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Brain function connectivity, derived from functional magnetic resonance imaging (fMRI), has enjoyed high popularity in studies of Autism Spectrum Disorder (ASD) diagnosis. Albeit rapid progress has been made, most studies still suffer from several knotty issues: (1) the hardship of effectively modeling the sophisticated brain neuronal connectivity; 
(2) the dimensionality explosion resulted from excessive voxels in each fMRI sample; (3) the poor interpretability giving rise to unpersuasive diagnosis. To ameliorate these issues, we propose a GCN-based model, namely ConResNet. Specifically, a convolutional restructuring attention is designed for efficiently ROI-interaction feature extraction. Besides, we embed a salient graph pooling method to sift salient nodes. Extensive experiments conducted on ABIDE dataset demonstrate ConResNet achieves state-of-the-art performance. Moreover, the most salient brain regions predicted by ConResNet are closely consistent with theoretical knowledge in the medical domain, providing potential biomarkers for ASD clinical diagnosis. Codes have been uploaded to Github.

CogSci 2023

GCN-based Autism Spectrum Disorder Diagnosis via Convolutional Restructuring Attention

The University Big Data Challenge (IUBDC) successfully concluded its latest edition, bringing together national health industry leaders, policymakers, and dignitaries for a conference centered on mental health research and innovation. Participants had the opportunity to showcase their projects in a poster session, engaging with representatives from industry and academia. The event provided a rich network of industry and academic professionals, offering pathways for scholarly publication, access to curated datasets, learning materials materials, and workshops.

Held at the Microsoft Headquarters in Toronto on July 26, 2024, the challenge featured hands-on data science and AI research projects focused on mental health. For over seven years, IUBDC has fostered collaboration between data science and health professionals and motivated university students. This year’s projects, which spanned topics from dementia to addictions, aimed to enhance our understanding of mental health, improve patient lifestyles, and reduce disease risk. Abstracts from teams were published in the NRC Research Press STEM Fellowship Journal, with all team's works featured by Underline publications.

Leveraging Open Data Analytics and Machine Learning to Improve Mental Health Research and Innovation

2024 Inter-University Big Data And AI Challenge -

Thee challenge featured hands-on data science and AI research projects focused on mental health. 

poster

The 45th Annual Meeting of the Cognitive Science Society will take place in Sydney, Australia as a modified hybrid conference. Presentations from the main-session room will be live-streamed every day for all events. This will include all invited talks and award sessions. Recordings of all CogSci 2023 sessions will be made available for on-demand viewing after the conference.<br>
The in-person component will be held at the International Convention Centre Sydney at Darling Harbour. The Conference will take place Wednesday July 26th – Saturday July 29th, 2023 (Wednesday, July 26th is the Workshops Day).

Join us!

<br>[**The Program at a Glance can be found here**](https://cognitivesciencesociety.org/wp-content/uploads/2023/07/COgSci-PAAG-V2.pdf)
<br>[**The Detailed Program can be found here**](https://online.fliphtml5.com/ebtyf/mswz/)
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#### **CogSci 2023 Program Book**
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The 45th Annual Meeting of the Cognitive Science Society presents the latest research across cognitive science and highlights the theme of Cognition in Context.

Most models in cognitive and computational neuroscience trained on one subject don’t generalize to other subjects due to individual differences. An ideal individual-to-individual neural converter is expected to generate real neural signals of one subject from those of another one, which can overcome the problem of individual differences for cognitive and computational models. In this study, we propose a novel individual-to-individual EEG converter, called EEG2EEG, inspired by generative models in computer vision. We applied THINGS EEG2 dataset to train and test 72 independent EEG2EEG models corresponding to 72 pairs across 9 subjects. Our results demonstrate that EEG2EEG is able to effectively learn the mapping of neural representations in EEG signals from one subject to another and achieve high conversion performance. Additionally, the generated EEG signals contain clearer representations of visual information than that can be obtained from real data. This method establishes a novel and state-of-the-art framework for neural conversion of EEG signals, which can realize a flexible and high-performance mapping from individual to individual and provide insight for both neural engineering and cognitive neuroscience.

