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EDITORIAL FOCUS
in this issue of Journal of Neurophysiology (p. 862–881) provide novel evidence that the perception of these second-order texture patterns entails additional processing in ventral and dorsal extrastriate areas beyond the first stages of visual analysis in the primary visual cortex (V1).
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for a review) have proposed different processing mechanisms for patterns that differ in their luminance intensity (first-order patterns) and texture patterns that cannot be detected by linear filters based on average luminance changes (second-order patterns). In particular, second-order processing mechanisms are tuned for orientation, similar to first-order mechanisms but have greater bandwidth, and there are weak or incomplete interactions between first– and second–order patterns. Based on these findings, Larsson et al. sought to identify brain regions with differential orientation selectivity for second-order (orientation- or contrast-defined patterns) rather than first-order patterns (luminance-defined patterns) that may support selective processing for second-order textures. Using an elegant orientation-selective adaptation paradigm in psychophysical and fMRI (functional magnetic resonance imaging) measurements, Larsson et al. tested for 1) sensitivity to orientation differences (vertical vs. horizontal) between an adapting and test stimulus and 2) cross-modal adaptation when the adapting stimulus was a first-order pattern and the test stimulus a second-order pattern. Adaptation to first- or second-order stimuli increased the observers' sensitivity in detecting a target pattern with different orientation than the adapted stimulus, consistent with classic behavioral adaptation effects. Similarly, fMRI responses in primary and extrastriate visual areas increased for test stimuli that differed in their orientation from the adapting stimulus. Interestingly, these adaptation effects were of comparable magnitude across visual occipitotemporal areas for first-order patterns, whereas orientation-selective adaptation for second-order patterns was larger in several higher extrastriate areas than in V1. What are the implications of these fMRI adaptation effects for understanding the neural mechanisms that mediate selective processing of second-order stimuli in the human brain?
fMRI adaptation capitalizes on neural adaptation after prolonged or repeated exposure to a stimulus and has been used extensively as a sensitive tool for discerning neuronal subpopulations selective for different stimulus attributes but intermingled within the measured voxels (e.g., Grill-Spector and Malach 2001). This method extends beyond the limited spatial resolution of the conventional fMRI paradigms that average across such populations. However, recent studies call for cautious interpretation of fMRI adaptation effects and their implications for neural processing across visual areas (Boynton and Finney 2003; Tolias et al. 2005). Larsson et al. demonstrate that fMRI adaptation is a powerful tool for investigating selectivity to a visual attribute (i.e., orientation) across visual areas when the stimuli used elicit strong fMRI responses but prevent neural saturation in these areas, and the observers' attention is controlled across stimulus conditions. Consistent with previous physiological studies, their findings show orientation selective adaptation for both first-order and second-order stimuli in primary and extrastriate areas, suggesting that the processing of second-order textures is distributed across visual areas rather than specialized within a single cortical region. The similarity in the magnitude of fMRI adaptation across visual areas for first-order stimuli suggests that these effects could be accounted for by orientation selective adaptation in V1 neurons that is propagated to downstream extrastriate areas. In contrast, the stronger orientation selective adaptation in extrastriate areas than V1, for second order stimuli, suggest a second stage of processing beyond the linear filter analysis in the primary visual cortex. Furthermore, the lack of consistent adaptation effects for the cross-modal condition support the hypothesis that different mechanisms are involved in the processing of first-order and second-order textures.
Based on these findings, Larsson et al. propose that the role of higher extrastriate areas in texture perception is to perform additional analysis (second stage filter) by pooling the output of the first-stage filters in primary visual cortex after rectification by the spiking threshold of V1 neurons, consistent with recent computational models for texture perception (Landy and Graham 2004 for a review). An alternative interpretation suggests that extrastriate areas contribute to texture perception by extracting salient surface regions (Stanley and Rubin 2003); that is, homogeneous regions of repeated structures in the case of texture patterns. This analysis is then evaluated by neurons in the primary visual cortex that may enhance texture boundaries (first- or second-order) defining a figure that pops-out from the background (i.e., the zebras in Vasarely's painting) compared with texture patterns that belong to the background (Fitzpatrick 2000; Lamme et al. 1998). This interpretation is consistent with the role of middle-level surface representations in the perception of textured figures and their surrounds (He and Nakayama 1994).
