Topic: sensory signals

Selection is the hallmark of attention: processing improves for attended items but is relatively impaired for unattended items. It is well known that visual spatial attention changes sensory signals and perception in this selective fashion. In the work I will present, we asked whether and how attentional selection happens across time. First, our experiments revealed that voluntary temporal attention (attention to specific points in time) is selective, resulting in perceptual tradeoffs across time. Second, we measured small eye movements called microsaccades and found that directing voluntary t

In nature, sensory inputs are often highly structured, and statistical regularities of these signals can be extracted to form expectation about future sensorimotor associations, thereby optimizing behavior. One of the fundamental questions in neuroscience concerns the neural computations that underlie these probabilistic sensorimotor processing. Through a recurrent neural network (RNN) model and human psychophysics and electroencephalography (EEG), the present study investigates circuit mechanisms for processing probabilistic structures of sensory signals to guide behavior. We first constructe

I will begin by describing recent research taking a new, model-based approach to perceptual development. This approach uncovers fundamental changes in information processing underlying the protracted development of perception, action, and decision-making in childhood. For example, integration of multiple sensory estimates via reliability-weighted averaging – widely used by adults to improve perception – is often not seen until surprisingly late into childhood, as assessed by both behaviour and neural representations. This approach forms the basis for a newer question: the scope for the nervous

Perceivers benefit from integrating their prior beliefs with sensory signals based on their relative reliability to mitigate the effects of stochastic noise. However, in volatile environments, one should avoid integrating these signals when changes in the signal source render their prior beliefs irrelevant. Bayesian causal inference could offer the brain an optimal strategy for adaptively adjusting its reliance on priors. Mechanistically, it has been suggested that the arousal system modulates the weights between prior beliefs and sensory signals accordingly. We tested these hypotheses using a

Animals continuously process information from multiple sensory modalities to assess their environment and guide their behavior. For example, a predator may rely on both visual and auditory cues to track its prey. Over time, various algorithms have been developed to describe multisensory processing, including linear and nonlinear fusion. However, these algorithms typically treat each time step independently and fail to account for temporal dependencies in these signals. To address this limitation, we introduce a novel set of multisensory tasks that systematically incorporate controlled temporal