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Neural Codes for Shape Perception
Neural Codes for Shape Perception Z Kourtzi, University of Cambridge, Cambridge, UK ã 2015 Elsevier Inc. All rights reserved.
Glossary Multivoxel pattern classification Multivariate analysis of fMRI signals that exploits distributed patterns of neural activity (i.e., weak signals across a pattern of voxels) to classify stimulus representations.
Shape Integration Along the Visual Stream Despite the ease with which we identify objects in complex environments, the computation of meaningful global forms from local image features on the retina is a challenging task for the visual system. Psychophysical and computational studies propose that midlevel vision mechanisms mediate shape perception by combining the output of local orientation detectors to higher-order features (Barlow & Olshausen, 2004; Geisler, Perry, Super, & Gallogly, 2001; Wilson & Wilkinson, 1998). A network of visual areas with selectivity for features of increasing complexity has been implicated in this task. Local image features (e.g., position and orientation) are processed in the primary visual cortex, while complex shapes and object categories (faces, bodies, and places) are represented toward the end of the visual pathway in the temporal cortex (Felleman & Van Essen, 1991; Grill-Spector & Malach, 2004; Reddy & Kanwisher, 2006; Ungerleider & Mishkin, 1982). Neurophysiological studies propose a functional architecture for the integration of global shapes in the human visual cortex. In particular, neurons in V1 and V2 (Hegde & Van Essen, 2000, 2003, 2004; Ito & Komatsu, 2004; Peterhans & von der Heydt, 1993; Smith, Bair, & Movshon, 2002; Smith, Kohn, & Movshon, 2007) have been suggested to compute local orientation signals within the area of their receptive fields and integrate collinear edges into contours within their extended receptive field or through a circuit of local (facilitative and suppressive) and recurrent (feedback from higher areas) interactions (for reviews, see Allman, Miezin, & McGuinness, 1985; Fitzpatrick, 2000; Gilbert, 1992, 1998; Lamme, Super, & Spekreijse, 1998; Murray, Schrater, & Kersten, 2004). Further, global spatial integration of multiple orientation signals has been attributed to V4 neurons that have larger receptive fields than neurons at earlier stages of processing (Desimone & Schein, 1987) and show selectivity for higher-order features of moderate complexity (e.g., curvature and angles) that define shape parts (Gallant, Braun, & Van Essen, 1993; Gallant, Connor, Rakshit, Lewis, & Van Essen, 1996; Kobatake & Tanaka, 1994; Pasupathy & Connor, 1999, 2001, 2002). Finally, information about object parts is converted based on both linear and nonlinear integration mechanisms into sparser representations of complex shapes (multipart configurations) at the posterior inferior temporal cortex (Baker, Behrmann, & Olson, 2002; Brincat & Connor, 2004, 2006; Fujita, Tanaka, Ito, & Cheng,
Brain Mapping: An Encyclopedic Reference
Recurrent processing Processing based on horizontal and feedback connections. Statistical learning Learning of regularities by mere exposure. Ventral cortex The ventral cortex processes object-related information, including shape, color, and texture.
1992; Riesenhuber & Poggio, 1999; Tsunoda, Yamane, Nishizaki, & Tanifuji, 2001). These shape configurations provide the basis for object recognition at more anterior IT regions where neurons selective even for entire objects have been identified (Gross, Rocha-Miranda, & Bender, 1972; Logothetis & Sheinberg, 1996; Rolls & Tovee, 1995; Tanaka, 1996; Tsao, Freiwald, Tootell, & Livingstone, 2006; Young & Yamane, 1992). Human brain imaging (fMRI, EEG, intracranial recordings, and lesion) studies show that global integration processes for global patterns occur at later stages of analysis corresponding to human ventral V4 (Allison, Puce, Spencer, & McCarthy, 1999; Dumoulin & Hess, 2007; Gallant, Shoup, & Mazer, 2000; Ohla, Busch, Dahlem, & Herrmann, 2005; Pei, Pettet, Vildavski, & Norcia, 2005; Wilkinson et al., 2000). Recent work (Ostwald, Lam, Li, & Kourtzi, 2008) using multivoxel pattern analyses demonstrates a continuum of integration processes that convert selectivity for local signals (orientation and position) in early visual areas to selectivity for global form structure in higher occipitotemporal areas. Higher occipitotemporal areas discern differences in global form structure rather than low-level stimulus properties with higher accuracy than early visual areas while relying on information from smaller but more selective neural populations consistent with global pooling mechanisms of local orientation signals. These findings suggest that the human visual system employs a code of increasing efficiency across stages of analysis that is critical for the successful detection and recognition of objects in complex environments.
