Topics in Cognitive Science最新文献

Languages are neither designed in classrooms nor drawn from dictionaries-they are products of human minds and human interactions. However, it is challenging to understand how structure grows in these circumstances because generations of use and transmission shape and reshape the structure of the languages themselves. Laboratory studies on language emergence investigate the origins of language structure by requiring participants, prevented from using their own natural language(s), to create a novel communication system and then transmit it to others. Because the participants in these lab studies are already speakers of a language, it is easy to question the relevance of lab-based findings to the creation of natural language systems. Here, we take the findings from a lab-based language emergence paradigm and test whether the same pattern is also found in a new natural language: Nicaraguan Sign Language. We find evidence that signers of Nicaraguan Sign Language may show the same biases seen in lab-based language emergence studies: (1) they appear to condition word order based on the semantic dimension of intensionality and extensionality, and (2) they adjust this conditioning to satisfy language-internal order constraints. Our study adds to the small, but growing literature testing the relevance of lab-based studies to natural language birth, and provides convincing evidence that the biases seen in the lab play a role in shaping a brand new language.

It is widely believed that play and curiosity are key ingredients as children develop models of the world. There is also an emerging consensus that children are Bayesian learners who combine their structured prior beliefs with estimations of the likelihood of new evidence to infer the most probable model of the world. An influential school of thought within developmental psychology, rational constructivism, combines these two ideas to propose that children learn intuitive theories of how the world works in part by engaging in play activities that allow them to gather new information for testing their theories. There are still, however, at least two pieces missing from rational constructivist theories of development. First, rational constructivism has so far devoted little attention to explaining why children's preferred form of learning, play, feels so fun, enjoyable, and rewarding. Rational constructivism may suggest that children are curious and like to play because reducing uncertainty and learning better theories of the causal workings of the world is enjoyable. What remains unclear, however, is why reducing uncertainty in play is interesting, fun, and joyful, while doing so in other forms of learning can be frustrating or boring. Second, rational constructivism may have overlooked how children, during play, will take control of and manipulate their environment, sometimes in an effort to create ideal niches for surprise-extraction, sometimes for developing strategies for making the world fit with their predictions. These missing elements from rational constructivism can be provided by understanding the contribution of play to development in terms of predictive processing, an influential framework in cognitive neuroscience that models many of the brain's cognitive functions as processes of model-based, probabilistic prediction.

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.

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.

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.

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.

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.

In this study, we explore the future of work by examining differences in productivity when teams are composed of only humans or both humans and machine agents. Our objective was to characterize the similarities and differences between human and human-machine teams as they work to coordinate across their specialized roles. This form of research is increasingly important given that machine agents are becoming commonplace in sociotechnical systems and playing a more active role in collaborative work. One particular class of machine agents, bots, is being introduced to these systems to facilitate both taskwork and teamwork. We investigated the association between bots and productivity outcomes in open source software development through an analysis of hundreds of project teams. The presence of bots in teams was associated with higher levels of productivity and higher work centralization in addition to greater amounts of observed communication. The adoption of bots in software teams may have tradeoffs, in that doing so may increase productivity, but could also increase workload. We discuss the theoretical and practical implications of these findings for advancing human-machine teaming research.

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.

Complex work in teams requires coordination across team members and their technology as well as the ability to change and adapt over time to achieve effective performance. To support such complex interactions, recent efforts have worked toward the design of adaptive human-autonomy teaming systems that can provide feedback in or near real time to achieve the desired individual or team results. However, while significant advancements have been made to better model and understand the dynamics of team interaction and its relationship with task performance, appropriate measures of team coordination and computational methods to detect changes in coordination have not yet been widely investigated. Having the capacity to measure coordination in real time is quite promising as it provides the opportunity to provide adaptive feedback that may influence and regulate teams' coordination patterns and, ultimately, drive effective team performance. A critical requirement to reach this potential is having the theoretical and empirical foundation from which to do so. Therefore, the first goal of the paper is to review approaches to coordination dynamics, identify current research gaps, and draw insights from other areas, such as social interaction, relationship science, and psychotherapy. The second goal is to collate extant work on feedback and advance ideas for adaptive feedback systems that have potential to influence coordination in a way that can enhance the effectiveness of team interactions. In addressing these two goals, this work lays the foundation as well as plans for the future of human-autonomy teams that augment team interactions using coordination-based measures.