RTify: Aligning Deep Neural Networks with Human Behavioral Decisions.

ABSTRACT: Current neural network models of primate vision focus on replicating overall levels of behavioral accuracy, often neglecting perceptual decisions' rich, dynamic nature. Here, we introduce a novel computational framework to model the dynamics of human behavioral choices by learning to align the temporal dynamics of a recurrent neural network (RNN) to human reaction times (RTs). We describe an approximation that allows us to constrain the number of time steps an RNN takes to solve a task with human RTs. The approach is extensively evaluated against various psychophysics experiments. We also show that the approximation can be used to optimize an "ideal-observer" RNN model to achieve an optimal tradeoff between speed and accuracy without human data. The resulting model is found to account well

SUBMITTER: Cheng YA 

PROVIDER: S-EPMC11703321 | biostudies-literature | 2024 Dec

REPOSITORIES: biostudies-literature