Topics in Cognitive Science最新文献

{"title":"Independent Effects of Age, Education, Verbal Working Memory, Motor Speed of Processing, Locality, and Morphosyntactic Category on Verb-Related Morphosyntactic Production: Evidence From Healthy Aging.","authors":"Marielena Soilemezidi, Maki Kubota, Marina Chrisikopoulou, Valantis Fyndanis","doi":"10.1111/tops.12750","DOIUrl":"https://doi.org/10.1111/tops.12750","url":null,"abstract":"<p><p>This study investigates the role of locality (a task/material-related variable), demographic factors (age, education, and sex), cognitive capacities (verbal working memory [WM], verbal short-term memory [STM], speed of processing [SOP], and inhibition), and morphosyntactic category (time reference and grammatical aspect) in verb-related morphosyntactic production (VRMP). A sentence completion task tapping production of time reference and grammatical aspect in local and nonlocal configurations, and cognitive tasks measuring verbal WM capacity, verbal STM capacity, motor SOP, perceptual SOP, and inhibition were administered to 200 neurotypical Greek-speaking participants, aged between 19 and 80 years. We fitted generalized linear mixed-effects models and performed path analyses. Significant main effects of locality, age, education, verbal WM capacity, motor SOP, and morphosyntactic category emerged. Production of time reference and aspect did not interact with any of the significant factors (i.e., age, education, verbal WM capacity, motor SOP, and locality), and locality did not interact with any memory system. Path analyses revealed that the relationships between age and VRMP, and between education and VRMP were partly mediated by verbal WM; and the relationship between verbal WM and VRMP was partly mediated by perceptual SOP. Results suggest that subject-, task/material- and morphosyntactic category-specific factors determine accuracy performance on VRMP; and the effects of age, education, and verbal WM on VRMP are partly indirect. The fact that there was a significant main effect of verbal WM but not of verbal STM on accuracy performance in the VRMP task suggests that it is predominantly the processing component (and not the storage component) of verbal WM that supports VRMP. Lastly, we interpret the results as suggesting that VRMP is also supported by a procedural memory system whose efficiency might be reflected in years of formal education.</p>","PeriodicalId":47822,"journal":{"name":"Topics in Cognitive Science","volume":" ","pages":""},"PeriodicalIF":2.9,"publicationDate":"2024-08-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142005571","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"心理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Predicting Learning: Understanding the Role of Executive Functions in Children's Belief Revision Using Bayesian Models.","authors":"Joseph A Colantonio, Igor Bascandziev, Maria Theobald, Garvin Brod, Elizabeth Bonawitz","doi":"10.1111/tops.12749","DOIUrl":"https://doi.org/10.1111/tops.12749","url":null,"abstract":"<p><p>Recent studies suggest that learners who are asked to predict the outcome of an event learn more than learners who are asked to evaluate it retrospectively or not at all. One possible explanation for this \"prediction boost\" is that it helps learners engage metacognitive reasoning skills that may not be spontaneously leveraged, especially for individuals with still-developing executive functions. In this paper, we combined multiple analytic approaches to investigate the potential role of executive functions in elementary school-aged children's science learning. We performed an experiment that investigates children's science learning during a water displacement task where a \"prediction boost\" had previously been observed-children either made an explicit prediction or evaluated an event post hoc (i.e., postdiction). We then considered the relation of executive function measures and learning, which were collected following the main experiment. Via mixed effects regression models, we found that stronger executive function skills (i.e., stronger inhibition and switching scores) were associated with higher accuracy in Postdiction but not in the Prediction Condition. Using a theory-based Bayesian model, we simulated children's individual performance on the learning task (capturing \"belief flexibility\"), and compared this \"flexibility\" to the other measures to understand the relationship between belief revision, executive function, and prediction. Children in the Prediction Condition showed near-ceiling \"belief flexibility\" scores, which were significantly higher than among children in the Postdiction Condition. We also found a significant correlation between children's executive function measures to our \"belief flexibility\" parameter, but only for children in the Postdiction Condition. These results indicate that when children provided responses post hoc, they may have required stronger executive function capacities to navigate the learning task. Additionally, these results suggest that the \"prediction boost\" in children's science learning could be explained by increased metacognitive flexibility in the belief revision process.