Neural plasticity in high-level visual cortex underlying object perceptual learning

doi:10.1007/s11515-013-1262-2

Front Biol

2013, Vol. 8

Issue (4) : 434-443 https://doi.org/10.1007/s11515-013-1262-2

REVIEW

Neural plasticity in high-level visual cortex underlying object perceptual learning

Taiyong BI¹(

), Fang FANG^1,2,3

1. Department of Psychology and Key Laboratory of Machine Perception (Ministry of Education), Peking University, Beijing 100871, China; 2. Peking-Tsinghua Center for Life Sciences, Peking University, Beijing 100871, China; 3. PKU-IDG/McGovern Institute for Brain Research, Peking University, Beijing 100871, China

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Abstract

With intensive training, human can achieve impressive behavioral improvement on various perceptual tasks. This phenomenon, termed perceptual learning, has long been considered as a hallmark of the plasticity of sensory neural system. Not surprisingly, high-level vision, such as object perception, can also be improved by perceptual learning. Here we review recent psychophysical, electrophysiological, and neuroimaging studies investigating the effects of training on object selective cortex, such as monkey inferior temporal cortex and human lateral occipital area. Evidences show that learning leads to an increase in object selectivity at the single neuron level and/or the neuronal population level. These findings indicate that high-level visual cortex in humans is highly plastic and visual experience can strongly shape neural functions of these areas. At the end of the review, we discuss several important future directions in this area.

Keywords plasticity object perceptual learning neural mechanism inferior temporal cortex lateral occipital

Corresponding Author(s): BI Taiyong,Email:bitaiyong@pku.edu.cn

Issue Date: 01 August 2013

Cite this article:

Taiyong BI,Fang FANG. Neural plasticity in high-level visual cortex underlying object perceptual learning[J]. Front Biol, 2013, 8(4): 434-443.

URL:

https://academic.hep.com.cn/fib/EN/10.1007/s11515-013-1262-2
https://academic.hep.com.cn/fib/EN/Y2013/V8/I4/434

Fig.1 Learning of object identification: specificity and transfer. (A) Two examples of the stimuli used for training. Objects were presented for a short period and followed by a mask. Subjects were instructed to namethe object. (B) The threshold exposure time of objects decreased gradually with training. Learning was specific to the trained objects and could persist up to 20 days after training. (C) Learning transferred nearly completely to the same objects with a different size.

Fig.2 Face view perceptual learning. First column denotes the stimuli for training. Subjects were asked to discriminate two side views of a stimulus around a 30° view angle. The second column denotes the stimuli for testing the transfer of the learned view discrimination ability to untrained stimuli. The test stimuli were different from the trained stimuli in size (A), part (B), identity (C), visual field (D), configural information (E), and face information (F).View discrimination performances at seven view angles (one was the trained view, others were the untrained views) were tested before and after training. The third column denotes the learning curve of each stimulus in the first column. The discrimination threshold decreased gradually along an eight-day training course for all the stimuli. The fourth column denotes the percent improvement of view discrimination performance on the test stimuli in the second column. The learning effects in A–D transferred to the test stimuli in a view specific manner. However, the learning effects of inverted face and non-face objects (E and F) could not transfer to the upright face stimuli.

Fig.3 Neural selectivity changes with categorization learning. (A) Stimuli and categories. The stimulus set consisted of line drawings of faces with four varying features: eye height, eye separation, nose length and mouth height. (B) Two-dimensional representation of the stimulus space. Monkeys were trained to categorize faces to two categories based on two of the four varying features. Dark line denotes the category boundary. Black stars represent the stimuli of the first category and red ovals represent the stimuli of the second category. Each number indicates the position of one corresponding stimulus from panel A. (C) Example of a neuron showing feature selectivity. Black traces indicate the responses of the neuron to the best feature value; gray traces indicate responses to the worst feature value. s.i. denotes the selectivity index. For each feature, one s.i. could be computed for each neuron. (D) Plots of the average selectivity index of each neuron for the diagnostic versus the non-diagnostic features. Each point represents one neuron. Red circles represent neurons with statistically significant selectivity for diagnostic features only. Blue circles represent neurons with statistically significant selectivity for both diagnostic and non-diagnostic features. Black triangles represent neurons with no significant selectivity. Insert diagram shows the distribution (numbers of neurons) of the selectivity index for the diagnostic features after subtraction of the selectivity index for the non-diagnostic features.

Fig.4 Example of an fMRI adaptation experiment and the learning effect on fMRI adaptation. (A) An illustration of the sequence of object images presented during an fMRI adaptation experiment. 32 pictures were sequentially presented in a repeating cycle. The number of different objects in each cycle is given on the left, ranging from 1 (the same object picture presented repeatedly) to 32 (32 different images). (B) Relative LOC signal of each repetition condition compared to the maximal activation (condition 32) as a function of number of different pictures in the cycle. Note the fMRI signal decreased monotonically as the repetition frequency increased. (C) The stimuli and result of an fMRI study on car categorization training. Subjects were trained to categorize morphed cars into two categories. Before and after training, subjects were scanned while viewing three kinds of stimuli. In condition M0, stimuli in a trial are always two identical cars which are one of the two prototypes. In condition M3, stimuli are two morphed car which are different from each other for 33% of each prototype. and refer to within the same category and between different categories respectively. Before training, LO responses in three conditions were similar, indicating no difference on the neural representation of different cars. However, after training, the response for different cars was significantly higher than that for identical car, revealing a release from adaptation after training.

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