Understanding decision criterion learning: From signal detection theory to neural implementation

• Applicants: Professor Dr. Frank Jäkel; Professor Dr. Maik C. Stüttgen
• Subject Area: Biological Psychology and Cognitive Neuroscience ; Human Cognitive and Systems Neuroscience
• Term: from 2019 to 2024
• Project identifier: Deutsche Forschungsgemeinschaft (DFG) - Project number 424828846

Animal behavior is controlled by stimuli and by action-produced outcomes. Through learning, organisms adapt their behavior to produce desirable outcomes (e.g., rewards) and avoid undesirable outcomes (e.g., punishments). In natural environments, however, it is not always clear which specific action should ideally follow upon encounter with a specific stimulus. Moreover, real-world stimulus-response-outcome contingencies are usually neither deterministic nor static, so behavior needs to be reshaped by experience.In the framework of signal detection theory (SDT), such learning under stimulus uncertainty can be described as adapting a flexible decision criterion. At the computational level of explanation ("why is the computation performed?"), the theory describes which decision criterion is optimal, e.g., in the sense of maximizing the number of rewards. While SDT has successfully been used for decades to analyze behavioral data from perceptual decision-making tasks, it says next to nothing about processes at the algorithmic level, i.e., how is the criterion learned in the first place, and how do observers adapt it when environmental conditions change?In previous work focusing on the algorithms of criterion learning, we have proposed and experimentally scrutinized an income-based criterion learning model that combines insights from SDT and animal learning theory. Here, we propose a series of behavioral experiments in both rats and humans to 1) contrast our reward-based model with error-learning accounts that have successfully been applied to describe human performance, and 2) challenge and further refine the model by observing behavior under different stimulus and outcome conditions. Furthermore, building on pilot experiments in rats, we will move from the algorithmic level of description ("which variables are represented, and how are they set off against each other?") to the implementational level ("how are the computations realized neurally?") by combining theoretical modelling and behavior analysis with optogenetically mediated transient inactivation of key brain structures.

• DFG Programme: Research Grants