurious feedback following the first presentation. This allowed participants the opportunity to repeat or change their response. Participants with ADHD changed their response more frequently than controls following a previously correct response, associated with reduced accuracy on the second trial. This was accompanied with smaller differences between change and repeat trials in the superior frontal gyrus, paracingulate gyrus and frontal pole compared to controls. Participants with OCD did not differ from healthy controls in their performance but exhibited greater activity on both change and repeat trials in the pre- and postcentral gyri than controls. These results point to distinct neurobehavioural differences in patients with ADHD and OCD underlying what is often termed more broadly inflexible behaviour.

Ostracism (i.e., social exclusion) threatens social security and individuals who are hypersensitive to it may face long-term negative mental health consequences. Clarifying how self-reported distress and neural responses to ostracism unfold moment-by-moment and across varying levels of individual traits may help better understand what contributes to hypersensitivity. To this objective, the current study employed a functional near-infrared spectroscopy (fNIRS) adapted Cyberball task and single-trial analytic techniques in a sample of 53 college students (aged 18-22 years). Results confirmed that Cyberball induced ostracism increased self-reported distress and neural activity in the ventrolateral prefrontal cortex (vlPFC), a region associated with emotion regulation. Self-reported distress varied with differences in social anxiety and need for belonging. Moreover, vlPFC activity sensitized across exclusion-specific trials and this neural sensitization was differentially modulated by social anxiety, need for belonging, and personality growth mindset. The current study highlights the value of single-trial approaches for capturing the nuanced temporal dynamics of ostracism responses. Additionally, it underscores the importance of examining how individual differences shape immediate responses to ostracism to better understand their association with downstream, longer-term consequences.

Cognitive flexibility is the ability to change the way of responding when the demands of the environment change. This study tested how cognitive flexibility develops across the lifespan. We used a new computerized task that gives a continuous score instead of just right or wrong answers. 221 healthy adults aged 18 to 71 completed the Continuous-score Probabilistic Reversal Learning Test (CPRLT). We calculated mean absolute error and adjusted error for rule-based learning, and fitted a Rescorla-Wagner model to estimate each persons learning rate (alpha) for reward-based learning. All three scores have one breakpoint, performance improved rapidly from childhood to young adulthood, then declined slowly. Rule-based learning peaked around age 20. Reward-based learning peaked earlier, around age 18. This suggests that reward-based learning matures before rule-based learning. The pattern fits with brain development: reward circuits mature earlier, while prefrontal regions for rule-based learning develop later. Our continuous measure captured this difference, which binary tasks would miss.

Time perception difficulties are frequently reported in Autism Spectrum Disorder, yet empirical findings remain inconsistent. A key methodological limitation is the failure to separate perceptual sensitivity from decision-making strategies. We applied Signal Detection Theory (SDT) to a subsecond duration discrimination task (100 and 500 ms) in 65 non-clinical adults varying in autistic traits, assessed via the Autism-Spectrum Quotient (AQ) and a Principal Component Analysis (PCA) of its subscales. Autistic traits did not predict reduced perceptual sensitivity (d'): temporal discrimination remained intact across the full autism-trait continuum, with Bayesian analyses providing converging evidence against a perceptual deficit. Instead, a PCA-derived cognitive component -- combining heightened Attention to Detail with reduced Imagination -- was systematically associated with a shift in decision bias (c). Individuals with this profile showed a graded attenuation of standard-based anchoring, with ordinal position progressively filling the gap. This shift operated consistently across both temporal scales, as confirmed by trial-level generalized linear mixed modelling, and reflects a quantitative redistribution of anchoring weight rather than a categorical switch in strategy. These findings reframe temporal "rigidity" in ASD not as a perceptual deficit, but as a suboptimal yet internally consistent decision-making style favouring within-trial information over accumulated representational knowledge. Lay AbstractMany autistic people report difficulties with time in daily life, but scientists have long disagreed on whether this reflects a genuine perceptual problem. This study found that autistic traits do not impair the basic ability to judge duration. Instead, people with more autistic traits tend to rely on which event came first, rather than accumulating experience across trials to refine their judgments -- a less effective but internally consistent strategy.

