Adaptation across the Cortical Hierarchy: Low-Level Curve Adaptation Affects High-Level Facial-Expression Judgments

Xu, Hong; Dayan, Peter; Lipkin, Richard M.; Niu, Qian

doi:10.1523/jneurosci.0182-08.2008

Cited by 100 publications

(149 citation statements)

References 59 publications

Supporting

Mentioning

122

Contrasting

Order By: Relevance

“…However, the average time from the offset of the second line in a trial to the onset of the first line in the next trial was 6.03 s (including a 2-s intertrial interval), much longer than the 0.5-s interstimulus interval between the two lines within a trial. Given the exponential decay of the tilt aftereffect (40,41), the cross-trial adaptation should be negligible compared with the within-trial adaptation. However, the observed backward aftereffect was as large as the forward aftereffect, ruling out the cross-trial interpretation.…”

Section: Resultsmentioning

confidence: 99%

Visual perception as retrospective Bayesian decoding from high- to low-level features

Ding

Cueva

Tsodyks

et al. 2017

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

View full text Add to dashboard Cite

When a stimulus is presented, its encoding is known to progress from low-to high-level features. How these features are decoded to produce perception is less clear, and most models assume that decoding follows the same low-to high-level hierarchy of encoding. There are also theories arguing for global precedence, reversed hierarchy, or bidirectional processing, but they are descriptive without quantitative comparison with human perception. Moreover, observers often inspect different parts of a scene sequentially to form overall perception, suggesting that perceptual decoding requires working memory, yet few models consider how working-memory properties may affect decoding hierarchy. We probed decoding hierarchy by comparing absolute judgments of single orientations and relative/ordinal judgments between two sequentially presented orientations. We found that lower-level, absolute judgments failed to account for higher-level, relative/ordinal judgments. However, when ordinal judgment was used to retrospectively decode memory representations of absolute orientations, striking aspects of absolute judgments, including the correlation and forward/backward aftereffects between two reported orientations in a trial, were explained. We propose that the brain prioritizes decoding of higher-level features because they are more behaviorally relevant, and more invariant and categorical, and thus easier to specify and maintain in noisy working memory, and that more reliable higher-level decoding constrains less reliable lower-level decoding.Bayesian prior | interreport correlation | bidirectional tilt aftereffect | efficient coding | adaptation theory V isual stimuli evoke neuronal responses (a process termed encoding), which lead to our perceptual estimation of the stimuli (decoding). Experimental studies have firmly established that encoding is hierarchical, progressing from lower-level representations of simpler and less invariant features to higher-level representations of more complex and invariant features along visual pathways (1). Researchers have also studied decoding by using models to relate neuronal responses to perceptual estimation. Most models posit, explicitly or implicitly, that decoding follows the same low-to high-level hierarchy, often in the form of what we call the absolute-to-relative assumption (2-6). For example, these models may decode V1 responses to a line into a perceived orientation of, say 51.2°(or a distribution around it). Psychophysically, this is termed an absolute judgment. To determine the relationship between two lines, the models first decode each orientation separately and the two resulting absolute orientations are then compared. For instance, the two absolute orientations may be subtracted to obtain the angle between the lines (relative orientation), or the sign of the difference may be used to determine whether the second line is clockwise or counterclockwise from the first (ordinal orientation discrimination). Absolute orientation of a single line is a simpler, less invariant, lower-...

show abstract

Section: Resultsmentioning

confidence: 99%

Visual perception as retrospective Bayesian decoding from high- to low-level features

Ding

Cueva

Tsodyks

et al. 2017

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

View full text Add to dashboard Cite

show abstract

“…Xu, Dayan, Lipkin, and Qian (2008) recently demonstrated that adaptation to concave and convex lines was sufficient to induce aftereffects for perceptions of happiness and sadness when judging the emotional expression of faces, suggesting that adaptation to different mouth shapes can induce aftereffects, at least for perceptions of emotional expressions. However, some models of face perception would suggest that dissociable cognitive routes underpin perceptions of lip speech and emotional expressions (e.g., Bruce & Young, 1986).…”

mentioning

confidence: 99%

Adaptation to different mouth shapes influences visual perception of ambiguous lip speech

