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People learn new categories on a daily basis, and the study of category learning is a major topic of research in cognitive science. However, most prior work has focused on how people learn categories over abstracted, artificial (and usually perceptual) representations. Little is known about how new categories are learnt for natural objects, for which people have extensive prior knowledge. We examine this question in three pre-registered studies involving the learning of new categories for everyday foods. Our models use word vectors derived from large-scale natural language data to proxy mental representations for foods, and apply classical models of categorization over these vectorized representations to predict participant categorization judgments. This approach achieves high predictive accuracy rates, and can be used to identify the real-world settings in which category learning is impaired. In doing so, it shows how existing theories of categorization can be used to predict and improve everyday cognition and behavior.

CogSci 2021

Learning New Categories for Natural Objects

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**Dear Colleagues,**

Welcome to the 43rd Annual Conference of the Cognitive Science Society! 

This year’s conference brings together the latest research from all Cognitive Science and also highlights this year’s theme **Comparative Cognition: Animal Minds.** 

The program features four plenary speakers: Friederike Range (University of Veterinary Medicine, Vienna), Onur Güntürkün (Ruhr University Bochum), Julia Fischer (University of Göttingen), and Erich Jarvis (The Rockefeller University). The keynotes and invited symposia showcase the broad spectrum of ideas that encompass the conference theme of comparative cognition. 

Further, the program includes the Rumelhart Prize keynote presentation by Susan Goldin-Meadow (University of Chicago), and symposia to celebrate the winner of the Elman Prize: Wei Ji Ma (New York University), the Heineken Award winner Robert Zatorre (McGill University), and the Robert J. Glushko Dissertation Prizes session.

The program committee for CogSci 2021 received 1,025 submissions, including 710 full papers, 275 member abstracts, 8 publication-based talks, as well as 15 proposals for symposia, and 17 for workshops and tutorials. After a rigorous review process, the committee selected 152 papers for oral presentation and inclusion in the conference proceedings (14.8%), 332 papers for poster presentation and inclusion in the proceedings (32.4%), and 175 papers for poster presentation with inclusion of abstracts in the proceedings (17.1%) We also selected 275 submitted member abstracts. In addition, we accepted 6 publication-based talks, 12 symposia, and 9 workshops and tutorials to make for an exciting and inclusive program.

We hope that you enjoy the conference this year!<br><br>

**Your Program Chairs,**
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**Learn more about the Vienna Cognitive Science Hub at the University of Vienna, hosts of CogSci 2021**

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To review the proceedings for the conference or review the searchable program, please visit the [website](https://cognitivesciencesociety.org/program/).


The Cognitive Science Society brings together researchers from around the world who hold a common goal: understanding the nature of the human mind. The mission of the Society is to promote Cognitive Science as a discipline, and to foster scientific interchange among researchers in various areas of study, including Artificial Intelligence, Linguistics, Anthropology, Psychology, Neuroscience, Philosophy, and Education. The Society is a non-profit professional organization and its activities include sponsoring an annual conference and publishing the journals Cognitive Science and TopiCS.

poster

Based on strategy-awareness (knowing which problem-solving strategy to use) and time-awareness (knowing when to use it), students are categorized into Rote (neither type of awareness), Dabbler (strategy-aware only) or Selective (both types of awareness). It was shown that Selective is often significantly more prepared for future learning than Rote and Dabbler (Abdelshiheed et al., 2020). In this work, we explore the impact of explicit strategy instruction on Rote and Dabbler students across two domains: logic and probability. During the logic instruction, our logic tutor handles both Forward- Chaining (FC) and Backward-Chaining (BC) strategies, with FC being the default; the Experimental condition is taught how to use BC via worked examples and when to use it via prompts. Six weeks later, all students are trained on a probability tutor that supports BC only. Our results show that Experimental significantly outperforms Control in both domains, and Experimental Rote catches up with Selective.

Preparing Unprepared Students For Future Learning

We use forecasting models for the 2020 US presidential election to test a model of human causal judgment. Across tens of thousands of simulations of possible outcomes of the election, we computed, for each US state, an adjusted measure of the correlation between a democratic victory in that state and a democratic victory at the national level. These scores accurately predicted the extent to which US participants (N=207, pre-registered) viewed victory in a given state as having caused Joe Biden to win the presidency. This supports the theory that people intuitively select as causes of an outcome the factors with the largest average causal effect on that outcome across possible worlds. This is the first evidence that the theory scales to real-world complex settings, and suggests a deep connection between cognitive processes for prediction and causal judgment.

Causal judgment in the wild

When exposed to perceptual sequences, we are able to gradually identify patterns within and form a compact internal description of the sequence. One proposal of how disparate sequential items can become one is people's ability to form chunks. We study chunking under the regime of serial reaction time tasks. We propose a rational model of chunking that progressively rearranges and modifies its representation to arrive at one that is beneficial to participants' utility under task demands. Our model predicts that participants should, on average, learn longer chunks when optimizing for speed than optimizing for accuracy. We tested this prediction experimentally by instructing and rewarding one group of participants to act as fast as possible, while the other group was instructed to act as accurately as possible. From several independent sources of evidence, we confirmed our model's predictions that participants in the fast condition chunked more than participants in the accurate condition. These results shed new light on the benefits of chunking and pave the way for future studies on structural and representation learning domains.

