Faculty of Social Sciences

The Colloquium of the School of Psychological Sciences

2021-2022 \ Semester B

MONDAY, 14:15-15:45

Dr. Uri Herts Abstract:

Social norms provide groups of individuals with behavioural prescriptions and therefore can be inferred by observing their behaviour. Changes to social settings caused by migration, cultural change or pandemics force us to adapt to new social norms. In my talk I will discuss the way cognitive learning processes account for the way people learn about social norms, and the way features of the norm’s prescribed behaviour affect learning. Using a multiplayer game, where bot-players displayed a variety of social norms, and computational learning models, we found that active behaviours were learned faster than omissions, and harmful behaviours were more readily attributed to all group members than beneficial behaviours. In a second study, we examine whether different learning mechanisms are used for group-level attribution for in-group players and out-group players. Our results and approach provide a cognitive foundation for learning and adaptation to descriptive norms and can inform future investigations of group-level learning and cross-cultural adaptation.

Prof. Alex Cristia Abstract: 

By the age of two, children are thought to know literally hundreds of words, including many that never appear on their own, like "bear". How do they go about it? Learning the answer to this question will likely take us 50 more years of research, but in the meantime, we can make significant progress by looking at how artificial learners (computers) solve this task. In this talk, I review some key studies looking at the learnability of word forms using computational modeling, a tool that yields surprising and interesting results, and which may be underused in our field.

Dr. Yaara Yeshurun-Dishon Abstract: 

Recent political polarization has highlighted the extent to which individuals with opposing views experience ongoing events in markedly different ways. In this talk I will discuss a project in which we explored the neural mechanisms underpinning this phenomenon. We conducted an fMRI scanning of right- and left-wing participants watching political videos just before the 2019 elections in Israel. Our results suggest that political polarization is not limited to higher-order processes as previously thought, but rather emerges already in motor and sensory regions.

Dr. Nava Levit-Binnun Abstract: 
Rapidly increasing research on mindfulness suggests that it has widespread beneficial effects on wellbeing, resilience and mental health. Still, the mechanisms underlying the beneficial effects of the practice remain vague. In search of these mechanisms, we transition from a stance of “mindfulness as relief” to a stance of “mindfulness as engagement”, suggesting that mindfulness is not merely a form of emotion regulation but rather a way of engaging with the world from moment-to-moment in a more beneficial way. In my talk, I will present our work in mapping mindfulness practices, understanding the beneficial effects of mindfulness-based interventions (including online) and our approach to assess the mechanisms underlying these beneficial effects. I will describe behavioral and physiological evidence demonstrating how the ability to move between emotion regulation strategies in accordance with changing situational demands - which is believed to underlie mental health - changed following an 8-week mindfulness course. If time allows, I will briefly describe our approach to advance diversity and inclusion in the field of mindfulness interventions by assessing culturally sensitive and adapted mindfulness protocols for various groups in Israeli society (e.g., Ultra-Orthodox Women, Ethiopian Jews, Arabs, People who stutter, etc), as well as our work in promoting mindfulness into the education system in Israel.  

Prof. Inbal Arnon Abstract: 

While the world’s languages differ in many respects, they share certain commonalities: these can provide crucial insight on our shared cognition and how it impacts language structure. In this project, we explore the learnability consequences and sources of one of the most striking commonalities across languages: the way word frequencies are distributed. Across languages, words follow a Zipfian distribution, showing a power law relation between a words’ frequency and its’ rank. Intuitively, this reflects the fact that languages have relative few high frequency words and many low frequency ones, and that frequency does not decrease in a linear way. The source of this distribution has been heavily debated with ongoing controversy about what it can tell us about language. Here, we propose that such distributions confer a learnability advantage, leading to enhanced language acquisition in children, and the to the creation of a cognitive pressure to maintain similarly skewed distributions over time. In this first part, we examine the learnability consequences of Zipfian distributions, focusing on their greater unigram predictability. We ask (1) Are different languages similarly predictable? (2) If so, is learning uniquely facilitated in language-like predictability? and (3) Is this limited to the linguistic domain? In the second part, we explore the learnability sources of Zipfian distributions to ask whether learning biases can help explain why such distributions are so common in language. 

Prof. Danny Horesh Abstract: 

Autism Spectrum Disorder (ASD) has been known to show high co-morbidity with different mental disorders, including anxiety and depression. Previous research indicates that individuals with ASD also face an increased risk of experiencing traumatic events. However, the co-morbidity between ASD and Post-Traumatic Stress Disorder (PTSD) has been almost entirely neglected by researchers. In my talk, I will discuss an ongoing study in which we examine the complex associations between PTSD and autism. We assessed both autistic traits (i.e., autistic features distributed among the general population) and individuals formally diagnosed with high functioning ASD. Overall, our findings reveal that individuals with ASD/high levels of autistic traits face an increased risk for PTSD, compared to typically developing controls. In addition, we show that among those with ASD, PTSD was best predicted by social stressors, such as bullying and ostracizing. We also identified a unique PTSD clinical profile among those high on autistic traits, characterized by elevations in specific symptom clusters. Finally, we propose several mechanisms, which may underlie the ASD-PTSD connection, including dysregulated emotion, rumination, and trait anger. Our findings highlight the need to study PTSD among those showing pre-existing neurodevelopmental vulnerabilities, rendering them more vulnerable to trauma and its effects. We will also discuss treatment and policy implications.