Perception of spatiotemporal random fractals: an extension of colorimetric methods to the study of dynamic texture

Billock, Vincent A.; Cunningham, Douglas W.; Havig, Paul R.; Tsou, Brian H.

doi:10.1364/josaa.18.002404

Cited by 17 publications

(24 citation statements)

References 24 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…(iii) For all subjects, the noise contrast required for pattern formation dropped dramatically as the spatial exponent was increased from 0 to Ϸ1.0-1.4 (Fig. 5 a and c); these exponents are typical of the statistics of natural images (29). This effect is similar to that predicted by Busch et al (28) from simulations of spatiotemporal stochastic resonance in pattern-forming neural networks.…”

Section: Methodssupporting

confidence: 83%

Neural interactions between flicker-induced self-organized visual hallucinations and physical stimuli

Billock

Tsou

2007

Proc. Natl. Acad. Sci. U.S.A.

Self Cite

View full text Add to dashboard Cite

Spontaneous pattern formation in cortical activity may have consequences for perception, but little is known about interactions between sensory-driven and self-organized cortical activity. To address this deficit, we explored the relationship between ordinary stimulus-controlled pattern perception and the autonomous hallucinatory geometrical pattern formation that occurs for unstructured visual stimulation (e.g., empty-field flicker). We found that flicker-induced hallucinations are biased by the presentation of adjacent geometrical stimuli; geometrical forms that map to cortical area V1 as orthogonal gratings are perceptually opponent in biasing hallucinations. Rotating fan blades and pulsating circular patterns are the most salient biased hallucinations. Apparent motion and fractal (1/f ) noise are also effective in driving hallucinatory pattern formation (the latter is consistent with predictions of spatiotemporal pattern formation driven by stochastic resonance). The behavior of these percepts suggests that selforganized hallucinatory pattern formation in human vision is governed by the same cortical properties of localized processing, lateral inhibition, simultaneous contrast, and nonlinear retinotopic mapping that govern ordinary vision.1/f ͉ MacKay effect ͉ spatiotemporal pattern formation ͉ spontaneous cortical activity ͉ stochastic resonance T he visual cortex is active even in the absence of retinal stimulation; this spatiotemporally structured neural activity may have perceptual implications (1-9). For example, Kenet et al. (3) (in visual cortex of anesthetized cats) find a succession of spontaneous patterned neural states that correspond to those induced by simple oriented geometric patterns. These specific neural patterns, if present in awake humans, must be subliminal. This result raises two intriguing questions (6). (i) Do analogous perceptible patterns arise in humans? (ii) If so, can they interact with neural activity evoked by physical stimuli to affect perception? To tackle these questions we consider stereotyped geometric hallucinations, often triggered by migraine, drug intoxications, and empty-field flicker (10-20); these phenomena are thought to arise from autonomous activity in visual cortex. Although little is known about how spontaneous activity affects the perception of physical stimuli, it is possible to approach the second question from another direction: How do physical stimuli affect perception of spontaneous activity? Here we explore three circumstances in which physical stimuli dictate perceivable cortical pattern formation.The most common geometric hallucinatory forms are lattices, spirals, concentric circles, and fan shapes (Fig. 1). We regard these hallucinations as spontaneous states because they are apparently self-organized by visual cortex (14-20) and can arise without visual stimulation; these perceptible states may be analogous to Kenet et al.'s (3) subliminal states (9). Their geometric forms are predicted by neural network models that generate stripe patterns of neura...

show abstract

Section: Methodssupporting

confidence: 83%

Neural interactions between flicker-induced self-organized visual hallucinations and physical stimuli

Billock

Tsou

2007

Proc. Natl. Acad. Sci. U.S.A.

Self Cite

View full text Add to dashboard Cite

show abstract

“…Neurons in early visual cortex (i.e., V1) process images by band-pass filtering, so their analysis of visual information can be considered to be optimally efficient (15, 16). Furthermore, it has been demonstrated that the correlational structure of adult human contrast sensitivity data displays a power law consistent with natural images (13,17).Behaviorally, discrimination of changes in amplitude spectrum is most accurate when ␣ is approximately 1.5 spatially (18)(19)(20)(21) and ␣ is between 0.8 and 1 temporally (21). Surround suppression is stronger with surrounds of natural amplitude spectra compared with less natural surrounds (22).…”

mentioning

confidence: 98%

“…Behaviorally, discrimination of changes in amplitude spectrum is most accurate when ␣ is approximately 1.5 spatially (18)(19)(20)(21) and ␣ is between 0.8 and 1 temporally (21). Surround suppression is stronger with surrounds of natural amplitude spectra compared with less natural surrounds (22).…”

mentioning

confidence: 99%

See 1 more Smart Citation

Natural images dominate in binocular rivalry

Baker

Graf²

2009

Proc. Natl. Acad. Sci. U.S.A.

