Cognitive, Affective, & Behavioral Neuroscience

Making choices about whether and when to engage cognitive effort is a common feature of human everyday experience with important consequences for academic, career, and health outcomes. Yet, despite its importance, little is understood about the neurochemical mechanisms that underlie individual differences in the willingness to engage in cognitively effortful activities. To examine this question, we employed a multi-modal neuroimaging approach in which participants performed the Cognitive Effort Discounting paradigm (Cog-ED) while simultaneous PET-fMRI was acquired. We assessed both individual differences in dopamine-D2 receptor availability and brain activity during decision-making. In addition, the same participants performed a parametric working memory task (N-Back) and reported the cognitive effort associated with their daily life activities through a seven-day Ecological Momentary Assessment (EMA) protocol. The findings suggest a central role for caudate dopamine-D2 receptors in modulating cognitive effort-related motivation. People with higher caudate dopamine-D2 receptor availability had better working memory function, heightened sensitivity to effort costs, and greater modulation of brain activity during effort valuation. These laboratory findings also translated directly to daily life: higher levels of caudate dopamine-D2 receptors were associated with greater engagement in cognitively effortful activities. Together, the results suggest that how individuals determine the motivational value of cognitively effortful activities strongly depends on the dopamine system.

Motivational incentives are known to improve working memory (WM) when items differ in value, but whether they enhance performance when all items are equally valued, and if so, by what mechanism, remains unclear. Improvements could reflect enhanced memory-based representations or speed-accuracy trade-offs, whereby incentives promote slower, more cautious responding without improving memory itself. Here, we tested these alternatives across two experiments using an auditory WM task in which incentives varied across trials, but all items on a trial were equally incentivised. In Experiment 1,we allowed participants to resubmit their responses if desired. Reward-related improvements in WM performance were selectively observed when memory load was high, and only at serial positions associated with greater interference. These improvements were also dissociated from systematic response slowing and resubmission behaviour. This indicated that the benefits of incentives were unlikely to be attributable to response caution. Critically, in Experiment 2, motivational benefits persisted under strict response deadlines, where speed cannot be traded for accuracy. Computational modelling showed that reward selectively improved the precision of memory-based responses without reducing guessing. Together, these findings indicate that motivational incentives improve WM by enhancing the quality of memory-based representations, rather than by strategic slowing or altering response strategies.

Spontaneous eye blinks have long been linked to cognitive processing, yet how task demands shape blink timing and its relationship to behavioral performance remains unclear. We examined spontaneous blink behavior in 576 adults performing two variants of the Continuous Performance Task (CPT). Blink occurrence and timing were most strongly modulated by the experimental condition in the more demanding CPT-AX task, whereas their association with response time was stronger in the CPT-X task, where more consistent blink timing predicted faster responses. This dissociation suggests that task structure changes not only blink behavior but also the behavioral relevance of blink timing. These findings are consistent with an adaptive scheduling account of spontaneous blinking and provide a conceptual framework for understanding when and why blink timing contains chronometric information about ongoing cognition.

How cognitive control influences the correction of motor errors is an important question. In this study, we examined how the slow and fast processes that constitute motor adaptation are modulated by cognitive load. To address this question, we used a novel approach that differed from traditional dual-task experiments that have assessed the role of fast processes by decreasing attention to the motor adaptation task. Here, cognitive load was manipulated using a decision-making task with easy and hard difficulty levels, while simultaneously adapting to a perturbation and maintaining attention on the task. Our results showed that the hard decision-making task increased cognitive load relative to the easy task, resulting in attenuated steady-state learning. The relative contributions of the fast and slow components were assessed using a dual state-space model, which showed that the former was larger under the high-load condition, whereas the latter was larger under the easy-load condition, consistent with a competition model in which a shared error signal is partitioned between the two processes. We also showed that the effect of cognitive load on fast processes was not attributable to increased reaction times or task performance. Taken together, these results support the notion that faster explicit motor adaptation processes derive from cognitive processes involved in action selection.

