Fig. 1: Inference models and experimental paradigm of adaptive behavior.
From: Neural variability in the medial prefrontal cortex contributes to efficient adaptive behavior
A, left model comprising three hierarchically organized probabilistic inference levels. First-level: inferences about environment latent states zt characterizing current external contingencies (stimulus-action-outcome contingencies: st-at-rt). Second-level: inferences about change probabilities τt (i.e., volatility) in current latent states. Third-level: inferences about the change rate ν of volatility4. Rate ν is assumed to be constant across time but its estimate may vary across time. Middle, model comprising only the two lower inference levels: volatility τ is assumed to be constant but its estimates may vary across time5. Right, models comprising only the lowest inference level: the environment latent state z is assumed to remain unchanged over time but beliefs about its identity may vary over time. The Weber-variability model comprises only the first inference level but these first-order inferences undergo computational imprecisions in agreement with Weber’s Law, providing the necessary flexibility to adapt to changing environments. B trial structure of the two-arm bandit task. In every trial, participants chose one of the two visually presented arms (square vs. circle with randomized left-right positions) by pressing the corresponding hand-held response button. 0.4–3.8 s later (jittered) and contingent upon participants’ choices, they received a visually presented reward (1 euro shown) or not (red cross) (duration: 700 ms). Inter-trial intervals were jittered from 0.5 to 3.9 seconds. C Reward probabilities associated with arms (15% vs. 85%) reversed with probability 0.05 (low volatility episodes), 0.07 (middle volatility episodes), and 0.1 (high volatility episodes). Episode order was pseudo-randomized.
