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The i5 Playbook—Transforming Business Education with 5 Impactful Methods, published by the Principles for Responsible Management Education (PRME), offers concrete pedagogical approaches to develop responsible business leaders. The playbook presents five teaching methods, known as the "impactful five" or "i5," that focus on making learning meaningful, facilitating active engagement, designing for iteration, developing supportive social interaction, and fostering joy and well-being. The resource emphasizes the integration of cognitive, creative, emotional, and social skills in responsible leadership education. It is structured in a clear and accessible manner, providing definitions, literature links, high-impact actions, illustrations, and quick exercises. The playbook is an open access resource, making it widely accessible, and encourages educators to reflect on their own teaching practices as responsible leaders. It is recommended for anyone seeking to enhance their pedagogical skills and develop more responsible leaders. [Extracted from the article]

This paper describes the SocialVidStim-a database of video stimuli available to the scientific community depicting positive and negative social evaluative and neutral statements. The SocialVidStim comprises 53 diverse individuals reflecting the demographic makeup of the USA, ranging from 9 to 41 years old, saying 20-60 positive and 20-60 negative social evaluative statements (e.g. 'You are a very trustworthy/annoying person'), and 20-60 neutral statements (e.g. 'The sky is blue'), totaling 5793 videos post-production. The SocialVidStim are designed for use in behavioral and functional magetic resonance imaging paradigms, across developmental stages, in diverse populations. This study describes stimuli development and reports initial validity and reliability data on a subset videos (N = 1890) depicting individuals aged 18-41 years. Raters perceive videos as expected: positive videos elicit positively valenced ratings, negative videos elicit negatively valenced ratings and neutral videos are rated as neutral. Test-retest reliability data demonstrate intraclass correlations in the good-to-excellent range for negative and positive videos and the moderate range for neutral videos. We also report small effects on valence and arousal that should be considered during stimuli selection, including match between rater and actor sex and actor believability. The SocialVidStim is a resource for researchers and we offer suggestions for using the SocialVidStim in future research.

Compositionality, or the ability to build complex representations from discrete elements, is an essential ingredient of human intelligence. Compositionality enables people to think productively, learn fast from limited experience, and generalize knowledge to new contexts without re-learning from scratch. It is also essential in information processing systems to efficiently represent structured data and has seen application in compression and symbolic Artificial Intelligence (AI). Historically, the notion of compositionality played a central role in linguistic theory and philosophy of mind. More recently, it is attracting a surge of interest throughout the domains of cognitive science. Compositional processes are leveraged for elucidating the nature of mental representations in cognition (Dehaene et al., 2022), understanding the functional organisation of the brain (Agrawal et al., 2019), or building Artificial Intelligence systems that are robust to changes in the environment (Hupkes et al., 2020).

In-context learning refers to the ability of a neural network to learn from information presented in its context. While traditional learning in neural networks requires adjusting network weights for every new task, in-context learning operates purely by updating internal activations without needing any updates to network weights. The emergence of this ability in large language models has led to a paradigm shift in machine learning and has forced researchers to reconceptualize how they think about learning in neural networks. Looking beyond language models, we can find in-context learning in many computational models relevant to cognitive science, including those that emerge from meta-learning.

To communicate is to move. There is no way around that. Ifwe pick up comprehensive handbooks or introductory textsin movement science (Hong and Bartlett (2008)) we seethat there is very rich knowledge and tractable mathematicalmodels about different aspects of movements. Yet, we findno chapter on communicative movements. While the fieldof speech motor control is a developed area on its own(Parrell and Lammert (2019)), there is no movement scienceof communication proper, which would include whole-body-,hand-gestural-, signed-, and inter-bodily actions.

Events are a fundamental part of human experience. Research on event cognition is rapidly developing and is revealing central aspects of how humans perceive, conceptualize, communicate about, and remember events. This symposium offers an interdisciplinary look at recent advances in the study of event cognition. The symposium brings together cognitive scientists from across continents, who are experts on the subject. The symposium contributors come from a variety of backgrounds and disciplines in developmental psychology, cognitive psychology, neuro-computational psychology, and linguistics. They combine a variety of innovative and integrative approaches and methodologies and study diverse populations across the lifespan and across languages. The overall goal of this symposium is to foster an interdisciplinary conversation on different aspects of event cognition.

Goals are a central pillar of everyday mental activity. From finding your way home to solving a puzzle and ordering food delivery, much of human action and cognition is goal-directed. Perhaps unsurprisingly, theories of goals are a central focus in the psychology of motivation (Elliott & Dweck, 1988), in social and personality psychology (Fishbach & Ferguson, 2007), as well as research aimed at understanding factors contributing to task achievement in educational and industrial settings (Ames & Ames, 1984; Locke & Latham, 2002). In this symposium, we highlight recent work emphasizing a goal-centric outlook on learning, exploration, and communication.