It has been shown that early-life adversity (ELA) shapes how individuals learn, remember, and make decisions, yet the precise computations altered by these experiences remain unclear. Here, we combine a structured foraging task with computational modeling to test a recently developed theory for how a particular kind of ELA, early-life unpredictability (ELU), specifically influences choice under uncertainty. Adult participants (N=297) performed a sequential foraging task requiring continuous trade-offs between exploiting depleting resources and exploring alternatives. Subsets also completed assessments of early-life unpredictability (QUIC) and for trauma symptoms arising from lifelong stressors (PCL). We fit participants behavior with a Bayesian learning-and-planning model in which uncertainty modulates the valuation of leaving a current resource patch. Critically, the subjective influence of local uncertainty was allowed to vary freely between participants. Consistent with theoretical proposals, computational model fits and mediation analyses revealed a robust indirect pathway: ELU predicted increased discounting in the face of uncertainty, which in turn predicted greater overharvesting. This pattern was consistent across environmental conditions, indicating that early-life unpredictability primarily influences behavior through a general influence on uncertainty processing. Importantly, although PCL scores were also correlated with uncertainty adaptation, these effects were fully accounted for by shared variance with ELU, offering a clear dissociation between developmental unpredictability and lifelong traumatic experience. Together, our results show that early-life unpredictability causes long-lasting changes in decision-making by amplifying the subjective experience of uncertainty.

Trust and fairness sustain cooperation, but social neuroscience has largely studied violations of these social norms in isolation. We asked whether neural sensitivity to betrayal carries forward into the evaluation of unfairness in a separate social exchange. In 132 adults who completed fMRI during the Trust Game and Ultimatum Game, betrayal and unfairness both recruited overlapping voxels within left anterior insula. Yet, individual differences in regional activation were uncorrelated across tasks, arguing against a simple shared-response account. Instead, cross-task structure emerged in connectivity: stronger anterior insula responses to social versus nonsocial betrayal predicted weaker anterior insula-ventromedial prefrontal coupling during social versus nonsocial fairness evaluation. These findings suggest that betrayal and unfairness are linked less by identical regional responses than by how salience signals engage valuation circuitry across contexts. By bridging two canonical social exchange tasks within the same individuals, this study reveals a network-level architecture for generalizing sensitivity to interpersonal norm violations.

Children often outperform adults in probabilistic statistical learning tasks, yet the mechanisms underlying this developmental advantage remain poorly understood. Here, we used eye-tracking measures of belief updating to examine how children and adults acquire and update predictions in a probabilistic sequence-learning task. Using the standard (oculomotor) reaction time measure, children showed stronger statistical learning than adults, replicating previous behavioral findings while revealing a more detailed profile of developmental differences in statistical learning. Critically, children updated their predictions more frequently: they were less likely to repeat previous predictions and more likely to shift their expectations in response to new input. Adults, in contrast, showed greater persistence, tending to maintain prior predictions even when those predictions were inconsistent with the underlying statistical structure. Despite these pronounced differences in updating behavior, the processing and use of prediction errors were remarkably similar across age groups. These findings indicate that developmental differences in statistical learning do not primarily arise from how prediction errors are computed, but rather from how prior beliefs and incoming information are weighted during belief updating. Children's enhanced learning may therefore reflect reduced reliance on stable priors and greater sensitivity to current sensory evidence, supporting a more exploratory learning strategy. Adults, by contrast, appear to favor an exploitative strategy that stabilizes existing predictions but reduces flexibility in probabilistic environments. More broadly, the results suggest that developmental changes in statistical learning may reflect age-related differences in how readily learners revise their predictions in response to incoming evidence. By integrating sensitive oculomotor measures with analyses that probe the mechanisms underlying belief updating, the present study provides a more fine-grained account of how predictive learning changes across development and offers a framework for reconciling previously inconsistent developmental findings in statistical learning.

Humans make emotional facial expressions and have a cardiac response when they catch themselves in a mistake or receive feedback about task performance. We tested whether rats exhibit similar visceral responses in the context of metacognition. We assessed heart rate variability (HRV) and machine learning-detected facial expressions as female and male rats detected and stopped in-progress mistakes and received post-choice rewards or error cues. HRV increased during internally detected mistakes, as well as in response to external error cues for both sexes. Errors were associated with an HRV response when parasympathetic tone was higher, while rewards were associated with an HRV response when sympathetic tone was higher. We observed sex-specific effects of cardiac interoception on cognitive control over real-time action correction, in that low parasympathetic tone was associated with reduced ability to stop in-progress mistakes exclusively in females. Rats made facial expressions during mistake detection and in response to task feedback. Outcome-related facial expressions were valence-specific, in that the facial expression after error feedback was delayed relative to the post-reward facial expression. Our results suggest that rats have a visceral experience during metacognitive monitoring.

