The Processing Fluency Theory posits that the ease of sensory information processing in the brain facilitates esthetic pleasure. Accordingly, the theory would predict that master painters should display biases towards visual properties such as symmetry, balance, and moderate complexity. Have these biases been occurring and if so, have painters been optimizing these properties (fluency variables)? Here, we address these questions with statistics of portrait paintings from the Early Renaissance period. To do this, we first developed different computational measures for each of the aforementioned fluency variables. Then, we measured their statistics in 153 portraits from 26 master painters, in 27 photographs of people in three controlled poses, and in 38 quickly snapped photographs of individual persons. A statistical comparison between Early Renaissance portraits and quickly snapped photographs revealed that painters showed a bias towards balance, symmetry, and moderate complexity. However, a comparison between portraits and controlled-pose photographs showed that painters did not optimize each of these properties. Instead, different painters presented biases towards different, narrow ranges of fluency variables. Further analysis suggested that the painters’ individuality stemmed in part from having to resolve the tension between complexity versus symmetry and balance. We additionally found that constraints on the use of different painting materials by distinct painters modulated these fluency variables systematically. In conclusion, the Processing Fluency Theory of Esthetic Pleasure would need expansion if we were to apply it to the history of visual art, since it cannot explain the lack of optimization of each fluency variables. To expand the theory, we propose the existence of a Neuroesthetic Space, which encompasses the possible values that each of the fluency variables can reach in any given art period. We discuss the neural mechanisms of this Space and propose that it has a distributed representation in the human brain. We further propose that different artists reside in different, small sub-regions of the Space. This Neuroesthetic-space hypothesis raises the question of how painters and their paintings evolve across art periods.

How do we come to like the things that we do? Each one of us starts from a relatively similar state at birth, yet we end up with vastly different sets of aesthetic preferences. These preferences go on to define us both as individuals and as members of our cultures. Therefore, it is important to understand how aesthetic preferences form over our lifetimes. This poses a challenging problem: to understand this process, one must account for the many factors at play in the formation of aesthetic values and how these factors influence each other over time. A general framework based on basic neuroscientific principles that can also account for this process is needed. Here, we present such a framework and illustrate it through a model that accounts for the trajectories of aesthetic values over time. Our framework is inspired by meta-analytic data of neuroimaging studies of aesthetic appraisal. This framework incorporates effects of sensory inputs, rewards, and motivational states. Crucially, each one of these effects is probabilistic. We model their interactions under a reinforcement-learning circuitry. Simulations of this model and mathematical analysis of the framework lead to three main findings. First, different people may develop distinct weighing of aesthetic variables because of individual variability in motivation. Second, individuals from different cultures and environments may develop different aesthetic values because of unique sensory inputs and social rewards. Third, because learning is stochastic, stemming from probabilistic sensory inputs, motivations, and rewards, aesthetic values vary in time. These three theoretical findings account for different lines of empirical research. Through our study, we hope to provide a general and unifying framework for understanding the various aspects involved in the formation of aesthetic values over time.

Introduction Although music has been said to have no apparent direct survival value (Pinker, 1997), it can elicit pleasurable reward responses in the human brain like those associated with food or sex (Blood & Zatorre, 1999; Blood & Zatorre, 2001; Salimpoor, et al., 2011; Salimpoor et al., 2013). Recent neuroimaging work has revealed many of the mechanisms recruited by the brain during the perception of reward from listening to music that is preferred (Pereira, et al., 2011; Salimpoor, et al., 2013). Less is known, however, about what structural aspects of music are required to drive these mechanisms. A prevailing theory of this process states that adherence to or deviation from listeners’ expectations leads to emotional reward (Meyer, 1956; Huron, 2006). One challenge in testing this theory of how music structure leads to reward is to quantify expectations. Another challenge is to test the theory in an ecologically valid way, that is, avoiding the artificial stimuli that are often used in laboratory tests, which can feature isolated chords (e.g., Koelsch, et al., 2001) or a novel musical system (Loui & Wessel, 2008). The framework of information theory has proved to be useful in quantifying expectations and deviations from them in naturalistic music. Through information theory, one can investigate expectations based on statistical measures of representative corpora. Such an approach was proposed by Leonard Meyer (1957), and has been expanded upon by other researchers in subsequent years (e.g., Knopoff & Hutchinson, 1981; Temperley, 2007; Agres, et al., 2013; for a review, see Rohrmeier & Koelsch, 2012). While he was cautious about overstating how much statistical analysis could reveal, Meyer claimed that knowledge about deviation from expectations, examined through probability, could help reveal their relationship to emotion, a primary source of meaning in music (Meyer, 1957). So far, however, information-theory-driven corpus analyses have primarily been aimed at describing aspects of music, with less emphasis on investigating any relationship these aspects might have with how the music is perceived. We set out to extend such analyses to determine the capacity of aspects within popular music to evoke a reward response. In a first step