@article {5636, title = {Decoding predicted future states from the brain{\textquoteright}s {\textquotedblleft}physics engine{\textquotedblright}}, journal = {Science Advances}, volume = {11}, year = {2025}, month = {05/2025}, abstract = {

Successful engagement with the physical world requires the ability to predict future events and plan interventions to alter that future. Growing evidence implicates a set of regions in the human parietal and frontal lobes [also known as the {\textquotedblleft}physics network{\textquotedblright} (PN)] in such intuitive physical inferences. However, the central tenet of this hypothesis, that PN runs forward simulations to predict future states, remains untested. In this preregistered study, we first show that PN abstractly represents whether two objects are in contact with each other, a physical scene property critical for prediction (because objects{\textquoteright} fates are intertwined when they are in contact). We then show that PN (but not other visual areas) carries abstract information about predicted future contact events (i.e., collisions). These findings support the hypothesis that PN contains a generative model of the physical world that conducts forward simulations, serving as the brain{\textquoteright}s {\textquotedblleft}physics engine.{\textquotedblright}

}, doi = {10.1126/sciadv.adr7429}, url = {https://www.science.org/doi/10.1126/sciadv.adr7429}, author = {Pramod, R. T. and Mieczkowski, Elizabeth and Fang, Cyn X. and Tenenbaum, Joshua B. and Kanwisher, Nancy} } @article {5529, title = {Dissociating language and thought in large language models}, journal = {Trends in Cognitive Sciences}, volume = {28}, year = {2024}, month = {03/2024}, pages = {517 - 540}, abstract = {

Large language models (LLMs) have come closest among all models to date to mastering human language, yet opinions about their linguistic and cognitive capabilities remain split. Here, we evaluate LLMs using a distinction between formal linguistic competence (knowledge of linguistic rules and patterns) and functional linguistic competence (understanding and using language in the world). We ground this distinction in human neuroscience, which has shown that formal and functional competence rely on different neural mechanisms. Although LLMs are surprisingly good at formal competence, their performance on functional competence tasks remains spotty and often requires specialized fine-tuning and/or coupling with external modules. We posit that models that use language in human-like ways would need to master both of these competence types, which, in turn, could require the emergence of separate mechanisms specialized for formal versus functional linguistic competence.

}, issn = {13646613}, doi = {10.1016/j.tics.2024.01.011}, url = {https://linkinghub.elsevier.com/retrieve/pii/S1364661324000275}, author = {Mahowald, Kyle and Ivanova, Anna A. and Blank, Idan A. and Kanwisher, Nancy and Tenenbaum, Joshua B. and Fedorenko, Evelina} } @article {5536, title = {Approaching human 3D shape perception with neurally mappable models}, journal = {arXiv}, year = {2023}, month = {08/2023}, abstract = {

Humans effortlessly infer the 3D shape of objects. What computations underlie this ability? Although various computational models have been proposed, none of them capture the human ability to match object shape across viewpoints. Here, we ask whether and how this gap might be closed. We begin with a relatively novel class of computational models, 3D neural fields, which encapsulate the basic principles of classic analysis-by-synthesis in a deep neural network (DNN). First, we find that a 3D Light Field Network (3D-LFN) supports 3D matching judgments well aligned to humans for within-category comparisons, adversarially-defined comparisons that accentuate the 3D failure cases of standard DNN models, and adversarially-defined comparisons for algorithmically generated shapes with no category structure. We then investigate the source of the 3D-LFN{\textquoteright}s ability to achieve human-aligned performance through a series of computational experiments. Exposure to multiple viewpoints of objects during training and a multi-view learning objective are the primary factors behind model-human alignment; even conventional DNN architectures come much closer to human behavior when trained with multi-view objectives. Finally, we find that while the models trained with multi-view learning objectives are able to partially generalize to new object categories, they fall short of human alignment. This work provides a foundation for understanding human shape inferences within neurally mappable computational architectures.

}, url = {https://arxiv.org/abs/2308.11300}, author = {Thomas P. O{\textquoteright}Connell and Tyler Bonnen and Yoni Friedman and Ayush Tewari and Joshua B. Tenenbaum and Vincent Sitzmann and Nancy Kanwisher} } @article {5542, title = {Behavioral signatures of face perception emerge in deep neural networks optimized for face recognition}, journal = {Proceedings of the National Academy of Sciences}, volume = {120}, year = {2023}, month = {07/2023}, abstract = {

Significance

For decades, cognitive scientists have collected behavioral signatures of face recognition. Here, we move beyond the mere curation of behavioral phenomena to ask why the human face system works the way it does. We find that many classic signatures of human face perception emerge spontaneously in convolutional neural networks (CNNs) trained on face discrimination, but not in CNNs trained on object classification (or on both object classification and face detection), suggesting that these long-documented properties of the human face perception system reflect optimizations for face recognition, not by-products of a generic visual categorization system. This work further illustrates how CNN models can be synergistically linked to classic behavioral findings in vision research, thereby providing psychological insights into human perception.

Abstract

Human face recognition is highly accurate and exhibits a number of distinctive and well-documented behavioral {\textquotedblleft}signatures{\textquotedblright} such as the use of a characteristic representational space, the disproportionate performance cost when stimuli are presented upside down, and the drop in accuracy for faces from races the participant is less familiar with. These and other phenomena have long been taken as evidence that face recognition is {\textquotedblleft}special{\textquotedblright}. But why does human face perception exhibit these properties in the first place? Here, we use deep convolutional neural networks (CNNs) to test the hypothesis that all of these signatures of human face perception result from optimization for the task of face recognition. Indeed, as predicted by this hypothesis, these phenomena are all found in CNNs trained on face recognition, but not in CNNs trained on object recognition, even when additionally trained to detect faces while matching the amount of face experience. To test whether these signatures are in principle specific to faces, we optimized a CNN on car discrimination and tested it on upright and inverted car images. As we found for face perception, the car-trained network showed a drop in performance for inverted vs. upright cars. Similarly, CNNs trained on inverted faces produced an inverted face inversion effect. These findings show that the behavioral signatures of human face perception reflect and are well explained as the result of optimization for the task of face recognition, and that the nature of the computations underlying this task may not be so special after all.

