Topics in Cognitive Science最新文献

For young children, gesture is found to precede and predict language development. However, we are still building a knowledge base about the specific nature of the relationship between gesture and speech. While much of the research on this topic has been conducted with neurotypical children, there is a growing body of work with children who have or are at increased likelihood of autism spectrum disorder (ASD). Here, we summarize the literature on relations between gesture and speech, including the role of child gesture production as well as that of gesture exposure (caregiver gesture). We include literature on both neurotypical children and children with or at likelihood of ASD, highlight the similarities and differences across populations, and offer implications for research as well as early identification and intervention.

Modulation of visual attention in the Visual World Paradigm relies on parallel processing of linguistic and visual information. Previous studies have argued that the human linguistic capacity includes an aspect of anticipation of upcoming material. Such anticipation can be triggered by both lexical and grammatical/morphosyntactic cues. In this study, we investigated the relationship between comprehension and prediction by testing how subtle changes in visual representations can affect the processing of grammatical case cues in Russian by Russian-German bilingual children (n = 49, age 8-13). The linguistic manipulation followed previous designs, contrasting SVO and OVS sentences, where the first NP (NP1) was marked with nominative or accusative case, respectively. Three types of visual displays were compared: (i) individual referents (potential agent/theme); (ii) pairs of referents (NP1 + potential agent/theme); and (iii) events (representing interactions between the referents). Participants were significantly more sensitive to the case manipulation when presented with events compared to the other two types of visual display. This suggests that they were able to quickly integrate the thematic role information signaled by grammatical case in the event representations. However, they were less likely to use the case information to anticipate upcoming arguments when the target pictures represented individual referents or pairs of noninteracting referents. We hypothesize that the process of argument anticipation is mediated by the activation of syntactic templates (SVO or OSV, depending on the case marking on NP1). The relatively weak anticipation effect observed may be attributed to the absence, or weak representation, of the noncanonical OVS template in the bilingual children's long-term memory.

Decades of research have established that learners benefit when instruction includes hand gestures. This benefit is seen when learners watch an instructor gesture, as well as when they are taught or encouraged to gesture themselves. However, there is substantial individual variability with respect to this phenomenon-not all individuals benefit equally from gesture instruction. In the current paper, we explore the sources of this variability. First, we review the existing research on individual differences that do or do not predict learning from gesture instruction, including differences that are either context-dependent (linked to the particular task at hand) or context-independent (linked to the learner across multiple tasks). Next, we focus on one understudied measure of individual difference: the learner's own spontaneous gesture rate. We present data showing rates of "non-gesturers" across a number of studies and we provide theoretical motivation for why this is a fruitful area for future research. We end by suggesting ways in which research on individual differences will help gesture researchers to further refine existing theories and develop specific predictions about targeted gesture intervention for all kinds of learners.

The International Conference on Cognitive Modelling is dedicated to understanding how the complex processes of the mind can be explained in terms of detailed inner processing. In this issue, we present four representative papers of this field of research from our 20th meeting, ICCM 2022. This meeting was our first hybrid meeting, with a virtual version happening July 11-15, 2022, and an in-person event from July 23-27, 2022, held in Toronto, Canada. The four papers presented here were the top-ranked papers across both the virtual and in-person events. Three of the papers develop novel computational theories about low-level components within the mind and how those components result in high-level phenomena such as motivation, anhedonia, and attention. The final paper demonstrates the use of cognitive modeling to develop novel explanations of a paired associate learning task, and uses those insights to develop and explain human performance in a more complex version of that task.

Internal representations guide our navigation of the world, while language allows us to share some of what is encoded internally with others. I have been interested in the content of thought, the nature of word meanings and what they reveal about thought, and how thoughts are expressed in words. My work has combined evidence from laboratory experimentation with observation of word use in natural settings, including from people who speak different languages. Some of the ideas guiding the work are these: understanding entities in the world non-linguistically engages different representations and processes than talking about them; patterns of word use in a language reflect cultural and linguistic history, not only conceptual representations of current speakers; linguistic and non-linguistic knowledge is therefore at least partially independent, and so language and thought will not always closely parallel one another; the beliefs people express about their concepts and word meanings may not accurately reflect the implicit knowledge they draw on in interacting with and talking about the world; and only by carefully observing actual word use can we understand how word meanings come about and how linguistic knowledge is used to select words for communication.