Deep Learning is a neural network approach where a network of multiple layers is trained to process complex data. Applications for deep learning include recognizing complex patterns in pictures, text, and sounds to, in some cases, produce insights into how human process information. Deep learning has garnered widescale interest, fostered by advances in, for instance, natural language processing and the release of innovative tools like Chat GPT. But does deep learning have implications for theory in the cognitive sciences; that is, is deep learning the answer for understanding human cognitive dynamics?

Adults are known to have superior working memory to children, but whether this improvement is driven primarily by differences in storage capacity or attentional control is debated. In particular, the understanding of how capacity and control influence the development of working memory is hampered by the fact that most theorizing about the effect of variation in either on behavior has been verbal. To address this, we extended a computational model of working memory to clearly separate the contributions of capacity and control, fitting the model to a recent developmental study. We find that the combined influence of capacity and control on working memory may be more complicated than previously appreciated. In particular, the general pattern of qualitative differences between children and adults could be produced by increasing either capacity or control alone. These results point to a need for additional experimental paradigms to clearly parse the differential impact of working memory components.

Reduced cognitive function in major depression (MDD) is often interpreted as a reduced ability to exert cognitive control. Here we used the Effort Foraging Task to test the hypothesis that reduced cognitive function may be due, in part, to decreased willingness to exert control in MDD because of increased cognitive effort "costs". Contrary to our predictions, neither cognitive nor physical effort costs differed with MDD diagnosis (N MDD=52, N Comparisons=27). However, we found distinct patterns of symptom relationships for cognitive and physical effort costs. In MDD, greater anxiety symptoms were selectively associated with lower cognitive, but not physical effort cost (i.e. greater willingness to exert cognitive effort), whereas greater anhedonia and behavioral apathy symptoms were selectively associated with increased physical (but not cognitive) effort costs. These findings support the measurement of both cognitive and physical effort as decision-making function markers that may inform heterogeneity of MDD.

Electroencephalogram (EEG) signals are physiological indicators of brain activity, offering the advantage of high temporal resolution for capturing subtle emotional changes and providing rich information for emotion recognition. However, extracting effective features from EEG data with a low signal-to-noise ratio poses a significant challenge that hinders progress in this research field. To address this issue, we propose a multi-view time-frequency contrastive learning framework called MV-TFCL to enhance the information representation capability of EEG signals from multiple perspectives. Firstly, we introduce a recursive neural network based on multi-scale time-frequency consistency, which integrates global semantic information across different scales through gated units. To our knowledge, this is the first proposal of the theory of multi-scale time-frequency consistency applied in emotion recognition research. Subsequently, we design a tree-structured time-frequency encoder to capture local semantic information within the time-frequency domain. Finally, we incorporate semantic consistency constraints from both global and local perspectives to learn more generalizable and robust features. Extensive experimental results on two publicly available datasets demonstrate the effectiveness and superiority of our proposed method.

An important issue in the semantic memory literature concerns the relative importance of experience-based sensorimotor versus language corpus-based distributional information in conceptual representations. Here we examine how each sort of information is associated with the EEG response to words in a property verification task in which participants indicated whether or not a property term (such as ”red”) is typically obtained for a concept term (such as ”APPLE”). To define and measure each type of information, we operationalized distributional and sensorimotor information using cosine distance measurements derived from GloVe Embeddings and Lancaster Sensorimotor Norms respectively. We then modeled single-trial EEG responses to property words in a property verification task using regression models. Our findings indicate that semantic processing in this task simultaneously incorporates distributional and sensorimotor information, and their contribution is shaped by task-relevant linguistic context. We aim for our study to contribute to a critical examination of such information operationalizations and also encourage a systematic evaluation of their performance across tasks, particularly forEEG measurements.

This review explores the complex relationship between Fear ofFalling (FoF) and Fear of Heights (FoH), and their impact onhuman postural control. FoF encompasses a spectrum of psychologicaland physiological responses that dynamically influencepostural control, while FoH involves perceptual distortionsand heightened physiological arousal in response to elevatedenvironments. Through a comprehensive literature review,we examine the research methods and findings of studieson FoF and FoH. We further propose that Optimal FeedbackControl (OFC) theory is a suitable framework to understandthe computational aspects of how these fears modify posturalcontrol. We aim to provide a nuanced understanding ofFoF and FoH, not only as psychological phenomena but ascomplex, dynamic interactions of cognitive, physiological, andmotor processes influencing an individual's interaction withtheir environment.

Exploration in sequential decision problems is a computationally challenging problem. Yet, animals exhibit effective exploration strategies, discovering shortcuts and efficient routes toward rewarding sites. Characterizing this efficiency in animal exploration is an important goal in many areas of research, from ecology to psychology and neuroscience to machine learning. In this study, we aim to understand the exploration behavior of animals freely navigating a complex maze with many decision points. We propose an algorithm based on a few simple principles of animal movement from foraging studies in ecology and formalized using reinforcement learning. Our approach not only captures the search efficiency and turning biases of real animals but also uncovers longer spatial and temporal dependencies in the decisions of animals during their exploration of the maze. Through this work, we aspire to unveil a novel approach in cognitive science of drawing interdisciplinary inspiration to advancing the field's understanding of complex decision-making.

