Dyslexia is a severe and persistent reading and spelling disorder caused by impairment to manipulate speech sounds. Here, we combine functional magnetic resonance brain imaging with multi-voxel pattern analysis and functional and structural connectivity analysis to disentangle whether dyslexics' phonological deficits are caused by poor quality of the phonetic representations or by difficulties in accessing intact phonetic representations. We show that phonetic representations are hosted bilaterally in primary and secondary auditory cortices, and that their neural quality is intact in adults with dyslexia. However, the functional and structural connectivity between bilateral auditory cortices and left inferior frontal gyrus (a region involved in higher-level phonological processing) is significantly hampered in dyslexics, suggesting deficient access to otherwise intact phonetic representations.Speech perception involves the mapping of spectrally complex and rapidly changing acoustic signals onto discrete and abstract phonetic sound categories or phonemes (1). Developmental dyslexia is a hereditary neurological disorder characterized by severe and persistent reading and/or spelling impairments (2). Individuals with dyslexia perform poorly on tasks that require phonological awareness, verbal short-term memory and lexical access. Performance on these phonological tasks predicts reading acquisition in both normal and dyslexic readers (3). One view is that success on these tasks reflects the quality of underlying phonological (phonetic) representations (4) and that these representations of speech sounds are distorted or less well specified in individuals with dyslexia (5). An alternative view holds that representations are intact, but access to the representations is problematic in people with dyslexia (6, 7). Here, we combine functional magnetic resonance imaging (fMRI) with multi-voxel pattern analysis (MVPA) (8-10) and functional and structural connectivity analysis to disentangle whether dyslexia is caused by poor quality of the phonetic representation or by difficulties in accessing an intact phonetic representation.We collected whole-brain functional images in 23 adults with a diagnosis of dyslexia and 22 matched normal readers (Table S1, 11-13), while they listened to different versions of four sublexical speech sounds (Fig. S1) and performed an easy phoneme discrimination task. The selection of stimuli allowed us to investigate both vowel and stop consonant discrimination, which relies on spectral versus spectrotemporal acoustic feature processing, respectively. If dyslexia is related to a deficit in the quality of phonetic representations, then we expect that the neural representations would be less robust and distinct in individuals with dyslexia than in normal readers. Given dyslexics' particular problems processing temporal cues, such as those involved in consonant discrimination (11), we expected the most prominent group differences for neural patterns distinguishing between consonants.We analyzed...

Visual object recognition relies critically on learning. However, little is known about the effect of object learning in human visual cortex, and in particular how the spatial distribution of training effects relates to the distribution of object and face selectivity across the cortex before training. We scanned human subjects with high-resolution functional magnetic resonance imaging (fMRI) while they viewed novel object classes, both before and after extensive training to discriminate between exemplars within one of these object classes. Training increased the strength of the response in visual cortex to trained objects compared with untrained objects. However, training did not simply induce a uniform increase in the response to trained objects: the magnitude of this training effect varied substantially across subregions of extrastriate cortex, with some showing a twofold increase in response to trained objects and others (including the right fusiform face area) showing no significant effect of training. Furthermore, the spatial distribution of training effects could not be predicted from the spatial distribution of either pretrained responses or face selectivity. Instead, training changed the spatial distribution of activity across the cortex. These findings support a dynamic view of the ventral visual pathway in which the cortical representation of an object category is continuously modulated by experience.

articlesThe capacity to categorize stimuli is fundamental to all living organisms 1,2 . Theories of categorization agree upon the importance of the similarity between stimuli to account for many aspects of categorization performance [3][4][5] . However, it is not straightforward to compute the degree of similarity between stimuli that can vary across a high number of dimensions, like complex shapes. Fortunately, the similarities among a set of complex stimuli can often be described in a more compact way [6][7][8] . Indeed, stimuli from many behaviorally relevant sets can be represented in a low-dimensional representation space in which the proximity between stimuli is related to their similarity. For example, by presenting the randomly ordered shapes of Fig. 1d in a particular order (Fig. 1a-c), the similarities can be easily described by a twodimensional square-like configuration. Several behavioral studies that have varied complex shape differences parametrically revealed that primates are able to represent the similarities between shapes in a low-dimensional representation space without ever seeing these stimuli in their parametric configuration 9-12 .Here we aim to study directly the neural basis of these lowdimensional representation spaces. Object recognition and categorization in macaques is thought to depend on the inferotemporal cortex (IT) 13,14 . Single IT neurons are selective for moderately complex object features 15 , but several studies have found little relationship between the similarities between complex objects and the responses of single IT neurons 16,17 . However, one needs to manipulate shape similarity parametrically to investigate how the responses of IT neurons to complex stimuli are related to the proximity of these stimuli in a low-dimensional space. Thus, we investigated whether the response pattern across a population of IT neurons can reveal a low-dimensional and faithful representation of shape similarity using parameterized shapes. Behavioral studies with parameterized shapes have shown that the similarities among these complex stimuli can be represented using a low number of dimensions. Using psychophysical measurements and single-cell recordings in macaque inferotemporal (IT) cortex, we found an agreement between low-dimensional parametric configurations of shapes and the representation of shape similarity at the behavioral and neuronal level. The shape configurations, computed from both the perceived and neuron-based similarities, revealed a low number of dimensions and contained the same stimulus order as the parametric configurations. However, at a metric level, the behavioral and neural representations deviated consistently from the parametric configurations. These findings suggest an ordinally faithful but metrically biased representation of shape similarity in IT.As the analysis of the visual input in the visual system is highly nonlinear, the neuronal representation space could deviate from the configurations in parameter space in several ways. Previous psychophysical stu...