}, issn = {0027-8424}, doi = {10.1073/pnas.2220642120}, url = {https://pnas.org/doi/10.1073/pnas.2220642120}, author = {Dobs, Katharina and Yuan, Joanne and Martinez, Julio and Kanwisher, Nancy} } @article {5313, title = {CNNs reveal the computational implausibility of the expertise hypothesis}, journal = {iScience}, volume = {26}, year = {2023}, month = {02/2023}, pages = {105976}, abstract = {

Face perception has long served as a classic example of domain specificity of mind and brain. But an alternative {\textquotedblleft}expertise{\textquotedblright} hypothesis holds that putatively face-specific mechanisms are actually domain-general, and can be recruited for the\ perception of other objects of expertise (e.g., cars for car experts). Here, we demonstrate the computational implausibility of this hypothesis: Neural network\ models optimized for generic object categorization provide a better foundation for expert fine-grained discrimination than do models optimized for face recognition.

}, issn = {25890042}, doi = {10.1016/j.isci.2023.105976}, url = {https://linkinghub.elsevier.com/retrieve/pii/S2589004223000536}, author = {Kanwisher, Nancy and Gupta, Pranjul and Dobs, Katharina} } @article {5268, title = {Preliminary evidence for selective cortical responses to music in one-month-old infants}, journal = {Developmental Science}, year = {2023}, month = {03/2023}, abstract = {

Prior studies have observed selective neural responses in the adult human auditory cortex to music and speech that cannot be explained by the differing lower-level acoustic properties of these stimuli. Does infant cortex exhibit similarly selective responses to music and speech shortly after birth? To answer this question, we attempted to collect functional magnetic resonance imaging (fMRI) data from 45 sleeping infants (2.0- to 11.9-weeks-old) while they listened to monophonic instrumental lullabies and infant-directed speech produced by a mother. To match acoustic variation between music and speech sounds we (1) recorded music from instruments that had a similar spectral range as female infant-directed speech, (2) used a novel excitation-matching algorithm to match the cochleagrams of music and speech stimuli, and (3) synthesized {\textquotedblleft}model-matched{\textquotedblright} stimuli that were matched in spectrotemporal modulation statistics to (yet perceptually distinct from) music or speech. Of the 36 infants we collected usable data from, 19 had significant activations to sounds overall compared to scanner noise. From these infants, we observed a set of voxels in non-primary auditory cortex (NPAC) but not in Heschl{\textquoteright}s Gyrus that responded significantly more to music than to each of the other three stimulus types (but not significantly more strongly than to the background scanner noise). In contrast, our planned analyses did not reveal voxels in NPAC that responded more to speech than to model-matched speech, although other unplanned analyses did. These preliminary findings suggest that music selectivity arises within the first month of life.

}, keywords = {auditory cortex, fMRI, infants, music, speech}, issn = {1363-755X}, doi = {10.1111/desc.13387}, url = {https://onlinelibrary.wiley.com/doi/10.1111/desc.13387}, author = {Heather L Kosakowski and Norman-Haignere, Samuel and Mynick, Anna and Takahashi, Atsushi and Saxe, Rebecca and Nancy Kanwisher} } @article {5320, title = {Using artificial neural networks to ask {\textquoteleft}why{\textquoteright} questions of minds and brains}, journal = {Trends in Neurosciences}, volume = {46}, year = {2023}, month = {03/2023}, pages = {240 - 254}, abstract = {

Neuroscientists have long characterized the properties and functions of the nervous system, and are increasingly succeeding in answering how brains perform the tasks they do. But the question {\textquoteleft}why{\textquoteright} brains work the way they do is asked less often. The new ability to optimize artificial neural networks (ANNs) for performance on human-like tasks now enables us to approach these {\textquoteleft}why{\textquoteright} questions by asking when the properties of networks optimized for a given task mirror the behavioral and neural characteristics of humans performing the same task. Here we highlight the recent success of this strategy in explaining why the visual and auditory systems work the way they do, at both behavioral and neural levels.

}, issn = {01662236}, doi = {10.1016/j.tins.2022.12.008}, url = {https://linkinghub.elsevier.com/retrieve/pii/S0166223622002624}, author = {Kanwisher, Nancy and Khosla, Meenakshi and Dobs, Katharina} } @article {5085, title = {Brain-like functional specialization emerges spontaneously in deep neural networks}, journal = {Science Advances}, volume = {8}, year = {2022}, month = {03/2023}, abstract = {

The human brain contains multiple regions with distinct, often highly specialized functions, from recognizing faces to understanding language to thinking about what others are thinking. However, it remains unclear why the cortex exhibits this high degree of functional specialization in the first place. Here, we consider the case of face perception using artificial neural networks to test the hypothesis that functional segregation of face recognition in the brain reflects a computational optimization for the broader problem of visual recognition of faces and other visual categories. We find that networks trained on object recognition perform poorly on face recognition and vice versa and that networks optimized for both tasks spontaneously segregate themselves into separate systems for faces and objects. We then show functional segregation to varying degrees for other visual categories, revealing a widespread tendency for optimization (without built-in task-specific inductive biases) to lead to functional specialization in machines and, we conjecture, also brains.

}, doi = {10.1126/sciadv.abl8913}, url = {https://www.science.org/doi/10.1126/sciadv.abl8913}, author = {Dobs, Katharina and Julio Martinez-Trujillo and Kell, Alexander J. E. and Nancy Kanwisher} } @article {5281, title = {A highly selective response to food in human visual cortex revealed by hypothesis-free voxel decomposition}, journal = {Current Biology}, volume = {32}, year = {2022}, month = {10/2022}, pages = {4159 - 4171.e9}, abstract = {

Prior work has identified cortical regions selectively responsive to specific categories of visual stimuli. However, this hypothesis-driven work cannot reveal how prominent these category selectivities are in the overall functional organization of the visual cortex, or what others might exist that scientists have not thought to look for. Furthermore, standard voxel-wise tests cannot detect distinct neural selectivities that coexist within voxels. To overcome these limitations, we used data-driven voxel decomposition methods to identify the main components underlying fMRI responses to thousands of complex photographic images. Our hypothesis-neutral analysis rediscovered components selective for faces, places, bodies, and words, validating our method and showing that these selectivities are dominant features of the ventral visual pathway. The analysis also revealed an unexpected component with a distinct anatomical distribution that responded highly selectively to images of food. Alternative accounts based on low- to mid-level visual features, such as color, shape, or texture, failed to account for the food selectivity of this component. High-throughput testing and control experiments with matched stimuli on a highly accurate computational model of this component confirm its selectivity for food. We registered our methods and hypotheses before replicating them on held-out participants and in a novel dataset. These findings demonstrate the power of data-driven methods and show that the dominant neural responses of the ventral visual pathway include not only selectivities for faces, scenes, bodies, and words but also the visually heterogeneous category of food, thus constraining accounts of when and why functional specialization arises in the cortex.

