Netherlands

Toddlers are sensitive to the reliability of speakers in their environment \cite{koenig2010}.
While previous work suggests that children prefer labels from reliable speakers, it remains unclear how these social representations guide lower-level information-seeking processes that affect speaker preferences. The current study introduces a gaze-contingent eye-tracking paradigm to investigate how children engage in sampling during word learning. Toddlers (22-24m) view videos of two speakers labeling familiar objects; one speaker provides reliable labels and the other speaker provides unreliable labels. Toddlers then sample novel labels from the speakers using a gaze-contingent interface: only the speaker they are fixating on provides a novel label. Preliminary data (N = 18) suggests that participants prefer to sample first from the reliable speaker over the unreliable speaker. However, there is little difference in overall sampling preferences. Our findings suggest that toddlers can assess speaker reliability, but remain open to exploring information from unreliable speakers.

**Authors:**

Jess Mankewitz: University of Wisconsin-Madison; Robert Hawkins: University of Wisconsin-Madison; Jenny Saffran: University of Wisconsin-Madison

CogSci 2024

Toddlers Actively Sample from Reliable and Unreliable Speakers

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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**Friday, July 26** 
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1600: [Invited Symposium: Dynamics between minds](https://underline.io/events/465/sessions?eventSessionId=18065&searchGroup=lecture) 
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**Saturday, July 27** 
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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 
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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

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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.

technical paper

A wealth of evidence indicates that children use their developing linguistic knowledge to incrementally interpret speech and predict upcoming reference to objects. For verbs, determiners, case-markers, and adjectives, hearing linguistic information that sufficiently constrains referent choice leads to anticipatory eye-movements. There is, however, limited evidence about whether children also use spatial prepositions predictively. This is surprising and theoretically important: spatial prepositions provide abstract semantic information that must interface with spatial properties of, and relations between, objects in the world. Making this connection may develop late because of the complex mapping required. In a visual-world eye-tracking task, we find that adults and 4-year-olds hearing 'inside' (but not 'near') look predictively to objects that afford the property of containment. We conclude that children make predictions about the geometric properties of objects from spatial terms that specify these properties, suggesting real-time use of language to guide analysis of objects in the visual world.

**Authors:**

Zoe Ovans: University of Pennsylvania; Heesu Yun: University of Pennsylvania; Sarah Yi: University of Pennsylvania; Barbara Landau: Johns Hopkins University; John Trueswell: University of Pennsylvania

A Look “Inside” Children’s Real-time Processing of Spatial Prepositions

Do children reason that people in close relationships accurately represent each other’s minds? In two experiments (total N = 123), we found that 7- to 9-year-old children from the US (i) reason that people who are close will accurately represent each other’s goals and desires and (ii) infer that people are socially close when they accurately predict each other’s emotional states. These findings suggest that children reason flexibly about mental state attributions within close relationships.

**Authors:**

Brandon Matthew Woo: Harvard University; Emma Yu: Harvard University; Ashley J Thomas: Harvard University

Children Expect People to Accurately Represent the Minds of Their Close Social Partners

We study referential communication about concepts at different levels of abstraction in an interactive concept-level reference game. To better understand processes of abstraction, we investigate superordinate referring expressions (animal). Previous work identified two main factors that influence speakers’ choice of referring expressions for concepts: the immediate context and the basic-level effect, i.e. a preference for basic-level terms such as dog. Here we introduce a new concept-level reference game that allows us to study differences in the basic-level effect between comprehension and production and to elicit superordinate referring expressions experimentally. We find that superordinate referring expressions become relevant for groups of objects. Further, we reproduce the basic-level effect in production but not in comprehension. In conclusion, even though basic-level terms are most readily accessible, speakers tailor their expressions to the context, allowing the listener to identify the target concept.

