Cognitive, Affective, & Behavioral Neuroscience

For methodological reasons, reward processing is commonly studied using random feedback and unlearnable tasks. It remains unclear whether task learnability influences reward-related brain activity, and whether this effect depends on individual differences such as reward responsiveness. We addressed this question by administering a behavioural activation system (BAS) scale before recording electroencephalography (EEG) while participants completed learnable and unlearnable versions of the "doors" task, a standard two-choice paradigm. Despite matched outcome likelihoods across conditions, participants reported greater motivation, enjoyment, and perceived performance in the learnable task. Contrary to our predictions, the amplitude of the reward positivity (RewP) - a frontocentral ERP index of reward processing - did not depend on task learnability and reward responsiveness. However, learnability and reward responsiveness effects became apparent when the analysis was restricted to high performers. Within this subgroup, participants low in reward responsiveness showed an enhanced RewP when the task was learnable. These findings suggest that contextual factors such as task learnability can interact with individual differences, informing ongoing efforts to identify the RewP as a biomarker of disordered reward processing.

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.

Decisions based on evidence accumulated over time require rules governing when to end the accumulation process and commit to a choice. These rules control inherent trade-offs between decision speed and accuracy, which require careful balance to maximize quantities that depend on both like reward rate. We previously showed that, to maximize reward rate, normative decision rules adapt to changing task conditions (Barendregt et al., 2022). Here we used a novel task to examine whether and how people use adaptive rules for individual decisions under a variety of conditions, including changes in decision outcomes across trials and changes in evidence quality both across and within trials. We found that the participants tended to use rules that adjusted, at least partially, to predictable changes in task conditions to improve reward rate, consistent with a rationally bounded implementation of normative principles. These findings help inform our understanding of the extent and limits of flexible decision formation in the brain.

People often switch tasks when attention wanes or an alternative task becomes more appealing. Such choices may reflect different control modes that may vary with working memory (WM) capacity. This study tested whether momentary attentional lapses prospectively predict voluntarily task switching and whether this relationship depends on WM capacity. Participants performed a continuous performance task involving face and scene images, with blocks in which they either freely chose the next task or followed an externally imposed sequence. A clear capacity-dependent crossover emerged where individuals with lower capacity were more likely to switch following lapse-prone blocks, whereas higher-capacity individuals tended to switch from relatively well-focused states. Eye-tracking revealed greater bias toward the competing irrelevant category before switches in lower-capacity individuals, accompanied by early conflict-related pupil dilation. Externally imposed task sequencing selectively reduced lapses in the lower-capacity group without affecting higher-capacity performance, suggesting that external structure can scaffold weaker internal goal maintenance. These findings suggest that the relationship between lapses and voluntary switching varies with WM capacity rather than being uniform across individuals. This pattern is consistent with a goal-competition account in which lapses reflect shifts in the balance between competing task goals, and voluntary switches may be preceded by different control states.

Multitasking is generally regarded as detrimental to performance. This deterioration effect is typically explained by the interference among tasks due to the limited capacity of information-processing resources, which in turn reduces the performance in each task. Contrary to this general view, we report evidence for a facilitation effect of multitasking on performance. This facilitation effect was observed in multitasking on a handgrip muscular endurance task and cognitive task, which are known to have little interference with each other. Specifically, we found that performance in the endurance task was facilitated with the difficulty of the concurrent cognitive task. This facilitation effect was mediated by additional pupil dilation due to the cognitive task. Increased effort with the difficulty of the cognitive task cannot explain the facilitated performance in the irrelevant endurance task. Instead, they suggest that the cognitive task elevated overall arousal to a level unattainable by the endurance task alone, which in turn facilitated performance in the irrelevant endurance task. To further test this arousal account, we manipulated participants motivation to the cognitive task by reward without changing its difficulty and found the same pattern of results. Thus, it is not effort or motivation specific to the cognitive task but rather overall arousal level that underlies the facilitation effect. These results unveiled a previously overlooked mechanism: a multitasking-induced arousal boost. Our findings suggest that multitasking can facilitate performance when the net effect of adding a concurrent task is governed less by the capacity limitation and more by the elevation of overall arousal.

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.

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.