}, issn = {09609822}, doi = {10.1016/j.cub.2022.08.009}, url = {https://linkinghub.elsevier.com/retrieve/pii/S0960982222012866}, author = {Khosla, Meenakshi and Ratan Murty, N. Apurva and Kanwisher, Nancy} } @article {5282, title = {Invariant representation of physical stability in the human brain}, journal = {eLife}, volume = {11}, year = {2022}, month = {05/2024}, abstract = {

Successful engagement with the world requires the ability to predict what will happen next. Here, we investigate how the brain makes a fundamental prediction about the physical world: whether the situation in front of us is stable, and hence likely to stay the same, or unstable, and hence likely to change in the immediate future. Specifically, we ask if judgments of stability can be supported by the kinds of representations that have proven to be highly effective at visual object recognition in both machines and brains, or instead if the ability to determine the physical stability of natural scenes may require generative algorithms that simulate the physics of the world. To find out, we measured responses in both convolutional neural networks (CNNs) and the brain (using fMRI) to natural images of physically stable versus unstable scenarios. We find no evidence for generalizable representations of physical stability in either standard CNNs trained on visual object and scene classification (ImageNet), or in the human ventral visual pathway, which has long been implicated in the same process. However, in frontoparietal regions previously implicated in intuitive physical reasoning we find both scenario-invariant representations of physical stability, and higher univariate responses to unstable than stable scenes. These results demonstrate abstract representations of physical stability in the dorsal but not ventral pathway, consistent with the hypothesis that the computations underlying stability entail not just pattern classification but forward physical simulation.

}, doi = {10.7554/eLife.71736}, url = {https://elifesciences.org/articles/71736}, author = {Pramod, RT and Cohen, Michael A and Tenenbaum, Joshua B and Kanwisher, Nancy} } @article {5027, title = {A neural population selective for song in human auditory cortex}, journal = {Current Biology}, year = {2022}, month = {02/2022}, abstract = {

How is music represented in the brain? While neuroimaging has revealed some spatial segregation between responses to music versus other sounds, little is known about the neural code for music itself. To address this question, we developed a method to infer canonical response components of human auditory cortex using intracranial responses to natural sounds, and further used the superior coverage of fMRI to map their spatial distribution. The inferred components replicated many prior findings, including distinct neural selectivity for speech and music, but also revealed a novel component that responded nearly exclusively to music with singing. Song selectivity was not explainable by standard acoustic features, was located near speech and music-selective responses, and was also evident in individual electrodes. These results suggest that representations of music are fractionated into subpopulations selective for different types of music, one of which is specialized for the analysis of song.

}, issn = {09609822}, doi = {10.1016/j.cub.2022.01.069}, url = {https://linkinghub.elsevier.com/retrieve/pii/S0960982222001312}, author = {Norman-Haignere, Sam V. and Jenelle Feather and Boebinger, Dana and Brunner, Peter and Ritaccio, Anthony and Josh H. McDermott and Schalk, Gerwin and Nancy Kanwisher} } @article {5090, title = {Using child-friendly movie stimuli to study the development of face, place, and object regions from age 3 to 12 years}, journal = {Human Brain Mapping}, year = {2022}, month = {03/2022}, abstract = {

Scanning young children while they watch short, engaging, commercially-produced movies has emerged as a promising approach for increasing data retention and quality. Movie stimuli also evoke a richer variety of cognitive processes than traditional experiments, allowing the study of multiple aspects of brain development simultaneously. However, because these stimuli are uncontrolled, it is unclear how effectively distinct profiles of brain activity can be distinguished from the resulting data. Here we develop an approach for identifying multiple distinct subject-specific Regions of Interest (ssROIs) using fMRI data collected during movie-viewing. We focused on the test case of higher-level visual regions selective for faces, scenes, and objects. Adults (N = 13) were scanned while viewing a 5.6-min child-friendly movie, as well as a traditional localizer experiment with blocks of faces, scenes, and objects. We found that just 2.7 min of movie data could identify subject-specific face, scene, and object regions. While successful, movie-defined ssROIS still showed weaker domain selectivity than traditional ssROIs. Having validated our approach in adults, we then used the same methods on movie data collected from 3 to 12-year-old children (N = 122). Movie response timecourses in 3-year-old children{\textquoteright}s face, scene, and object regions were already significantly and specifically predicted by timecourses from the corresponding regions in adults. We also found evidence of continued developmental change, particularly in the face-selective posterior superior temporal sulcus. Taken together, our results reveal both early maturity and functional change in face, scene, and object regions, and more broadly highlight the promise of short, child-friendly movies for developmental cognitive neuroscience.

}, issn = {1065-9471}, doi = {10.1002/hbm.25815}, url = {https://onlinelibrary.wiley.com/doi/10.1002/hbm.25815}, author = {Kamps, Frederik S. and Richardson, Hilary and N. Apurva Ratan Murty and Nancy Kanwisher and Rebecca Saxe} } @article {5070, title = {Computational models of category-selective brain regions enable high-throughput tests of selectivity}, journal = {Nature Communications}, volume = {12}, year = {2021}, month = {12/2021}, abstract = {

Cortical regions apparently selective to faces, places, and bodies have provided important evidence for domain-specific theories of human cognition, development, and evolution. But claims of category selectivity are not quantitatively precise and remain vulnerable to empirical refutation. Here we develop artificial neural network-based encoding models that accurately predict the response to novel images in the fusiform face area, parahippocampal place area, and extrastriate body area, outperforming descriptive models and experts. We use these models to subject claims of category selectivity to strong\ tests, by screening for and synthesizing images predicted to produce high responses. We find that these high-response-predicted images are all unambiguous members of the hypothesized preferred category for each region. These results provide accurate, image-computable encoding models of each category-selective region, strengthen evidence for domain specificity in the brain, and point the way for future research characterizing the functional organization of the brain with unprecedented computational precision.

}, doi = {10.1038/s41467-021-25409-6}, url = {https://www.nature.com/articles/s41467-021-25409-6}, author = {N. Apurva Ratan Murty and Pouya Bashivan and Abate, Alex and James J. DiCarlo and Nancy Kanwisher} } @article {4969, title = {The neural architecture of language: Integrative modeling converges on predictive processing}, journal = {Proceedings of the National Academy of Sciences}, volume = {118}, year = {2021}, month = {11/2021}, pages = {e2105646118}, abstract = {

Significance

Language is a quintessentially human ability. Research has long probed the functional architecture of language in the mind and brain using diverse neuroimaging, behavioral, and computational modeling approaches. However, adequate neurally-mechanistic accounts of how meaning might be extracted from language are sorely lacking. Here, we report a first step toward addressing this gap by connecting recent artificial neural networks from machine learning to human recordings during language processing. We find that the most powerful models predict neural and behavioral responses across different datasets up to noise levels. Models that perform better at predicting the next word in a sequence also better predict brain measurements{\textemdash}providing computationally explicit evidence that predictive processing fundamentally shapes the language comprehension mechanisms in the brain.