Adaptive Coding for Shape Perception Typically, in the search for neural codes, we measure responses to input alone (e.g., global shapes and object categories) without taking into account context in space (i.e., scene configuration) or time (i.e., previous experiences with a given object). However, there is accumulating evidence in favor of an adaptive neural code that is dynamically shaped by experience. Specifically, learning is shown to facilitate and shape the neural processes that mediate binding of local elements and parts into objects, recognition of objects across image changes that preserve identity (e.g., position, orientation, and clutter), and selection of behaviorally relevant features for object categorization.
http://dx.doi.org/10.1016/B978-0-12-397025-1.00035-X
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INTRODUCTION TO SYSTEMS | Neural Codes for Shape Perception
Learning to See Objects Evolution and development have been proposed to shape the organization of the visual system and facilitate visual recognition in cluttered scenes (Gilbert, Sigman, & Crist, 2001; Simoncelli & Olshausen, 2001). Recent studies suggest that the primate brain is sensitive to regularities that occur frequently in natural scenes (e.g., orientation similarity in neighboring elements) and has developed a network of connections that mediate integration of object features based on these correlations (Geisler, 2008; Sigman, Cecchi, Gilbert, & Magnasco, 2001). However, long-term experience is not the only means by which visual processes become optimized. Learning through everyday experiences in adulthood has been shown to be a key facilitator in the detection and recognition of targets in cluttered scenes (Brady & Kersten, 2003; Dosher & Lu, 1998; Gilbert et al., 2001; Gold, Bennett, & Sekuler, 1999; Goldstone, 1998; Schyns, Goldstone, & Thibaut, 1998; Sigman & Gilbert, 2000). Observers are shown to learn distinctive target features by using image regularities to integrate relevant object features and by suppressing background noise (Brady & Kersten, 2003; Dosher & Lu, 1998; Gold et al., 1999; Li, Levi, & Klein, 2004). Recent work proposes that long-term experience and short-term training interact to shape the optimization of visual recognition processes. While long-term experience through evolution and development hones the principles of organization that mediate feature grouping for object recognition, short-term training in adulthood may establish new principles for the interpretation of natural scenes. For example, long-term experience with the high prevalence of collinear edges in natural environments (Geisler, 2008; Sigman et al., 2001) has been shown to result in enhanced sensitivity for the detection of collinear contours in clutter. However, short-term training has been shown to alter the behavioral relevance of image regularities that violate the typical principles of contour linking (Geisler, 2008; Sigman et al., 2001; Simoncelli & Olshausen, 2001). Although collinearity is a prevalent principle for perceptual integration in natural scenes, there is recent evidence (Schwarzkopf & Kourtzi, 2008) that the brain can learn to exploit other image regularities (i.e., orthogonal alignments) that typically signify discontinuities for contour linking. Recent studies combining behavioral and brain imaging measurements (Zhang & Kourtzi, 2010) propose two routes to visual learning in clutter with distinct signatures of brain plasticity. These studies show that long-term experience with statistical regularities (i.e., collinearity) may facilitate opportunistic learning (i.e., learning to exploit image cues), while learning to integrate discontinuities (i.e., elements orthogonal to contour paths) entails bootstrap-based training (i.e., learning new features) for the detection of contours in clutter. Learning to integrate collinear contours was shown to simply occur through frequent exposure, generalize across untrained stimulus features, and shape processing in higher occipitotemporal regions implicated in the representation of global forms. In contrast, learning to integrate discontinuities (i.e., elements orthogonal to contour paths) required task-specific training (bootstrap-based learning), was stimulus-dependent, and enhanced processing in intraparietal regions implicated in attention-gated learning. Similarly, recent neuroimaging studies suggest that a ventral cortex region becomes specialized through experience and development for letter integration
and word recognition (Dehaene, Cohen, Sigman, & Vinckier, 2005), while parietal regions are recruited for recognizing words presented in unfamiliar formats (Cohen, Dehaene, Vinckier, Jobert, & Montavont, 2008). Taken together, these findings propose that opportunistic learning of statistical regularities shapes bottom-up object processing in occipitotemporal areas, while learning new features and rules for perceptual integration recruits parietal regions involved in the attentional gating of recognition processes.