To fully understand human memory, it is necessary to understand its lifespan development. However, memory assessments often rely on significantly different methodologies for different age groups, and their results are typically not directly comparable. In this paper, we present a quantitative assessment of memory function spanning an age range of five to 85 years that is based on a model-based memory assessment. This approach yields a uniform metric that is directly interpretable and can be compared across different tasks and materials that are appropriate for different age groups. The results show a robust U-shape function, with long-term memory function at age 5 being comparable to that of cognitively impaired elderly individuals. These results and the method utilized could provide a new foundation for future studies on memory development across life stages.

Reservoir computing (RC) offers distinct advantages in extracting spatiotemporal information with low training costs by separating recurrent neural networks into a fixed network with recurrent connections. The quality of the fixed network, known as the reservoir, plays a pivotal role in the performance of the RC system. Our work aims to provide a unified synaptic development framework for RC, constructing a more biologically plausible reservoir to model and understand the neural networks development within the human brain. In this paper, we propose an Autonomous Synaptic Development Reservoir Computing model (ASD-RC) based on an adaptive network of phase oscillators. The reservoir autonomously adjusts the distribution of connection weights in response to external stimuli, forming a task-specific structure. Through experiments and theoretical analyses, we demonstrate that ASD-RC can emulate various synaptic development rules of biological neural networks in \textit{vivo}, including the Hebbian rule and STDP. Furthermore, experiments reveal that combining different development rules can enhance performance on prediction tasks compared to using a single development rule, showcasing the emergence and effects of synergistic development that improve information processing capacity.

Computational neuromodeling methods for evaluating representational dynamics involve intricate analysis choices at every stage of the analysis pipeline. Analysis choices for data processing pipelines are generally chosen based upon end to end accuracy metrics and corresponding performance metrics. Psychology research has recently begun to acknowledge the importance of controlling for potential bias introduced by degrees of freedom in data analysis, with specification curve analysis introduced as a principled method for correcting for such biases. In this paper, we conduct a specification curve analysis (SCA) for representational similarity analysis pipelines reported in the literature for fMRI and EEG datasets, respectively. We show that EEG-based RSA analyses are relatively robust to alternative specifications but that fMRI based analyses are not. Using a novel decision-tree analysis to supplement SCA, we present a potentially more robust pipeline for such analyses.

Decoding naturalistic stimuli from neural recordings is a significant challenge in systems neuroscience, primarily due to the high-dimensional and nonlinear nature of stimulus-response interactions, and is further exacerbated by the limited availability and noisiness of neural data. While contemporary approaches that incorporate generative models, such as Generative Adversarial Networks (GANs), attempt to address these issues by mapping neural responses to latent representations, they do not fully overcome these obstacles. In this work, we present a novel paradigm of differential neural decoding (dicoding) that focuses on the relative changes in response patterns, which not only expands the neural training data quadratically but also inherently denoises it. To determine the corresponding stimulus changes, this method leverages the Euclidean and feature-disentangled properties of the underlying latents through vector arithmetic. As such, we not only effectively exploit the latent space but also achieve semantically meaningful latent offsets in the context of the stimuli, resulting in improved sample efficiency. We trained a decoder to predict changes in latent vectors based on the corresponding changes in neural responses. The absolute latent vector itself was derived by vector addition of the predicted latent change (indicative of stimulus shift) to a reference latent, which was fed to the generator for the reconstruction of the perceived stimulus. Our results show that this geometrically principled approach facilitates more effective reconstruction of naturalistic stimuli from noisy and limited neural data.

Our visual system tends to prioritise novel information, and this allocation of attention, as examined with the Visual Paired Comparison Task (VPC), is taken as an indirect index of memory processes. At present, research on the emergence of a novelty preference (NP) remains unclear about its temporal dynamics and agnostic about the role that the organisation of conceptual knowledge may play in it. These two gaps are addressed in this eye-tracking study, which adapts the VPC task to enable a finer temporal tracking of the NP while manipulating categorical and functional relationships between pairs of real-world visual objects to examine the impact conceptual associations bear on it. We found that NP significantly increases with increasing delay between the familiarisation and the test phase, especially for pairs of objects that were both categorically and functionally related (e.g., dart/dartboard). Our findings provide fresh evidence about the interplay between overt attention, conceptual knowledge and memory processes on novelty preference while offering valuable insights into the temporal dynamics of NP and its conceptual implications for mechanisms governing visual short-term memory.