Abstract

The neuroscience of perception has recently been revolutionized with an integrative modeling approach in which computation, brain function, and behavior are linked across many datasets and many computational models. By revealing trends across models, this approach yields novel insights into cognitive and neural mechanisms in the target domain. We here present a systematic study taking this approach to higher-level cognition: human language processing, our species{\textquoteright} signature cognitive skill. We find that the most powerful {\textquotedblleft}transformer{\textquotedblright} models predict nearly 100\% of explainable variance in neural responses to sentences and generalize across different datasets and imaging modalities (functional MRI and electrocorticography). Models{\textquoteright} neural fits ({\textquotedblleft}brain score{\textquotedblright}) and fits to behavioral responses are both strongly correlated with model accuracy on the next-word prediction task (but not other language tasks). Model architecture appears to substantially contribute to neural fit. These results provide computationally explicit evidence that predictive processing fundamentally shapes the language comprehension mechanisms in the human brain.

}, issn = {0027-8424}, doi = {10.1073/pnas.2105646118}, url = {http://www.pnas.org/lookup/doi/10.1073/pnas.2105646118}, author = {Martin Schrimpf and Blank, Idan Asher and Tuckute, Greta and Kauf, Carina and Hosseini, Eghbal A. and Nancy Kanwisher and Joshua B. Tenenbaum and Fedorenko, Evelina} } @article {4980, title = {Selective responses to faces, scenes, and bodies in the ventral visual pathway of infants}, journal = {Current Biology}, volume = {32}, year = {2021}, month = {11/2021}, chapter = {1-20}, abstract = {

Three of the most robust functional landmarks in the human brain are the selective responses to faces in the fusiform face area (FFA), scenes in the parahippocampal place area (PPA), and bodies in the extrastriate body area (EBA). Are the selective responses of these regions present early in development or do they require many years to develop? Prior evidence leaves this question unresolved. We designed a new 32-channel infant magnetic resonance imaging (MRI) coil and collected high-quality functional MRI (fMRI) data from infants (2{\textendash}9\ months of age) while they viewed stimuli from four conditions{\textemdash}faces, bodies, objects, and scenes. We find that infants have face-, scene-, and body-selective responses in the location of the adult FFA, PPA, and EBA, respectively, powerfully constraining accounts of cortical development.

}, doi = {10.1016/j.cub.2021.10.064}, url = {https://www.sciencedirect.com/science/article/pii/S0960982221015086}, author = {Heather L Kosakowski and Cohen, Michael A. and Takahashi, Atsushi and Keil, Boris and Nancy Kanwisher and Rebecca Saxe} } @article {4553, title = {Response patterns in the developing social brain are organized by social and emotion features and disrupted in children diagnosed with autism spectrum disorder}, journal = {Cortex}, volume = {125}, year = {2020}, month = {Jan-04-2020}, pages = {12 - 29}, issn = {00109452}, doi = {10.1016/j.cortex.2019.11.021}, url = {https://www.ncbi.nlm.nih.gov/pubmed/31958654}, author = {Richardson, Hilary and Hyowon Gweon and Dodell-Feder, David and Malloy, Caitlin and Pelton, Hannah and Keil, Boris and Nancy Kanwisher and Rebecca Saxe} } @article {4552, title = {The speed of human social interaction perception}, journal = {NeuroImage}, year = {2020}, month = {Jan-04-2020}, pages = {116844}, abstract = {

The ability to perceive others{\textquoteright} social interactions, here defined as the directed contingent actions between two or more people, is a fundamental part of human experience that develops early in infancy and is shared with other primates. However, the neural computations underlying this ability remain largely unknown. Is social interaction recognition a rapid feedforward process or a slower post-perceptual inference? Here we used magnetoencephalography (MEG) decoding to address this question. Subjects in the MEG viewed snapshots of visually matched real-world scenes containing a pair of people who were either engaged in a social interaction or acting independently. The presence versus absence of a social interaction could be read out from subjects{\textquoteright} MEG data spontaneously, even while subjects performed an orthogonal task. This readout generalized across different people and scenes, revealing abstract representations of social interactions in the human brain. These representations, however, did not come online until quite late, at 300 ms after image onset, well after feedforward visual processes. In a second experiment, we found that social interaction readout still occurred at this same late latency even when subjects performed an explicit task detecting social interactions. We further showed that MEG responses distinguished between different types of social interactions (mutual gaze vs joint attention) even later, around 500 ms after image onset. Taken together, these results suggest that the human brain spontaneously extracts information about others{\textquoteright} social interactions, but does so slowly, likely relying on iterative top-down computations.

}, issn = {10538119}, doi = {10.1016/j.neuroimage.2020.116844}, url = {https://www.ncbi.nlm.nih.gov/pubmed/32302763}, author = {Leyla Isik and Mynick, Anna and Pantazis, Dimitrios and Nancy Kanwisher} } @conference {4525, title = {Using task-optimized neural networks to understand why brains have specialized processing for faces }, booktitle = {Computational and Systems Neurosciences}, year = {2020}, month = {02/2020}, address = {Denver, CO, USA}, author = {Dobs, Katharina and Alexander J. E. Kell and Julio Martinez-Trujillo and Michael Cohen and Nancy Kanwisher} } @conference {4524, title = {Why Are Face and Object Processing Segregated in the Human Brain? Testing Computational Hypotheses with Deep Convolutional Neural Networks }, booktitle = {Conference on Cognitive Computational Neuroscience}, year = {2020}, month = {09/2020}, address = {Berlin, Germany}, author = {Dobs, Katharina and Alexander J. E. Kell and Julio Martinez-Trujillo and Michael Cohen and Nancy Kanwisher} } @conference {4323, title = {Are topographic deep convolutional neural networks better models of the ventral visual stream?}, booktitle = {Conference on Cognitive Computational Neuroscience}, year = {2019}, author = {K.M. Jozwik and Lee, H. and Nancy Kanwisher and James J. DiCarlo} } @article {4255, title = {Divergence in the functional organization of human and macaque auditory cortex revealed by fMRI responses to harmonic tones}, journal = {Nature Neuroscience}, year = {2019}, month = {06/10/2019}, abstract = {

We report a difference between humans and macaque monkeys in the functional organization of cortical regions implicated in pitch perception. Humans but not macaques showed regions with a strong preference for harmonic sounds compared to noise, measured with both synthetic tones and macaque vocalizations. In contrast, frequency-selective tonotopic maps were similar between the two species. This species difference may be driven by the unique demands of speech and music perception in humans.