Learning Object Category Extensive behavioral work on visual categorization (e.g., Goldstone, Lippa, & Shiffrin, 2001) suggests that the brain learns the relevance of visual features for categorical decisions rather than simply representing physical similarity. That is, learning may reduce object space dimensionality by reweighting feature representations based on their behavioral relevance in the context of a task. Although a large network of brain areas has been implicated in visual category learning, the role of the temporal cortex in the learning and representation of visual categories remains controversial. Recent imaging studies have revealed a distributed pattern of activations for object categories in the temporal cortex (Haxby et al., 2001) including regions specialized for categories of biological importance (e.g., faces, bodies, and places) (Reddy & Kanwisher, 2006). However, some neurophysiological studies propose that the temporal cortex represents primarily the visual similarity between stimuli (Freedman, Riesenhuber, Poggio, & Miller, 2003; Jiang et al., 2007; Op de Beeck, Torfs, & Wagemans, 2008; Op de Beeck, Wagemans, & Vogels, 2001; Thomas, Van Hulle, & Vogels, 2001), while others suggest that it represents learned stimulus categories (Meyers, Freedman, Kreiman, Miller, & Poggio, 2008) and diagnostic stimulus dimensions for categorization (Mirabella et al., 2007; Sigala & Logothetis, 2002). Further, recent work suggests that the representations of object categories in the temporal cortex are modulated by task demands (Koida & Komatsu, 2007) and experience (e.g., Op de Beeck, Baker, DiCarlo, & Kanwisher, 2006; Gillebert, Op de Beeck, Panis, & Wagemans, 2009). Recent brain imaging work combining high-resolution imaging with multivoxel pattern analysis methods provides evidence for neural representations of perceived shape categories that are established by training. First, studies comparing the performance of human observers and pattern classifiers have demonstrated that learning alters the observers’ sensitivity to global forms and the tuning of fMRI activation patterns in visual areas selective for task-relevant features (Zhang, Meeson, Welchman, & Kourtzi, 2010). In particular, training resulted in (a) improved behavioral performance in categorizing global forms embedded in noise as radial or concentric and (b) enhanced amplitude with reduced width of voxel patternbased tuning functions in higher dorsal and ventral visual areas. Increased amplitude after training indicates higher stimulus discriminability that may relate to enhanced neural responses for the preferred stimulus category at the level of large neural populations. Reduced tuning width after training indicates fewer classification mispredictions, suggesting that learning decreases neural responses to nonpreferred stimuli.
INTRODUCTION TO SYSTEMS | Neural Codes for Shape Perception
Thus, these findings suggest that learning of visual patterns is implemented in the human visual cortex by enhancing the response to the preferred stimulus category while reducing the response to nonpreferred stimuli. Second, studies combining multivoxel pattern classification and reverse correlation approaches (Kuai, Levi, & Kourtzi, 2013) have demonstrated that learning optimizes mental templates for categorical decisions by tuning the representation of informative image parts in the higher ventral cortex. Observers were trained to discriminate novel visual forms (i.e., polygons) that were randomly perturbed by noise into two categories. Reverse-correlating behavioral and multivoxel pattern responses with noisy stimulus trials showed that observers learned to integrate information across locations and weigh the discriminative image parts that were critical for shape categorization. Importantly, training enhanced shape processing in human LO that was shown to reflect size-invariant representations of informative image parts. Taken together, these findings demonstrate that learning tunes the representation of image parts that are critical for shape categorization in higher ventral areas. Further, recent imaging work testing for whole brain circuits involved in shape learning proposes that adaptive categorical coding is implemented by interactions between top-down mechanisms related to the formation of rules and local processing of task-relevant object features. For example, recent neuroimaging studies (Li, Ostwald, Giese, & Kourtzi, 2007) using multivariate methods provide evidence that learning shapes feature and object representations in a network of areas with dissociable roles in visual categorization. In particular, observers were trained to categorize dynamic shape configurations based on a single stimulus dimension (form vs. motion) or feature conjunctions. Temporal and parietal areas were shown to encode the perceived similarity in form and motion features, respectively. In contrast, frontal areas and the striatum were shown to represent task-relevant conjunctions of spatiotemporal features critical for forming more complex categorization rules. These findings suggest that neural representations in these areas are shaped by the behavioral relevance of sensory features and previous experience to reflect the perceptual (categorical) rather