Chunking as a Rational Solution to the Speed-Accuracy Trade-off in a Serial Reaction Time Task

Social interactions are composed of coordinated, multimodal behaviors with each individual taking turns and sharing attention. By the second year of life, infants are able to engage in coordinated interactions with their caregivers. Although research has focused on the social behaviors that enable parent-infant dyads to engage in joint attention, little work has been done to understand the sensorimotor mechanisms underlying coordination. Using wireless head-mounted eye trackers and motion sensing, we recorded 31 dyads as they played freely in a home-like laboratory. We identified moments of visual joint attention, when parent and infant were looking at the same object, and then measured the dyad’s head and hand movements during and around joint attention. We found evidence that both parents and infants still their bodies during joint attention. We also compared instances of joint attention that were led by the parent or by the infant and identified different sensorimotor pathways that support the two types of joint attention. These results provide the foundation for continued exploration of the critical role of sensorimotor processes on coordinated social behavior and its development.

The Sensorimotor Dynamics of Joint Attention

Despite the rise in communicative technologies for healthy aging, little research has focused on how effectively older adults process language spoken by artificial agents. We explore whether a robot's redundant (but potentially helpful) descriptions facilitate real-time comprehension in younger and older listeners. Gaze was recorded as participants heard instructions like "Tap on the [purple/closed] umbrella" for a display containing eight unique objects. We manipulated the description (no-adjective, color-adjective, state-adjective) and the visual context, specifically whether there was another object bearing the property denoted by the adjective (purple/closed notebook). Relative to the no-adjective condition, redundant color adjectives speeded comprehension when they uniquely identified targets, whereas (less-salient) state adjectives always impeded comprehension. No age-related differences were observed. Paralleling human-human studies, language processing in human-robot communication is facilitated when salient information narrows visual search. Together, these findings help inform the future design of communicative technologies.

Aging and Social Robots: How Overspecification Affects Real-Time Language Processing

Foraging has been suggested to provide a more ecologically-valid context for studying decision-making. However, the environments used in human foraging tasks fail to capture the structure of real world environments which contain multiple levels of spatio-temporal regularities. We ask if foragers detect these statistical regularities and use them to construct a model of the environment that guides their patch-leaving decisions. We propose a model of how foragers might accomplish this, and test its predictions in a foraging task with a structured environment that includes patches of varying quality and predictable transitions. Here, we show that human foraging decisions reflect ongoing, statistically-optimal structure learning. Participants modulated decisions based on the current and potential future context. From model fits to behavior, we can identify an individual's structure learning ability and relate it to decision strategy. These findings demonstrate the utility of leveraging model-based reinforcement learning to understand foraging behavior.

Model-based foraging using latent-cause inference

Is analogical reasoning a task that must be learned to solve from scratch by applying deep learning models to massive numbers of reasoning problems? Or are analogies solved by computing similarities between structured representations of analogs? We address this question by comparing human performance on visual analogies created using images of familiar three-dimensional objects (cars and their subregions) with the performance of alternative computational models. Human reasoners achieved above-chance accuracy for all problem types, but made more errors in several conditions (e.g., when relevant subregions were occluded). We compared human performance to that of two recent deep learning models (Siamese Network and Relation Network) directly trained to solve these analogy problems, as well as to that of a compositional model that assesses relational similarity between part-based representations. The compositional model based on part representations, but not the deep learning models, generated qualitative performance similar to that of human reasoners.

Visual Analogy: Deep Learning Versus Compositional Models

Humans have the ability to rapidly understand rich combinatorial concepts from limited data. Here we investigate this ability in the context of auditory signals, which have been evolved in a cultural transmission experiment to study the emergence of combinatorial structure in language. We propose a neuro-symbolic generative model which combines the strengths of previous approaches to concept learning. Our model performs fast inference drawing on neural network methods, while still retaining the interpretability and generalization from limited data seen in structured generative approaches. This model outperforms a purely neural network-based approach on classification as evaluated against both ground truth and human experimental classification preferences, and produces superior reproductions of observed signals as well. Our results demonstrate the power of flexible combined neural-symbolic architectures for human-like generalization in raw perceptual domains and offers a step towards developing precise computational models of inductive biases in language evolution.

Learning Evolved Combinatorial Symbols with a Neuro-symbolic Generative Model

Multidimensional scaling (MDS) has provided insight into the structure of human perception and conceptual knowledge. However, MDS usually requires participants to produce large numbers of similarity judgments, leading to prohibitively long experiments for most developmental research. Here we propose a method that combines simple grouping tasks with recent neural network models to uncover participants’ psychological spaces. We validate the method on simulated data and find that it can uncover the true structure even when given heterogeneous groupings. We then apply the method to data from the World Color Survey and find that it can uncover language-specific color organization. Finally, we apply the method to a novel developmental experiment and find age-dependent differences in conceptual spaces.

Recovering human category structure across development using sparse judgments

Event is a fuzzy term that refers to bounded spatio-temporal units. Events guide behavior to allow adaptation to complex environments. The study of event segmentation investigates mechanisms behind the ability to segment the continuous information flow into discrete units. Event Segmentation Theory states that people predict observed ongoing activities and monitor their prediction errors for event segmentation. In this study, inspired by Event Segmentation Theory and predictive processing, we introduced a computational model of event segmentation and learning. In order to verify that our method can segment ongoing activity into meaningful parts and learn them via passive observation, we compared the performance of our method with humans for fine and coarse segmentation tasks in two psychological experiments. The results demonstrated that our model not only learned segmented behavioral units accurately but also displayed similar segmentation performances with human subjects.

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