Wiley Interdisciplinary Reviews-Cognitive Science最新文献

This article focuses on familiarity, the form of memory allowing humans to recognize stimuli that have been encountered before. We aim to emphasize its complex nature which includes representational and phenomenological dimensions. The former implies that its neural correlates depend on the type and complexity of the cue stimulus, as different classes of stimuli are represented in distributed ventral visual and medial temporal regions. The second dimension relates to the subjective feeling of familiarity, which results from a fluency signal that is attributed to past encounters with the stimulus. We review mnemonic and non-mnemonic sources of fluency that can induce a sense of familiarity, as well as cases where fluency is not attributed to memory, among which the phenomenological experience of déjà-vu. Across these two dimensions, we highlight key questions to be answered by future studies to improve our understanding of the underpinnings of this form of memory and contribute to building an integrative neurocognitive model of familiarity. Essential to this aim is the clarification of the computational, cognitive, and neural mechanisms involved, namely global matching, fluency attribution, and sharpening. Furthermore, future research is needed to unravel the relationships between these mechanisms. We argue that to achieve these goals, researchers must use appropriate behavioral paradigms and clearly define which dimension of familiarity they investigate.

Perception involves the processing of content or information about the world. In what form is this content represented? I argue that perception is widely compositional. The perceptual system represents many stimulus features (including shape, orientation, and motion) in terms of combinations of other features (such as shape parts, slant and tilt, common and residual motion vectors). But compositionality can take a variety of forms. The ways in which perceptual representations compose are markedly different from the ways in which sentences or thoughts are thought to be composed. I suggest that the thesis that perception is compositional is not itself a concrete hypothesis with specific predictions; rather it affords a productive framework for developing and evaluating specific empirical hypotheses about the form and content of perceptual representations. The question is not just whether perception is compositional, but how. Answering this latter question can provide fundamental insights into perception. This article is categorized under: Philosophy > Representation Philosophy > Foundations of Cognitive Science Psychology > Perception and Psychophysics.

This primer describes research on the development of motor behavior. We focus on infancy when basic action systems are acquired-posture, locomotion, manual actions, and facial actions-and we adopt a developmental systems perspective to understand the causes and consequences of developmental change. Experience facilitates improvements in motor behavior and infants accumulate immense amounts of varied everyday experience with all the basic action systems. At every point in development, perception guides behavior by providing feedback about the results of just prior movements and information about what to do next. Across development, new motor behaviors provide new inputs for perception. Thus, motor development opens up new opportunities for acquiring knowledge and acting on the world, instigating cascades of developmental changes in perceptual, cognitive, and social domains. This article is categorized under: Cognitive Biology > Cognitive Development Psychology > Motor Skill and Performance Neuroscience > Development.

The study of consciousness is considered by many one of the most difficult contemporary scientific endeavors and confronts several methodological and theoretical challenges. A central issue that makes the study of consciousness so challenging is that, while the rest of science is concerned with problems that can be verified from a "third person" view (i.e., objectively), the study of consciousness deals with the phenomenon of subjective experience, only accessible from a "first person" view. In the present article, we review early (starting during the late 19th century) and later efforts on measuring consciousness and its absence, focusing on the two main approaches used by researchers within the field: objective (i.e., performance based) and subjective (i.e., report based) measures of awareness. In addition, we compare the advantages and disadvantages of both types of awareness measures, evaluate them according to different methodological considerations, and discuss, among other issues, the possibility of comparing them by transforming them to a common sensitivity measure (d'). Finally, we explore several new approaches-such as Bayesian models to support the absence of awareness or new machine-learning based decoding models-as well as future challenges-such as measuring the qualia, the qualitative contents of awareness-in consciousness research.

Mind-wandering (MW) refers to the shift of attention away from an ongoing task and/or external environment towards mental contents (e.g., memories, prospective thoughts) unrelated to the task. Physiological measures (e.g., pupil size, EEG, and fMRI) have often been acquired as objective markers for MW states, which has greatly helped their study as well as triangulation with other measures. Pupillometry in particular has been used as a covert biomarker of MW because it is reliably modulated by several distinct processes spanning arousal, emotion, and attention, and it signals attentional lapses. Yet, coupling MW and the measurement of pupil size has led to seemingly contrasting results. We argue that, common to the studies reviewed here, one reason is resolving to the measurement of tonic pupil size, which reflects low-frequency, slow changes in one's physiological state, and thus implicitly assumes that MW is a static, long-lasting process. We then additionally focus on three major axes of variability in the reviewed studies: (i) the definition and measurement of MW; (ii) the impact of contextual aspects, such as task demands and individual arousal levels; (iii) the identification and tracking of MW in combination with pupillary measures. We provide an overview of these differences and put forward recommendations for using physiological measures-including, but not limited to, pupil size-in MW research effectively. In conclusion, pupillometry can be a very informative tool for MW research, provided that it is used with the due methodological caution.

