@article {141, title = {The inferior temporal cortex is a potential cortical precursor of orthographic processing in untrained monkeys}, journal = {Nature Communications}, volume = {11}, year = {2020}, month = {Jan-12-2020}, abstract = {

The ability to recognize written letter strings is foundational to human reading, but the underlying neuronal mechanisms remain largely unknown. Recent behavioral research in baboons suggests that non-human primates may provide an opportunity to investigate this question. We recorded the activity of hundreds of neurons in V4 and the inferior temporal cortex (IT) while na\ïve macaque monkeys passively viewed images of letters, English words and non-word strings, and tested the capacity of those neuronal representations to support a battery of orthographic processing tasks. We found that simple linear read-outs of IT (but not V4) population responses achieved high performance on all tested tasks, even matching the performance and error patterns of baboons on word classification. These results show that the IT cortex of untrained primates can serve as a precursor of orthographic processing, suggesting that the acquisition of reading in humans relies on the recycling of a brain network evolved for other visual functions.

}, doi = {10.1038/s41467-020-17714-3}, url = {http://www.nature.com/articles/s41467-020-17714-3}, author = {Rajalingham, Rishi and Kar, Kohitij and Sanghavi, Sachi and Dehaene, Stanislas and DiCarlo, James J.} } @article {147, title = {Integrative Benchmarking to Advance Neurally Mechanistic Models of Human Intelligence}, journal = {Neuron}, year = {2020}, month = {Jan-09-2020}, abstract = {

A potentially organizing goal of the brain and cognitive sciences is to accurately explain domains of human intelligence as executable, neurally mechanistic models. Years of research have led to models that capture experimental results in individual behavioral tasks and individual brain regions. We here advocate for taking the next step: integrating experimental results from many laboratories into suites of benchmarks that, when considered together, push mechanistic models toward explaining entire domains of intelligence, such as vision, language, and motor control. Given recent successes of neurally mechanistic models and the surging availability of neural, anatomical, and behavioral data, we believe that now is the time to create integrative benchmarking platforms that incentivize ambitious, unified models. This perspective discusses the advantages and the challenges of this approach and proposes specific steps to achieve this goal in the domain of visual intelligence with the case study of an integrative benchmarking platform called Brain-Score.

}, issn = {08966273}, doi = {10.1016/j.neuron.2020.07.040}, url = {https://linkinghub.elsevier.com/retrieve/pii/S089662732030605X}, author = {Schrimpf, Martin and Kubilius, Jonas and Lee, Michael J. and Murty, NAR and Ajemian, Robert and DiCarlo, James J.} } @proceedings {67, title = {Increases in selectivity are offset by increases in tolerance ("invariance") to maintain sparseness across the ventral visual pathway}, journal = {Society for Neuroscience}, year = {2008}, month = {11/2008}, pages = {514.8}, publisher = {SFN}, address = {Washington, DC, USA}, abstract = {

Popular accounts of visual processing suggest that neurons become increasingly selective for particular objects and scenes as signals propagate through the ventral visual pathway, but this hypothesis has proven difficult to test. To investigate this issue systematically, we recorded the responses of neurons in a mid-level visual area (V4) and a high-level visual area (IT) under well-controlled conditions (same monkeys, same task, same region of the visual field, same stimuli). We assessed the selectivity of neurons in each area by determining how well each population could discriminate between natural images and \“scrambled\” versions of those images that have the same local structure but configured randomly. We found that the V4 population discriminated between members of the two image sets with similar fidelity whereas discrimination by the IT population was considerably degraded for the scrambled as compared to the natural images. These results suggest that IT neurons are more selective than V4 neurons in terms of the image features that drive these cells. As a second estimate of selectivity, we measured the tuning bandwidth of neurons for natural images (commonly called \“sparseness\”). Surprisingly, we found that distributions of sparseness values were indistinguishable between V4 and IT. Similarly, we found that individual images activated the same fraction of neurons in V4 and IT, suggesting that a coding principle is conserved at each level of processing. How can the selectivity for natural image features increase while the tuning bandwidth for natural images remains constant? One possible explanation is that increases in selectivity for particular image features are offset by increases in tolerance for the (e.g.) position and scale of those features. Indeed, when we measured the tolerance of neurons to changes in the position and scale of images, we found that tolerance increases as signals propagate from V4 to IT. Moreover, we found that equivalent sparseness values were correlated with higher levels of selectivity and tolerance in IT as compared to V4. These results confirm that neurons increase both their selectivity for image features and their tolerance to changes in the position and scale of those features as signals propagate through the ventral visual pathway. Remarkably, the rates of increase of these two parameters appear to be set such that an equally distributed coding scheme is maintained at each level of visual processing.

