Behavioral and Brain Sciences最新文献

In the human mind, what is a social relationship, and what are the developmental origins of this representation? I consider findings from infant psychology and propose that our representations of social relationships are intuitive theories built on core knowledge. I propose three central components of this intuitive theory. The purpose of the first component is to recognize whether a relationship exists, the purpose of the second is to characterize the relationship by categorizing it into a model and to compute its strength (i.e., intensity, pull, or thickness), and the purpose of the third is to understand how to change relationships through explicit or implicit communication. I propose that infants possess core knowledge on which this intuitive theory is built. This paper focuses on the second component and considers evidence that infants characterize relationships. Following Relational Models Theory (A. P. Fiske, 1992, 2004) I propose that from infancy humans recognize relationships that belong to three models: communal sharing (where people are 'one'), authority ranking (where people are ranked), and equality matching (where people are separate, but evenly balanced). I further propose that humans, and potentially infants, recognize a relationship's strength which can be thought of as a continuous representation of obligations (the extent to which certain actions are expected), and commitment (the likelihood that people will continue the relationship). These representations and the assumption that others share them allow us to form, maintain, and change social relationships throughout our lives by informing how we interpret and evaluate the actions of others and plan our own.

Guided by concepts from life history (LH) theory, a large human research literature has tested the hypothesis that exposures to extrinsic mortality (EM) promote the development of faster LH strategies (e.g., earlier/faster reproduction, higher offspring number). A competing model proposes that, because EM in the past was intimately linked to energetic constraints, such exposures specifically led to the development of slower LH strategies. We empirically address this debate by examining (1) LH variation among small-scale societies under different environmental conditions; (2) country-, regional- and community-level correlations between ecological conditions, mortality, maturational timing, and fertility; (3) individual-level correlations between this same set of factors; and (4) natural experiments leveraging the impact of externally-caused changes in mortality on LH traits. Partially supporting each model, we found that harsh conditions encompassing energetic stress and ambient cues to EM (external cues received through sensory systems) have countervailing effects on the development of LH strategies, both delaying pubertal maturation and promoting an accelerated pace of reproduction and higher offspring number. We conclude that, although energetics are fundamental to many developmental processes, providing a first tier of environmental influence, this first tier alone cannot explain these countervailing effects. An important second tier of environmental influence is afforded by ambient cues to EM. We advance a 2-tiered model that delineates this second tier and its central role in regulating development of LH strategies. Consideration of the first and second tier together is necessary to account for the observed countervailing shifts toward both slower and faster LH traits.

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.

Among non-human animals, crows, octopuses and honeybees are well-known for their complex brains and cognitive abilities. Widening the lens from the idiosyncratic abilities of exemplars like these to those of animals across the phylogenetic spectrum begins to reveal the ancient evolutionary process by which complex brains and cognition first arose in different lineages. The distribution of 35 phenotypic traits in 17 metazoan lineages reveals that brain and cognitive complexity in only three lineages (vertebrates, cephalopod mollusks, and euarthropods) can be attributed to the pivotal role played by body, sensory, brain and motor traits in active visual sensing and visuomotor skills. Together, these pivotal traits enabled animals to transition from largely reactive to more proactive behaviors, and from slow and two-dimensional motion to more rapid and complex three-dimensional motion. Among pivotal traits, high-resolution eyes and laminated visual regions of the brain stand out because they increased the processing demands on and the computational power of the brain by several orders of magnitude. The independent acquisition of pivotal traits in cognitively complex (CC) lineages can be explained as the completion of several multi-trait transitions over the course of evolutionary history, each resulting in an increasing level of complexity that arises from a distinct combination of traits. Whereas combined pivotal traits represent the highest level of complexity in CC lineages, combined traits at lower levels characterize many non-CC lineages, suggesting that certain body, sensory and brain traits may have been linked (the trait-linkage hypothesis) during the evolution of both CC and non-CC lineages.

I propose that meta-learned models, and in particular the situation-aware deployment of "learning-to-infer" modules can be advantageously extended to domains commonly thought to lie outside the cognitive, such as motivations and preferences on one hand, and the effectuation of micro- and coping-type behaviors.

Binz et al. propose meta-learning as a promising avenue for modelling human cognition. They provide an in-depth reflection on the advantages of meta-learning over other computational models of cognition, including a sound discussion on how their proposal can accommodate neuroscientific insights. We argue that active inference presents similar computational advantages while offering greater mechanistic explanatory power and biological plausibility.

Quantum active Bayesian inference and quantum Markov blankets enable robust modeling and simulation of difficult-to-render natural agent-based classical inferential paradoxes interfaced with task-specific environments. Within a non-realist cognitive completeness regime, quantum Markov blankets ensure meta-learned irrational decision making is fitted to explainable manifolds at optimal free energy, where acceptable incompatible observations or temporal Bell-inequality violations represent important verifiable real-world outcomes.

Binz et al. propose a general framework for meta-learning and contrast it with built-by-hand Bayesian models. We comment on some architectural assumptions of the approach, its relation to the active inference framework, its potential applicability to living systems in general, and the advantages of the latter in addressing the explanation problem.

We are encouraged by the many positive commentaries on our target article. In this response, we recapitulate some of the points raised and identify synergies between them. We have arranged our response based on the tension between data and architecture that arises in the meta-learning framework. We additionally provide a short discussion that touches upon connections to foundation models.

Abundant experimental evidence illustrates violations of Bayesian models across various cognitive processes. Quantum cognition capitalizes on the limitations of Bayesian models, providing a compelling alternative. We suggest that a generalized quantum approach in meta-learning is simultaneously more robust and flexible, as it retains all the advantages of the Bayesian framework while avoiding its limitations.