Canada

The capacity to generate recursive sequences is argued to be a behavioral marker of rich, algorithmic cognition, like that found perhaps distinctively in humans. However, the precise mechanisms underlying recursive sequence generation remain mysterious. We investigate three potential building blocks of recursive pattern processing: hierarchical reasoning, ordinal reasoning, and associative chaining. We develop a Bayesian mixture model to quantify the extent to which these three cognitive mechanisms contribute to a center-embedded sequence generation task. We further test whether recursive rule discovery depends upon relational information (either perceptual or conceptual) present in the task stimuli. The presence of relational information facilitates hierarchical reasoning that drives the generation of recursive sequences across various depths of center-embedding. Without relational information, the use of ordinal reasoning predominates. Our results suggest that hierarchical reasoning is an important building block of recursive pattern processing and can be deployed across embedding depths and relational domains.

CogSci 2022

Building Blocks of Recursive Pattern Processing in Human Adults

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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>

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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/).


CogSci 2021

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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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.
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<br>[**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)
<br>[**The Detailed Program can be found here**](https://cognitivesciencesociety.org/wp-content/uploads/2022/08/DeArmond-CogSci2022-program-v113-final.pdf)
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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. 





Third party punishment (TPP), or altruistic punishment, is specifically human prosocial behavior. TPP denotes the administration of a sanction to a transgressor by an individual that is not affected by the transgression. In some evolutionary accounts, TPP is considered crucial for the stability of cooperation and solidarity in larger groups formed by genetically unrelated individuals. Belief in free will (BFW), on the other hand, is the idea that humans have control over their behavior. BFW is a human universal notion that, in some studies, has been found to be supportive of prosocial behavior. In our study, we examined the effect of BFW on TPP under high and low affect scenarios through optical brain imaging (fNIRS). We hypothesized that in low affect cases, there would be a positive correlation between the strength of the BFW and the severity of the punishment inflicted. Obtained results and related statistical analyses indicate that participants with higher degree of BFW have more neural activation in their right dorsolateral prefrontal cortex (DLPFC) (hbo and hbt measures) in high affect scenarios, whereas the participants with lower degree of BFW have higher levels of neural activation in the medial PFC (hbo and hbt measures) in low affect scenarios. These empirical findings are in line with the research findings in the relevant academic literature and support the hypothesis that the degree of BFW influences punishment decisions.

The Neural Correlates of the Effect of Belief in Free Will on Third-Party Punishment: An Optical Brain Imaging (fNIRS) Study

Dual character concepts have recently received significant attention from experimental philosophers. They are usually defined as possessing two independent criteria for categorization, each associated with its own sense. One of these criteria is descriptive, while the other is normative. Almost all examples discussed in the literature involve a positively evaluated normative element (e.g., the concept of scientist is associated with the pursuit of empirical truth, something that we value). But can dual character concepts also involve morally bad elements? We report the first evidence suggesting that they can. Our study contrasted pairs of positive and negative concepts (e.g., friend/enemy, joy/sadness) and neutral concepts (e.g., baker). We found that negatively valenced concepts such as traitor, enemy, superficial, or sadness have a dual character. We also observed that positive concepts were significantly more likely to have a dual character than negative ones, but the effect size of this difference was small.

Are There Bad Dual Character Concepts?

Morality is a critical aspect of life––it influences how we think, design systems, and even the stories we tell. Looking to the popularity of true crime stories and characters like Dexter Morgan, it seems that our preferences are toward exploring moral ambiguity and moral badness. Across two experiments, we examine what moral ambiguity is and what kinds of moral information spark curiosity and explanation-seeking. In Experiment 1, we manipulate moral ambiguity to mean someone with conflicting moral character, and we predict those individuals will trigger curiosity more than morally consistent people. Results suggest that both morally ambiguous and immoral minds pique curiosity for explanations. In Experiment 2, we find that when ambiguity is instead operationalized as what is typical or average, we are curious about morally deviant things. This research points to critical differences in the kinds of moral minds we are curious to learn more about.


What is moral ambiguity and when does it trigger curiosity?

Artificial Intelligence (AI) can be harnessed to create sophisticated social and moral scoring systems – enabling people and organisations to form judgements of others at scale. While this capability has many useful applications – e.g., matching romantic partners who are aligned in their moral principles, it also raises many ethical questions. For example, there is widespread concern about the use of social credit systems in the political domain. In this project, we approach this topic from a psychological perspective. With experimental evidence, we show that the acceptability of moral scoring by AI depends on its perceived accuracy, and that perceived accuracy is compromised by people's tendency to see themselves as morally peculiar, and thus less characterizable by AI. That is, we suggest that people overestimate the peculiarity of their moral profile, believe that AI will neglect this peculiarity, and resist for this reason the introduction of moral scoring by AI.


