Work

Augmented Hebbian reweighting accounts for accuracy and induced bias in perceptual learning with reverse feedback

Year: 2015

Type: article

Abstract: Using an asymmetrical set of vernier stimuli (-15″, -10″, -5″, +10″, +15″) together with reverse feedback on the small subthreshold offset stimulus (-5″) induces response bias in performance (Aberg & Herzog, 2012; Herzog, Eward, Hermens, & Fahle, 2006; Herzog & Fahle, 1999). These conditions are of interest for testing models of perceptual learning because the world does not always present balanced stimulus frequencies or accurate feedback. Here we provide a comprehensive model for the complex set of asymmetric training results using the augmented Hebbian reweighting model (Liu, Dosher, & Lu, 2014; Petrov, Dosher, & Lu, 2005, 2006) and the multilocation integrated reweighting theory (Dosher, Jeter, Liu, & Lu, 2013). The augmented Hebbian learning algorithm incorporates trial-by-trial feedback, when present, as another input to the decision unit and uses the observer's internal response to update the weights otherwise; block feedback alters the weights on bias correction (Liu et al., 2014). Asymmetric training with reversed feedback incorporates biases into the weights between representation and decision. The model correctly predicts the basic induction effect, its dependence on trial-by-trial feedback, and the specificity of bias to stimulus orientation and spatial location, extending the range of augmented Hebbian reweighting accounts of perceptual learning.

Source: Journal of Vision

DOI: 10.1167/15.10.10

Authors: Jiajuan Liu, Barbara Anne Dosher, Zhong-Lin Lu

Institutions: University of California, Irvine, The Ohio State University

Language: English

Topic: Visual perception and processing mechanisms

Subfield: Cognitive Neuroscience

Field: Neuroscience

Domain: Life Sciences

Open Access status: gold

Estimated APC paid: $1,850

Funders: National Institutes of Health, National Eye Institute