This article describes the use of Bayes factors for comparing Dynamic Causal Models (DCMs). DCMs are used to make inferences about effective connectivity from functional Magnetic Resonance Imaging (fMRI) data. These inferences, however, are contingent upon assumptions about model structure, that is, the connectivity pattern between the regions included in the model. Given the current lack of detailed knowledge on anatomical connectivity in the human brain, there are often considerable degrees of freedom when defining the connectional structure of DCMs. In addition, many plausible scientific hypotheses may exist about which connections are changed by experimental manipulation, and a formal procedure for directly comparing these competing hypotheses is highly desirable. In this article, we show how Bayes factors can be used to guide choices about model structure, both with regard to the intrinsic connectivity pattern and the contextual modulation of individual connections. The combined use of Bayes factors and DCM thus allows one to evaluate competing scientific theories about the architecture of large-scale neural networks and the neuronal interactions that mediate perception and cognition.

... This article models how these interactions help visual cortex to realize: (1) the binding process whereby cortex groups distributed data into coherent object representations; (2) the attentional process whereby cortex selectively processes important events; and (3) the developmental and learning processes whereby cortex shapes its circuits to match environmental constraints. New computational ideas about feedback systems suggest how neocortex develops and learns in a stable way, and why top-down attention requires converging bottom-up inputs to fully activate cortical cells, whereas perceptual groupings do not.

Spatial attention: Visual selection and deployment over space The attentional spotlight and spatial cueing Attentional shifts, splits, and resolution Object-based Selection The visual search paradigm Top-down and bottom-up control of attention Inhibitory mechanisms of attention Invalid cueing Negative priming Inhibition of return Temporal attention: Visual selection and deployment over time Single target search Attentional blink and attentional dwell time Repetition blindness NEURAL MECHANISMS OF SELECTION Single-cell physiological method Event-related potentials Functional imaging: PET and fMRI

Abstract not found

& A still photograph of an object in motion may convey dynamic information about the position of the object immediately before and after the photograph was taken (implied motion). Medial temporal/medial superior temporal cortex (MT/MST) is one of the main brain regions engaged in the perceptual analysis of visual motion. In two experiments we examined whether MT/MST is also involved in representing implied motion from static images. We found stronger functional magnetic resonance imaging (fMRI) activation within MT/MST during viewing of static photographs with implied motion compared to viewing of photographs without implied motion. These results suggest that brain regions involved in the visual analysis of motion are also engaged in processing implied dynamic information from static images. & The perception of motion is critical for our ability to interact with a dynamic environment. Neurophysiological studies in monkeys (for example, Britten, Newsome,

& What happens in the brain when you conjure up a mental image in your mind’s eye? We tested whether the particular regions of extrastriate cortex activated during mental imagery depend on the content of the image. Using functional magnetic resonance imaging (fMRI), we demonstrated selective activation within a region of cortex specialized for face perception during mental imagery of faces, and selective activation within a place-selective cortical region during imagery of places. In a further study, we compared the activation for imagery and perception in these regions, and found greater response magnitudes for perception than for imagery of the same items. Finally, we found that it is possible to determine the content of single cognitive events from an inspection of the fMRI data from individual imagery trials. These findings strengthen evidence that imagery and perception share common processing mechanisms, and demonstrate that the specific brain regions activated during mental imagery depend on the content of the visual image. &

Neural correlates of perceptual awareness, until very recently an elusive quarry, are now almost commonplace findings. This article first describes a variety of neural correlates of perceptual awareness based on fMRI, ERPs, and single-unit recordings. It is then argued that our quest should ultimately focus not on mere correlates of awareness, but rather on the neural events that are both necessary and sufficient for perceptual awareness. Indeed, preliminary evidence suggests that although many of the neural correlates already reported may be necessary for the corresponding state of awareness, it is unlikely that they are sufficient for it. The final section considers three hypotheses concerning the possible sufficiency conditions

Selective visual attention to objects and locations depends both on deliberate behavioral goals that regulate even early visual representations (goal-directed influences) and on autonomous neural responses to sensory input (stimulus-driven influences). In this chapter, I argue that deliberate goal-directed attentional strategies are always constrained by involuntary, ”hard-wired computations, and that an appropriate research strategy is to delineate the nature of the interactions imposed by these constraints. To illustrate the inter-action between goal-directed and stimulus-driven attentional control, four domains of visual selection are reviewed. First, selection by location is both spatially and temporally limited, reflecting in part early visual representations of the scene. Second, selection by feature is an available attentional strategy, but it appears to be mediated by location, and feature salience alone does not govern the deployment of attention. Third, early visual seg-mentation processes that parse a scene into perceptual object representations enable object-based selection, but they also enforce selection of entire objects, and not just isolated features. And fourth, the appearance of a new perceptual object captures attention in a stimulus-driven fashion, but even this is subject to some top-down attentional control.

processing

The effects of lexical context on phonological processing are pervasive and there have been indications that such effects may be modulated by attention. However, attentional modulation in speech processing is neither well-documented nor well-understood. Experiment 1 demonstrated attentional modulation of lexical facilitation of speech sound recognition when task and critical stimuli were identical across attention conditions. We propose modulation of lexical activation as a neurophysiologically-plausible computational mechanism that can account for this type of modulation. Contrary to the claims of critics, this mechanism can account for attentional modulation without violating the principle of interactive processing. Simulations of the interactive TRACE model extended to include two different ways of modulating lexical activation showed that each can account for attentional modulation of lexical feedback effects. Experiment 2 tested conflicting predictions from the two implementations and provided evidence that is consistent with bias input as the mechanism of attentional control of lexical activation.