Netherlands

Exploration in sequential decision problems is a computationally challenging problem. Yet, animals exhibit effective exploration strategies, discovering shortcuts and efficient routes toward rewarding sites. Characterizing this efficiency in animal exploration is an important goal in many areas of research, from ecology to psychology and neuroscience to machine learning. In this study, we aim to understand the exploration behavior of animals freely navigating a complex maze with many decision points. We propose an algorithm based on a few simple principles of animal movement from foraging studies in ecology and formalized using reinforcement learning. Our approach not only captures the search efficiency and turning biases of real animals but also uncovers longer spatial and temporal dependencies in the decisions of animals during their exploration of the maze. Through this work, we aspire to unveil a novel approach in cognitive science of drawing interdisciplinary inspiration to advancing the field&#39;s understanding of complex decision-making.

**Authors:**

Umesh K Singla: Princeton University; Marcelo G Mattar: New York University

CogSci 2024

Temporal Persistence Explains Mice Exploration in a Labyrinth

The purpose of the AAAI conference is to promote research in artificial intelligence (AI) and scientific exchange among AI researchers, practitioners, scientists, and engineers in affiliated disciplines. 

AAAI 2021

Exploration in sequential decision problems is a computationally challenging problem. Yet, animals exhibit effective exploration strategies, discovering shortcuts and efficient routes toward rewarding sites. Characterizing this efficiency in animal exploration is an important goal in many areas of research, from ecology to psychology and neuroscience to machine learning. In this study, we aim to understand the exploration behavior of animals freely navigating a complex maze with many decision points. We propose an algorithm based on a few simple principles of animal movement from foraging studies in ecology and formalized using reinforcement learning. Our approach not only captures the search efficiency and turning biases of real animals but also uncovers longer spatial and temporal dependencies in the decisions of animals during their exploration of the maze. Through this work, we aspire to unveil a novel approach in cognitive science of drawing interdisciplinary inspiration to advancing the field's understanding of complex decision-making.

**Authors:**

Umesh K Singla: Princeton University; Marcelo G Mattar: New York 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.

Recent neuroscience studies suggest that the hippocampus encodes a low-dimensional ordered representation of evidence through sequential neural activity. Cognitive modelers have proposed a mechanism by which such sequential activity could emerge through the modulation of the decay rate of neurons with exponentially decaying firing profiles. Through a linear transformation, this representation gives rise to neurons tuned to a specific magnitude of evidence, resembling neurons recorded in the hippocampus. Here we integrated this cognitive model inside reinforcement learning agents and trained the agents to perform an evidence accumulation task designed to mimic a task used in experiments on animals. We found that the agents were able to learn the task and exhibit sequential neural activity as a function of the amount of evidence, similar to the activity reported in the hippocampus.

**Authors:**

James Mochizuki-Freeman: Indiana University; Md Rysul Kabir: Indiana University; Zoran Tiganj: Indiana University

Incorporating a cognitive model for evidence accumulation into deep reinforcement learning agents

Predicting Click-Through Rate (CTR) is crucial in product and content recommendation, as it involves estimating the likelihood of a user engaging with a specific advertisement or content link. This task encompasses understanding the complex cognitive processes behind human interactions with recommended content. Learning varied feature embeddings that reflect different cognitive responses in various circumstances is significantly important. However, traditional methods typically learn fixed feature representations, leading to suboptimal performance. Some recent approaches attempt to address this issue by learning bit-wise weights or augmented embeddings for feature representations, but suffer from uninformative or redundant features in the context. To tackle this problem, inspired by the Global Workspace Theory in conscious processing, which posits that only a specific subset of the product features are pertinent while the rest can be noisy and even detrimental to human-click behaviors, we propose a CTR model that enables Dynamic Embedding Learning  with Truncated Conscious Attention for CTR prediction, termed DELTA. DELTA contains two key components:

**Authors:**

Chen Zhu: Tsinghua University; Liang Du: IEG; Hong Chen: Tsinghua University; Shuang Zhao: Tencent; Zixun Sun: Tencent; Xin Wang: Tsinghua University; Wenwu Zhu: Tsinghua University

DELTA: Dynamic Embedding Learning with Truncated Conscious Attention for CTR Prediction

