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Sengupta, R.

Publications and source records attributed to Sengupta, R..

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Exogenous attention facilitates performance through dissociable sensitivity and bias mechanisms

Selective attention enables prioritizing the most important information for differential sensory processing and decision-making. Here, we address an active debate regarding whether attention reflects a unitary phenomenon or involves the operation of dissociable sensory enhancement (perceptual sensitivity) and decisional gating (choice bias) processes. We developed a multialternative task in which participants detected and localized orientation changes in gratings at one of four spatial locations. Exogenous attention cues (high contrast flashes) preceded or followed the change events in close temporal proximity. Analysis of participants behavior with a multidimensional signal detection model revealed markedly distinct effects of exogenous cueing on perceptual sensitivity and choice bias. Whereas sensitivity enhancement was localized to the stimulus proximal to the exogenous cue, bias enhancement occurred even for distal stimuli in the cued hemifield. Modulations of sensitivity and bias were uncorrelated at both cued and uncued locations. Finally, exogenous cueing produced reaction time benefits only at the cued location and costs only at locations contralateral to the cue. These disparate effects of exogenous cueing on sensitivity, bias and reaction times could be parsimoniously explained within the framework of a diffusion-decision model, in which the drift rate was determined by a linear combination of sensitivity and bias at each location. Exogenous cueing effects on sensitivity and bias differed systematically from previously reported effects of endogenous cueing. We propose that the search for shared neural substrates of exogenous and endogenous attention would benefit from investigating neural correlates of their component sensory and decisional mechanisms.\n\nSignificance statementWhen we voluntarily direct attention \"endogenously\", we are able to better perceive stimuli at the attended location (sensitivity), and to prioritize information from that location for guiding behavioral decisions (bias). But when a salient stimulus, such as a flash of lightning, captures our attention \"exogenously\", does it also produce these same effects? To answer this question, we designed a multiple alternative task in which task events occurred in close conjunction with salient exogenous cues (high contrast flashes). We discovered that exogenous attention enhanced both sensitivity and bias for cued stimuli, but each of these changes followed distinct spatial patterns across locations. Our results provide novel insights into component processes of exogenous attention and motivate the search for their neural correlates.

neuroscience

From Neural Network To Psychophysics Of Time: Exploring Emergent Properties Of RNNs Using Novel Hamiltonian Formalism

The stability analysis of dynamical neural network systems generally follows the route of finding a suitable Liapunov function after the fashion Hopfields famous paper on content addressable memory network or by finding conditions that make divergent solutions impossible. For the current work we focused on biological recurrent neural networks (bRNNs) that require transient external inputs (Cohen-Grossberg networks). In the current work we have proposed a general method to construct Liapunov functions for recurrent neural network with the help of a physically meaningful Hamiltonian function. This construct allows us to explore the emergent properties of the recurrent network (e.g., parameter configuration needed for winner-take-all competition in a leaky accumulator design) beyond that available in standard stability analysis, while also comparing well with standard stability analysis (ordinary differential equation approach) as a special case of the general stability constraint derived from the Hamiltonian formulation. We also show that the Cohen-Grossberg Liapunov function can be derived naturally from the Hamiltonian formalism. A strength of the construct comes from its usability as a predictor for behavior in psychophysical experiments involving numerosity and temporal duration judgements.

neuroscience