Netherlands

People who have no experience with programming can create informal programs to rearrange the order of cars in trains. To find out whether they rely on kinematic mental simulations, the current studies examined participants’ eye movements in two experiments in which participants performed various moves and rearrangements on a railway consisting of a main track running from left to right and a siding entered from and exited to the left track.  In Experiment 1, they had to imagine different sorts of single moves of cars on the railway. The sequences of their fixations resembled iconic gestures: they tended to look at the starting location of the imagined move, and then at its final location. In Experiment 2, the task was to create descriptions of how to solve four sorts of rearrangements that differ in their Kolmogorov complexity.  It predicted the time to find the correct solution and the relative number and duration of fixations recorded during the description of each move for rearrangements of different complexity. Participants were more likely to fixate on the symbols on the cars than anything else, and they fixated longer when the rearrangement was more difficult. They also tended to fixate regions of the tracks where a car’s movement began or ended, as if they were imagining a car moving along the tracks. The results suggest that humans rely on a kinematic mental simulation when creating informal algorithms.

**Authors:**

Robert Mackiewicz: SWPS University of Social Science and Humanities; Monica Bucciarelli: Università di Torino; Sangeet Khemlani: Naval Research Laboratory; Phil Johnson-Laird: Princeton University

CogSci 2024

Eye Movements are like Gestures in the Creation of Informal Algorithms

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AAAI 2022

technical paper

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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**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) 
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1500: [Invited Symposium: Dynamics within minds](https://underline.io/events/465/sessions?eventSessionId=18100&searchGroup=lecture) 


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#### **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** 
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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.

The study focuses on the mechanisms through which dance brings people together. We recorded 7 improvised dance duets and asked 5 skilled improvisers to rate the perceived togetherness in the recorded dances. Subsequently, we employed pose tracking techniques and developed a quantitative measure of the stability of interpersonal movement coordination between dancers, demonstrating that it strongly correlates with experts' togetherness ratings. Based on follow-up interviews, we revealed that experts' understanding of togetherness converges to a stable construct, involving a state of responsive, mindful attention. This construct can be grounded in the objective properties of movement coordination. These properties can be framed within the context of dynamical systems, suggesting potential systemic organization principles, such as moment-to-moment adaptation, that promote togetherness. Our mixed-methods research has implications for various fields, including psychology, cognitive science, and art studies.

**Authors:**

Julian Zubek: University of Warsaw; Klara Łucznik: University of Warsaw

Movement coordination as a measure of togetherness in improvised dance duets

People thinking creatively will shift their bodies, wander around, move. Why? Here we investigate one explanation: Movement is a canny strategy for changing the information that is available visually, in ways that facilitate insight. We first analyzed video footage of mathematicians engrossed in creative thought. We found that sudden "aha" insights were reliably preceded by movements away far from the blackboard, as if mathematicians were stepping back to "see the big picture." To confirm the causal impact of changing proximity on creativity, we conducted an experiment that manipulated proximity to a whiteboard while participants worked on insight puzzles represented by diagrams. Participants had greater creative success when they could survey the entire whiteboard from a distance. Whether in real-world expert reasoning or a controlled experiment, movements away and toward visual representations facilitated insight. Wandering is sometimes a kind of epistemic action, facilitating the discovery of novel connections.

**Authors:**

Shadab Tabatabaeian: University of California Merced; Alyssa Viviana Ortega: University of California, Merced; Artemisia O'bi: -; David Landy: Indiana University, Bloomington; Tyler Marghetis: University of California, Merced

Stepping back to see the connection: Movement during problem solving facilitates creative insight

Pointing is an intuitive and commonplace communication modality. In human-robot collaborative tasks, human pointing has been modeled using a variety of approaches, such as the forearm vector or the vector from eye to hand. However, models of the human pointing vector have not been uniformly or comprehensively evaluated. We performed a user study to compare five different representations of the pointing vector and their accuracies in identifying the human's intended target in an object selection task. We also compare the vectors' performances to that of domestic dogs to assess a non-human baseline known to be successful at following human points. Additionally, we developed an observation model to transform the vector into a probability map for object search. We implemented our system on our robot, enabling it to locate and fetch the user's desired objects efficiently and accurately.

