Neurocognitive Foundations of Adaptive Rationality

How are people able to navigate uncertainty in our complex and ever-changing world despite their limited cognitive resources? Possible answers to this question may lie in neurocognitive processes that are not easily observable in overt actions—for example, in the workings of human memory.

Limited Cognitive Resources

Much of our daily adaptive behavior hinges on information that is no longer present in the environment. For instance, when deciding between alternative routes to work, we may draw on current observations (e.g., it’s raining and the traffic is heavy) as well as on knowledge and preferences acquired through past experience. This may include information from previous commutes stored in long-term memory. Despite its apparently limitless capacity, however, long-term memory does not keep a complete or necessarily accurate record of the past. Recent information (e.g., a traffic report heard a few seconds ago) is often stored only briefly in working memory—and forgotten again soon after. The small capacity of working memory limits the amount of information decision makers can consider at a time.

How do the idiosyncrasies of human memory shape adaptive decision making and vice versa? How does the brain deal with the abundance of information from past and present in ways that enable good decisions despite limited cognitive resources?


Our studies combine human neuroimaging techniques, such as electroencephalography (EEG) and functional magnetic resonance imaging (fMRI), with multivariate analysis methods and computational modeling. We study the neural underpinnings of adaptive cognition in a variety of tasks, including perceptual decisions, decisions based on how gratifying the options are, decisions under known risks, and decisions under uncertainty.


  • Christophel, T. B., Klink, P. C., Spitzer, B., Roelfsema, P. R., & Haynes, J.-D. (2017). The Distributed nature of working memory. Trends in Cognitive Sciences, 21, 111–124.
  • Juechems, K., Balager, J., Spitzer, B., & Summerfield, C. (2021). Optimal utility and probability functions for agents with finite computational precision. PNAS, 118(2), e2002232118.
  • Spitzer, B., Blankenburg, F., & Summerfield, C. (2016). Rhythmic gain control during supramodal integration of approximate number. NeuroImage, 129, 470–479. 
  • Spitzer, B., Fleck, S., & Blankenburg, F. (2014). Parametric alpha- and beta-band signatures of supramodal numerosity information in human working memory. The Journal of Neuroscience, 34, 4293–4302. 
  • Spitzer, B., Waschke, L., & Summerfield, C. (2017). Selective overweighting of larger magnitudes during noisy numerical comparison. Nature Human Behaviour, 1, 0145. 
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