A computational developmental model for specificity and transfer in perceptual learning

Abstract

How and under what circumstances the training effects of perceptual learning (PL) transfer to novel situations is critical to our understanding of generalization and abstraction in learning. Although PL is generally believed to be highly specific to the trained stimulus, a series of psychophysical studies have recently shown that training effects can transfer to untrained conditions under certain experimental protocols. In this article, we present a brain-inspired, neuromorphic computational model of the Where-What visuomotor pathways which successfully explains both the specificity and transfer of perceptual learning. The major architectural novelty is that each feature neuron has both sensory and motor inputs. The network of neurons is autonomously developed from experience, using a refined Hebbian-learning rule and lateral competition, which altogether result in neuronal recruitment. Our hypothesis is that certain paradigms of experiments trigger two-way (descending and ascending) off-task processes about the untrained condition which lead to recruitment of more neurons in lower feature representation areas as well as higher concept representation areas for the untrained condition, hence the transfer. We put forward a novel proposition that gated self-organization of the connections during the off-task processes accounts for the observed transfer effects. Simulation results showed transfer of learning across retinal locations in a Vernier discrimination task in a double-training procedure, comparable to previous psychophysical data (Xiao et al., 2008). To the best of our knowledge, this model is the first neurally-plausible model to explain both transfer and specificity in a PL setting.

The role of alpha-rhythm states in perceptual learning: insights from experiments and computational models

“…A possibility in such cases is to build feature detectors capable of changing their tuning properties, and to adapt them to the statistical properties of the training set (Sigala et al, 2005; Serre et al, 2007). In a recent publication, Solgi et al (2013) propose a model that explains generalization (transfer) learning effects to untrained features. According to this model, transfer learning occurs since particular tasks are able to trigger neuronal recruitment in lower-feature and higher-association areas, relevant for both the trained and the untrained conditions.…”

Spatial frequency tuning of perceptual learning and transfer in global motion

“…Global processes are investigated 139 using stimuli or tasks that can only be resolved through integration and 140 segregation of coherent or conflicting information [81][82][83]. Cells higher in the 141 processing cortical hierarchy are involved in the perception of global aspects of 142 an image and generalise across individual features such as spatial frequency and 143 location. Based on the predictions of the Reverse Hierarchy Theory, modification 144 of these generalising receptive fields may produce perceptual learning that also 145 generalises over these stimulus parameters.…”

“…While the two positions are pitched as competing theories, they may not be 964 mutually exclusive. In an attempt to combine the two models, Solgi and Weng 965 (2013) [143] model learning as a two-way process (descending and ascending).…”

“…The model attributes training effects to the reweighting of connections between 967 early and higher sensory areas, but crucially also as a result of increased 968 neuronal activity representing the trained feature [143]. For transfer to occur, 969 Solgi and Weng (2013) [143] argue there is an important role for the re-entrant 970 connections from higher levels.…”

“…The model attributes training effects to the reweighting of connections between 967 early and higher sensory areas, but crucially also as a result of increased 968 neuronal activity representing the trained feature [143]. For transfer to occur, 969 Solgi and Weng (2013) [143] argue there is an important role for the re-entrant 970 connections from higher levels. Similarly, Bejjanki et al (2011) [144] propose a 971 model that is computationally similar to the reweighting models, however it 972 illustrates how changing the population codes in the early sensory areas can 973 create similar changes in response to those made by high-level re-weighting 974 models.…”