Sense of agency (SoA), the experience of controlling ones actions and their consequences, is crucial for self-representation and adaptive goal-directed behavior. Classic comparator models explain SoA as the match between predicted and actual sensorimotor outcomes, whereas inference-based and Bayesian accounts emphasize cue integration and probabilistic weighting. Besides the influence of action-outcome contingencies on SoA, the feedback effect of perceived SoA on cognitive processing is also crucial for cognitive performance. Much of todays cognitive work is performed through interaction with devices that are not entirely reliable or are prone to operator error. Against this background, it is of particular interest whether the impact of an expectancy violation differs depending on whether the outcome is attributed to a malfunctioning system or to ones own mistake. To investigate this, the present EEG study deploys manipulated performance feedback in a color-discrimination task, while EEG was recorded. Thirty-five participants performed in this task with periods of veridical feedback, periods with feedback simulating an increased error rate, and periods of feedback suggesting malfunctioning response buttons. Behavioral performance was decomposed using the EZ-diffusion model, and time-frequency EEG analyses focused on event-related alpha, beta, and theta oscillations. The participants responded significantly slower in the self-attribution of errors condition compared to neutral feedback, and also significantly slower in the system-attribution of errors condition compared to self-attribution of errors. Decomposing behavior using drift-diffusion modeling indicates that a general decrease of response times with manipulated feedback can be attributed to decreased drift rates, whereas the difference between the self and system error conditions are reflected in the non-decision time. In the EEG, the manipulated feedback was reflected in attenuated decreases of occipital alpha and sensorimotor beta power during the cue-target interval. Furthermore, system-versus self-attributed errors elicited stronger feedback-locked midfrontal theta responses. Our findings suggest a functional dissociation within the agency inference process, where perceived controllability regulates preparatory investment of cognitive resources, while the attribution of action-outcome discrepancies seem to modulate sensory processes or motor-execution.

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.

I ntroduction: Prospective memory (PM) deficits in children with attention-deficit/hyperactivity disorder (ADHD) significantly impact academic and daily functioning. Through two experiments, this study investigated how cognitive load and encoding strategies modulate PM performance. Methods: Experiment 1 included 43 children (21 ADHD, 22 typically developing) who completed an n-back task under high and low cognitive load. Experiment 2 included 44 children with ADHD who were randomly assigned to either a standard encoding group or an implementation intention encoding group, also completing the n-back task under both load conditions. Results: Experiment 1 showed that children with ADHD had significantly lower PM accuracy than typically developing peers. Signal detection analysis revealed that this deficit stemmed from a more conservative response bias rather than impaired perceptual sensitivity. Unexpectedly, PM accuracy and perceptual sensitivity were higher under high cognitive load than low load for both groups. Experiment 2 demonstrated that implementation intention encoding significantly enhanced PM accuracy and perceptual sensitivity in children with ADHD, with stable effects across load conditions and no interference with ongoing task performance. Discussion: These findings indicate that PM deficits in children with ADHD reflect a conservative response strategy rather than an inability to detect target cues. Implementation intention encoding provides an effective, load-independent cognitive strategy for enhancing PM performance. These results offer novel insights into the cognitive mechanisms underlying PM deficits in ADHD and provide evidence-based guidance for targeted interventions.

Cooperative behavior is a cornerstone of human interaction. Although both "betrayal aversion" (the affective cost of being betrayed) and "loss aversion" (the financial detriment incurred from betrayal) are established determinants of cooperative behavior, their relative potency remains undetermined. Here, we investigated these effects by integrating computational modeling and event-related potential (ERP) techniques. In two tasks involving risk and cooperation, participants decided whether to take financial risks or to cooperate under possible betrayal. Our results showed that betrayal aversion had a stronger effect on reducing cooperation compared to loss aversion. Furthermore, ERP data demonstrated sequential processing: betrayal was encoded early in decision-making, reflected by increased P3 with weaker betrayal aversion, whereas loss aversion manifested later, marked by increased LPP. By dissociating the contributions of betrayal and loss, our finding provides novel insights into the cognitive and neural mechanisms underlying cooperative behavior.

Fairness decisions often integrate affective responses within a social context, yet emotion regulation in this literature has been largely studied as a self-directed process rather than an interpersonal one. We examined how individual differences in other-directed emotion regulation--measured with the Emotion Regulation of Others and Self (EROS) scale--relate to behavioral and neural responses during fairness decisions in 138 adults completing a variant of the Ultimatum Game with human and computer partners during fMRI. Behaviorally, participants who more strongly endorsed worsening others emotions rejected unfair offers more frequently, and this tendency interacted with offer fairness to amplify rejection of unfair offers. At the neural level, the left anterior insula tracked offer unfairness more strongly in social versus nonsocial contexts, consistent with sociality modulating the neural encoding of fairness. Right dlPFC activation during socially unfair offers was greater among individuals who preferred to improve others emotions. Connectivity analyses revealed that social fairness sensitivity predicted stronger amygdala-orbitofrontal and amygdala-dmPFC coupling; the latter was further amplified among individuals higher in other-directed emotion worsening. Together, these findings identify interpersonal emotion regulation as an understudied source of variation in the affective and prefrontal systems supporting fairness-based social decisions.

