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In this study we conducted further tests of the ability of a computational model to fit human category learning performance. The Divergent Autoencoder (DIVA) is a generative-connectionist model of human categorization that learns through error-driven adjustments to predict class membership on the basis of the fidelity of auto-associative item reconstructions with respect to each category (Kurtz 2007). DIVA was tested on the classic SHJ category structures resulting in new qualitative and quantitative fits to both the original and revised SHJ orderings that show the impact of proposed design modifications and parameters. Theoretical implications for psychological accounts of category learning are discussed.

CogSci 2023

Modeling Human Performance on SHJ Category Structures with a Divergent Autoencoder

Join us for CogSci2022! Our 44th Annual Conference will be centered around the theme of diversity, with an accompanying range of keynotes and invited speakers who themselves highlight the diversity of our field. Our three symposia are Cognition across Diverse Populations, Cognition within Diverse Societies, and Global Challenges for Cognitive Science. The conference is being offered in a fully hybrid environment for onsite and online attendees.
 
 [**The Program at a Glance can be found here**](https://cognitivesciencesociety.org/wp-content/uploads/2022/07/Cogsci-2022-PAAG-Detailed-July-12final-1.pdf)
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CogSci 2022

The 44th Annual Meeting of the Cognitive Science Society highlighted research on the theme Cognitive Diversity in addition to the full breadth and diversity of research topics offered by the society’s membership. 





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The 45th Annual Meeting of the Cognitive Science Society will take place in Sydney, Australia as a modified hybrid conference. Presentations from the main-session room will be live-streamed every day for all events. This will include all invited talks and award sessions. Recordings of all CogSci 2023 sessions will be made available for on-demand viewing after the conference. 
The in-person component will be held at the International Convention Centre Sydney at Darling Harbour. The Conference will take place Wednesday July 26th – Saturday July 29th, 2023 (Wednesday, July 26th is the Workshops Day).

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 [**The Program at a Glance can be found here**](https://cognitivesciencesociety.org/wp-content/uploads/2023/07/COgSci-PAAG-V2.pdf)
 [**The Detailed Program can be found here**](https://online.fliphtml5.com/ebtyf/mswz/)
 

#### **CogSci 2023 Program Book**
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The 45th Annual Meeting of the Cognitive Science Society presents the latest research across cognitive science and highlights the theme of Cognition in Context.

It is well known that continuous encoding and recall of similar semantic content leads to memory deficits caused by the build-up of proactive interference. This build-up can be released if the content is switched to a different semantic category. While release from proactive interference has been studied using non-semantic content, it has yet to be studied with relational categories (i.e., categories that cohere based on fulfilling a particular semantic relationship such as obstacles). Recent findings suggest that relational information may be stored as individual entities similar to traditional entity categories. To assess the extent to which entity and relational content are similarly stored and retrieved, participants repeatedly encode and recall lists of word singletons in either an entity or relational category condition. Recall performance is evaluated for each list to compare the build-up and release of proactive interference for participants exposed to a semantic content switch or corresponding no-switch controls.

Release from Proactive Interference with Relational Categories Versus Traditional Entity Categories

Humans can readily generalize their learning to new visual concepts, and infer their associated meanings. How do people align the different conceptual systems learned from different modalities? In the present paper, we examine emojis— pictographs uniquely situated between visual and linguistic modalities—to explore the role of alignment and multimodality in visual and linguistic semantics. Simulation experiments show that relational structures of emojis captured in visual and linguistic conceptual systems can be aligned, and that the ease of alignment increases as the number of emojis increases. We also found that emojis with subjective impressions of high popularity are easier to align between their visual and linguistic representations, suggesting the expressive power of these emojis is likely associated with their popularity. A behavioral experiment was conducted to measure similarity patterns between 48 emojis, and to compare human similarity judgments with three models. We found that the model trained with multimodal data by aligning visual and semantic spaces best accounts for human judgments.

Human similarity judgments of emojis support alignment of conceptual systems across modalities

This work tests three coping mechanisms for time pressure in similarity-based inferences from multiple features. Specifically, we test whether time pressure makes people attend to fewer object features, respond less precisely, or simplify the psychological similarity computation. We ran two preregistered time pressure experiments (Ns = 61 and 175), in which people classified objects or rated the similarity of object pairs. Inferential statistics and computational modeling in an exemplar-similarity framework indicate that time pressure mostly reduced participants’ response precision, especially concerning their similarity judgments. Specifically, participants’ categorizations were less deterministic and their similarity judgments became more variable as time pressure increased. In contrast, we do not find evidence that participants attend to fewer features or simplify the similarity computation. These results add to the evidence that time pressure and other cognitive load do not necessarily affect cognitive processes themselves but rather lower the precision of response selection.

