What is the evolutionary function of cuteness? Research on cuteness originated with the study of juvenile morphology following Lorenz’s Kindchenschema proposal, which identified specific cues of juvenility that reliably elicit a distinctive affective response. Because juvenile cues activate protective responses typically associated with vulnerability and distress (e.g., compassion, harm avoidance), cuteness has often been interpreted as an emotion whose primary function is protection. However, this leaves a key pattern unexplained: children are perceived as cutest not when they are helpless, but when they are sociable, curious, and eager to interact. More surprisingly, we also perceive adults as cute when they display not vulnerability, but naïveté, enthusiasm, and teachability. To solve this issue, we propose a developmental investment theory of cuteness, according to which cuteness is not merely a protection-eliciting mechanism but a motivational system that evolved to support prosocial investment in the development of inexperienced individuals. Crucially, this account expands the range of cues that elicit cuteness beyond juvenile morphology to include behavioral and cognitive signals of immaturity and learning potential. Drawing on evolutionary anthropology, cognitive neuroscience, developmental psychology and social sciences, we argue that cuteness identifies motivated but inexperienced individuals, and elicits not rescue, but teaching, guidance, and social play. This theory provides a better account of which traits elicit cuteness, what responses they provoke, and how cuteness is strategically deployed. It explains why cuteness can be attractive in romantic interactions, why modern, learning-rich environments foster an explosion of cuteness and why women disproportionately perform cuteness in patriarchal societies. Finally, we suggest that, over evolutionary time, this system may have contributed to the domestication of other species, and to the self-domestication of our own.

Interpersonal synchrony, such as dancing to a shared rhythm, elicits bonding and rewards, leading many to see it as a mechanism for group cohesion. Yet synchronized activities also reduce ingroup bias, rapidly bond strangers, flatten hierarchies and blur roles, and evoke trance states. Synchrony thus softens many boundaries that “groupishness” requires. Perhaps in consequence, cultural authorities frequently condemn expressive dance music rather than embracing it as a tool for social cohesion. Aiming to account for these multifarious features, this review integrates findings from cognitive motor science, biomusicology, and adjacent fields to synthesize the relaxed priors through synchrony (RePS) model. Highlighting synchrony’s effects on high-level social predictive processing, RePS depicts the sensorimotor signals associated with beat perception and synchrony as lending precision to bottom-up prediction error at temporally granular (< 2-second) scales. Precision for abstract social priors is thereby reduced, inverting the social brain’s characteristic “top-heaviness.” With categorical beliefs about roles and groups no longer scaffolding coordination and joint action, local-timescale planning and processing evoke low psychological construal. Tighter links between motivation and reward facilitate disinhibited enjoyment orthogonal to status and group boundaries. In the entropic cognitive state that results, relaxed social priors are more easily revisable, enabling coordinated change and updating, as in ritual celebrations. This model affords rich opportunities for empirical testing and interdisciplinary theory-building. Overall, RePS explains why authorities frequently dislike synchrony, why dance plays centrally in social transitions such as weddings, and why rhythmic music is associated with trance. More than a bonding device, synchrony facilitates change.

We introduce a new framework for understanding how cognitive systems (e.g., humans) learn from experience, based on the concept of representational capacity—the relative amount of representational resources devoted to encoding past experiences. Most paradigms in cognitive science have operated under the assumption that these resources are constrained, forcing cognitive systems to compress rich and noisy experiences to effectively generalize to new situations. We leverage recent advances in computer science to outline the implications of learning with excess capacity, or applying even more representational resources than needed to perfectly memorize all the details of one’s past experiences. In particular, we review evidence suggesting that excess capacity systems can exhibit many of the characteristics of human learning, such as the simultaneous ability to memorize individual experiences and generalize knowledge to new situations. We define and differentiate between constrained (not enough), sufficient (just enough), and excess (more than enough to perfectly capture all the details of one’s past experiences) capacity. We derive empirical properties of learning in each of these capacity regimes, and compare these predictions to effects documented for human learning. We highlight the broad implications of this framework for advancing theoretical and empirical work across cognitive, clinical, and developmental psychology.