Attention prioritizes relevant information from the environment, but does not do so consistently over time. Instead, we sometimes experience poor attentional states which are characterized by a higher degree of performance failures, or "lapses." The source of these lapses--whether they arise from attentional disengagement, failures of attentional selection, or simply failures of response control--is not clear. This study tests whether the strength of neural representations differs as a function of both attentional selection and modulation over time. We recorded electroencephalography (EEG) data while participants performed a sustained attention task requiring the selection of task-relevant items and withholding of responses to rare targets. Results show that relevant items receive prioritized processing in brain signals, as measured with EEG decoding. Yet, their neural representational fidelity weakens under poor sustained attentional states, foreshadowing upcoming behavioral failures. These findings support a neural basis of disengagement during poor attentional states, suggesting that lapses may arise from a decoupling of brain activity from task processing. Finally, pre-item representational strength predicted memory success for targets, suggesting that neural fidelity indexes an encoding-ready state. Results suggest that the quality of neural representations is affected by ongoing attention and plays a key role in processing and memory.

Financial exploitation often arises in social contexts in which individuals must evaluate fairness, trust, and interpersonal information during financial decisions. We examined whether neural responses to fairness in social versus nonsocial contexts relate to financial exploitation vulnerability and whether these relationships vary with age, emotional intelligence, and affect. A total of 310 adults spanning ages 21-89 completed an fMRI Ultimatum Game in which they accepted or rejected offers from human and computer partners, along with measures of financial exploitation vulnerability, emotional intelligence, and affect. Participants were strongly sensitive to offer fairness, and individual differences in behavioral sensitivity to partner type were associated with social-relative anterior insula responses to unfairness. Greater baseline negative affect was associated with stronger anterior insula differentiation between social and nonsocial fairness. Financial exploitation vulnerability was associated with prefrontal responses to social-relative fairness in ways that depended on individual characteristics: age moderated its association with dorsomedial prefrontal cortex activation, whereas emotional intelligence moderated its association with dorsolateral prefrontal cortex activation. Whole-brain connectivity analyses further showed that age moderated associations between emotional intelligence and executive control network connectivity with posterior cingulate and right temporoparietal regions. These findings suggest that vulnerability to financial exploitation is reflected in how social context is integrated with affective and individual characteristics across adulthood.

In visuomotor sequential tasks, chaining actions smoothly requires tight coordination between sensory and motor systems to deal with competing demands, such as reducing uncertainty about future opportunities for action while controlling ongoing ones. In a football-based decision-making task, experts and novices used different strategies to overcome these competing demands, demonstrating a proactive and a sequential visuomotor strategy, respectively. We propose a computational explanation of expertise-related difference in policy selection, grounded in the active inference framework. We simulated the selection of sequence of actions using a partially observable Markov decision process (POMDP) model, and we manipulated three parameters: the reward structure, the prior beliefs about state transition, and the sensory precision. Simulations showed that selection of the proactive strategy relied on strong prior beliefs, high sensory precision and high valuation of fast passing. Conversely, the sequential policy was selected when rapid performance was not highly valued, or under noisy prior beliefs, or lower level of sensory precision. Decreasing sensory precision and adding noise in prior beliefs also decreased decision accuracy. These results suggest that policy selection leading to novice or expert visuomotor strategy reflects differences in the underlying generative model, particularly in how agents value different aspects of the task.