Recent findings in dorsal visual stream areas and computational work raise the question whether neurons at the end station of the ventral visual stream can code for stimulus position. The authors provide the first detailed, quantitative data on the spatial sensitivity of neurons in the anterior part of the inferior temporal cortex (area TE) in awake, fixating monkeys. They observed a large variation in receptive field (RF) size (ranging from 2.8 degrees to 26 degrees ). TE neurons differed in their optimal position, with a bias toward the foveal position. Moreover, the RF profiles of most TE neurons could be fitted well with a two-dimensional Gaussian function. Most neurons had only one region of high sensitivity and showed a smooth decline in sensitivity toward more distal positions. In addition, the authors investigated some of the possible determinants of such spatial sensitivity. First, testing with low-pass filtered versions of the stimuli revealed that the general preference for the foveal position and the size of the RFs was not due simply to TE neurons receiving input with a lower spatial resolution at more eccentric positions. The foveal position was still preferred after intense low-pass filtering. Second, although an increase in stimulus size consistently broadened spatial sensitivity profiles, it did not change the qualitative features of these profiles. Moreover, size selectivity of TE neurons was generally position invariant. Overall, the results suggest that TE neurons can code for the position of stimuli in the central region of the visual field.

The mammalian visual system contains an extensive web of feedback connections projecting from “higher” cortical areas to “lower” areas including primary visual cortex. Although multiple theories have been proposed, the role of these connections in perceptual processing is not understood. Here we report a surprising new phenomenon not predicted by prior theories of feedback: the pattern of fMRI response in human foveal retinotopic cortex contains information about objects presented in the periphery, far away from the fovea. This information is position invariant, correlated with perceptual discrimination accuracy, and found only in foveal, not peripheral, retinotopic cortex. Our data cannot be explained by differential eye movements, activation from the fixation cross, or spillover activation from peripheral retinotopic cortex or from LOC. Instead, our findings indicate that position-invariant object information from higher cortical areas is fed back to foveal retinotopic cortex, enhancing task performance.

Neuroimaging research over the past decade has revealed a detailed picture of the functional organization of the human brain. Here we focus on two fundamental questions that are raised by the detailed mapping of sensory and cognitive functions and illustrate these questions with findings from the object-vision pathway. First, are functionally specific regions that are located close together best understood as distinct cortical modules or as parts of a larger-scale cortical map? Second, what functional properties define each cortical map or module? We propose a model in which overlapping continuous maps of simple features give rise to discrete modules that are selective for complex stimuli.Advances in brain imaging technology (especially functional MRI (fMRI)) have radically improved our understanding of the functional organization of the human brain (BOX 1). In this Review we describe the organization of the ventral visual pathway, which is characterized by strong selectivity for particular object categories (for example, faces and bodies) at the level of both individual neurons and larger cortical regions. We then consider two central questions: whether this organization reflects maps or modules, and what properties are mapped. In each case we derive clues from the literature on the primary sensory cortex, in which cortical maps have been studied extensively using electrophysiology in animals. We find that apparently modular cortical regions, such as orientation columns and face-selective regions, might be parts of larger maps, and show that it is a substantial challenge to determine the basic properties and dimensions that describe functional organization most parsimoniously. We then propose a new framework that reconciles the existence of graded cortical maps and distinct functional modules. In this framework, the strong category selectivity that exists for faces and other objects might arise from the nonlinear combination of multiple correlated maps for simpler stimulus properties. The ventral visual pathwayThe ventral visual pathway comprises a large cortical region that occupies the ventral and lateral surfaces of the occipital and temporal lobes (FIG. 1). A substantial proportion of fMRI voxels in this pathway are 'object-selective' -that is, they respond more strongly when people view images of objects than when people view scrambled versions of these objects or texture patterns. This object-selective region is often referred to as the lateral occipital complex (LOC) 1 . The LOC has little selectivity for particular stimulus categories 2-4 , but several regions of NIH-PA Author ManuscriptNIH-PA Author Manuscript NIH-PA Author Manuscript cortex near the LOC are selective for particular object categories: they respond at least twice as strongly to their 'preferred' stimuli than to other stimuli. For example, in essentially all humans cortical regions can be found that respond selectively to faces (the fusiform face area (FFA) 5,6 and, in many individuals, the occipital face area (OFA)) 7,8 , to pl...

Humans rely heavily on shape similarity among objects for object categorization and identification. Studies using functional magnetic resonance imaging (fMRI) have shown that a large region in human occipitotemporal cortex processes the shape of meaningful as well as unfamiliar objects. Here, we investigate whether the functional organization of this region as measured with fMRI is related to perceived shape similarity. We found that unfamiliar object classes that are rated as having a similar shape were associated with a very similar response pattern distributed across object-selective cortex, whereas object classes that were rated as being very different in shape were associated with a more different response pattern. Human observers, as well as object-selective cortex, were very sensitive to differences in shape features of the objects such as straight versus curved versus "spiky" edges, more so than to differences in overall shape envelope. Response patterns in retinotopic areas V1, V2, and V4 were not found to be related to perceived shape. The functional organization in area V3 was partially related to perceived shape but without a stronger sensitivity for shape features relative to overall shape envelope. Thus, for unfamiliar objects, the organization of human object-selective cortex is strongly related to perceived shape, and this shape-based organization emerges gradually throughout the object vision pathway.

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