A widespread observation is that people avoid mentally effortful courses of action, and much recent work examining cognitive effort has explained subjective effort evaluation – and, consequently, preferences – in economic terms, which assumes that the expenditure of cognitive effort is experienced as costly. However, this economic perspective is largely tacit about the source of these costs. Here, we review recent theoretical treatments of effort costs, which take vastly different perspectives (information-theoretic, psychological, and biological) to explain how the subjective experience of cognitive effort arises from controlled information processing, exploring their predictions concerning the simple observation that people experience tasks with high (versus low) working memory demands as costly. Finally, we identify open questions that might help bridge across these accounts.

Roelfsema and Serre [1] contend that coherent object perception generally requires feature binding across segregated maps. We agree that selective control – attention, grouping, and recurrence – is essential when natural regularities are disrupted by occlusion, camouflage, isoluminance, or clutter. However, we observe specialization but no segregation: functional biases arise as local amplification within broadly overlapping, mixed-selectivity populations (see Box 1), not as isolated feature silos.

Mental life is filled with thoughts about the social world and one’s place in it. Mentalizing, or ascribing mental content (e.g., preferences, beliefs, visuospatial perspectives) to others and oneself, often requires considering self-representations and target representations in relation to each other. We propose a model of mentalizing wherein simulation, which, minimally, involves activating a self-representation, facilitates two phenomena: projection (using self-representations to construct target representations) is an inherent element of simulation-driven mentalizing, and introjection (using target representations to shift self-representations) arises incidentally from simulating another’s mind. We review evidence primarily from adults supporting this model, identify theoretically-relevant factors that amplify and attenuate projection and introjection, and discuss implications for questions of longstanding interest in cognitive science (e.g., Is the self special?).

A long-standing question in cognitive sciences concerns the specific contribution of linguistic and sensorimotor experience in shaping conceptual knowledge. A new study by Xu et al. shows that large language models (LLMs) represent a powerful tool to advance this debate, helping to disentangle the relative contribution of different experiential modalities.

Blindsight patients lack conscious visual perception yet perform visual tasks effectively, suggesting many animals may similarly rely on non-conscious vision. Here, we discuss how to investigate visual consciousness in miniature brains, using bees as a case study. This new endeavor can reveal the minimal neural requirements for visual awareness.

Rapid progress in artificial intelligence (AI) capabilities has drawn fresh attention to the prospect of consciousness in AI. There is an urgent need for rigorous methods to assess AI systems for consciousness, but significant uncertainty about relevant issues in consciousness science. We present a method for assessing AI systems for consciousness that involves exploring what follows from existing or future neuroscientific theories of consciousness. Indicators derived from such theories can be used to inform credences about whether particular AI systems are conscious. This method allows us to make meaningful progress because some influential theories of consciousness, notably including computational functionalist theories, have implications for AI that can be investigated empirically.

Episodic memories are highly dynamic and change in nonlinear ways over time. This dynamism is not captured by existing systems consolidation theories …

Electrophysiological markers of spontaneous thought are well established, yet less is known regarding the timescales of two core dimensions: task relatedness and thought orientation. In a recent study, Hua and colleagues reported dissociable timescales in the behavioral and neural correlates of these thought dimensions, offering insights into their distinct temporal dynamics.

In recent years, scientific interest in sensory processing sensitivity (SPS), a personality trait reflecting increased sensitivity, reactivity, and deeper processing of stimuli, has grown exponentially. Building on this momentum, we synthesise recent SPS literature to discuss several central themes, including SPS assessment, relations to other personality traits and diverse positive and negative outcomes. We propose a novel account of SPS grounded in predictive processing, that bridges cognitive, neural, and computational domains. Specifically, we posit that brains of high-SPS individuals consistently assign high precision to incoming sensory signals. This account offers a unified explanation for the phenotypic consequences of heightened sensory sensitivity. We review behavioural and neural evidence that indirectly supports this account, and delineate important avenues for future research.

Episodic memories are highly dynamic and change in nonlinear ways over time. This dynamism is not captured by existing systems consolidation theories that predict a unidirectional process where memories are first supported by the hippocampus and then the neocortex. Here, I propose a 3D state space for episodic memories. The first two dimensions relate to whether episodic retrieval is driven by the hippocampus and the neocortex, critically allowing for independent and additive contributions from both regions. The third dimension relates to the episodic specificity of retrieval. Memories can be located at any point in this state space and move to any other location. The state space captures the dynamic nature of episodic memory and broadens the search space of possible memory states and transformations across time.

