Topics in Cognitive Science最新文献

Elections, jury deliberations, lawmaking, high-stakes negotiations and related activities are human attempts to answer the question "How should we live?" Collectively, we know these activities as politics. Politics are how societies attempt to reconcile diverse individual needs with potential benefits of social coordination. People's beliefs about what others will do ainfluence many political strategies and outcomes. This article reviews how properties of cognition affect these political phenomena. Contrary to the common belief that many citizens are too ignorant to make competent political decisions, we focus on a central finding of social science-how societies can design contexts and environments to overcome individual cognitive limitations. These adaptations expand societal capacities to provide essential goods, services, and protections. In addition to explaining these adaptations, we also show how greater collaborations between cognitive science and the social sciences can help societies do even better.

Children's testimonial learning often occurs in epistemic collaborations with others. In this paper, we will discuss ways in which cultural learning emerges in social and interpersonal contexts, and is intrinsically supported and guided by children's collaborative capacities. Much work in cultural learning has focused on children's examination of speaker and model characteristics, but more recent research has investigated the interactive aspects of testimonial exchanges. We will review evidence that children (1) participate in the interpersonal commitments that are shared in testimonial transactions by way of direct address and epistemic buck passing, (2) participate in social groups that affect their selective learning in nuanced ways, and (3) may detect epistemic harms by listeners who refuse to believe sincere and accurate speakers. Implications for conceptualizing children's testimonial learning as an interactive mechanism of collaboration will be discussed.

Humans routinely form groups to achieve goals that no individual can accomplish alone. Group coordination often brings to mind synchrony and alignment, where all individuals do the same thing (e.g., driving on the right side of the road, marching in lockstep, or playing musical instruments on a regular beat). Yet, effective coordination also typically involves differentiation, where specialized roles emerge for different members (e.g., prep stations in a kitchen or positions on an athletic team). Role specialization poses a challenge for computational models of group coordination, which have largely focused on achieving synchrony. Here, we present the CARMI framework, which characterizes role specialization processes in terms of five core features that we hope will help guide future model development: Communication, Adaptation to feedback, Repulsion, Multi-level planning, and Intention modeling. Although there are many paths to role formation, we suggest that roles emerge when each agent in a group dynamically allocates their behavior toward a shared goal to complement what they expect others to do. In other words, coordination concerns beliefs (who will do what) rather than simple actions. We describe three related experimental paradigms-"Group Binary Search," "Battles of the Exes," and "Find the Unicorn"-that we have used to study differentiation processes in the lab, each emphasizing different aspects of the CARMI framework.

As organizations gravitate to group-based structures, the problem of improving performance through judicious selection of group members has preoccupied scientists and managers alike. However, which individual attributes best predict group performance remains poorly understood. Here, we describe a preregistered experiment in which we simultaneously manipulated four widely studied attributes of group compositions: skill level, skill diversity, social perceptiveness, and cognitive style diversity. We find that while the average skill level of group members, skill diversity, and social perceptiveness are significant predictors of group performance, skill level dominates all other factors combined. Additionally, we explore the relationship between patterns of collaborative behavior and performance outcomes and find that any potential gains in solution quality from additional communication between the group members are outweighed by the overhead time cost, leading to lower overall efficiency. However, groups exhibiting more "turn-taking" behavior are considerably faster and thus more efficient. Finally, contrary to our expectation, we find that group compositional factors (i.e., skill level and social perceptiveness) are not associated with the amount of communication between group members nor turn-taking dynamics.

In the present paper, we describe the Enhanced Literate Mind (ELM) hypothesis. As individuals learn to read and write, they are, from then on, exposed to extensive written-language input and become literate. We propose that acquisition and proficient processing of written language ("literacy") leads to, both, increased language knowledge as well as enhanced language and nonlanguage (perceptual and cognitive) skills. We also suggest that all neurotypical native language users, including illiterate, low literate, and high literate individuals, share a Basic Language Cognition (BLC) in the domain of oral informal language. Finally, we discuss the possibility that the acquisition of ELM leads to some degree of "knowledge parallelism" between BLC and ELM in literate language users, which has implications for empirical research on individual and situational differences in spoken language processing.