The Quantified Moral Self

What is the relationship between trust and perceived agency? The present study experimentally investigated the effect of people’s perception of a robot’s compliance (and resistance) to social norms on their evaluation of a robot’s perceived agency, performance trust, and moral trust. Participants reported a norm-conforming robot to have higher perceived agency and a greater sense of trust than a robot that violated social norms. 
We also found that perceived agency, regardless of how much a robot followed norms, was correlated with trust. We interpret this finding as evidence that as people see a robot as having agency, they trust it more

Perceived Agency Changes Performance and Moral Trust in Robots

Dennett’s (1981) call to use computational frameworks in philosophical analysis can assist in clarifying the distinction between System-1 and System-2 metacognition. Metacognition has been described as “cognition about cognitive phenomena” (Flavell, 1979) and is renowned for its particularly difficult issues. Understanding its “mysterious mechanisms” (Brown, 1987) has been obscured by the subject’s abstractness and disputed terminology. Computation is uniquely suited to overcome these barriers as it forces definitional clarity. New research is supportive of the Common Model of Cognition as the best candidate for computationally modeling the functional organization of the human brain (Stocco et al., 2021). The Common Model represents a powerful tool for investigating unresolved cognitive issues as it is informed by a convergence of research and is well-defined. We propose that applying the Common Model as a philosophical tool can assist in clarifying the problematic distinction between System-1 and System-2 metacognition.


Clarifying Metacognition System 1 & 2 with the Common Model

The primary phenomenon within the field of collaborative memory is collaborative inhibition: the tendency for collaborative groups to underperform in free recall tasks compared to nominal groups of comparable size. Previously, we adapted the Search of Associative Memory (SAM; Raaijmakers & Shiffrin, 1981) model to collaborative free recall and found support for the retrieval disruption hypothesis (Mannering, Rajaram, Shiffrin, & Jones, 2022). In this paper, we investigate another possible cause of collaborative inhibition: memory homogenization. Previous modeling attempts have shown that memory homogenization may contribute to collaborative inhibition (Luhmann & Rajaram, 2015). In this paper, we prevent SAM models from learning during collaborative retrieval which subsequently prevents memory homogenization. We found that even when SAM model memories remained diversified, collaborative inhibition persisted—though the strength of the effect was diminished. These results suggest that both retrieval disruption and memory homogenization may contribute to collaborative inhibition in SAM.

Modeling the Effect of Learning During Retrieval on Collaborative Inhibition

Demonstratives---simple referential devices like this and that---are linguistic universals, but their meaning varies cross-linguistically. In languages like English and Italian, demonstratives are thought to encode the referent's distance from the producer (e.g., that one means ``the one far away from me"), while in others, like Portuguese and Spanish, they encode relative distance from both producer and receiver (e.g., aquel means ``the one far away from both of us"). Here we propose that demonstratives are also sensitive to the receiver's focus of attention, hence requiring a deeper form of social cognition than previously thought. We provide initial empirical and computational evidence for this idea, suggesting that producers use demonstratives to redirect the receiver's attention towards the intended referent, rather than only to indicate its physical distance.

Language universals rely on social cognition: Computational models of the use of this and that to redirect the receiver’s attention

“Each” and “every” can be used to express the same truth-conditions but differ in their contexts of use. We adopt a particular psycho-semantic proposal about the meanings of these universal quantifiers: “each” has a meaning that interfaces with the psychological system for representing object-files whereas “every” has a meaning that interfaces with the psychological system for representing ensembles. In five experiments (n=798 total) we demonstrate that this mentalistic account correctly predicts newly-observed constraints on how “each” and “every” are pragmatically used. More generally, these results demonstrate that canonical patterns of language use are affected in predictable ways by fine-grained differences in semantic representations and the cognitive systems to which those representations connect. By treating the output of semantics as mental representations that are more finely articulated than truth-conditions—and by taking seriously the relationship between linguistic meanings and non-linguistic cognitive systems—we can explain otherwise puzzling patterns of language use.

A Mentalistic Semantics Explains “Each” and “Every” Quantifier Use

How do humans produce and comprehend language in pragmatic ways? A variety of models of pragmatic inferences have been proposed, and these models are typically evaluated on their ability to account for human inferences in reference game experiments. However, these experiments are not tailored to target theoretical differences between models or clearly tease apart model predictions. We propose an optimal experiment design approach to systematically construct reference games that can optimally differentiate between models of human pragmatic reasoning. We demonstrate this approach and apply it to four models that have been debated in the literature: Grammar-based, Iterated Best Response (IBR), Rational Speech Act (RSA), and a recent variant of RSA grounded in rate-distortion theory (RD-RSA). Using these optimal reference game experiments, we find empirical evidence favoring iterated rationality models over the grammar-based model, as well as support for the relevance of rate-distortion theory to human pragmatic inferences. These results suggest that our optimal reference game design framework may help adjudicate between computational theories of pragmatic reasoning.

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The Neural Correlates of the Effect of Belief in Free Will on Third-Party Punishment: An Optical Brain Imaging (fNIRS) Study

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