Language production requires the coordination of multiple cognitive processes. The ability to anticipate and override a habitual response in favor of a contextually-appropriate response are key subprocesses of cognitive control which enable speakers to communicate effectively. Word retrieval involves the co-activation of semantically related alternatives from which the speaker must select the appropriate target representation. Although cognitive control mechanisms have been proposed to contribute to resolving semantic interference during language production, the nature of these control processes remain unclear. Studies investigating the temporal dynamics of cognitive control during decision making tasks have led to a distinction between two operating processes: proactive control, initiated prior to the occurrence of conflict, and reactive control recruited after conflict is detected. We investigated the roles of proactive and reactive control in resolving interference between competing linguistic representations during word retrieval. We analyzed congruency sequence effects combined with delta-plot distributional analyses to dissociate potential adjustments in proactive versus reactive cognitive control in a picture-naming task manipulating semantic context compared to a minimally-linguistic Stroop-like paradigm. Reaction time distributional properties following semantically related trials revealed the engagement of proactive control in semantic interference resolution during word retrieval in the PWI task. In contrast, reactive inhibitory control was engaged in resolving semantic interference following low conflict trials. This distinction was not present in the minimally-linguistic task, which did not appear to engage adaptive control to the same extent. These findings demonstrate that both proactive and reactive cognitive control mechanisms contribute to language production, and are engaged dynamically, adjusting trial-by-trial to resolve semantic interference during word retrieval. In addition, our study provides important insight into the comparison of language with other cognitive domains and positions linguistic paradigms as being instrumental in the study of cognitive control dynamics.

Human decision-making behavior varies widely across individuals and task conditions. This variability is often interpreted in terms of different suboptimal decision strategies, but the principles that govern these suboptimalities remain poorly understood. We propose that some of these suboptimalities can be understood in terms of limited-capacity, but information-efficient, inference processes that inform decision-making. We developed and used new theoretical and empirical approaches to compare the amount of information used (capacity) to the effectiveness with which it was used (accuracy) by individual participants performing simple inference tasks. Variable, suboptimal performance was explained largely by inference that had variable, limited information capacity. Across these capacity limits, and regardless of whether the inference strategy was based on optimal or heuristic principles, the information was used effectively to maximize accuracy for a given capacity. This form of flexible and efficient information bottleneck reflects fundamental capacity-accuracy tradeoffs that structure individual variability.

Attentional control balances proactive suppression of predictable distractors with reactive suppression of unexpected ones. Yet, how internal states such as alertness shape this balance is unclear. Using pupillometry and eye tracking across two probability-cueing experiments (conducted in 2024) with varying distractor prevalence, we distinguished tonic (baseline pupil size across blocks) from trial-level pupil size fluctuations (trial-by-trial residual variability in pre-stimulus pupil size). With moderate prevalence, suppression of frequent-region distractors developed gradually, whereas high prevalence induced near-immediate suppression. Behavioral measures (e.g., reaction times) were closely linked to tonic and trial-level pupil size fluctuations. Critically, both alertness components jointly influenced control: during early learning, heightened trial-level pupil size increased distractor capture and reduced target fixations, whereas later on, suppression shifted to a proactive mode resilient to trial-level fluctuations. Under high prevalence, this shift occurred faster. Notably, higher trial-level pupil size generally accelerated first target selection. These findings show that tonic alertness and trial-level alertness fluctuations dynamically regulate reactive and proactive control during statistical learning. Impact StatementThis study shows that people become better at ignoring predictable distractions over time, but that this improvement depends not only on what they have learned about the task environment, but also on their current level of alertness. By combining eye tracking and pupil measures, we found that temporary increases in alertness can sometimes help people orient more quickly to relevant information, yet during earlier stages of learning they can also make attention more vulnerable to distracting events. These findings suggest that successful focus in complex environments depends on a dynamic interplay between learned expectations and moment-to-moment fluctuations in mental state, with implications for understanding sustained attention in settings such as monitoring, driving, and other tasks that require people to stay engaged while resisting distraction.