A recurrent neural network framework for flexible and adaptive decision making based on sequence learning
Fig 8
Task 2: Multisensory integration.
a. The psychometric curve. The model is trained with the unimodal conditions and tested with both the unimodal (green: visual, orange: vestibular) and the bimodal (black) conditions. Each data point represents the proportion of the left choice for a given motion direction. The model performs better and shows a steeper psychometric curve for the bimodal condition. b. The performance thresholds. The model’s thresholds (blue) are compared against the thresholds calculated with the optimal Bayesian inference (orange). The thresholds under the bimodal condition are significantly lower than those under either unimodal condition. The differences between the thresholds of the network and the thresholds calculated with Bayesian inference are not significant. (two-tailed t-test with Bonferroni correction, p-value threshold = 0.05) The error bars indicate the SE across runs.