In sum, Larsson et al. provide novel insights in understanding the neural mechanisms that mediate the analysis of texture patterns in natural images. This work provides the foundations for investigating the mechanisms that underlie the analysis of "the stuff (e.g., wood, metal, plastic) that objects are made of" (Bergen and Adelson 1988; Fleming et al. 2003; Heeger and Bergen 1995; Portilla and Simoncelli 2000) and our perception of 3D objects defined by texture (Li and Zaidi 2000; Todd 2004) in combination with other depth cues (Knill 2003). Finally, recent work on multimodal object perception (Amedi et al. 2001) raises novel questions in understanding the role of texture in our unified visual and haptic experiences of objects that is critical for successful actions and interactions in our complex environments.
University of Birmingham, School of Psychology, Edgbaston, Birmingham, United Kingdom
Address for reprint requests and other correspondence: Z. Kourtzi, University of Birmingham, School of Psychology, Edgbaston, Birmingham, B15 2TT, UK (E-mail: z.kourtzi{at}bham.ac.uk)
REFERENCES
Amedi A, Malach R, Hendler T, Peled S, and Zohary E. Visuo-haptic object-related activation in the ventral visual pathway. Nat Neurosci 4: 324–330, 2001.[CrossRef][ISI][Medline]
Bergen JR and Adelson EH. Early vision and texture perception. Nature 333: 363–364, 1988.[CrossRef][Medline]
Boynton GM and Finney EM. Orientation-specific adaptation in human visual cortex. J Neurosci 23: 8781–8787, 2003.
Fitzpatrick D. Seeing beyond the receptive field in primary visual cortex. Curr Opin Neurobiol 10: 438–443, 2000.[CrossRef][ISI][Medline]
Fleming RW, Dror RO, and Adelson EH. Real-world illumination and the perception of surface reflectance properties. J Vis 3: 347–368, 2003.[Medline]
Grill-Spector K and Malach R. fMR-adaptation: a tool for studying the functional properties of human cortical neurons. Acta Psychol (Amst) 107: 293–321, 2001.[CrossRef][Medline]
He ZJ and Nakayama K. Perceiving textures: beyond filtering. Vision Res 34: 151–162, 1994.[Medline]
Heeger DJ and Bergen JR. Pyramid-based texture analysis/synthesis. Proc SIGGRAPH: 229–238, 1995.
Knill DC. Mixture models and the probabilistic structure of depth cues. Vision Res 43: 831–854, 2003.[Medline]
Lamme VA, Super H, and Spekreijse H. Feedforward, horizontal, and feedback processing in the visual cortex. Curr Opin Neurobiol 8: 529–535, 1998.[CrossRef][ISI][Medline]
Landy MS and Graham N. Visual perception of texture. In: The Visual Neurosciences, edited by Chalupa L.M. Werner JS Cambridge: MIT Press, 2004, p. 1106–1118.
Larsson J, Landy MS, and Heeger DJ. Orientation-selective adaptation to first- and second-order patterns in human visual cortex. J Neurophysiol 95: 862–881, 2006.
Li A and Zaidi Q. Perception of three-dimensional shape from texture is based on patterns of oriented energy. Vision Res 40: 217–242, 2000.[CrossRef][ISI][Medline]
Portilla J and Simoncelli EP. A Parametric Texture Model Based on Joint Statistics of Complex Wavelet Coefficients. Internat J Comp Vis 40: 49–71, 2000.[CrossRef]
Stanley DA and Rubin N. fMRI activation in response to illusory contours and salient regions in the human lateral occipital complex. Neuron 37: 323–331, 2003.[CrossRef][ISI][Medline]
Todd JT. The visual perception of 3D shape. Trends Cogn Sci 8: 115–121, 2004.[Medline]
Tolias AS, Keliris GA, Smirnakis SM, and Logothetis NK. Neurons in macaque area V4 acquire directional tuning after adaptation to motion stimuli. Nat Neurosci 8: 591–593, 2005.[CrossRef][ISI][Medline]
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