Trends in Cognitive Sciences最新文献

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.

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.

Affect labeling can shape how emotions are experienced and shared, with important consequences for both well-being and relationships. While decades of research have explored the impact of articulating emotions through language, the labeling process itself has received limited attention until recently. We suggest that affect labeling can be considered analogous to perceptual decision making, as both involve accumulating evidence toward a decision. Building on perceptual theories of emotion, we explore how this perspective provides new insights into the mechanisms underlying affect labeling. We then review existing research applying sequential sampling models to affect labeling, illustrating how it accounts for the different processes involved in labeling and may explain mechanisms underlying individual differences in the labeling process.

Research on 'cognitive listening' has grown exponentially in recent years. Lacking, however, is a conceptual framework to organize the abundance of data from the hearing, cognitive, and linguistic sciences. We offer the data-resource-language (DRL) framework that draws from the notions of data-limited and resource-limited processes to provide a roadmap for understanding the interaction between auditory sensitivity, cognitive resources, and linguistic knowledge during speech perception, especially in adverse conditions. The DRL framework explains how these three sets of abilities predict performance and resource engagement as a function of signal quality. It also provides a platform for characterizing similarities and differences in how normal-hearing, impaired-hearing, and non-native listeners process speech in challenging conditions.

Cooperation enables humans to reshape entire environments and build complex societies. Although often celebrated, cooperation also has hidden costs. By presenting core mechanisms behind its emergence, we demonstrate that maintaining cooperation frequently relies on social control and coercion, which can lead to extortion and discrimination. Group cooperation further necessitates defining who belongs to the group, fostering exclusion and intergroup conflict. Free-rider concerns fuel scapegoating and polarization. These downsides challenge the notion of cooperation as a simple success story. The resulting conundrum for scientists is not just to explain cooperation but to identify institutions that harness its benefits while limiting its risks. Understanding these complexities is crucial to ensuring that human cooperation serves the common good rather than deepening social divides.

The basic mechanisms that underlie developmental dyslexia - a difficulty in acquiring reading expertise - are still debated. We propose that such difficulties should be understood within the broad framework of learning and skill acquisition. Behavioral and neural studies, as well as computational analyses, imply that acquiring expertise has atypical dynamics in dyslexia, largely due to reduced perceptual memory, which is manifested in faster decay of perceptual traces of both speech and non-speech stimuli. This faster behavioral decay is associated with faster decay of neural adaptation to stimulus regularities in perceptual cortices. We propose that these atypical dynamics lead to a slower accumulation of language statistics, manifested in reduced complexity of perceptual categories, slower acquisition of words, and - counterintuitively - larger relative difficulties as exposure to stimuli grows.

Computational functionalism claims that executing certain computations is sufficient for consciousness, regardless of the physical mechanisms implementing those computations. This view neglects a compelling alternative: that subcomputational biological mechanisms, which realize computational processes, are necessary for consciousness. By contrasting computational roles with their subcomputational biological realizers, I show that there is a systematic tension in our criteria for consciousness: prioritizing computational roles favors consciousness in AI, while prioritizing subcomputational biological realizers favors consciousness in simpler animals. Current theories of consciousness are 'meat-neutral', but if specific physical substrates are necessary, AI may never achieve consciousness. Understanding whether consciousness depends on computational roles, biological realizers, or both, is crucial for assessing the prospects of consciousness in AI and less complex animals.

Longitudinal measurements of brain structure and function are critical for understanding how humans change over time. Traditional longitudinal approaches sample sparsely across large windows of time to estimate coarse, long-term brain changes. This review showcases insights from dense longitudinal neuroimaging (DLN), an emerging approach that samples densely across relatively short windows of time to precisely estimate individual trajectories of brain change. DLN measures multiple samples from individuals throughout critical periods of rapid change. It allows precise estimates of nonlinear trajectories to advance a mechanistic understanding of brain change. Novel findings from this approach are improving our understanding of human cognition, such as the role of the motor system in visual development and learning.