Musical aesthetic experience can reveal students' cognitive, aesthetic, and emotional responses to music. However, how professional vocal training alters brain functions in female college students with varying levels of musicianship to promote these is unknown. Here we applied fMRI, psychological measures and Goldsmiths musical sophistication index (Gold-MSI) assessments to study 27 non-music major female college students consisting of instrument group (normal pitch accuracy with instrumental training), general group (normal pitch accuracy without instrumental training) and poor-pitch group (poor pitch accuracy and without instrumental training). After 1 year of one-on-one instructional practice, we found that professional vocal training enhanced information processing related to musical cognition and pleasure during musical aesthetic experiences across all three groups, involving improvements in motor control of vocalization, auditory feedback processing, musical memory and attention together with musical pleasure. Specifically, instrument group showed activation of reward circuits, general group exhibited activation of laryngeal motor control circuits, and poor-pitch group displayed activation of sensory/sensorimotor/motor control circuits. Both pre- and post-training, the instrument group exhibited higher connectivity than the other two groups in the left amygdala-dorsal anterior cingulate cortex (dACC) and the right posterior superior temporal gyrus (pSTG)-left insula pathway. After professional vocal training, the general group showed higher connectivity in the left amygdala-dACC, whereas the poor-pitch group demonstrated increased connectivity in the right pSTG-left insula and dACC-nucleus accumbens pathways. Surprisingly, professional vocal training activated the bed nucleus of the stria terminalis (BNST), a subcortical emotion center, and connectivity with the medial prefrontal cortex associated with musical information processing. Thus, vocal training can significantly improve musical cognition and aesthetic pleasure, and facilitates neural modulation in the brain's emotional centers.

Naming pictures from the same semantic category can interfere with subsequent word retrieval. Although the left inferior frontal gyrus (LIFG) has been implicated in resolving this semantic interference, its causal role in this process remains unclear. Across three experiments, we investigated the contribution of the LIFG to interference resolution during blocked-cyclic naming. Experiment 1 tested whether continuous theta-burst stimulation over the LIFG increased semantic interference relative to vertex stimulation. Although the overall interference effect did not differ significantly between stimulation sites, exploratory analyses revealed an unexpected pattern based on the initial context (related vs. unrelated) with which items were named, suggesting that LIFG disruption affected retrieval of previously related items even when they were subsequently named in unrelated contexts. This observation motivated two further experiments examining the consequences of prior naming context more directly. Experiment 2 established that, without stimulation, interference observed in related blocks was no longer evident after the items were redistributed into unrelated blocks. Experiment 3 combined this design with cathodal or sham transcranial direct current stimulation (tDCS) over the LIFG. Planned comparisons showed that cathodal stimulation slowed naming of initially related items both within related blocks and after redistribution into unrelated blocks; corresponding comparisons for items named consistently in unrelated blocks were not significant. The similar patterns across two stimulation methods suggest that LIFG-mediated control may influence both selection under current competition and subsequent retrieval of previously competing words. These findings could be accommodated within a hybrid account in which working memory supports context-guided selection and selection-related control helps recalibrate lexical accessibility, limiting the carryover of interference into subsequent naming.

Humans efficiently learn the temporal structure of speech, yet the underlying cognitive mechanisms remain unclear. Recent research in visuospatial sequential memory has proposed the successor representation (SR), important in reinforcement learning, which encodes multi-step transitional relationships. To test whether SR also supports the structural learning of rapidly changing speech syllable sequences, we adapted a pyramid-structured sequential learning paradigm using syllable streams presented at a natural speech rate (~ 4 Hz). Participants exhibited robust structural learning, evidenced by asymmetric memory performance modulated by adjacent transitional probabilities. Notably, the SR model captured recognition performance for non-adjacent syllable pairs. Moreover, SR-based learning effects generalized across all syllable sequences, regardless of adjacency. Furthermore, the effectiveness of SR learning was associated with cognitive flexibility and working memory capacity. Together, these findings suggest that humans rely on effortful SR-like mechanisms for the structural learning of syllable sequences, highlighting a modality-general computational principle for sequential learning across various temporal modulation scales.

Task demand has a long-standing history in experimental psychology, with numerous investigations of the capacity of different systems and processes under varying cognitive load. Whilst demand is a broadly used term in the literature, it is limited by a lack of cross-domain operationalisation, and thus can be a confound in many inter-domain investigations. To address this, we generated and validated two large-scale, open-source, widely applicable stimuli sets that: (i) can probe a wide, continuous range of objective behaviourally-measured processing demand, and (ii) can be directly matched across two highly contrastive cognitive domains (semantic and visuospatial processing). We normed these tasks in a large, online cohort of healthy adults, enabling future studies to utilise and benefit from reliable baseline measures of demand. Using a matching algorithm that takes into account both speed and accuracy, we then closely paired each semantic judgement trial with a trial of comparable difficulty from the corresponding visuospatial task to produce a cross domain matched set of over 400 items. Finally, we analysed the psycholinguistic and perceptual properties of these stimuli to explore some of the underlying factors driving demand.

