Netherlands

Pragmatic implicature derivation presupposes that the cooperative principle is observed and critically depends on interlocutors expecting each other to behave cooperatively. It is much less clear, however, whether people extend this assumption to communication with artificial agents. People might therefore not draw the same pragmatic inferences when interacting with an artificial agent as they would with other conversationally competent humans, even if the agent is in principle believed to be similarly competent. In our study, we ask participants to interpret messages in a pragmatic reference game which they are told were generated by ChatGPT. Additionally, participants report whether they believe ChatGPT to be capable of the reasoning needed to select the optimal message. We observe a noteworthy discrepancy: in the reference game, participants interpret ChatGPT&#39;s messages less pragmatically than those of another adult human, but in the post-test questionnaire, they overwhelmingly rate ChatGPT&#39;s pragmatic ability very highly.

**Authors:**

Alexandra Mayn: Saarland University; Jia Loy: Saarland University; Vera Demberg: Saarland University

CogSci 2024

Capable but not cooperative? Perceptions of ChatGPT as a pragmatic speaker

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Pragmatic implicature derivation presupposes that the cooperative principle is observed and critically depends on interlocutors expecting each other to behave cooperatively. It is much less clear, however, whether people extend this assumption to communication with artificial agents. People might therefore not draw the same pragmatic inferences when interacting with an artificial agent as they would with other conversationally competent humans, even if the agent is in principle believed to be similarly competent. In our study, we ask participants to interpret messages in a pragmatic reference game which they are told were generated by ChatGPT. Additionally, participants report whether they believe ChatGPT to be capable of the reasoning needed to select the optimal message. We observe a noteworthy discrepancy: in the reference game, participants interpret ChatGPT's messages less pragmatically than those of another adult human, but in the post-test questionnaire, they overwhelmingly rate ChatGPT's pragmatic ability very highly.

**Authors:**

Alexandra Mayn: Saarland University; Jia Loy: Saarland University; Vera Demberg: Saarland University

poster

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.

**ON-DEMAND PROGRAM ACCESS AFTER THE CONFERENCE** 
Recordings of the invited program are now available. You can access them by clicking on the 'Schedule' icon on the left. Select view by week and navigate to the day and time slot of the recording you wish to view. Click on the time slot to access the recording.

Recordings available (click on each recording to view):

**Thursday, July 25** 
0900: [Keynote Speaker Morgan Barense on Enhancing real-world event memory](https://underline.io/events/465/sessions/17997/lecture/99084-dynamics-between-minds-and-the-environment) 
1000: [Gleitman Award Winner Isabelle Dautriche on Language Foundations: Insights from acquisition, communication, cognition, and more](https://underline.io/events/465/sessions/18000/lecture/99085-gleitman-talk) 
1415: [Invited Symposium: Dynamics between minds and the environment](https://underline.io/events/465/sessions?eventSessionId=18016&searchGroup=lecture) 
1700: [Rumelhart Prize Presentation Speaker Alison Gopnik on Exploit, explore, empower: Three ages and three intelligences](https://underline.io/events/465/sessions/18028/lecture/99161-rp1-rumelhart-prize-presentation) 

**Friday, July 26** 
0900: [C.L. de Carvalho-Heineken Prize Keynote Speaker Kia Nobre on Focusing in memory](https://underline.io/events/465/sessions/18035/lecture/99162-hp1-c-l-de-carvalho-heineken-prize-keynote-address) 
1030: [Rumelhart Symposium: Childhood as exploration](https://underline.io/events/465/sessions/18038/lecture/100346-childhood-as-exploration) 
1415: [Elman Prize Symposium](https://underline.io/events/465/sessions?eventSessionId=18053&searchGroup=lecture) 
1600: [Invited Symposium: Dynamics between minds](https://underline.io/events/465/sessions?eventSessionId=18065&searchGroup=lecture) 
1745: [Keynote Speaker Andrea E. Martin on Neural dynamics encode the structure and statistics of language](https://underline.io/events/465/sessions/18077/lecture/99271-neural-dynamics-encode-the-structure-and-statistics-of-language) 

**Saturday, July 27** 
0900: [Keynote Speaker Gregor Schöner on How higher cognition emerges from the dynamics of strongly interacting neural populations](https://underline.io/events/465/sessions/18083/lecture/99273-dynamics-within-the-mind) 
1000: [Glushko Talks](https://underline.io/events/465/sessions?eventSessionId=18086&searchGroup=lecture) 
1500: [Invited Symposium: Dynamics within minds](https://underline.io/events/465/sessions?eventSessionId=18100&searchGroup=lecture) 


#### **CogSci 2024 Program Book**
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**The Program Book can be downloaded [here](https://drive.google.com/file/d/1yRVp1cBqRnAbdYi4GXWV_pZeyWNsRquv/view?usp=sharing).**

#### **Letter from the President**

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 
their experience inspires them to join our future meetings for many years to come.

