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.

How has human culture become so complex? We argue that a key process is social tinkering: the gradual accumulation of ad hoc innovations to the social rules that coordinate behavior in response to immediate challenges. Momentary innovations provide precedents that can be reused, entrenched, adapted and recombined to handle future challenges. Interactions between these social rules create rich cultural systems (languages, ethics, political organization) of increasing complexity through processes of spontaneous order, not deliberate design. To explain the historical emergence of cumulative cultural complexity, we distinguish between six overlapping and interacting stages: (1) non-social tinkering to solve problems in the natural world; (2) learning and copying from the tinkering of others; (3) social tinkering involving jointly agreeing on momentary conventions to coordinate interactions, typically for mutual benefit; (4) creating communicative conventions (language) to support more complex social interactions; (5) social tinkering of linguistically-formulated cultural rules leading to laws, organizations, institutions, etc.; and (6) tinkering with linguistically-formulated non-social knowledge, allowing for the creation of science and technology. The rich interplay of innovation across the six stages is crucial for explaining increasing cultural and organizational complexity and our collective mastery of the natural world. Because social and non-social tinkering requires two different kinds of learning, this analysis has important implications for the understanding of human learning and cognition, including moral and evolutionary psychology, theory of mind, and the view of the child-as-scientist. Social tinkering also has substantial implications for current theories of cultural evolution.

Human success in navigating the social world is typically attributed to our capacity to represent other minds—a mentalistic stance. We argue that humans are endowed with a second equally powerful intuitive theory: an institutional stance. In contrast to the mentalistic stance, which helps us predict and explain unconstrained behavior via unobservable mental states, the institutional stance interprets social interactions in terms of role-based structures that constrain and regulate behavior via rule-like behavioral expectations. We argue that this stance is supported by a generative grammar that builds structured models of social collectives, enabling people to rapidly infer, track, and manipulate the social world. The institutional stance emerges early in development and its precursors can be traced across social species, but its full-fledged generative capacity is uniquely human. Once in place, the ability to reason about institutional structures takes on a causal role, allowing people to create and modify social structures, supporting new forms of institutional life. Human social cognition is best understood as an interplay between a system for representing the unconstrained behavior of individuals in terms of minds and a system for representing the constrained behavior of social collectives in terms of institutional structures composed of interlocking sets of roles.

Many traditional subsistence groups have been described as ‘egalitarian societies’. Definitions of ‘egalitarianism’, especially beyond anthropology, have often emphasised equality in resource access, prestige or rank, alongside generalised preferences for fairness and equality. However, there are no human societies where equality is genuinely realised in all areas of life. Here we demonstrate, empirically, that nominally egalitarian societies are often unequal across seven important interconnected domains: embodied capital, social capital, leadership, gender, age/knowledge, material capital/land tenure, and reproduction. We also highlight evidence that individuals in nominally egalitarian societies do not unfailingly adhere to strong equality preferences. We propose a new operational framework for understanding egalitarianism in traditional subsistence groups, focussing on individual motivations, rather than equality. We redefine “egalitarianism” societies as those where socio-ecological circumstances enable most individuals to successfully secure their own resource access, status, and autonomy. We show how this emphasis on self-interest — particularly status concerns, resource access and autonomy — dispels naive enlightenment notions of the ‘noble savage’, and clarifies the plural processes (demand-sharing, risk-pooling, status-levelling, prosocial reputation-building, consensus-based collective decision-making, and residential mobility) by which relative equality is maintained. We finish with suggestions for better operationalizing egalitarianism in future research.

The human brain makes up just 2% of body mass but consumes closer to 20% of the body’s energy. Nonetheless, it is significantly more energy-efficient than most modern computers. Although these facts are well-known, models of cognitive capacities rarely account for metabolic factors. In this paper, we argue that metabolic considerations should be integrated into cognitive models. We distinguish two uses of metabolic considerations in modeling. First, metabolic considerations can be used to evaluate models. Evaluative metabolic considerations function as explanatory constraints. Metabolism limits which types of computation are possible in biological brains. Further, it structures and guides the flow of information in neural systems. Second, metabolic considerations can be used to generate new models. They provide: a starting point for inquiry into the relation between brain structure and information processing, a proof-of-concept that metabolic knowledge is relevant to cognitive modeling, and potential explanations of how a particular type of computation is implemented. Evaluative metabolic considerations allow researchers to prune and partition the space of possible models for a given cognitive capacity or neural system, while generative considerations populate that space with new models. Our account suggests cognitive models should be consistent with the brain’s metabolic limits, and modelers should assess how their models fit within these bounds. Our account offers fresh insights into the role of metabolism for cognitive models of mental effort, philosophical views of multiple realization and medium independence, and the comparison of biological and artificial computational systems.

