Netherlands

It is now well-established that the visual features of objects influence the sounds we make to refer to them. This is called sound symbolism. We present the results of a two-part study that explores the extent to which the visual features of writing systems correspond to the smallest spoken units of language. In Study 1, participants (n = 322) classified the shape of a set of glyphs, representative of the world’s script families. The purpose was to create an open-source database of normed glyphs for future research in cognitive linguistics. In Study 2, participants (n = 73) were prompted to select either a round or angular glyph after hearing one of two kinds of phonemes (vowel or consonant) from the International Phonetic Alphabet. Results from a logistic regression suggest that the type of sound had a significant effect on the choice of glyph, and that vowel sounds increased the likelihood of choosing round glyphs by 30%. The significant correlation between what subjects heard and their choice of glyph suggests that the effect may extend to such sound symbolic relations in real-world writing systems. Our ongoing research seeks to substantiate these findings with increased glyph contrast and more diverse populations.

**Authors:**

Alexander Porto: Duquesne University; Alexander Basalyga: Independent Researcher; Mr. Nikolai Huckle: SCHUFA Holding AG; Julio Santiago: University of Granada; Elizabeth Fein: Duquesne University; Alexander Kranjec: Duquesne University

CogSci 2024

Sound Symbolism Across Diverse Writing Systems

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The 62nd Annual Meeting of the Association for Computational Linguistics (ACL 2024) will take place in Bangkok, Thailand from August 11th to 16th, 2024. More information will be announced soon.

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The 46th Annual Meeting of the Cognitive Science Society was an in-person meeting held in Rotterdam, The Netherlands at the Postillion Hotel & Conference Centre.

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Welcome to the 2024 Meeting of the Cognitive Science Society in Rotterdam! CogSci 2024 brings together a large community of cognitive scientists who have traveled here from around the world, as well as a smaller group of remote presenters. I would like to extend an especially warm welcome to our first-time CogSci meeting attendees, and hope that 
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**Anna Papafragou** 
President, Cognitive Science 
Society 2023-2024

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The 46th Annual Meeting of the Cognitive Science Society presents the latest research across cognitive science and highlights the theme of Cognition in Context.

Large Language Models (LLMs), which match or exceed human performance on many linguistic tasks, are nonetheless commonly criticized for not “understanding” language. These critiques are hard to evaluate because they conflate “understanding” with reasoning and common sense—abilities that, in human minds, are dissociated from language processing per se. Here, we instead focus on a form of understanding that is tightly linked to language: mapping sentence structure onto an event description of “who did what to whom” (thematic roles). Whereas LLMs can be directly trained to solve to this task, we asked whether they naturally learn to extract such information during their regular, unsupervised training on word prediction. In two experiments, we evaluated sentence representations in two commonly used LLMs—BERT and GPT-2. Experiment 1 tested hidden representations distributed across all hidden units, and found an unexpected pattern: sentence pairs that had opposite (reversed) agent and patient, but shared syntax, were represented as more similar than pairs that shared the same agent and same patient, but differed in syntax. In contrast, human similarity judgments were driven by thematic role assignment. Experiment 2 asked whether thematic role information was localized to a subset of units and/or to attention heads. We found little evidence that this information was available in hidden units (with one exception). However, we found attention heads that reflected thematic roles independent of syntax. Therefore, some components within LLMs capture thematic roles, but such information exerts a much weaker influence on their sentence representations compared to its influence on human judgments.

**Authors:**

Joseph Denning: UCLA; Xiaohan (Hannah) Guo: The University of Chicago; Bryor Snefjella: UCLA; Idan A Blank: University of California, Los Angeles

Do Large language Models know who did what to whom?

