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Moral disengagement is a mechanism whereby people distance or disconnect their actions from their moral evaluation. This work presents a novel knowledge graph schema, dataset, and transformer-based NLP model to identify and represent indicators of moral disengagement in text. Our graph schema is informed by Albert Bandura’s psychosocial mechanisms of moral disengagement, including dehumanization, victimization, moral condemnation and justification, and attribution (or displacement) of responsibility. Our preliminary dataset is comprised of online posts from five different communities. We present initial evidence that (1) our theory-based schema can represent moral disengagement indicators across these communities and (2) our transformer-based NLP model can identify indicators of moral disengagement in text. As it matures, this thread of computational social science research can help us understand the spread of morally-disengaged language and its effect on online communities.

CogSci 2021

Toward Transformer-Based NLP for Extracting Psychosocial Indicators of Moral Disengagement

**31st IEEE International Conference on Robot & Human Interactive Communication**
 This conference is a leading forum where state-of-the-art innovative results, the latest developments as well as future perspectives relating to robot and human interactive communication are presented and discussed.
 
 The conference covers a wide range of topics related to Robot and Human Interactive Communication, involving theories, methodologies, technologies, empirical and experimental studies. Papers related to the study of robotic technology, psychology, cognitive science, artificial intelligence, human factors, ethics and policies, interaction-based robot design and other topics related to human-robot interaction are welcome.
 
 **CONFERENCE THEME:**
 Social, asocial, and antisocial robots
 
 Robots are starting to populate our lives: at work, in our homes, hotels, shops. However, not everybody is comfortable with robots finding a place in society. For example, people may be afraid that robots will replace humans in their jobs. This belief is exacerbated by the media that, on one side, raise expectations of robots’ capabilities, while amplifying fears and worries on the other side.
 
 In the next years, due to the increasing request for interaction with people in social environments, academic research and market analyses predict that robots will become more and more “social”. But, at the same time, robots may incur the risk of being perceived as “asocial” as their pervasiveness in everyday environments may become an obstacle to social interaction between humans. Or they may even be rejected as “antisocial” if researchers do not take care to develop robots that behave according to the customs and habits of the communities where they are expected to operate.
 
 To prevent this from happening, robotic researchers can no longer take the luxury of being mere technicians. They must be capable of addressing all the challenges raised by future robot-populated communities in a multidisciplinary way. They need to share their knowledge with the public to make people aware of what robots really are, what expectations we can have from them, the risks we may incur, and the possible mitigation plans.
 
 While being open to any kind of contribution related to Human-Robot Interactive Communication, the RO-MAN 2022 conference will emphasize these aspects of robotic research. How can we create robots capable of being welcomed as our assistants and companions in society while, at the same time, not being perceived as “asocial” or “antisocial?” How can we create robots perceived as facilitators of the natural development of human beings and communities, rather than obstacles?
 
 Contributions from different disciplines, not exclusively related to technological development, will be welcomed as crucial to pave the way to global acceptance of future robots with an informed attitude.
 
 [![](https://assets.underline.io/uploads/markdown_image/1/image/af259abe105b3791c498ae1552d75710.png)](https://drive.google.com/file/d/18w73BZPB_JVOgcrcdZDqYpEupwVt4CnU/view?usp=sharing)

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IEEE RO-MAN 2022

31st IEEE International Conference on Robot & Human Interactive Communication
This conference is a leading forum where state-of-the-art innovative results, the latest developments as well as future perspectives relating to robot and human interactive communication are presented and discussed.

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### Looking for Cogsci 2023? Please [click here](https://underline.io/events/405/reception) to switch to 2023 edition.