Language is inherently multimodal. In spoken languages, combined spoken and visual signals (e.g., co-speech gestures) are an integral part of linguistic structure and language representation. This requires an extension of the parallel architecture, which needs to include the visual signals concomitant to speech. We present the evidence for the multimodality of language. In addition, we propose that distributional semantics might provide a format for integrating speech and co-speech gestures in a common semantic representation.

Successful spoken discourse requires a speaker to be informative to deliver a coherent, meaningful message. The informativeness of discourse can be conveyed by the variety of vocabulary produced (i.e., lexical diversity [LD]), the typicality of vocabulary items used (i.e., core lexicon [CL]), and the amount of relevant content produced (i.e., information units). Yet, it is well documented that older adults produce less informative content compared to younger adults despite relatively subtle changes to LD. The typicality of core lexical items has not been assessed in healthy aging. Paradoxically, these results indicate that some aspects of discourse informativeness remain stable or even improve across the adult lifespan, while other aspects decline. The purpose of the current study is to understand how microlinguistic processes of informativeness change across the adult lifespan. The cross-sectional study included narrative language samples from two wordless picture books collected from 420 healthy participants between 20 and 89 years old. LD and percent of correct information units (%CIUs) were analyzed, as well as CL nouns and verbs. The results indicate that %CIUs and CL nouns demonstrate a quadratic decline starting around the ages of 40 and 60, respectively. LD shows a slight linear decline as a function of age. CL verbs are resistant to age-related changes but are influenced greater by education. The differing findings across the microlinguistic measures can be explained by the weakened connections within the language system and the differential characteristics of the measures. The findings contribute to the aging literature by systematically identifying the trajectory of how variables of informativeness change with age.

Ruminative thinking, characterized by a recurrent focus on negative and self-related thought, is a key cognitive vulnerability marker of depression and, therefore, a key individual difference variable. This study aimed to develop a computational cognitive model of rumination focusing on the organization and retrieval of information in memory, and how these mechanisms differ in individuals prone to rumination and individuals less prone to rumination. Adaptive Control of Thought-Rational (ACT-R) was used to develop a rumination model by adding memory chunks with negative valence to the declarative memory. In addition, their strength of association was increased to simulate recurrent negative focus, thereby making it harder to disengage from. The ACT-R models were validated by comparing them against two empirical datasets containing data from control and depressed participants. Our general and ruminative models were able to recreate the benchmarks of free recall while matching the behavior exhibited by the control and the depressed participants, respectively. Our study shows that it is possible to build a computational theory of rumination that can accurately simulate the differences in free recall dynamics between control and depressed individuals. Such a model could enable a more fine-tuned investigation of underlying cognitive mechanisms of depression and potentially help to improve interventions by allowing them to more specifically target key mechanisms that instigate and maintain depression.

Over two decades have passed since the publication of van Gelder's (1998) "dynamical hypothesis." In that paper, van Gelder proposed that cognitive agents were not digital computers-per the representational computational approach-but dynamical systems. The evolution of the dynamical hypothesis was driven by parallel advances in three areas. Theoretically, a deeper understanding of genetics, biology, neuroscience, and cognitive science inspired questions about how systems within each domain dynamically interact and extend their effects across spatiotemporal scales. Methodologically, more sophisticated and domain-general tools allowed researchers to discover, model, and quantify system dynamics, structure, and patterns across multiple scales to generate a more comprehensive system-level understanding of behaviors. Empirically, we can analyze a system's behavior while preserving its natural dynamics, revealing evidence that the reductionist approach leads to an incomplete understanding of the components and the overall system. Researchers have traditionally reduced a complex system into its component processes and assumed that the parts can be recombined to explain the whole. These three advances fundamentally altered our understanding of a "cognitive agent:" How their behaviors are driven by long-range coordination across multiple processes, how the interdependent and nested structure of interacting variables produces behaviors that are greater than the sum of its parts, and how environmental constraints shape adaptive yet stable behavioral patterns.