</p>","PeriodicalId":47822,"journal":{"name":"Topics in Cognitive Science","volume":" ","pages":""},"PeriodicalIF":2.9,"publicationDate":"2024-08-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141894571","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"心理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Comprehension of English for-adverbials: The Nature of Lexical Meanings and the Neurocognitive Architecture of Language.","authors":"Maria M Piñango, Yao-Ying Lai, Ashwini Deo, Emily Foster-Hanson, Cheryl Lacadie, Todd Constable","doi":"10.1111/tops.12746","DOIUrl":"https://doi.org/10.1111/tops.12746","url":null,"abstract":"<p><p>What is the nature of lexical meanings such that they can both compose with others and also appear boundless? We investigate this question by examining the compositional properties of for-time adverbial as in \"Ana jumped for an hour.\" At issue is the source of the associated iterative reading which lacks overt morphophonological support, yet, the iteration is not disconnected from the lexical meanings in the sentence. This suggests an analysis whereby the iterative reading is the result of the interaction between lexical meanings under a specific compositional configuration. We test the predictions of two competing accounts: Mismatch-and-Repair and Partition-Measure. They differ in their assumptions about lexical meanings: assumptions that have implications for the possible compositional mechanisms that each can invoke. Mismatch-and-Repair assumes that lexical meaning representations are discrete, separate from the conceptual system from which they originally emerged and brought into sentence meaning through syntactic composition. Partition-Measure assumes that lexical meanings are contextually salient conceptual structures substantially indistinguishable from the conceptual system that they inhabit. During comprehension, lexical meanings construe a conceptual representation, in parallel, morphosyntactic and morphophonological composition as determined by the lexical items involved in the sentence. Whereas both hypotheses capture the observed cost in the punctual predicate plus for-time adverbial composition (e.g., jump (vs. swim) for an hour), their predictions differ regarding iteration with durative predicates; for example, swim for a year (vs. for an hour). Mismatch-and-Repair predicts contrasting processing profiles and nonoverlapping activation patterns along punctuality differences. Partition-Measure predicts overlapping processing and cortical distribution profiles, along the presence of iterativity. Results from a self-paced reading and an functional Magnetic Resonance Imaging (fMRI) studies bear out the predictions of the Partition-Measure account, supporting a view of linguistic meaning composition in line with an architecture of language whereby combinatoriality and generativity are distributed, carried out in parallel across linguistic and nonlinguistic subsystems.</p>","PeriodicalId":47822,"journal":{"name":"Topics in Cognitive Science","volume":" ","pages":""},"PeriodicalIF":2.9,"publicationDate":"2024-07-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141621224","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"心理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Syntactic Variation in Reduced Registers Through the Lens of the Parallel Architecture.","authors":"Anastasia Smirnova","doi":"10.1111/tops.12747","DOIUrl":"https://doi.org/10.1111/tops.12747","url":null,"abstract":"<p><p>Diversion from the syntactic norm, as manifested in the absence of otherwise expected lexical and syntactic material, has been extensively studied in theoretical syntax. Such modifications are observed in headlines, telegrams, labels, and other specialized contexts, collectively referred to as \"reduced\" registers. Focusing on search queries, a type of reduced register, I propose that they are generated by a simpler grammar that lacks a full-fledged syntactic component. The analysis is couched in the Parallel Architecture framework, whose assumption of relative independence of linguistic components-their parallelism-and the rejection of syntactocentrism are essential to explain properties of queries.</p>","PeriodicalId":47822,"journal":{"name":"Topics in Cognitive Science","volume":" ","pages":""},"PeriodicalIF":2.9,"publicationDate":"2024-07-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141535576","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"心理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Understanding Human Cognition Through Computational Modeling.","authors":"Janet Hui-Wen Hsiao","doi":"10.1111/tops.12737","DOIUrl":"10.1111/tops.12737","url":null,"abstract":"<p><p>One important goal of cognitive science is to understand the mind in terms of its representational and computational capacities, where computational modeling plays an essential role in providing theoretical explanations and predictions of human behavior and mental phenomena. In my research, I have been using computational