**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! 

**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.

Context-sensitive communication not only requires speakers to choose relevant utterances from alternatives, but also to retrieve and evaluate the relevant utterances from memory in the first place. In this work, we compared different proposals about how underlying semantic representations work together with higher-level selection processes to enable individuals to flexibly utilize context to guide their language use. We examined speaker and guesser performance in a two-player iterative language game based on Codenames, which asks speakers to choose a single `clue' word that allows their partner to select a pair of target words from a context of distractors. The descriptive analyses indicated that speakers were sensitive to the shared semantic neighborhood of the target word pair and were able to use guesser feedback to shift their clues closer to the unguessed word. We also formulated a series of computational models combining different semantic representations with different selection processes. Model comparisons suggested that a model which integrated contextualized lexical representations based on association networks with a contextualized model of pragmatic reasoning was better able to predict behavior in the game compared to models that lacked context at either the representational or process level. Our findings suggest that flexibility in communication is driven by context-sensitivity at at the level of both representations and processes.

Contextual Flexibility Guides Communication in a Cooperative Language Game

Signalling systems of various species (humans and non-human animals) as well as our world both exhibit discrete and continuous properties. However, continuous meanings are not always expressed using continuous forms but instead frequently categorised into discrete symbols. While discrete symbols are ubiquitous in communication, the emergence of discretisation from a continuous form space is not well understood. We investigate the emergence of discrete symbols by simulating the learning process of two agents that acquire a shared signalling system. The task is formalised as a reinforcement learning problem in continuous form and meaning space. We identify two central causes for the emergence of discretisation: 1) sub-optimal signalling conventions and 2) a topological mismatch between form and meaning space. A long version of this paper has been accepted for publication in Cognition (International Journal of Cognitive Science).

Discretisation and Continuity: Simulating the Emergence of Symbols in Communication Games

People have the fascinating ability to infer causality by observing other humans’ actions. We modelled this process using a Bayesian rational agent model and showed how people can reason about another agent’s beliefs and, by extension, infer the world’s causal structure. We compared the model’s predictions against humans’ causal judgements on a novel inference task. Participants (N = 171) were shown a dynamic scene depicting either a human agent, robot agent, or both agents acting on two objects sequentially before observing an outcome. After observing the human (vs the less intentional robot) agent, people were more likely to infer that both objects (in sequential order) caused the outcome. When two agents of different intentionality were shown, people favored the object that the intentional agent interacted with as the cause of the outcome. Our model captured these inference patterns well and revealed insights into reasoning about semi-intentional agents and multi-agent contexts.

Learning from Agentic Actions: Modelling Causal Inference from Intention

Defying polarization, U.S. support for marijuana legalization grew from 36% to 67% during 2005-2019 (Pew Research Center, 2019). To identify discourse properties that accompanied this shift, we present the largest social media corpus on the topic (>3M documents) and the first to target Reddit (2008-2019). We used geolocation inference to distinguish U.S. discourse from international chatter and separate pre- and post-legalization content given marijuana’s uneven legal status across U.S. states. Research on online discourse has focused on generalized statements (e.g., moral arguments/sentiments). However, we combine topic modeling with hierarchical clustering to show that personal anecdotes and attitudes were a major driver of discourse especially during the legalization drive, at the expense of certain more generalizable themes. We classified comments by expressed attitude and presence of persuasion attempts using neural networks to show that the anecdotal discourse reflected not only social sharing, but also active argumentation.

Narratives of Consensus: a Decade of Reddit Discourse on Marijuana Legalization

We introduce and implement a cognitively plausible model for learning from generic language, statements that express generalizations about members of a category and are an important aspect of concept development in language acquisition (Carlson & Pelletier, 1995; Gelman, 2009). We extend a computational framework designed to model grounded language acquisition by introducing the concept network. This new layer of abstraction enables the system to encode knowledge learned from generic statements and represent the associations between concepts learned by the system. Through three tasks that utilize the concept network, we demonstrate that our extensions to ADAM can acquire generic information and provide an example of how ADAM can be used to model language acquisition.