This pupillometric study investigates the relevance of domain-final intonation for attention-orienting in German, employing a changing-state oddball paradigm with rising, falling and neutral intonation on deviant stimuli. Pupil dilation responses (PDR) to deviants were shown to be affected by their intonation contours, strengthening the case for the role of intonational edge tones in attention-orienting. Moreover, the magnitude and duration of the PDR response was higher for rises than falls, indicating the fundamental role of intonational rises for the activation of the attention-orienting mechanism in speech perception.

We investigate whether more accurate representation of syntacticinformation in Transformer-based language models isassociated with better alignment to brain activity. We use fMRIrecordings from a large dataset (MOUS) of a Dutch sentencereading task, and perform Representational Similarity Analysisto measure alignment with 2 mono- and 3 multilinguallanguage models. We focus on activity in a region knownfor syntactic processing (the Left posterior Medial TemporalGyrus). We correlate model-brain similarity scores with theaccuracy of dependency structures extracted from model internalstates using a labelled structural probe. We report three keyfindings: 1) Accuracy of syntactic dependency representationscorrelates with brain similarity, 2) The link between brain similarityand dependency accuracy persists regardless of sentencecomplexity, although 3) Sentence complexity decreases dependencyaccuracy while increasing brain similarity. These resultshighlight how interpretable, linguistic features such as syntacticdependencies can mediate the similarity between languagemodels and brains

Infants' bodies, brains, and environments are ever-changing. Although this continuous transformation is a fundamental feature of development, how infants actively adapt and learn amidst such volatility is still unknown. To address this, we devised a novel learning task in which the location of a reward was systematically altered, transitioning from stable to volatile periods. Through computational modelling, we inferred from the infants' gaze and pupil data the learning processes that enabled them to navigate these changing environments. We found that infants' tonic pupil size reflected trial-by-trial changes in the level of environmental volatility. Moreover, phasic changes in pupil size when observing the reward indicated that infants relied on the information about volatility to optimize their learning. This resulted in the successful performance of the task, as indicated by the pattern of anticipatory looks to the correct reward locations. Together, these results identify the active role that infants play in adapting to change.

Choice deadlines are commonly imposed in decision-making research to incentivize speedy responses and sustained attention to the task settings. However, computational models of choice and response times routinely overlook this deadline, instead simply omitting trials past the deadline from further analysis. This choice is made under the implicit assumption that parameter estimation is not significantly affected by ignoring these omissions. Using new tools from likelihood-free inference, here we elucidate the degree to which omitting omissions, even in seemingly benign settings, can lead researchers astray. We explore the phenomenon using a Sequential Sampling Model (SSM) with collapsing boundaries as a test-bed.

The cognitive processes underlying mental folding have been investigated for decades, while the neural correlates associated with this spatial transformation are barely understood. This study combines cognitive modeling with EEG recordings from 41 subjects to investigate the general mechanisms of mental spatial transformation. By linking model-based simulation and electrocortical activity, we identified brain areas involved during mental folding. Our novel approach showed active central parietal and left parietal, as well as occipital areas during spatial storage, while the right parietal cortex was associated with spatial transformation. The left occipital and parietal regions were active especially during visual baseline trials, while the right parietal region exhibited stronger activity for more difficult folding trials, replicating previous results. The varying activation patterns imply different cognitive loads for storage and for transformation depending on task difficulty.

Cognitive control is thought to regulate the conflict between stability---maintaining the current task in the face of distraction---and flexibility---switching to a new task of greater priority. However, evidence conflicts regarding when and to what extent stability and flexibility trade-off. A normative theory of flexibility and stability may help clarify when and why we should expect such trade-offs to occur. Towards such a theory, we model task-switching as a problem of decision-making under uncertainty, in which the decision-maker must simultaneously infer both the identity of a stimulus and the task governing the correct response to that stimulus. We find that optimal behavior is either extremely stable or extremely flexible, but not both, indicating a normative basis for a trade-off between the two. However, we also show that a sub-optimal but more realistic decision-maker exhibits behavior between these two extremes, and more closely resembles experimental data.

Our choices often require us to prioritize some features of our rich sensory experience over others. Past work suggests that humans solve this problem by focusing on relevant information while discarding that which is irrelevant. Yet learning which features to prioritize requires extensive experience. Moreover, features that are irrelevant now may become relevant in the future. One way to address these issues is by sampling individual richly encoded experiences from episodic memory. Here we hypothesize that episodic memory is used to guide decisions based on multiple features of past events. We test this hypothesis using an experiment in which participants made choices about the value of features that were present in multiple past experiences. We find evidence suggesting that participants used episodic memories to flexibly access features of past events during decision making. Overall, these results suggest that episodic memory promotes adaptive decisions when knowledge of multiple features is necessary.

