Canada

**Abstract:**

Politeness is a social linguistic phenomenon. Modeling polite language production and understanding is difficult, as it may contradict conversational maxims and is shaped by extralinguistic social influences, such as the speaker-hearer relationship. This paper extends Yoon et al.&#39;s (2016) Rational Speech Act-based model of politeness by mapping speaker-hearer relationship influences to the utility weights of the model and instantiates it in German. Three online experiments, for empirical analysis and collection of behavioural data for model training and evaluation, are presented. These confirm the influence of the speaker-hearer relations on indirect politeness. Furthermore, two versions of the model are trained and evaluated to find out which part of the model is better suited for the integration of social influences. Overall, both model versions yielded similar results and were able to predict the meaning of polite speech acts.

**List of authors and affiliations:**

Eleonore Lumer: Bielefeld University; Hendrik Buschmeier: Bielefeld University

CogSci 2022

Modeling Social Influences on Indirectness in a Rational Speech Act Approach to Politeness

**Abstract:**

Politeness is a social linguistic phenomenon. Modeling polite language production and understanding is difficult, as it may contradict conversational maxims and is shaped by extralinguistic social influences, such as the speaker-hearer relationship. This paper extends Yoon et al.'s (2016) Rational Speech Act-based model of politeness by mapping speaker-hearer relationship influences to the utility weights of the model and instantiates it in German. Three online experiments, for empirical analysis and collection of behavioural data for model training and evaluation, are presented. These confirm the influence of the speaker-hearer relations on indirect politeness. Furthermore, two versions of the model are trained and evaluated to find out which part of the model is better suited for the integration of social influences. Overall, both model versions yielded similar results and were able to predict the meaning of polite speech acts.

**List of authors and affiliations:**

Eleonore Lumer: Bielefeld University; Hendrik Buschmeier: Bielefeld University

poster

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

This conference requires a registration to attend. Please use the link below to secure your ticket. The website will be available to all registered attendees on July 25th. 

[Register to Attend](https://cognitivesciencesociety.org/registration/)

Registration Is Required

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. 





**Abstract:**

The present study investigated whether making judgments of learning (JOLs) can enhance the learning of the studied materials (backward effect) and newly studied materials (forward effect) in inductive learning. In two experiments, participants studied various butterfly species through Sections A and B. After learning Section A, participants made either target-present JOLs, target-absent JOLs, or merely restudied Section A (Experiment 1). We also manipulated whether participants make an explicit retrieval attempt before making JOLs (Experiment 2). Then all participants learned Section B and took a final test for both sections. The results revealed that simply making target-present or target-absent JOLs did not produce learning benefits. However, when people made the target-absent JOLs combined with explicit retrieval attempts they outperformed the other groups in both sections, showing backward and forward effects of JOLs. In conclusion, JOLs seem to be effective only when learners actively retrieve the learned information while making metacognitive judgments.

**List of authors and affiliations:**

Hyorim Ha: Yonsei University; Hee Seung Lee: Yonsei University

Do Judgments of Learning Improve Inductive Learning?

People reciprocate another person’s altruistic or spiteful intentions toward themselves over repeated interactions, but it is unclear how such other-regarding intentions are represented in people’s minds and how the dynamics of reciprocity unfold. Recent work formalizes other-regarding intentions as welfare tradeoff ratios (WTR) and demonstrates that people reciprocate by adjusting their own WTR in response to the perceived WTR of another person. However, due to the complexity and inaccuracy of existing WTR measurements, it is still infeasible to study the long-term dynamics of people’s WTR adjustment beyond a few trials. Here we develop an experimental paradigm based on nonlinear continuous decomposed games that precisely reveals to the participant the opponent's WTR and measures the participant’s WTR in a single trial. We find that participants are sensitive to the opponent’s WTR and adjust their WTR accordingly. This experimental framework enables the fine-grained investigation of the long-term dynamics of people’s intention-based reciprocity.