BackgroundPavlovian responding is a core component of behavior and can be measured via Pavlovian-instrumental transfer (PIT), where Pavlovian responses bias instrumental actions. Standard single-lever PIT paradigms, which assess responses using a single-choice option, cannot dissociate the contribution of model-free versus model-based reinforcement learning. While indirect evidence suggests a role for model-free responding in single-lever PIT, the contribution of model-based strategies is unclear. It also remains unknown whether internal cognitive states, such as mind wandering, impair specifically model-based but not model-free PIT, as is theoretically expected. MethodsWe developed a novel, trial-by-trial two-stage PIT paradigm designed to computationally dissociate model-free and model-based Pavlovian responding by leveraging probabilistic state transitions and trial-wise outcome predictions. After each two-stage Pavlovian learning trial, participants performed a single-lever PIT trial as well as a query trial of explicit value judgment. Detailed task instructions were provided to support potential model-based strategies. Computational modeling was used to quantify individual learning strategies. We assessed mind-wandering questionnaires and thought probes. ResultsAnalysis of query and PIT trials revealed trial-by-trial updating of outcome expectations based on probabilistic task structure, consistent with model-based Pavlovian responding. Behavioral responses during PIT were best explained by a computational model-based reinforcement learning model. In contrast, we found little evidence for model-free Pavlovian responding. Higher levels of mind wandering were associated with reduced model-based control but did not impact model-free indices. ConclusionWe introduce a novel single-lever PIT paradigm that enables fine-grained dissociation of model-free versus model-based Pavlovian response systems. Our findings provide evidence that single-lever PIT can operate through model-based mechanisms, challenging the assumption that single-lever PIT is predominantly model-free. Our findings also indicate that internal attentional states selectively modulate model-based PIT. Given the involvement of Pavlovian responding in numerous psychiatric conditions, our paradigm offers new avenues for understanding maladaptive behavior. Author SummaryOur daily actions are often influenced by cues like the smell of food or the sound of phone notifications that signal potential rewards or losses. These Pavlovian cues can shape our instrumental behavior even though their outcomes do not depend on what we do - a process known as Pavlovian-instrumental transfer (PIT). Here we study the computational learning mechanisms that underlie such PIT effects. While it is often assumed that Pavlovian responding follows simple, automatic rules without a cognitive model of cue consequences (i.e., model-free), evidence also shows a role for cognitive anticipations in Pavlovian responding (i.e., model-based). In this study, we extend this evidence by showing that PIT responding can be driven by flexible model-based learning. We designed a task to test whether participants use model-free versus model-based strategies to guide PIT, providing detailed task instructions. Using reinforcement learning models, we found that most participants used model-based learning when forming cue-outcome associations. Importantly, peoples attention mattered: when they were more distracted and doing mind wandering, they relied less on model-based strategies. Our findings suggest that Pavlovian learning is complex, flexible, and influenced by internal mental states, opening new windows to understand decision-making problems in mental health conditions like addiction.

Obesity is a growing public health concern with more than 40% of adults meeting criteria for obesity in the United States. Although many treatments seek to lower individuals weight, few treatments have focused on cognitive strategies to change the way individuals think about food, therefore, decreasing consumption of non-nutrient-dense foods. Cognitive reappraisal is one strategy that involves changing the way one thinks about a situation and can be used to downregulate responses to those stimuli. Leveraging this intuitive, cost-effective strategy to decrease ones desire to eat unhealthy food and therefore, decrease overeating, could improve physical and mental health. The present study identified brain regions that are differentially activated when using cognitive reappraisal to downregulate responses to food (FR) versus when using the same strategy to downregulate negative emotions (ER). We collected functional magnetic resonance imaging (fMRI) data in 63 undergraduate students while participants completed both tasks. There was increased reappraisal-related activation in widespread regions across both tasks, including in expected subcortical (i.e., striatum) and cortical areas (i.e., visual, frontoparietal). We also found domain-specific activity, with greater insula activation in the FR than the ER task and greater hippocampal activation in the ER than the FR task. These results reveal domain-general and domain-specific effects of cognitive reappraisal in FR and ER tasks that inform future work examining eating behavior. Taken together, a better explication of the overlapping and discrete processes of food regulation, as it compares to other applications of this regulatory strategy can inform new intervention targets.

