A network of multiple brain regions is recruited in face perception. Our understanding of the functional properties of this network can be facilitated by explicating the structural white matter connections that exist between its functional nodes. We accomplished this using functional MRI (fMRI) in combination with fiber tractography on high angular resolution diffusion weighted imaging data. We identified the three nodes of the core face network: the “occipital face area” (OFA), the “fusiform face area” (mid-fusiform gyrus or mFus), and the superior temporal sulcus (STS). Additionally, a region of the anterior temporal lobe (aIT), implicated as being important for face perception was identified. Our data suggest that we can further divide the OFA into multiple anatomically distinct clusters – a partitioning consistent with several recent neuroimaging results. More generally, structural white matter connectivity within this network revealed: 1) Connectivity between aIT and mFus, and between aIT and occipital regions, consistent with studies implicating this posterior to anterior pathway as critical to normal face processing; 2) Strong connectivity between mFus and each of the occipital face-selective regions, suggesting that these three areas may subserve different functional roles; 3) Almost no connectivity between STS and mFus, or between STS and the other face-selective regions. Overall, our findings suggest a re-evaluation of the “core” face network with respect to what functional areas are or are not included in this network.

Humans’ ability to rapidly and accurately detect, identify, and classify faces under variable conditions derives from a network of brain regions highly tuned to face information. The fusiform face area (FFA) is thought to be a computational hub for face processing, however temporal dynamics of face information processing in FFA remains unclear. Here we use multivariate pattern classification to decode the temporal dynamics of expression-invariant face information processing using electrodes placed directly upon FFA in humans. Early FFA activity (50-75 ms) contained information regarding whether participants were viewing a face. Activity between 200-500 ms contained expression-invariant information about which of 70 faces participants were viewing along with the individual differences in facial features and their configurations. Long-lasting (500+ ms) broadband gamma frequency activity predicted task performance. These results elucidate the dynamic computational role FFA plays in multiple face processing stages and indicate what information is used in performing these visual analyses.

Vision science, particularly machine vision, has been revolutionized by introducing large-scale image datasets and statistical learning approaches. Yet, human neuroimaging studies of visual perception still rely on small numbers of images (around 100) due to time-constrained experimental procedures. To apply statistical learning approaches that include neuroscience, the number of images used in neuroimaging must be significantly increased. We present BOLD5000, a human functional MRI (fMRI) study that includes almost 5,000 distinct images depicting real-world scenes. Beyond dramatically increasing image dataset size relative to prior fMRI studies, BOLD5000 also accounts for image diversity, overlapping with standard computer vision datasets by incorporating images from the Scene UNderstanding (SUN), Common Objects in Context (COCO), and ImageNet datasets. The scale and diversity of these image datasets, combined with a slow event-related fMRI design, enables fine-grained exploration into the neural representation of a wide range of visual features, categories, and semantics. Concurrently, BOLD5000 brings us closer to realizing Marr’s dream of a singular vision science–the intertwined study of biological and computer vision.

Studies of biological motion have identified specialized neural machinery for the perception of human actions. Our experiments examine behavioral and neural responses to novel, articulating and non-human 'biological motion'. We find that non-human actions are seen as animate, but do not convey body structure when viewed as point-lights. Non-human animations fail to engage the human STSp, and neural responses in pITG, ITS and FFA/FBA are reduced only for the point-light versions. Our results suggest that STSp is specialized for human motion and ventral temporal regions support general, dynamic shape perception. We also identify a region in ventral temporal cortex 'selective' for non-human animations, which we suggest processes novel, dynamic objects.

Feedforward visual object perception recruits a cortical network that is assumed to be hierarchical, progressing from basic visual features to complete object representations. However, the nature of the intermediate features related to this transformation remains poorly understood. Here, we explore how well different computer vision recognition models account for neural object encoding across the human cortical visual pathway as measured using fMRI. These neural data, collected during the viewing of 60 images of real-world objects, were analyzed with a searchlight procedure as in Kriegeskorte, Goebel, and Bandettini (2006): Within each searchlight sphere, the obtained patterns of neural activity for all 60 objects were compared to model responses for each computer recognition algorithm using representational dissimilarity analysis (Kriegeskorte et al., 2008). Although each of the computer vision methods significantly accounted for some of the neural data, among the different models, the scale invariant feature transform (Lowe, 2004), encoding local visual properties gathered from "interest points," was best able to accurately and consistently account for stimulus representations within the ventral pathway. More generally, when present, significance was observed in regions of the ventral-temporal cortex associated with intermediate-level object perception. Differences in model effectiveness and the neural location of significant matches may be attributable to the fact that each model implements a different featural basis for representing objects (e.g., more holistic or more parts-based). Overall, we conclude that well-known computer vision recognition systems may serve as viable proxies for theories of intermediate visual object representation.

Neuroimaging studies of biological motion perception have found a network of coordinated brain areas, the hub of which appears to be the human posterior superior temporal sulcus (STSp). Understanding the functional role of the STSp requires characterizing the response tuning of neuronal populations underlying the BOLD response. Thus far our understanding of these response properties comes from single-unit studies of the monkey anterior STS, which has individual neurons tuned to body actions, with a small population invariant to changes in viewpoint, position and size of the action being viewed. To measure for homologous functional properties on the human STS, we used fMR-adaptation to investigate action, position and size invariance. Observers viewed pairs of point-light animations depicting human actions that were either identical, differed in the action depicted, locally scrambled, or differed in the viewing perspective, the position or the size. While extrastriate hMT+ had neural signals indicative of viewpoint specificity, the human STS adapted for all of these changes, as compared to viewing two different actions. Similar findings were observed in more posterior brain areas also implicated in action recognition. Our findings are evidence for viewpoint invariance in the human STS and related brain areas, with the implication that actions are abstracted into object-centered representations during visual analysis.

SUMMARY Investigations of functional (re)organization in children who have undergone large cortical resections offer a unique opportunity to elucidate the nature and extent of cortical plasticity. We report findings from a 3-year investigation of a child, U.D., who underwent surgical removal of the right occipital and posterior temporal lobes at age 6 years 9 months. Relative to controls, post-surgically, U.D. showed age-appropriate intellectual performance and visuoperceptual face and object recognition skills. Using fMRI at five different time points, we observed a persistent hemianopia and no visual field remapping. In category-selective visual cortices, however, object- and scene-selective regions in the intact left hemisphere were stable early on, but regions subserving face and word recognition emerged later and evinced competition for cortical representation. These findings reveal alterations in the selectivity and topography of category-selective regions when confined to a single hemisphere and provide insights into dynamic functional changes in extrastriate cortical architecture.

The study reported here asks whether the use of probabilistic information indicating forecast uncertainty improves the quality of deterministic weather decisions. Participants made realistic wind speed forecasts based on historical data in a controlled laboratory setting. They also decided whether it was appropriate to post an advisory for winds greater than 20 knots during the same time intervals and in the same geographic locations. On half of the forecasts each participant also read a chart showing color-coded probability of winds greater than 20 knots (10.29 m s -1 ). Participants had a general tendency to post too many advisories in the low probability situations (0-10%) and too few advisories in very high probability situations (90-100%). However, the probability product attenuated these biases. When participants used the probability product they posted fewer advisories when the probability of high winds was low and they posted more advisories when the probability of high winds was high. The difference was due to the probability product alone because the within subjects design and counterbalancing of forecast dates ruled out alternative explanations. The data suggest that the probability product improved threshold forecast decisions.