View full text Add to dashboard Cite

Ecological approaches to perception have demonstrated that information encoding by the visual system is informed by the natural environment, both in terms of simple image attributes like luminance and contrast, and more complex relationships corresponding to Gestalt principles of perceptual organization. Here, we ask if this optimization biases perception of visual inputs that are perceptually bistable. Using the binocular rivalry paradigm, we designed stimuli that varied in either their spatiotemporal amplitude spectra or their phase spectra. We found that noise stimuli with ''natural'' amplitude spectra (i.e., amplitude content proportional to 1/f, where f is spatial or temporal frequency) dominate over those with any other systematic spectral slope, along both spatial and temporal dimensions. This could not be explained by perceived contrast measurements, and occurred even though all stimuli had equal energy. Calculating the effective contrast following attenuation by a model contrast sensitivity function suggested that the strong contrast dependency of rivalry provides the mechanism by which binocular vision is optimized for viewing natural images. We also compared rivalry between natural and phase-scrambled images and found a strong preference for natural phase spectra that could not be accounted for by observer biases in a control task. We propose that this phase specificity relates to contour information, and arises either from the activity of V1 complex cells, or from later visual areas, consistent with recent neuroimaging and single-cell work. Our findings demonstrate that human vision integrates information across space, time, and phase to select the input most likely to hold behavioral relevance.amplitude spectrum ͉ interocular suppression ͉ natural image statistics ͉ phase spectrum ͉ bistable perception T he human visual system is tasked with processing and organizing perceptual information relevant to the tasks we routinely perform. Recent investigations of the statistical properties of natural images indicate that the tuning characteristics of early visual mechanisms reflect measurable properties of the world (1). From simple image attributes such as luminance and contrast information (2) to Gestalt rules of perceptual organization such as proximity and good continuation (3, 4), known properties of perceptual systems appear tuned to the statistics of natural images (5-8). As a whole, these studies support the hypothesis that human visual processing has evolved to efficiently encode images from our natural environment (see ref. 1 for a recent review). Here, we directly address this hypothesis by asking if, when the visual system must choose between two competing inputs, it prefers the one most representative of the natural environment.During binocular rivalry, conscious perception alternates between different images presented to the two eyes. It is well established that some stimuli are preferred to others, for example, high-contrast stimuli will dominate over low-contrast stimuli, and thus will be ...

show abstract

“…As a result, the perceptual and emotional properties of static textures in general (eg, Machajdik and Hanbury 2010; Mao et al 2003), and the effects of their color distribution in particular (eg, Lucassen et al 2011; Simmons and Russell 2008), have been well documented. It appears that the human visual system is optimized for the perception of natural images (Field 1987, 1994; Parraga et al 2000), which typically have fractal-like spatiotemporal spectra (Billock 2000; Billock et al 2001a, 2001b). The amplitude spectra of dynamic natural textures closely follow an inverse power law relationship:

A (f - s, f_{t}) = c f_{s}^{- β} f_{t}^{- α}

where f s and f t are, respectively, the spatial and temporal frequency, with 0.9 ≤ β ≤ 1.2 ( M = 1.08; Billock 2000), and 0.61 ≤ α ≤ 1.2 (Billock et al 2001b).…”

Section: Introductionmentioning

confidence: 99%

Emotional Effects of Dynamic Textures

Toet

Henselmans²,

Lucassen

et al. 2011

i-Perception

View full text Add to dashboard Cite

This study explores the effects of various spatiotemporal dynamic texture characteristics on human emotions. The emotional experience of auditory (eg, music) and haptic repetitive patterns has been studied extensively. In contrast, the emotional experience of visual dynamic textures is still largely unknown, despite their natural ubiquity and increasing use in digital media. Participants watched a set of dynamic textures, representing either water or various different media, and self-reported their emotional experience. Motion complexity was found to have mildly relaxing and nondominant effects. In contrast, motion change complexity was found to be arousing and dominant. The speed of dynamics had arousing, dominant, and unpleasant effects. The amplitude of dynamics was also regarded as unpleasant. The regularity of the dynamics over the textures' area was found to be uninteresting, nondominant, mildly relaxing, and mildly pleasant. The spatial scale of the dynamics had an unpleasant, arousing, and dominant effect, which was larger for textures with diverse content than for water textures. For water textures, the effects of spatial contrast were arousing, dominant, interesting, and mildly unpleasant. None of these effects were observed for textures of diverse content. The current findings are relevant for the design and synthesis of affective multimedia content and for affective scene indexing and retrieval.

show abstract

Perception of spatiotemporal random fractals: an extension of colorimetric methods to the study of dynamic texture

Cited by 17 publications

References 24 publications

Neural interactions between flicker-induced self-organized visual hallucinations and physical stimuli

Neural interactions between flicker-induced self-organized visual hallucinations and physical stimuli

Natural images dominate in binocular rivalry

Emotional Effects of Dynamic Textures

Contact Info

Product

Resources

About