**Authors:**

Madeline Helmer Pelgrim: Brown University; Ivy Xiao He: Brown University; Kyle Lee: Brown University; Falak Pabari: brown university ; Stefanie Tellex: Brown University; Thao Nguyen: Brown University; Daphna Buchsbaum: Brown University

Find it like a dog: Using Gesture to Improve Object Search

Pragmatic theories assume that during communicative exchanges humans strive to be optimally informative and spontaneously adjust their communicative signals to satisfy their addressee’s epistemic needs. To investigate this ability in infants, we designed a task in which 18-month-olds had to point at the target object they wanted to receive. In Experiment 1, we found that when the target was placed behind a distractor object, infants appropriately modified their pointing to avoid mistakenly indicating the distractor to their partner. When the objects were covered, and their communicative partner had no information (Experiment 2) or incorrect information (Experiment 3) about the target’s location – as opposed to being knowledgeable about it – infants pointed at the target more often and employed modified pointing more frequently when it was necessary. This demonstrates that 18-month-olds can take into account their communicative partner’s epistemic states and provide her with relevant information through optimally informative deictic gestures.

**Authors:**

Tibor Tauzin: University of Vienna; Josep Call: University of St Andrews; Gyorgy Gergely: Central European University

Infants Point to Satisfy the Epistemic Needs of Their Communicative Partner

Emblems (also called conventional gestures) are a powerful, yet often overlooked part of humans’ communicative tool-kit. These gestures rapidly express encapsulated messages, such as waving a hand to greet someone and shoulder shrugging to reveal a lack of knowledge. We hypothesized that emblems are shaped by a universal pressure to reveal their communicative purpose, and they should therefore be unconfounded with movement typically produced to accomplish non-communicative goals. We present evidence for this hypothesis using a novel dataset of over 250 emblems from around the world: Over 95% of these gestures have forms that support observers’ inferences, suggesting that emblems are shaped to ease observers’ inferential burden. Finally, in a gesture-creation experiment, we show that these inference-guiding features emerge spontaneously without the need for observer feedback or cultural transmission. Taken together, these complementary approaches provide insight into how goal inferential processes may explain the shape of communicative actions across cultures.

**Authors:**

Amanda L Royka: Yale University; Gwyneth Heuser: Allendale Columbia School; Marieke Schouwstra: University of Amsterdam; Simon Kirby: The University of Edinburgh; Julian Jara-Ettinger: Yale University

Emblems and Improvised Gestures are Structured to Guide their Own Detection

Inspired by inductive reasoning models, we test whether generalized shared reality (i.e., the sense of being on the same page) arises through probabilistic inference about latent commonalities. Using a naturalistic text-based chat paradigm, we manipulated whether conversation partners discussed a belief they shared, a belief on which their opinions differed, or a random prompt. Participants discussing shared opinions reported experiencing greater shared reality compared to those discussing differences or random topics. Moreover, participants who made broader inferences about additional beliefs they might share with their partners also reported greater shared reality. While discussing shared opinions can induce an overall greater sense of shared reality, participants discussing differences leveraged their conversation to establish shared realities about other topics. We demonstrate that shared reality can emerge in multiple ways during initial interactions, establishing a foundation for future mechanistic investigations within an inductive inference framework.

**Authors:**

Wasita Mahaphanit: Dartmouth College; Christopher Welker: Dartmouth College; Helen Schmidt: Temple University; Luke Chang: Dartmouth College; Robert Hawkins: University of Wisconsin-Madison

When and why does shared reality generalize?

