Which Mask is the Most Efficient: A Pattern or a Noise? It Depends on the Task

Delord, Sandrine

doi:10.1080/713756789

Cited by 17 publications

(16 citation statements)

References 30 publications

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“…On the one hand, unstructured visual patterns might have masked the representations of the letters, which disrupted their being entered into the visual cache. In fact, studies have demonstrated that visual patterns that do not resemble the visual stimuli can produce backward masking (e.g., Delord, 1998). On the other hand, some memory tasks are affected by unstructured DVN.…”

Section: Discussionmentioning

confidence: 99%

Representations in mental imagery and working memory: Evidence from different types of visual masks

Ganis¹,

Thompson²,

Kosslyn³

2011

Mem Cogn

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Although few studies have systematically investigated the relationship between visual mental imagery and visual working memory, work on the effects of passive visual interference has generally demonstrated a dissociation between the two functions. In four experiments, we investigated a possible commonality between the two functions: We asked whether both rely on depictive representations. Participants judged the visual properties of letters using visual mental images or pictures of unfamiliar letters stored in short-term memory. Participants performed both tasks with two different types of interference: sequences of unstructured visual masks (consisting of randomly changing white and black dots) or sequences of structured visual masks (consisting of fragments of letters). The structured visual noise contained elements of depictive representations (i.e., shape fragments arrayed in space), and hence should interfere with stored depictive representations; the unstructured visual noise did not contain such elements, and thus should not interfere as much with such stored representations. Participants did in fact make more errors in both tasks with sequences of structured visual masks. Various controls converged in demonstrating that in both tasks participants used representations that depicted the shapes of the letters. These findings not only constrain theories of visual mental imagery and visual working memory, but also have direct implications for why some studies have failed to find that dynamic visual noise interferes with visual working memory.

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Section: Discussionmentioning

confidence: 99%

Representations in mental imagery and working memory: Evidence from different types of visual masks

Ganis¹,

Thompson²,

Kosslyn³

2011

Mem Cogn

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“…Numerous studies on visual masking using unnatural masks have shown that thresholds can be affected by controlled, often low-level mask properties. For example, thresholds have been shown to be affected by changes in contrast (e.g., Legge & Foley, 1980;Watson & Solomon, 1997), changes in luminance (e.g., Peli, 1990;Watson, 1993a;Eckert & Bradley, 1998), changes in pattern or noise properties (e.g., Ahumada, 1967;Legge, Kersten, & Burgess, 1987;Delord, 1998;Nadenau & Reichel, 2000;Winkler & Susstrünk, 2004), and changes in subject familiarity .…”

Section: Single or Multiple Forms Of Masking?mentioning

confidence: 99%

Local masking in natural images: A database and analysis

et al. 2014

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Studies of visual masking have provided a wide range of important insights into the processes involved in visual coding. However, very few of these studies have employed natural scenes as masks. Little is known on how the particular features found in natural scenes affect visual detection thresholds and how the results obtained using unnatural masks relate to the results obtained using natural masks. To address this issue, this paper describes a psychophysical study designed to obtain local contrast detection thresholds for a database of natural images. Via a three-alternative forced-choice experiment, we measured thresholds for detecting 3.7 cycles/8 vertically oriented log-Gabor noise targets placed within an 85 · 85pixels patch (1.98 patch) drawn from 30 natural images from the CSIQ image database (Larson & Chandler, Journal of Electronic Imaging, 2010). Thus, for each image, we obtained a masking map in which each entry in the map denotes the root mean squared contrast threshold for detecting the log-Gabor noise target at the corresponding spatial location in the image. From qualitative observations we found that detection thresholds were affected by several patch properties such as visual complexity, fineness of textures, sharpness, and overall luminance. Our quantitative analysis shows that except for the sharpness measure (correlation coefficient of 0.7), the other tested low-level mask features showed a weak correlation (correlation coefficients less than or equal to 0.52) with the detection thresholds. Furthermore, we evaluated the performance of a computational contrast gain control model that performed fairly well with an average correlation coefficient of 0.79 in predicting the local contrast detection thresholds. We also describe specific choices of parameters for the gain control model. The objective of this database is to provide researchers with a large ground-truth dataset in order to further investigate the properties of the human visual system using natural masks.

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“…Using logic similar to that of Delord (1998), we have argued that the information that most efficiently masks scene gist is also the most useful for recognizing scene gist. Our results are entirely consistent with arguments that second-order image statistics based on the unlocalized amplitude spectrum provide insufficient information for recognizing scenes.…”

Section: The Role Of Unlocalized Amplitude Spectrum Information In Scmentioning

confidence: 99%

The importance of information localization in scene gist recognition.

Loschky

Sethi

Simons³

et al. 2007

Journal of Experimental Psychology: Human Perception and Perfor

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People can recognize the meaning or gist of a scene from a single glance, and a few recent studies have begun to examine the sorts of information that contribute to scene gist recognition. The authors of the present study used visual masking coupled with image manipulations (randomizing phase while maintaining the Fourier amplitude spectrum; random image structure evolution [RISE]; J. Sadr & P. Sinha, 2004) to explore whether and when unlocalized Fourier amplitude information contributes to gist perception. In 4 experiments, the authors found that differences between scene categories in the Fourier amplitude spectrum are insufficient for gist recognition or gist masking. Whereas the global 1/f spatial frequency amplitude spectra of scenes plays a role in gist masking, local phase information is necessary for gist recognition and for the strongest gist masking. Moreover, the ability to recognize the gist of a target image was influenced by mask recognizability, suggesting that conceptual masking occurs even at the earliest stages of scene processing.

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Which Mask is the Most Efficient: A Pattern or a Noise? It Depends on the Task

Cited by 17 publications

References 30 publications

Representations in mental imagery and working memory: Evidence from different types of visual masks

Representations in mental imagery and working memory: Evidence from different types of visual masks

Local masking in natural images: A database and analysis

The importance of information localization in scene gist recognition.

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