Attention serves to select objects from often complex scenes for enhanced processing and perception. In particular, the perception of shape depends critically on attention for integrating the various parts of the selected object into a coherent representation of object shape. To study whether oscillatory neuronal synchrony may serve as a mechanism of attention in shape perception, we introduced a novel shape-tracking task requiring sustained attention to a morphing shape. Attention was found to strongly increase oscillatory currents underlying the recorded field potentials in the gamma-frequency range, thus indicating enhanced neuronal synchrony within the population of V4 neurons representing the attended stimulus. Errors indicating a misdirection of attention to the distracter instead of the target were preceded by a corresponding shift of oscillatory activity from the target's neuronal representation to that of the distracter. No such effect was observed for errors unrelated to attention. Modulations of the attention-dependent enhancement of oscillatory activity occurred in correspondence with changing attentional demands during the course of a trial. The specificity of the effect of attentional errors together with the close coupling between attentional demand and oscillatory activity support the hypothesis that oscillatory neuronal synchrony serves as a mechanism of attention.

Receptive fields (RFs) of cortical sensory neurons increase in size along consecutive processing stages. When multiple stimuli are present in a large visual RF, a neuron typically responds to an attended stimulus as if only that stimulus were present. However, the mechanism by which a neuron selectively responds to a subset of its inputs while discarding all others is unknown. Here, we show that neurons can switch between subsets of their afferent inputs by highly specific modulations of interareal gamma-band phase-coherence (PC). We measured local field potentials, single-and multi-unit activity in two male macaque monkeys (Macaca mulatta) performing an attention task. Two small stimuli were placed on a screen; the stimuli were driving separate local V1 populations, while both were driving the same local V4 population. In each trial, we cued one of the two stimuli to be attended. We found that gamma-band PC of the local V4 population with multiple subpopulations of its V1 input was differentially modulated. It was high with the input subpopulation representing the attended stimulus, while simultaneously it was very low between the same V4 population and the other input-providing subpopulation representing the irrelevant stimulus. These differential modulations, which depend on stimulus relevance, were also found in the locking of spikes from V4 neurons to the gamma-band oscillations of the V1 input subpopulations. This rapid, highly specific interareal locking provides neurons with a powerful dynamic routing mechanism to select and process only the currently relevant signals.

Oscillatory synchrony has been proposed to dynamically coordinate distributed neural ensembles, but whether this mechanism is effectively used in neural processing remains controversial. We trained two monkeys to perform a delayed matching-to-sample task using new visual shapes at each trial. Measures of population-activity patterns (cortical field potentials) were obtained from a chronically implanted array of electrodes placed over area V4 and posterior infero-temporal cortex. In correct trials, oscillatory phase synchrony in the beta range (15-20 Hz) was observed between two focal sites in the inferior temporal cortex while holding the sample in short-term memory. Error trials were characterized by an absence of oscillatory synchrony during memory maintenance. Errors did not seem to be due to an impaired stimulus encoding, since various parameters of neural activity in sensory area V4 did not differ in correct and incorrect trials during sample presentation. Our findings suggest that the successful performance of a visual short-term memory task depends on the strength of oscillatory synchrony during the maintenance of the object in short-term memory. The strength of oscillatory synchrony thus seems to be a relevant parameter of the neural population dynamics that matches behavioral performance.

Integration of oriented elements into a contour has been investigated extensively in human psychophysics whereas electrophysiological experiments exploring the neuronal mechanism of contour integration were most often done with macaque monkeys. To bridge the gap between human psychophysics and physiology we estimated spatial and temporal constraints of contour integration in two macaque monkeys. Our results show that contour integration in monkeys depends in a similar way on element distance and alignment between contour path and contour elements as in human subjects. The grouping process was surprisingly fast: In a backward masking experiment we show that a stimulus duration of 30-60 ms is sufficient to perceive a contour and to identify its shape.

For processing and segmenting visual scenes, the brain is required to combine a multitude of features and sensory channels. It is neither known if these complex tasks involve optimal integration of information, nor according to which objectives computations might be performed. Here, we investigate if optimal inference can explain contour integration in human subjects. We performed experiments where observers detected contours of curvilinearly aligned edge configurations embedded into randomly oriented distractors. The key feature of our framework is to use a generative process for creating the contours, for which it is possible to derive a class of ideal detection models. This allowed us to compare human detection for contours with different statistical properties to the corresponding ideal detection models for the same stimuli. We then subjected the detection models to realistic constraints and required them to reproduce human decisions for every stimulus as well as possible. By independently varying the four model parameters, we identify a single detection model which quantitatively captures all correlations of human decision behaviour for more than 2000 stimuli from 42 contour ensembles with greatly varying statistical properties. This model reveals specific interactions between edges closely matching independent findings from physiology and psychophysics. These interactions imply a statistics of contours for which edge stimuli are indeed optimally integrated by the visual system, with the objective of inferring the presence of contours in cluttered scenes. The recurrent algorithm of our model makes testable predictions about the temporal dynamics of neuronal populations engaged in contour integration, and it suggests a strong directionality of the underlying functional anatomy.