Netherlands

During sentence processing, comprehenders have to maintain a mapping between lexical items and their position in the sentence (syntactic position). We propose a model of morpheme-position binding in working memory, based on models such as &#39;serial-order-in-a-box&#39; and its SOB-complex-span version. Like those working memory models, our sentence processing version derives a range of attested memory interference effects from the process of item-position binding. We present simulation results capturing similarity-based interference and item-distortion. These two major classes of interference effects have not received a unified account before, and are not fully captured by cue-based retrieval models.

**Authors:**

Maayan Keshev: The Hebrew University of Jerusalem; Mandy Cartner: Tel Aviv University; Aya Meltzer-Asscher: Tel Aviv University; Brian Dillon: University of Massachusetts Amherst

CogSci 2024

A working memory model of sentence processing as binding morphemes to syntactic positions

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EMNLP 2023

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During sentence processing, comprehenders have to maintain a mapping between lexical items and their position in the sentence (syntactic position). We propose a model of morpheme-position binding in working memory, based on models such as 'serial-order-in-a-box' and its SOB-complex-span version. Like those working memory models, our sentence processing version derives a range of attested memory interference effects from the process of item-position binding. We present simulation results capturing similarity-based interference and item-distortion. These two major classes of interference effects have not received a unified account before, and are not fully captured by cue-based retrieval models.

**Authors:**

Maayan Keshev: The Hebrew University of Jerusalem; Mandy Cartner: Tel Aviv University; Aya Meltzer-Asscher: Tel Aviv University; Brian Dillon: University of Massachusetts Amherst

technical paper

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**Friday, July 26** 
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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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**ABOUT THE COGNITIVE SCIENCE SOCIETY** 
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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.

In this paper, we report three reading time (RT) experiments (one using self-paced reading and two using A-Maze) that tested the cognitive mechanisms underlying the processing of classifier-noun dependencies in Mandarin Chinese (MC). We leveraged prenominal relative clauses and the contrast between general and specific classifiers in MC, which offered a good testing ground for existing theories of sentence processing. Results from the A-Maze experiments showed both locality and expectation effects. More importantly, we observed an interaction between locality and expectation in the way of Information Locality (Futrell, 2019; Futrell, Gibson, & Levy, 2020): Expectation-driven facilitation was highly constrained by locality effects. To capture the results, we implemented a resource-rational Lossy-Context Surprisal model (Hahn et al., 2022) for MC, which successfully replicated the key patterns in the A-Maze experiments.

**Authors:**

Hailin Hao: University of Southern California; Yang Yang: Guangdong University of Foreign Studies; Michael Hahn: Saarland University

Information Locality in the Processing of Classifier-Noun Dependencies in Mandarin Chinese

Computational psycholinguistics has traditionally employed a complexity metric called Node Count, which counts the number of syntactic nodes representing syntactic structures and predicts processing costs in human sentence processing. However, Node Count does not dissociate distinct syntactic operations deriving those syntactic structures, so that how much processing cost each syntactic operation induces remains to be investigated. In this paper, we introduce a novel complexity metric dubbed Composition Count, which counts the number of syntactic operations deriving syntactic structures, allowing us to understand the computational system of human sentence processing from the derivational, not representational, perspective. Specifically, employing Combinatory Categorial Grammar (CCG) which is equipped with multiple syntactic operations and thus suitable for the purpose here, we investigate (i) how much distinct syntactic operations of CCG contribute to predicting human reading times, and (ii) whether the same holds across languages. The results demonstrate that distinct syntactic operations of CCG have independent and cross-linguistic contributions to predicting human reading times, while Node Count turns out not to be robust cross-linguistically. In conclusion, these results strongly suggest the importance of Composition Count to dissociate distinct syntactic operations, not whole syntactic representations, and understand the computational system of human sentence processing.

**Authors:**

Kohei Kajikawa: University of Tokyo; Ryo Yoshida: University of Tokyo; Yohei Oseki: University of Tokyo

Dissociating Syntactic Operations via Composition Count

Both prediction and working memory constraints have been established as key factors in characterizing incremental sentence processing difficulty. Here we investigate the less explored question: Whether and how predictive expectation and working memory interact with each other using data from naturalistic reading time corpora. We provide broad-coverage evaluations of two hypotheses that make divergent predictions regarding the interaction of expectation and memory constraints: the Information Locality and Prediction Maintenance hypotheses. We first confirmed the predictions of both expectation- and working memory-based theories. Regarding their interactions, we find support the Information Locality hypothesis: Strong mutual predictability can enhance locality effects. We argue that future theory building in sentence processing should therefore take into consideration both prediction and memory constraints, as well as their potential interaction.

**Authors:**

Hailin Hao: University of Southern California; Himanshu Yadav: Indian Institute of Technology Kanpur; Elsi Kaiser: University of Southern California

Naturalistic Reading Time Data Support Information Locality

How do humans decide what to look at and when to stop looking? The Rational Action, Noisy Choice for Habituation (RANCH) model formulates looking behaviors as a rational information acquisition process. RANCH instantiates a hypothesis about the perceptual encoding process using a neural network-derived embedding space, which allows it to operate on raw images. In this paper, we show that the model not only captures key looking time patterns such as habituation and dishabituation, but also makes fine-grained, out-of-sample predictions about magnitudes of dishabituation to previously unseen stimuli. We validated those predictions experimentally with a self-paced looking time task in adults (N = 468). We also show that model fits are robust across parameters, but that assumptions about the perceptual encoding process, the learning process and the decision process are all critical for predicting human performance.

