The default-mode network represents aesthetic appeal that generalizes across visual domains

Abstract

Visual aesthetic evaluations, which impact decision-making and well-being, recruit the ventral visual pathway, subcortical reward circuitry, and parts of the medial prefrontal cortex overlapping with the default-mode network (DMN). However, it is unknown whether these networks represent aesthetic appeal in a domain-general fashion, independent of domain-specific representations of stimulus content (artworks versus architecture or natural landscapes). Using a classification approach, we tested whether the DMN or ventral occipitotemporal cortex (VOT) contains a domain-general representation of aesthetic appeal. Classifiers were trained on multivoxel functional MRI response patterns collected while observers made aesthetic judgments about images from one aesthetic domain. Classifier performance (high vs. low aesthetic appeal) was then tested on response patterns from held-out trials from the same domain to derive a measure of domain-specific coding, or from a different domain to derive a measure of domain-general coding. Activity patterns in category-selective VOT contained a degree of domain-specific information about aesthetic appeal, but did not generalize across domains. Activity patterns from the DMN, however, were predictive of aesthetic appeal across domains. Importantly, the ability to predict aesthetic appeal varied systematically; predictions were better for observers who gave more extreme ratings to images subsequently labeled as "high" or "low." These findings support a model of aesthetic appreciation whereby domain-specific representations of the content of visual experiences in VOT feed in to a "core" domain-general representation of visual aesthetic appeal in the DMN. Whole-brain "searchlight" analyses identified additional prefrontal regions containing information relevant for appreciation of cultural artifacts (artwork and architecture) but not landscapes.

Keywords: architecture; artwork; default-mode network; natural landscape; visual aesthetics.

Fig. 1.

Stimuli and experimental design. Examples of images used in the experiment: (A) visual art, (B) interior and exterior architecture, and (C) natural landscapes. (D) Each trial began with a fixation point (1 s), followed by an image of an artwork (4 s) and a rating period (4 s) during which the observer used a trackball to indicate their response on a visual slider. (E) Classifiers were trained on multivoxel patterns of trialwise (beta-series) estimates taken from individual observer ROIs. Three sets of “within-domain” classification scores and 6 sets of “across-domain” classification scores were derived for each observer using cross-validation. (Fig. 1 A, Left) Reprinted from ref. . (Fig. 1 A, Right) Reprinted from ref. . (Fig. 1 B, Left) Image courtesy of Alec Hartill (photographer). (Fig. 1 B, Right) Image courtesy of R. Hoekstra (photographer). (Fig. 1C) Images are examples from the SUN database, https://groups.csail.mit.edu/vision/SUN/.

Fig. 2.

Predicting aesthetic appeal from activity patterns in DMN and VOT. (A) For each region, a set of classifiers were trained on voxelwise activity patterns from trials of one stimulus domain and tested on separate trials of another domain to produce a 3 × 3 performance matrix. Classification performance (percent correct) was averaged across the 3 diagonal elements to produce a within-domain accuracy score, and the 6 off-diagonal elements were averaged to produce an across-domain score. (B) Six bilateral DMN ROIs (Left) were identified in individual participants: aMPFC, vMPFC, dMPFC, PCC, IPL, and LTC. The regions shown here are the “master” ROIs drawn on an average template brain—these regions were then used to mask individual DMN maps derived from a separate “rest” scan. In addition, 3 category-selective, bilateral VOT regions (Right) were identified in individual participants using a separate object/face/place localizer scan (approximate location on the average template brain shown here): PPA, VOA, and FFA. An overall DMN mask was created by summing all 6 DMN subregions together, and an overall VOT mask was created by projecting the larger VOT region shown here onto individual participant surfaces. (C) Within- and across- domain classification accuracy scores for each region. DMN regions are left of the dashed line in cool colors, and VOT regions are right of the dashed line in warm colors; n = 16. Error bars are 95% CIs; **P < 0.005, tested by comparison to a null distribution derived from 5,000 permutations of individual trial labels.

Fig. 3.

Characterization of domain-general and domain-specific ROI signatures. Each dot represents one ROI (color key same as Fig. 2; gray, DMN; purple, aMPFC; maroon, vMPFC; indigo, dMPFC; blue, PCC; green, IPL; light green, LTC; beige, VOT; yellow, PPA; orange, VOA; red, FFA). Average across-domain performance (y axis) is plotted against average within-domain performance (x axis). The DMN ROIs (cool colors) all tend to fall near the diagonal, illustrating a “domain-general” signature (similar across-domain and within-domain performance levels). The VOT ROIs (warm colors), however, tend to fall along the horizontal and are thus better characterized as “domain-specific” (significant within-domain performance but poor across-domain performance), with the exception of the overall VOT ROI (tan), which is above the horizontal line; n = 16. Error bars are 95% CIs.

Fig. 4.

Variability in DMN classifier performance reflects the strength of observers’ aesthetic experiences. For each observer, the distance between the top-rated trials (labeled “high”) and the bottom-rated trials (labeled “low”) was calculated based on behavioral ratings (d’). Therefore, observers with greater variability in their ratings produced higher d’ values. Separately, classifiers were trained on BOLD signal patterns from each observers’ DMN to distinguish trials labeled as “high” vs. “low.” Across observers, classifier performance (vertical axis) was strongly correlated with each observers’ behavioral d’ distance measure (horizontal axis); n = 16.

Fig. 5.

Topography of domain-general and domain-specific information. (A) Average of the 6 across-domain classifier maps (orange), rendered on an average flattened cortical surface. (B) Three within-domain classifier maps and their overlap, as indicated by the color key. Outlines from the across-domain maps are shown in orange. The right hemisphere is on the left side. Maps were cluster-corrected for multiple comparisons using Monte Carlo simulations (P < 0.05); n = 16. Dark gray areas indicate regions of cortex where data from all participants were not available and were therefore not included in the map.