Cognitive Science最新文献

Research shows that high- and low-pitch sounds can be associated with various meanings. For example, high-pitch sounds are associated with small concepts, whereas low-pitch sounds are associated with large concepts. This study presents three experiments revealing that high-pitch sounds are also associated with open concepts and opening hand actions, while low-pitch sounds are associated with closed concepts and closing hand actions. In Experiment 1, this sound-meaning correspondence effect was shown using the two-alternative forced-choice task, while Experiments 2 and 3 used reaction time tasks to show this interaction. In Experiment 2, high-pitch vocalizations were found to facilitate opening hand gestures, and low-pitch vocalizations were found to facilitate closing hand gestures, when performed simultaneously. In Experiment 3, high-pitched vocalizations were produced particularly rapidly when the visual target stimulus presented an open object, and low-pitched vocalizations were produced particularly rapidly when the target presented a closed object. These findings are discussed concerning the meaning of intonational cues. They are suggested to be based on cross-modally representing conceptual spatial knowledge in sensory, motor, and affective systems. Additionally, this pitch-opening effect might share cognitive processes with other pitch-meaning effects.

Metaphor generation is both a creative act and a means of learning. When learning a new concept, people often create a metaphor to connect the new concept to existing knowledge. Does the manner in which people generate a metaphor, via sudden insight (Aha! moment) or deliberate analysis, influence the quality of generation and subsequent learning outcomes? According to some research, deliberate processing enhances knowledge retention; hence, generation via analysis likely leads to better concept learning. However, other research has shown that solutions generated via insight are better remembered. In the current study, participants were presented with science concepts and descriptions, then generated metaphors for the concepts. They also indicated how they generated each metaphor and rated their metaphor for novelty and aptness. We assessed participants’ learning outcomes with a memory test and evaluated the creative quality of the metaphors based on self- and crowd-sourced ratings. Consistent with the deliberate processing benefit, participants became more familiar with the target science concept if they previously generated a metaphor for the concept via analysis compared to via insight. We also found that metaphors generated via analysis did not differ from metaphors generated via insight in quality (aptness or novelty) nor in how well they were remembered. However, participants’ self-evaluations of metaphors generated via insight showed more agreement with independent raters, suggesting the role of insight in modulating the creative ideation process. These preliminary findings have implications for understanding the nature of insight during idea generation and its impact on learning.

Literacy is in decline in many parts of the world, accompanied by drops in associated cognitive skills (including IQ) and an increasing susceptibility to fake news. It is possible that the recent explosive growth and widespread deployment of Large Language Models (LLMs) might exacerbate this trend, but there is also a chance that LLMs can help turn things around. We argue that cognitive science is ideally suited to help steer future literacy development in the right direction by challenging and informing current educational practices and policy. Cognitive scientists have the right interdisciplinary skills to study, analyze, evaluate, and change LLMs to facilitate their critical use, to encourage turn-taking that promotes rather than hinders literacy, to support literacy acquisition in diverse and equitable ways, and to scaffold potential future changes in what it means to be literate. We urge cognitive scientists to take up this mantle—the future impact of LLMs on human literacy skills is too important to be left to the large, predominately U.S.-based tech companies.

People learn language variation through exposure to linguistic interactions. The way we take part in these interactions is shaped by our lexical representations, the mechanisms of language processing, and the social context. Existing work has looked at how we learn and store variation in the ambient language. How this is mediated by the social context is less understood.

We report on the results of an innovative experimental battery designed to test how learning variation is affected by a variable's social indexicality. Hungarian native speakers played a co-operative game involving verb nonwords. These were built on existing inflectional variation in Hungarian. Participant behavior shifted in response to an automated co-player's preferences, and this reflected a change in the overall lexical patterns of the players, affected by the particular verbs introduced by the co-player. Patterns persisted in subsequent testing. Learning was similar for variables with or without social meaning.

Results show that participants can learn and retain a range of variable morphological patterns in a simulated interaction. Participants seem to have equal capacity to pick up variables with and without social meaning. This suggests that the social meaning of a pattern does not clearly constrain learning morphological variation and becomes relevant downstream in learning.