I want to thank this year’s conference co-chairs, Larissa K Samuelson, Stefan Frank, Mariya Toneva, Allyson Mackey, and Eliot Hazeltine, for putting together an amazing series of invited talks and symposia around the theme of Dynamics of Cognition. The co-chairs also deserve credit for handling a record-breaking number of submissions and producing a truly exciting conference program 

Beyond the invited talks and symposia dedicated to this year’s conference theme, I would like to highlight the presentations and events honoring the 
Rumelhart, Elman, Gleitman and C .L . de Carvalho Heineken prize winners, including the Rumelhart reception on Thursday. As you may know, we are recording all of the invited talks and symposia, and all prize-winners’ talks, and will be making this content available after the conference. 

The Cognitive Science Society relies on the volunteer efforts of our Governing Board members who work on matters related to our membership; conference policies; diversity and inclusion, international, and outreach initiatives; prizes and much more. We welcome interest from Society members who would like to become more involved in what we do. The 
Society is deeply grateful to Rick Dale and Andrea Bender, who serve as editors of the two Society journals, Cognitive Science and Topics in Cognitive Science respectively. We especially acknowledge our editors’ generosity in donating their stipends each year to D&I and outreach efforts. 

This year’s Annual Meeting depends critically on the support of Marischal De Armond and his team at Podium Conference Specialists: Sharon Zwack, Cendrine De Vis, Sarah-Kate Burke, and Rachel Waller worked tirelessly to secure the venue and handled a multitude of logistical and organizational issues, from arranging coffee breaks and receptions to making sure that a huge number of posters fits comfortably into the available space. Finally, and importantly, I would like to acknowledge the Cognitive Science Society Executive Officer, Erica Wojcik, for managing the complex and ever expanding sphere of our Society’s activities.

I hope you enjoy this year’s meeting and the many different opportunities to engage with the vibrant community of cognitive scientists gathered here. I also hope you get a chance to explore the great city of Rotterdam with its many attractions, museums, restaurants and scenic views. If you’d like to talk more about our Society, or simply want to say hello, please stop by!

![](https://assets.underline.io/markdown_image/1/image/3c66d0e21fff30e204df1138ffd15173.jpeg)

**Anna Papafragou** 
President, Cognitive Science 
Society 2023-2024

**CODE OF CONDUCT** 
By attending the CogSci 2024 Conference, you are required to adhere to the society’s [Code of Conduct](https://drive.google.com/file/d/16-6KkptF0Gn3ZYGJDlpqpwTdImw45Ng0/view?usp=drive_link).

**ABOUT THE COGNITIVE SCIENCE SOCIETY** 
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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The 46th Annual Meeting of the Cognitive Science Society presents the latest research across cognitive science and highlights the theme of Cognition in Context.

Humans can flexibly bind familiar functional roles to novel entities in their environment. For example, children who have the concept of ``goal posts'' can bind this abstract role to two hats placed on the street. In doing so, they can port over existing expectations of ``goal posts" for the duration of the game. In this paper, we seek to explore artificial agents' ability to perform flexible role binding and rebinding. To this end, we designed a Gridworld navigation game and tested a popular CNN-based agent which has had success in other tasks involving visual and spatial state spaces (e.g. Atari or Minigrid). To our surprise, we found that while this architecture was capable of over-fitting to the training set, it was not able to learn flexible role binding without intervention. We ultimately show that with carefully engineered data augmentation techniques, our artificial agent is able to learn the task. This suggests that the diversity of the training dataset was a limiting factor.

**Authors:**

Brian Pennisi: New York University; Todd M Gureckis: New York University; Rheza Budiono: New York University; Mark K Ho: Stevens Institute of Technology

Investigating Flexible Role Binding in AI Agents

This study investigated visual similarity modeling of Chinese characters across proficiency levels. Similarity models of Chinese characters from previous research were evaluated against human judgments. Extended tree analysis highlighted the importance of characters’ configurations in expert similarity judgments, with character components as support. In contrast, Chinese learners’ and novices’ criteria for similarity judgments were less clear as indicated by the low proportion of variance that their extended tree models accounted for. Although cophenetic correlation coefficients revealed only weak correlations between similarity models and human judgments, their scores on configurations and components pointed to the nonlinear development of visual expertise. The computational challenges in replicating human perception and judgments were discussed and future directions, including machine learning with larger datasets for improved model performance, were proposed.

