Information search in multi-attribute decisions and probabilistic inferences

• Applicant: Professor Dr. Sebastian Gluth
• Subject Area: General, Cognitive and Mathematical Psychology ; Biological Psychology and Cognitive Neuroscience ; Human Cognitive and Systems Neuroscience ; Personality Psychology, Clinical and Medical Psychology, Methodology
• Term: since 2023
• Project identifier: Deutsche Forschungsgemeinschaft (DFG) - Project number 517387490

Humans are confronted with an increasingly complex world, in which many choice situations are characterized by a large number of options described by different types of information. To meet this challenge, they must find a suitable trade-off between making informed decisions and accurate inferences on the one hand and limiting invested resources such as time and effort on the other hand. In the present research project, we propose that humans achieve this balance by searching systematically for relevant information in an efficient and goal-directed manner. Thereto, we develop and test a hierarchical Bayesian cognitive model of information search that can be applied to both multi-attribute, value-based decisions as well as probabilistic inferences with multiple cues. The search process is assumed to be driven by three factors: the importance of attributes or cues that provide information, the uncertainty of the information, and the accumulated evidence for different options. Our novel theory goes beyond existing theories by making specific predictions about the cognitive and neural dynamics of both the search process and the decision-making processes, whereas existing models often only focus on search or decisions alone. We will rigorously test our theory in a series of eye-tracking and electroencephalography (EEG) studies. To gain a deeper insight of the cognitive mechanisms underlying search and decisions we will complement this effort with extensive cognitive modelling. In our behavioural paradigms of multi-attribute decisions and multi-cue probabilistic inferences, we will systematically vary the uncertainty of information to test the model’s predictions. With the first work package A we test the model’s predictions for situations in which aggregated information with little uncertainty is available. With work package B we will test the model for a situation in which the information provided by the attributes and cues is uncertain and therefore requires an extensive sampling phase. Moreover, we will test the hypothesis that patients suffering from obsessive compulsive disorder (OCD) as well as undiagnosed people that score high on OCD-related symptoms struggle with achieving a good balance between the desire to be accurate and the need to be efficient (Work package C). Specifically, we predict that the OCD and OCD-related symptoms are associated with putting too much emphasis on accuracy with respect to the selection of single pieces of information and with respect to deciding whether to terminate the search process. Our project will provide critical insights into the computational and neurocognitive basis of information search in complex decision-making and inference problems, and it will elucidate maladaptive mechanisms associated with OCD symptomatology that can make it difficult to achieve efficiency in information search.

• DFG Programme: Research Grants
• International Connection: Switzerland
• Partner Organisation: Schweizerischer Nationalfonds (SNF)
• Co-Investigator: Professorin Dr. Anja Riesel, Ph.D.
• Cooperation Partner: Professor Dr. Jörg Rieskamp