Generalization to new tasks requires learning of task representations that accurately reflect the similarity structure of the task space. Here, we argue that episodic memory (EM) plays an essential role in this process by stabilizing task representations, thereby supporting the accumulation of structured knowledge. We demonstrate this using a neural network model that infers task representations that minimize the current task's objective function; crucially, the model can retrieve previously encoded task representations from EM and use these to initialize the task inference process. With EM, the model succeeds in learning the underlying task structure; without EM, task representations drift and the network fails to learn the structure. We further show that EM errors can support structure learning by promoting the activation of similar task representations in tasks with similar sensory inputs. Overall, this model provides a novel account of how EM supports the acquisition of structured task representations.

Expanding on previous research highlighting the learning benefits of errors, this study explores the enduring effects of error-induced learning. Using an adaptive fact-learning system, 23 participants engaged in recognition, recall, and error tasks, with repeated testing for memory assessment. Initial findings echoed previous results: items learned through errors initially took longer to retrieve. However, a significant shift occurred over time; error items demonstrated faster retrieval speeds compared to study items, and, most notably, they exhibited greater resilience against forgetting. This study reaffirms the positive role of errors in learning and uncovers their contribution to enhanced long-term memory retention. These insights challenge traditional learning paradigms, advocating for an educational approach that recognizes and leverages the value of errors in learning processes.

When children use online apps, they often share personal information, such as their name, address, and birthday. In the present study, we investigated the mental models children use to reason about what apps are allowed to do with personal data after it has been willingly shared with an app. 57 children ages 8- to 11-years-old were read a story in which they were asked to judge whether an online game (app) was allowed or not allowed to perform four different actions: looking, saving, selling, and showing. We compared these judgments to a comparison condition where we asked children what users themselves should be allowed to do with their data. We found that children viewed the app as less permitted to act on the data than users as well as some further differences by action-type. Our findings suggest that children use something akin to a “lending” model to conceptualize data transfers, in which apps have less rights than users despite the data being willingly transferred to the app. Our findings also suggest that children differentiate among the uses of information as children think certain actions by the app are less permissible than others (e.g., looking is more permissible than selling).

This paper tests the similarity in neural responses across repeated words and morphosyntactic features both within and between two languages. Prior work using priming has revealed robust cross-linguistic lexical effects and effects for shared grammatical form, such as argument structure; these methods have been less successful when applied to morphosyntactic features. Combining machine-learning based neural decoding with EEG data collected from Korean-English bilinguals we, first, replicate prior work showing successful classification of lexical items from EEG signals. We then extend this to demonstrate successful classification of morphosyntactic features of number and tense. Finally, we find that EEG decoding in one language does not successfully generalize to another, even when temporal differences are considered. Taken together, these results point to stable EEG representations for lexical items and morphosyntactic features, but suggest that these representations are different between the two languages investigated here.

Models based on the Transformer neural network architecture have seen success on a wide variety of tasks that appear to require complex ``cognitive branching''-- or the ability to maintain pursuit of one goal while accomplishing others. In cognitive neuroscience, success on such tasks is thought to rely on sophisticated frontostriatal mechanisms for selective gating, which enable role-addressable updating-- and later readout-- of information to and from distinct ``addresses'' of memory, in the form of clusters of neurons. However, Transformer models have no such mechanisms intentionally built-in. It is thus an open question how Transformers solve such tasks, and whether the mechanisms that emerge to help them to do so bear any resemblance to the gating mechanisms in the human brain. In this work, we analyze the mechanisms that emerge within a vanilla attention-only Transformer trained on a simple sequence modeling task inspired by a task explicitly designed to study working memory gating in computational cognitive neuroscience. We find that, as a result of training, the self-attention mechanism within the Transformer specializes in a way that mirrors the input and output gating mechanisms which were explicitly incorporated into earlier, more biologically-inspired architectures. These results suggest opportunities for future research on computational similarities between modern AI architectures and models of the human brain.

Prediction error, both at the level of sentence meaning and at the level of the next presented word, has been shown to successfully account for N400 amplitudes. Here we investigate whether people differ in the representational level at which they implicitly predict upcoming language. We computed a measure of prediction error at the level of sentence meaning (termed semantic update) and a measure of prediction error at the level of the next word (surprisal). Both measures significantly accounted for N400 amplitudes even when the other measure was controlled for. Most important for current purposes, both effects were significantly negatively correlated. Moreover, random-effects model comparison showed that individuals differ in whether their N400 amplitudes are driven by semantic update only, by surprisal only, or by both, and that the most common model in the population was either semantic update or the combined model but clearly not the pure surprisal model.

How humans process and utilize experienced outcomes and actions to adapt to a constantly evolving and noisy world is an important area of research. We investigate the role of the pupil-linked arousal system in adaptive value-based decision-making in an uncertain and changing environment using a two-armed bandit task with occasional changes in reward contingencies. We find that pupil size fluctuation encodes reward- and uncertainty-related values across trials; moreover, pupil size reflects future-choice-dependent contributions of these variables to learning and decision-making: larger pupil encoding of reward prediction error (RPE) promotes reward-driven switches in choice, while larger pupil encodings of estimation uncertainty (EU) promotes uncertainty-driven switches in choice. Furthermore, individual differences in pupil's encoding of RPE and EU correlate with individual variabilities in choice bias and task performance. Given the relationship of pupil size to noradrenergic and cholinergic modulations, these results provide insights into the computational and neural process underlying adaptive decision-making.