Often the only source of information for learning a word is its surrounding language context. For example, even without seeing a rambutan, one can learn that it is a fruit just from hearing “I like sweet, juicy rambutans”. What processes foster learning words from context? We investigated candidate processes that can unfold when the context precedes a new word and can foster learning via prediction, versus when the context occurs after and can only be used retroactively. We particularly sought to illuminate a role for working memory in linking a new word to the meaning implied by its context. Experiment 1 probed word learning during reading with eye tracking, and Experiment 2 probed word learning from speech. We found convergent evidence that regardless of whether the context precedes or follows a new word, word learning depends on maintaining the context in working memory while linking it to a new word.

**Authors:**

Layla Unger: University of York; Vladimir Sloutsky: The Ohio State University

Dynamic Processes of Learning Words from Context

Learning names for novel objects has been shown to be impacted by the context in which they appear. Manipulations of context, therefore, provide a key pathway to explore these learning dynamics. Here we use a neural process model that instantiates the details of ‘context’ to generate novel, counterintuitive predictions about how similarity in object properties influence learning. Specifically, we use a dynamic field model, WOLVES, to simulate and predict learning in a cross-situational word learning task in two conditions: one where the two objects presented on each learning trial are metrically similar in a property (‘NEAR’) and another condition where the two objects are always dissimilar (‘FAR’). WOLVES predicts—counterintuitively—that participants should learn better in the ‘NEAR’ condition (where objects are potentially confusable) than in ‘FAR’ condition (where objects are distinctive). We then tested this prediction empirically, finding support for the novel prediction. This study shows the utility of process models which instantiate the details of ‘context’ during learning and provides support for WOLVES. We know of no other theory of cross-situational word learning that captures these novel findings.

**Authors:**

Ajaz Ahmad Bhat: University Brunei Darussalam; John Spencer: University of East Anglia; Larissa K Samuelson: University of East Anglia

Similarity in object properties supports cross-situational word learning: Predictions from a dynamic neural model confirmed

Speech perception requires inferring category membership from varied acoustic cues, with listeners adeptly adjusting cue utilization upon encountering novel speech inputs. This adaptivity has been examined through the dimension-based statistical learning (DBSL) paradigm, which reveals that listeners can quickly de-emphasize secondary cues when cue correlations deviate from long-term expectations, resulting in cue-reweighting. Although multiple accounts of cue-reweighting have been proposed, direct comparisons of these accounts against human perceptual data are scarce. This study evaluates three computational models–cue normalization, Bayesian ideal adaptor, and error-driven learning–against classic DBSL findings to elucidate how cue reweighting supports adaptation to new speech patterns. These models differ in how they map cues onto categories for categorization and in how recent exposure to atypical input patterns influences this mapping. Our results show that both the error-driven learning and ideal adaptor models effectively capture the key patterns of cue-reweighting phenomena, whereas prelinguistic cue normalization does not. This comparison not only highlights the models' relative efficacy but also advances our understanding of the dynamic processes underlying speech perception adaptation.

**Authors:**

Yiming Lu: University of California, Irvine; Xin Xie: University of California, Irvine

Modeling cue re-weighting in dimension-based statistical learning

Compositional generalization that requires production and comprehension of novel _structures_ through observed constituent parts has been shown to be challenging for even very powerful neural network models of language. However, one of the test cases that poses the greatest difficulty---generalization of modifiers to unobserved syntactic positions---has not been empirically attested in human learners under the same exposure conditions assumed by these tests. In this work, we test adult human learners on whether they generalize or withhold the production of modification in novel syntactic positions using artificial language learning. We find that adult native speakers of English are biased towards producing modifiers in unobserved positions (therefore producing novel structures), even when they only observe modification in a single syntactic position, and even when the knowledge of their native language actively biases them against the plausibility of the target structures.

**Authors:**

Najoung Kim: Boston University; Paul Smolensky: Johns Hopkins University

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