Preferences need inferences

Abstract

In this chapter, we describe how the individual’s active involvement with works of art contributes to the experience of them. Based on a review of recent experimental work, we show how predictions rooted in previous experiences shape perceptual, cognitive and emotional processes involved in the experience of art. Specifically, we demonstrate that mechanisms of valuation—understood as the process of how we come to value, prefer or like things—are built on Bayesian inference. This view that art experiences, including affective evaluations, emerge as a function of learning and inference stands in contrast to the traditional view first formulated by Robert Zajonc that “preferences need no inferences.” We argue that this traditional conception of valuation fails to account for experimental findings and discuss how our predictive framework allows us to resolve traditional conundrums in the science of aesthetic experience, such as the nature of the “beholder’s share,” the link between curiosity and appreciation, Keats’ “negative capability” and the tension between the mere exposure principle and the Goldilocks (optimal level) principle.