**Authors:**

Zhuqian Zhou: Teachers College, Columbia University; James E. Corter: Columbia University

Visual Similarity Modeling of Chinese Characters Across Natives, Second-Language Learners, and Novices

Gonering & Corina (2023) argued that abstractions over visual scenes (i.e. schemas or situation models) provide a semantic scaffold for acquiring verb argument structures. This work intends to support their argument through systematic meta-analysis of 158 fMRI studies of verb processing (from NeuroSynth) and 208 fMRI studies of visual event processing (from NeuroQuery). Activation Likelihood Estimation using the Neuroimaging Meta-Analysis Research Environment package (Salo et al., 2022) will be used to assess separate and overlapping regions of significant neural activity (cluster-level family-wise error corrected using Monte Carlo method). Meta-analyses on additional visual scene and verb processing studies from NeuroSynth and NeuroQuery are also underway. We further intend to show that a hierarchical Bayesian model can learn verb argument structures from input statistics, even when they deviate from strong prior event semantic knowledge.

**Authors:**

Brennan T Gonering: University of California, Davis; Zoey Liu: University of Florida

Real world event schemas offer modality-independent conceptual bases for verb argument structures

This paper explores how speakers choose between two utterance alternatives with similar syntactic properties and distinct yet related meanings. In doing so, we consider the interaction of two speaker pressures: accessibility of lexical items, and pressure to be informative at early stages in the utterance; the latter of which is explicitly predicted by an incremental Rational Speech Act (IRSA) model. A significant effect of accessibility on speaker choice is established based on the results of an online spoken production task, which asked speakers to describe the relationship between two objects using a provided verb. We find that foregrounding objects leads speakers to mention them earlier, even when the resulting utterance has a different meaning. This task is then extended to an interactive context, where speakers describe images while paired with a partner who must click on the corresponding visual scene based on the description. In this goal-oriented context, IRSA predicts more informative words to come earlier in the sentence. The interactive task does not show a significant effect of foregrounding or the predicted informativity effect.

**Authors:**

Emily Goodwin: Stanford University; Judith Degen: Stanford University

Production of Syntactic Alternations Displays Accessibility But Not Informativity Effects

Infants learn actively in their environments, shaping their own learning curricula. They selectively pay attention, orient themselves, and later move to particular stimuli and entities. As a result, infants master progressively more complex tasks, and exhibit physical and agentive knowledge. Essentially, they learn about their environments' affordances, that is, how their behavior can affect the environment under local circumstances. Here we model this type of behavior by means of a deep learning architecture. The architecture mediates between global cognitive map exploration and local affordance learning. Inference processes actively move the simulated agent towards regions with high uncertainty, where they expect knowledge gain about local affordances. We contrast three measures of uncertainty: predicted uncertainty of a model, standard deviation between the means of several models (SD), and the Jensen-Shannon Divergence (JSD) between several models. We show that the first measure gets fooled by aleatoric uncertainty inherent in the environment, while the two other measures focus learning on epistemic uncertainty. However, SD yields behavior that is attracted by extreme behavior regimes, while JSD shows the most balanced epistemic exploration strategy. While our brain may have other means to implement the proposed processes, on computational levels our model suggests three key ingredients for coordinating the active generation of learning curricula: (1) Navigation behavior needs to be coordinated with local motor behavior for enabling the active search for affordances in an environment. (2) Affordances need to be encoded locally for acquiring generalized knowledge about affordances. (3) Effective active affordance learning mechanism may use density comparison techniques for estimating expected knowledge gain. Future work may seek collaborations with developmental psychology to model active play in children in more realistic scenarios.

**Authors:**

Fedor Scholz: University of Tübingen; Erik Ayari: University of Tübingen; Johannes Bertram: University of Tübingen; Martin V. Butz: University of Tübingen

Quick and Accurate Affordance Learning

German has two demonstrative series, the der (die, das) series and the dieser (diese, dieses) series. Both have been claimed to be topic shifters, taking up a non-topical antecedent and promoting it to topichood. However, der can form topical referential chains, while dieser cannot. We operationalize discourse topichood via questions and provide evidence from a corpus study and an acceptability study that while dieser is indeed sensitive to topichood and avoids topical antecedents, der is compatible with topical antecedents. We hypothesize that only dieser is a discourse topic shifter, while der marks a sentence topic.

**Authors:**

Timo Buchholz: Universität zu Köln; Klaus von Heusinger: University of Cologne

German demonstratives and topic questions

While effects of animacy and attention have been studied quite extensively in adult speakers, less is known about their role in child language production. In the present study we fill this gap by testing German-speaking preschool children in two language production tasks using eye-tracking. We find that animacy does neither affect the production of transitive sentences nor the production of conjoined noun phrases. By contrast, we find significant effects of attentional orienting. Children were more likely to first fixate an entity when it had been preceded by a visual cue and was hence in their focus of attention. While this held true across tasks, attentional orienting only affected children’s production of conjoined noun phrases but not the production of transitive sentences. Effects of attentional orienting therefore seem conditioned by language production affordances. In sum, our findings provide new evidence that animacy and attention play different roles in children’s language production.

