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Previous work investigated a range of spatio-temporal constraints for fMRI data analysis to provide robust detection of neural activation. We present a mixture-based method for the spatio-temporal modelling of fMRI data. This approach assumes that fMRI time series are generated by a probabilistic superposition of a small set of spatio-temporal prototypes (mixture components). Each prototype comprises a temporal model that explains fMRI signals on a single voxel and the model's “region of influence” through a spatial prior over the voxel space. As the key ingredient of our temporal model, the Hidden Process Model (HPM) framework proposed in Hutchinson et al. (2009) is adopted to infer the overlapping cognitive processes triggered by stimuli. Unlike the original HPM framework, we use a parametric model of Haemodynamic Response Function (HRF) so that biological constraints are naturally incorporated in the HRF estimation. The spatial priors are defined in terms of a parameterised distribution. Thus, the total number of parameters in the model does not depend on the number of voxels. The resulting model provides a conceptually principled and computationally efficient approach to identify spatio-temporal patterns of neural activation from fMRI data, in contrast to most conventional approaches in the literature focusing on the detection of spatial patterns. We first verify the proposed model in a controlled experimental setting using synthetic data. The model is further validated on real fMRI data obtained from a rapid event-related visual recognition experiment (Mayhew et al., 2012). Our model enables us to evaluate in a principled manner the variability of neural activations within individual regions of interest (ROIs). The results strongly suggest that, compared with occipitotemporal regions, the frontal ones are less homogeneous, requiring two HPM prototypes per region. Despite the rapid event-related experimental design, the model is capable of disentangling the perceptual judgement and motor response processes that are both activated in the frontal ROIs. Spatio-temporal heterogeneity in the frontal regions seems to be associated with diverse dynamic localizations of the two hidden processes in different subregions of frontal ROIs., Highlights • We present a “spatio-temporal prototype”-based approach to fMRI data analysis. • Our model is capable of disentangling overlapping mental processes evoked by stimuli. • We found frontal ROIs were functionally less homogeneous than occipitotemporal ones. • We detected spatio-temporal heterogeneity of mental processes in the frontal regions.

Summary the properties of neurons in different visual areas (Hubel and Wiesel, 1968; Felleman and Van Essen, 1991; Van The integration of local image features into global Essen et al., 1992), and in humans, where recent fMRI shapes was investigated in monkeys and humans us- studies provide evidence for functional organization of ing fMRI. An adaptation paradigm was used, in which the visual cortex (Wandell, 1999, for review). The strong stimulus selectivity was deduced by changes in the correlation between the fMRI signal and the underlying course of adaptation of a pattern of randomly oriented neural responses (Logothetis et al., 2001) emphasizes elements. Accordingly, we observed stronger activity the importance of such studies for bridging the gap when orientation changes in the adapting stimulus between the extensive neurophysiological findings in resulted in a collinear contour than a different random monkeys and those reported in combined psychophysipattern. This selectivity to collinear contours was ob- cal and imaging investigations with humans. Our goal served not only in higher visual areas that are impli- was to investigate the visual integration processes in cated in shape processing, but also in early visual both the monkey and the human brain by using the same areas where selectivity depended on the receptive fMRI technique and similar experimental paradigms. field size. These findings suggest that unified shape We tested responses across visual areas to collinear perception in both monkeys and humans involves mul- contours versus random patterns. The collinear patterns tiple visual areas that may integrate local elements to consisted of a number of similarly oriented elements global shapes at different spatial scales. embedded into a background of randomly oriented elements, while the random patterns consisted of a field of randomly oriented elements. Such displays yield the

Successful actions and interactions in the complex environments we inhabit entail making fast and optimal perceptual decisions. Extracting the key features from our sensory experiences and deciding how to interpret them is a computationally challenging task that is far from understood. Accumulating evidence suggests that the brain may solve this challenge by combining sensory information and previous knowledge about the environment acquired through evolution, development, and everyday experience. Here, we review the role of visual learning and experience-dependent plasticity in shaping decisions. We propose that learning plays an important role in translating sensory experiences to decisions and actions by shaping neural representations across cortical circuits in a task-dependent manner.

