Pragmatic information of aesthetic appraisal

TL;DR

This work develops a phenomenological model of aesthetic appraisal based on pragmatic information within a dynamic update semantics framework. By viewing aesthetic objects as operators that update an agent's belief state and using a KL-divergence-like measure to quantify information gain, it links context uncertainty with stimulus surprise to hedonic value. The model is instantiated in a tractable binary-repertoire setting for cadential chords in Western tonal music, yielding a simple phenomenological formula that qualitatively matches experimental pleasure ratings. The discussion situates the approach within Kantian aesthetics, Meyer’s embodied meaning, and neural-dynamical interpretations (including GAN-like dynamics) and points to broader implications for computational and neuroaesthetic theories.

Abstract

A phenomenological model for aesthetic appraisal is proposed in terms of pragmatic information for a dynamic update semantics over belief states on an aesthetic appreciator. The model qualitatively correlates with aesthetic pleasure ratings in an experimental study on cadential effects in Western tonal music. Finally, related computational and neurodynamical accounts are discussed.

Figures (3)

• Figure 1: Comparison of Fermi-Dirac entropy $H(q)$ with logistic map $L(q)$ against probability parameter $q$ of a binary distribution. Blue: Fermi-Dirac entropy \ref{['eq:fdent']}; red: logistic map \ref{['eq:logi']}.
• Figure 2: Comparison of aesthetic pleasure rating $P(I \| H)$ from the experimental study of CheungHarrisonEA19 with pragmatic information model $S(I(p) \| H(q))$. (a) Normalized $z$-scores of aesthetic appreciation in dependence of context uncertainty $H(q)$ (abscissa) and stimulus surprise $I(p)$ (ordinate). Reprinted after slight modifications from CheungHarrisonEA19 with permission from Elsevier. (b) Pragmatic information \ref{['eq:pi3']} for probabilistic context updating dynamics \ref{['eq:update']} under a cadential operator $\mathbf{A}$.
• Figure 3: Kullback-Leibler divergence for the lower branch of the probabilistic context updating dynamics \ref{['eq:update']}, depending on context uncertainty $H(q)$ (abscissa) and stimulus surprise $I(p)$ (ordinate).