Generate your neural signals from mine: individual-to-individual EEG converters

Recent advances in Natural Language Processing (NLP) make it possible to quantify relationships among different words extracted from large-scale human text corpora. Using a word embeddings model, we quantified the semantic distance between pairs of adjectives that could describe people or objects (e.g., smart, friendly; round, wooden) and scanned participants using fMRI while they had the opportunity to generalize from one attribute to another across parametrically varying degrees of semantic distance (e.g., given that this person is smart, how likely are they to be friendly?; given that this furniture is round, how likely is it to be wooden?). Across categories, we observed a positive parametric effect of semantic distance on activation in the dorsomedial prefrontal cortex (DMPFC). Results connect to this region’s role in abstraction and inference under reducible uncertainty, with implications for understanding how people generalize beyond what they know to make inferences about novel individuals, items, or experiences.

Measures of Semantic Distance from Word Embeddings Predict Neural Responses During Inferences about People and Objects

Action perception plays a crucial role in human life and is significant both evolutionarily and socially. It is supported by the well-established action observation network (AON), which consists of three core nodes in the occipitotemporal, parietal, and pre-motor cortex of the human brain. In the present study, we investigated how top-down attention affects the AON using fMRI and representational similarity analysis. In the first session, human participants viewed short videos while they performed tasks in which they attended different features of actions in separate blocks, including the actor, the target, or the effector. In a second fMRI session, participants viewed the videos in a passive manner to be able to compare the neural representations in the AON across passive and active attention tasks. Our results show that attention to different features of observed actions modulates the nodes of the AON differently indicating their distinct roles in supporting action perception.

Top-down effects of attention on the action observation network

The accurate reconstruction of deep cortical regional source activities from EEG recordings is crucial for understanding spatiotemporal cognitive processes and the origins of impairments in brain disorders. Recently, several state-of-the-art source localization algorithms have been developed that claim to outperform standard methods. However, a rigorous comparison of the strengths and weaknesses of these methods in EEG source localization has yet to be conducted. This study aims to evaluate the algorithms, namely Full Dugh, Thin Dugh, and Mackay, against standard methods, namely minimum norm and eLORETA, using a realistic head model and simulated spatially-propagated source signals. Results indicate that Thin Dugh performed superiorly in both spatial and temporal source reconstruction, followed by Mackay. Both Thin Dugh and Mackay effectively identified contributing sources and diminished the amplitudes of non-contributing sources. Additionally, we have also proven that there are serious theoretical flaws within Full Dugh algorithm through both mathematical analysis and simulation results.

Evaluating State-of-the-Art EEG Source Localization Algorithms Using Spatially Propagated Sources and Realistic Head Model

Brain networks have evolved to be highly efficient and robust while consuming minimal energy. They possess unique properties such as sparsity, complexity, and small-world nature. Studies suggest that a trade-off between minimal wiring cost and efficient communication shapes brain network development. However, it's not entirely clear what factors are influential and to what extent each factor contributes to this development. This research examines several potential underlying factors and finds that with connectivity guaranteed by fixed degree distribution, minimizing wiring cost has the largest impact on network structure among all factors, such as maximizing the clustering coefficient or coefficient of variation for wiring length distribution. Without degree constraints, the cost-efficiency balance reproduces optimally brain networks of five species, but minimizing wiring cost remains the primary determinant.

Wiring Cost Minimization: A Dominant Factor in the Evolution of Brain Networks across Five Species

There is major potential for using electroencephalography (EEG) in brain decoding that has been untapped due to the need for large amounts of data. Advances in machine learning have mitigated this need through data augmentation techniques, such as Generative Adversarial Networks (GANs). Here, we gauged the extent to which GANs can augment EEG data to enhance classification performance. Our objectives were to determine which classifiers benefit from GAN-augmented EEG and to estimate the impact of sample sizes on GAN-enhancements. We investigated three classifiers---neural networks, support vector machines, and logistic regressions---across seven sample sizes ranging from 5 to 100 participants. GAN-augmented EEG enhanced classification for neural networks and support vector machines, but not logistic regressions. Further, GAN-enhancements diminished as sample sizes increased---suggesting it is most effective with small samples, which may facilitate research that is unable to collect large amounts of data, such as with clinical populations.