modeling, together with behavioral experiments and cognitive neuroscience methods, to investigate the information processing mechanisms underlying learning and visual cognition in terms of perceptual representation and attention strategy. In perceptual representation, I have used neural network models to understand how the split architecture in the human visual system influences visual cognition, and to examine perceptual representation development as the results of expertise. In attention strategy, I have developed the Eye Movement analysis with Hidden Markov Models method for quantifying eye movement pattern and consistency using both spatial and temporal information, which has led to novel findings across disciplines not discoverable using traditional methods. By integrating it with deep neural networks (DNN), I have developed DNN+HMM to account for eye movement strategy learning in human visual cognition. The understanding of the human mind through computational modeling also facilitates research on artificial intelligence's (AI) comparability with human cognition, which can in turn help explainable AI systems infer humans' belief on AI's operations and provide human-centered explanations to enhance human-AI interaction and mutual understanding. Together, these demonstrate the essential role of computational modeling methods in providing theoretical accounts of the human mind as well as its interaction with its environment and AI systems.</p>","PeriodicalId":47822,"journal":{"name":"Topics in Cognitive Science","volume":" ","pages":"349-376"},"PeriodicalIF":2.9,"publicationDate":"2024-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141088953","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"心理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"When a Robot Is Your Teammate.","authors":"Filipa Correia, Francisco S Melo, Ana Paiva","doi":"10.1111/tops.12634","DOIUrl":"10.1111/tops.12634","url":null,"abstract":"<p><p>Creating effective teamwork between humans and robots involves not only addressing their performance as a team but also sustaining the quality and sense of unity among teammates, also known as cohesion. This paper explores the research problem of: how can we endow robotic teammates with social capabilities to improve the cohesive alliance with humans? By defining the concept of a human-robot cohesive alliance in the light of the multidimensional construct of cohesion from the social sciences, we propose to address this problem through the idea of multifaceted human-robot cohesion. We present our preliminary effort from previous works to examine each of the five dimensions of cohesion: social, collective, emotional, structural, and task. We finish the paper with a discussion on how human-robot cohesion contributes to the key questions and ongoing challenges of creating robotic teammates. Overall, cohesion in human-robot teams might be a key factor to propel team performance and it should be considered in the design, development, and evaluation of robotic teammates.</p>","PeriodicalId":47822,"journal":{"name":"Topics in Cognitive Science","volume":" ","pages":"527-553"},"PeriodicalIF":2.9,"publicationDate":"2024-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"10787386","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"心理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Introduction to the Emerging Cognitive Science of Distributed Human-Autonomy Teams.","authors":"Christopher W Myers, Nancy J Cooke, Jamie C Gorman, Nathan J McNeese","doi":"10.1111/tops.12744","DOIUrl":"10.1111/tops.12744","url":null,"abstract":"<p><p>Teams are a fundamental aspect of life-from sports to business, to defense, to science, to education. While the cognitive sciences tend to focus on information processing within individuals, others have argued that teams are also capable of demonstrating cognitive capacities similar to humans, such as skill acquisition and forgetting (cf., Cooke, Gorman, Myers, & Duran, 2013; Fiore et al., 2010). As artificially intelligent and autonomous systems improve in their ability to learn, reason, interact, and coordinate with human teammates combined with the observation that teams can express cognitive capacities typically seen in individuals, a cognitive science of teams is emerging. Consequently, new questions are being asked about teams regarding teamness, trust, the introduction and effects of autonomous systems on teams, and how best to measure team behavior and phenomena. In this topic, four facets of human-autonomy team cognition are introduced with leaders in the field providing in-depth articles associated with one or more of the facets: (1) defining teams; (2) how trust is established, maintained, and repaired when broken; (3) autonomous systems operating as teammates; and (4) metrics for evaluating team cognition across communication, coordination, and performance.