Rasmus Bruckner

Freie Universität Berlin | FUB · Institute of Psychology

Healthy aging can lead to impairments in learning that affect many laboratory and real-life tasks. These tasks often involve the acquisition of dynamic contingencies, which requires adjusting the rate of learning to environmental statistics. For example, learning rate should increase when expectations are uncertain (uncertainty), outcomes are surpr...

Learning should be adjusted according to the surprise associated with observed outcomes but calibrated according to statistical context. For example, when occasional changepoints are expected, surprising outcomes should be weighted heavily to speed learning. In contrast, when uninformative outliers are expected to occur occasionally, surprising out...

Learning is pervasive across the human lifespan and essential for adaptive behavior. Children and older adults are often slower to learn cognitive tasks than young adults. Here we build on established theory formalizing learning as predictive inference and consider the possibility that age-related learning differences emerge from satisficing in thi...

In natural settings, learning and decision making often takes place under considerable perceptual uncertainty. Here we investigate the computational principles that govern reward-based learning and decision making under perceptual uncertainty about environmental states. Based on an integrated perceptual and economic decision-making task where unobs...

Learning allows humans and animals to make predictions about the environment and increases the likelihood of adaptive behavior. Studying the mechanisms through which humans regulate learning to update their predictions in the face of uncertainty can also shed light on maladaptive behaviors in various clinical and age groups. Drawing on normative le...

Decisions that require taking prospective effort costs into account are ubiquitous in real life. The common currency theory hypothesizes that a neural network integrates different costs and rewards into a common scale to facilitate value comparison. Although there has been a surge of interest in the computational and neural basis of effort-reward i...

Predictive processing accounts propose that our brain constantly tries to match top-down internal representations with bottom-up incoming information from the environment. Predictions can lead to prediction errors of varying degrees depending on the extent to which the information encountered in the environment conforms with prior expectations. The...

Influential theories emphasize the importance of predictions in learning: we learn from feedback to the extent that it is surprising, and thus conveys new information. Here we explore the hypothesis that surprise depends not only on comparing current events to past experience, but also on online evaluation of performance via internal monitoring. Sp...

Influential theories emphasize the importance of predictions in learning: we learn from feedback to the extent that it is surprising, and thus conveys new information. Here we explore the hypothesis that surprise depends not only on comparing current events to past experience, but also on online evaluation of performance via internal monitoring. Sp...

Recent discussions on the reproducibility of task-related functional magnetic resonance imaging (fMRI) studies have emphasized the importance of power and sample size calculations in fMRI study planning. In general, statistical power and sample size calculations are dependent on the statistical inference framework that is used to test hypotheses. B...

Successful decision-making requires learning expectations based on experienced outcomes. This learning should be calibrated according to the surprise associated with an outcome, but also to the statistical context dictating the most likely source of surprise. For example, when occasional changepoints are expected, surprising outcomes should be weig...

P-values and null-hypothesis significance testing are popular data-analytical tools in functional neuroimaging. Sparked by the analysis of resting-state fMRI data, there has recently been a resurgence of interest in the validity of some of the p-values evaluated with the widely used software SPM. The default parametric p-values reported in SPM are...

P-values and null-hypothesis significance testing are popular data-analytical tools in functional neuroimaging. Sparked by the analysis of resting-state fMRI data, there has recently been a resurgence of interest in the validity of some of the p-values evaluated with the widely used software SPM. The default parametric p-values reported in SPM are...

In recent years, the application of computational modeling in studies on age-related changes in decision making and learning has gained in popularity. One advantage of computational models is that they provide access to latent variables that cannot be directly observed from behavior. In combination with experimental manipulations, these latent vari...

Supplementary Figures 1-7

Mather and colleagues provide an impressive cross-level account of how arousal levels modulate behavior, and they support it with data ranging from receptor pharmacology to measures of cognitive function. Here we consider two related questions: (1) Why should the brain engage in different arousal levels? and (2) What are the predicted consequences...

Recent findings suggest that older adults show decision-making deficits in situations that involve learning about the expected value of choice outcomes. In this chapter we provide a link between these age-related impairments in decision-making and age-related deficits in different types of reinforcement learning. We will focus on age-related change...