Augmenting EEG with Generative Adversarial Networks Enhances Brain Decoding Across Classifiers and Sample Sizes

Conservation experiments are at the heart of Piaget’s theory that children are easily misled by appearance and struggle to grasp that quantities are unaffected by superficial trans- formations. An independent line of research on the development of counterfactual thinking suggests that children struggle to imagine what the world would be if particular events had not occurred. Although seemingly unrelated, we suspect that counterfactual thinking might be in play when children perform the mental operation of reversing the changes in conservation tasks. The experiment reported here examines whether children’s counterfactual thinking predicts their ability to solve conservation problems. Children (N = 48; 24 females) between 6 and 8 years old (72 - 95 months), completed conservation and counterfactual thinking tasks in a single Zoom session. In the conservation task, children received four conservation problems (liquid, matter, number, and length) delivered via pre-recorded video clips in which the experimenter altered the appearance of two qualitatively identical objects (e.g., a ball made with modeling clay was pressed down to a flat sheet by hand). In the counterfactual thinking task, children received four cartoon vignettes in which the experimenter described events live along with animated slides shown on the computer screen. In a given vignette, two simultaneous yet causally independent events led to the same outcome (e.g., a towel getting wet). Results showed that both children’s performance on the counterfactual thinking task and age significantly predicted their performance on the conservation task, although the former effect was much stronger than the latter. The findings suggest that there is a so-far undetected involvement of counterfactual thinking in solving Piagetian conservation problems. We will discuss the role of mentally reversing or undoing events in understanding the conservation of substance.

The Relation Between the Development of Counterfactual Thinking and Piagetian Conservation

The ability to form novel conventions is a key signature of efficient linguistic communication. Reduction of referring expressions, one measure of convention formation, is found robustly in dyadic repeated reference games when the target images are initially difficult to name. In reference games, participants share the explicit goal of establishing joint reference. However, establishing reference is a key subgoal of many conversations where interlocuters have more complex goals. In the current work, we analyze a dataset where reference was embedded in strategic 3- player negotiation and coordination games. In these more complex games, we found that the patterns of reduction and convergence to conventions still held across two different incentive conditions, with some modest differences between the conditions.

Communicative reduction in referring expressions within a multi-player negotiation game

While human communication occurs across groups of varying sizes, current cognitive models typically focus on the dynamics between two individuals. However, generalizing to group communication is not trivial as it requires a joint inference over what a signal means and who it is intended for. Prevailing research relies on agents that recursively model each other to infer a signal's meaning which becomes incredibly complex in group settings. Here we demonstrate how a shared agency approach to communication called the Imagined We (IW) can naturally be leveraged to address challenges of group communication in simulation. In a cooperative communication task, we show that IW agents perform better and that their strategies for communicating under ambiguity are robust to the group size, unlike a baseline comparison model based on recursion. These results offer insight into how the cooperative logic of shared agency can constrain the uncertainty introduced by communicating in group settings.

Simplifying Group Communication: A Shared Agency Modeling Approach

Moral injury describes the set of psychological symptoms resulting from traumatic experiences that violate one’s moral presuppositions. Such disruption occurs when an individual encounters information from the environment that cannot be reconciled with the fundamental assumptions underlying their predictive models of the world. Examination of predictive models has been rapidly developing within cognitive science, with the predictive processing framework emerging as a central paradigm. Predictive processing entails estimations of sensory uncertainty scaffolded by previous predictions and modified by attention. This model describes cognition as seeking to minimize sensory prediction error using dynamic interactions between top-down and bottom-up processes. Therefore, the predictive processing framework may be fruitfully used to examine psychological changes related to moral injury. Towards this end, we will consider moral injury as a form of belief updating, dysregulation in precision estimates of predictive models, and a breakdown in what Ramstead et al. (2016) call ‘regimes of shared attention.

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Generate your neural signals from mine: individual-to-individual EEG converters

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