The ability to exert cognitive control is central to human brain function, facilitating goal-directed task performance. However, humans exhibit limitations in the duration over which they can exert cognitive control---a phenomenon referred to as cognitive fatigue. This study explores a computational rationale for cognitive fatigue in continual learning scenarios: cognitive fatigue serves to limit the extended performance of one task to avoid the forgetting of previously learned tasks. Our study employs a meta-learning framework, wherein cognitive control is optimally allocated to balance immediate task performance with forgetting of other tasks. We demonstrate that this model replicates common patterns of cognitive fatigue, such as performance degradation over time and sensitivity to reward. Furthermore, we discuss novel predictions, including variations in cognitive fatigue based on task representation overlap. This approach offers a novel perspective on the computational role of cognitive fatigue in neural systems.

The decision to engage in lifelong learning often entails a motivational conflict, requiring individuals to balance potential benefits against the costs of engagement. Approach- avoidance motivation occurs when an action involves simultaneous positive and negative outcomes, necessitating a choice. This concept has primarily been studied in emotionally charged decisions linked to fear or anxiety, relevant for clinical settings. Our aim is to shift the focus to the cost of engagement in learning and educational settings. In a society marked by high demands and numerous tools for knowledge updating, lifelong learning may be beneficial for continuous individual development and societal contribution. We introduce a new framework that intricately connects motivation and learning processes with cognition, highlighting the pivotal roles of executive functions and decision-making processes. This article delves into the confluence of lifelong learning, cognitive conflict, and approach- avoidance motivation within the context of education and learning processes.

Spontaneous internally directed attention, such as mind wandering, typically hinders performance in cognitive tasks. The impact of intentional internally directed attention (IDA) – for instance, deliberately thinking about past or future events – on task performance, however, remains unclear. In our study, we employed a dual-task paradigm that involved self-referential stimuli in a color-recall visual working memory task. This approach revealed that intentional IDA more significantly influences performance compared to intentional externally directed attention (EDA). We observed larger late positive potentials (LPP) over medial frontal sensors, suggesting sustained stimulus processing over frontal sensors under IDA. Additionally, we noted a pattern of neural activity associated with internal attention: event-related desynchronization (ERD) in the alpha band (8-12 Hz) during the encoding phase and event-related synchronization (ERS) in the delay phase. In contrast, the EDA condition was marked by theta (4-8 Hz) band ERS during the delay period. These findings highlight distinct behavioral impacts and neural patterns associated with internally versus externally directed attention in dual-task settings.

Emerging folklore in the cognitive sciences suggests that interpretability techniques to reverse-engineer artificial neural networks (ANNs) could speed up discovery and theory-building. For many researchers in psychology, linguistics, neuroscience, and artificial intelligence (AI), the full observability and perturbability of ANNs trained on complex tasks affords a shortcut to domain insights, cognitive theories, neurocognitive models, application improvement, and user safety. Folklore intuitions, however, are typically disconnected from other relevant knowledge. Here we examine these intuitions formally by drawing relevant connections to computational complexity theory. We model interpretability queries computationally and analyze their resource demands for biological/artificial high-level cognition. We prove mathematically that, contrary to folklore, basic circuit-finding queries in classic ANNs are already infeasibly demanding to answer even approximately. We discuss how interdisciplinary integration can mitigate this disconnect and situate the broader implications for the cognitive sciences, the philosophy of AI-fueled discovery, and AI ethics.

Empirical investigations demonstrate similar cognitive processing patterns for objects and sexualized women. However, sexual objectification (SO) extends beyond sexualized women. To explore SO, we apply eye-tracking technique in conjunction with local/global and body-inversion paradigms. Ninety-four college students participated in the study. The visual gaze on non-sexualized South-Asian wo(men) images and the response time in Navon task post-priming with upright and inverted images is analyzed. Results indicate that participants of both genders gaze objectify females. Interestingly, male images are also gaze objectified. A comparison of attention allocation to face versus sexual body parts in upright versus inverted female images shows a reduced face-to-body ratio for the latter orientation, indicating a gender-specific attention shift. Combining the two SO theories, the study objectively substantiates the claim that women undergo objectification in even in non-sexual attire.

