Topics in Cognitive Science最新文献

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.

An enigma for human languages is that children learn to understand words in their mother tongue extremely fast. The cognitive sciences have not been able to fully understand the mechanisms behind this highly efficient learning process. In order to provide at least a partial answer to this problem, I have developed a cognitive model of the semantics of natural language in terms of conceptual spaces. I present a background to conceptual spaces and provide a brief summary of their main features, in particular how it handles learning of concepts. I then apply the model to give a geometric account of the semantics of different word classes. In particular, I propose a "single-domain hypotheses" for the semantics of all word classes except nouns. These hypotheses provide a partial answer to the enigma of how words are learned. Next, a dynamic cognitive model of events is introduced that replaces and extends the function of thematic roles. I apply it to analyze the meanings of different kinds of verbs. I argue that the model also explains some aspects of syntactic structure. In particular, I propose that a sentence typically refers to an event. Some further applications of conceptual spaces are briefly presented.

Scientific thinking is one of the most creative expressions of human cognition. This paper discusses my research contributions to the cognitive science of science. I have advanced the position that data on the cognitive practices of scientists drawn from extensive research into archival records of historical science or collected in extended ethnographic studies of contemporary science can provide valuable insight into the nature of scientific cognition and its relation to cognition in ordinary contexts. I focus on contributions of my research on analogy, model-based reasoning, and conceptual change and on how scientists enhance their natural cognitive capacities by creating modeling environments that integrate cognitive, social, material, and cultural resources. I provide an outline of my trajectory from a physicist to a philosopher of science to a hybrid cognitive scientist in my quest to understand the nature of scientific thinking.

Automated moral inference is an emerging topic of critical importance in artificial intelligence. The contemporary approach typically relies on language models to infer moral relevance or moral properties of a concept. This approach demands complex parameterization and costly computation, and it tends to disconnect with existing psychological accounts of moralization. We present a simple cognitive model for moral inference, Moral Association Graph (MAG), inspired by psychological work on moralization. Our model builds on word association network for inferring moral relevance and draws on rich psychological data. We demonstrate that MAG performs competitively to state-of-the-art language models when evaluated against a comprehensive set of data for automated inference of moral norms and moral judgment of concepts, and in-context moral inference. We also show that our model yields interpretable outputs and is applicable to informing short-term moral change.

As they process complex linguistic input, language comprehenders must maintain a mapping between lexical items (e.g., morphemes) and their syntactic position in the sentence. We propose a model of how these morpheme-position bindings are encoded, maintained, and reaccessed in working memory, based on working memory models such as "serial-order-in-a-box" and its SOB-Complex Span version. Like those models, our model of linguistic working memory derives a range of attested memory interference effects from the process of binding items to positions in working memory. We present simulation results capturing similarity-based interference as well as item distortion effects. Our model provides a unified account of these two major classes of interference effects in sentence processing, attributing both types of effects to an associative memory architecture underpinning linguistic computation.

This study delves into how various musical factors influence the experience of auditory illusions, building on Diana Deutsch's scale illusion experiments and subsequent studies. Exploring the interaction between scale mode and timbre, this study assesses their influence on auditory misperceptions, while also considering the impact of an individual's musical training and ability to discern absolute pitch. Participants were divided into nonmusicians, musicians with absolute pitch, and musicians with relative pitch, and were exposed to stimuli modified across three scale modes (tonal, dissonant, atonal) and two timbres (same, different). The findings suggest that scale illusions occur less frequently with different timbres and vary with scale mode. Crucially, the absolute pitch ability appears to have a more significant impact on the perception of illusions than the duration of musical training. This research contributes to understanding the complex interplay between various factors in auditory perception and the mechanisms behind the experience of auditory illusions.

Retrieval practice-the process of actively calling information to mind rather than passively studying materials-has been proven to be a highly effective learning strategy. However, only recently, researchers have started to examine differences between learners in terms of the optimal conditions of retrieval practice in applied educational settings. In this study (N = 118), we focus on learners with dyslexia. We compare their performance to the performance of typical learners in an adaptive retrieval practice task using both typing-based and speech-based response conditions. We find that typical learners outperform learners with dyslexia when they are asked to respond by typing, but that this difference disappears when learners respond by speech. Using a mathematical model to decompose response times, we demonstrate that this typing-specific disadvantage in learners with dyslexia is mainly a consequence of processing delays, rather than poorer memory performance. These findings contribute to a better understanding of the mechanisms underlying declarative learning in dyslexia, and they can be used to tailor educational technology toward the needs of neurodiverse learners.

Whereas cognitive models of learning often assume direct experience with both the features of an event and with a true label or outcome, much of everyday learning arises from hearing the opinions of others, without direct access to either the experience or the ground-truth outcome. We consider how people can learn which opinions to trust in such scenarios by extending the hedge algorithm: a classic solution for learning from diverse information sources. We first introduce a semi-supervised variant we call the delusional hedge capable of learning from both supervised and unsupervised experiences. In two experiments, we examine the alignment between human judgments and predictions from the standard hedge, the delusional hedge, and a heuristic baseline model. Results indicate that humans effectively incorporate both labeled and unlabeled information in a manner consistent with the delusional hedge algorithm-suggesting that human learners not only gauge the accuracy of information sources but also their consistency with other reliable sources. The findings advance our understanding of human learning from diverse opinions, with implications for the development of algorithms that better capture how people learn to weigh conflicting information sources.

Efficiency principles are increasingly called upon to study features of human language and communication. Zipf's law of abbreviation is widely seen as a classic instance of a linguistic pattern brought about by language users' search for efficient communication. The "law"-a recurrent correlation between the frequency of words and their brevity-is a near-universal principle of communication, having been found in all of the hundreds of human languages where it has been tested, and a few nonhuman communication systems as well. The standard explanation for the law of abbreviation derives from pressures for efficiency: speakers minimize their cumulative effort by using shorter words for frequent occurrences. This explanation, we argue here, fails to explain why long words exist at all. It also fails to explain why the law of abbreviation, despite being robust, is systematically weakened by many short and rare words. We propose an alternative account of the law of abbreviation, based on a simple cultural evolutionary model. Our model does not require any pressure for efficiency. Instead, it derives the law of abbreviation from a general pressure for brevity applying to all words regardless of their frequency. This model makes two accurate predictions that the standard model misses: the correlation between frequency and brevity is consistently weak, and it is characterized by heteroskedasticity, with many short and rare words. We argue on this basis that efficiency considerations are neither necessary nor sufficient to explain the law.