**Authors:**

Kristina Kobrock: University of Osnabrück; Charlotte Uhlemann: University of Osnabrück; Nicole Gotzner: University of Osnabrück

Superordinate referring expressions in abstraction: Introducing the concept-level reference game

Adults often learn new semantic information in face-to-face communication with other adults (e.g., teachers, colleagues). More knowledgeable individuals provide an ensemble of multimodal behaviours that can shape the information that their interlocutors learn. Using the naturalistic ECOLANG corpus of dyadic conversations, we ask whether multimodal behaviours (pitch, speaking rate, representational gestures, points, object manipulations, and gaze) support adults’ semantic learning of unknown objects above and beyond verbal properties of utterances (number of utterances, lexical diversity, mean length of utterances, concreteness) and learners’ individual differences (vocabulary, working memory). We found that individual differences, pointing and object manipulations affected learning, with verbal and multimodal factors also interacting to predict adult semantic learning. Our results highlight the relevance of accounts of multimodal learning in adulthood and the importance of considering naturalistic interaction in its complexity to understand the factors that influence adult learning.

**Authors:**

Christopher Edwards: University College London; Francesco Cabiddu: University College London; Harriet Hill-Payne: University College London; Quitterie D'Estalenx: University of Exeter; Ed Donnellan: University College London; Yan Gu: University of Essex; Gabriella Vigliocco: University College London

The impact of speakers’ multimodal behaviours on adults’ learning of semantic information: A corpus-based investigation

Advancements in deep neural networks have led to significant progress in computer vision and natural language processing. These networks, trained on real-world stimuli, develop high-level feature representations of stimuli. It is hypothesized that these representations, stemming from different inputs, should converge into similar conceptual systems, as they reflect various perspectives of the same underlying reality. This paper examines the degree to which different conceptual systems can be aligned in an unsupervised manner, using feature-based representations from deep neural networks. Our investigation centers on the alignment between the image and word representations produced by diverse neural networks, emphasizing those trained via self-supervised learning methods. Subsequently, to probe comparable alignment patterns in human learning, we extend this examination to models trained on developmental headcam data from children. Our findings reveal a more pronounced alignment in models trained through self-supervised learning compared to supervised learning, effectively uncovering higher-level structural connections among categories. However, this alignment was notably absent in models trained with limited developmental headcam data, suggesting more data, more inductive biases, or more supervision are needed to establish alignment from realistic input.

**Authors:**

Kexin Luo: NYU; Bei Zhang: NYU; Yajie Xiao: NYU; Brenden Lake: NYU

Finding Unsupervised Alignment of Conceptual Systems in Image-Word Representations

Our visual system tends to prioritise novel information, and this allocation of attention, as examined with the Visual Paired Comparison Task (VPC), is taken as an indirect index of memory processes. At present, research on the emergence of a novelty preference (NP) remains unclear about its temporal dynamics and agnostic about the role that the organisation of conceptual knowledge may play in it. These two gaps are addressed in this eye-tracking study, which adapts the VPC task to enable a finer temporal tracking of the NP while manipulating categorical and functional relationships between pairs of real-world visual objects to examine the impact conceptual associations bear on it. We found that NP significantly increases with increasing delay between the familiarisation and the test phase, especially for pairs of objects that were both categorically and functionally related (e.g., dart/dartboard). Our findings provide fresh evidence about the interplay between overt attention, conceptual knowledge and memory processes on novelty preference while offering valuable insights into the temporal dynamics of NP and its conceptual implications for mechanisms governing visual short-term memory.

**Authors:**

Elena Allegretti: La Sapienza, University of Rome; Jennifer D/ Ryan: Baycrest; Moreno I. Coco: Sapienza, University of Rome

Conceptual Knowledge Modulates the Temporal Dynamics of Novelty Preference for Real-world Objects in a Visual Paired Comparison Task

The decision to engage in lifelong learning often entails a motivational conflict, requiring individuals to balance potential benefits against the costs of engagement. Approach- avoidance motivation occurs when an action involves simultaneous positive and negative outcomes, necessitating a choice. This concept has primarily been studied in emotionally charged decisions linked to fear or anxiety, relevant for clinical settings. Our aim is to shift the focus to the cost of engagement in learning and educational settings. In a society marked by high demands and numerous tools for knowledge updating, lifelong learning may be beneficial for continuous individual development and societal contribution. We introduce a new framework that intricately connects motivation and learning processes with cognition, highlighting the pivotal roles of executive functions and decision-making processes. This article delves into the confluence of lifelong learning, cognitive conflict, and approach- avoidance motivation within the context of education and learning processes.

