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Research on discrimination-based transitive inference (TI) has demonstrated a widespread capacity for relational inference in people and non-human animals. In this domain individuals learn to choose the reinforced item in a set of interrelated discriminations (e.g., A–/B+; B–/C+) and are tested for transitive inference on novel, non-adjacent pairs (e.g., A vs. C). Existing models suggest that transitive responding can be supported by associative learning mechanisms, but they fail to account for evidence that knowledge about the hierarchical nature of the task, whether instructed or discovered during training, has a dramatic influence on learning. I present a model which formalizes TI as the estimation of items’ positions along a latent dimension and tracks learners’ uncertainty about the mapping between item position and feedback. The model naturally accounts for standard effects in TI, while going beyond associative models in explaining the effects of knowledge and rich feedback on relational inference.

CogSci 2021

Capturing uncertainty in relational learning: A Bayesian model of discrimination-based transitive inference

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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.

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Lay perceptions of medical conditions and treatments determine people’s health behaviors, guide biomedical research funding, and have important consequences for both individual and societal wellbeing. Yet it has been nearly impossible to quantitatively predict lay health perceptions for hundreds of everyday diseases due to the myriad psychological forces governing health-related attitudes and beliefs. Here we present a data-driven approach that uses text explanations on healthcare websites, combined with large-scale survey data, to train a machine learning model capable of predicting lay health perception. We use our model to analyze how language influences health perceptions, interpret the psychological underpinnings of health judgment, and quantify differences between different descriptions of disease states. Our model is accurate, cost-effective, and scalable, and offers researchers and practitioners a new tool for studying health-related attitudes and beliefs.

Machine Learning Models for Predicting, Understanding, and Influencing Health Perception

Previous studies have suggested testing and feature highlighting each facilitates category learning. We investigated whether the beneficial effects of interim testing change with the presence of feature highlighting in natural category learning. Participants learned various rock categories that were divided into two sections. They studied a series of rocks with or without feature descriptions highlighted on each image and either took an interim test or not between the two sections. On the final test, participants classified studied and new rock exemplars from both sections. The testing group outperformed the no-testing group for both sections, indicating both backward and forward effects of testing. Such beneficial effects of testing occurred regardless of whether the feature highlighting was provided or not. The feature highlighting, however, showed negative effects on learning of both sections, suggesting that provision of explicit instruction may impede learning when it is not appropriately embedded in the learning material.

Effects of interim testing and feature highlighting on natural category learning

We often organize objects around both visual and semantic boundaries in space. Across four experiments, we examined how semantically consistent partitions influenced memory for object locations. Participants learned the locations of items in a semantically partitioned display (where each partition contained objects from a single category), as well as a purely visually partitioned display (where each partition contained a random assortment of objects from different categories). While semantic partitions significantly improved location memory over the purely visually partitioned display, this advantage was significantly reduced when participants were cued to the correct partition during recall. Our results suggest that semantic category information benefits memory via strengthening the association between a given category and a spatial region delineated by a partition. Further, there was some indication that this benefit may come with the drawback of reducing memory precision for objects within a partitioned space.

The effect of semantic categorization on object location memory

Persuasion is hard. Then why do some consumers think that marketers can easily manipulate them? Three studies and an internal meta-analysis suggest that consumers’ beliefs about marketing manipulation are rooted in humans’ deeper and older psychology—motivation to understand life events. Consumers higher in motivation to make sense of their environments not only detect persuasion where it exists but also where none (make false-positive errors). Whereas consumers higher in sense-making motives believe that manipulations are more effective, objective sense-making abilities negatively predict false manipulation detection. We also tested how manipulation beliefs are related to personality traits, gender, and age. These findings help 1) understand the origins of lay (false) beliefs about the marketplace and persuasion in general, 2) consider marketing segmentation strategies to reach out to particularly skeptical consumers, and 3) understand ways to attenuate false-positive beliefs and foster accurate persuasion detection, which is critical in the era of infodemic.

Lay Theories of Manipulation: Do Consumers Believe They are Susceptible to Marketers’ Trickery?

Most machine learning based decision support systems are black box models that are not interpretable for humans. However, the demand for explainable models to create comprehensible and trustworthy systems is growing, particularly in complex domains involving risky decisions. In many domains, decision making is based on visual information. We argue that nevertheless, explanations need to be verbal to communicate the relevance of specific feature values and critical relations for a classification decision. To address that claim, we introduce a fictitious visual domain from archeology where aerial views of ancient grave sites must be classified. Trustworthiness among other factors relies on the perceived or assumed correctness of a system's decisions. Models learned by induction of data, in general, cannot have perfect predictive accuracy and one can assume that unexplained erroneous system decisions might reduce trust. In a 2×2 factorial online experiment with 190 participants, we investigated the effect of verbal explanations and information about system errors. Our results show that explanations increase comprehension of the factors on which classification of grave sites is based and that explanations increase the joint performance of human and system for new decision tasks. Furthermore, explanations result in more confidence in decision making and higher trust in the system.

