The system contained written information on the fictive care recipient "Asta", i.e., on her current situation, what occurred during the latest visit and tasks to be carried out on this occasion.
Computational models of reward processing suggest that foregone or fictive outcomes serve as important information sources for learning and augment those generated by experienced rewards (e.g. reward prediction errors).
The hospitals compared in the ACBC are not actual hospitals with their data but fictive hospitals designed to assess the importance of hospital information without any distortion being introduced due to naming the hospitals.
Similarly, in a design that allowed participants to discover not only what the earnings resulting from their choices were, but also what their earnings would have been if they had chosen differently, activation in the striatum was found to be correlated with the "fictive error signal" provided by the information about the unchosen outcome (Lohrenz et al., 2007).
Calculating the mutual information of our two fictive sequences (see next section for details of this calculation) gives the following result: Sequence 1 (20% errors, auditory feedback on all errors) gives MI = 0.722; Note that this is the same as H o) because there is no uncertainty in the outcome after feedback (i.e., H(o| f) is zero).
Fictive errors are reinforcement learning signals based on hypothetical information about "what could have been".
It was conceived as a group card game in order to allow patients to become partners of a fictive character (Michael) interacting together with cards containing impersonal information which may however reflect their own problems.
In line with their behavioral differences, f-learners and TD-learners also showed different patterns of brain activations related to fictive and reward prediction errors (Fig. 4c,d, and Supporting Information Tables S9 S12).
We further conducted unbiased ROI analysis based on seeds selected from the main effect of fictive error averaged across both task conditions (Fig. 2c and Supporting Information Table S4), including AIC (centered at [−34, 16, −12]), LOFC (centered at [44, 22, −8]), MOFC (centered at [−4, 32, −16]), and striatum (centered at [−16, −4, 14]).
First, external information can be directly included by converting this information into pseudo-records for fictive daughters of external animals (e.g., [ 2]).
