Topics in Cognitive Science最新文献

Some of the required characteristics for a true machine theory of mind (MToM) include the ability to (1) reproduce the full diversity of human thought and behavior, (2) develop a personalized model of an individual with very limited data, and (3) provide an explanation for behavioral predictions grounded in the cognitive processes of the individual. We propose that a certain class of cognitive models provide an approach that is well suited to meeting those requirements. Being grounded in a mechanistic framework like a cognitive architecture such as ACT-R naturally fulfills the third requirement by mapping behavior to cognitive mechanisms. Exploiting a modeling paradigm such as instance-based learning accounts for the first requirement by reflecting variations in individual experience into a diversity of behavior. Mechanisms such as knowledge tracing and model tracing allow a specific run of the cognitive model to be aligned with a given individual behavior trace, fulfilling the second requirement. We illustrate these principles with a cognitive model of decision-making in a search and rescue task in the Minecraft simulation environment. We demonstrate that cognitive models personalized to individual human players can provide the MToM capability to optimize artificial intelligence agents by diagnosing the underlying causes of observed human behavior, projecting the future effects of potential interventions, and managing the adaptive process of shaping human behavior. Examples of the inputs provided by such analytic cognitive agents include predictions of cognitive load, probability of error, estimates of player self-efficacy, and trust calibration. Finally, we discuss implications for future research and applications to collective human-machine intelligence.

Mastering how to convey meanings using language is perhaps the main challenge facing any language learner. However, satisfactory accounts of how this is achieved, and even of what it is for a linguistic item to have meaning, are hard to come by. Nick Chater was one of the pioneers involved in the early development of one of the most successful methodologies within the cognitive science of language for discovering meaning: distributional semantics. In this article, we review this approach and discuss its successes and shortcomings in capturing semantic phenomena. In particular, we discuss what we dub the "distributional paradox:" how can models that do not implement essential dimensions of human semantic processing, such as sensorimotor grounding, capture so many meaning-related phenomena? We conclude by providing a preliminary answer, arguing that distributional models capture the statistical scaffolding of human language acquisition that allows for communication, which, in line with Nick Chater's more recent ideas, has been shaped by the features of human cognition on the timescale of cultural evolution.

Predictive processing is an influential theoretical framework for understanding human and animal cognition. In the context of predictive processing, learning is often reduced to optimizing the parameters of a generative model with a predefined structure. This is known as Bayesian parameter learning. However, to provide a comprehensive account of learning, one must also explain how the brain learns the structure of its generative model. This second kind of learning is known as structure learning. Structure learning would involve true structural changes in generative models. The purpose of the current paper is to describe the processes involved upstream of these structural changes. To do this, we first highlight the remarkable compatibility between predictive processing and the processing fluency theory. More precisely, we argue that predictive processing is able to account for all the main theoretical constructs associated with the notion of processing fluency (i.e., the fluency heuristic, naïve theory, the discrepancy-attribution hypothesis, absolute fluency, expected fluency, and relative fluency). We then use this predictive processing account of processing fluency to show how the brain could infer whether it needs a structural change for learning the causal regularities at play in the environment. Finally, we speculate on how this inference might indirectly trigger structural changes when necessary.

The foundation of ancient, invented writing systems lies in the predominant iconicity of their sign shapes. However, these shapes are often used not for their referential meaning but in a metaphorical way, whereby one entity stands for another. Metaphor, including its subcategories pars pro toto and metonymy, plays a crucial role in the formation of the earliest pristine invented scripts, yet this mechanism has been understudied from a cognitive, contextual, and comparative perspective. This article aims to address issues pertaining to the definition, development, and application of these mechanisms in the formation of the Mesopotamian, Egyptian, and Chinese scripts. We analyze the local cases of metaphor-in-action in primary inventions, focusing first on visual metaphors and, second, on the typical or idiosyncratic uses of metonyms.

