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Aphantasia, namely the inability to voluntarily form visual mental imagery, does not, counterintuitively, impair the affected from successfully performing mental imagery tasks. One way of explaining this finding is to posit that aphantasics, despite their claim to the contrary, can form visual imagery, a position here referred to as aphantasia skepticism. This article outlines and rejects two types of aphantasia skepticism and argues that the position results from what is coined the visualizer’s fallacy, namely the false belief that visual mental imagery is necessary to carry out mental imagery tasks. Furthermore, it is argued that the visualizer’s fallacy and the resulting aphantasia skepticism are not only potentially harmful to aphantasics but may also lead to an impoverished view of the dynamics of cognition in general.

**Authors:**

Christian Oliver Scholz: Ruhr-Universität Bochum

CogSci 2024

The Visualizer&#39;s Fallacy: Why Aphantasia Skepticism Underestimates the Dynamics of Cognition

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The Visualizer's Fallacy: Why Aphantasia Skepticism Underestimates the Dynamics of Cognition

technical paper

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**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) 
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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 
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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!

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**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.

This paper uses phylogenetic modeling to investigate the evolutionary mechanisms responsible for the maintenance of sound symbolism in the world's languages. 
Applying our model to sound-meaning correspondences reported in the literature, we find that many previously established associations are weaker than expected when analyzed using our framework. This is possibly because certain sound-meaning associations are artifacts of slow-changing vocabulary items rather than specific preferences for certain sounds in words with certain meanings. For sound-meaning associations for which we find evidence, the maintenance of sound symbolism appears to be due to a tendency to preserve words in certain meanings if certain sounds are present.

**Authors:**

Chundra Cathcart: University of Zurich; Gerhard Jäger: Tübingen University

Exploring the evolutionary dynamics of sound symbolism

Languages spoken in larger populations seem to be relatively simple. One possible explanation is that this is a consequence of the simplifying influence of non-native speakers: adult learners tend to reduce complexity during learning, and large languages tend to have a higher proportion of non-native speakers. This Linguistic Niche Hypothesis, that languages adapt to their social niche, receives some statistical support from typological studies which show negative correlations between population size or number of non-native speakers and morphological complexity. Here I report an experimental test of this hypothesis, using two artificial language learning experiments to explore the impact of simplifications made by non-native-like early learners on morphological complexity. These experiments show that the presence of non-native-like early learners in a population can lead to the simplification of that language's morphology as a result of inter-generational language transmission, providing experimental support for the Linguistic Niche Hypothesis.

**Authors:**

Kenny Smith: University of Edinburgh

Simplifications made early in learning can reshape language complexity: an experimental test of the Linguistic Niche Hypothesis

Electroencephalogram (EEG) signals are physiological indicators of brain activity, offering the advantage of high temporal resolution for capturing subtle emotional changes and providing rich information for emotion recognition. However, extracting effective features from EEG data with a low signal-to-noise ratio poses a significant challenge that hinders progress in this research field. To address this issue, we propose a multi-view time-frequency contrastive learning framework called MV-TFCL to enhance the information representation capability of EEG signals from multiple perspectives. Firstly, we introduce a recursive neural network based on multi-scale time-frequency consistency, which integrates global semantic information across different scales through gated units. To our knowledge, this is the first proposal of the theory of multi-scale time-frequency consistency applied in emotion recognition research. Subsequently, we design a tree-structured time-frequency encoder to capture local semantic information within the time-frequency domain. Finally, we incorporate semantic consistency constraints from both global and local perspectives to learn more generalizable and robust features. Extensive experimental results on two publicly available datasets demonstrate the effectiveness and superiority of our proposed method.

**Authors:**

Lei Wang: Dalian University of Technology; Jianping Zhu: Dalian University of Technology; Bo Jin: DaLian University of Technology; XiaoPeng Wei: Dalian University of Technology

Multi-view Time-frequency Contrastive Learning for Emotion Recognition

Computational neuromodeling methods for evaluating representational dynamics involve intricate analysis choices at every stage of the analysis pipeline. Analysis choices for data processing pipelines are generally chosen based upon end to end accuracy metrics and corresponding performance metrics. Psychology research has recently begun to acknowledge the importance of controlling for potential bias introduced by degrees of freedom in data analysis, with specification curve analysis introduced as a principled method for correcting for such biases. In this paper, we conduct a specification curve analysis (SCA) for representational similarity analysis pipelines reported in the literature for fMRI and EEG datasets, respectively. We show that EEG-based RSA analyses are relatively robust to alternative specifications but that fMRI based analyses are not. Using a novel decision-tree analysis to supplement SCA, we present a potentially more robust pipeline for such analyses.

**Authors:**

Satwick Sen Sarma: Indian Institute of Technology Kanpur; Gouravmoy Boruah: Indian Institute of Technology Kanpur; Nisheeth Srivastava: Indian Institute of Technology

How robust are fMRI and EEG data to alternative specifications in representational similarity analyses?

Human language is an example of a natural system that can be described by both statistical (e.g., surprisal) and structured (e.g., phonology, syntax) information. I focus on a foundational question at the heart of this issue –  how can linguistic structure be encoded in a neural system that is often driven by statistics? I reconcile cognitive neuroimaging data with computational simulations to outline a theory of language representation and processing in the brain that integrates basic insights from linguistics and psycholinguistics with the currency of neural computation, population rhythmic activity.

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