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. See our recent chapter summarizing the main focus of our work.

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 our most recent work on this topic:

Ganesh, P., Cichy, R. M., Schuck N. W., Finke, C., & Bruckner, R. (2024). Adaptive integration of perceptual and reward information in an uncertain world. eLife, 13:RP99266. <Link>

Adaptive learning in changing environments

We 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. In one line of work led by Hashim Satti, we examine learning in changing environments under threat. In particular, 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. See our most recent conference contribution on this topic:

Satti, M. H., Wille, K., Nassar, M. R., Cichy, R. M., Schuck, N. W., Dayan, P., & Bruckner, R. (2024). Absence of systematic effects of trait anxiety on learning under uncertainty. Proceedings of the conference on Cognitive Computational Neuroscience 2024. <Link>

Moreover, we examine learning under uncertainty across the human lifespan. We have suggested 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".

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