Older adults typically exhibit reduced episodic memory precision, while maintaining relatively preserved retrieval success. However, little is known about whether these age-related differences are influenced by internal biological factors (e.g., chronotype) or external testing conditions (e.g., experiment format, time of day). To address this question, younger and older adults completed an object-location memory task online, either in the morning or evening, and either with or without video-based supervision. Memory performance was assessed using mean absolute error and Bayesian hierarchical mixture modelling, which dissociated memory precision ({kappa}) from the probability of memory-based responding ({theta}). Immediate memory performance was high across age groups, irrespective of time of day. Greater encoding error predicted greater retrieval error, and participants tested in the video condition exhibited lower recall error and higher recognition performance than those tested without supervision. Consistent with prior studies, older adults exhibited reduced memory precision relative to younger adults, whereas retrieval success remained largely intact. However, Bayesian Hierarchical Modelling provided no evidence that time of day, experiment format, or their interactions with age influenced either memory precision or retrieval probability. Older adults reported stronger morning preferences than younger adults, and participants generally selected testing times that aligned with their chronotype. Nevertheless, alignment between chronotype and testing time did not modulate memory performance. The results indicate that age-related differences in episodic memory precision are robust across variations in testing context, including time of day, experiment format, and individual differences in chronotype. More broadly, the results support the feasibility of remote and unsupervised testing for memory precision research and suggest that chronotype and testing time are unlikely to be major sources of variability in immediate object-location memory performance.

We investigated how auditory and visual presentation of small numerical magnitudes and non- numerical magnitudes are passively processed at the neural level. Earlier research suggests automatic processing of quantities occurs rapidly in the brain. However, there are no studies on passive, pre-attentive change detection of magnitudes utilizing mismatch negativity (MMN) across sensory modalities in same participants, important to understand amodal processing of magnitudes. We recorded EEG in passive auditory and visual multi-feature oddball and control paradigms from the same adult individuals (N=104/105, respectively). In the auditory paradigm, the standard stimulus was a 2-part sound, while the deviant stimuli varied in frequency, intensity, gap length, and number of sounds (1-part and 3-part stimuli). In the visual paradigm, the standard stimulus was a dot array containing two medium-sized dots, while the deviant stimuli varied in numerosity (1 or 3 dots) and dot size (small or large). The results for the auditory paradigm showed significant MMN across all features, including comparisons with deviant-alone control stimuli, except for the 2-part vs. 1-part contrast. The results for the visual paradigm suggested differences in processing across all features apart from large and medium-size stimuli. These results replicate earlier MMN findings that the brain distinguishes pre-attentively changes in sound frequency, amplitude and gap. Auditorily presented small numerical magnitudes also showed pre-attentive processing but less robustly to the contrast 2 vs. 1. Visual pre-attentive processing of small numerical magnitudes was also observed. Together these findings could be interpreted to support the existence of abstract, pre- attentive processing of numerical magnitudes. Highlights* Auditory and visual processing of numerical and non-numerical magnitudes was examined with EEG * Auditory non-numerical magnitudes and medium vs. small dots replicated earlier MMN studies * aMMN and vMMN showed pre-attentive processing of changes in small numerosities 2 vs. 3 and 2 vs. 1 * 2 vs. 1 tone processing was less robust than 2 vs. 3 or the processing of the visual contrasts * The brain processes changes in small non-symbolic magnitudes pre-attentively