}, issn = {1097-6256}, doi = {10.1038/s41593-019-0410-7}, url = {https://www.nature.com/articles/s41593-019-0410-7}, author = {Sam V Norman-Haignere and Nancy Kanwisher and Josh H. McDermott and B. R. Conway} } @conference {4533, title = {Effects of Face Familiarity in Humans and Deep Neural Networks }, booktitle = {European Conference on Visual Perception}, year = {2019}, month = {09/2019}, address = {Leuven, Belgium}, author = {Dobs, Katharina and Ian A Palmer and Joanne Yuan and Yalda Mohsenzadeh and Aude Oliva and Nancy Kanwisher} } @article {4555, title = {Eye movements and retinotopic tuning in developmental prosopagnosia}, journal = {Journal of Vision}, volume = {19}, year = {2019}, month = {Jan-08-2019}, pages = {7}, issn = {1534-7362}, doi = {10.1167/19.9.7}, url = {https://www.ncbi.nlm.nih.gov/pubmed/31426085}, author = {M.F. Peterson and Ian Zaun and Hoke, Harris and Jiahui, Guo and Duchaine, Brad and Nancy Kanwisher} } @article {4180, title = {How face perception unfolds over time}, journal = {Nature Communications}, volume = {10}, year = {2019}, month = {01/2019}, abstract = {

Within a fraction of a second of viewing a face, we have already determined its gender, age and identity. A full understanding of this remarkable feat will require a characterization of the computational steps it entails, along with the representations extracted at each. Here, we used magnetoencephalography (MEG) to measure the time course of neural responses to faces, thereby addressing two fundamental questions about how face processing unfolds over time. First, using representational similarity analysis, we found that facial gender and age information emerged before identity information, suggesting a coarse-to-fine processing of face dimensions. Second, identity and gender representations of familiar faces were enhanced very early on, suggesting that the behavioral benefit for familiar faces results from tuning of early feed-forward processing mechanisms. These findings start to reveal the time course of face processing in humans, and provide powerful new constraints on computational theories of face perception.

}, doi = {10.1038/s41467-019-09239-1}, url = {http://www.nature.com/articles/s41467-019-09239-1}, author = {Dobs, Katharina and Leyla Isik and Pantazis, Dimitrios and Nancy Kanwisher} } @article {4178, title = {An integrative computational architecture for object-driven cortex}, journal = {Current Opinion in Neurobiology}, volume = {55}, year = {2019}, month = {01/2019}, pages = {73 - 81}, abstract = {

Computational architecture for object-driven cortex

Objects in motion activate multiple cortical regions in every lobe of the human brain. Do these regions represent a collection of independent systems, or is there an overarching functional architecture spanning all of object-driven cortex? Inspired by recent work in artificial intelligence (AI), machine learning, and cognitive science, we consider the hypothesis that these regions can be understood as a coherent network implementing an integrative computational system that unifies the functions needed to perceive, predict, reason about, and plan with physical objects{\textemdash}as in the paradigmatic case of using or making tools. Our proposal draws on a modeling framework that combines multiple AI methods, including causal generative models, hybrid symbolic-continuous planning algorithms, and neural recognition networks, with object-centric, physics-based representations. We review evidence relating specific components of our proposal to the specific regions that comprise object-driven cortex, and lay out future research directions with the goal of building a complete functional and mechanistic account of this system.

}, issn = {09594388}, doi = {10.1016/j.conb.2019.01.010}, url = {https://linkinghub.elsevier.com/retrieve/pii/S0959438818301995}, author = {Ilker Yildirim and Jiajun Wu and Nancy Kanwisher and Joshua B. Tenenbaum} } @article {4554, title = {Invariant representations of mass in the human brain}, journal = {eLife}, volume = {8}, year = {2019}, month = {May-12-2020}, doi = {10.7554/eLife.46619}, url = {https://www.ncbi.nlm.nih.gov/pubmed/31845887}, author = {Schwettmann, Sarah and Joshua B. Tenenbaum and Nancy Kanwisher} } @article {4556, title = {Representational similarity precedes category selectivity in the developing ventral visual pathway}, journal = {NeuroImage}, volume = {197}, year = {2019}, month = {Jan-08-2019}, pages = {565 - 574}, issn = {10538119}, doi = {10.1016/j.neuroimage.2019.05.010}, url = {https://www.ncbi.nlm.nih.gov/pubmed/31077844}, author = {Cohen, Michael A. and Dilks, Daniel D. and Kami Koldewyn and Weigelt, Sarah and Jenelle Feather and Alexander J. E. Kell and Keil, Boris and Fischl, Bruce and Z{\"o}llei, Lilla and Lawrence Wald and Rebecca Saxe and Nancy Kanwisher} } @conference {4322, title = {To find better neural network models of human vision, find better neural network models of primate vision}, booktitle = {BioRxiv}, year = {2019}, abstract = {

Specific deep artificial neural networks (ANNs) are the current best models of ventral visual processing and object recognition behavior in monkeys. We here explore whether models of non-human primate vision generalize to visual processing in the human primate brain. Specifically, we asked if model match to monkey IT is a predictor of model match to human IT, even when scoring those matches on different images. We found that the model match to monkey IT is a positive predictor of the model match to human IT (R = 0.36), and that this approach outperforms the current standard predictor of model accuracy on ImageNet. This suggests a more powerful approach for pre-selecting models as hypotheses of human brain processing.

}, url = {https://www.biorxiv.org/content/10.1101/688390v1.full}, author = {K.M. Jozwik and Martin Schrimpf and Nancy Kanwisher and James J. DiCarlo} } @article {3517, title = {What is changing when: decoding visual information in movies from human intracranial recordings}, journal = {NeuroImage}, volume = {180, Part A}, year = {2018}, month = {10/2018}, pages = {147-159}, abstract = {

The majority of visual recognition studies have focused on the neural responses to repeated presentations of static stimuli with abrupt and well-defined onset and offset times. In contrast, natural vision involves unique renderings of visual inputs that are continuously changing without explicitly defined temporal transitions. Here we considered commercial movies as a coarse proxy to natural vision. We recorded intracranial field potential signals from 1,284 electrodes implanted in 15 patients with epilepsy while the subjects passively viewed commercial movies. We could rapidly detect large changes in the visual inputs within approximately 100 ms of their occurrence, using exclusively field potential signals from ventral visual cortical areas including the inferior temporal gyrus and inferior occipital gyrus. Furthermore, we could decode the content of those visual changes even in a single movie presentation, generalizing across the wide range of transformations present in a movie. These results present a methodological framework for studying cognition during dynamic and natural vision.

