Research

A multimodal approach to human cooperation

The following schema illustrates the three aspects of cooperation to be investigated within the framework of the Emmy-Noether research group.

 

(a) Before cooperating, humans usually learn about each other’s personality (by generalizing across similar traits).

(b) The basic problems of cooperation itself are encapsulated in the prisoner’s dilemma (depicted payoff table). Mutual cooperation yields the best joint payoff but unilateral defection is tempting. Further difficulties arise when one person (green) is sometimes forced to defect but the other person (blue) does not know this.

(c) Once cooperation is firmly established, partners have to decide how to share the benefits.

To address these aspects of human cooperation, the planned projects will draw on insights from our previous studies (see research topics below) and combine the following approaches: Game theory (especially notions from stochastic games and derivations of the Shapley value), computational modeling (related to Markov decision processes, reinforcement learning, risk-return models), fMRI (univariate model-based analyses and representational similarity analyses), pupillometry, virtual reality (VR), and testing of psychiatric patients, especially borderline personality disorder (BPD) patients.

All projects will benefit from close interactions within the Institute of Systems Neuroscience and in particular the lab of Jan GläscherTesting of the BPD patients will take place in collaboration with Sabine Herpertz and her lab at the Heidelberg University Hospital. Gabriela Rosenblau and Kevin Pelphrey at George Washington University (Washington, DC) will collaborate on projects involving social learning models.

Multisensory integration

In ongoing projects, Jan Gläscher and Christoph harness representational similarity analyses of fMRI data to investigate how reward learning relates to the integration of multimodal stimuli (i.e., audiovisual stimuli).

Optimal versus heuristic decision-making

A series of (partly published) studies from Christoph’s postdoc at the University of Zurich clarify how humans combine myopic heuristics and rationally optimal decision policies, which necessitate integrating over a multiple future steps (Korn & Bach, 2015; 2018). Christoph developed complex decision-making tasks on the basis of Markov decision processes and compared novel decision-making models against standard economic models of valuation (using Bayesian model comparison). Results provide evidence for a conflict computation related to activity in medial prefrontal cortex that guides the trade-off between heuristic and optimal policies. Such multi-step decisions play an integral role in many approach-avoidance conflicts, which are sensitive to anxiolytic medication and lesions of the amygdala and the hippocampus (Korn, Vunder, Miró, et al., 2017; Bach, Korn, Vunder, Bantel, in 2018).

 

Results from Korn & Bach (2018) show that in a sequential “foraging” task humans combine a simple heuristic (called here “p foraging success”) with the optimal policy. The optimal policy and the uncertainty thereof relate to parts of the MPFC.

Apparent decision biases in social and non-social information processing

Studies from Christoph’s Master’s thesis in London and his PhD in Berlin demonstrate that humans process self-relevant information about their future or their character in an apparently irrational and positively biased way (Sharot*, Korn*, Dolan, 2011; Sharot, Guitart-Masip, Korn, et al., 2012; Korn, Prehn, Park, et al., 2012; see also Oganian, Heekeren, Korn, in press). In my PhD thesis, Christoph developed a neurocognitive model that relates this type of biased information processing to the neural processing of social rewards and mentalizing (Korn, Prehn, Park, et al., 2012).

Follow-up studies show the relevance of these processes for psychiatric disorders, i.e., depression (Korn*, Sharot*, Walter, et al., 2014) and borderline personality disorder (Korn, La Rosée, Heekeren, et al., 2016a, 2016b) and also for cultural differences (Korn, Fan, Zhang, et al., 2014).

In additional studies, we specified some of the boundary conditions for other types of apparent decision biases, namely the self-related attributions (Korn*, Rosenblau*, Rodriguez Buritica, et al., 2016) and framing effects (Oganian*, Korn*, Heekeren, 2016; Korn, Ries, Schalk, et al., 2017; Korn, Heekeren, Oganian, 2018).

*equal contribution

Model-based approaches to pupillometry

Together with Dominik Bach, Christoph developed model-based procedures for analyzing pupil responses. In a first step, we established and validated a parsimonious description of luminance-elicited pupil responses (on the basis of two response functions). This description furnishes a characterization of both prolonged and brief pupil responses (Korn & Bach, 2016). In a second step, we validated an analysis procedure for fear conditioning paradigms that provides better discrimination than previous methods (Korn, Staib, Tzovara, et al., 2016). These models have recently summarized within a review paper (Bach, Castegnetti, Korn, et al., 2018).

Commitment to open science

We believe that science should be open and reproducible. Therefore, we have made data of recent projects publicly available. We have pre-registered two recent studies (Korn, Ries, Schalk et al., 2017 and Oganian, Heekeren, Korn, 2018). We are thankful to the Center for Open Science for awarding us their pre-registartion prize for both of these studies.

Thanks to the efforts by Dominik Bach and many people in his lab, all developed models for analyzing psychophysiological data are incorporated in the MATLAB toolbox PsPM and most datasets are publicly available.