Inferring Master Painters' Esthetic Biases from the Statistics of Portraits

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 toward 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 toward 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 toward different, narrow ranges of fluency variables. Further analysis suggested that the painters' individuality stemmed in part from having to resolve the tension between complexity vs. 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

Probabilistic Motion Estimation Based on Temporal Coherence

We develop a theory for the temporal integration of visual motion motivated by psychophysical experiments. The theory proposes that input data are temporally grouped and used to predict and estimate the motion flows in the image sequence. This temporal grouping can be considered a generalization of the data association techniques used by engineers to study motion sequences. Our temporal-grouping theory is expressed in terms of the Bayesian generalization of standard Kalman filtering. To implement the theory we derive a parallel network which shares some properties of cortical networks. Computer simulations of this network demonstrate that our theory qualitatively accounts for psychophysical experiments on motion occlusion and motion outliers.Comment: 40 pages, 7 figure

Occlusions and their relationship with the distribution of contrasts in natural images

AbstractAn Information-Theory-like hypothesis recently proposed for early visual processing (the Minimal Local-Asperity hypothesis) accounts for the adaptive behavior with intensity of horizontal cells. It has been shown that for this to hold, the probability that a point is traversed by an occluding border must increase supralinearly (that is, with a positive second derivative) as a function of contrast. We test this condition by analyzing the distribution of contrasts and their relationship with occluding borders in natural images. We find that the distribution of contrasts in natural images falls exponentially as a function of contrast. Moreover, the probability that a point is traversed by an occluding border in natural images always rises with contrast until reaching one. This rise tends to be supralinear and addition of noise (at low intensities) increases the supralinearity, shifting the rising portion of the curve towards higher contrasts. These findings lend support to the Minimal Local-Asperity hypothesis, which proposes that one of the main roles of early retinal processing is to extract optimally edge, contrast, and luminance attributes from the visual world based on previous knowledge about natural images

Parametric decomposition of optic flow by humans

Ego motion and natural motions in the world generate complex optic flows in the retina. These optic flows, if produced by rigid surface patches, can be decomposed into four components, including rotation and expansion. We showed previously that humans can precisely estimate parameters of these components, such as the angular velocity of a rotational motion and the rate of expansion of a radial motion. However, natural optic flows mostly display motions containing a combination of more than one of these components. Here, we report that when a pure motion (e.g., rotation) is combined with its orthogonal component (e.g., expansion), no bias is found in the estimate of the component parameters. This suggests that the visual system can decompose complex motions. However, this decomposition is such that the presence of the orthogonal component increases the discrimination threshold for the original component. We propose a model for how the brain decomposes the optic flow into its elementary components. The model accounts for how errors in the estimate of local-velocity vectors affect the decomposition, producing the increase of discrimination thresholds.Fil: Barraza, Jose Fernando. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Tucumán. Instituto de Investigación en Luz, Ambiente y Visión. Universidad Nacional de Tucumán. Facultad de Ciencias Exactas y Tecnología. Instituto de Investigación en Luz, Ambiente y Visión; ArgentinaFil: Grzywacz, Norberto M.. University of Southern California; Estados Unido

Estimation of linear and nonlinear spatial receptive fields from natural Images

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A Statistical Analysis of the Relationship between Harmonic Surprise and Preference in Popular Music

Studies have shown that some musical pieces may preferentially activate reward centers in the brain. Less is known, however, about the structural aspects of music that are associated with this activation. Based on the music cognition literature, we propose two hypotheses for why some musical pieces are preferred over others. The first, the Absolute-Surprise Hypothesis, states that unexpected events in music directly lead to pleasure. The second, the Contrastive-Surprise Hypothesis, proposes that the juxtaposition of unexpected events and subsequent expected events leads to an overall rewarding response. We tested these hypotheses within the framework of information theory, using the measure of “surprise.” This information-theoretic variable mathematically describes how improbable an event is given a known distribution. We performed a statistical investigation of surprise in the harmonic structure of songs within a representative corpus of Western popular music, namely, the McGill Billboard Project corpus. We found that chords of songs in the top quartile of the Billboard chart showed greater average surprise than those in the bottom quartile. We also found that the different sections within top-quartile songs varied more in their average surprise than the sections within bottom-quartile songs. The results of this study are consistent with both the Absolute- and Contrastive-Surprise Hypotheses. Although these hypotheses seem contradictory to one another, we cannot yet discard the possibility that both absolute and contrastive types of surprise play roles in the enjoyment of popular music. We call this possibility the Hybrid-Surprise Hypothesis. The results of this statistical investigation have implications for both music cognition and the human neural mechanisms of esthetic judgments

A Bayesian model for the measurement of visual velocity

Several models have been proposed for how the brain measures velocity from the output of motion-energy units. These models make some unrealistic assumptions such as the use of Gabor-shaped temporal filters, which are non causal, or flat spatial spectra, which are invalidated by existing data. We present a Bayesian model of velocity perception, which makes more realistic assumptions and allows the estimation of local retinal velocity regardless of the specific mathematical form of the spatial and temporal filters used. The model is consistent with several aspects of speed perception, such as the dependence of perceived spee