Flexible integration of continuous sensory evidence in perceptual estimation tasks
Esnaola-Acebes, Jose M. (Centre de Recerca Matemàtica)
Roxin, Alex (Centre de Recerca Matemàtica)
Wimmer, Klaus (Centre de Recerca Matemàtica)

Date:	2022
Abstract:	Accumulating sensory information over time is crucial for making accurate judgments when acting in the face of noisy or ambiguous sensory information. For example, a hunting predator needs to compute the net direction of motion of a large group of prey (e. g. , shoals of fish or birds flying in flock). Here, we study the underlying neural mechanisms by developing a neural network model that can average angular sensory input near-optimally and also signal the reliability of the estimated average direction. Moreover, the network can flexibly give larger weight to either initial or more recent sensory information, as we observe in humans performing an estimation task. Our findings shed light on the neural circuit mechanisms underlying continuous perceptual judgments. Temporal accumulation of evidence is crucial for making accurate judgments based on noisy or ambiguous sensory input. The integration process leading to categorical decisions is thought to rely on competition between neural populations, each encoding a discrete categorical choice. How recurrent neural circuits integrate evidence for continuous perceptual judgments is unknown. Here, we show that a continuous bump attractor network can integrate a circular feature, such as stimulus direction, nearly optimally. As required by optimal integration, the population activity of the network unfolds on a two-dimensional manifold, in which the position of the network's activity bump tracks the stimulus average, and, simultaneously, the bump amplitude tracks stimulus uncertainty. Moreover, the temporal weighting of sensory evidence by the network depends on the relative strength of the stimulus compared to the internally generated bump dynamics, yielding either early (primacy), uniform, or late (recency) weighting. The model can flexibly switch between these regimes by changing a single control parameter, the global excitatory drive. We show that this mechanism can quantitatively explain individual temporal weighting profiles of human observers, and we validate the model prediction that temporal weighting impacts reaction times. Our findings point to continuous attractor dynamics as a plausible neural mechanism underlying stimulus integration in perceptual estimation tasks.
Grants:	Agencia Estatal de Investigación PCI2018-093095 Ministerio de Economía y Competitividad RYC-2015-17236 Agencia Estatal de Investigación BFU2017-86026-R Agencia Estatal de Investigación PID2020-112838RB-I00 Agencia Estatal de Investigación RTI2018-097570-B-100 Agencia Estatal de Investigación RED2018-102323-T Agencia Estatal de Investigación CEX2020-001084-M
Rights:	Aquest document està subjecte a una llicència d'ús Creative Commons. Es permet la reproducció total o parcial, la distribució, i la comunicació pública de l'obra, sempre que no sigui amb finalitats comercials, i sempre que es reconegui l'autoria de l'obra original. No es permet la creació d'obres derivades.
Language:	Anglès
Document:	Article ; recerca ; Versió publicada
Subject:	Recurrent neural networks ; Perceptual decision making ; Evidence integration ; Attractor dynamics
Published in:	Proceedings of the National Academy of Sciences of the United States of America, Vol. 119, Issue 45 (November 2022) , art. e2214441119, ISSN 1091-6490

DOI: 10.1073/pnas.2214441119
PMID: 36322720

11 p, 2.6 MB

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