than the physical similarity between stimuli. This is consistent with neurophysiological evidence for recurrent processes that modulate selectivity for perceptual categories along the behaviorally relevant stimulus dimensions in a top-down manner (Freedman et al., 2003; Mirabella et al., 2007; Smith, Gosselin, & Schyns, 2004) resulting in enhanced selectivity for the relevant stimulus features in the visual areas. Finally, additional evidence for recurrent processing for flexible categorical representations comes from recent work (Li, Mayhew, & Kourtzi, 2009, 2012) showing that category learning shapes decision-related processes in the frontal and higher occipitotemporal regions rather than signal detection or response execution in the primary visual or motor areas. In particular, in prefrontal circuits, learning shapes the estimation of the decision criterion only in the context of the categorization task. In contrast, in higher occipitotemporal regions, the representations of perceived categories are sustained after training independent of the task and may serve as selective readout signals for optimal decisions.
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Summary Investigating the neural mechanisms that mediate our ability to integrate features into global shapes is critical for understanding the building blocks of perception and action. Here, we review evidence for an adaptive neural code that optimizes shape representations through training and experience to support our ability to translate sensory signals to perceptual decisions (i.e., shape detection and categorization). Future challenges involve understanding the high-dimensional code for the recognition of complex objects and the brain circuits that translate this code to decisions and actions.
See also: INTRODUCTION TO ACQUISITION METHODS: Temporal Resolution and Spatial Resolution of fMRI; INTRODUCTION TO ANATOMY AND PHYSIOLOGY: Functional Organization of the Primary Visual Cortex; Topographic Layout of Monkey Extrastriate Visual Cortex; INTRODUCTION TO COGNITIVE NEUROSCIENCE: Category Learning; INTRODUCTION TO SYSTEMS: Expertise and Object Recognition.
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Glossary Multivoxel pattern classification Multivariate analysis of fMRI signals that exploits distributed patterns of neural activity (i.e., weak signals across a pattern of voxels) to classify stimulus representations.
Shape Integration Along the Visual Stream Despite the ease with which we identify objects in complex environments, the computation of meaningful global forms from local image features on the retina is a challenging task for the visual system. Psychophysical and computational studies propose that midlevel vision mechanisms mediate shape perception by combining the output of local orientation detectors to higher-order features (Barlow & Olshausen, 2004; Geisler, Perry, Super, & Gallogly, 2001; Wilson & Wilkinson, 1998). A network of visual areas with selectivity for features of increasing complexity has been implicated in this task. Local image features (e.g., position and orientation) are processed in the primary visual cortex, while complex shapes and object categories (faces, bodies, and places) are represented toward the end of the visual pathway in the temporal cortex (Felleman & Van Essen, 1991; Grill-Spector & Malach, 2004; Reddy & Kanwisher, 2006; Ungerleider & Mishkin, 1982). Neurophysiological studies propose a functional architecture for the integration of global shapes in the human visual cortex. In particular, neurons in V1 and V2 (Hegde & Van Essen, 2000, 2003, 2004; Ito & Komatsu, 2004; Peterhans & von der Heydt, 1993; Smith, Bair, & Movshon, 2002; Smith, Kohn, & Movshon, 2007) have been suggested to compute local orientation signals within the area of their receptive fields and integrate collinear edges into contours within their extended receptive field or through a circuit of local (facilitative and suppressive) and recurrent (feedback from higher areas) interactions (for reviews, see Allman, Miezin, & McGuinness, 1985; Fitzpatrick, 2000; Gilbert, 1992, 1998; Lamme, Super, & Spekreijse, 1998; Murray, Schrater, & Kersten, 2004). Further, global spatial integration of multiple orientation signals has been attributed to V4 neurons that have larger receptive fields than neurons at earlier stages of processing (Desimone & Schein, 1987) and show selectivity for higher-order features of moderate complexity (e.g., curvature and angles) that define shape parts (Gallant, Braun, & Van Essen, 1993; Gallant, Connor, Rakshit, Lewis, & Van Essen, 1996; Kobatake & Tanaka, 1994; Pasupathy & Connor, 1999, 2001, 2002). Finally, information about object parts is converted based on both linear and nonlinear integration mechanisms into sparser representations of complex shapes (multipart configurations) at the posterior inferior temporal cortex (Baker, Behrmann, & Olson, 2002; Brincat & Connor, 2004, 2006; Fujita, Tanaka, Ito, & Cheng,
Brain Mapping: An Encyclopedic Reference
Recurrent processing Processing based on horizontal and feedback connections. Statistical learning Learning of regularities by mere exposure. Ventral cortex The ventral cortex processes object-related information, including shape, color, and texture.
