Model connectivity: leveraging the power of encoding models to overcome the limitations of functional connectivity.

Functional connectivity (FC) is the most popular method for recovering functional networks of brain areas with fMRI. However, because FC is defined as temporal correlations in brain activity, FC networks are confounded by noise and lack a precise functional role. To overcome these limitations, we developed model connectivity (MC). MC is defined as similarities in encoding model weights, which quantify reliable functional activity in terms of interpretable stimulus- or task-related features. To compare FC and MC, both methods were applied to a naturalistic story listening dataset. FC recovered spatially broad networks that are confounded by noise, and that lack a clear role during natural language comprehension. By contrast, MC recovered spatially localized networks that are robust to noise, and that represent distinct categories of semantic concepts. Thus, MC is a powerful data-driven approach for recovering and interpreting the functional networks that support complex cognitive processes.

Visual noise consisting of X-junctions has only a minimal adverse effect on object recognition.

In 1968, Guzman showed that the myriad of surfaces composing a highly complex and novel assemblage of volumes can readily be assigned to their appropriate volumes in terms of the constraints offered by the vertices of coterminating edges. Of particular importance was the L-vertex, produced by the cotermination of two contours, which provides strong evidence for the termination of a 2-D surface. An X-junction, formed by the crossing of two contours without a change of direction at the crossing, played no role in the segmentation of a scene. If the potency of noise elements to affect recognition performance reflects their relevancy to the segmentation of scenes, as was suggested by Guzman, gaps in an object's contours bounded by irrelevant X-junctions would be expected to have little or no adverse effect on shape-based object recognition, whereas gaps bounded by L-junctions would be expected to have a strong deleterious effect when they disrupt the smooth continuation of contours. Guzman's roles for the various vertices and junctions have never been put to systematic test with respect to human object recognition. By adding identical noise contours to line drawings of objects that produced either L-vertices or X-junctions, these shape features could be compared with respect to their disruption of object recognition. Guzman's insights that irrelevant L-vertices should be highly disruptive and irrelevant X-vertices would have only a minimal deleterious effect were confirmed.

A face in a (temporal) crowd.

Familiar objects, specified by name, can be identified with high accuracy when embedded in a rapidly presented sequence of images at rates exceeding 10 images/s. Not only can target objects be detected at such brief presentation rates, they can also be detected under high uncertainty, where their classification is defined negatively, e.g., "Not a Tool." The identification of a familiar speaker's voice declines precipitously when uncertainty is increased from one to a mere handful of possible speakers. Is the limitation imposed by uncertainty, i.e., the number of possible individuals, a general characteristic of processes for person individuation such that the identifiability of a familiar face would undergo a similar decline with uncertainty? Specifically, could the presence of an unnamed celebrity, thus any celebrity, be detected when presented in a rapid sequence of unfamiliar faces? If so, could the celebrity be identified? Despite the markedly greater physical similarity of faces compared to objects that are, say, not tools, the presence of a celebrity could be detected with moderately high accuracy (∼75%) at rates exceeding 7 faces/s. False alarms were exceedingly rare as almost all the errors were misses. Detection accuracy by moderate congenital prosopagnosics was lower than controls, but still well above chance. Given the detection of the presence of a celebrity, all subjects were almost always able to identify that celebrity, providing no role for a covert familiarity signal outside of awareness.

The cognitive neuroscience of person identification.

We compare and contrast five differences between person identification by voice and face. 1. There is little or no cost when a familiar face is to be recognized from an unrestricted set of possible faces, even at Rapid Serial Visual Presentation (RSVP) rates, but the accuracy of familiar voice recognition declines precipitously when the set of possible speakers is increased from one to a mere handful. 2. Whereas deficits in face recognition are typically perceptual in origin, those with normal perception of voices can manifest severe deficits in their identification. 3. Congenital prosopagnosics (CPros) and congenital phonagnosics (CPhon) are generally unable to imagine familiar faces and voices, respectively. Only in CPros, however, is this deficit a manifestation of a general inability to form visual images of any kind. CPhons report no deficit in imaging non-voice sounds. 4. The prevalence of CPhons of 3.2% is somewhat higher than the reported prevalence of approximately 2.0% for CPros in the population. There is evidence that CPhon represents a distinct condition statistically and not just normal variation. 5. Face and voice recognition proficiency are uncorrelated rather than reflecting limitations of a general capacity for person individuation.