Summary Visually guided behavior depends on (1) extracting and (2) discriminating signals from complex retinal inputs, and these perceptual skills improve with practice [1]. For instance, training on aerial reconnaissance facilitated World War II Allied military operations [2]; analysts pored over stereoscopic photographs, becoming expert at (1) segmenting pictures into meaningful items to break camouflage from (noisy) backgrounds, and (2) discriminating fine details to distinguish V-weapons from innocuous pylons. Training is understood to optimize neural circuits that process scene features (e.g., orientation) for particular purposes (e.g., judging position) [3–6]. Yet learning is most beneficial when it generalizes to other settings [7, 8] and is critical in recovery after adversity [9], challenging understanding of the circuitry involved. Here we used repetitive transcranial magnetic stimulation (rTMS) to infer the functional organization supporting learning generalization in the human brain. First, we show dissociable contributions of the posterior parietal cortex (PPC) versus lateral occipital (LO) circuits: extracting targets from noise is disrupted by PPC stimulation, in contrast to judging feature differences, which is affected by LO rTMS. Then, we demonstrate that training causes striking changes in this circuit: after feature training, identifying a target in noise is not disrupted by PPC stimulation but instead by LO stimulation. This indicates that training shifts the limits on perception from parietal to ventral brain regions and identifies a critical neural circuit for visual learning. We suggest that generalization is implemented by supplanting dynamic processing conducted in the PPC with specific feature templates stored in the ventral cortex., Highlights • Before training, dorsal, but not ventral, rTMS disrupts perception in noisy displays • Training on fine differences boosts feature templates, facilitating detection in noise • After training, ventral, but not dorsal, rTMS becomes disruptive for targets in noise • Functional reweighting of dorsal and ventral circuits supports learning transfer, Using TMS in human subjects, Chang et al. reveal differential roles of parietal and ventral cortex in extracting and discriminating visual targets, respectively. Training on fine discriminations causes a reweighting of the circuit: following learning, ventral, not parietal, activity determines target extraction task performance.

Highlights • Exposure to temporal sequences improves prediction of future events. • Learning to predict from temporal sequences generalizes to untrained stimuli. • Learning to predict is sensitive to the global structure of the trained sequence. • Learning to predict is compromised by increased attentional load., Previous experience is thought to facilitate our ability to extract spatial and temporal regularities from cluttered scenes. However, little is known about how we may use this knowledge to predict future events. Here we test whether exposure to temporal sequences facilitates the visual recognition of upcoming stimuli. We presented observers with a sequence of leftwards and rightwards oriented gratings that was interrupted by a test stimulus. Observers were asked to indicate whether the orientation of the test stimulus matched their expectation based on the preceding sequence. Our results demonstrate that exposure to temporal sequences without feedback facilitates our ability to predict an upcoming stimulus. In particular, observers’ performance improved following exposure to structured but not random sequences. Improved performance lasted for a prolonged period and generalized to untrained stimulus orientations rather than sequences of different global structure, suggesting that observers acquire knowledge of the sequence structure rather than its items. Further, this learning was compromised when observers performed a dual task resulting in increased attentional load. These findings suggest that exposure to temporal regularities in a scene allows us to accumulate knowledge about its global structure and predict future events.

The relationship between brain activity and conscious visual experience is central to our understanding of the neural mechanisms underlying perception. Binocular rivalry, where monocular stimuli compete for perceptual dominance, has been previously used to dissociate the constant stimulus from the varying percept. We report here fMRI results from humans experiencing binocular rivalry under a dichoptic stimulation paradigm that consisted of two drifting random dot patterns with different motion coherence. Each pattern had also a different color, which both enhanced rivalry and was used for reporting which of the two patterns was visible at each time. As the perception of the subjects alternated between coherent motion and motion noise, we examined the effect that these alternations had on the strength of the MRsignal throughout the brain. Our results demonstrate that motion perception is able to modulate the activity of several of the visual areas which are known to be involved in motion processing. More specifically, in addition to area V5 which showed the strongest modulation, a higher activity during the perception of motion than during the perception of noise was also clearly observed in areas V3A and LOC, and less so in area V3. In previous studies, these areas had been selectively activated by motion stimuli but whether their activity reflects motion perception or not remained unclear; here we show that they are involved in motion perception as well. The present findings therefore suggest a lack of a clear distinction between processing versus perceptual areas in the brain, but rather that the areas involved in the processing of a specific visual attribute are also part of the neuronal network that is collectively responsible for its perceptual representation. � 2005 Elsevier Ltd. All rights reserved.