Learning in adulthood is embedded in everyday social life, in which periods of psychosocial stress alternate with recovery. The autonomic nervous system regulates how the body responds to environmental demands, yet individuals differ markedly in this regulation. It remains unknown whether such individual differences in bodily regulation modulate the ability to learn probabilistic patterns from sensory input. Here, we investigated statistical learning of probabilistic patterns in speech streams in a six-hour experiment incorporating psychosocial stress and recovery to approximate everyday conditions. Sixty-five adults were exposed to novel speech streams in high- and low-stress contexts, with learning assessed immediately after exposure and following a rest period. Heart rate variability was recorded throughout the experiment to capture individual differences in autonomic reactivity to stress and recovery. From these measures, we constructed composite proxies of sympathetic (SNS) and parasympathetic (PNS) nervous system reactivity. Individuals with congruent SNS-PNS reactivity--either jointly high or jointly low--showed superior statistical learning outcomes across stress contexts. SNS reactivity preferentially supported encoding, whereas PNS reactivity supported consolidation. Moreover, the effect of SNS activation during speech exposure on statistical learning depended on individuals SNS reactivity profiles. These findings demonstrate that individual differences in bodily regulation are tightly linked to the ability to learn statistical dependencies in stressful environments. Overall, the findings highlight the essential role of brain-body-environment interactions in statistical learning.

Flow state, characterized by optimal engagement and performance, represents a key concept in understanding human performance and cognitive resource allocation. Grounded in Csikszentmihalyis and Sherrys flow theory and the Limited Capacity Model of Motivated Mediated Message Processing (LC4MP), this study investigated physiological and neural correlates of flow state during a simulated driving task under different music conditions and difficulty levels. Using a 2 x 3 factorial design with 20 participants, this study examined self-selected versus non-self-selected music across three difficulty levels, testing the relationship between task switching, cognitive resource allocation, and flow state. Physiological measures included heart rate and EEG (alpha/theta power) using a 4-channel Muse 2 headband, alongside a self-report measure of flow experience. Hierarchical linear modeling revealed significant physiological changes during self-selected music: heart rate decreased ({beta} = -5.15, p < .001), while alpha ({beta} = 5829.77, p < .001) and theta power ({beta} = 7637.24, p < .001) increased. Task difficulty also showed significant effects, with heart rate decreasing during hard ({beta} = -6.70, p < .001) and moderate ({beta} = -3.40, p = .001) conditions. In particular, while physiological measures showed robust changes, the self-reported flow state did not reach significance. Task switching rates showed significant decreases during self-selected music ({beta} = -0.86, p < .001) and hard difficulty ({beta} = -0.61, p < .001), supporting the LC4MP frameworks predictions regarding cognitive resource allocation. These findings demonstrate how task switching and cognitive resource allocation relate to flow state induction. The results highlight the importance of multimodal measurement approaches and demonstrate that personal relevance through music selection and task difficulty significantly influence physiological and neural responses during performance. Future research should employ more comprehensive measurement approaches to better capture the complexity of flow-related neural activity and its relationship to task switching and cognitive resource allocation.

Attention-deficit/hyperactivity disorder (ADHD) has been associated with atypical temporal processing across multiple cognitive domains. However, most evidence derives from simplified paradigms that isolate timing from spatial behaviour. Here, we examine how temporal prediction operates within a continuous, dynamic visual environment. Using the Dynamic Visual Search (DVS) task, we embedded spatiotemporal regularities into a sustained stream of visual events, allowing observers to implicitly learn and anticipate predictable targets. Continuous mouse tracking provided a fine-grained measure of action planning beyond discrete reaction time and accuracy metrics. Young adults diagnosed with ADHD (N=40) were compared to matched neurotypical controls (N=38). Both groups benefited from target predictability and reduced distractor load, indicating intact early spatiotemporal learning in ADHD. Across the duration of the task, however, the groups diverged. Neurotypical participants showed progressive increases in behavioural benefits from prediction, accompanied by increasingly direct and efficient mouse trajectories. In contrast, individuals with ADHD reached a plateau in prediction benefits midway through the experiment. Their performance remained stable, with minimal evidence of resource depletion, but did not show further optimisation based on learned regularities. These findings suggest that while prediction formation is preserved in ADHD, its progressive utilisation across longer timescales is attenuated. Rather than reflecting a primary deficit in learning or sustained attention, ADHD may involve altered long-timescale integration or weighting of predictive information in dynamic environments.