1992; Riesenhuber & Poggio, 1999; Tsunoda, Yamane, Nishizaki, & Tanifuji, 2001). These shape configurations provide the basis for object recognition at more anterior IT regions where neurons selective even for entire objects have been identified (Gross, Rocha-Miranda, & Bender, 1972; Logothetis & Sheinberg, 1996; Rolls & Tovee, 1995; Tanaka, 1996; Tsao, Freiwald, Tootell, & Livingstone, 2006; Young & Yamane, 1992). Human brain imaging (fMRI, EEG, intracranial recordings, and lesion) studies show that global integration processes for global patterns occur at later stages of analysis corresponding to human ventral V4 (Allison, Puce, Spencer, & McCarthy, 1999; Dumoulin & Hess, 2007; Gallant, Shoup, & Mazer, 2000; Ohla, Busch, Dahlem, & Herrmann, 2005; Pei, Pettet, Vildavski, & Norcia, 2005; Wilkinson et al., 2000). Recent work (Ostwald, Lam, Li, & Kourtzi, 2008) using multivoxel pattern analyses demonstrates a continuum of integration processes that convert selectivity for local signals (orientation and position) in early visual areas to selectivity for global form structure in higher occipitotemporal areas. Higher occipitotemporal areas discern differences in global form structure rather than low-level stimulus properties with higher accuracy than early visual areas while relying on information from smaller but more selective neural populations consistent with global pooling mechanisms of local orientation signals. These findings suggest that the human visual system employs a code of increasing efficiency across stages of analysis that is critical for the successful detection and recognition of objects in complex environments.
Adaptive Coding for Shape Perception Typically, in the search for neural codes, we measure responses to input alone (e.g., global shapes and object categories) without taking into account context in space (i.e., scene configuration) or time (i.e., previous experiences with a given object). However, there is accumulating evidence in favor of an adaptive neural code that is dynamically shaped by experience. Specifically, learning is shown to facilitate and shape the neural processes that mediate binding of local elements and parts into objects, recognition of objects across image changes that preserve identity (e.g., position, orientation, and clutter), and selection of behaviorally relevant features for object categorization.
