Inferring on the Intentions of Others by Hierarchical Bayesian Learning
Figure 2
Hierarchical structure of the model space: Perceptual models, response models, specific models.
The models considered in this study have a 3×2×2 factorial structure and can be displayed as a tree. The leaves at the bottom represent individual models of social learning in which both social and non-social sources of information are considered. The nodes at the first level represent the perceptual model families (three-level HGF, reduced two-level HGF, and RW). Two response models were formalized under the HGF model: decision noise in the mapping of beliefs to decisions either (1) depended dynamically on the estimated volatility of the adviser's intentions (“Volatility” model) or (2) was a fixed entity over trials (“Decision noise” model). At the third level, the response model parameters can be divided further according to the weight of social versus non-social information – these models propose that participants' beliefs are based on (1) both cue and advice information and (2) advice only. The branch on the left-hand side proposes a model in which only the given cue probabilities (i.e., the pie chart) enter the response model (Cue Probability).