A Grounded Approach to Modeling Generic Knowledge Acquisition

Recent work has paired classic category learning models with convolutional neural networks (CNNs), allowing researchers to study categorization behavior from raw image inputs. However, this research typically uses naturalistic images, which assess participant responses to existing categories; yet, much of traditional category learning research has focused on using novel, artificial stimuli to examine the learning process behind how people acquire categories. In this work, we pair a CNN with ALCOVE (Kruschke, 1992), a well-known exemplar model of categorization, and attempt to examine whether this model can reproduce the classic type ordering effect from Shepard, Hovland, and Jenkins (1961) on raw images rather than abstract features. We examine this question with a variety of CNN architectures and image datasets and compare ALCOVE-CNN to two other models that lacked certain key features of ALCOVE. We found that our ALCOVE-CNN model could reproduce the type ordering effect more often than the other models we tested, but in limited situations. Our results showed that success varied greatly across the various configurations we tested, suggesting that the feature representations from CNNs provide strong constraints in properly capturing this effect.

Modeling artificial category learning from pixels: Revisiting Shepard, Hovland, and Jenkins (1961) with deep neural networks

The framework of emotional Biologically Inspired Cognitive Architecture (eBICA) is used to define a cognitive model, producing believable socially emotional behavior in social interaction paradigms in a virtual environment. The paradigm selected for this study is a virtual pet (a penguin) interacting with a human user. Its implementation in Unity on a desktop PC in two versions, with and without an Oculus VR headset, was used in experiments involving 20 college student participants. Several versions of the model were compared. Results support the validity of the eBICA framework and indicate that the combination of somatic factors, cognitive appraisals and moral schemas in one model has the potential to make behavior of a virtual actor believable and socially attractive. At the same time, partial randomization of behavior does not affect the general result. The work has implications for the design of future emotionally intelligent collaborative robots and virtual assistants.

A virtual actor with socially emotional behavior

Order effects occur when judgments about a hypothesis's probability given a sequence of information do not equal the probability of the same hypothesis when the information is reversed. Different experiments have been performed in the literature that supports evidence of order effects.

We proposed a Bayesian update model for order effects where each question can be thought of as a mini-experiment where the respondents reflect on their beliefs. We showed that order effects appear, and they have a simple cognitive explanation: the respondent's prior belief that two questions are correlated.

The proposed Bayesian model allows us to make several predictions: (1) we found certain conditions on the priors that limit the existence of order effects; (2) we show that, for our model, the QQ equality is not necessarily satisfied (due to symmetry assumptions); and (3) the proposed Bayesian model has the advantage of possessing fewer parameters than its quantum counterpart.

Order Effects in Bayesian Updates

The Abstraction and Reasoning Corpus (ARC) is a collection program induction tasks that was recently proposed by Chollet (2019) as a measure of machine intelligence. Here, we report a preliminary set of results from a behavioral study of humans solving a subset of tasks from ARC (40 out of 1000). We found that humans were able to infer the underlying program and generate the correct test output for a novel test input example, with an average of 84% of tasks solved per participant, and with 65% of tasks being solved by more than 80% of participants. Additionally, we find interesting patterns of behavioral consistency and variability across the action sequences to generate their responses, the natural language descriptions used to describe the rule for each task, and the errors people make. Our findings suggest that people can quickly and reliably determine the relevant features and properties of a task to compose a correct solution, despite limited experience in this domain. This dataset offers useful insights for designing AI systems that can solve abstract reasoning tasks such as ARC with the fluidity of human intelligence.

Fast and Flexible: Human program induction in abstract reasoning tasks

People think and learn abstractly and compositionally. These two key properties of human cognition are shared with natural language: we use a finite, composable vocabulary of nameable concepts to generate and understand a combinatorially large space of new sentence. In this paper, we present a domain of compositional reasoning tasks and an artificial language learning paradigm designed to probe the role language plays in bootstrapping learning. We discuss results from a language-guided program learning model suggesting that language can play an important role in bootstrapping learning by providing an important signal for search on individual problems, and a cue towards named, reusable abstractions across the domain as a whole. We evaluate adults on the same domain, comparing learning performance between those tasked with jointly learning language and solving reasoning tasks, and those who only approach the domain as a collection of inductive reasoning problems. We find that adults provided with abstract language prompts are better equipped to generalize and compose concepts learned across a domain than adults solving the same problems using reasoning alone.

Toward Transformer-Based NLP for Extracting Psychosocial Indicators of Moral Disengagement

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Contextual Flexibility Guides Communication in a Cooperative Language Game

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