Description of proposed project:
Part of the project: Ecologically rational strategy selection
How do people select strategies that allow them to solve a given cognitive or behavioral task in a manner that matches their available resources (in terms of time or computational abilities)? To address this question, the project will investigate how organisms infer the anticipated accuracy and costs associated with candidate strategies and how, when choosing from among the repertoire of available strategies, the consideration of the implementation costs is attuned to the resources that are available to the organism. These questions will be investigated both for strategies for decision making under risk and for exploration strategies, and both with human and robot participants.
Project Leads: Thorsten Pachur, Ralph Hertwig, Falk Lieder
Description of the postdoctoral project
The project focuses on the mechanisms underlying strategy selection in risky choice. One question is which features of a decision problem are predictive of the accuracy of each strategy and the costs associated with implementing the strategy, and how, based on the accuracy and cost assessments of the decision maker, an appropriate strategy is selected. The project will involve both machine learning analyses to identify such features of task environment as well as behavioral experiments and computational modeling of the observed behavior.
Applicants must hold PhD and a Diploma/Master’s degree in psychology or cognitive science and should have proven skills/background in following topics:
- Interest in interdisciplinary research in the context of the Cluster of Excellence “Science of Intelligence”
- Designing, programming, and running behavioral experiments and online experiments
- Interest in the cognitive underpinnings of decision making
- Proficiency in computational statistics and data analysis using either frequentist or Bayesian approaches (e.g., in R or Python)
- Experience in developing computational models of decision-making and reinforcement learning
- Strong programming skills (e.g., in Matlab or Python) and experience with machine learning --especially Bayesian inference and reinforcement learning
- Excellent English writing and communication skills
Application deadline: 29 November 2019 23:59 CET