The current study examines the interaction between predisposed mood, perceptual processing, and induced mood using music. We conducted an experiment in which participants were asked to identify stimuli at global or local (G/L) perceptual levels with four different background music conditions, which had different valence and arousal ratings. We used BMIS to assess current mood and PHQ-9 and GAD-7 to assess depression and anxiety, and divided the participants into two groups: distress and no distress (encompassing both disorders). We found a main effect of background music on mood. However, the distress group showed an overall low mood. Further, we observed an overarching effect of predisposed mood, encompassing depression and anxiety, on individuals’ transient mood experience and perceptual task performance. Individuals in the non-distress group showed a larger global-precedence effect. The results are discussed in light of emotional reactivity theories and the theory of positive emotion.

**Authors:**

Manas Kabre: International Institute of information technology, Hyderabad; Priyanka Srivastava: International Institute of Information Technology, Hyd

Predisposed Mood and Music in Perceptual Judgement Task

Neural language models, particularly large-scale ones, have been consistently proven to be most effective in predicting brain neural activity across a range of studies. However, previous research overlooked the comparison of these models with psychologically plausible ones. Moreover, evaluations were reliant on limited, single-modality, and English cognitive datasets. To address these questions, we conducted an analysis comparing encoding performance of various neural language models and psychologically plausible models. Our study utilized extensive multi-modal cognitive datasets, examining bilingual word and discourse levels. Surprisingly, our findings revealed that psychologically plausible models outperformed neural language models across diverse contexts, encompassing different modalities such as fMRI and eye-tracking, and spanning languages from English to Chinese. Among psychologically plausible models, the one incorporating embodied information emerged as particularly exceptional. This model demonstrated superior performance at both word and discourse levels, exhibiting robust prediction of brain activation across numerous regions in both English and Chinese.

**Authors:**

Yunhao Zhang: State Key Laboratory of Multimodal Artificial Intelligence Systems, Institute of Automation, CAS, Beijing, China.; Shaonan Wang: State Key Laboratory of Multimodal Artificial Intelligence Systems, Institute of Automation, CAS, Beijing, China.; Xinyi Dong: Beijing Normal University; Jiajun Yu: China Agricultural University; Chengqing Zong: State Key Laboratory of Multimodal Artificial Intelligence Systems, Institute of Automation, CAS, Beijing, China

Navigating Brain Language Representations: A Comparative Analysis of Neural Language Models and Psychologically Plausible Models

Counterfactual statements are famously difficult to process, and so are negated sentences and infrequent clause orders. Here, we argue that their combination can ease much of the processing cost when these difficult constructions align to clarify what is being referred to, thereby reducing referential uncertainty. In Experiment 1, we tested how affirmative and negative counterfactual statements (e.g., If there had been (no) zebras, there would have been (no) lions) are interpreted using a web-based eye-tracking paradigm. We found that negation facilitates processing, particularly when a Question under Discussion is about the actual state of affairs. In Experiment 2, reversing the clause order resulted in easier comprehension. These results provide support for a model of incremental language processing that puts the construal of semantic representations front and center.

**Authors:**

Ebru Evcen: University of California San Diego; Eva Wittenberg: Central European University

Two Negatives Make a Positive: Reducing Referential Uncertainty through Negation and Order Reversal Eases Processing in Counterfactuals

While the ability of recursion is considered universally available, there are considerable cross- and with-linguistic differences regarding the rules for recursive embedding, which must be learned from language-specific experience. One proposal argues that the recursivity of a structure is learnable as a productive generalization from distributional information in non-embedded input, and adults can indeed use such distributional cues to acquire recursive structures in an artificial language. However, it is not yet known whether children can use distributional information in this way. In this work, we examine children’s distributional learning of recursive structures. We exposed children to non-embedded sentences in an artificial grammar, where we manipulated the productivity of the structure across conditions. At test, we found that children exposed to productive input were more likely to accept recursively embedded sentences unattested during the exposure phase. The results suggest that children can make use of distributional information to acquire recursive structures.