Many cultures share common constellations and common narratives about the stars in the night sky. Previous research has shown that this overlap in asterisms, minimal star groupings inside constellations, is clearly present across 27 distinct culture groups and can be explained in part by properties of individual stars (brightness) and properties of pairs of stars (proximity) (Kemp, Hamacher, Little, & Cropper, 2022). The same work, however, found no evidence that properties of triples (angle) and quadruples (good continuation) predicted constellation formation. We developed a behavioral experiment to explore how individuals form constellations under conditions that reduce cultural learning. We found that participants independently selected and connected similar stars, and that their responses were predicted by two properties of triples (angle and even spacing) in addition to the properties of brightness and proximity supported by previous work. Our findings lend further evidence to the theory that commonality of constellations across cultures is not a result of shared human history but rather stems from shared human nature.

Great storytelling takes us on a journey the way ordinary reality rarely does. But what exactly do we mean by this "journey?" Recently, literary theorist Karin Kukkonen proposed that storytelling is "probability design:" the art of giving an audience pieces of information bit by bit, to craft the journey of their changing beliefs about the fictional world. A good "probability design" choreographs a delicate dance of certainty and surprise in the reader's mind as the story unfolds from beginning to end. In this paper, we computationally model this conception of storytelling. Building on the classic Bayesian inverse planning model of human social cognition, we treat storytelling as inverse inverse planning: the task of choosing actions to manipulate an inverse planner's inferences, and therefore a human audience's beliefs. First, we use an inverse inverse planner to depict social and physical situations, and present behavioral studies indicating that inverse inverse planning produces more expressive behavior than ordinary "naïve planning." Then, through a series of examples, we demonstrate how inverse inverse planning captures many storytelling elements from first principles: character, narrative arcs, plot twists, irony, flashbacks, and deus ex machina are all naturally encoded in the flexible language of probability design.

Motivation is the driving force that influences people's behaviors and interacts with many cognitive functions. Computationally, motivation is represented as a cost-benefit analysis that weighs efforts and rewards in order to choose the optimal actions. Shenhav and colleagues proposed an elegant theory, the Expected Value of Control (EVC), which describes the relationship between cognitive efforts, costs, and rewards. In this paper, we propose a more fine-grained and detailed motivation framework that incorporates the principles of EVC into the ACT-R cognitive architecture. Specifically, motivation is represented as a specific slot in the Goal buffer with a corresponding scalar value, M, that is translated into the reward value R_t that is delivered when the goal is reached. This implementation is tested in two models. The first model is a high-level model that reproduces the EVC predictions with abstract actions. The second model is an augmented version of an existing ACT-R model of the Simon task. The motivation mechanism is shown to permit optimal effort allocation and reproduce known phenomena. Finally, the broader implications of our mechanism are discussed.

Adverse early life experiences can have remarkably enduring negative consequences on mental health, with numerous, varied psychiatric conditions sharing this developmental origin. Yet, the mechanisms linking adverse experiences to these conditions remain poorly understood. Here, we draw on a principled model of interval timing to propose that statistically optimal adaptation of temporal representations to an unpredictable early life environment can produce key characteristics of anhedonia, a transdiagnostic symptom associated with affective disorders like depression and anxiety. The core observation is that early temporal unpredictability produces broader, more imprecise temporal expectations. As a result, reward anticipation is diminished, and associative learning is slowed. When agents with such representations are later introduced to more stable environments, they demonstrate a negativity bias, responding more to the omission of reward than its receipt. Increased encoding of negative events has been proposed to contribute to disorders with anhedonia as a symptom. We then examined how unpredictability interacts with another form of adversity, low reward availability. We found that unpredictability's effect was most strongly felt in richer environments, potentially leading to categorically different phenotypic expressions. In sum, our formalization suggests a single mechanism can help to link early life adversity to a range of behaviors associated with anhedonia, and offers novel insights into the interactive impacts of multiple adversities.

This paper presents two studies in which a peer-assisted learning condition was compared to an individual learning condition. The first study used the paired-associates learning task and the second study used an incrementally more complex task-the remote associate test. Participants in the peer-assisted learning condition worked in groups of four. They had to solve a given problem individually and give a first answer before being able to request to see their peers' solutions; then, a second answer was issued. After six sessions of peer-assisted practice, a final individual test was administered. Peer interaction was found to benefit learning in both studies but the benefit transferred to the final test only in the second study. Fine-grained behavioral analyses and computational modeling suggested that the benefits of peer interaction were (partially) offset by its costs, particularly increased cognitive load and error exposure. Overall, the superiority of peer-assisted learning over individual learning was more pronounced in the more complex task and for the more difficult problems in that task.