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Bruce C. Hansen, Lester C. Loschky; The contribution of amplitude and phase spectra-defined scene statistics to the masking of rapid scene categorization. Journal of Vision ;13 13 The masks used in those paradigms Metamorpho)))sis (for electric bass and time woven filter matrix) - Agitation Phi - Studies On Ambie often Paljon Sanomatta Jää - Jukka Kuoppamäki - Paljon Sanomatta Jää by either specific amplitude spectra only, or amplitude and phase spectra-defined structural properties.
The current study addresses this issue. Experiments 1—3 explored amplitude spectra defined contributions to category masking and revealed that the slope of the amplitude spectrum Mauvais Rêves - William Sheller - Chemin De Traverse more important for modulating scene category masking strength Metamorpho)))sis (for electric bass and time woven filter matrix) - Agitation Phi - Studies On Ambie amplitude orientation.
Specifically, the masking effects observed in Experiment 5 followed a target-mask categorical dissimilarity principle whereby the more dissimilar the mask category is to the target image category, the stronger the masking. Purchase this article with an account. Jump To Hansen ; Lester C.
Author Affiliations. Journal of Vision NovemberVol. Alerts User Alerts. The contribution of amplitude and phase spectra-defined scene Metamorpho)))sis (for electric bass and time woven filter matrix) - Agitation Phi - Studies On Ambie to the masking of rapid scene categorization. You will receive an email whenever this article is corrected, updated, or cited in the literature.
You can manage this and all other alerts in My Account. This feature is available to authenticated users only. Get Citation Citation. Get Permissions. Rapid scene categorization masking paradigms typically employ backward masking though some paradigms involve the direct manipulation of target scenes and can be construed as rudimentary forms of simultaneous masking. Backward masking paradigms often utilize masks that are characterized by specific second and higher order statistical regularities of luminance contrast within different real-world scene categories i.
A detailed account of these relationships is provided in the Supplementary material. Briefly, the masks typically consist of spatial contrast characteristics defined only by amplitude spectral relationships e.
Thus, there are several gaps in our knowledge regarding a whether the two types of masks will produce different masking functions, and if so b how the different mask spectral characteristics e. Thus, the current study addresses these questions regarding backward masking. We have focused on backward masking because, in contrast to simultaneous masking, it ensures that the participants will have direct access to all target image information in its original unmanipulated state prior to being exposed to a mask.
Additionally, by varying the stimulus onset asynchrony SOAbackward masking allows the experimenter to assess when amplitude or phase information becomes relevant to the categorization process but see VanRullen,though here, we primarily focus on the spatial aspects of the masking.
Amplitude-only backward masking effects have been investigated in a pair of studies by Loschky et al. Those studies used masks created by phase-randomizing scenes, thereby leaving only amplitude information intact, which we therefore refer to as amplitude-only masks.
Those masks were unrecognizable, and thus devoid of any semantic meaning, which eliminated masking effects caused by conceptual processes, known as conceptual masking e.
Doing so crucially limited masking effects to those due to visual mechanisms responding to image structure defined by amplitude spectral properties, rather than those attributable to conceptual masking.
Loschky et al. These comparisons produced two critical results: a there were no differences in the masking between amplitude-only masks derived from the same versus different scene category than the target, and b both masking conditions based on phase-randomized scene masks produced significantly stronger effects than the white noise masking condition. The former result suggests that amplitude spectral properties produce little if any category-specific masking effects.
On the other hand, the latter result suggests that general spatial frequency and orientation differences relative to white noise play a critical role in masking rapid scene categorization. That is, greater masking was caused by the phase-randomized amplitude-only masks that possessed typical amplitude spectrum characteristics of real-world scenes i. Thus, the question is which aspect of the amplitude spectrum is responsible for such masking?
Experiments 1—3 aimed at answering this question. The studies by Loschky et al. Thus, an alternative explanation for such differences in masking effects would be conceptual masking. Experiments 4—5 aimed at answering this question. It is important to note that utilizing backward masking paradigms to study rapid scene categorization is not without its limitations. One point worth reiterating here is that masking has a long history in the study of early visual processes such as the detection and discrimination of sinusoidal gratings or Gabor patterns.
Thus, backward masking Back To The Worms - Cryptopsy - And Then Youll Beg in rapid scene categorization must be considered in Anarchy For Sale - Dead Kennedys - Bedtime For Democracy terms of early spatial vision mechanisms assessed by visual masking.
