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Learning Lab Research

Many environments do not immediately provide information that is necessary for successful learning and decision making (e.g., where should I invest my money?). Instead, we often need to combine information across many experiences to learn the outcome contingencies of an environment. This can be particularly challenging under uncertainty, strongly depends on the affective state of an organism, and differs across clinical and age groups. 

Learning under perceptual uncertainty. We investigate the computational principles of reward-based learning under perceptual uncertainty. In this project, we have developed and tested formal models that algorithmically describe how humans take perceptual uncertainty into consideration. In an ongoing project led by Prashanti Ganesh, we study the role of pupil-linked arousal systems that might help the brain adjust learning to perceptual uncertainty. 

See preprint: Belief states and categorical-choice biases determine reward-based learning under perceptual uncertainty

Adaptive learning in changing environments. We also study how humans dynamically adjust to changing environments. Humans and animals have to flexibly regulate how they combine newly arriving information with information from past experiences during learning in these environments. Our current work suggests that age-related differences in these abilities can be explained by differences in the degree of satisficing behavior. Subsequently, seemingly impaired learning abilities in children and older adults might be the consequence of a learning strategy where outcomes are more rapidly perceived as "good enough", which might be an efficient way of learning that requires less cognitive resources than more "optimal strategies".

See preprint: Differences in adaptive learning across the lifespan are driven by satisficing

Moreover, we examine adaptive learning under threat. In this project led by Hashim Satti, we try to better understand how trait anxiety and the proximity of threat changes human abilities to take uncertainty into account during aversive learning and decision making. 

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