Rat sensitivity to multipoint statistics is predicted by efficient coding of natural scenes

eLife

Eugenio Piasini¹  Vijay Balasubramanian¹  Anna Carboncino²  Andrea Buccellato²  Davide Zoccolan²  Riccardo Caramellino² 

[1] Computational Neuroscience Initiative, University of Pennsylvania, Philadelphia, United States;Visual Neuroscience Lab, International School for Advanced Studies, Trieste, Italy;

关键词: texture perception;    image statistics;    pattern vision;    shape perception;    efficient coding;    ideal observer;    Rat;   

DOI  :  10.7554/eLife.72081

来源: eLife Sciences Publications, Ltd

【 摘 要 】

Efficient processing of sensory data requires adapting the neuronal encoding strategy to the statistics of natural stimuli. Previously, in Hermundstad et al., 2014, we showed that local multipoint correlation patterns that are most variable in natural images are also the most perceptually salient for human observers, in a way that is compatible with the efficient coding principle. Understanding the neuronal mechanisms underlying such adaptation to image statistics will require performing invasive experiments that are impossible in humans. Therefore, it is important to understand whether a similar phenomenon can be detected in animal species that allow for powerful experimental manipulations, such as rodents. Here we selected four image statistics (from single- to four-point correlations) and trained four groups of rats to discriminate between white noise patterns and binary textures containing variable intensity levels of one of such statistics. We interpreted the resulting psychometric data with an ideal observer model, finding a sharp decrease in sensitivity from two- to four-point correlations and a further decrease from four- to three-point. This ranking fully reproduces the trend we previously observed in humans, thus extending a direct demonstration of efficient coding to a species where neuronal and developmental processes can be interrogated and causally manipulated.

【 授权许可】

CC BY