}, url = {https://www.abstractsonline.com/Plan/ViewAbstract.aspx?sKey=fc0d0a2d-b563-4b41-8311-0805f08bde8a\&cKey=8a2c998e-bc76-4d92-96ac-ad5199da59bf\&mKey=\%7bAFEA068D-D012-4520-8E42-10E4D1AF7944\%7d}, author = {Rust, N. C. and James J. DiCarlo} } @proceedings {72, title = {Inferior temporal cortex robustly signals encounters with new objects, but is not an online representation of the visual world}, journal = {Society for Neuroscience}, year = {2008}, month = {11/2008}, pages = {316.6}, publisher = {SFN}, address = {Washington, DC, USA}, abstract = {

Popular accounts of visual processing suggest that neurons become increasingly selective for particular objects and scenes as signals propagate through the ventral visual pathway, but this hypothesis has proven difficult to test. To investigate this issue systematically, we recorded the responses of neurons in a mid-level visual area (V4) and a high-level visual area (IT) under well-controlled conditions (same monkeys, same task, same region of the visual field, same stimuli). We assessed the selectivity of neurons in each area by determining how well each population could discriminate between natural images and \“scrambled\” versions of those images that have the same local structure but configured randomly. We found that the V4 population discriminated between members of the two image sets with similar fidelity whereas discrimination by the IT population was considerably degraded for the scrambled as compared to the natural images. These results suggest that IT neurons are more selective than V4 neurons in terms of the image features that drive these cells. As a second estimate of selectivity, we measured the tuning bandwidth of neurons for natural images (commonly called \“sparseness\”). Surprisingly, we found that distributions of sparseness values were indistinguishable between V4 and IT. Similarly, we found that individual images activated the same fraction of neurons in V4 and IT, suggesting that a coding principle is conserved at each level of processing. How can the selectivity for natural image features increase while the tuning bandwidth for natural images remains constant? One possible explanation is that increases in selectivity for particular image features are offset by increases in tolerance for the (e.g.) position and scale of those features. Indeed, when we measured the tolerance of neurons to changes in the position and scale of images, we found that tolerance increases as signals propagate from V4 to IT. Moreover, we found that equivalent sparseness values were correlated with higher levels of selectivity and tolerance in IT as compared to V4. These results confirm that neurons increase both their selectivity for image features and their tolerance to changes in the position and scale of those features as signals propagate through the ventral visual pathway. Remarkably, the rates of increase of these two parameters appear to be set such that an equally distributed coding scheme is maintained at each level of visual processing.

}, url = {https://www.abstractsonline.com/Plan/ViewAbstract.aspx?sKey=ee83e7f7-5aea-4ec8-a948-436658d20e37\&cKey=fc64f0af-c81e-4b0e-b809-796349279531\&mKey=\%7bAFEA068D-D012-4520-8E42-10E4D1AF7944\%7d}, author = {Rust, N. C. and DiCarlo, James J.} } @proceedings {106, title = {Inferotemporal representations underlying object recognition in the free viewing monkey}, journal = {Society for Neuroscience}, year = {2000}, month = {10/2000}, publisher = {SFN}, address = {New Orleans, LA, USA}, author = {DiCarlo, James J. and Maunsell, John H. R.} }