Canada

CogSci 2022

On the Rational Bounds of Cognitive Control

Join us for CogSci2022! Our 44th Annual Conference will be centered around the theme of diversity, with an accompanying range of keynotes and invited speakers who themselves highlight the diversity of our field. Our three symposia are Cognition across Diverse Populations, Cognition within Diverse Societies, and Global Challenges for Cognitive Science. The conference is being offered in a fully hybrid environment for onsite and online attendees.
 
 [**The Program at a Glance can be found here**](https://cognitivesciencesociety.org/wp-content/uploads/2022/07/Cogsci-2022-PAAG-Detailed-July-12final-1.pdf)
 [**The Detailed Program can be found here**](https://cognitivesciencesociety.org/wp-content/uploads/2022/08/DeArmond-CogSci2022-program-v113-final.pdf)

This conference requires a registration to attend. Please use the link below to secure your ticket. The website will be available to all registered attendees on July 25th. 

[Register to Attend](https://cognitivesciencesociety.org/registration/)

Registration Is Required

The 44th Annual Meeting of the Cognitive Science Society highlighted research on the theme Cognitive Diversity in addition to the full breadth and diversity of research topics offered by the society’s membership. 





invited talk

How does the brain represent different modes of information? Can we design a system that can automatically understand what the user is thinking? We can make progress towards answering such questions by studying brain recordings from devices such as functional magnetic resonance imaging (fMRI). The brain encoding problem aims to automatically generate fMRI brain representations given a stimulus. The brain decoding problem is the inverse problem of reconstructing the stimuli given the fMRI brain representation. Both the brain encoding and decoding problems have been studied in detail in the past two decades and the foremost attraction of studying these solutions is that they serve as additional tools for basic research in cognitive science and cognitive neuroscience. Recently, inspired by the effectiveness of deep learning models for natural language processing and computer vision, such models have been applied for neuroscience as well. In this tutorial, we plan to discuss different kinds of stimulus representations, and popular encoding and decoding architectures in detail. The tutorial will provide a working knowledge of the state of the art methods for encoding and decoding, a thorough understanding of the literature, and a better understanding of the benefits and limitations of encoding/decoding with deep learning.

Deep Learning for Brain Encoding and Decoding (1)

Deep Learning for Brain Encoding and Decoding (3)

The lack of diverse participants in our research can lead to issues of generalizability, inability to address social and health disparities, and perpetuation of stereotypes, among many other issues. For example, it became quite clear during COVID-19 in the United States, the inequities that existed in health care access and quality and disparities in health outcomes for minoritized communities. Such inequities and disparities are also found in research practice in many fields, including cognitive science. For example, research with Western, educated, industrialized, rich, and democratic (WEIRD) populations has been highlighted as problematic (Ceci, Kahan, & Braman, 2010; Henrich, Heine, & Norenzayan, 2010), yet the inclusion of diverse participants in research remains lacking.

Building Trust and Conducting Research with Minoritized Communities: A SPARK-Sponsored Tutorial

Drawing is a powerful cognitive technology for creating external representations of thought. While drawings have long provided inspiration to researchers in many areas of cognitive science, including psychology, artificial intelligence, and neuroscience, these communities have not generally had opportunities to interact and share insights. The goal of this workshop is to bring together perspectives from multiple disciplines to explore the question of how humans use drawings to communicate knowledge, catalyzing new opportunities for multidisciplinary collaboration. We are introducing a novel “flipped” format wherein we will hold three virtual seminars in the weeks leading up to CogSci 2022, each highlighting insights from Machine Learning, Neuroscience, and Developmental Science, respectively. Holding these thematic seminars in advance will enable us to attract a broader audience for our event and focus on promoting informal interaction among workshop attendees at the on-site event.

Motivation and Metacognition in Learning by Drawing

From Images to a Lexicon: Getting Human-like Meaning out of Drawings

From Images to Symbols: Drawing as a Window into the Mind (W3.4)

Applied Category Theory (aCT) is both a language for describing and a method for analyzing the abstract structures that populate cognitive science. It can serve as the lingua franca for cross domain discussions, and as a mathematical tool for probing the consequences of a model or theory’s structure. As many cognitive scientists are unfamiliar with aCT this workshop will provide an introduction to its terminology and key features. The morning session will emphasize key concepts and tutorial exercises. The afternoon session will use recent research applications as case studies of its potential and as the basis for demonstrations of how it can be used productively.

Category Theory for Cognitive Science - Some formal and conceptual analogies

The production and promotion of so-called "AI" technology involves dehumanization on many fronts: The computational metaphor valorizes one kind of cognitive activity as "intelligence", devaluing many other aspects of human experience (Baria and Cross, 2021), while also focusing on agents as isolated individuals rather than seeing the importance of being located within webs of relationships (Birhane, 2021). AI research paradigms frequently frame the purpose of humans to be labelers of data or interchangeable machine components (Gray and Suri, 2019). Meanwhile, data collected from people is understood as "ground truth" even while it lies about those people, especially marginalized people (Noble, 2018; Raji, 2021). In this talk, I will explore these processes of dehumanization and the role that cognitive science can play in resisting these trends by bringing a deeper and richer picture of human cognition to the discourse.

Resisting dehumanization in the age of AI

Globalized humanity is changing and needs to change. Consider climate change and extreme weather events, geopolitical tensions and conflict, and rising inequalities within and between nations. To these contemporary adaptational challenges, human cognition and action are changing – both individually as a person and collectively as a species. These changes occur at times blindly, but all the while accompanied by human strivings to gain foresights and perspectives beyond the current horizon. Given the importance of culture as a mechanism for human adaptation, anthropology’s answer has been cultural evolution – an attempt to understand and explain cumulative cultural changes over the millennia. Whereas this has provided us with the knowledge and insights into long-term macro-level cultural dynamics, micro-level psychological knowledge complements our understanding of how and why macro-level cultural changes occur. Moreover, psychological insights are imperative in contemplating how to cultivate and craft human cultures – behaviour change, if you will – to navigate the uncharted waters into our collective futures. The psychology of cultural dynamics has accumulated knowledge and insights into micro-level cognitive and communicative processes that constitute and are constituted by macro-level cultural processes. In this presentation, I will examine the micro-macro dynamics of cultural maintenance and change mediated by dynamically changing social networks at the meso-level and outline future directions for the psychology of cultural dynamics.

On the Rational Bounds of Cognitive Control

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