**Authors:**

Angélica Mendes: University of Luxembourg; Samuel Greiff: University of Luxembourg; David Levi Tekampe: University of Luxembourg; Katarzyna Bobrowicz: University of Luxembourg

Approach-Avoidance Motivation in Lifelong Learning: A New Framework for Understanding the Decision-Making Process behind Voluntary Learning

How humans process and utilize experienced outcomes and actions to adapt to a constantly evolving and noisy world is an important area of research. We investigate the role of the pupil-linked arousal system in adaptive value-based decision-making in an uncertain and changing environment using a two-armed bandit task with occasional changes in reward contingencies. We find that pupil size fluctuation encodes reward- and uncertainty-related values across trials; moreover, pupil size reflects future-choice-dependent contributions of these variables to learning and decision-making: larger pupil encoding of reward prediction error (RPE) promotes reward-driven switches in choice, while larger pupil encodings of estimation uncertainty (EU) promotes uncertainty-driven switches in choice. Furthermore, individual differences in pupil’s encoding of RPE and EU correlate with individual variabilities in choice bias and task performance. Given the relationship of pupil size to noradrenergic and cholinergic modulations, these results provide insights into the computational and neural process underlying adaptive decision-making.

**Authors:**

Zoe W He: University of California, San Diego; Maëva L’Hôtellier: CEA Paris-Saclay, Gif-sur-Yvette, France Université de Paris; Alexander Paunov: CEA Paris-Saclay, Gif-sur-Yvette, France Université de Paris; Dalin Guo: University of California, San Diego; Florent Meyniel: CEA Paris-Saclay, Gif-sur-Yvette, France Université de Paris; Angela J Yu: Technical University of Darmstadt

Pupil size reflects the relevance of reward prediction error and estimation uncertainty in upcoming choice

Whereas cognitive models of learning often assume direct experience with both the features of an event and with a true label or outcome, much of everyday learning arises from hearing the opinions of others, without direct access to either the experience or the ground truth outcome. We consider how people can learn which opinions to trust in such scenarios by extending the hedge algorithm: a classic solution for learning from diverse information sources. We first introduce a semi-supervised variant we call the delusional hedge capable of learning from both supervised and unsupervised experiences. In two experiments, we examine the alignment between human judgments and predictions from the standard hedge, the delusional hedge, and a heuristic baseline model. Results indicate that humans effectively incorporate both labeled and unlabeled information in a manner consistent with the delusional hedge algorithm---suggesting that human learners not only gauge the accuracy of information sources but also their consistency with other reliable sources. The findings advance our understanding of human learning from diverse opinions, with implications for the development of algorithms that better capture how people learn to weigh conflicting information sources.

**Authors:**

Yun-Shiuan Chuang: University of Wisconsin - Madison; Jerry Zhu: University of Wisconsin-Madison; Timothy Rogers: University of Wisconsin - Madison

The Delusional Hedge Algorithm as a Model of Human Learning from Diverse Opinions

We propose a neural dynamic architecture that models negation processing. The architecture receives a visual scene and a relational phrase like ``The blue object is not to the right of the yellow object'' or ``The blue object is to the right of the green object'' as input, and autonomously determines whether the phrase correctly describes the visual scene. The model is built out of empirically founded components for perceptually grounded cognition and constrained by neural principles. We demonstrate that the model can explain two commonly found reaction time effects: the negation effect in which reaction times are higher for negated than for affirmative phrases, and the polarity-by-truth-value interaction effect in which reaction times for false negated phrases are  faster than those for true negated phrases whereas the opposite is true for affirmative phrases. The model is consistent with some aspects of the two-step simulation theory.

**Authors:**

Lea Kati: Ruhr-University Bochum; Daniel Sabinasz: Institut für Neuroinformatik; Gregor Schöner: Ruhr-Universität Bochum; Barbara Kaup: University of Tübingen

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