</p>","PeriodicalId":47822,"journal":{"name":"Topics in Cognitive Science","volume":" ","pages":"377-390"},"PeriodicalIF":2.9,"publicationDate":"2024-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141297036","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"心理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Estimating Systemic Cognitive States from a Mixture of Physiological and Brain Signals.","authors":"Matthias Scheutz, Shuchin Aeron, Ayca Aygun, J P de Ruiter, Sergio Fantini, Cristianne Fernandez, Zachary Haga, Thuan Nguyen, Boyang Lyu","doi":"10.1111/tops.12669","DOIUrl":"10.1111/tops.12669","url":null,"abstract":"<p><p>As human-machine teams are being considered for a variety of mixed-initiative tasks, detecting and being responsive to human cognitive states, in particular systematic cognitive states, is among the most critical capabilities for artificial systems to ensure smooth interactions with humans and high overall team performance. Various human physiological parameters, such as heart rate, respiration rate, blood pressure, and skin conductance, as well as brain activity inferred from functional near-infrared spectroscopy or electroencephalogram, have been linked to different systemic cognitive states, such as workload, distraction, or mind-wandering among others. Whether these multimodal signals are indeed sufficient to isolate such cognitive states across individuals performing tasks or whether additional contextual information (e.g., about the task state or the task environment) is required for making appropriate inferences remains an important open problem. In this paper, we introduce an experimental and machine learning framework for investigating these questions and focus specifically on using physiological and neurophysiological measurements to learn classifiers associated with systemic cognitive states like cognitive load, distraction, sense of urgency, mind wandering, and interference. Specifically, we describe a multitasking interactive experimental setting used to obtain a comprehensive multimodal data set which provided the foundation for a first evaluation of various standard state-of-the-art machine learning techniques with respect to their effectiveness in inferring systemic cognitive states. While the classification success of these standard methods based on just the physiological and neurophysiological signals across subjects was modest, which is to be expected given the complexity of the classification problem and the possibility that higher accuracy rates might not in general be achievable, the results nevertheless can serve as a baseline for evaluating future efforts to improve classification, especially methods that take contextual aspects such as task and environmental states into account.</p>","PeriodicalId":47822,"journal":{"name":"Topics in Cognitive Science","volume":" ","pages":"485-526"},"PeriodicalIF":2.9,"publicationDate":"2024-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9752704","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"心理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Understanding Human-Autonomy Teams Through a Human-Animal Teaming Model.","authors":"Heather C Lum, Elizabeth K Phillips","doi":"10.1111/tops.12713","DOIUrl":"10.1111/tops.12713","url":null,"abstract":"<p><p>The relationship between humans and animals is complex and influenced by multiple variables. Humans display a remarkably flexible and rich array of social competencies, demonstrating the ability to interpret, predict, and react appropriately to the behavior of others, as well as to engage others in a variety of complex social interactions. Developing computational systems that have similar social abilities is a critical step in designing robots, animated characters, and other computer agents that appear intelligent and capable in their interactions with humans and each other. Further, it will improve their ability to cooperate with people as capable partners, learn from natural instruction, and provide intuitive and engaging interactions for human partners. Thus, human-animal team analogs can be one means through which to foster veridical mental models of robots that provide a more accurate representation of their near-future capabilities. Some digital twins of human-animal teams currently exist but are often incomplete. Therefore, this article focuses on issues within and surrounding the current models of human-animal teams, previous research surrounding this connection, and the challenges when using such an analogy for human-autonomy teams.</p>","PeriodicalId":47822,"journal":{"name":"Topics in Cognitive Science","volume":" ","pages":"554-567"},"PeriodicalIF":2.9,"publicationDate":"2024-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138446605","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"心理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Introduction to topiCS Volume 16, Issue 3.","authors":"Andrea Bender","doi":"10.1111/tops.12748","DOIUrl":"10.1111/tops.12748","url":null,"abstract":"","PeriodicalId":47822,"journal":{"name":"Topics in Cognitive Science","volume":" ","pages":"346-348"},"PeriodicalIF":2.9,"publicationDate":"2024-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141535575","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"心理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}