The finding that people tend to prefer logically valid conclusions over invalid ones is known in the literature as the logic-liking effect and has traditionally been interpreted as evidence for the notion of so-called logical intuitions. Results of more recent empirical studies investigating conditional and categorical syllogisms suggest, however, that previous instances of the logic-liking effect can be accounted for by a confound in terms of surface-feature atmosphere. But the true nature of this atmosphere effect has so far remained largely elusive. Here, we address this issue and introduce two variants of disjunctive syllogisms that enable us to deconfound validity, possibility of the conclusion, and surface-feature atmosphere, which has been impossible with simple disjunctive syllogisms used in earlier studies. Three experiments, in which participants were asked to provide liking and logic ratings for these arguments, revealed that the logic-liking effect in disjunctive syllogisms can be explained by an atmosphere confound in combination with implied demand to consider logicality when judging likability. We also observed a strong atmosphere effect in logic ratings over and above an effect of logical validity per se. Furthermore, atmosphere effects appear to be induced only by specific surface features, namely those that are ecologically valid, if fallible, predictors for logicality. We conclude that acquired atmosphere heuristics provide proxies for logical validity that reasoners often take at face value. A comparison of the present results with previous findings from experiments that focused on conditional and categorical syllogisms additionally indicates that these atmosphere heuristics are used irrespective of an argument's complexity.

How do teachers learn about what learners already know? How do learners aid teachers by providing them with information about their background knowledge and what they find confusing? We formalize this collaborative reasoning process using a hierarchical Bayesian model of pedagogy. We then evaluate this model in two online behavioral experiments (N = 312 adults). In Experiment 1, we show that teachers select examples that account for learners' background knowledge, and adjust their examples based on learners' feedback. In Experiment 2, we show that learners strategically provide more feedback when teachers' examples deviate from their background knowledge. These findings provide a foundation for extending computational accounts of pedagogy to richer interactive settings.

How do cognitive pressures shape the lexicons of natural languages? Here, we reframe George Kingsley Zipf's proposed “law of abbreviation” within a more general framework that relates it to cognitive pressures that affect speakers and listeners. In this new framework, speakers' drive to reduce effort (Zipf's proposal) is counteracted by the need for low-frequency words to have word forms that are sufficiently distinctive to allow for accurate recognition by listeners. To support this framework, we replicate and extend recent work using the prevalence of subword phonemic sequences (phonotactic probability) to measure speakers' production effort in place of Zipf's measure of length. Across languages and corpora, phonotactic probability is more strongly correlated with word frequency than word length. We also show this measure of ease of speech production (phonotactic probability) is strongly correlated with a measure of perceptual difficulty that indexes the degree of competition from alternative interpretations in word recognition. This is consistent with the claim that there must be trade-offs between these two factors, and is inconsistent with a recent proposal that phonotactic probability facilitates both perception and production. To our knowledge, this is the first work to offer an explanation why long, phonotactically improbable word forms remain in the lexicons of natural languages.

Gestures—hand movements that accompany speech and express ideas—can help children learn how to solve problems, flexibly generalize learning to novel problem-solving contexts, and retain what they have learned. But does it matter who is doing the gesturing? We know that producing gesture leads to better comprehension of a message than watching someone else produce gesture. But we do not know how producing versus observing gesture impacts deeper learning outcomes such as generalization and retention across time. Moreover, not all children benefit equally from gesture instruction, suggesting that there are individual differences that may play a role in who learns from gesture. Here, we consider two factors that might impact whether gesture leads to learning, generalization, and retention after mathematical instruction: (1) whether children see gesture or do gesture and (2) whether a child spontaneously gestures before instruction when explaining their problem-solving reasoning. For children who spontaneously gestured before instruction, both doing and seeing gesture led to better generalization and retention of the knowledge gained than a comparison manipulative action. For children who did not spontaneously gesture before instruction, doing gesture was less effective than the comparison action for learning, generalization, and retention. Importantly, this learning deficit was specific to gesture, as these children did benefit from doing the comparison manipulative action. Our findings are the first evidence that a child's use of a particular representational format for communication (gesture) directly predicts that child's propensity to learn from using the same representational format.

Previous research described different cognitive processes on how individuals process distributional information. Based on these processes, the current research uncovered a novel phenomenon in distribution perception: the Endpoint Leverage Effect. Subjective endpoints influence distribution estimations not only locally around the endpoint but also influence estimations across the whole value range of the distribution. The influence is largest close to the respective endpoint and decreases in size toward the opposite end of the value range. Three experiments investigate this phenomenon: Experiment 1 provides correlational evidence for the Endpoint Leverage Effect after presenting participants with a numerical distribution. Experiment 2 demonstrates the Endpoint Leverage Effect by manipulating the subjective endpoints of a numerical distribution directly. Experiment 3 generalizes the phenomenon by investigating a general population sample and estimations regarding a real-world income distribution. In addition, quantitative model analysis examines the cognitive processes underlying the effect. Overall, the novel Endpoint Leverage Effect is found in all three experiments, inspiring further research in a wide area of contexts.