}, keywords = {Electrocorticography (ECoG), Movies, Natural vision, neural decoding, object recognition, Ventral pathway}, doi = {10.1016/j.neuroimage.2017.08.027}, url = {https://www.sciencedirect.com/science/article/pii/S1053811917306742}, author = {Leyla Isik and Jedediah Singer and Nancy Kanwisher and Madsen JR and Anderson WS and Gabriel Kreiman} } @article {2568, title = {Organization of high-level visual cortex in human infants}, journal = {Nature Communications}, year = {2017}, month = {01/2017}, abstract = {

How much of the structure of the human mind and brain is already specified at birth, and how much arises from experience? In this article, we consider the test case of extrastriate visual cortex, where a highly systematic functional organization is present in virtually every normal adult, including regions preferring behaviourally significant stimulus categories, such as faces, bodies, and scenes. Novel methods were developed to scan awake infants with fMRI, while they viewed multiple categories of visual stimuli. Here we report that the visual cortex of 4{\textendash}6-month-old infants contains regions that respond preferentially to abstract categories (faces and scenes), with a spatial organization similar to adults. However, precise response profiles and patterns of activity across multiple visual categories differ between infants and adults. These results demonstrate that the large-scale organization of category preferences in visual cortex is adult-like within a few months after birth, but is subsequently refined through development.

}, doi = {10.1038/ncomms13995}, url = {http://www.nature.com/doifinder/10.1038/ncomms13995}, author = {Ben Deen and Richardson, Hilary and Dilks, Daniel D. and Takahashi, Atsushi and Keil, Boris and Lawrence Wald and Nancy Kanwisher and Rebecca Saxe} } @article {3450, title = {Perceiving social interactions in the posterior superior temporal sulcus}, journal = {Proceedings of the National Academy of Sciences}, volume = {114}, year = {2017}, month = {10/2017}, abstract = {

Primates are highly attuned not just to social characteristics of individual agents, but also to social interactions between multiple agents. Here we report a neural correlate of the representation of social interactions in the human brain. Specifically, we observe a strong univariate response in the posterior superior temporal sulcus (pSTS) to stimuli depicting social interactions between two agents, compared with (i) pairs of agents not interacting with each other, (ii) physical interactions between inanimate objects, and (iii) individual animate agents pursuing goals and interacting with inanimate objects. We further show that this region contains information about the nature of the social interaction{\textemdash}specifically, whether one agent is helping or hindering the other. This sensitivity to social interactions is strongest in a specific subregion of the pSTS but extends to a lesser extent into nearby regions previously implicated in theory of mind and dynamic face perception. This sensitivity to the presence and nature of social interactions is not easily explainable in terms of low-level visual features, attention, or the animacy, actions, or goals of individual agents. This region may underlie our ability to understand the structure of our social world and navigate within it.

}, issn = {Print ISSN: 0027-8424; Online ISSN: 1091-6490}, doi = { https://doi.org/10.1073/pnas.1714471114 }, url = {http://www.pnas.org/content/early/2017/10/06/1714471114.short}, author = {Leyla Isik and Kami Koldewyn and David Beeler and Nancy Kanwisher} } @article {2569, title = {The Quest for the FFA and Where It Led}, journal = {The Journal of Neuroscience}, volume = {37}, year = {2017}, month = {02/2017}, pages = {1056 - 1061}, abstract = {

This article tells the story behind our first paper on the fusiform face area (FFA): how we chose the question, developed the methods, and followed the data to find the FFA and subsequently many other functionally specialized cortical regions. The paper{\textquoteright}s impact had less to do with the particular findings in the paper itself and more to do with the method that it promoted and the picture of the human mind and brain that it led to. The use of a functional localizer to define a candidate region in each subject individually enabled us not just to make pictures of brain activation, but also to ask principled, hypothesis-driven questions about a thing in nature. This method enabled stronger and more extensive tests of the function of each cortical region than had been possible before in humans and, as a result, has produced a large body of evidence that the human cortex contains numerous regions that are specifically engaged in particular mental processes. The growing inventory of cortical regions with distinctive and often very specific functions can be seen as an initial sketch of the basic components of the human mind. This sketch also serves as a roadmap into the vast and exciting new landscape of questions about the computations, structural connections, time course, development, plasticity, and evolution of each of these regions, as well as the hardest question of all: how do these regions work together to produce human intelligence?

}, issn = {0270-6474}, doi = {10.1523/JNEUROSCI.1706-16.2016}, url = {http://www.jneurosci.org/lookup/doi/10.1523/JNEUROSCI.1706-16.2016}, author = {Nancy Kanwisher} } @article {3451, title = {What is changing when: Decoding visual information in movies from human intracranial recordings}, journal = {Neuroimage}, year = {2017}, abstract = {

The majority of visual recognition studies have focused on the neural responses to repeated presentations of static stimuli with abrupt and well-defined onset and offset times. In contrast,\ natural vision\ involves unique renderings of visual inputs that are continuously changing without explicitly defined temporal transitions. Here we considered commercial movies as a coarse proxy to natural vision. We recorded intracranial\ field potential\ signals from 1,284 electrodes implanted in 15 patients with\ epilepsy\ while the subjects passively viewed commercial movies. We could rapidly detect large changes in the visual inputs within approximately 100\ ms of their occurrence, using exclusively field potential signals from ventral visual cortical areas including the inferior temporal\ gyrus\ and inferior\ occipitalgyrus. Furthermore, we could decode the content of those visual changes even in a single movie presentation, generalizing across the wide range of transformations present in a movie. These results present a methodological framework for studying cognition during dynamic and natural vision.

