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Reinterpreting behavioral receptive fields: lightness induction alters visually completed shape.

Keane BP, Lu H, Papathomas TV, Silverstein SM, Kellman PJ - PLoS ONE (2013)

Bottom Line: This pattern arose when pixels immediately adjacent to the discriminated boundaries were excluded from the analysis (Experiment 2) and also when the noise was restricted to the key regions so that the noise never overlapped with the physically visible edges (Experiment 3).Moreover, behavioral receptive fields derived in CI studies do not correspond to contours per se but to filled-in surface regions contained by those contours.The relevance of lightness to two-dimensional shape completion supplies a new constraint for models of object perception.

View Article: PubMed Central - PubMed

Affiliation: Center for Cognitive Science, Rutgers University, New Brunswick, Piscataway, New Jersey, USA. Brian.Keane@gmail.com

ABSTRACT

Background: A classification image (CI) technique has shown that static luminance noise near visually completed contours affects the discrimination of fat and thin Kanizsa shapes. These influential noise regions were proposed to reveal "behavioral receptive fields" of completed contours-the same regions to which early cortical cells respond in neurophysiological studies of contour completion. Here, we hypothesized that 1) influential noise regions correspond to the surfaces that distinguish fat and thin shapes (hereafter, key regions); and 2) key region noise biases a "fat" response to the extent that its contrast polarity (lighter or darker than background) matches the shape's filled-in surface color.

Results: To test our hypothesis, we had observers discriminate fat and thin noise-embedded rectangles that were defined by either illusory or luminance-defined contours (Experiment 1). Surrounding elements ("inducers") caused the shapes to appear either lighter or darker than the background-a process sometimes referred to as lightness induction. For both illusory and luminance-defined rectangles, key region noise biased a fat response to the extent that its contrast polarity (light or dark) matched the induced surface color. When lightness induction was minimized, luminance noise had no consistent influence on shape discrimination. This pattern arose when pixels immediately adjacent to the discriminated boundaries were excluded from the analysis (Experiment 2) and also when the noise was restricted to the key regions so that the noise never overlapped with the physically visible edges (Experiment 3). The lightness effects did not occur in the absence of enclosing boundaries (Experiment 4).

Conclusions: Under noisy conditions, lightness induction alters visually completed shape. Moreover, behavioral receptive fields derived in CI studies do not correspond to contours per se but to filled-in surface regions contained by those contours. The relevance of lightness to two-dimensional shape completion supplies a new constraint for models of object perception.

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Hypothesis and motivations for the experiments.(A) When subjects discriminated noise-corrupted fat and thin rectangles, noise near real and illusory contours correlated with response. (Modified from a figure courtesy of Jason Gold) (B) The present hypothesis is that correlated noise pixels correspond not to contours but to the surfaces that distinguish the fat and thin shapes–the “key regions”. (C) Component images from Keane, Lu, and Kellman (2007) are shown for the illusory and real conditions along with superimposed fat and thin shapes. There is one component image for the fat response and one for the thin response. Each image is the summation of the average noise fields for correct and incorrect trials. The component images suggest that key region pixels biased a fat and thin response by being light and dark, respectively. The final CIs, which result from a simple subtraction, reveal that noise pixel contrast within the key regions positively correlated with a fat response and negatively correlated with a thin response (NB: Images in 1 C are of opposite polarity to Gold et al. (2000), only because in that study the subtraction sequence was reversed).
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pone-0062505-g001: Hypothesis and motivations for the experiments.(A) When subjects discriminated noise-corrupted fat and thin rectangles, noise near real and illusory contours correlated with response. (Modified from a figure courtesy of Jason Gold) (B) The present hypothesis is that correlated noise pixels correspond not to contours but to the surfaces that distinguish the fat and thin shapes–the “key regions”. (C) Component images from Keane, Lu, and Kellman (2007) are shown for the illusory and real conditions along with superimposed fat and thin shapes. There is one component image for the fat response and one for the thin response. Each image is the summation of the average noise fields for correct and incorrect trials. The component images suggest that key region pixels biased a fat and thin response by being light and dark, respectively. The final CIs, which result from a simple subtraction, reveal that noise pixel contrast within the key regions positively correlated with a fat response and negatively correlated with a thin response (NB: Images in 1 C are of opposite polarity to Gold et al. (2000), only because in that study the subtraction sequence was reversed).

Mentions: The neural mechanisms that implement completion include superficial and deep layers of V2 and V1 [1]. Recent work has suggested that the receptive fields for these cells can be revealed behaviorally through a technique termed “classification imaging” [2]–[6]. In an influential study, observers discriminated “fat” and “thin” rectangles, the edges of which were luminance-defined (“real”) or completed (“illusory”; see Figure 1). The rectangles were embedded in a new luminance noise field on each trial and correlations were calculated (across trials) between noise contrast and observer response for each pixel in a noise field. The resulting map of correlations–the classification image–revealed that the noise near illusory contours correlated with response about as much as the noise near real contours. These correlated noise regions were thought to specify the path of completion–the same regions to which cortical cells respond in single-unit studies [7]–[8].


Reinterpreting behavioral receptive fields: lightness induction alters visually completed shape.