Anxiety disorder is a complex disease with the influence of environmental and genetic factors and multimolecular participation, and it is also one of the most common mental disorders. The causes of disorders are not clear but may include a variety of social, psychological, and biological factors. Therefore, neither genetics, neurobiology, nor neuroimaging can independently explain the pathological mechanism. By searching the Web of Science databases, Derwent Innovation Patent database, ClinicalTrials.gov database, and Cortellis database, we analyze the current situation of papers, patents, clinical trials, and drugs of anxiety disorder. Second, the existing literature was reviewed to summarize the neurophysiological mechanism, brain imaging, gene, anti-anxiety drugs, and other aspects of anxiety disorders. This article reviews the research status of anxiety disorders. The heterogeneity of the disease, lack of treatment effectiveness, and gaps in translational medicine still present barriers to further advancement. Thus, in-depth explorations of the underlying biological mechanisms of anxiety disorders, the detection and intervention of biological targets, and further developments based on existing intervention strategies will drive future research on anxiety disorders. This article is categorized under: Neuroscience > Clinical.

Working memory is thought to be the psychological capacity that enables us to maintain or manipulate information no longer in our environment for goal-directed action. Recent work argues that working memory is not a so-called natural kind and in turn cannot explain the cognitive processes attributed to it. This paper first clarifies the scope of this earlier critique and argues for a pluralist account of working memory. Under this account, working memory is variously realized by many mechanisms that contribute to the maintenance and manipulation of information across tasks. This view in effect updates one of the earliest pluralist formulations of working memory. Juxtaposing this view against deflationary descriptions allows us to delineate two gradients that help us chart various accounts of working memory and identify their respective theoretical commitments. In turn, we can isolate those accounts that fail to accord with the evidence supporting a pluralist view, and we can begin to rehabilitate working memory as a pluralist, and ultimately more informative, construct.

Humans and other animals possess the remarkable ability to effectively navigate a shared perceptual environment by discerning which objects and spaces are perceived by others and which remain private to themselves. Traditionally, this capacity has been encapsulated under the umbrella of joint attention or joint action. In this comprehensive review, we advocate for a broader and more mechanistic understanding of this phenomenon, termed co-perception. Co-perception encompasses the sensitivity to the perceptual engagement of others and the capability to differentiate between objects perceived privately and those perceived commonly with others. It represents a distinct concept from mere simultaneous individual perception. Moreover, discerning between private and common objects doesn't necessitate intricate mind-reading abilities or mutual coordination. The act of perceiving objects as either private or common provides a comprehensive account for social scenarios where individuals simply share the same context or may even engage in competition. This conceptual framework encourages a re-examination of classical paradigms that demonstrate social influences on perception. Furthermore, it suggests that the impacts of shared experiences extend beyond affective responses, also influencing perceptual processes. This article is categorized under: Psychology > Attention Philosophy > Foundations of Cognitive Science Philosophy > Psychological Capacities.

Perceptual learning is commonly understood as conferring some benefit to the learner, such as allowing for the extraction of more information from the environment. However, perceptual learning can be biased in several different ways, some of which do not appear to provide such a benefit. Here we outline a systematic framework for thinking about bias in perceptual learning and discuss how several cases fit into this framework. We argue these biases are compatible with an understanding in which perceptual learning is beneficial, but that its benefits are tied to both a person's narrow interests and the training environment or domain, and so if there are changes to either of these, then benefits can turn into liabilities, though these are often temporary. This article is categorized under: Psychology > Learning Philosophy > Value Linguistics > Language Acquisition.

Deep learning has enabled major advances across most areas of artificial intelligence research. This remarkable progress extends beyond mere engineering achievements and holds significant relevance for the philosophy of cognitive science. Deep neural networks have made significant strides in overcoming the limitations of older connectionist models that once occupied the center stage of philosophical debates about cognition. This development is directly relevant to long-standing theoretical debates in the philosophy of cognitive science. Furthermore, ongoing methodological challenges related to the comparative evaluation of deep neural networks stand to benefit greatly from interdisciplinary collaboration with philosophy and cognitive science. The time is ripe for philosophers to explore foundational issues related to deep learning and cognition; this perspective paper surveys key areas where their contributions can be especially fruitful. This article is categorized under: Philosophy > Artificial Intelligence Computer Science and Robotics > Machine Learning.