Standard models of lexical production assume that speakers access representations of meaning, grammar, and different aspects of sound in a roughly sequential manner (whether or not they admit cascading or interactivity). In contrast, we review evidence for a parallel activation model in which these representations are accessed in parallel. According to this account, word learning involves the binding of the meaning, grammar, and sound of a word into a single representation. This representation is then activated as a whole during production, and so all linguistic components are available simultaneously. We then note that language comprehension involves extensive use of prediction and argue that comprehenders use production mechanisms to determine (roughly) what they would say next if they were speaking. So far, theories of prediction-by-production have assumed sequential lexical production. We therefore reinterpret such evidence in terms of parallel lexical production.

Studies of homesigns have shed light on the human capacity for language and on the challenging problem of language acquisition. The study of homesign has evolved from a perspective grounded in gesture studies and child development to include sign language linguistics and the role of homesigns in language emergence at the community level. One overarching finding is that homesigns more closely resemble sign languages used by linguistic communities than they resemble the gestures produced by hearing people along with spoken language. Homesigns may not exhibit all of the linguistic properties of community languages, but the properties they do exhibit are language properties, and for the people who use them, homesigns are their language. Further, the linguistic structures in homesigns are innovated by the deaf people who use them and are imperfectly learned by their hearing communication partners. I close with a call to action: We cannot celebrate discoveries about the mind made possible by studies of homesigns and emerging languages while ignoring the pervasiveness of language deprivation among deaf people, and the relative lack of deaf participation in science, even in studies of sign languages. While the scientific community learns much from studying homesigns and sign languages, we also have a responsibility to work toward ensuring that every deaf person has access to language, communication, and education.

The 1998 article by van Gelder proposed a Dynamical Hypothesis (DH) in cognitive science consisting of Nature (cognitive agents are dynamical systems) and Knowledge (cognitive agents should be understood dynamically) hypotheses in contrast to the Computational Hypothesis (CH) that cognitive agents are computers. My commentary focuses on the contributions of Paxton and Necaise et al. in interpersonal motor coordination and radicalization across social media. I do not think that either contribution supports the Nature hypothesis but does conform with the Knowledge hypothesis. I conclude by describing cognitive agents as living systems (or nonliving systems that mimic aspects of living systems) that can be alternately viewed to support the DH or CH or both at the same time.

This paper explores the role of measurement as a cognitive technology across human history, emphasizing the coexistence of formal and informal measurement systems. While standardized systems dominate contemporary culture and are well documented across large-scale societies of the past, this manuscript highlights the less explored domain of informal measurement practices that have been integral to daily life from the past to the present. Through the examination of body-based measurement systems and proportional heuristics, we demonstrate how these informal strategies were not merely precursors to formal standards but essential adaptive tools for solving everyday problems. Often, these informal solutions come with practical advantages. This manuscript calls for a broader recognition of their significance in cultural and technological evolution.

Throughout the long history of Classic Maya hieroglyphs, a logosyllabic writing system used from the late first millennium BCE through the mid-second millennium CE in southern Mesoamerica, the most commonly recorded phonetic value was the syllable u (/ʔu/). With over a dozen different u hieroglyphs, Classic Maya scribes had more options for recording /ʔu/ than any other syllable or logograph. Cognitive approaches to writing systems typically attribute graphemic variation (i.e., alternation between signs with equivalent linguistic value) to semantic differences like animacy or to non-linguistic factors like identity. Distribution of Classic Maya u hieroglyphs, however, suggests that morphosyntactic context influenced which grapheme scribes wrote and when. This case suggests that scribal knowledge of Classic Maya hieroglyphs included ideas about writing's relationship to language. It also highlights the cognitive relevance of morphosyntax for a writing system's users as they differentiate among graphic signs with identical linguistic denotation.

The interaction-dominant approach to perception and action, originally formulated in the mid-1990s, has matured and gained remarkable momentum as an entailment of the dynamical hypotheses proposed at that time. This framework seeks to explain the fluid and intricate interplay of causality spanning the entire organism by integrating high-dimensional details with low-dimensional constraints across various scales of behavior. Both Chemero (2024) and Wallot et al. (2024) have skillfully explored the theoretical implications and methodological challenges this perspective introduces. We echo Chemero's (2024) and Wallot et al.'s (2024) focus on multifractality, while also underscoring new efforts to model the synergetic relationships and cascading dynamics inherent in this interaction-dominant approach.