“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.

Language models can produce fluent, grammatical text. Nonetheless, some maintain that language models don’t really learn language and also that, even if they did, that would not be informative for the study of human learning and processing. On the other side, there have been claims that the success of LMs obviates the need for studying linguistic theory and structure. We argue that both extremes are wrong. LMs can contribute to fundamental questions about linguistic structure, language processing, and learning. They force us to rethink arguments and ways of thinking that have been foundational in linguistics. While they do not replace linguistic structure and theory, they serve as model systems and working proofs of concept for gradient, usage-based approaches to language. We offer an optimistic take on the relationship between language models and linguistics.

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.

Human societies reliably develop complex cultural traditions with striking similarities. These “super-attractors” span the domains of magic and religion (e.g., shamanism, supernatural punishment beliefs), aesthetics (e.g., heroic tales, dance songs), and social institutions (e.g., justice, corporate groups), and collectively constitute what I call the “cultural manifold.” The cultural manifold represents a set of equilibrium states of social and cultural evolution: hypothetically cultureless humans placed in a novel and empty habitat will eventually produce most or all of these complex traditions. Although the study of the super-attractors has been characterized by explanatory pluralism, particularly an emphasis on processes that favor individual- or group-level benefits, I here argue that their development is primarily underlain by a process I call “subjective selection,” or the production and selective retention of variants that are evaluated as instrumentally useful for satisfying goals. Humans around the world are motivated towards similar ends, such as healing illness, explaining misfortune, calming infants, and inducing others to cooperate. As we shape, tweak, and preferentially adopt culture that appears most effective for achieving these ends, we drive the convergence of complex traditions worldwide. The predictable development of the cultural manifold reflects the capacity of humans to sculpt traditions that apparently provide them with what they want, attesting to the importance of subjective selection in shaping human culture.

Most scientists are aware that developmental databases derive primarily from Western, middle-class samples, but fewer are cognizant that developmental theories can be similarly biased. There is urgency in revising developmental theories, both scientifically (embracing diversity is essential to the study of human psychology) and applied (it is damaging to apply WEIRD standards/methods/theories to evaluate development in the multitude of non-WEIRD contexts).

We evaluate the extent to which two prominent developmental theories are inclusive. We find that Shared Intentionality Theory is based on a WEIRD bias in the foundational databases: the core constructs lack culturally diverse data, undermining claims that this theory explains human-general social cognition. In Attachment Theory, we illuminate the lack of inclusivity in the core assumptions and resulting claims of the meaning and measure of the attachment system: this theory excludes cultural diversity in social-emotional constructs focused on communal orientations (e.g., interdependence, attachment networks) found in many people of the Global South, and neglects culture-specific adaptive behavior patterns.

Acknowledging the lack of inclusivity at the level of theory is necessary. We urge researchers to take a more WILD approach: obtain Worldwide samples, study development In situ, focus on Local cultural practices and ethnotheories, and consider development as Diverse. Being WILD entails attending to inclusivity during the entire research process, from framing the research questions to interpreting the data (e.g., respecting all adaptive behaviors in development). Five Steps for Increasing Inclusivity can be used as a practical guide to decenter psychological theories from their current WEIRD mindset.