Alterations in impulsivity characterize several neuropsychiatric disorders, however the neural processes that underlie an impulsive choice have not been identified. A recent body of literature suggests that there may be generalizable aspects of neural representations across animals. Whether this is also true for dynamic rather than just static (like geometrical) properties of neural representations, and specifically in the context of impulsive behaviors, is less clear. Therefore, the goal of this study is to characterize generalizable features of latent dynamics that disambiguate an impulsive from non-impulsive choice across many different data sets and animals. A deep learning-based latent factor model was constructed from neural recordings that were acquired in rat anterior cingulate cortex while performing a delay discounting task, which is commonly used to assess impulsivity. Prior work shows this brain region is critical for the performance of this task. We leveraged piecewise linear recurrent neural networks (PLRNNs) to perform a nonlinear latent factor analysis across animals and experimental sessions. Our PLRNN explained a substantially larger percent of variance in fewer dimensions than linear approaches. We cross-validated performance of the model on test data and following the disruption of recurrent connectivity or auto-regressive latent dynamics. These tests confirmed that our fitting procedure consistently embedded generalizable neuronal dynamics within the PLRNN. Further, we found that the latent dynamics exhibited strong rotational features and progressed faster for impulsive choices. In summary, our model captures properties of the latent dynamics that are consistent across multiple data sets and disambiguate impulsive choices.

Artificial intelligence (AI) assistants can improve individual performance but may weaken unaided performance and limit interest diversity, raising questions about their long-term effects on collective intelligence (CI). Here, we consider an evolutionary model in which individuals predict, consult AI, and imitate better-performing peers. Classic reward structures that promote CI without AI can fail under Chatbot AI, whose predictions depend on users' interests, by reducing interest diversity or propagating bias. Directly rewarding or penalizing AI use does not reliably mitigate these effects. Instead, a balanced incentive that preserves minority expertise and rewards collective contributions can promote CI, but an additional incentive is needed to restore unaided collective accuracy. Our framework offers an explanation for emerging evidence of AI's societal impact and clarifies its long-term implications.

Neuromarketing mainly seeks to enhance the prediction of marketing stimuli success, such as commercials and movie trailers, by integrating neurophysiological measures with traditional behavioral measures. In the current study, the authors tested whether neural activity could predict consumer valuation and how preferences change after watching commercials, both immediately and over time. Participants (n=161) watched images of products followed by commercials advertising those products while their neural activity was recorded using electroencephalograph (EEG). They watched the product images again a week later without EEG recordings. Immediately after each stimulus exposure, participants stated their willingness to pay (WTP) for the product and how much they liked the commercial. Standard EEG measures showed weak and inconsistent relationships with behavior and yielded near-chance predictions. In contrast, deep learning models applied to the raw neural data achieved substantially higher predictive accuracy, successfully predicting both immediate and delayed WTP and ad liking. Importantly, the authors were able to successfully predict preferences for new participants and/or new products the models were not trained on. However, accuracy declined as generalization demands increased. Moreover, prediction performance declined as value differences narrow. Together, these findings show that neural responses carry reliable and temporally persistent information about consumer valuation and its evolution over time.

Social isolation and loneliness are major determinants of well-being in older adults. Shrinking social networks and age-related shifts in social behavior and decision-making may increase vulnerability to loneliness. These changes in social decision-making strategies can be conceptualized within an exploration-exploitation tradeoff framework, in which social options with known outcomes are prioritized over uncertain ones. Yet, no study has characterized how complementary decision-making strategies, undirected novelty seeking and goal-directed exploration, relate to social network constructs or are supported by distinct neural systems. These strategies capture distinct forms of decision-making: undirected novelty seeking is driven by the intrinsic salience of novel options independent of expected informational gain, whereas goal-directed exploration involves deliberately sampling uncertain options to reduce uncertainty and optimize future choices. Here, we used fMRI and a novel social bandit reinforcement task to identify dissociable social constructs and brain systems linked to undirected novelty seeking and goal-directed exploration in older adults. Undirected novelty seeking was associated with social network size and with activity of temporoparietal and mid-cingulate brain areas. Goal-directed exploration predicted subjective feelings of social connectedness and was associated with activity of anterior cingulate and prefrontal brain areas. Crucially, choice probabilities from a non-social version of the three-arm bandit task were not associated with social decision-making nor with social constructs. Our findings suggest that, in later life, dissociable social decision-making strategies, when faced with unknown options, rely on distinct neural systems supporting social network size as well as subjective feelings of social connectedness.