Empirical evidence shows that gendered languages influence speaker’s perception of gender of inanimate nouns. In this framework, we aimed to explore whether grammatical gender can override gender stereotypes. One hundred fourteen native Greek speakers whose second language was English were asked to match stereotypically male- and female-associated nouns presented in Greek or in their English translation with a male or female face. The nouns denoted agency and communality. Participants were presented with nouns both congruent and incongruent in terms of conceptual and grammatical gender. Responses for both Greek and English nouns were provided consistently with gender stereotypes. Critically, although responses were not dominated by grammatical gender, for a female-associated noun, a grammatically masculine gender would reduce female responses. Moreover, participants assigned a male face faster for male-associated nouns than for female associated nouns irrespective of grammatical gender. Male participants provided fewer female responses for agentic nouns than female participants.

**Authors:**

Nikoletta Galeraki: Deree, The American College of Greece; Eleni Orfanidou: Deree, The American College of Greece

Can Grammatical Gender Override Gender Stereotypes?

The present study examines the feasibility of conducting iconic artificial language learning (ALL) experiments in a fieldwork setting. We taught the pictographic language from Shapiro and Steinert-Threlkeld (2023) to speakers of San Martín Peras Mixtec in Oaxaca, Mexico. In a qualitative analysis, we explore whether these speakers display similar word-ordering behaviors to those observed among other populations, while developing insights for future ALL field experiments. We show that iconic ALL offers a promising path forward for considering understudied communities in the cognitive sciences.

**Authors:**

Naomi Tachikawa Shapiro: Radboud Universiteit; Andrew Hedding: University of Washington; Shane Steinert-Threlkeld: University of Washington

Iconic Artificial Language Learning in the Field: An Experiment with San Martín Peras Mixtec Speakers

The present study investigated the effects of presentational timing, operationalized as different levels of temporal overlap, on cross-modal priming of written words. We used a paradigm where the playback of spoken word primes was shifted relative to the presentation of written targets (asynchronous, partially overlapping, and synchronous presentation). Our participants (n = 48) carried out a speeded lexical decision task on the written targets. Presenting the spoken primes, albeit the words’ onset, before the written targets reduced lexical decision times to both words and pseudowords. Asynchronous presentation of the spoken primes resulted in the largest difference between word and pseudoword response times. We discuss our results in relation to the mental structure of human word knowledge and in the context of word form acquisition.

**Authors:**

Johanna Funk: Philipps University of Marburg; Louis Terenci Hessler Carbonell: Philipps Universität Marburg ; Denise Al-Rubaye-Jung: DSA; Michelle Morgenstern: Philipps-Universität Marburg; Elisa Schmied: Philipps-Universität Marburg ; Jasemina Tzallas: Philipps-Universität Marburg ; Florian Hintz: Philipps University

Cross-modal priming of written words across different timing conditions

What words are central in our semantic representations? In this experiment, we compared the core vocabulary derived from different association-based and language-based distributional models of semantic representation. Our question was: what kinds of words are easiest to guess given the surrounding sentential context? This task strongly resembles the prediction tasks on which distributional language models are trained, so core words from distributional models might be expected to be easier to guess. Results from 667 participants revealed that people's guesses were affected by word predictability, but that aspects of their performance could not be explained by distributional language models and were better captured by association-based semantic representations.

**Authors:**

Andrew Wang: The University of Melbourne; Simon De Deyne: University of Melbourne; Meredith McKague: University of Melbourne; Andrew Perfors: University of Melbourne

Word prediction is more than just predictability: An investigation of core vocabulary

It has been proposed that Chinese classifiers facilitate efficient communication by reducing the noun uncertainty in context. Although recent evidence has undermined this proposal, it was obtained using the common method of equating noun occurrence probabilities with corpus frequencies. This method implicity assumes words occur uniformly across contexts, yet this is inconsistent with empirical findings showing word distributions to be bursty. We hypothesized that if language users are sensitive to burstiness, and if classifiers provide information about upcoming nouns, this information will be less important in reducing uncertainty about noun after their first mention. We show that classifier usage provides more information at earlier mentions of nouns and and less information at later mentions, and that the actual classifier distribution appears inconsistent with previous proposals. These results support the idea that classifiers facilitate efficient communication and indicate that language users representations of lexical probabilities in context are dynamic.