**Authors:**

Sarah Dolscheid: University of Cologne; Martina Penke: University of Cologne

Animacy and attention play different roles in children’s language production

At birth, the human brain is tuned to spoken language in general and to some extent also to native language in particular. In behavioral studies, infants also prefer to listen to infant-directed speech (IDS) to adults-directed speech (ADS), apparently most robustly in their native language. Recent studies demonstrated that this preference has correlates at the neural level as well. We test whether newborns show differential neural tracking of native over foreign, rhythmically different, language.

We assess neural tracking of native and non-native speech in Czech-exposed newborns. Newborns' were played a children's story in two rhythmically different languages, Czech (lacking acoustic cues to word-level stress) and Russian (acoustically salient word-level stress), in IDS or ADS, while their EEG was recorded.

We predicted stronger neural tracking of the native Czech, evident in larger inter-trial phase coherence (ITC), and total power. Preliminary data (n = 27 out of planned 60) suggest this language-specific effect is most prominent in the theta band corresponding to the syllable rate. We will further test whether this native-language effect would be more prominent in ADS or IDS. Data collection is underway and the results will be presented & discussed at the conference.

**Authors:**

Martina Dvořáková: Charles University; Katerina Chladkova: Institute of Psychology, Czech Academy of Sciences; Josef Urbanec: Faculty od Medicine in Hradec Kralove, Charles University; Jan Kremláček: Faculty od Medicine in Hradec Kralove, Charles University

Newborns' neural tracking of infant-directed and adult-directed speech in native and foreign language

Recently the principles of efficient communication have provided useful characterizations of semantic typology: the diversity of attested languages can be described by competing pressures for simplicity and informativeness. While this approach has achieved success in several semantic domains, the formalizations used to define complexity across domains vary. In this note, we list the conditions under which the two main approaches of defining complexity: channel rate and description length, unify and, thus, conclusions about near-optimal communicative efficiency generalize across formalizations. We illustrate this equivalence using simulations of communicative efficiency for Boolean concepts. We round out this note discussing the (un)importance of description languages and the limits on generalizing this equivalence for other behavioral targets for explanation.

**Authors:**

Francis Mollica: University of Melbourne

A note on complexity in efficient communication analyses of semantic typology

Research suggests that the meanings of iconic signs are not easily accessible to sign-naive people; however, some signs' meanings are more easily guessed than others'. What causes some signs to be more easily guessable (more transparent) than others is not well-understood. In our previous research, we showed that signs whose form is based on more cross-linguistically common underlying motivations are more transparent than signs based on less common underlying motivations (Authors, Year). In the current study, we ask whether signs that are rated as more iconic are more interpretable. We asked 104 English speakers with no knowledge of sign languages to rate signs from our previous study for how iconic they are. We then reanalyzed the data from our previous study in light of the obtained iconicity ratings. We found that when people choose between a relevant sign and an irrelevant sign (e.g., when participants pick a sign that is supposed to mean 'cat' from two signs, one of which indeed means 'cat' but the other does not), iconicity does not matter: participants tend to choose the sign with the relevant meaning, regardless of whether it is highly iconic or not. However, when participants are presented with two signs with the same meaning (e.g., signs for 'cat' from two different sign languages), not only do they choose the sign that is based on a more common cross-linguistic motivation (as we reported in [Authors, Year]), but iconicity does appear to influence interpretability. Specifically, participants were more likely to pick the more cross-linguistically common sign if the difference in iconicity ratings between the two signs was bigger. Taken together, the data show that participants are sensitive to at least three factors: the underlying motivation of the sign (choosing the sign with a relevant meaning over one with an irrelevant meaning), the cross-linguistic frequency of the sign (choosing the relevant sign based on more cross-linguistically common underlying motivation over the relevant sign based on a less common one), and the iconicity of the sign (choosing the more cross-linguistically common sign more the higher the difference in iconicity ratings is). These results shed additional light on the ongoing debate on the connection between iconicity and transparency: Iconicity by itself does not make a sign transparent, but it can enhance transparency under certain conditions.

**Authors:**

Oksana Tkachman: University of British Columbia; Emily Sadlier-Brown: The University of British Columbia; Roger Yu-Hsiang Lo: University of British Columbia ; Carla Hudson Kam: University of British Columbia

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