Studying the long-term dynamics of reciprocity based on welfare tradeoff ratios

**Abstract:**

When considering the location of objects and places, we often take perspectives in reference to ourselves or someone/something else. Using ourselves as a reference is considered using an egocentric reference frame, while using something external as a reference is considered using an allocentric reference frame. Of interest is the similarity of how these reference frames inform our understanding of both spatial and social cognitive processes. Similar to how we understand objects in relation to ourselves or an external reference, mentalizing and theory of mind processes have also been described using reference frames. Whether there is a common mechanism for using reference frames for processing both spatial and social information is unclear. The present study explored this idea with an online study where participants performed both a spatial and social (i.e., mentalizing) perspective taking task, along with questionnaire gauging personality, visualization ability, and anxiety. Participants who were better at taking someone else’s spatial perspective tended to be better at mentalizing. This relationship was not present when taking one’s own spatial perspective or when mentalizing was not necessary. We provide preliminary evidence that reference frames contribute to both spatial and social cognitive processes.

**List of authors and affiliations:**

Sungjoon Park: Texas A&M University; Brandon K Watanabe: Texas A&M University; Heather Burte: Texas A&M University

Perspective taking and reference frames for spatial and social cognition

**Abstract:**

Groups of individuals need to coordinate in many real world
domains. However, coordination failure is common and not
well understood. There are few coordination measurements,
analyses focus on averaged data, and models lack coordination
strategies and clear correspondence to cognitive mechanisms.
Here, we present a thorough analysis of human data from a
difficult coordination scenario and a cognitive model implemented
within the ACT-R cognitive architecture to fit and explain
the data. Data were explored to better understand coordination
strategies and group dynamics. The cognitive model included
pre-game preferences, coordination strategies like signaling,
and other player choice predictions. This work highlights
the need for deeper data explorations and presents challenges
for modeling related to coordination dynamics, strategies,
and how players form beliefs about others.

**List of authors and affiliations:**

Alexander Hough: Air Force Research Lab; Ion Juvina: Wright State University

Understanding and Modeling Coordination in the Minimum Effort Game

Theory of Mind (ToM)– the capacity to attribute mental states to others, plays a central role in social cognition and behaviour. Social categorization (SC) - the classification of people as within or outside of one’s own group- also shapes our evaluation of others, often without our awareness. The features that give rise to social categorizations are often static and can be extracted by looking at human faces. However, little is known about the effect of SC in ToM judgements. The most widely-used instrument to assess ToM, Baron-Cohen’s Reading the Mind in the Eyes (RmE), uses exclusively white faces. We created a new database of high-quality videos and stills of ethnically diverse actors depicting 93 different mental states, suitable to study cross-cultural ToM, to be validated using a) the classic RmE four-alternative forced-choice paradigm and b) a task where raters locate each face within a 2-D space of valence and arousal.

Transcultural mindreading: Building a new database of Facial Expressions with actors of diverse ethnicities to assess social cognition.

**Abstract:**

A previous study used the Antarjami gaming framework to determine the OCEAN personality traits. In this study, a reinforcement learning agent is being compared to its human counterpart through various levels in the game. A Deep-Q Network(DQN) is proposed here for playing the Antarjami game autonomously. The DQN takes as input the image of the game screen and decides the moves/actions it wants to play in the game. A collection of DQNs having uniform architectures is used, where each DQN is trained on a particular level of the game. The starting position of each player within a game level is random in the Antarjami framework,  therefore the training of the reinforcement learning agent is agnostic to any bias about the initial positions of the players. The work shows how an RL-Agent gathers scores in a greedy fashion irrespective of any psychological inclinations.

**List of authors and affiliations:**

Utanko Mitra: Indian Institute of Technology, Kharagpur; Shivam Raj: Indian Institute of Technology; Anirban Lahiri: Arndit Ltd.; Sunreeta Sen: Arndit Ltd.; Rajlakshmi Guha: Indian Institute of Technology; Pabitra Mitra: Indian Institute of Technology

A Study on Relative Performance of an Reinforcement Learning Agent and Human in a Psychometric Assessment Game