**Authors:**

Anjie Cao: Stanford ; Gal Raz: MIT; Rebecca Saxe: MIT; Michael C. Frank: Stanford University

Predicting graded dishabituation in a rational learning model using perceptual stimulus embeddings

Differentiating letter shapes accurately is an increasingly crucial competence. Are letters as distinctive as they could be? We used a unique dataset of crowdsourced letter descriptions across 43 writing systems to produce a comprehensive typology of letter shapes for these diverse scripts. We extracted from 19,591 letter classifications, contributed by 1,683 participants, enough features to provide a unique description of all letters in each system. We show that scripts, compared to phoneme inventories, are feature-extensive: they use additional features to do what could be done with a lower number of features, used more efficiently. Compared to 516 phoneme inventories from the P-base dataset, our 43 scripts have lower feature economy (fewer symbols for a given number of features) and lower feature informativeness (a less balanced distribution of feature values). Letter shapes, we argue, having more degrees of freedom than speech sounds, use features in a more wasteful way.

**Authors:**

Yoolim Kim: Wellesley College; Marc Allassonnière-Tang: CNRS; Helena Miton: Santa Fe Institute; Olivier Morin: PSL University

Letter shapes phonology: Feature economy and informativeness in 43 writing systems

Here we used deep neural network + hidden Markov model (DNN+HMM) to provide a computational account for the relationship among holistic processing (HP), face scanning pattern and face recognition performance. The model accounted for the positive associations between HP and eyes-focused face scanning pattern/face recognition performance observed in the literature regardless of the version of the composite task used to measure HP. Interestingly, we observed a quadratic relationship between HP and face scanning pattern, where models being highly eyes-focused or highly nose-focused had lower HP. By inspecting fixation locations and associated attention window size in the model and XAI methods, we found that the eyes- and nose-focused models both developed local and holistic internal representations during training, and their difference was in the temporal dynamics of how these representations were used. Our findings demonstrated how computational modeling could unravel the mechanisms underlying cognition not readily observable in human data.

**Authors:**

Wei Xing: University of Hong Kong; Yueyuan Zheng: University of Hong Kong; Antoni B. Chan: City University of Hong Kong; Janet Hsiao: Hong Kong University of Science & Technology

Is Holistic Processing Associated with Face Scanning Pattern and Performance in Face Recognition? Evidence from Deep Neural Network with Hidden Markov Modeling

In this paper, we test the effect of manipulating discrimination difficulty on subsequent generalization of learning and in particular, on the peak shift effect. Participants learned a discrimination where one stimulus led to an outcome (S+) and another stimulus led to no outcome (S-). Difficulty was manipulated by varying the degree of similarity between the S+ and S- across groups (easy/medium/hard). In contrast to similar studies in animals, we found that increasing the difficulty of the discrimination resulted in less peak shift. Using a hierarchical mixture model, we characterize the effects of discrimination difficulty on relational- and similarity based responding, and show for the first time, a similar mixture of responding on stimulus identification gradients. We conclude that peak shift on generalization and identification measures can be explained by mixtures of participants responding in different ways.

**Authors:**

Jessica C. Lee: University of Sydney; Tamara Cahyadi: Macquarie University; Peter Lovibond: University of New South Wales; René Schlegelmilch: University of Bremen

Effects of Discrimination Difficulty on Peak Shift and Generalization

We propose that kinds relate to particular things either constitutively or contingently. Taxonomic categories of animals and artifacts constitutively relate their members: DOG and CAR group things by aspects of the forms of their matter; the forms that make them things instead of stuff. Categories of things in roles or with diseases contingently relate to their members: LAWYER and DIABETIC group things by forms other than the forms that make them things. We confirm this distinction in five experiments with American adults.

**Authors:**

Alexander Noyes: New York University; Katherine Ritchie: University of California, Irvine; Marjorie Rhodes: New York University

Constitutive and Contingent Kinds: Relations between kind, form, and identity

In term series problems where multiple mental models can be constructed, partial-order models can be created as mental representations, which make it easier to perceive the symmetry of the terms. To test these hypotheses, we categorized multi-model (indeterminate) term series problems according to the patterns of partial-order models that could be constructed, and analyzed the reasoning performance for each pattern. These results suggest that reasoners tend to use the symmetry of terms to reduce the cognitive load of reasoning. Analysis of the patterns of incorrect answers also suggests that attempts to exploit the symmetry of the term may be biased, leading to errors in reasoning.

**Authors:**

Takafumi Aoi: Japan Advanced Institute of Science and Technology; Shohei Hidaka: Japan Advanced Institute of Science and Technology

Symmetric Bias in Reasoning: Error Analysis of Indeterminate Term Series Problems

We can evaluate features of problems and their potential solutions well before we can effectively solve them. When considering a game we have never played, for instance, we might infer whether it is likely to be challenging, fair, or fun simply from hearing the game rules, prior to deciding whether to invest time in learning the game or trying to play it well. Many studies of game play have focused on optimality and expertise, characterizing how people and computational models play based on moderate to extensive search and after playing a game dozens (if not thousands or millions) of times. Here, we study how people reason about a range of simple but novel connect-n style board games. We ask people to judge how fair and how fun the games are from very little experience: just thinking about the game for a minute or so, before they have ever actually played with anyone else, and we propose a resource-limited model that captures their judgments using only a small number of partial game simulations and almost no lookahead search. For more information about this project, see https://sites.google.com/view/intuitive-game-theory

**Authors:**

Cedegao E Zhang: Massachusetts Institute of Technology; Katherine M Collins: University of Cambridge; Lionel Wong: Massachusetts Institute of Technology; Adrian Weller: University of Cambridge; Josh Tenenbaum: MIT

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