From spider dances to human language, multimodality is ubiquitous in natural communication systems. Much scholarship has been devoted to investigating why multimodality evolved and the role it plays in communication. Here, we highlight the role of multimodality in safeguarding the most fundamental prerequisite of all functioning, extant communication systems: honesty. We begin by introducing the arms race between honesty and deception in natural communication systems, and the critical role socially-mediated controls can play in maintaining signal honesty when classic, intrinsic costs are not sufficient. We next introduce three ways by which multimodality buffers signal honesty by 1) providing insurance against signal unreliability in dynamic environments, 2) forming an honest, multimodal gestalt with which to cross-validate signal honesty, and 3) increasing signal complexity, making the entire signal harder to fake. We then discuss the case of highly cooperative societies, with human language emphasized, and argue that signal honesty is important especially in complex and cooperative societies wherein the need to cooperate and be accepted as part of the group may supersede honesty. Finally, we propose future directions wherein human and non-human communication research could expand beyond the well trodden realms of competition and mate attraction to investigate the role of multimodality and honesty in cooperative, “cheap” signals, and emphasize the importance of drawing from both the human and non-human literatures in investigating the forces that have shaped the evolution of communication.

The impacts of poverty and material scarcity on human decision making appear paradoxical. One set of findings associates poverty with risk aversion, whilst another set associates it with risk taking. We present an idealised rational-choice model, the Desperation Threshold Model (DTM), that explains how both these accounts can be correct. The DTM assumes that there are basic needs whose satisfaction is not fully divisible. This generates an S-shaped utility function for material resources. The value of gaining a dollar is at first small (because even with the extra dollar, basic needs still cannot be met); then large (because the extra dollar enables basic needs to be met); and then small again. Just above the basic needs threshold, people’s main concern is not falling below, and they are predicted to avoid risk especially strongly. Below the threshold, their most important concern is jumping above, and they are predicted to take risks that would otherwise be avoided. Versions of the DTM have been proposed under various names across biology, anthropology, economics and psychology. We review a broad range of relevant empirical evidence from a variety of societal contexts. Though the model primarily concerns individual decision making, it connects to a range of population-scale and societal issues such as: the consequences of economic inequality; the deterrence of crime; and the optimal design and behavioural consequences of the welfare state. We discuss interpretative issues, and suggest areas for future DTM research that bridges disciplines.

“Core knowledge” refers to a set of cognitive systems that underwrite early representations of the physical and social world, appear universally across cultures, and likely result from our genetic endowment. Although this framework is canonically considered as a hypothesis about early-emerging conception — how we think and reason about the world — here we present an alternative view: that many such representations are inherently perceptual in nature. This “core perception” view explains an intriguing (and otherwise mysterious) aspect of core-knowledge processes and representations: that they also operate in adults, where they display key empirical signatures of perceptual processing. We first illustrate this overlap using recent work on “core physics”, the domain of core knowledge concerned with physical objects, representing properties such as persistence through time, cohesion, solidity, and causal interactions. We review evidence that adult vision incorporates exactly these representations of core physics, while also displaying empirical signatures of genuinely perceptual mechanisms, such as rapid and automatic operation on the basis of specific sensory inputs, informational encapsulation, and interaction with other perceptual processes. We further argue that the same pattern holds for other areas of core knowledge, including geometrical, numerical, and social domains. In light of this evidence, we conclude that many infant results appealing to precocious reasoning abilities are better explained by sophisticated perceptual mechanisms shared by infants and adults. Our core-perception view elevates the status of perception in accounting for the origins of conceptual knowledge, and generates a range of ready-to-test hypotheses in developmental psychology, vision science, and more.

The scope of unconscious processing has long been, and still remains, a hotly debated issue. This is driven in part by the current diversity of methods to manipulate and measure perceptual consciousness. Here, we provide ten recommendations and nine outstanding issues about designing experimental paradigms, analyzing data, and reporting the results of studies on unconscious processing. These were formed through dialogue among a group of researchers representing a range of theoretical backgrounds. We acknowledge that some of these recommendations naturally do not align with some existing approaches and are likely to change following theoretical and methodological development. Nevertheless, we hold that at this stage of the field they are instrumental in evoking a much-needed discussion about the norms of studying unconscious processes and helping researchers make more informed decisions when designing experiments. In the long run, we aim for this paper and future discussions around the outstanding issues to lead to a more convergent corpus of knowledge about the extent – and limits – of unconscious processing.