Topics in Cognitive Science最新文献

People's choices of food and drink, the attitudes they express, and the beliefs that they state are influenced by their political and other identities. At the same time, people's everyday choices depend on the context of available options in ways that are difficult to explain in terms of the choosers' preferences and beliefs. Such phenomena provoke various questions. Do partisans or conspiracy theorists really believe what they are saying? Given the systematic inconsistency of their choices, in what sense do consumers prefer the items they purchase? More generally, how "flat" is the mind-do we come to decision-making and choice with pre-existing preferences, attitudes, and beliefs, or are our explanations for our behavior mere post-hoc narratives? Here, we argue that several apparently disparate difficulties are rooted in a failure to separate psychologically different types of preferences, attitudes, and beliefs. We distinguish between underlying, inferred, and expressed preferences. These preferences may be expressed in different coordinate spaces and hence support different types of explanatory generalizations. Choices that appear inconsistent according to one type of preference can appear consistent according to another, and whether we can say that a person "really" prefers something depends on which type of preference we mean. We extend the tripartite classification to the case of attitudes and beliefs, and suggest that attributions of attitudes and beliefs may also be ambiguous. We conclude that not all of the mental states and representations that govern our behavior are context-dependent and constructed, although many are.

This is an introduction to the special issue of Topics in Cognitive Science, honoring Nick Chater's award of the 2023 David E. Rumelhart Prize for Contributions to the Theoretical Foundations of Human Cognition. It provides a condensed overview of his contributions to cognitive science within which the articles to this special issue are situated, finishing off with two short personal recollections by the Editors.

How do teachers' gestures influence students' learning? This article reviews research investigating the role of gestures in communication, focusing on teachers' communication with their students, primarily in mathematics and science instruction. We first briefly consider gesture's role in communication more generally as a backdrop for considering teaching as a special context for communication. We then describe teachers' spontaneous gesturing in teaching contexts, and we consider how teachers' spontaneous gestures might influence students' learning. We then consider experimental studies that provide causal support for the effects of teachers' gestures on students' learning. We conclude by discussing future directions and implications for educational practice.

Studies of the evolution of language rely heavily on comparisons to nonhuman primates, particularly the gestural communication of nonhuman apes. Differences between human and ape gestures are largely ones of degree rather than kind. For example, while human gestures are more flexible, ape gestures are not inflexible. In this piece, I closely consider two features of the gestural communication of apes and humans that might display differences in kind: iconicity and temporal alignment. Iconicity has long played a privileged role in theories of gestural language origins, the proposal being that it provided a steppingstone into language through a stage of pantomime. However, iconicity is not as easy as it seems. Evidence from co-speech gestures of hearing children and from homesign suggests that iconic reference is both cognitively complex and slow to develop in humans. There is no conclusive evidence that nonhuman apes understand or produce iconic gestures; some gestures may appear iconic to human observers but the apes themselves may not understand the similarity between form and meaning. Far from providing an easy pathway into the emergence of symbolic communication, iconicity relies on sophisticated capacities for analogy and abstraction, ones often lacking in apes and young children. Temporal alignment between gesture and vocalization is another area that may show sharp contrasts between adult humans and apes, though data here is sparser. I discuss the tight synchronization of gesture and speech that emerges over the first year of life in human children and contrast it with gesture and vocalization in apes, which typically are described as overlapping but not simultaneous. Human ancestors probably communicated in ways similar to other apes, but the dual emergence of the ability to use iconic reference and the alignment of hand and mouth may have set our ancestors down a unique evolutionary road toward language.

How we judge the similarity between stimuli in the world is connected ultimately to how we represent them. Because of this, decisions about how we model similarity, either in terms of human behavior or patterns of neural activity, can provide key insights into how representations are structured and organized. Despite this, psychology and cognitive neuroscience continue to be dominated by a narrow range of similarity models, particularly those that characterize similarity as distance within "cognitive space." Despite the appeal of such models, their topological nature places fundamental constraints on their ability to capture relationships between objects and events in the world. To probe this, we created a stimulus set in which the predicted similarity relationships (based on an alternative model of similarity) could not be simply embedded within Euclidean space. This approach revealed that the spatial model distorts these predictions, and the perceived similarities of human observers. These findings indicate that cognitive spaces-that underlie much recent work probing both visual and conceptual representations in cognitive neuroscience-are limited in fundamental ways that restrict their theoretical and practical utility.