}, doi = {https://doi.org/10.1016/j.neuroimage.2017.08.027}, url = {https://www.sciencedirect.com/science/article/pii/S1053811917306742}, author = {Leyla Isik and Jedediah Singer and Joseph Madsen and Nancy Kanwisher and Gabriel Kreiman} } @article {2572, title = {Color-Biased Regions of the Ventral Visual Pathway Lie between Face- and Place-Selective Regions in Humans, as in Macaques}, journal = {Journal of Neuroscience}, volume = {36}, year = {2016}, month = {02/2016}, pages = {1682 - 1697}, abstract = {

The existence of color-processing regions in the human ventral visual pathway (VVP) has long been known from patient and imaging studies, but their location in the cortex relative to other regions, their selectivity for color compared with other properties (shape and object category), and their relationship to color-processing regions found in nonhuman primates remain unclear. We addressed these questions by scanning 13 subjects with fMRI while they viewed two versions of movie clips (colored, achromatic) of five different object classes (faces, scenes, bodies, objects, scrambled objects). We identified regions in each subject that were selective for color, faces, places, and object shape, and measured responses within these regions to the 10 conditions in independently acquired data. We report two key findings. First, the three previously reported color-biased regions (located within a band running posterior{\textendash}anterior along the VVP, present in most of our subjects) were sandwiched between face-selective cortex and place-selective cortex, forming parallel bands of face, color, and place selectivity that tracked the fusiform gyrus/collateral sulcus. Second, the posterior color-biased regions showed little or no selectivity for object shape or for particular stimulus categories and showed no interaction of color preference with stimulus category, suggesting that they code color independently of shape or stimulus category; moreover, the shape-biased lateral occipital region showed no significant color bias. These observations mirror results in macaque inferior temporal cortex (Lafer-Sousa and Conway, 2013), and taken together, these results suggest a homology in which the entire tripartite face/color/place system of primates migrated onto the ventral surface in humans over the course of evolution.

SIGNIFICANCE STATEMENT Here we report that color-biased cortex is sandwiched between face-selective and place-selective cortex on the bottom surface of the brain in humans. This face/color/place organization mirrors that seen on the lateral surface of the temporal lobe in macaques, suggesting that the entire tripartite system is homologous between species. This result validates the use of macaques as a model for human vision, making possible more powerful investigations into the connectivity, precise neural codes, and development of this part of the brain. In addition, we find substantial segregation of color from shape selectivity in posterior regions, as observed in macaques, indicating a considerable dissociation of the processing of shape and color in both species.

}, issn = {0270-6474}, doi = {10.1523/JNEUROSCI.3164-15.2016}, url = {http://www.jneurosci.org/cgi/doi/10.1523/JNEUROSCI.3164-15.2016}, author = {R. Lafer-Sousa and B. R. Conway and Nancy Kanwisher} } @article {2570, title = {Functional neuroanatomy of intuitive physical inference}, journal = {Proceedings of the National Academy of Sciences}, volume = {113}, year = {2016}, month = {06/2016}, pages = {E5072 - E5081}, abstract = {

To engage with the world{\textemdash}to understand the scene in front of us, plan actions, and predict what will happen next{\textemdash}we must have an intuitive grasp of the world{\textquoteright}s physical structure and dynamics. How do the objects in front of us rest on and support each other, how much force would be required to move them, and how will they behave when they fall, roll, or collide? Despite the centrality of physical inferences in daily life, little is known about the brain mechanisms recruited to interpret the physical structure of a scene and predict how physical events will unfold. Here, in a series of fMRI experiments, we identified a set of cortical regions that are selectively engaged when people watch and predict the unfolding of physical events{\textemdash}a {\textquotedblleft}physics engine{\textquotedblright} in the brain. These brain regions are selective to physical inferences relative to nonphysical but otherwise highly similar scenes and tasks. However, these regions are not exclusively engaged in physical inferences per se or, indeed, even in scene understanding; they overlap with the domain-general {\textquotedblleft}multiple demand{\textquotedblright} system, especially the parts of that system involved in action planning and tool use, pointing to a close relationship between the cognitive and neural mechanisms involved in parsing the physical content of a scene and preparing an appropriate action.

}, issn = {0027-8424}, doi = {10.1073/pnas.1610344113}, url = {http://www.pnas.org/lookup/doi/10.1073/pnas.1610344113}, author = {Fischer, Jason and Mikhael, John G. and Joshua B. Tenenbaum and Nancy Kanwisher} } @article {1792, title = {Individual Differences in Face Looking Behavior Generalize from the Lab to the World}, journal = {Journal of Vision}, volume = {16}, year = {2016}, month = {05/2016}, chapter = {12}, abstract = {

Recent laboratory studies have found large, stable individual differences in the location people first fixate when identifying faces, ranging from the brows to the mouth. Importantly, this variation is strongly associated with differences in fixation-specific identification performance such that an individual{\textquoteright}s recognition ability is maximized when looking at their preferred location (Mehoudar, Arizpe, Baker, \& Yovel, 2014; Peterson \& Eckstein, 2013). This finding suggests that face representations are retinotopic and individuals enact gaze strategies that optimize identification, yet the extent to which this behavior reflects real-world gaze behavior is unknown. Here, we used mobile eye-trackers to test whether individual differences in face-gaze generalize from lab to real-world vision. In-lab fixations were measured with a speeded face identification task, while real-world behavior was measured as subjects freely walked around the MIT campus. We found a strong correlation between the patterns of individual differences in face-gaze in the laboratory and real-world settings. Our findings support the hypothesis that individuals optimize real-world face identification by consistently fixating the same location and thus strongly constraining the space of retinotopic input. The methods developed for this study entailed collecting a large set of high-definition, wide field-of-view natural videos from head-mounted cameras and the viewer{\textquoteright}s fixation position, allowing us to characterize subject{\textquoteright}s actually-experienced real-world retinotopic images. These images enable us to ask how vision is optimized not just for the statistics of the {\textquotedblleft}natural images{\textquotedblright} found in web databases, but of the truly natural, retinotopic images that have landed on actual human retinae during real-world experience.

}, doi = {10.1167/16.7.12.}, url = {http://jov.arvojournals.org/article.aspx?articleid=2524135\&resultClick=1}, author = {M.F. Peterson and Jing Lin and Ian Zaun and Nancy Kanwisher} } @article {2867, title = {Individual differences in face-looking behavior generalize from the lab to the world.}, journal = {Journal of Vision}, year = {2016}, author = {M.F. Peterson and J. Lin and Ian Zaun and Nancy Kanwisher} } @article {2326, title = {Measuring and modeling the perception of natural and unconstrained gaze in humans and machines}, year = {2016}, month = {11/2016}, abstract = {

Humans are remarkably adept at interpreting the gaze direction of other individuals in their surroundings. This skill is at the core of the ability to engage in joint visual attention, which is essential for establishing social interactions. How accurate are humans in determining the gaze direction of others in lifelike scenes, when they can move their heads and eyes freely, and what are the sources of information for the underlying perceptual processes? These questions pose a challenge from both empirical and computational perspectives, due to the complexity of the visual input in real-life situations. Here we measure empirically human accuracy in perceiving the gaze direction of others in lifelike scenes, and study computationally the sources of information and representations underlying this cognitive capacity. We show that humans perform better in face-to-face conditions compared with recorded conditions, and that this advantage is not due to the availability of input dynamics. We further show that humans are still performing well when only the eyes-region is visible, rather than the whole face. We develop a computational model, which replicates the pattern of human performance, including the finding that the eyes-region contains on its own, the required information for estimating both head orientation and direction of gaze. Consistent with neurophysiological findings on task-specific face regions in the brain, the learned computational representations reproduce perceptual effects such as the Wollaston illusion, when trained to estimate direction of gaze, but not when trained to recognize objects or faces.

