Netherlands

Neural Episodic Control is a powerful reinforcement learning framework that employs a differentiable dictionary to store non-parametric memories. It was inspired by episodic memory on the functional level, but lacks a direct theoretical connection to the associative memory models generally used to implement such a memory. We first show that the dictionary is an instance of the recently proposed Universal Hopfield Network framework. We then introduce a continuous approximation of the dictionary readout operation in order to derive two energy functions that are Lyapunov functions of the dynamics. Finally, we empirically show that the dictionary outperforms the Max separation function, which had previously been argued to be optimal, and that performance can further be improved by replacing the Euclidean distance kernel by a Manhattan distance kernel. These results are enabled by the generalization capabilities of the dictionary, so a novel criterion is introduced to disentangle memorization from generalization when evaluating associative memory models.

**Authors:**

Hugo Chateau-Laurent: Inria centre at the university of Bordeaux; Frederic Alexandre: Inria centre at the university of Bordeaux

CogSci 2024

Relating Hopfield Networks to Episodic Control

### Welcome to ACL 2023, the 61st Annual Meeting of the Association for Computational Linguistics! 
 The conference will be held in Toronto, Canada, July 9-14, 2023. 
Following the succession of the recent conferences in our field, ACL 2023 will adopt a hybrid format.
While the impact of Covid has considerably diminished in terms of traveling, obtaining visas to Canada
entails a very long process. Moreover, the global economic conditions pose challenges for many individuals to travel to conferences. Recognizing these circumstances, we know many participants may not be
able to attend the conference in person. Therefore, we are committed to providing a great virtual platform
so everyone has the opportunity to interact with other participants and enjoy the conference. Based on the
current registered participants, approxiately 30% have chosen to attend the conference virtually. Whether
you join us in person or virtually, we sincerely hope everyone has a remarkable conference experience. 
This General Chair’s message is where I express my gratitude to the many individuals who have made
enormous contributions to the conference over the past year.

Read [**ACL 2023 General Chair's message**](https://docs.google.com/document/d/1WobYM7norbG4dI48s75HfJoD89qgX5a_F-6U8AteLSA/edit?usp=sharing/) in full.

##### **[Conference Handbook](https://2023.aclweb.org/downloads/acl2023-handbook.pdf)**

ACL 2023

The Association for Computational Linguistics (ACL) is the premier international scientific and professional society for people working on computational problems involving human language, a field often referred to as either computational linguistics or natural language processing.

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**
<div style="position:relative;padding-top:0;width:900px;height:500px;"><iframe style="position:absolute;border:none;width:100%;height:100%;left:0;top:0;" src="https://online.fliphtml5.com/ebtyf/cble/" seamless="seamless" scrolling="no" frameborder="0" allowtransparency="true" allowfullscreen="true" ></iframe></div>


**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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Please register!

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 increasing prevalence of dementia and cognitive impairments in the aging global population poses significant challenges to healthcare and society. Detecting cognitive impairment is crucial for managing diseases like Alzheimer's, yet current research faces limitations such as reliance on cross-sectional studies and a lack of understanding of causal relationships. In response, our study introduces a dynamic causal graph-based learning approach for predicting cognitive impairment risk in middle-aged and older adults. Employing a longitudinal perspective, we uncover causal structures through causal discovery methods, offering profound insights into cognitive changes over time. Our model, utilizing dynamic input variables, outperforms traditional algorithms while enhancing interpretability. This innovative approach not only improves prediction accuracy but also contributes to a deeper comprehension of the causal mechanisms underlying cognitive impairment. The longitudinal insight offers a comprehensive understanding of evolving factors associated with cognitive changes, making our model valuable for both research and practical applications.

**Authors:**

Linna Wang: College of Computer Science, Sichuan University; Xinyu Guo: Department of Health Policy and Management, West China School of Public Health and West China Fourth Hospital, Sichuan University; Yunyi Zhou: College of Computer Science, Sichuan University; Zhenchao Li: Sichuan University; Lihua Jiang: Department of Health Policy and Management, West China School of Public Health and West China Fourth Hospital, Sichuan University; Li Zhao: Department of Health Policy and Management, West China School of Public Health and West China Fourth Hospital, Sichuan University; Ziliang Feng: College of Computer Science, Sichuan University; Li Lu: College of Computer Science, Sichuan University

Dynamic Causal Graph-Based Learning Approach for Predicting Cognitive Impairment in Middle-Aged and Older Adults