Explaining Machine Learned Relational Concepts in Visual Domains - Effects of Perceived Accuracy on Joint Performance and Trust

Learnability has been a topic of great interest in phonology. In particular is the question of the relative learnability of process interactions. In both historical and experimental domains, researchers have noted that certain kinds of interactions are harder to learn than others. In both domains, however, the results are seemingly in conflict. One potential source of the conflicting outcome is the types of processes involved. In this paper, we investigate the effect of process types on the learnability of different interaction types, using an ideal minimum-description-length learner (MDL). We find that the model indeed predicts different learnability outcomes for each interaction type; however, the asymmetry is largely independent of the process type. This computational model explains certain elements of both the historical as well as some experimental findings of the relative learnability of linguistics process interactions, while contradicting other behavioral findings.

Phonological Interactions, Process Types, and Minimum Description Length Principles

Text-generation algorithms like GPT-2 (Radford et al., 2019) and GPT-3 (Brown et al., 2020) produce documents which resemble coherent human writing. But no study has compared the discourse linguistics features of the artificial text with that of comparable human content. We used a sample of Reddit and news discourse as prompts to generate artificial text using fine-tuned GPT-2 (Grover; Zellers et al., 2019). Blind annotators identified clause-level discourse features (e.g., states and events; Smith, 2003), and coherence relations (e.g., contrast; Wolf and Gibson, 2005) in prompts and generated text. Comparing the >20000 clauses, Grover recreates human word co-occurrence patterns and clause types across discourse modes. However, its coherence relations have shorter length and lower quality, with many nonsensical instances. Therefore, annotators could perfectly guess the human/algorithmic source of documents. Using a corresponding GPT-3 sample, we discuss aspects of generation that have and have not improved since Grover.

Can computers tell a story? Discourse Structure in Computer-generated Text and Humans

Conditionals like 'If A, then C' can be used, among others, to convey important knowledge about rules, dependencies and causal relationships.
Much work has been devoted to the interpretation of conditional sentences, but much less is known about when speakers choose to use a conditional over another type of utterance in communication.
To fill this gap, we consider a recently proposed computational model from probabilistic pragmatics, adapted for modeling the use of conditionals in natural language, by comparing its predictions to experimental production data from a behavioral experiment.
In a novel experimental approach, we manipulate relevant causal beliefs that might influence whether utterances with conditional structure are preferred over utterances without conditional structure.
This is a step towards a systematic, quantitative investigation of the situations that do or do not elicit the natural use of conditionals.

Communicating uncertain beliefs with conditionals: Probabilistic modeling and experimental data

Despite recent progress in promoting gender equality, gender bias continues to post challenges to women's career advancement. Here, we use a statistically grounded framework to investigate how face-based social perception may contribute to gender biases in political and job application settings. By analyzing a large face dataset and performing a novel behavioral experiment, we find that: 1) female faces exhibit stronger anti-correlation between perceived trustworthiness and dominance, 2) this anti-correlation is due to distinct sets of facial features humans utilize to assess female and male faces for trustworthiness and dominance, 3) perceived dominance positively contributes to social decision preferences for female faces, contrary to prior suggestions that perceived dominance affects female candidates negatively, and 4) the anti-correlated perception of trustworthiness and dominance put females at a disadvantage in competitive environments. More generally, our findings reveal the important role of face-based trait perceptions underlying gender biases in social decision making.

Gender differences in face-based trait perception and social decision making

A critical challenge for any intelligent system is to infer structure from continuous data streams. Theories of event-predictive cognition suggest that the brain segments sensorimotor information into compact event encodings, which are used to anticipate and interpret environmental dynamics. Here, we introduce a SUrprise-GAted Recurrent neural network (SUGAR) using a novel form of counterfactual regularization. We test the model on a hierarchical sequence prediction task, where sequences are generated by alternating hidden graph structures. Our model learns to both compress the temporal dynamics of the task into latent event-predictive encodings and anticipate event transitions at the right moments, given noisy hidden signals about them. The addition of the counterfactual regularization term ensures fluid transitions from one latent code to the next, whereby the resulting latent codes exhibit compositional properties. The implemented mechanisms offer a host of useful applications in other domains, including hierarchical reasoning, planning, and decision making.

Capturing uncertainty in relational learning: A Bayesian model of discrimination-based transitive inference

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