Model-based Quantification of Social Learning and Decision

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Institute for Biomedical Engineering Translational Neuromodelling Unit
Model-based Quantification of
Social Learning and DecisionMaking
Background:
The process of how we represent others’
intentions forms the basis of social exchange.
Social learning, i.e., simulating another
person’s intentions, becomes even more
crucial when we need to rely on advice
regarding a course of action. Advice-taking
can be very beneficial as it entails utilizing the
wisdom of others to make more informed
decisions. However, since advice is motivated
by unknown goals, which may also change in
time, we are constantly challenged by the
question of how accurately we infer on others’
intentions.
The purpose of the project is to develop and test computational models of learning that
quantify how people infer on the “hidden” intentions of others.
To pursue this goal, our group has developed a hierarchical Bayesian model of learning,
which postulates that the brain constructs and updates a generative model of its sensory
inputs, which are caused by hidden (environmental) states that are hierarchically organized.
We demonstrated that the proposed model best described participants’ learning in an
interactive game: We found that when inferring on the intentions of others, participants not
only track the validity of their advice, but also incorporate their estimation of the volatility of
the advisers’ intentions into their predictions about the advice accuracy.
Now, we want to examine how people infer on others’ intentions under multiple levels of
social uncertainty as they interact with different advisers. This project will be an additional
step towards quantifying maladaptive social learning, which is of great relevance in
psychiatry.
The goal of your project:
Implement and test a computational model of social learning.
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Establish a set of candidate computational models that could be used to infer on how
participants learn from social input.
Using simulations, test the internal validity and robustness of the proposed computational
models and their parameters.
Collect behavioral data on an already established social learning paradigm.
Test the proposed computational models on real participant data.
Type of work:
Master project: 40% Theory and Modeling, 35% Programming, 25% Data Acquisition
Supervisors:
Andreea Diaconescu, [email protected], WIL F-102, Tel. +41 44 634 91 09
Professor:
Institute for Biomedical Engineering Translational Neuromodelling Unit
Klaas Enno Stephan, [email protected], WIL G-203, Tel. +41 44 634 91 25
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