Specifically, one must consider whether a given set of backward masking effects can be predicted by the known response characteristics of early visual mechanisms, which could apply equally well to a wide array of visual tasks including rapid scene categorization. For example, if a given backward masking effect can be accounted for by a reduction of the signal-to-noise ratio in early spatial channels, then the information in the mask may simply have been more effective at stopping informative scene information from being passed along to higher levels where scene categorization is thought to occur e.
We therefore apply this approach to interpreting the results of the current study. In doing so, we attempt to dissociate those masking effects more likely to be explained by earlier spatial channel interactions from those less likely to be so Tabala - Vol.
10. Experiments 1—3 were concerned with the effects of the spectral characteristics of amplitude-only masks. Experiment 1 employed noise masks containing only amplitude-defined structure i. Experiment 2 Yann Leguay - Unstatic (File, Album) up Experiment 1 by extending its crucial masking conditions into the temporal domain.
Experiment 3 controlled for the perceived contrast rather than physical contrast of the noise masks used in Experiments 1 and 2. This was necessary to ensure minimal to no recognizability in order eliminate conceptual masking effects.
These were driven by Optiplex L Pentium 4 processors 2. Stimuli were displayed using a linearized look-up table, generated by calibrating with a Color-Vision Spyder2 Pro sensor.
Maximum luminance output of the display monitors was Single pixels subtended. For experiments conducted at Colgate University Experiment 2all stimuli were presented on a 21 in. Viewsonic GfB monitor. Stimuli were displayed using a linearized look-up table, generated by calibrating with a Color-Vision Spyder3 Pro sensor.
Maximum luminance output of the display monitor was set to yield Single pixels subtended 0. A total of grayscale scene images selected from the Internet and free of any copyright restrictions were used in the current study images per scene category as either target stimuli or as images used to construct mask stimuli. There were six basic level categories: a beaches, b forests, c airports, d streets, e home interiors, and f store interiors. These could be further categorized as three conjoint superordinate categories: natural outdoor beaches and forestsman-made outdoor airports and streetsand man-made indoor home and store interiors.
Here, rms was set to 0. The particular choice of rms allowed for a suprathreshold contrast that did not result in pixel grayscale values outside the 0— range. The current experiment was designed to determine which aspect of the amplitude spectrum slope or orientation in amplitude-only masks is responsible for masking rapid scene categorization performance: a the slope of the amplitude spectrum, b the orientation biases, or c both?
However, it also contained the typical spatial frequency contrast bias in i. The result is a flat isotropic broadband spectrum i. Here, O mag was a scalar value controlling the orientation bias magnitude, which took on one of five values from zero to one in steps of 0. The rms contrast of all noise masks was fixed at 0.
Examples of the 20 mask types are shown in Figure 1A. Figure 1. View Original Download Slide. A Example noise masks used in Experiment 1. The examples are arranged in rows for amplitude spectrum slope i. B Shows orientation averaged amplitude spectra for the four different noise mask slopes. Note the distinct horizontal and vertical orientation bias.
Experiment 1 used a six alternative forced choice AFC scene categorization backward masking paradigm. The order of target category and mask orientation bias magnitude was randomized.
Participants were randomly assigned to between-subjects conditions. A given trial sequence consisted of a ms fixation point 0. The positions of the category labels were randomized on each trial to avoid category location response bias. That is, by randomizing the locations of response choices on every trial, any location-based response bias e.
Note however, Metamorpho)))sis (for electric bass and time woven filter matrix) - Agitation Phi - Studies On Ambie this requires participants to first search for the correct answer on each trial, adding to their reaction time, making this method better suited to analyzing accuracy than reaction times.
Take It (Gians Bass Rock Mix) - Tom Novy & Lima - Take It 2. Averaged data for Experiment 1. A Shows amplitude spectrum slope masking functions for the different mask orientation bias magnitudes OM. B Shows mask orientation bias magnitude OM masking functions for the four different mask amplitude spectrum slopes.
Lastly, while unlikely see Introduction and Loschky et al. Thus, in terms of which amplitude-only spectral characteristics most strongly mask rapid scene categorization, the current results show that the distribution of contrast across spatial frequency i. Finally, the masking functions shown in Figure 2b show an interesting trend that seems related to the typical amplitude spectrum slope found in natural scenes i.
Specifically, it is tempting to conclude that the more similar the mask slopes were to the typical slope observed in natural scenes, the stronger the masking effect. However, since the entire target image set had a mean slope of 1. Specifically, the more similar the mask amplitude slope was to the target image slope, the stronger the masking effect. We will return to this notion in Experiments 4 and 5as well as in the General discussion.
We note, along with VanRullenthat the relationship between masking SOA and processing time is not as simple as suggested by many studies using backward masking to study the time course of perception.
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