Canada

People learn to have emotions that fit the values central to their groups or cultures, and that benefit the types of relationships that are valued there. By sharing similar emotions, individuals align themselves –often unwittingly-- with these central values. Therefore, cultural variations in emotional phenomena can be understood from culturally normative relationship goals that tell individuals how to feel, when to feel and why to feel. Considered this way, having the “right” emotions is a key social competence in achieving belongingness and fit. In this talk I present cross-sectional, longitudinal and social network research on emotional acculturation, and its consequences for the belongingness and fit of immigrant minorities. I will also discuss how our insights on emotional acculturation may inform interventions to improve the belongingness of immigrant minorities.

CogSci 2022

Having the “right” emotions: The role of emotional acculturation in minority belonging

invited talk

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. 





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

The spread of misinformation has substantially sped up in recent years, posing tangible risks to public health, democratic life, and the fight against climate change. For this reason, finding novel solutions to scale and reduce the time-intensive work of fact-checking agencies has become an urgent issue in social science. In this work, we evaluated crowdsourcing as a strategy to fact-check statements made by politicians, a type of misinformation that has been understudied in comparison to fake news. We performed a behavioural experiment (N=180) where the structure of social influence was manipulated, with the objective of identifying which communication network designs enhanced or undermined detection of 20 statements made by political leaders from a left-wing and a right-wing political party in Argentina. We found that crowds of laypeople are generally able to classify political discourse in a similar way as a fact-checking agency, but they are subject to partisan biases at the individual level. We also observed that social influence in heterogeneous dyadic interactions works as an antidote to these biases. Altogether, our results support recent efforts on developing crowdsourcing tools for aiding fact-checking over a variety of misinformation formats, adding a precautionary note on how partisanship may limit its performance, at least in the context of fact-checking politicians.

Crowdsourcing strategies to fact-check politicians are benefited from heterogeneous social influence

In the current globalized world, innovation in science and technology are vital for 
economic competitiveness, quality of life, and national security. This trend is accelerating the increasing reliance on virtual teams and their collaborative effort to solve complex environmental, 
social and public health problems. To contend with these dynamic conditions, communication, and 
collaborative problem-solving (CPS) competencies have taken a principal role in educational policy, research, and technology. Adaptive educational technologies provide a platform to deliver 
personalized training to improve learners’ CPS skills. However, for these systems to optimally 
tailor instruction, they must have key insights into learners’ interaction dynamics and team behaviors. We have been exploring these properties by employing Group Communication Analysis 
(GCA), a computational linguistics methodology for quantifying and characterizing the socio-cognitive processes between learners in online interactions. This talk will focus on recent studies 
where we have used GCA to gain a deeper understanding of role ecologies, learning and problemsolving, and issues of inclusivity in digitally mediated group interactions. The scalability of GCA 
opens the door for future research efforts directed towards improving collaborative competencies 
and creating more inclusive online interactions.

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