Keane BP, Lu H, Papathomas TV, Silverstein SM, Kellman PJ - PLoS ONE (2013)

Hypothesis and motivations for the experiments.(A) When subjects discriminated noise-corrupted fat and thin rectangles, noise near real and illusory contours correlated with response. (Modified from a figure courtesy of Jason Gold) (B) The present hypothesis is that correlated noise pixels correspond not to contours but to the surfaces that distinguish the fat and thin shapes–the “key regions”. (C) Component images from Keane, Lu, and Kellman (2007) are shown for the illusory and real conditions along with superimposed fat and thin shapes. There is one component image for the fat response and one for the thin response. Each image is the summation of the average noise fields for correct and incorrect trials. The component images suggest that key region pixels biased a fat and thin response by being light and dark, respectively. The final CIs, which result from a simple subtraction, reveal that noise pixel contrast within the key regions positively correlated with a fat response and negatively correlated with a thin response (NB: Images in 1 C are of opposite polarity to Gold et al. (2000), only because in that study the subtraction sequence was reversed).
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Related In: Results  -  Collection

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getmorefigures.php?uid=PMC3672097&req=5

pone-0062505-g001: Hypothesis and motivations for the experiments.(A) When subjects discriminated noise-corrupted fat and thin rectangles, noise near real and illusory contours correlated with response. (Modified from a figure courtesy of Jason Gold) (B) The present hypothesis is that correlated noise pixels correspond not to contours but to the surfaces that distinguish the fat and thin shapes–the “key regions”. (C) Component images from Keane, Lu, and Kellman (2007) are shown for the illusory and real conditions along with superimposed fat and thin shapes. There is one component image for the fat response and one for the thin response. Each image is the summation of the average noise fields for correct and incorrect trials. The component images suggest that key region pixels biased a fat and thin response by being light and dark, respectively. The final CIs, which result from a simple subtraction, reveal that noise pixel contrast within the key regions positively correlated with a fat response and negatively correlated with a thin response (NB: Images in 1 C are of opposite polarity to Gold et al. (2000), only because in that study the subtraction sequence was reversed).
Mentions: The neural mechanisms that implement completion include superficial and deep layers of V2 and V1 [1]. Recent work has suggested that the receptive fields for these cells can be revealed behaviorally through a technique termed “classification imaging” [2]–[6]. In an influential study, observers discriminated “fat” and “thin” rectangles, the edges of which were luminance-defined (“real”) or completed (“illusory”; see Figure 1). The rectangles were embedded in a new luminance noise field on each trial and correlations were calculated (across trials) between noise contrast and observer response for each pixel in a noise field. The resulting map of correlations–the classification image–revealed that the noise near illusory contours correlated with response about as much as the noise near real contours. These correlated noise regions were thought to specify the path of completion–the same regions to which cortical cells respond in single-unit studies [7]–[8].

Bottom Line: This pattern arose when pixels immediately adjacent to the discriminated boundaries were excluded from the analysis (Experiment 2) and also when the noise was restricted to the key regions so that the noise never overlapped with the physically visible edges (Experiment 3).Moreover, behavioral receptive fields derived in CI studies do not correspond to contours per se but to filled-in surface regions contained by those contours.The relevance of lightness to two-dimensional shape completion supplies a new constraint for models of object perception.

View Article: PubMed Central - PubMed

Affiliation: Center for Cognitive Science, Rutgers University, New Brunswick, Piscataway, New Jersey, USA. Brian.Keane@gmail.com

ABSTRACT

Background: A classification image (CI) technique has shown that static luminance noise near visually completed contours affects the discrimination of fat and thin Kanizsa shapes. These influential noise regions were proposed to reveal "behavioral receptive fields" of completed contours-the same regions to which early cortical cells respond in neurophysiological studies of contour completion. Here, we hypothesized that 1) influential noise regions correspond to the surfaces that distinguish fat and thin shapes (hereafter, key regions); and 2) key region noise biases a "fat" response to the extent that its contrast polarity (lighter or darker than background) matches the shape's filled-in surface color.

Results: To test our hypothesis, we had observers discriminate fat and thin noise-embedded rectangles that were defined by either illusory or luminance-defined contours (Experiment 1). Surrounding elements ("inducers") caused the shapes to appear either lighter or darker than the background-a process sometimes referred to as lightness induction. For both illusory and luminance-defined rectangles, key region noise biased a fat response to the extent that its contrast polarity (light or dark) matched the induced surface color. When lightness induction was minimized, luminance noise had no consistent influence on shape discrimination. This pattern arose when pixels immediately adjacent to the discriminated boundaries were excluded from the analysis (Experiment 2) and also when the noise was restricted to the key regions so that the noise never overlapped with the physically visible edges (Experiment 3). The lightness effects did not occur in the absence of enclosing boundaries (Experiment 4).

Conclusions: Under noisy conditions, lightness induction alters visually completed shape. Moreover, behavioral receptive fields derived in CI studies do not correspond to contours per se but to filled-in surface regions contained by those contours. The relevance of lightness to two-dimensional shape completion supplies a new constraint for models of object perception.

Show MeSH
Related in: MedlinePlus