**Authors:**

Sihan Chen: MIT; Edward Gibson: Massachusetts Institute of Technology; Michael Ramscar: University of Tübingen

Availability, informatively and bustiness: Why average corpus measures are an inaccurate guide to surprisal in language

Recent advances in generative language models, such as ChatGPT, have demonstrated an uncanny ability to produce texts that appear to be comparable to those produced by humans. Several key empirical results related to human processing of language, such as analogical reasoning, have been replicated using these models. Nevertheless, there are some important differences between the language generated by these models and language produced by humans. In this paper, I examine how LLMs performs on two pronoun disambiguation tasks reported on by Rhode, Levy, and Kehler (2011) and Sagi and Rips (2014). While LLMs performed reasonably in these tasks, their responses demonstrate stronger language-based biases while the influence of world knowledge, such as causal relationships, was lessened. Because LLMs replicate language produced by humans, these results can help shed light on which aspects of language use are directly encoded in language and which require additional reasoning faculties beyond language processing.

**Authors:**

Eyal Sagi: University of St. Francis

Large Language Models and Human Discourse Processing

Polysemy is pervasive in modern language and represents one key factor that allows for the unlimited expressive potential of human language. One important process of historical meaning extension is that of metaphor (Anderson 2017). Recently, the paradigm of Experimental Semiotics has been proposed as a novel methodology to investigate semantic change (Bowerman & Smith 2022). In experimental semiotics, participants have to converge on a novel signalling system in the absence of a shared language. Here we adopt this approach to experimentally investigate the role of metaphor in meaning extensions. Specifically, we will present results of a study in which participants have to communicate about a novel meaning space in a referential communication game. Importantly, participants will only be able to use symbols for which they have previously established symbol-meaning mappings in a prior game. The results show to which degree participants make use of metaphor in in this task.

**Authors:**

Michael Pleyer: Nicolaus Copernicus University in Toruń; Darya Namednikava: Centre for Language Evolution Studies, Nicolaus Copernicus University ; Klaudia Karkowska: Nicolaus Copernicus University; Svetlana Kuleshova: Nicolaus Copernicus University in Torun; Marek Placiński: Nicolaus Copernicus University in Toruń; Slawomir Wacewicz: Nicolaus Copernicus University

An Experimental-Semiotic Approach to the Emergence of Metaphor and Polysemy in Interaction

Experiments in psycholinguistics allow us to test hypotheses and build theories. However, psycholinguistic experiments often suffer from low ecological validity, because participants are often required to perform an unusual task in the face of unusual materials. In the current experiment, we test the predictions of Noisy Channel Processing in a naturalistic task: identifying the lyrics of a song. We conducted an experiment where participants heard short excerpts from songs and then indicated which one out of four possible transcriptions they had heard. We found that the predictions of Noisy Channel Processing bear out: options with higher prior and likelihood were chosen more often by participants as the heard song lyrics. Thus, Noisy Channel Processing is successful in explaining the everyday phenomenon of misheard song lyrics. More broadly, this suggests that Noisy Channel Processing captures everyday language processing, and that it is not dependent on unnatural experimental tasks and materials.

**Authors:**

Moshe Poliak: MIT; Hannah Kimura: MIT; Edward Gibson: Massachusetts of Institute Technology

Mis-Heard Lyrics: an Ecologically-Valid Test of Noisy Channel Processing

This paper presents prevalence norms collected from a representative sample of Singapore English speakers for a set of 240 concepts unique to Singapore English. Prevalence refers to the proportion of people who know or recognize a particular concept. Because large-scale, diachronic language corpora are scarce for non-standard varieties of English, the present study aims to establish the collection of prevalence norms from a cross-sectional sample as a potential alternative for tracking changes in word usage patterns over time. Preliminary analyses indicate that lexical knowledge of Singapore English concepts differs across gender, age, and ethnic groups. In particular, while most concepts are generally well known, some concepts are better known by younger participants and others are better known by older participants. These results underline the dynamic nature of Singapore English vocabulary and demonstrate how simple psycholinguistic tasks could be used to study lexical change in under-resourced languages and varieties.

**Authors:**

Cynthia S.Q. Siew: National University of Singapore

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