Mushrooms are a ubiquitous and essential component in our biological environment and have been of interest to humans around the globe for millennia. Knowledge about mushrooms represents a prime example of cumulative culture, one of the key processes in human evolution. Based on a review of available research, we argue that the cognitive mechanisms of cultural transmission impact this knowledge in a twofold manner. First and foremost, they secure the accumulation of (folk-)mycological knowledge, with the principal objective to capture reliable information on edibility and means for safe distinction. However, they also shape attitudes toward mushrooms, practices involved in foraging and consumption, and appraisals of edibility in distinct ways, with even regression and eventual loss of knowledge as one possible outcome. In using the domain of mushrooms as an example for expounding this dual role that culture plays during knowledge transmission, our paper contributes to theoretical debates around the cognitive and cultural mechanisms involved in human evolution.

Peer-assisted learning has the potential to improve learning in academic settings and beyond. However, the cognitive and motivational mechanisms of learning through interaction with other learners are not fully understood. Here, we present an empirical study in which we compare a peer-assisted learning condition with two individual learning conditions. The empirical findings suggest that both positive and negative peer effects occurred. On the positive side, learners placed in a peer-assisted learning condition allocated more time to practice and they benefited from selectively interacting with the more knowledgeable peers. On the negative side, error exposure and increased cognitive load may have hindered learning in the peer-assisted learning condition. A computational cognitive model developed in the ACT-R cognitive architecture is presented and used to explain the mechanisms of knowledge spillover, trust, and error exposure. This research has implications for designing collaborative learning protocols to increase human collective intelligence and designing artificial intelligence systems that can support human-machine teaming.

This manuscript documents a systematic ethnomycological analysis of ethnographic archives. Focusing on texts describing human-fungi interactions, I conduct a global, cross-cultural review of mushroom use, covering 193 societies worldwide. The study reveals diverse mushroom-related cultural practices, emphasizing the significance of fungi beyond culinary value to include domains such as rituals, medicine, folklore, and fire-making. Special attention is given to exploring how mushrooms and their foraging involve human cognition. The findings also expose a lack of detail in descriptions of human-mushroom relations. Ethnomycology continues to receive limited attention, largely due to Western mycophobic biases. This highlights the need for expanded ethnomycological research to enrich our understanding of past and present human encounters with the fungal kingdom.

In recent years, we have experienced rapid development of advanced technology, machine learning, and artificial intelligence (AI), intended to interact with and augment the abilities of humans in practically every area of life. With the rapid growth of new capabilities, such as those enabled by generative AI (e.g., ChatGPT), AI is increasingly at the center of human communication and collaboration, resulting in a growing recognition of the need to understand how humans and AI can integrate their inputs in collaborative teams. However, there are many unanswered questions regarding how human-AI collective intelligence will emerge and what the barriers might be. Truly integrated collaboration between humans and intelligent agents may result in a different way of working that looks nothing like what we know now, and it is important to keep the essential goal of human societal well-being and prosperity a priority. In this special issue, we begin to scope out the underpinnings of a socio-cognitive architecture for Collective HUman-MAchine INtelligence (COHUMAIN), which is the study of the capability of an integrated human and machine (i.e., intelligent technology) system to achieve goals in a wide range of environments. This topic consists of nine papers including a description of the conceptual foundation for a socio-cognitive architecture for COHUMAIN, empirical tests of some aspects of this architecture, research on proposed representations of intelligent agents that can jointly interact with humans, empirical tests of human-human and human-machine interactions, and philosophical and ethical issues to consider as we develop these systems.

Artificial intelligence (AI) is often used to predict human behavior, thus potentially posing limitations to individuals' and collectives' freedom to act. AI's most controversial and contested applications range from targeted advertisements to crime prevention, including the suppression of civil disorder. Scholars and civil society watchdogs are discussing the oppressive dangers of AI being used by centralized institutions, like governments or private corporations. Some suggest that AI gives asymmetrical power to governments, compared to their citizens. On the other hand, civil protests often rely on distributed networks of activists without centralized leadership or planning. Civil protests create an adversarial tension between centralized and decentralized intelligence, opening the question of how distributed human networks can collectively adapt and outperform a hostile centralized AI trying to anticipate and control their activities. This paper leverages multi-agent reinforcement learning to simulate dynamics within a human-machine hybrid society. We ask how decentralized intelligent agents can collectively adapt when competing with a centralized predictive algorithm, wherein prediction involves suppressing coordination. In particular, we investigate an adversarial game between a collective of individual learners and a central predictive algorithm, each trained through deep Q-learning. We compare different predictive architectures and showcase conditions in which the adversarial nature of this dynamic pushes each intelligence to increase its behavioral complexity to outperform its counterpart. We further show that a shared predictive algorithm drives decentralized agents to align their behavior. This work sheds light on the totalitarian danger posed by AI and provides evidence that decentrally organized humans can overcome its risks by developing increasingly complex coordination strategies.