http://dx.doi.org/10.1016/B978-0-12-397025-1.00035-X
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Learning to See Objects Evolution and development have been proposed to shape the organization of the visual system and facilitate visual recognition in cluttered scenes (Gilbert, Sigman, & Crist, 2001; Simoncelli & Olshausen, 2001). Recent studies suggest that the primate brain is sensitive to regularities that occur frequently in natural scenes (e.g., orientation similarity in neighboring elements) and has developed a network of connections that mediate integration of object features based on these correlations (Geisler, 2008; Sigman, Cecchi, Gilbert, & Magnasco, 2001). However, long-term experience is not the only means by which visual processes become optimized. Learning through everyday experiences in adulthood has been shown to be a key facilitator in the detection and recognition of targets in cluttered scenes (Brady & Kersten, 2003; Dosher & Lu, 1998; Gilbert et al., 2001; Gold, Bennett, & Sekuler, 1999; Goldstone, 1998; Schyns, Goldstone, & Thibaut, 1998; Sigman & Gilbert, 2000). Observers are shown to learn distinctive target features by using image regularities to integrate relevant object features and by suppressing background noise (Brady & Kersten, 2003; Dosher & Lu, 1998; Gold et al., 1999; Li, Levi, & Klein, 2004). Recent work proposes that long-term experience and short-term training interact to shape the optimization of visual recognition processes. While long-term experience through evolution and development hones the principles of organization that mediate feature grouping for object recognition, short-term training in adulthood may establish new principles for the interpretation of natural scenes. For example, long-term experience with the high prevalence of collinear edges in natural environments (Geisler, 2008; Sigman et al., 2001) has been shown to result in enhanced sensitivity for the detection of collinear contours in clutter. However, short-term training has been shown to alter the behavioral relevance of image regularities that violate the typical principles of contour linking (Geisler, 2008; Sigman et al., 2001; Simoncelli & Olshausen, 2001). Although collinearity is a prevalent principle for perceptual integration in natural scenes, there is recent evidence (Schwarzkopf & Kourtzi, 2008) that the brain can learn to exploit other image regularities (i.e., orthogonal alignments) that typically signify discontinuities for contour linking. Recent studies combining behavioral and brain imaging measurements (Zhang & Kourtzi, 2010) propose two routes to visual learning in clutter with distinct signatures of brain plasticity. These studies show that long-term experience with statistical regularities (i.e., collinearity) may facilitate opportunistic learning (i.e., learning to exploit image cues), while learning to integrate discontinuities (i.e., elements orthogonal to contour paths) entails bootstrap-based training (i.e., learning new features) for the detection of contours in clutter. Learning to integrate collinear contours was shown to simply occur through frequent exposure, generalize across untrained stimulus features, and shape processing in higher occipitotemporal regions implicated in the representation of global forms. In contrast, learning to integrate discontinuities (i.e., elements orthogonal to contour paths) required task-specific training (bootstrap-based learning), was stimulus-dependent, and enhanced processing in intraparietal regions implicated in attention-gated learning. Similarly, recent neuroimaging studies suggest that a ventral cortex region becomes specialized through experience and development for letter integration
and word recognition (Dehaene, Cohen, Sigman, & Vinckier, 2005), while parietal regions are recruited for recognizing words presented in unfamiliar formats (Cohen, Dehaene, Vinckier, Jobert, & Montavont, 2008). Taken together, these findings propose that opportunistic learning of statistical regularities shapes bottom-up object processing in occipitotemporal areas, while learning new features and rules for perceptual integration recruits parietal regions involved in the attentional gating of recognition processes.
Learning Object Category Extensive behavioral work on visual categorization (e.g., Goldstone, Lippa, & Shiffrin, 2001) suggests that the brain learns the relevance of visual features for categorical decisions rather than simply representing physical similarity. That is, learning may reduce object space dimensionality by reweighting feature representations based on their behavioral relevance in the context of a task. Although a large network of brain areas has been implicated in visual category learning, the role of the temporal cortex in the learning and representation of visual categories remains controversial. Recent imaging studies have revealed a distributed pattern of activations for object categories in the temporal cortex (Haxby et al., 2001) including regions specialized for categories of biological importance (e.g., faces, bodies, and places) (Reddy & Kanwisher, 2006). However, some neurophysiological studies propose that the temporal cortex represents primarily the visual similarity between stimuli (Freedman, Riesenhuber, Poggio, & Miller, 2003; Jiang et al., 2007; Op de Beeck, Torfs, & Wagemans, 2008; Op de Beeck, Wagemans, & Vogels, 2001; Thomas, Van Hulle, & Vogels, 2001), while others suggest that it represents learned stimulus categories (Meyers, Freedman, Kreiman, Miller, & Poggio, 2008) and diagnostic stimulus dimensions for categorization (Mirabella et al., 2007; Sigala & Logothetis, 2002). Further, recent work suggests that the representations of object categories in the temporal cortex are modulated by task demands (Koida & Komatsu, 2007) and experience (e.g., Op de Beeck, Baker, DiCarlo, & Kanwisher, 2006; Gillebert, Op de Beeck, Panis, & Wagemans, 2009). Recent brain imaging work combining high-resolution imaging with multivoxel pattern analysis methods provides evidence for neural representations of perceived shape categories that are established by training. First, studies comparing the performance of human observers and pattern classifiers have demonstrated that learning alters the observers’ sensitivity to global forms and the tuning of fMRI activation patterns in visual areas selective for task-relevant features (Zhang, Meeson, Welchman, & Kourtzi, 2010). In particular, training resulted in (a) improved behavioral performance in categorizing global forms embedded in noise as radial or concentric and (b) enhanced amplitude with reduced width of voxel patternbased tuning functions in higher dorsal and ventral visual areas. Increased amplitude after training indicates higher stimulus discriminability that may relate to enhanced neural responses for the preferred stimulus category at the level of large neural populations. Reduced tuning width after training indicates fewer classification mispredictions, suggesting that learning decreases neural responses to nonpreferred stimuli.
