Figure 1.

Examples of computational theories and models

(A) Levels of abstraction and update of theory and model via error feedback.

(B) Schematic diagram of predictive coding for pain perception.

(C) The perception-action cycle is intertwined with internal prediction loops, where different stages of prediction can be applied in pain-related processing.

(D) Schematic diagram of reinforcement learning (RL). The agent receives nociceptive input x_k(t) at the k-th trial, updates the value function, selects the action, receives the reward (or punishment), produces a prediction error (PE), which is further used to update the weights and value function.

(E) Schematic diagram of the computational model with arbitration between model-based and model-free learning systems (Wang et al., 2018, ref. ³⁴, figure modified with permission, CCBY 4.0 license).

(F) The predicted response outcome (PRO) model (Alexander and Brown, 2011, ref. ³⁵, figure reproduced with permission, Springer Nature). (i) The model learns predictions of future outcomes (e.g., error or correct feedback) based on task-related cues (S) signaling the onset of a trial is presented. Over the course of a task, the model learns a timed prediction (V) of possible responses and outcomes (r). The temporal difference learning signal (δ) is decomposed into its positive and negative components (ω^P and ω^N, respectively), indicating unpredicted occurrences and unpredicted non-occurrences, respectively. (ii) ω^N accounts for typical effects observed in the PFC from human imaging studies. Conflict and error likelihood panels show activity magnitude aligned on trial onset; error and error unexpectedness panels show activity magnitude aligned on feedback. Model activity (vertical axis) is in arbitrary units. HEL, high error likelihood; LEL, low error likelihood. (iii) Typical time courses for components of the PRO model.