Mistakes make up our daily lives. Once a mistake has been committed, it is common to pause before you continue. This slowing is called post-error slowing (PES). Although several theories have been proposed to account for PES in humans, findings across cognitive tasks show variation in how PES occurs, and it is unclear how this phenomenon may translate across modalities to a key model organism of human cognition, macaque monkeys. To determine whether monkeys exhibit PES, we tested three rhesus macaque monkeys in components of cognitive sequential decision-making tasks that used either eye tracking or touch screens to report responses. Here we show that monkeys exhibit non-adaptive, error type-dependent PES across modalities. Task structure and context also modulated PES in the eye tracking task, and no modality improved task performance following PES. These results suggest that monkey PES is a combination of cognitive and non-cognitive related responses to error that are modulated by task structures such as error type and task context. Overall, the flexibility in monkey PES is comparable to that of humans, further supporting the importance of monkeys as a translational model in investigating the mechanisms of PES.

Working memory (WM), a core executive function that supports learning and reasoning, undergoes rapid development during early childhood years. Neural activation patterns underlying WM have been extensively studied in both adults and children, revealing increasing involvement of the frontoparietal network (FPN) with age and coordinated interactions with the default mode network (DMN). However, relatively little is known about how these network interactions develop in children, particularly within the visuospatial domain. The present study investigated the influence of age and task performance on effective connectivity within the FPN and its interactions with DMN hubs during a visuospatial WM task in early childhood. Functional MRI data were collected during a visual n-back task with two WM load levels (0-back and 2-back) in 120 typically developing children aged 5-8 years. Dynamic Causal Modeling (DCM) with parametric empirical Bayes was used to estimate effective connectivity among eight regions of interest spanning the FPN and DMN. Behaviorally, age-related gains were steepest under high-load for accuracy, and under low-load for reaction time, suggesting WM capacity is a demand-sensitive marker of development at this age. At the network level, high-load robustly engaged the FPN, whereas low-load was associated with relatively greater engagement of DMN and episodic memory-related regions. Baseline effective connectivity was dominated by parietal-to-frontal drive and strong self-inhibition in the anterior cingulate cortex. Age-related effects on this architecture were sparse, involving asymmetric interhemispheric frontal coupling and reduced parieto-cingulate coupling, whereas accuracy-related effects were larger in magnitude, particularly at self-connections. More accurate children showed greater excitability in frontal and parietal FPN nodes alongside increased self-inhibition in the precuneus, a DMN hub, consistent with a neural efficiency account in which better performance reflects more selective, rather than more global, network engagement. Task load further shaped connectivity in distinct ways: low-load elicited widespread inter-regional modulation, whereas high-load predominantly modulated self-connections, with age and accuracy effects on this modulation dissociating by load. These findings suggest that, in primary school children, current task performance was associated with more pronounced differences in frontoparietal effective connectivity than was age, and that WM-related network engagement becomes increasingly selective and load-dependent across this developmental window.

Everyday problems often require choosing among actions with different future consequences, even though fully evaluating those consequences is often computationally infeasible. The human brain is remarkable in its ability to solve such complex problems efficiently. In this paper, we hypothesize that this capacity arises from a principle inspired by Bayesian accounts of perception and sensorimotor control: evidence derived from effortful forward planning is integrated with task-specific preferences akin to priors, weighted by the uncertainty of these preferences. To test this hypothesis, we used a sequential decision-making task with a relatively large state space. Model comparison showed that participants' deviations from optimal forward planning are best captured by task-specific preferences, rather than by unsystematic errors, recent action-history effects, or offer-context biases. Moreover, the influence of forward planning on participants' responses depended on preference uncertainty, as evidenced by both choice behavior and reaction times, and preferences were not arbitrary but instead supported above-stochastic performance even in the absence of forward planning. Together, these results are consistent with a Bayesian-inspired interpretation of decision making, in which costly forward planning is integrated with state-dependent priors or preferences. These preferences may provide an inexpensive, quickly available first-pass solution, which is complemented by forward planning when preference-based guidance is uncertain.