Understanding Speeded Categorizations and Similarity Judgments Using Computational Cognitive Modeling

Conceptions of social disparities as natural and biologically-caused, known as “essentialist” conceptions, lead to the maintenance social disparities. Surprisingly little research has examined whether formal educational experiences – directly teaching people about the nature and origins of social disparities – can reduce essentialist conceptions. We investigate how social science coursework (e.g., sociology, history, anthropology) impact (a) essentialist vs. structural explanations for racial disparities, and (b) social essentialism more broadly, in a diverse group of undergraduates (n = 246). Results suggest that students who have completed such coursework show reduced endorsement of essentialist explanations for racial disparities, but enhanced endorsement of other dimensions of social essentialist thought (e.g., the view that social categories are meaningfully distinct from one another). Thus, social science coursework may have a nuanced impact on social essentialism. We discuss the questions these results raise regarding the relations between explanatory thinking and other dimensions of social essentialism.

How does higher education in the social sciences impact social essentialist thinking?

In two experiments (N=888), we explored to what extent the folk concept of art is compatible with the most famous philosophical definitions of art. We also investigated whether the folk concept of art is an essentialist or a non-essentialist concept. We manipulated three factors: whether an object is created intentionally, whether it has aesthetic value, and whether it is institutionally recognized. In addition, we also manipulated the artistic domain (visual art or music). The results suggest that none of the three properties is seen by the folk as necessary for an object to be considered art. The folk concept of art might be a cluster concept.

What Is Art? The Role of Intention, Beauty, and Institutional Recognition

How do children – who are undeniably productive learners – think about their learning? Do children understand, as adults do, that learning is a process of continuous improvement over time? To explore children’s emerging representations of the learning process, we created a non-verbal motor learning paradigm where 4- to 8-year-olds predicted their own learning without prior experience. We found that by age 7, children conceptualized learning as a process of getting better over time. Younger children, however, were overly optimistic about how well they would do at the game and often predicted near- perfect performance across trials. This work suggests that children’s understanding of learning as a process develops across childhood, which may have implications for young children’s learning decisions.

Children’s developing understanding of learning as improvement over time

A key debate in language learning centers on how people successfully learn the extension of a novel word, despite inherent ambiguity in the input. Across two studies, we tested whether learners reduce ambiguity about a word’s extension by actively sampling the environment. Adult participants were first presented with ambiguous learning situations in which novel words were presented with a set of exemplars that were drawn from a subordinate-level category (e.g., Dalmatians), a basic-level category (e.g., dogs), or a superordinate-level category (e.g., animals). Learners then had the opportunity to sample the label of additional exemplars. Participants systematically adapted their sampling choices as a function of training. Moreover, participants varied in their sampling strategies, pursuing both confirmatory strategies (selecting exemplars similar to the training set) and constraining strategies (selecting exemplars that constrain the word’s extension). Overall, these findings show that learners spontaneously pursue sampling strategies that support generalizing novel word meanings.

Active information-seeking in support of learning extensions of novel words

People make subjective judgments about the healthiness of food. These judgments are highly influenced by personal beliefs, associations, and attitudes, as well as group-level cultural constructs. Consequently, people rely upon different latent representations of food when judging what is healthy and deciding what to consume. In this paper, we model shared representations of food healthiness by aligning learned word representations to the consensus among a group of respondents. To do so, we extend Cultural Consensus Theory to include latent constructs structured as fine-tuned word representations. We then apply the model to a dataset of people’s judgments of food healthiness. We show that our method creates a robust mapping between learned word representations and culturally constructed representations that guide consumer behavior.

Predicting judgments of food healthiness with deep latent-construct cultural consensus theory

An influential theory of human fairness has suggested that norms of equity evolved as community sizes grew, markets and institutions stabilized and world religions came about. The theory assumes that humans predominantly interacted with kin in the evolutionary past, lived in genetically related groups and did not have formal norms of cooperation. In this article I present anthropological evidence to the contrary. Contemporary hunter-gatherer societies from around the globe live in nomadic camps with fluid membership and low genetic relatedness; cooperate extensively based on principles of need, equity and reciprocity; have high prevalence of food sharing, cooperative hunting, alloparenting; and demonstrate formal norms of redistribution for essential goods. This directly contradicts the claim that fairness is a recently evolved disposition for market-integrated societies.

Markets, Religion, Community Size and the Evolution of Fairness? Not really

We introduce the Cognitive Science Community to the challenges faced in human-in-the-loop (HiTL) decision-making for complex and dynamic industrial chemical processes. A microworld is presented which includes advanced dynamic simulations, an optimal control-based decision-support algorithm and a preliminary human machine interface. Three example applications are discussed, including decision-making in plant startup and shutdown, shared control using human and HiTL supervisory Model Predictive Control (MPC), and a pilot study of operator eye-tracking during a complex startup task. The paper also discusses ongoing and future experimental efforts highlighting the utility of the microworld in understanding human cognition and designing advanced operator training software for various process operation related complex tasks.

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Release from Proactive Interference with Relational Categories Versus Traditional Entity Categories

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