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Frequency counters are computational mechanisms that track the frequency or probability of speech units. Such counters are idealizations which re-describe frequency effects in early word segmentation, not providing an underlying learning mechanism from which these effects arise. Previous work has shown that Implicit Chunking represents a plausible learning mechanism explaining infants’ sensitivity to statistical cues when segmenting small-scale artificial languages (French et al., 2011). However, no work has examined whether Implicit Chunking allows to segment naturalistic speech in a developmentally plausible way. Here, we show how a novel symbolic model of Implicit Chunking – CLASSIC-Utterance-Boundary - performs better or as well as previous frequency-based models (i.e., transitional probability, chunking) at predicting children’s word age of first production and a range of word-level characteristics of children’s vocabularies (word frequency, word length, neighborhood density, phonotactic probability). We suggest that explicit frequency counters are not necessary to explain infants’ speech segmentation in naturalistic settings.

CogSci 2021

Are Explicit Frequency Counters Necessary in Computational Models of Early Word Segmentation?

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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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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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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

A fundamental goal in cognitive and historical linguistic research on semantic change is to characterize the regularity in how word meanings change over time. We examine a common belief that has not yet been evaluated comprehensively, which asserts that gender of a word influences its direction of semantic change. By this account, female terms like mistress should undergo pejorative change in meaning systematically more so than male terms like master. We evaluate this claim in gender-marked word pairs in English and French respectively as languages without and with grammatical gender. Our results provide supporting evidence for gender asymmetry in semantic change of English words but not French words. Our study raises questions about the generality of the claim about gender asymmetry in semantic change and provides a scalable computational framework for understanding the social roots of word meaning change.

A computational evaluation of gender asymmetry in semantic change

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.

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

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

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.

Are Explicit Frequency Counters Necessary in Computational Models of Early Word Segmentation?

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Contextual Flexibility Guides Communication in a Cooperative Language Game

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