**Authors:**

Daoxin Li: University of Pennsylvania; Kathryn Schuler: University of Pennsylvania

Children can use distributional cues to acquire recursive structures

In the domain of Linguistics, the categorization of intransitive verbs, namely the unaccusativity, is a long-debating topic. Unaccusativity suggests that intransitive verbs can be divided into unergative and unaccusative verbs, based on their subjects’ similarity to the subjects of transitive verbs or the objects of transitive verbs. Previous research has discussed how the meaning of verbs can decide the unaccusativity of intransitive verbs, but the meanings of verbs alone still cannot predict the unaccusativity of intransitive verbs cross-linguistically. On the other hand, the sentential environment can have an impact on the categories of intransitive verbs, but previous studies did not investigate how the environment plays a role in the categorization. This paper examines this issue from child language acquisition. We selected four sentential environments in the children’s corpus of Mandarin and conducted a qualitative analysis that suggested that these sentence environments indeed possess the properties of either category. In a child acquisition experiment, we further found that when the category of verbal meanings and sentential environments align, the categorization is the most obvious and efficient. These results suggest that children can infer the unaccusativity of verbs from a variety of sentence environments in language that may not be directly linked to unaccusativity. We introduce the concept of 'compatibility' to describe the relationship between verb meaning and the sentential environment as a crucial factor in this categorization process.

**Authors:**

Kaiying Lin: University of Hawai'i

How categories of intransitive verbs are formed? The interaction between meaning and grammar based on evidence from children’s acquisition

The complexity of studying behavior and cognitive processes in realistic ecological tasks is a major challenge for cognitive scientists, behavioral ecologists, community ecologists, and the cognitive ecology community that subsumes all these fields. Here we describe a modeling approach that can be used to study the decision-making trade-offs that emerge from the coupling of nervous systems, bodies, and ecological context. To demonstrate the method, we describe an agent that must balance its need to consume resources with its need to avoid predation. We then show how to analyze the resulting behavior through the lens of behavioral trade-off schemas synthesized with neural traces measured during real-time behavior. The employment of model agents will be an important contributor to ecological theory of cognitive processes, and here we hope to convince the reader of that methodological potential.

**Authors:**

Eden Forbes: Indiana University; Randall Beer: Indiana University

Minimal Modeling for Cognitive Ecologists: Measuring Decision-Making Trade-Offs in Ecological Tasks

(II) explicit embedding optimization (EEO), which applies an auxiliary task during training that directly and independently propagates the gradient from the loss layer to the embedding layer, thereby optimizing the embedding explicitly via linear feature crossing. Extensive experiments on five challenging CTR datasets demonstrate that DELTA achieves new state-of-the-art performance among current CTR methods.

**Authors:**

Grace Qiyuan Miao: University of California, Los Angeles (UCLA); Yanru Jiang: University of California Los Angeles; Ashley Binnquist: University of California, Los Angeles; Agnieszka Pluta: University of Warsaw; Francis Steen: University of California; Rick Dale: UCLA; Matthew Lieberman: University of California, Los Angeles

Integrative Multimodal Dynamics of Neural and Behavioral Signals: An fNIRS Hyperscanning and Deep Neural Network (DNN) Investigation

We investigated the Gratton effect and the proportion congruency effect using a parity/magnitude task-switching paradigm. The conflict-driven control model posits that conflict between stimulus dimensions triggers a controller, directing attention to the relevant dimension. In our study, participants decided the parity or magnitude of a digit. These tasks alternated across trials. Congruency was defined as the match between the stimulus-response rules of the current task with the other task. Therefore, there was no irrelevant dimension, no conflict within the stimulus, or no focus on the relevant dimensions. We observed both the proportion congruency effect and the Gratton effect in both reaction times and error rates. Our results suggest that the conflict-driven control model should be extended to incorporate conflict between rules, as holding conflicting rules in immediate memory calls for control.

**Authors:**

Kadriye Rüveyda Öztürk: TOBB University of Economics and Technology; Kaan Berk Taç: TOBB University of Economics and Technology; Nart Bedin Atalay: TOBB University of Economics and Technology

Exploring the Gratton and Proportion Congruency Effects in a Parity/Magnitude Task-Switching Paradigm: Implications for the Conflict-Driven Control Model

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Incorporating a cognitive model for evidence accumulation into deep reinforcement learning agents

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