Emotion recognition is crucial for enhancing human-computer interaction. Due to considerable individual differences in emotion manifestation, traditional models do not adapt well to new individuals. Moreover, existing algorithms typically focus on identifying a single emotion, overlooking intrinsic connections among multiple emotions. Therefore, we propose a multi-task adversarial domain adaption (MADA) model for EEG-based emotion recognition. First, domain matching is employed to identify the most similar individual from the dataset as the source domain, alleviating individual differences and reducing training time. Subsequently, multi-task learning is utilized to simultaneously classify multiple emotions, capturing their intrinsic connections. Finally, adversarial domain adaption is applied to learn the individual differences between the source and target domains. Cross-subject experiments on the DEAP dataset indicate that our model achieves accuracies of 78.08%, 68.36%, and 69.64% on the valence, arousal, and dominance, respectively, surpassing state-of-the-art methods. This indicates the effectiveness of our model in recognizing multi-dimensional emotions.

Our ability to re-experience the events from our personal past tends to decline with age, which can have profound effects on well-being. HippoCamera is a smartphone-based application developed to mitigate age-related decline by guiding users to record and review cues for real-world events using established mnemonic strategies, with previous work demonstrating improved episodic recollection and enhanced hippocampal activity following use. Here, we followed-up with older adult participants who had used HippoCamera several years prior to investigate whether any benefits persisted following use. Using a mixed-methods approach, we found stronger subjective re-experiencing of events that were recorded with HippoCamera compared to those that were not. Further, participants reported extended benefits to their overall sense of meaning and well-being. These results provide preliminary evidence characterizing the long-lasting effects of a smartphone-based tool that improves memory for everyday events in aging.

Intentional binding (IB) is the experience of temporal interval compression between voluntary actions and subsequent events when the latter are perceived to be caused on purpose by the agent's actions. It can be measured experimentally by comparing the judgments of temporal intervals between either a voluntary act or an external event, and a later sensory consequence. Evidence suggests this might be modulated by the emotional valence of the consequence. However, controversies have arisen over the consistency of the results and the methodology they were obtained with. Here, we aimed to measure this affective modulation using a two-interval forced-choice (2AFC) discrimination task and word stimuli. Three factors were employed: agency (agency and passive), emotional valence (neutral, positive, and negative words), and interval duration ratio determined based on individual values of just noticeable differences (JND). Participants had to judge which of two intervals presented in each trial was shorter. Generalized linear mixed model analysis indicated that there was an effect of IB, but no affective modulation. Dissociation of component mechanisms of SoA are discussed to better understand results and suggest further directions.

The complexity of visual stimuli plays an important role in many cognitive phenomena, including attention, engagement, memorability, time perception and aesthetic evaluation. Despite its importance, complexity is poorly understood and ironically, previous models of image complexity have been quite \textit{complex}. There have been many attempts to find handcrafted features that explain complexity, but these features are usually dataset specific, and hence fail to generalise. On the other hand, more recent work has employed deep neural networks to predict complexity, but these models remain difficult to interpret, and do not guide a theoretical understanding of the problem. Here we propose to model complexity using segment-based representations of images. We use state-of-the-art segmentation models, SAM and FC-CLIP, to quantify the number of segments at multiple granularities, and the number of classes in an image respectively. We find that complexity is well-explained by a simple linear model with these two features across six diverse image-sets of naturalistic scene and art images. This suggests that the complexity of images can be surprisingly simple.

Recent research has identified substantial individual differences in how people solve value-based tasks. Here, we examine such differences in the motivational Go/NoGo task, which orthogonalizes action and valence, using open-source data from 817 participants. Using computational modeling and behavioral analysis, we identified four distinct clusters of people. Three clusters corresponded to previous models of the task, including people with different learning rates for cues that signal rewarding and punishing states and with different sensitives for rewards and punishments. The fourth cluster of people acted like naïve reinforcement learners, with their responses shaped by outcomes in a manner that was independent of the state information provided by the cues. In addition to providing evidence that state-independent learning is a common disposition, we show that not considering such learning can dramatically affect the results of computational modeling. We discuss the implications for the modeling of data from heterogeneous populations.

Causal reasoning enables us to explain the past, predict the future, and intervene in the present. Does the brain allocate specialized cortical regions to causal reasoning? And if so, are they involved in reasoning about both physical and social causal relationships, or are they domain-specific? In a pre-registered experiment (Exp 1) we scanned adults using fMRI while they matched physical and social causes to effects (e.g., ‘The car swerved to avoid a crash' -> ‘Coffee spilled all over the car seat'; ‘He was late for work' -> ‘Tom was scolded by his boss') or physical and social descriptions of the same entity matched for difficulty and linguistic variables to the causal conditions (e.g., ‘The brightest object in the sky'-> ‘The closest star to earth'; ‘She works at a hotel' -> ‘She brings in guests' luggage'). A region in the left lateral prefrontal cortex (LPFC) responded significantly more strongly to causal than descriptive conditions in most subjects individually. Responses in this region in held-out data were high for both social and physical causal conditions, yet no greater than baseline for the two descriptive (non-causal) conditions. In a follow-up exploratory experiment (Exp 2), we tested a different task (answering causal versus non-causal questions about physical and social narratives, matched for linguistic variables). Again, we found that both the physical and social causal stimuli selectively engaged the LPFC region. Finally, in both experiments, we found that brain regions previously implicated in intuitive physical reasoning responded more to the physical causal than the physical non-causal stimuli. Collectively, these results suggest that a) a region in the LPFC is selectively engaged in causal reasoning independent of content domain and b) the hypothesized physics network (hPN) is selectively involved in physical causal reasoning across modalities (visual vs. linguistic).

