A cost minimisation and Bayesian inference model predicts startle reflex modulation across species

Abstract

In many species, rapid defensive reflexes are paramount to escaping acute danger. These reflexes are modulated by the state of the environment. This is exemplified in fear-potentiated startle, a more vigorous startle response during conditioned anticipation of an unrelated threatening event. Extant explanations of this phenomenon build on descriptive models of underlying psychological states, or neural processes. Yet, they fail to predict invigorated startle during reward anticipation and instructed attention, and do not explain why startle reflex modulation evolved. Here, we fill this lacuna by developing a normative cost minimisation model based on Bayesian optimality principles. This model predicts the observed pattern of startle modification by rewards, punishments, instructed attention, and several other states. Moreover, the mathematical formalism furnishes predictions that can be tested experimentally. Comparing the model with existing data suggests a specific neural implementation of the underlying computations which yields close approximations to the optimal solution under most circumstances. This analysis puts startle modification into the framework of Bayesian decision theory and predictive coding, and illustrates the importance of an adaptive perspective to interpret defensive behaviour across species.

Keywords: Emotion; Fear; Motivational priming.

Fig. 1

Examples for cost functions and change of parameters. (A) Startle cost (light grey) increases with increasing startle magnitude, while expected cost of a blow (dark grey) decreases with increasing startle magnitude. They are added into a total cost function (black), and this is minimized to determine optimal startle magnitude. (B) Increasing blow probability scales the expected cost of blow and increases the minimiser for the total cost function – hence, optimal startle magnitude is increased. Blow probability given sensory input can, by Bayes theorem, be increased via increased prior probability of a blow (see text). (C) Opportunity costs are potential benefits, foregone due to the startle response (dotted light grey) or due to the blow (dotted dark grey). They are combined with the direct costs to give a total cost function (black). (D) Increasing the potential benefits scales the opportunity cost function. This shifts the minimising startle magnitude towards higher values – provided that the opportunity costs of startle have shallower slope than the opportunity costs of the blow, a biological meaningful assumption (see text).