This article describes a computational model, called DIVA, that provides a quantitative framework for understanding the roles of various brain regions involved in speech acquisition and production. An overview of the DIVA model is first provided, along with descriptions of the computations performed in the different brain regions represented in the model. Particular focus is given to the model's speech sound map, which provides a link between the sensory representation of a speech sound and the motor program for that sound. Neurons in this map share with "mirror neurons" described in monkey ventral premotor cortex the key property of being active during both production and perception of specific motor actions. As the DIVA model is defined both computationally and anatomically, it is ideal for generating precise predictions concerning speechrelated brain activation patterns observed during functional imaging experiments. The DIVA model thus provides a well-defined framework for guiding the interpretation of experimental results related to the putative human speech mirror system.

Whereas many laboratory-studied decisions involve a highly trained animal identifying an ambiguous stimulus, many naturalistic decisions do not. Consumption decisions, for instance, involve determining whether to eject or consume an already identified stimulus in the mouth and are decisions that can be made without training. By standard analyses, rodent cortical single-neuron taste responses come to predict such consumption decisions across the 500 ms preceding the consumption or rejection itself; decision-related firing emerges well after stimulus identification. Analyzing single-trial ensemble activity using hidden Markov models, we show these decision-related cortical responses to be part of a reliable sequence of states (each defined by the firing rates within the ensemble) separated by brief state-to-state transitions, the latencies of which vary widely between trials. When we aligned data to the onset of the (late-appearing) state that dominates during the time period in which single-neuron firing is correlated to taste palatability, the apparent ramp in stimulusaligned choice-related firing was shown to be a much more precipitous coherent jump. This jump in choice-related firing resembled a step function more than it did the output of a standard (ramping) decision-making model, and provided a robust prediction of decision latency in single trials. Together, these results demonstrate that activity related to naturalistic consumption decisions emerges nearly instantaneously in cortical ensembles.

The brightness and color of a surface depends on its contrast with nearby surfaces. For example, a gray surface can appear very light when surrounded by a black surface or dark when surrounded by a white surface. Some theories suggest that perceived surface brightness and color is represented explicitly by neural signals in cortical visual field maps; these neural signals are not initiated by the stimulus itself but rather by the contrast signals at the borders. Here, we use functional magnetic resonance imaging (fMRI) to search for such neural "filling-in" signals. Although we find the usual strong relationship between local contrast and fMRI response, when perceived brightness or color changes are induced by modulating a surrounding field, rather than the surface itself, we find there is no corresponding local modulation in primary visual cortex or other nearby retinotopic maps. Moreover, when we model the obtained fMRI responses, we find strong evidence for contributions of both local and long-range edge responses. We argue that such extended edge responses may be caused by neurons previously identified in neurophysiological studies as being brightness responsive, a characterization that may therefore need to be revised. We conclude that the visual field maps of human V1 and V2 do not contain filled-in, topographical representations of surface brightness and color.

An ongoing debate in developmental cognitive neuroscience is whether individuals with autism are able to learn prototypical category representations from multiple exemplars. Prototype learning and memory were examined in a group of high-functioning autistic boys and young men, using a classic paradigm in which participants learned to classify novel dot patterns into one of two categories. Participants were trained on distorted versions of category prototypes until they reached a criterion level of performance. During transfer testing, participants were shown the training items together with three novel stimulus sets manifesting variable levels of physical distortion (low, medium, or high distortion) relative to the unseen prototypes. Two experiments were conducted, differing only in the manner in which the physical distortions were defined. In the first experiment, a subset of autistic individuals learned categories more slowly than controls, accompanied by an overall diminution in transfer-testing performance. The autism group did, however, manifest a typical pattern of performance across the testing conditions, relative to controls. In the second experiment, group means did not differ statistically in either the training or testing phases. Taken together, these data indicate that high-functioning autistic individuals do not manifest gross deficits in prototypical category learning. A theoretical discussion is given in terms of how perceptual grouping may interact with category learning.