Abstract
It remains unclear how the visual system is able to extract affective content from complex scenes even with extremely brief (< 100 millisecond) exposures. One possibility, suggested by findings in machine vision, is that low-level features such as unlocalized, two-dimensional (2-D) Fourier spectra can be diagnostic of scene content. To determine whether Fourier image amplitude carries any information about the affective quality of scenes, we first validated the existence of image category differences through a support vector machine (SVM) model that was able to discriminate our intact aversive and neutral images with ~ 70% accuracy using amplitude-only features as inputs. This model allowed us to confirm that scenes belonging to different affective categories could be mathematically distinguished on the basis of amplitude spectra alone. The next question is whether these same features are also exploited by the human visual system. Subsequently, we tested observers’ rapid classification of affective and neutral naturalistic scenes, presented briefly (~33.3 ms) and backward masked with synthetic textures. We tested categorization accuracy across three distinct experimental conditions, using: (i) original images, (ii) images having their amplitude spectra swapped within a single affective image category (e.g., an aversive image whose amplitude spectrum has been swapped with another aversive image) or (iii) images having their amplitude spectra swapped between affective categories (e.g., an aversive image containing the amplitude spectrum of a neutral image). Despite its discriminative potential, the human visual system does not seem to use Fourier amplitude differences as the chief strategy for affectively categorizing scenes at a glance. The contribution of image amplitude to affective categorization is largely dependent on interactions with the phase spectrum, although it is impossible to completely rule out a residual role for unlocalized 2-D amplitude measures.
Highlights
Perceptual processing in the natural world is strongly influenced by motivational factors, allowing for adaptive behavioral routines in response to threats and opportunities in the environment [1,2,3,4]
We tested behavioral accuracy across three distinct experimental conditions, using: (i) original images, (ii) images having their amplitude spectra swapped within a single affective image category or (iii) images having their amplitude spectra swapped between affective categories
To determine whether Fourier amplitude, independent of phase spectra, contains sufficient information to differentiate among scenes with distinct affective tone, we first considered the results from the support vector machine (SVM) classifier
Summary
Perceptual processing in the natural world is strongly influenced by motivational factors, allowing for adaptive behavioral routines in response to threats and opportunities in the environment [1,2,3,4]. Complex scenes can be affectively discriminated even with very rapid exposure times [5]. Enhanced brain physiological responses elicited by emotional, relative to neutral, scenes are detectable by ~200 ms using non-invasive recordings [6,7,8], earlier latency modulations are apparent in intracranial studies [9,10]. The visual system forms a rough, initial sketch of complex scenes based on such low-level sources of information, which can later be filled in with higher level processing. The affective Gestalt of scenes (i.e., whether they signal something aversive or neutral) is inseparable from the lower, physical aspects [16,17] and emerges only after these more basic semantic kinds of categorization have occurred [18,19,20,21]
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