Despite decades of progress, gender inequalities in workplace authority and compensation persist. This review reveals recent advances in understanding how gendered careers emerge through three psychological mechanisms prevalent in organizations: (i) intrapersonal gender biases, (ii) interpersonal power dynamics, and (iii) intergroup gender relations. Because power is perceived as masculine, women in authority face systematic backlash. While status offers women an alternative leadership route, it proves less valuable than power – lacking resource control and requiring constant social validation rather than conferring formal authority. We suggest that mindsets about human malleability moderate these dynamics: fixed mindsets amplify stereotyping and power-based challenges for women leaders, while growth mindsets attenuate barriers. Our framework aims to illuminate gender disparities and to suggest promising intervention pathways.

A fundamental question regarding the human mind is how we derive knowledge from others’ episodic memories, to learn about things we did not experience directly. This learning is vital for understanding the world around us and guides our actions and decisions. We propose a novel framework to investigate how people learn from others' episodic memories, hypothesizing that in such learning, people take an evaluative stance to avoid acquiring misleading information. Information evaluated as more veridical will be more likely to be learned from. We review the cues used to evaluate others’ memories. These cues may be conveyed and interpreted automatically (e.g., various aspects of prosody), or more deliberatively (e.g., description of recollective content, overt metacognitive claims of certainty or specificity).

Humans exhibit a striking tendency to persist with chosen goals. This strong attachment to goals can often appear irrational – a perspective captured by terms such as perseverance or sunk-cost biases. In this review, we explore how goal commitment could stem from several adaptive mechanisms, including those that optimise cognitive resources, shield decisions from interference, and scaffold motivation in the absence of accessible reward signals. We propose that these computational considerations have important implications for algorithmic architectures supporting decision making, including separate algorithms for goal selection and implementation, and for monitoring ongoing goals versus alternative sources of reward. Finally, we discuss how a variety of mechanisms supporting goal commitment and abandonment could relate to dimensions affected in mental health.

Communication errors are typically viewed as detrimental, yet they can benefit collective foraging in social insects. Temnothorax ants provide a powerful model for studying how such errors arise during tandem running and how they might improve group performance under certain environmental conditions.

Billions of people around the world believe that the events in their lives are determined by the law of karma: good deeds lead to good outcomes, whereas bad deeds lead to bad outcomes, because of supernatural forces that transcend the boundaries of human justice. This belief is increasingly recognized in psychological research as an important dimension of religious diversity across the world, and has unique impacts on the psychology and social behavior of believers. This review highlights the distinctive cognitive profile of karma beliefs and summarizes recent empirical research showing that belief in karma can lead to both positive outcomes (e.g., optimism, generosity) and negative outcomes (e.g., depression, victim blaming) which impact on individual well-being and social dynamics.

For most mammals, the ability to form, maintain, retrieve, and reshape memories of social experience is essential for individual survival and cooperative behavior. Considerable recent progress has been made in understanding how the hippocampus forms internal representations of social experience, with the CA2 region having emerged as an important integrator of multiple socially relevant inputs. In this review we discuss recent studies exploring neural substrates of social recognition with a focus on the potential role of plasticity mechanisms in hippocampal circuits and their downstream targets. We also consider the neural bases of binding social with nonsocial and abstract features of the environment to create multidimensional representations that support adaptive social behavior.

Does including emotions improve reinforcement-learning models? A recent EEG study by Heffner and colleagues presents separate neural signatures for reward and emotion prediction errors. This advance invites questions about, and even holds clues to, which ingredients of emotion and prediction errors most improve reinforcement-learning models.

Recent advances in artificial intelligence (AI) have generated enthusiasm for using AI simulations of human research participants to generate new know…

Recent advances in artificial intelligence (AI) have generated enthusiasm for using AI simulations of human research participants to generate new know…

Recent advances in artificial intelligence (AI) have generated enthusiasm for using AI simulations of human research participants to generate new knowledge about human cognition and behavior. This vision of ‘AI Surrogates’ promises to enhance research in cognitive science by addressing longstanding challenges to the generalizability of human subjects research. AI Surrogates are envisioned as expanding the diversity of populations and contexts that we can feasibly study with the tools of cognitive science. Here, we caution that investing in AI Surrogates risks entrenching research practices that narrow the scope of cognitive science research, perpetuating ‘illusions of generalizability’ where we believe our findings are more generalizable than they actually are. Taking the vision of AI Surrogates seriously helps illuminate a path toward a more inclusive cognitive science.

Perceptual experience depends on recurrent interactions between lower and higher cortices. One theory, predictive coding, posits that feedback from higher to lower brain regions decreases neuronal activity predicted by higher-level representations. Despite the widespread adoption of predictive coding in neuroscience, the correspondence to neurophysiological findings in sensory cortices remains elusive. Here, we review how the canonical patterns of intra- and inter-cortical interactions that occur during perception and shifts of attention deviate from those predicted by predictive coding. We argue that these circuit interactions are better captured by alternative theories, which we summarize under the umbrella term BELIEF. We review how BELIEF theories account for the inter-areal interactions during attentive perception.