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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.

CogSci 2023

Top-down effects of attention on the action observation network

ICPR 2020 is the premier international conference on Pattern Recognition, gathering leading researchers from around the world to present their latest achievements in Pattern Analysis and Recognition, applied Artificial Intelligence and Machine Learning. The topic of the year is " Putting Artificial intelligence to work on Patterns". 
For the first time, ICPR has employed a two-round review process similar to journal submissions, with new papers allowed to be submitted in either the first or second round and papers submitted in the first round and not accepted allowed to be revised and re-submitted for second round review. 

In this edition of the **ICPR 2020** Conference, there are 1407 Oral/Poster presentations organized in five Tracks: 
- Artificial Intelligence, Machine Learning for Pattern Analysis; 
- Biometrics, Human Analysis and Behavior Understanding; Computer Vision, Robotics and Intelligent Systems; 
- Document and Media Analysis; and 
- Image and Signal Processing. 

The **ICPR 2020** Main Conference program also includes 7 Keynotes from prominent world-renowned scientists, 8 Tutorials, 28 Demos, 8 Challenges, 2 Panels, as well as an Industry/Sponsor Exhibition. In addition, 41 Workshops on a range of important current and emerging research topics complete the ICPR 2020 program. 


ICPR 2020

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.

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.

Examining Moral Injury using a Predictive Processing Framework

This paper introduces a philosophy of cognition inspired by recent developments in computational neuroscience relative to the hippocampal formation showing us how thinking, remembering, and navigating are nested measurements of a common process. Reframing cognition as 'all a body does to make its way through its encounter' allows us to understand mind and life as continuous and alleviates current obstacles to progress such as those arising from the so-called mind-body problem and the existence and definition of mental representations. Through hippocampal models, waymaking frames a philosophy whereby pathfinding, memory-development, and knowledge-acquisition are flexibly algorithmic measurements of the same process from sensory and developmental positions of statistical regularity—in other words, where patterns of bodily activity scale fractally once sensory regularities are statistically accounted for at all experimental levels. Taking this seriously, however, means statistically measuring the stimulus as much as the response in future neuroscientific experiments.

Waymaking: a nested approach to cognition inspired by cognitive and computational hippocampal models

In this study, we investigate how human choosers adapt their value encoding strategy to the statistics of the choice environment. Specifically, we ask whether human value encoding mechanism exhibits divisive normalization only in Pareto distributed environments in which it is information-maximizing. To test this theory, we conduct a risky choice experiment in which subjects are presented with blocks of choice stimuli drawn from either a Pareto-distributed environment or a uniform-distributed environment. Our results show that subjects exhibit some degree of normalization regardless of whether it is efficient or not, but do adapt the curvature of the encoding function to the environment. These findings suggest that human value coding mechanisms are flexible but biologically constrained to be perfectly efficient only in specific environments. This study provides new insights into the neural mechanism of human decision-making and the role of environmental statistics in shaping it.

Choosers Adapt Value Coding to the Environment, But Do Not Attain Efficiency

In this paper we present a new modelling framework for concepts based on quantum theory, and demonstrate how the conceptual representations can be learned from data. Our approach builds upon Gardenfors’ classical framework of conceptual spaces, in which cognition is modelled geometrically through the use of convex spaces, which in turn factorise in terms of simpler spaces called domains. We show how concepts from the domains of shape, colour, size and position can be learned from images of simple shapes, where individual images are represented as quantum states and concepts as quantum effects. Concepts are learned by a hybrid classical-quantum network trained to perform concept classification. We also use discarding to produce mixed effects, which can then be used to learn concepts which only apply to a subset of the domains, and show how entanglement (together with discarding) can be used to capture interesting correlations across domains.

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

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