Sharpened cortical tuning and enhanced cortico-cortical communication contribute to the long-term neural mechanisms of visual motion perceptual learning
Introduction
Training can improve performance for many visual tasks, which is referred to as visual perceptual learning (VPL) (Sagi, 2011, Watanabe and Sasaki, 2014). The neural basis of VPL has generated a lot of interest in the past decades and at the same time is highly controversial. VPL is commonly characterized by its specificity to the trained stimulus, leading to the hypothesis that the underlying neural changes occur in early visual areas (Fahle and Poggio, 2002). However, this hypothesis was mainly based on psychophysical data and was supported only inconsistently from neurophysiological studies (Schoups et al., 2001, Ghose et al., 2002, Yang and Maunsell, 2004, Furmanski et al., 2004, Hua et al., 2010). Even within psychophysics, however, several recent studies show that the specificity of VPL can be eliminated by training with an easy task (Liu, 1999, Liu and Weinshall, 2000), training with a different task (Xiao et al., 2008), or mere exposure to a different stimulus (Zhang et al., 2010b), suggesting that VPL is mediated by higher cortical areas. So far, this alternative hypothesis has been little tested with neurophysiological methods, though researchers have found learning-related neural changes in decision-making and attention-related areas such as intraparietal sulcus (IPS) and anterior cingulate cortex (Lewis and Van Essen, 2000, Law and Gold, 2008, Kahnt et al., 2011).
Another important, but unanswered question in VPL is its long-term neural mechanisms. To date, almost all VPL studies have focused on immediate neural changes after training. However, persistency, a hallmark feature of VPL, remains largely unknown inside the brain. Yotsumoto et al. (2008) tracked the neural changes induced by a texture discrimination task, and found a transient activity enhancement in V1. This enhancement receded after two weeks, while the behavioral improvement persisted. Parallel findings have been reported in auditory and motor modalities. For example, cortical expansion was detected immediately after training in both modalities, but faded out weeks later (Molina-Luna et al., 2008, Reed et al., 2011). These findings raised questions on previous claims that are based on neural changes immediately after training, and prompted us to investigate what longer-lasting neural changes may be associated with VPL.
In our current investigation, we studied visual motion perceptual learning, with two specific aims: whether the neural modifications occurred at low- or high-level, and what neural modifications may be longer-lasting. Human subjects were trained in a motion direction discrimination task. Their behavioral performance and BOLD signals were measured before, immediately after, and two weeks after training. We examined not only how learning affected the local representation of the trained motion direction within individual visual cortical areas and IPS, a motion decision-making area and homologue of monkey LIP (lateral intraparietal area) (Kayser et al., 2010), but also how learning changed the effective connectivities between the visual areas and IPS. Law and Gold, 2008, Law and Gold, 2009 modeled the learning process as a high-level decision unit refining its connectivities to sensory neurons tuned to a specific motion direction through response reweighting (Poggio et al., 1992, Dosher and Lu, 1998, Bejjanki et al., 2011). However, there is no empirical evidence yet directly supporting this hypothesis.
Here, we report that, parallel to the long-lasting motion discrimination improvement, the neural selectivity in V3A and the effective connectivity from V3A to IPS for the trained direction exhibited a persistent increase after training, as revealed by both decoding and encoding analyses and dynamic causal modeling (DCM). We found that the behavioral learning could be well explained by a linear combination of improvements from these two sources. These findings make headways towards resolving previous controversies and demonstrate that perceptual learning should be attributed to changes both in the sensory representation of trained stimuli and the transmission of sensory signals to decision circuitry.
Section snippets
Subjects
Seventeen subjects (nine female) participated in the study. They were naïve to the purpose of the study and had never participated in any perceptual learning experiment before. All subjects were right-handed with reported normal or corrected-to-normal vision and had no known neurological or visual disorders. Their ages ranged from 20 to 25 years. They gave written, informed consent in accordance with the procedures and protocols approved by the human subject review committee of Peking University.
Psychophysical results
Subjects underwent eight daily training sessions (1000 trials per session) to discriminate motion directions around a pre-specified, but randomly selected direction (hereafter the direction is referred to as 0°). In a trial, two random-dot kinematograms (RDKs) with slightly different directions were presented sequentially. Subjects were asked to make a two-alternative forced-choice (2-AFC) judgment of the direction of the second RDK relative to the first one (clockwise or counter-clockwise) (
Discussion
Our study provides the following psychophysical and neuroimaging findings. (1) Motion direction discrimination training improved behavioral performance, which was specific to the trained direction and persisted for at least two weeks. This finding replicated the work by Ball and Sekuler (1987). (2) Immediately after training, the mean BOLD signal in V3A responding to the trained direction decreased, but this decrease mostly vanished two weeks later. (3) The decoding accuracy and neural
Acknowledgments
We thank Zhong-lin Lu for his helpful discussion. This work was supported by the National Natural Science Foundation of China (31230029, 31421003, 91132302 and 90820307) and the Ministry of Science and Technology of China (2015CB351800 and 2012CB825500). Zili Liu was supported in part by a US NSF grant (BCS 0617628) and a China NSFC grant (31228009).
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These authors contributed equally to this work.