Jones

Feinberg

Bestelmeyer

et al. 2010

Psychonomic Bulletin & Review

View full text Add to dashboard Cite

522Many studies have used visual adaptation to investigate whether recent visual experience influences face perception (e.g., Calder, Jenkins, Cassel, & Clifford, 2008;DeBruine, Jones, Unger, Little, & Feinberg, 2007;Little, DeBruine, & Jones, 2005;Rhodes, Jeffery, Watson, Clifford, & Nakayama, 2003;Webster, Kaping, Mizokami, & Duhamel, 2004;Webster & MacLin, 1999). For example, adaptation to faces with expanded feature spacing causes the feature spacing of unmanipulated faces to appear contracted, whereas adaptation to faces with contracted feature spacing causes the feature spacing of unmanipulated faces to appear expanded (Webster & MacLin, 1999). Other studies have shown that adaptation to faces with either expanded or contracted feature spacing causes the adapted feature spacing to appear more normal and more attractive (Rhodes et al., 2003). This effect of adaptation on attractiveness judgments is thought to reflect the correlation between perceived normality and attractiveness (DeBruine et al., 2007;Rhodes et al., 2003).The effects of visual adaptation on face perception occur when the same individuals (see, e.g., Yamashita, Hardy, De Valois, & Webster, 2005) and when different individuals (see, e.g., Rhodes et al., 2003;Yamashita et al., 2005) are shown at adaptation and test. Furthermore, aftereffects for normality and attractiveness judgments of faces occur when the faces shown during the adaptation and test phases differ in size (e.g., Bestelmeyer et al., 2008;Little et al., 2005) or in orientation (e.g., Jeffery, Rhodes, & Busey, 2006;Rhodes et al., 2003), suggesting that face aftereffects reflect adaptation of neurons that code high-level aspects of faces, rather than low-level (e.g. University of Stirling, Stirling, ScotlandWe investigated the effects of adaptation to mouth shapes associated with different spoken sounds (sustained / / or / /) on visual perception of lip speech. Participants were significantly more likely to label ambiguous faces on an / /-to-/ / continuum as saying / / following adaptation to / / mouth shapes than they were in a preadaptation test. By contrast, participants were significantly less likely to label the ambiguous faces as saying / / following adaptation to / / mouth shapes than they were in a preadaptation test. The magnitude of these aftereffects was equivalent when the same individual was shown in the adaptation and test phases of the experiment and when different individuals were presented in the adaptation and test phases. These findings present novel evidence that adaptation to natural variations in facial appearance influences face perception, and they extend previous research on face aftereffects to visual perception of lip speech.

show abstract

“…Little is known about this issue, to the best of our knowledge. Only one very recent study [23] investigated whether low-level curve adaptation affects high-level facial expression judgment. This issue is however crucial to theoretically understand neural coding and computation mechanism, the better understanding for cross-level adaptation interaction will illuminates cross-hierarchy neural response transmission mechanism and implicate how the visual system integrates the complex stimuli from basic visual property.…”

Section: Introductionmentioning

confidence: 99%

The Influence of a Low-Level Color or Figure Adaptation on a High-Level Face Perception

Miao

Shinomori

Zhang

2010

IEICE Trans. Inf. & Syst.

View full text Add to dashboard Cite

SUMMARYVisual adaptation is a universal phenomenon associated with human visual system. This adaptation affects not only the perception of low-level visual systems processing color, motion, and orientation, but also the perception of high-level visual systems processing complex visual patterns, such as facial identity and expression. Although it remains unclear for the mutual interaction mechanism between systems at different levels, this issue is the key to understand the hierarchical neural coding and computation mechanism. Thus, we examined whether the low-level adaptation influences on the high-level aftereffect by means of cross-level adaptation paradigm (i.e. color, figure adaptation versus facial identity adaptation). We measured the identity aftereffects within the real face test images on real face, color chip and figure adapting conditions. The cross-level mutual influence was evaluated by the aftereffect size among different adapting conditions. The results suggest that the adaptation to color and figure contributes to the high-level facial identity aftereffect. Besides, the real face adaptation obtained the significantly stronger aftereffect than the color chip or the figure adaptation. Our results reveal the possibility of cross-level adaptation propagation and implicitly indicate a high-level holistic facial neural representation. Based on these results, we discussed the theoretical implication of cross-level adaptation propagation for understanding the hierarchical sensory neural systems. key words: human visual system, low level neural representation, highlevel neural representation, adaptation paradigm, face perception

show abstract

Adaptation across the Cortical Hierarchy: Low-Level Curve Adaptation Affects High-Level Facial-Expression Judgments

Cited by 100 publications

References 59 publications

Visual perception as retrospective Bayesian decoding from high- to low-level features

Visual perception as retrospective Bayesian decoding from high- to low-level features

Adaptation to different mouth shapes influences visual perception of ambiguous lip speech

The Influence of a Low-Level Color or Figure Adaptation on a High-Level Face Perception

Contact Info

Product

Resources

About