One of the ways to make reinforcement learning (RL) more ef-
ficient is by utilizing human advice. Because human advice is
expensive, the central question in advice-based reinforcement
learning is, how to decide in which states the agent should ask
for advice. To approach this challenge, various advice strate-
gies have been proposed. Although all of these strategies dis-
tribute advice more efficiently than naive strategies (such as
choosing random states), they rely solely on the agent’s inter-
nal representation of the task (the action-value function, the
policy, etc.) and therefore, are rather inefficient when this rep-
resentation is not accurate, in particular, in the early stages of
the learning process. To address this weakness, we propose an
approach to advice-based RL, in which the human’s role is not
limited to giving advice in chosen states, but also includes hint-
ing apriori (before the learning procedure) which sub-domains
of the state space require more advice. Specifically, we sug-
gest different ways to improve any given advice strategy by
utilizing the concept of critical states: states in which it is very
important to choose the correct action. Finally, we present ex-
periments in 2 environments that validate the efficiency of our
approach.

Criticality-Based Advice in Reinforcement Learning

**Abstract:**

Making inferences about claims we do not have direct experience with is a common feature of everyday life. In these situations, it makes sense to consult others: an apparent consensus may be a useful cue to the truth of a claim. This strategy is not without its challenges. The utility of a consensus should depend in part on the sources of evidence that underlie it. If each person based their conclusion on independent data then the fact that they agree is informative. If, instead, everyone relied on the same primary source, the consensus is less meaningful. However, the extent to which people are actually sensitive to this kind of source independence is still unclear. Here, we present the results of three experiments that examine this issue in a social media setting, by varying the sources of primary data cited via retweets. In each experiment, participants rated their agreement with 12 different claims before and after reading four tweets that were retweeted on the basis of either the same or different primary data. We found that people were sensitive to source independence only when it was clear that the tweeters had relied on the primary data to reach their conclusion. Implications for existing research are discussed.

**List of authors and affiliations:**

Manikya Alister: The University of Melbourne; Keith James Ransom: University of Adelaide; Andrew Perfors: University of Melbourne

Source independence affects argument persuasiveness when the relevance is clear

**Abstract:**

Decision-making can be automatic (“rote”) or on-the-fly (“reflective”). Are people sensitive to whether others are behaving rotely or reflectively? The rote-versus-reflective inference may be particularly relevant when learning from others: good teachers should be actively considering the learner’s needs. When teachers rely on rote systems, this may “break” the mental state recursion that facilitates learning from pedagogy. This study takes a first step in investigating learning and inference when teachers use rote-versus-reflective reasoning, using uniqueness of feedback as a cue to reflectivity. Adult participants viewed videos of teachers providing either identical, similar, or unique feedback to three different groups of students; participants evaluated that teacher along several metrics. Consistent with our predictions, rote teachers were evaluated as poorer informants than reflective teachers, and students paired with rote teachers were expected to learn less. These results are the first to demonstrate sensitivity to, and impact from, inferences about others’ rote-versus-reflective behaviors.

**List of authors and affiliations:**

Ilona Bass: Harvard University; Elizabeth Bonawitz: Harvard University; Tomer D. Ullman: Harvard University

Adults' Evaluations of Rote and Reflective Teachers

**Abstract:**

Social environments often impose tradeoffs between pursuing personal goals and maintaining a favorable reputation. We studied how individuals navigate these tradeoffs using Reinforcement Learning (RL), paying particular attention to the role of social value orientation (SVO). We had human participants play an interated Trust Game against various software opponents and analyzed the behaviors. We then incorporated RL into two cognitive models, trained these RL agents against the same software opponents, and performed similar analyses. Our results show that the RL agents reproduce many interesting features in the human data, such as the dynamics of convergence during learning and the tendency to defect once reciprocation becomes impossible. We also endowed some of our agents with SVO by incorporating terms for altruism and inequality aversion into their reward functions. These prosocial agents differed from proself agents in ways that resembled the differences between prosocial and proself participants. This suggests that RL is a useful framework for understanding how people use feedback to make social decisions.

**List of authors and affiliations:**

Peter Duggins: University of Waterloo; Terrence C Stewart: University of Waterloo; Chris Eliasmith: University of Waterloo

Modeling Social Influences on Indirectness in a Rational Speech Act Approach to Politeness

Next from CogSci 2022

Do Judgments of Learning Improve Inductive Learning?

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