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Diagnostic spatial frequencies and human efficiency for discriminating actions.

Thurman SM, Grossman ED - Atten Percept Psychophys (2011)

Bottom Line: The first, more traditional, approach measured signal-to-noise ratio (s/n) thresholds for videos filtered by one of six Gaussian band-pass filters ranging from 4 to 128 cycles/image.The second approach used SF "bubbles", Willenbockel et al. (Journal of Experimental Psychology.Efficiency on this task was estimated by comparing s/n thresholds for humans to an ideal observer, and was estimated to be quite low (>.04%) for both experiments.

View Article: PubMed Central - PubMed

Affiliation: Department of Cognitive Sciences, University of California, Irvine, 3151 Social Science Plaza, Irvine, CA 92697-5100, USA. sthurman@uci.edu

ABSTRACT
Humans extract visual information from the world through spatial frequency (SF) channels that are sensitive to different scales of light-dark fluctuations across visual space. Using two methods, we measured human SF tuning for discriminating videos of human actions (walking, running, skipping and jumping). The first, more traditional, approach measured signal-to-noise ratio (s/n) thresholds for videos filtered by one of six Gaussian band-pass filters ranging from 4 to 128 cycles/image. The second approach used SF "bubbles", Willenbockel et al. (Journal of Experimental Psychology. Human Perception and Performance, 36(1), 122-135, 2010), which randomly filters the entire SF domain on each trial and uses reverse correlation to estimate SF tuning. Results from both methods were consistent and revealed a diagnostic SF band centered between 12-16 cycles/image (about 1-1.25 cycles/body width). Efficiency on this task was estimated by comparing s/n thresholds for humans to an ideal observer, and was estimated to be quite low (>.04%) for both experiments.

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Classification vector results using SF bubbles. a. Z-scored classification vectors for individual human observers and the group-averaged classification vector. b. Classification vectors for independent simulations of the ideal observer and the group-average. The dotted line represents the critical z-score level for statistical significance (Zcrit=3.45, p=0.05)
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Fig4: Classification vector results using SF bubbles. a. Z-scored classification vectors for individual human observers and the group-averaged classification vector. b. Classification vectors for independent simulations of the ideal observer and the group-average. The dotted line represents the critical z-score level for statistical significance (Zcrit=3.45, p=0.05)

Mentions: Figure 4 shows classification vector results for human and ideal observers. The overall pattern of diagnostic spatial frequencies for individual subjects (thin gray lines) was quite consistent. The average correlation between each human observer and the group-averaged classification vector was r=0.88, SD=0.06. Similarly for individual ideal observers, based on the same number of simulated trials each, the average correlation was r=0.71, SD=0.19. The dotted line in Fig. 4 represents the threshold for statistical significance at the p=0.05 level, corrected for multiple comparisons (Zcrit=3.45; see Chauvin, Worsley, Schyns, Arguin, & Gosselin, 2005 and Willenbockel et al., 2010)Fig. 4


Diagnostic spatial frequencies and human efficiency for discriminating actions.

Thurman SM, Grossman ED - Atten Percept Psychophys (2011)

Classification vector results using SF bubbles. a. Z-scored classification vectors for individual human observers and the group-averaged classification vector. b. Classification vectors for independent simulations of the ideal observer and the group-average. The dotted line represents the critical z-score level for statistical significance (Zcrit=3.45, p=0.05)
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Related In: Results  -  Collection

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getmorefigures.php?uid=PMC3037475&req=5

Fig4: Classification vector results using SF bubbles. a. Z-scored classification vectors for individual human observers and the group-averaged classification vector. b. Classification vectors for independent simulations of the ideal observer and the group-average. The dotted line represents the critical z-score level for statistical significance (Zcrit=3.45, p=0.05)
Mentions: Figure 4 shows classification vector results for human and ideal observers. The overall pattern of diagnostic spatial frequencies for individual subjects (thin gray lines) was quite consistent. The average correlation between each human observer and the group-averaged classification vector was r=0.88, SD=0.06. Similarly for individual ideal observers, based on the same number of simulated trials each, the average correlation was r=0.71, SD=0.19. The dotted line in Fig. 4 represents the threshold for statistical significance at the p=0.05 level, corrected for multiple comparisons (Zcrit=3.45; see Chauvin, Worsley, Schyns, Arguin, & Gosselin, 2005 and Willenbockel et al., 2010)Fig. 4

Bottom Line: The first, more traditional, approach measured signal-to-noise ratio (s/n) thresholds for videos filtered by one of six Gaussian band-pass filters ranging from 4 to 128 cycles/image.The second approach used SF "bubbles", Willenbockel et al. (Journal of Experimental Psychology.Efficiency on this task was estimated by comparing s/n thresholds for humans to an ideal observer, and was estimated to be quite low (>.04%) for both experiments.

View Article: PubMed Central - PubMed

Affiliation: Department of Cognitive Sciences, University of California, Irvine, 3151 Social Science Plaza, Irvine, CA 92697-5100, USA. sthurman@uci.edu

ABSTRACT
Humans extract visual information from the world through spatial frequency (SF) channels that are sensitive to different scales of light-dark fluctuations across visual space. Using two methods, we measured human SF tuning for discriminating videos of human actions (walking, running, skipping and jumping). The first, more traditional, approach measured signal-to-noise ratio (s/n) thresholds for videos filtered by one of six Gaussian band-pass filters ranging from 4 to 128 cycles/image. The second approach used SF "bubbles", Willenbockel et al. (Journal of Experimental Psychology. Human Perception and Performance, 36(1), 122-135, 2010), which randomly filters the entire SF domain on each trial and uses reverse correlation to estimate SF tuning. Results from both methods were consistent and revealed a diagnostic SF band centered between 12-16 cycles/image (about 1-1.25 cycles/body width). Efficiency on this task was estimated by comparing s/n thresholds for humans to an ideal observer, and was estimated to be quite low (>.04%) for both experiments.

Show MeSH
Related in: MedlinePlus