This paper presents an approach to model the internal cognitive state of decision-makers when interacting with AI to understand exchanges between agents and improve future interactions. We focus on realizing how the process of AI suggestions is perceived by a human agent using an approach based on the technology acceptance model. The variation in the user's state has been investigated when perceiving the interaction with AI by considering it as a hidden (latent) state. Using human evaluation data collected from two cases of clinical decision-making and software development domains respectively we have analysed the data to understand the perceptional state during interaction. The experiment was conducted using the Bayesian belief network to represent the human perceptional model and provide a prediction of the usefulness of AI suggestions in the considered case. Upon introduction of cognitive states in the model we observed an increase in predictive performance by 76–77%. Our investigation can be concluded as an attempt to identify implicit static and dynamic cognitive characteristics of a user to provide personalized assistance in human-AI interaction and collaboration in the complex domain of decision-making.

**Authors:**

Sergey Kovalchuk: Huawei; Ashish Tara Shivakumar Ireddy: ITMO University

Prediction of Users Perceptional State for Human-Centric Decision Support Systems in Complex Domains through Implicit Cognitive State Modeling

How does language input lead to meaning? Is the answer in large language models - or certain "translations" they afford? Or does the answer remain in formal semantics theories? Here, we take a different approach. We cast language understanding as Bayesian inference in a generative model of how world states arise and project to utterances. We develop this model in a case study of action understanding from language input: inferring the goal of an agent in 2D gridworlds from utterances. The generative model provides a prior over agents' goals, a planner that maps these goals to actions, and a `language-renderer' that creates utterances from these actions. The generative model also incorporates GPT-2 as a noisy language production model. We invert this process with sequential Monte Carlo. In a behavioral experiment, the resulting model, called GSTP, explains evolving goal inferences of humans as utterances unfold.

**Authors:**

Lorenss Martinsons: Yale University; John Muchovej: Yale University; Ilker Yildirim: Yale University

Generative Semantic Transformation Process: A Case Study in Goal Prediction via Online Bayesian Language Inference

We integrate emotional mechanisms into the RL framework, conceptualizing emotions as a hierarchical controller influencing a baseline RL agent. This approach is designed to be broad, encompassing various contemporary perspectives on embedding emotions in RL agents. Rather than predefining the specific functional form of emotional control, we treat emotional mechanisms as the result of an optimization problem. This optimization aligns with evolutionary theories of emotion, aiming to endow RL agents with emotional mechanisms that optimize their survival across diverse environments. While this perspective has previously been adapted to generate intrinsic reward signals, we extend it more broadly to other aspects of emotional control - learning, planning and action selection. In this specific work we present a first step in this direction: i.e. we formulate a generalized version of the optimal reward problem where intrinsic rewards are temporally extended. We study the framework in the context of 2 armed bandit tasks.

**Authors:**

Prakhar Godara: TU Darmstadt

Meta-learning emotional control in bandit tasks

Artificial Neural Networks can be used to build a general theory of intelligent systems, connecting the computational, algorithmic and implementational levels. I analyze the generalization of learning in simple but challenging problems as a way to build the theory. I report simulations of learning and generalizing sameness, using Simple Recurrent Networks (SRN), Long-Short Term Memories (LSTM) and Transformers. We show that even when minimal requirements to implement sameness in SRNs are met, and a SRN network that can compute sameness theoretically exists, we failed to obtain it by training with backpropagation using all the possible input pairs. LSTMs come close to learn sameness, but the best networks require an inordinate amount of examples and the enrichment of the sample with positive examples. The same happens with Transformers. A similar task applied to ChatGPT revealed related problems. We discuss what this implies for Cognitive and Neural Science.

**Authors:**

Juan C Valle-Lisboa: Universidad de la República

Deep learning and the rules and statistics debate in cognitive science, applied to a simple case

Although data visualizations are a relatively recent invention, people are now expected to know how to read them. Recent advances in machine learning have produced algorithms that show strong performance on various visual understanding tasks. However, existing results for machine data visualization understanding rely upon weak benchmarks that do not resemble the tests used to assess similar abilities in humans. We evaluated several state-of-the-art algorithms on data visualization literacy assessments designed for humans, and compared their responses to human participants with different levels of high-school math education. We found that these models systematically underperform humans, and are highly sensitive to small changes in how they are prompted. Among the models we tested, GPT-4V most closely approximates human error patterns, but gaps remain between all models and humans. Our findings highlight the need for stronger benchmarks for data visualization understanding to advance artificial systems towards human-like visual reasoning.