As artificial intelligence (AI) continues to advance, it is natural to ask whether AI systems can be not only intelligent, but also conscious. I consider why people might think AI could develop consciousness, identifying some biases that lead us astray. I ask what it would take for conscious AI to be a realistic prospect, challenging the assumption that computation provides a sufficient basis for consciousness. I'll instead make the case that consciousness depends on our nature as living organisms – a form of biological naturalism. I lay out a range of scenarios for conscious AI, concluding that real artificial consciousness is unlikely along current trajectories, but becomes more plausible as AI becomes more brain-like and/or life-like. I finish by exploring ethical considerations arising from AI that either is, or convincingly appears to be, conscious. If we sell our minds too cheaply to our machine creations, we not only overestimate them – we underestimate ourselves.

It is not obvious why we are conscious. Why can't all of our mental activities take place unconsciously? What is consciousness for? We aim to make progress on this question, focusing on conscious vision. We review evidence on the timescale of visual consciousness, showing that it is surprisingly slow: postdictive effects reveal windows of unconscious integration lasting up to 400 milliseconds. We argue that if consciousness is slow, it cannot be for online action-guidance. Instead, we propose that conscious vision evolved to support offline cognition, in tandem with the larger visual sensory horizons afforded by the water-to-land transition. Smaller visual horizons typical in aquatic environments require fast, reflexive actions of the sort that are guided unconsciously in humans. Conversely, larger terrestrial visual horizons allow benefits to accrue from “model-based” planning of the sort that is associated with consciousness in humans. We further propose that the acquisition of these capacities for internal simulation and planning provided pressures for the evolution of reality monitoring—the capacity to distinguish between internally and externally triggered signals, and to solve “Hamlet's problem” in perception—the problem of when to stop integrating evidence, and fix a particular model of reality. In line with higher-order theories of consciousness, we associate the emergence of consciousness with the emergence of this reality monitoring function. We discuss novel empirical predictions that arise from this account, and explore its implications for the distribution of conscious (vs. unconscious) vision in aquatic and terrestrial animals.

The human capacity for culture is a key determinant of our success as a species. While much work has examined adults’ abilities to create and transmit cultural knowledge, relatively less work has focused on the role of children (approx. 3-17 years) in this important process. In the cases where children are acknowledged, they are largely portrayed as acquirers of cultural knowledge from adults, rather than cultural producers in their own right. In this paper, we bring attention to the important role that children play in cultural adaptation by highlighting the structure, function, and ubiquity of the large body of knowledge produced and transmitted by children, known as peer culture. Supported by evidence from diverse disciplines, we argue that children are independent producers and maintainers of these autonomous cultures, which exist with regularity across diverse societies, and persist despite compounding threats. Critically, we argue peer cultures are a source of community knowledge diversity, encompassing both material and immaterial knowledge related to geography, ecology, subsistence, norms, and language. Through a number of case studies, we further argue that peer culture products and associated practices — including exploration, learning, and the retention of abandoned adult cultural traits — may help populations adapt to changing ecological and social conditions, contribute to community resilience, and even produce new cultural communities. We end by highlighting the pressing need for research to more carefully investigate children's roles as active agents in cultural adaptation.

It is widely agreed upon that morality guides people with conflicting interests towards agreements of mutual benefit. We therefore might expect numerous proposals for organizing human moral cognition around the logic of bargaining, negotiation, and agreement. Yet, while “contractualist” ideas play an important role in moral philosophy, they are starkly underrepresented in the field of moral psychology. From a contractualist perspective, ideal moral judgments are those that would be agreed to by rational bargaining agents—an idea with wide-spread support in philosophy, psychology, economics, biology, and cultural evolution. As a practical matter, however, investing time and effort in negotiating every interpersonal interaction is unfeasible. Instead, we propose, people use abstractions and heuristics to efficiently identify mutually beneficial arrangements. We argue that many well-studied elements of our moral minds, such as reasoning about others’ utilities (“consequentialist” reasoning) or evaluating intrinsic ethical properties of certain actions (“deontological” reasoning), can be naturally understood as resource-rational approximations of a contractualist ideal. Moreover, this view explains the flexibility of our moral minds—how our moral rules and standards get created, updated and overridden and how we deal with novel cases we have never seen before. Thus, the apparently fragmentary nature of our moral psychology—commonly described in terms of systems in conflict—can be largely unified around the principle of finding mutually beneficial agreements under resource constraint. Our resulting “triple theory” of moral cognition naturally integrates contractualist, consequentialist and deontological concerns.