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1.  Prefrontal cortex activity during flexible categorization 
Items are categorized differently depending on the behavioral context. For instance, a lion can be an African animal or a type of cat. We recorded lateral prefrontal cortex (PFC) neural activity while monkeys switched between categorizing the same image set along two different category schemes with orthogonal boundaries. We found that each category scheme was largely represented by independent PFC neuronal populations and that activity reflecting a category distinction was weaker, but not absent, when that category was irrelevant. We suggest that the PFC represents competing category representations independently to reduce interference between them.
PMCID: PMC3709835  PMID: 20573899
prefrontal cortex; categorization; monkey; flexibility; goal directed; object vision
2.  A quantitative link between face discrimination deficits and neuronal selectivity for faces in autism☆ 
NeuroImage : Clinical  2013;2:320-331.
Individuals with Autism Spectrum Disorder (ASD) appear to show a general face discrimination deficit across a range of tasks including social–emotional judgments as well as identification and discrimination. However, functional magnetic resonance imaging (fMRI) studies probing the neural bases of these behavioral differences have produced conflicting results: while some studies have reported reduced or no activity to faces in ASD in the Fusiform Face Area (FFA), a key region in human face processing, others have suggested more typical activation levels, possibly reflecting limitations of conventional fMRI techniques to characterize neuron-level processing. Here, we test the hypotheses that face discrimination abilities are highly heterogeneous in ASD and are mediated by FFA neurons, with differences in face discrimination abilities being quantitatively linked to variations in the estimated selectivity of face neurons in the FFA. Behavioral results revealed a wide distribution of face discrimination performance in ASD, ranging from typical performance to chance level performance. Despite this heterogeneity in perceptual abilities, individual face discrimination performance was well predicted by neural selectivity to faces in the FFA, estimated via both a novel analysis of local voxel-wise correlations, and the more commonly used fMRI rapid adaptation technique. Thus, face processing in ASD appears to rely on the FFA as in typical individuals, differing quantitatively but not qualitatively. These results for the first time mechanistically link variations in the ASD phenotype to specific differences in the typical face processing circuit, identifying promising targets for interventions.
► fMRI-RA, local correlations are used to estimate neuronal tuning in the FFA in ASD. ► Both techniques reveal a link of neuronal selectivity and face discrimination ability. ► These results suggest weaker experience-driven learning in the FFA in ASD.
PMCID: PMC3777682  PMID: 24179786
Face; Autism; ASD; fMRI; fMRI-RA; Local correlation
3.  Task effects, performance levels, features, configurations, and holistic face processing: A reply to Rossion 
Acta psychologica  2009;132(3):286-292.
A recent article in Acta Psychologica (“Picture-plane inversion leads to qualitative changes of face perception” by B. Rossion, 2008) criticized several aspects of an earlier paper of ours (Riesenhuber et al., “Face processing in humans is compatible with a simple shape-based model of vision”, Proc Biol Sci, 2004). We here address Rossion’s criticisms and correct some misunderstandings. To frame the discussion, we first review our previously presented computational model of face recognition in cortex (Jiang et al., “Evaluation of a shape-based model of human face discrimination using fMRI and behavioral techniques”, Neuron, 2006) that provides a concrete biologically plausible computational substrate for holistic coding, namely a neural representation learned for upright faces, in the spirit of the original simple-to-complex hierarchical model of vision by Hubel and Wiesel. We show that Rossion’s and others’ data support the model, and that there is actually a convergence of views on the mechanisms underlying face recognition, in particular regarding holistic processing.
PMCID: PMC2788156  PMID: 19665104
4.  Evidence for highly selective neuronal tuning to whole words in the “Visual Word Form Area” 
Neuron  2009;62(2):199-204.
Theories of reading have posited the existence of a neural representation coding for whole real words (i.e. an orthographic lexicon), but experimental support for such a representation has proved elusive. Using fMRI rapid adaptation techniques, we provide evidence that the human left ventral occipitotemporal cortex (specifically the “visual word form area”, VWFA) contains a representation based on neurons highly selective for individual real words, in contrast to current theories that posit a sublexical representation in the VWFA.
PMCID: PMC2706007  PMID: 19409265
5.  Categorization Training Results in Shape- and Category-Selective Human Neural Plasticity 
Neuron  2007;53(6):891-903.
Object category learning is a fundamental ability, requiring combination of “bottom-up” stimulus-driven with “top-down” task-specific information. It therefore may be a fruitful domain for study of the general neural mechanisms underlying cortical plasticity. A simple model predicts that category learning involves the formation of a task-independent shape-selective representation that provides input to circuits learning the categorization task, with the computationally appealing prediction of facilitated learning of additional, novel tasks over the same stimuli. Using fMRI rapid-adaptation techniques, we find that categorization training (on morphed “cars”) induced a significant release from adaptation for small shape changes in lateral occipital cortex irrespective of category membership, compatible with the sharpening of a representation coding for physical appearance. In contrast, an area in lateral prefrontal cortex, selectively activated during categorization, showed sensitivity post-training to explicit changes in category membership. Further supporting the model, categorization training also improved discrimination performance on the trained stimuli.
PMCID: PMC1989663  PMID: 17359923
6.  Face processing in humans is compatible with a simple shape-based model of vision. 
Understanding how the human visual system recognizes objects is one of the key challenges in neuroscience. Inspired by a large body of physiological evidence, a general class of recognition models has emerged, which is based on a hierarchical organization of visual processing, with succeeding stages being sensitive to image features of increasing complexity. However, these models appear to be incompatible with some well-known psychophysical results. Prominent among these are experiments investigating recognition impairments caused by vertical inversion of images, especially those of faces. It has been reported that faces that differ 'featurally' are much easier to distinguish when inverted than those that differ 'configurally'; a finding that is difficult to reconcile with the physiological models. Here, we show that after controlling for subjects' expectations, there is no difference between 'featurally' and 'configurally' transformed faces in terms of inversion effect. This result reinforces the plausibility of simple hierarchical models of object representation and recognition in the cortex.
PMCID: PMC1810084  PMID: 15801600

Results 1-6 (6)