}, keywords = {computational evaluation, computational modeling, Computer vision, empirical evaluation, estimation of gaze direction, Gaze perception, joint attention, Machine Learning}, author = {Daniel Harari and Tao Gao and Nancy Kanwisher and Joshua B. Tenenbaum and Shimon Ullman} } @article {2198, title = {Neural Representations Integrate the Current Field of View with the Remembered 360{\textdegree} Panorama}, journal = {Current Biology}, year = {2016}, month = {09/08/2016}, abstract = {

We experience our visual environment as a seamless, immersive panorama. Yet, each view is discrete and fleeting, separated by expansive eye movements and discontinuous views of our spatial surroundings. How are discrete views of a panoramic environment knit together into a broad, unified memory representation? Regions of the brain{\textquoteright}s {\textquotedblleft}scene network{\textquotedblright} are well poised to integrate retinal input and memory [ 1 ]: they are visually driven [ 2, 3 ] but also densely interconnected with memory structures in the medial temporal lobe [ 4 ]. Further, these regions harbor memory signals relevant for navigation [ 5{\textendash}8 ] and adapt across overlapping shifts in scene viewpoint [ 9, 10 ]. However, it is unknown whether regions of the scene network support visual memory for the panoramic environment outside of the current field of view and, further, how memory for the surrounding environment influences ongoing perception. Here, we demonstrate that specific regions of the scene network{\textemdash}the retrosplenial complex (RSC) and occipital place area (OPA){\textemdash}unite discrete views of a\ 360{\textdegree} panoramic environment, both current and out of sight, in a common representational space. Further, individual scene views prime associated representations of the panoramic environment in behavior, facilitating subsequent perceptual judgments. We propose that this dynamic interplay between memory and perception plays an important role in weaving the fabric of continuous visual experience.

}, doi = {10.1016/j.cub.2016.07.002}, url = {http://www.cell.com/current-biology/abstract/S0960-9822(16)30753-9}, author = {Robertson, Caroline~E. and Katherine Hermann and Mynick, Anna and Kravitz, Dwight~J. and Nancy Kanwisher} } @article {2571, title = {The occipital place area represents the local elements of scenes}, journal = {NeuroImage}, volume = {132}, year = {2016}, month = {02/2016}, pages = {417 - 424}, abstract = {

Neuroimaging studies have identified three scene-selective regions in human cortex: parahippocampal place area (PPA), retrosplenial complex (RSC), and occipital place area (OPA). However, precisely what scene information each region represents is not clear, especially for the least studied, more posterior OPA. Here we hypothesized that OPA represents local elements of scenes within two independent, yet complementary scene descriptors: spatial boundary (i.e., the layout of external surfaces) and scene content (e.g., internal objects). If OPA processes the local elements of spatial boundary information, then it should respond to these local elements (e.g., walls) themselves, regardless of their spatial arrangement. Indeed, we found that OPA, but not PPA or RSC, responded similarly to images of intact rooms and these same rooms in which the surfaces were fractured and rearranged, disrupting the spatial boundary. Next, if OPA represents the local elements of scene content information, then it should respond more when more such local elements (e.g., furniture) are present. Indeed, we found that OPA, but not PPA or RSC, responded more to multiple than single pieces of furniture. Taken together, these findings reveal that OPA analyzes local scene elements - both in spatial boundary and scene content representation - while PPA and RSC represent global scene properties.

}, issn = {10538119}, doi = {10.1016/j.neuroimage.2016.02.062}, url = {https://www.ncbi.nlm.nih.gov/pubmed/26931815}, author = {Kamps, Frederik S. and Julian, Joshua B. and Jonas Kubilius and Nancy Kanwisher and Dilks, Daniel D.} } @article {1751, title = {Functional organization of social perception and cognition in the superior temporal sulcus}, journal = {Cerebral Cortex}, volume = {25}, year = {2015}, month = {11/2015}, pages = {4596-4609}, abstract = {

The superior temporal sulcus (STS) is considered a hub for social perception and cognition, including the perception of faces and human motion, as well as understanding others{\textquoteright} actions, mental states, and language. However, the functional organization of the STS remains debated: Is this broad region composed of multiple functionally distinct modules, each specialized for a different process, or are STS subregions multifunctional, contributing to multiple processes? Is the STS spatially organized, and if so, what are the dominant features of this organization? We address these questions by measuring STS responses to a range of social and linguistic stimuli in the same set of human participants, using fMRI. We find a number of STS subregions that respond selectively to certain types of social input, organized along a posterior-to-anterior axis. We also identify regions of overlapping response to multiple contrasts, including regions responsive to both language and theory of mind, faces and voices, and faces and biological motion. Thus, the human STS contains both relatively domain-specific areas, and regions that respond to multiple types of social information.

}, doi = { 10.1093/cercor/bhv111}, url = {http://cercor.oxfordjournals.org/content/25/11/4596.full}, author = {Ben Deen and Kami Koldewyn and Nancy Kanwisher and Rebecca Saxe} } @article {1749, title = {Functional organization of the human superior temporal sulcus}, number = { Poster Number: 4380 }, year = {2015}, month = {6/2015}, address = {Honolulu, HI}, abstract = {

The human superior temporal sulcus (STS) has been implicated in a broad range of social perceptual and cognitive processes, including the perception of faces, biological motion, and vocal sounds, and the understanding of language and mental states. However, little is known about the overall functional organization of these responses. Does the STS contain distinct, specialized regions for processing different types of social information? Or is cortex in the STS largely multifunctional, with each region engaged in multiple different computations (Hein, 2008)? Because prior work has largely studied these processes independently, this question remains unanswered. Here, we first identify distinct functional subregions of the STS, and then examine their response to a broad range of social stimuli.

}, url = {https://ww4.aievolution.com/hbm1501/index.cfm?do=abs.viewAbs\&abs=3635}, author = {Ben Deen and Nancy Kanwisher and Rebecca Saxe} } @article {940, title = {Exploring the functional organization of the superior temporal sulcus with a broad set of naturalistic stimuli}, year = {2014}, author = {Ben Deen and Nancy Kanwisher and Rebecca Saxe} }