The influence of learning context on response times: A reinforcement learning sequential sampling model analysis
In previous work, we showed how different learning contexts affected not only choice proportion but also decision time: Participants tend to give faster responses in higher-value contexts compared to low-value contexts. To explain these effects, we combined traditional reinforcement learning models–which model across-trial dynamics–with sequential sampling models–which model within-trial dynamics. However, it remains to be assessed whether the magnitude and sign of rewards are associated with different decision mechanisms (i.e., decision caution or motor facilitation). In this study, we manipulated both the magnitude and sign of rewards in a within-participant design. We found that the two manipulations had overall different effects on the joint choice proportion and response times patterns. We propose a new model that attempts to explain such patterns and therefore provide a concise and comprehensive account of value effects on decision-making in reinforcement learning.
Hi, thank you for this interesting and amazing talk. I have a question in terms of the learning model. It seems the reward is continued and probabilistic, would it be better to use Bayesian model that can capture the perceived reward precision? Because in the previous studies, like Daw et al., 2006 Nature, the author found kalman filter is better t...