Trends in Cognitive Sciences最新文献

Recent research by Lavan et al. explores how individuals form complex impressions from voices. Using electroencephalography and behavioral measures, the study identifies distinct time courses for discerning traits, with early acoustic processing preceding higher-order perception. These findings shed light on the temporal dynamics of voice-based person perception and its neural underpinnings.

The evolution of whole-body endothermy occurred independently in dinosaurs and mammals and was associated with some of the most significant neurocognitive shifts in life's history. These included a 20-fold increase in neurons and the evolution of new brain structures, supporting similar functions in both lineages. We propose the endothermic brain hypothesis, which holds that elaborations in endotherm brains were geared towards increasing caloric intake through efficient foraging. The hypothesis is grounded in the intrinsic coupling of cognition and organismic self-maintenance. We argue that coevolution of increased metabolism and new forms of cognition should be jointly investigated in comparative studies of behaviors and brain anatomy, along with studies of fossil species. We suggest avenues for such research and highlight critical open questions.

Online communication is central to modern social life, yet it is often linked to toxic manifestations and reduced well-being. How and why online communication enables these toxic social effects remains unanswered. In this opinion, we propose three roots of online toxicity: disembodiment, limited accountability, and disinhibition. We suggest that virtual disembodiment results in a chain of psychological states primed for deleterious social interaction. Drawing from differences between face-to-face and online interactions, the framework highlights and addresses the fundamental problems that result in impaired communication between individuals and explicates its effects on social toxicity online.

Decision making is often necessary before performing an action. Traditionally, it has been assumed that decision making and motor control are independent, sequential processes. Ogasa et al. challenge this view, and demonstrate that the decision-making process significantly impacts on the formation and retrieval of motor memory by tagging it with the level of confidence.

A central challenge for cognitive science is to explain how abstract concepts are acquired from limited experience. This has often been framed in terms of a dichotomy between connectionist and symbolic cognitive models. Here, we highlight a recently emerging line of work that suggests a novel reconciliation of these approaches, by exploiting an inductive bias that we term the relational bottleneck. In that approach, neural networks are constrained via their architecture to focus on relations between perceptual inputs, rather than the attributes of individual inputs. We review a family of models that employ this approach to induce abstractions in a data-efficient manner, emphasizing their potential as candidate models for the acquisition of abstract concepts in the human mind and brain.

Understanding why individuals are more confident of the existence of invisible scientific phenomena (e.g., oxygen) than invisible religious phenomena (e.g., God) remains a puzzle. Departing from conventional explanations linking ontological beliefs to direct experience, we introduce a model positing that testimony predominantly shapes beliefs in both scientific and religious domains. Distinguishing direct experience (personal observation) from cultural input (testimony-based evidence), we argue that even apparently direct experiences often stem from others' testimony. Our analysis indicates that variability in direct experience cannot explain belief disparities between science and religion, within each domain, or across cultures. Instead, variability in testimony is the primary driver of ontological beliefs. We present developmental evidence for testimony-based beliefs and elucidate the mechanisms underlying their impact.

For decades, cognitive scientists have debated what kind of representation might characterize human concepts. Whatever the format of the representation, it must allow for the computation of varied properties, including similarities, features, categories, definitions, and relations. It must also support the development of theories, ad hoc categories, and knowledge of procedures. Here, we discuss why vector-based representations provide a compelling account that can meet all these needs while being plausibly encoded into neural architectures. This view has become especially promising with recent advances in both large language models and vector symbolic architectures. These innovations show how vectors can handle many properties traditionally thought to be out of reach for neural models, including compositionality, definitions, structures, and symbolic computational processes.