Prognostic assessment of patients with disorders of consciousness (DoC) remains one of the most challenging problems in contemporary medicine. The long treatment cycle and high costs of treatment are heavy burdens to our society. In this paper, we use deep network to investigate potential indicators of consciousness within brain signals of DoC patients. In the experiments, we study P300 and resting-state Electroencephalogram (rs-EEG) signals of 22 DoC patients to investigate neural correlation between brain signals and the improvement of consciousness. Synergistic integration of P300 and rs-EEG signals demonstrated superior predictive proficiency for cross-subject and cross-paradigm prognosis in DoC, achieving an accuracy of 81.1%. Our investigation is the first known to the literature to combine P300 and rs-EEG signals for analyzing DoC. This novel approach leverages advanced neural network models to elucidate the complex neural patterns associated with DoC, setting a precedent for future research in the field.

Existing models of the basal ganglia assume the existence of separate channels of neuron populations for representing each available action. This type of localist mapping limits models to small, discrete action spaces, since additional actions require additional channels, costing neural resources and imposing new connective tracts. In contrast, evidence suggests that the basal ganglia plays a role in the selection of both discrete action units, and continuously-valued action kinematics. In this work, we model the basal ganglia with distributed action representations, using high-dimensional vectors. This method lends itself to representing both discrete and continuous action spaces. Vectors that represent actions are weighted by a scalar value (their salience to the current task), and bundled together to form a single input vector. This paper provides an overview of the encoding method and network structure, as well as a demonstration of the model solving an action selection task using spiking neurons.

Bipolar disorder (BD) is a mental health condition characterized by large fluctuations in goal-directed energy and mood. BD is defined by the presence of at least one lifetime episode of mania, a prolonged period of excessive goal-directed behavior, hyperactivity and elevated mood. Previous computational models of BD have primarily focused on explaining mood fluctuations in mania, placing less emphasis on goal-directed symptoms. In this work, we use reinforcement learning (RL), a principled model of goal-directed behavior and learning, to show how augmenting RL agents with \textit{dynamic self-efficacy beliefs} can give rise to goal-directed and mood symptoms characteristic of the mania phase of BD. Our simulations demonstrate that a model-free RL agent that dynamically updates its self-efficacy beliefs learns optimistic overgeneralized value representations. We suggest that these representations may underlie several behaviors associated with mania, such as increased motivational drive and faster initiation of approach behavior (i.e. impatience). We further show that agents with more sensitive self-efficacy beliefs display increased willingness to exert effort in order to achieve higher goals even in the face of costs, a characteristic that is observed in individuals at risk for BD. Finally, unrealistically high self-efficacy beliefs that emerged with learning were accompanied by behaviors such as distractibility and compulsive action selection that have clinical parallels to symptoms of mania.

Our remarkable ability to infer complex cause-effect relationships is thought to distinguish humans from all other species. Despite that causal inferencing pervades human cognition, it remains unclear whether this fundamental cognitive ability is supported by a unified, domain-general mechanism or multiple domain-specific mechanisms. Both the language and logical reasoning systems have been described as possible unified substrates of causal inferencing. The current study uses neuroimaging to offer insight into this debate. We specifically focus on the culturally universal and highly motivationally relevant case of inferring illness causes. Participants read causal and noncausal vignettes about illness and mechanical failure while undergoing fMRI. We find that inferring the causes of illness selectively activates the brain's ‘animacy network,' particularly the precuneus. By contrast, a domain-general (i.e., ‘content-invariant') preference for causal inferencing did not emerge, including in the language and logical reasoning networks. Together, this evidence suggests that domain-specific mechanisms enable causal inferencing.

The desire to move to music appears to be a human universal. This behavioral response seems to be supported by a tight coupling of auditory and motor networks, even in the absence of overt movement. The prevailing theories explain this phenomenon either in terms of passive brain network entrainment to musical periodicity or motor system involvement in predictive coding. Both explanations recognize the role of rhythmic complexity in modulating motor activity. However, the precise nature of the relationship between rhythmic complexity and motor activity remains unclear. In this work, we conducted an fMRI literature review to examine this relationship. Out of 110 screened articles, 24 met inclusion criteria, reporting findings ranging from non-existent to linear or inverted-U-shaped. Underlying these findings, we encountered significant heterogeneity in the measurement and conceptualization of rhythmic complexity. We provide a summary of the relationships found, the approaches to measuring rhythmic complexity and the different types of tasks and stimuli used. We conclude that, in order to move forward, more agreement is needed regarding measures and notions of complexity.