Search

Saccades can either be elicited automatically by salient peripheral stimuli or can additionally depend on explicit cognitive goals. Similarly, it is thought that motor adaptation is driven by the combination of a more automatic, implicit process and a more explicit, cognitive process. However, the degree to which such implicit and explicit learning contribute to the adaptation of more reactive and voluntary saccades remains elusive. To study this question, we employed a global saccadic adaptation paradigm with both increasing and decreasing saccade amplitudes. We assessed the resulting adaptation using a dual state model of motor adaptation. This model decomposes learning into a fast and slow process, which are thought to constitute the explicit and implicit learning, respectively. Our results show that adaptation of reactive saccades is equally driven by fast and slow learning, while fast learning is nearly absent when adapting voluntary (i.e. scanning) saccades. This pattern of results was present both when saccade gain was increased or decreased. Our results suggest that the increased cognitive demands associated with voluntary compared to reactive saccade planning interfere specifically with explicit learning.

Spatial attention changes the sampling of visual space. Behavioral studies suggest that feature-based attention modulates this resampling to optimize the attended feature's sampling. We investigate this hypothesis by estimating spatial sampling in visual cortex while independently varying both feature-based and spatial attention. Our results show that spatial and feature-based attention interacted: resampling of visual space depended on both the attended location and feature (color vs. temporal frequency). This interaction occurred similarly throughout visual cortex, regardless of an area's overall feature preference. However, the interaction did depend on spatial sampling properties of voxels that prefer the attended feature. These findings are parsimoniously explained by variations in the precision of an attentional gain field. Our results demonstrate that the deployment of spatial attention is tailored to the spatial sampling properties of units that are sensitive to the attended feature., eLife digest Much like digital cameras record images using a grid of tiny pixels, our own visual experience results from the activity of many neurons, each with its own receptive field. A neuron’s receptive field is the area of visual space – the scene in front of our eyes – to which that neuron responds. But whereas digital pixels have fixed locations, the receptive fields of neurons do not. If we switch our attention to a different area of the scene in front of us, visual neurons move their receptive fields to cover that area instead. We do not need to move our eyes for this to happen, just the focus of our attention. Moving receptive fields in this way enables the visual system to generate more detailed vision at the new attended location. Unlike a digital camera, the brain is thus much more than a passive recording device. But does the movement of receptive fields also depend on what we are attending to at a given location? Paying attention to tiny details, for example, might require many receptive fields to move by large amounts to produce vision with high enough resolution. Van Es et al. have now answered this question by using a brain scanner to measure receptive fields in healthy volunteers. The volunteers focused on different visual features, such as color or motion, and to various visual locations. When the volunteers attended to color, their attention was more tightly focused than when they attended to motion. This might be because processing color requires fine-detail vision, whereas we can detect movement with our attention spread over a larger area. As a result, receptive fields moved more when the volunteers attended to color than when they attended to motion. Movement of visual receptive fields thus depends on what we attend to, as well as where we focus our attention. This adds to our understanding of how the brain filters the information bombarding our senses. This might lead to better diagnosis and treatment of disorders that include attentional problems, such as autism and ADHD. The results could also help develop artificial intelligence systems that, like the visual system, can process information flexibly to achieve different goals.

In a continuously changing environment, time is a key property that tells us whether information from the different senses belongs together. Yet, little is known about how the brain integrates temporal information across sensory modalities. Using high-density EEG combined with a novel psychometric timing task in which human subjects evaluated durations of audiovisual stimuli, we show that the strength of alpha-band (8-12. Hz) phase synchrony between localizer-defined auditory and visual regions depended on cross-modal attention: during encoding of a constant 500. ms standard interval, audiovisual alpha synchrony decreased when subjects attended audition while ignoring vision, compared to when they attended both modalities. In addition, alpha connectivity during a variable target interval predicted the degree to which auditory stimulus duration biased time estimation while attending vision. This cross-modal interference effect was estimated using a hierarchical Bayesian model of a psychometric function that also provided an estimate of each individual's tendency to exhibit attention lapses. This lapse rate, in turn, was predicted by single-trial estimates of the stability of interregional alpha synchrony: when attending to both modalities, trials with greater stability in patterns of connectivity were characterized by reduced contamination by lapses. Together, these results provide new insights into a functional role of the coupling of alpha phase dynamics between sensory cortices in integrating cross-modal information over time. © 2014 Elsevier Inc.