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Contextual inference through flexible integration of environmental features and behavioural outcomes
Fig 2
Design of feature and outcome inference models.
(A) Setup of the cued T-maze task, showing different location and cue-based features, (B) Algorithm underlying feature inference showing how observed feature transitions 
to 
are compared with learnt successor feature maps M using Bayesian inference to determine which context 
is currently most likely, followed by using the corresponding temporal difference map TD to choose the current best action at, (C) Algorithm underlying outcome inference showing how observed outcomes crt are compared with learnt convolved reward maps C using Bayesian inference to determine which context is currently most likely, (D) Schematic of how the feature and outcome inference models react to a change in trial type, showing selection of the relevant map for feature inference following the cue and for outcome inference following the lack of reward, and how the inferred context allows for action selection using separate temporal difference maps.
doi: https://doi.org/10.1371/journal.pcbi.1014093.g002