Curiosity, a key driver of exploration and learning, is reinforced by reward-related neurochemical systems, yet the role of the opioidergic system in modulating this behavior remains unclear. Music, as a highly rewarding stimulus, offers a unique context to investigate the neurochemical basis of curiosity, particularly the unexplored role of opioids in music-driven exploration. To fill this gap, we performed a double-blind within-subject pharmacological design, in which 26 participants received, in two different sessions, either a placebo or the opioid antagonist naltrexone. During each session, participants engaged in a music exploration/exploitation trade-off paradigm designed to assess their willingness to pay for exploring unfamiliar electronic music. Using logistic regression mixed-effects models, we found that while naltrexone did not affect overall curiosity ratings, it significantly reduced exploratory behavior in states of heightened curiosity. These findings suggest that the opioidergic system plays a critical role in regulating the relationship between curiosity and exploration, particularly in the context of novel and rewarding stimuli like music. Overall, the present research provides new and compelling evidence on the important relationship between curiosity and exploration and its regulation with the opioidergic neurotransmitter subsystem. Significance StatementThe present research aimed to advance our understanding of the neurochemical mechanisms underlying curiosity and information seeking. In our study, we employed a pharmacological design to examine the role of the opioidergic system in music-related exploration. Using a novel music exploration/exploitation paradigm, we found that while naltrexone, an opioid antagonist, did not affect baseline curiosity ratings, it markedly reduced exploratory behavior during high-curiosity states in the presence of potential monetary losses. These results provide new evidence that opioidergic modulation plays a critical role in regulating curiosity-driven exploration. This new evidence might be relevant in the future for better understanding how neurochemical systems shape learning, motivation, and affective responses in complex cognitive domains such as music.

Subthreshold depression is associated with significant functional impairment and elevated risk of major depressive disorder. A negative self-concept may disrupt the implicit positive association evoked by ones own face, impairing incidental encoding of self-relevant information. Whether subthreshold depression involves a selective deficit in encoding self-face identity remains unclear. The attribute amnesia paradigm is well suited to address this question because it can dissociate attentional selection from working memory encoding. Using this paradigm, we examined the issue across two experiments. Experiment 1 employed nonsocial stimuli (animal drawings) and confirmed an intact attribute amnesia effect in subthreshold depression (n = 30) comparable to healthy controls (n = 30), ruling out a generalized encoding deficit. Experiment 2 replaced targets with faces (self or other) and revealed a selective enhancement of the attribute amnesia effect for self-face identity in subthreshold depression. Specifically, on the surprise trial, accuracy for self-face identity dropped to near-chance levels in the subthreshold depression group, whereas no such deficit emerged for other-faces or in controls. Encoding recovered rapidly once explicit memory expectations were introduced, indicating intact basic encoding capacity. These findings suggest that subthreshold depression is associated with a specific impairment in incidentally encoding self-face identity. This impairment likely stems from a negative self-concept that weakens self-face salience under incidental encoding conditions. By capturing this selective encoding failure, the present study reveals that the self-processing deficit in subthreshold depression can arise at the gating stage between attention and working memory consolidation.

Human behavior is highly flexible, allowing efficient performance across a wide range of task contexts. A distributed set of frontal and parietal regions, commonly termed the multiple-demand network (MDN), is consistently engaged during diverse cognitively demanding tasks and is thought to support this flexibility. However, it remains unclear how patterns of MDN engagement relate to the qualitative features of ongoing cognition experienced during task performance. To address this issue, we examined the reliability of self-reported experiential features sampled during performance of a broad range of tasks. Across tasks, we found little evidence that particular patterns of thought were intrinsically more reliable than others, nor that individual tasks were associated with stable, characteristic thought profiles. Instead, the reliability of specific experiential features varied systematically across task contexts, with the same patterns showing high stability in some tasks and low stability in others. We next asked whether stable patterns of thought were associated with distinct neural signatures. We found that patterns of brain activity resembling the MDN tended to be present for tasks in which deliberate task focus was high, and when distraction was lower, adding to an emerging body of research suggesting that coordinated activity within frontal and parietal regions helps to establish a stable goal-focused mode of thoughts and actions.