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1.  There Is a “U” in Clutter: Evidence for Robust Sparse Codes Underlying Clutter Tolerance in Human Vision 
The Journal of Neuroscience  2015;35(42):14148-14159.
The ability to recognize objects in clutter is crucial for human vision, yet the underlying neural computations remain poorly understood. Previous single-unit electrophysiology recordings in inferotemporal cortex in monkeys and fMRI studies of object-selective cortex in humans have shown that the responses to pairs of objects can sometimes be well described as a weighted average of the responses to the constituent objects. Yet, from a computational standpoint, it is not clear how the challenge of object recognition in clutter can be solved if downstream areas must disentangle the identity of an unknown number of individual objects from the confounded average neuronal responses. An alternative idea is that recognition is based on a subpopulation of neurons that are robust to clutter, i.e., that do not show response averaging, but rather robust object-selective responses in the presence of clutter. Here we show that simulations using the HMAX model of object recognition in cortex can fit the aforementioned single-unit and fMRI data, showing that the averaging-like responses can be understood as the result of responses of object-selective neurons to suboptimal stimuli. Moreover, the model shows how object recognition can be achieved by a sparse readout of neurons whose selectivity is robust to clutter. Finally, the model provides a novel prediction about human object recognition performance, namely, that target recognition ability should show a U-shaped dependency on the similarity of simultaneously presented clutter objects. This prediction is confirmed experimentally, supporting a simple, unifying model of how the brain performs object recognition in clutter.
SIGNIFICANCE STATEMENT The neural mechanisms underlying object recognition in cluttered scenes (i.e., containing more than one object) remain poorly understood. Studies have suggested that neural responses to multiple objects correspond to an average of the responses to the constituent objects. Yet, it is unclear how the identities of an unknown number of objects could be disentangled from a confounded average response. Here, we use a popular computational biological vision model to show that averaging-like responses can result from responses of clutter-tolerant neurons to suboptimal stimuli. The model also provides a novel prediction, that human detection ability should show a U-shaped dependency on target–clutter similarity, which is confirmed experimentally, supporting a simple, unifying account of how the brain performs object recognition in clutter.
PMCID: PMC4683683  PMID: 26490856
clutter; HMAX; sparse coding; vision
2.  Adding Words to the Brain's Visual Dictionary: Novel Word Learning Selectively Sharpens Orthographic Representations in the VWFA 
The Journal of Neuroscience  2015;35(12):4965-4972.
The nature of orthographic representations in the human brain is still subject of much debate. Recent reports have claimed that the visual word form area (VWFA) in left occipitotemporal cortex contains an orthographic lexicon based on neuronal representations highly selective for individual written real words (RWs). This theory predicts that learning novel words should selectively increase neural specificity for these words in the VWFA. We trained subjects to recognize novel pseudowords (PWs) and used fMRI rapid adaptation to compare neural selectivity with RWs, untrained PWs (UTPWs), and trained PWs (TPWs). Before training, PWs elicited broadly tuned responses, whereas responses to RWs indicated tight tuning. After training, TPW responses resembled those of RWs, whereas UTPWs continued to show broad tuning. This change in selectivity was specific to the VWFA. Therefore, word learning appears to selectively increase neuronal specificity for the new words in the VWFA, thereby adding these words to the brain's visual dictionary.
PMCID: PMC4389595  PMID: 25810526
language; learning; object recognition; plasticity; reading; VWFA
3.  Task Dependence of Visual and Category Representations in Prefrontal and Inferior Temporal Cortices 
The Journal of Neuroscience  2014;34(48):16065-16075.
Visual categorization is an essential perceptual and cognitive process for assigning behavioral significance to incoming stimuli. Categorization depends on sensory processing of stimulus features as well as flexible cognitive processing for classifying stimuli according to the current behavioral context. Neurophysiological studies suggest that the prefrontal cortex (PFC) and the inferior temporal cortex (ITC) are involved in visual shape categorization. However, their precise roles in the perceptual and cognitive aspects of the categorization process are unclear, as the two areas have not been directly compared during changing task contexts. To address this, we examined the impact of task relevance on categorization-related activity in PFC and ITC by recording from both areas as monkeys alternated between a shape categorization and passive viewing tasks. As monkeys viewed the same stimuli in both tasks, the impact of task relevance on encoding in each area could be compared. While both areas showed task-dependent modulations of neuronal activity, the patterns of results differed markedly. PFC, but not ITC, neurons showed a modest increase in firing rates when stimuli were task relevant. PFC also showed significantly stronger category selectivity during the task compared with passive viewing, while task-dependent modulations of category selectivity in ITC were weak and occurred with a long latency. Finally, both areas showed an enhancement of stimulus selectivity during the task compared with passive viewing. Together, this suggests that the ITC and PFC show differing degrees of task-dependent flexibility and are preferentially involved in the perceptual and cognitive aspects of the categorization process, respectively.
PMCID: PMC4244472  PMID: 25429147
categorization; inferior temporal cortex; neurophysiology; object recognition; prefrontal cortex; vision
4.  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
5.  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
6.  Functional Correlates of the Antero-Lateral Processing Hierarchy in Human Auditory Cortex 
Converging evidence supports the hypothesis that an antero-lateral processing pathway mediates sound identification in auditory cortex, analogous to the role of the ventral cortical pathway in visual object recognition. Studies in nonhuman primates have characterized the antero-lateral auditory pathway as a processing hierarchy, composed of three anatomically and physiologically distinct initial stages: core, belt and parabelt. In humans, potential homologues of these regions have been identified anatomically, but reliable and complete functional distinctions between them have yet to be established. Because the anatomical locations of these fields vary across subjects, investigations of potential homologues between monkeys and humans require these fields to be defined in single subjects. Using functional MRI, we presented three classes of sounds (tones, band-passed noise bursts, and conspecific vocalizations), equivalent to those used in previous monkey studies. In each individual subject, three regions showing functional similarities to macaque core, belt and parabelt were readily identified. Furthermore, the relative sizes and locations of these regions were consistent with those reported in human anatomical studies. Our results demonstrate that the functional organization of the antero-lateral processing pathway in humans is largely consistent with that of nonhuman primates. Because our scanning sessions last only 15 min/subject, they can be run in conjunction with other scans. This will enable future studies to characterize functional modules in human auditory cortex at a level of detail previously possible only in visual cortex. Furthermore, the approach of employing identical schemes in both humans and monkeys will aid with establishing potential homologies between them.
PMCID: PMC3142575  PMID: 21697384
Auditory; cortex; fMRI; ventral stream; hierarchy; recognition
7.  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
8.  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
9.  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
10.  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 1-10 (10)