**Authors:**

Arnav Verma: Stanford University; Kushin Mukherjee: University of Wisconsin-Madison; Christopher Potts: Stanford University; Elisa Kreiss: University of California, Los Angeles; Judith E. Fan: Stanford University

Evaluating human and machine understanding of data visualizations

Large language models have performed well on several reasoning benchmarks, including ones that test analogical reasoning abilities. However, it is debated whether they are actually performing humanlike abstract reasoning, or instead employing less general processes that rely on similarity to training data. We investigate the generality of analogy-making abilities previously claimed for LLMs (Webb, Holyoak, & Lu, 2023). We take one set of analogy problems used to evaluate LLMs and create a set of “counterfactual” variants—versions that test the same abstract reasoning abilities but are likely dissimilar from pre-training data. We test humans and three GPT models on the original and counterfactual problems, and show that, while performance of humans remains high for all problems, GPT models’ performance declines sharply on the counterfactual set. This provides evidence that, despite previously reported successes of LLMs on analogical reasoning, these models lack the robustness and generality of human analogy-making.

**Authors:**

Martha Lewis: University of Bristol; Melanie Mitchell: Santa Fe Institute

Using Counterfactual Tasks to Evaluate the Generality of Analogical Reasoning in Large Language Models

While humans learn within the first months of their lives that temporarily occluded objects continue to exist, deep-learning models are challenged by this. Here, we introduce the first self-supervised interpretable learning model that learns about object permanence directly from video data without supervision. To overcome the limitations in other models, we augment a slot-based autoregressive deep learning system with the ability to fuse latent imaginations with pixel-based observations into consistent object-specific what and where encodings over time. We show that, (i) Loci-Looped tracks objects through occlusions and anticipates their reappearance, while outperforming state-of-the-art baseline models, (ii) Loci-Looped is surprised when the principle of object permanence is violated (iii) Loci-Looped’s internal latent loop is key for learning object permanence.

**Authors:**

Frederic Becker: University of Tübingen; Manuel Traub: Eberhard Karls University of Tübingen; Sebastian Otte: University of Lübeck; Martin V. Butz: University of Tuebingen

Developing Object Permanence from Videos

In curriculum learning, teaching involves cooperative selection of sequences of data via plans to facilitate efficient and effective learning. One-off cooperative selection of data has been mathematically formalized as entropy-regularized optimal transport and the limiting behavior of myopic sequential interactions has been analyzed, both yielding theoretical and practical guarantees. We recast sequential cooperation with curriculum planning in a reinforcement learning framework and analyze performance mathematically and by simulation. We prove that infinite length plans are equivalent to not planning under certain assumptions on the method of planning, and isolate instances where monotonicity and hence convergence in the limit hold, as well as cases where it does not. We also demonstrate through simulations that argmax data selection is the same across planning horizons and demonstrate problem-dependent sensitivity of learning to the teacher's planning horizon. Thus, we find that planning ahead yields efficiency at the cost of effectiveness. This failure of alignment is illustrated in particular with grid world examples in which the teacher must attempt to steer the learner away from a particular location in order to reach the desired grid square. We conclude with implications and directions for efficient and effective curricula.

**Authors:**

Patrick Shafto: Rutgers University - Newark; Benjamin Sheller: Rutgers University - Newark

The alignment problem in curriculum learning

The nature of consciousness has been a long-debated concept related to human cognition and self-understanding. As AI systems become more capable and autonomous, it is an increasingly pressing matter whether they can be called conscious. In line with narrative-based theories, here we present a simple but concrete computational criterion for consciousness grounded in the querying of a virtual self-representation. We adopt a reinforcement learning (RL) setting and implement these ideas in SubjectZero, a planning-based deep RL agent which has an explicit virtual self-model and whose architecture draws similarities to multiple prominent consciousness theories. Being able to self-localize, simulate the world, and model its own internal state, it can support a primitive virtual narrative, the quality of which depends on the number of abstractions that the underlying generative model sustains. Task performance still ultimately depends on the modeling capabilities of the agent where intelligence, understood simply as the ability to model complicated relationships, is what matters.

**Authors:**

Asen Nachkov: INSAIT, Sofia University

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