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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.


CogSci 2022

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

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.


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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.

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

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.

Perspective taking and reference frames for spatial and social cognition

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.

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.

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.

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

Deep learning (DL) makes it possible to automate the work of theorists, developers, and programmers in creating intelligent agents. At the same time, it has limitations related to the statistical nature of the method, which largely ignores available domain knowledge and requires large training datasets. These limitations become a barrier in the field of modeling and characterization of human social-emotional behavior. Biologically inspired cognitive architectures (BICA) are based on facts from cognitive and brain sciences and therefore can be successful here, although at the great cost of intellectual human labor. The challenge therefore is to integrate the two approaches, combining their strengths and compensating for their weaknesses. Here a particular form of such integration is presented, which involves a scaffolding of DL by BICA. Experimental paradigms include a virtual registrar, a virtual partner dance, a virtual clownery, and more. This work was supported by the Russian Science Foundation Grant #22-11-00213.

Socially emotional intelligent agents based on BICA and deep learning

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.

Source independence affects argument persuasiveness when the relevance is clear

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.

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

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Do Judgments of Learning Improve Inductive Learning?

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