Dr. Alexandra Witt

@alexthewitty.bsky.social

342 Followers 276 Following

Postdoctoral Researcher at RIKEN-CBS working on computational models of social cognition 🧠🤖 she/her

Me at the RIKEN Center for Brain Science, my new place of work!
Bar chart showing the proportion of participants best fit by an unbiased learning model vs. positivity- and negativity-biased participants (determined by the difference in bias in participants best fit by a biased learning model). Proportions vary across conditions, with the highest proportion of positivity bias in social + poor, followed by individual + poor. Individual + rich has the highest proportion of unbiased participants (but no negativity biased participants), while social + rich has the highest proportion of negativity-biased participants.
Beeswarm plots comparing positive and negative learning rates across conditions. The positive learning rate is significantly higher than the negative learning rate in poor and rich environments for individual learning, and in poor environments for social learning. There is no significant difference between learning rates in rich environments for social learning.
Experiment design: participants went through poor and rich environments for both individual and social learning conditions. In the individual learning condition, they made choices and received direct feedback. In the social learning condition, they observed multiple reviews, before making one choice.
Simulation results across poor and rich environments, and individual and social learning. Regardless of learning type, positivity biased agents perform better than negativity biased agents in poor environments, and vice versa.
A plot showing the value of the directed exploration parameter within participant across condition (solo vs. group rounds). Directed exploration is significantly higher in solo than in group rounds.
A plot showing the protected exceedance probability (a measure of model fit) across different social correlations. Asocial Learning is the best fitting model when social correlations are low (0.1), while Social Generalization is the best fitting model at correlations of 0.6.
A plot showing the significant negative correlation between social noise (a parameter that is low when relying on social information) and mean reward.
A screenshot of the socially correlated bandit task. Groups of 4 participants explore spatially correlated bandits, which are also socially correlated. They have a limited number of clicks to explore, and are trying to maximize their payoff.
Multiple people give different star ratings about the same product
The poster is standing in front of the Princeton Neuroscience Institute and pointing at the sign.