INTRODUCTION TO SYSTEMS | Neural Codes for Shape Perception
Thus, these findings suggest that learning of visual patterns is implemented in the human visual cortex by enhancing the response to the preferred stimulus category while reducing the response to nonpreferred stimuli. Second, studies combining multivoxel pattern classification and reverse correlation approaches (Kuai, Levi, & Kourtzi, 2013) have demonstrated that learning optimizes mental templates for categorical decisions by tuning the representation of informative image parts in the higher ventral cortex. Observers were trained to discriminate novel visual forms (i.e., polygons) that were randomly perturbed by noise into two categories. Reverse-correlating behavioral and multivoxel pattern responses with noisy stimulus trials showed that observers learned to integrate information across locations and weigh the discriminative image parts that were critical for shape categorization. Importantly, training enhanced shape processing in human LO that was shown to reflect size-invariant representations of informative image parts. Taken together, these findings demonstrate that learning tunes the representation of image parts that are critical for shape categorization in higher ventral areas. Further, recent imaging work testing for whole brain circuits involved in shape learning proposes that adaptive categorical coding is implemented by interactions between top-down mechanisms related to the formation of rules and local processing of task-relevant object features. For example, recent neuroimaging studies (Li, Ostwald, Giese, & Kourtzi, 2007) using multivariate methods provide evidence that learning shapes feature and object representations in a network of areas with dissociable roles in visual categorization. In particular, observers were trained to categorize dynamic shape configurations based on a single stimulus dimension (form vs. motion) or feature conjunctions. Temporal and parietal areas were shown to encode the perceived similarity in form and motion features, respectively. In contrast, frontal areas and the striatum were shown to represent task-relevant conjunctions of spatiotemporal features critical for forming more complex categorization rules. These findings suggest that neural representations in these areas are shaped by the behavioral relevance of sensory features and previous experience to reflect the perceptual (categorical) rather than the physical similarity between stimuli. This is consistent with neurophysiological evidence for recurrent processes that modulate selectivity for perceptual categories along the behaviorally relevant stimulus dimensions in a top-down manner (Freedman et al., 2003; Mirabella et al., 2007; Smith, Gosselin, & Schyns, 2004) resulting in enhanced selectivity for the relevant stimulus features in the visual areas. Finally, additional evidence for recurrent processing for flexible categorical representations comes from recent work (Li, Mayhew, & Kourtzi, 2009, 2012) showing that category learning shapes decision-related processes in the frontal and higher occipitotemporal regions rather than signal detection or response execution in the primary visual or motor areas. In particular, in prefrontal circuits, learning shapes the estimation of the decision criterion only in the context of the categorization task. In contrast, in higher occipitotemporal regions, the representations of perceived categories are sustained after training independent of the task and may serve as selective readout signals for optimal decisions.
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Summary Investigating the neural mechanisms that mediate our ability to integrate features into global shapes is critical for understanding the building blocks of perception and action. Here, we review evidence for an adaptive neural code that optimizes shape representations through training and experience to support our ability to translate sensory signals to perceptual decisions (i.e., shape detection and categorization). Future challenges involve understanding the high-dimensional code for the recognition of complex objects and the brain circuits that translate this code to decisions and actions.
See also: INTRODUCTION TO ACQUISITION METHODS: Temporal Resolution and Spatial Resolution of fMRI; INTRODUCTION TO ANATOMY AND PHYSIOLOGY: Functional Organization of the Primary Visual Cortex; Topographic Layout of Monkey Extrastriate Visual Cortex; INTRODUCTION TO COGNITIVE NEUROSCIENCE: Category Learning; INTRODUCTION TO SYSTEMS: Expertise and Object Recognition.
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