Modeling mechanisms of perceptual learning with augmented Hebbian re-weighting

Abstract

Using the external noise plus training paradigm, we have consistently found that two independent mechanisms, stimulus enhancement and external noise exclusion, support perceptual learning in a range of tasks. Here, we show that re-weighting of stable early sensory representations through Hebbian learning (Petrov et al., 2005, 2006) can generate performance patterns that parallel a large range of empirical data: (1) perceptual learning reduced contrast thresholds at all levels of external noise in peripheral orientation identification (Dosher & Lu, 1998, 1999), (2) training with low noise exemplars transferred to performance in high noise, while training with exemplars embedded in high external noise transferred little to performance in low noise (Dosher & Lu, 2005), and (3) pre-training in high external noise only reduced subsequent learning in high external noise, whereas pre-training in zero external noise left very little additional learning in all the external noise conditions (Lu et al., 2006). In the augmented Hebbian re-weighting model (AHRM), perceptual learning strengthens or maintains the connections between the most closely tuned visual channels and a learned categorization structure, while it prunes or reduces inputs from task-irrelevant channels. Reducing the weights on irrelevant channels reduces the contributions of external noise and additive internal noise. Manifestation of stimulus enhancement or external noise exclusion depends on the initial state of internal noise and connection weights in the beginning of a learning task. Both mechanisms reflect re-weighting of stable early sensory representations.

Perceptual learning leads to long lasting visual improvement in patients with central vision loss

“…Whereas in both tasks learning may improve sensory signals and modulate lateral interactions, feedback in the temporal-2AFC task may reinforce learning by maximizing decision mechanism through reward (Petrov, Dosher, & Lu, 2005;Lu & Dosher, 2010;Kumano & Uka, 2013).…”

Modeling trial by trial and block feedback in perceptual learning

“…First we briefly describe all the subsystems of the AHRM along with the experimental stimuli and procedure in Herzog & Fahle (1997). A detailed description of the model can be found in previous studies (Petrov et al 2005, 2006; Lu et al 2010; Liu et al 2010, 2012). We provide a mathematical description of the model in Appendix A.…”

“…The parameters that control the front end were set a-priori as in Petrov et al (2005, 2006), or were fixed based on model fits to experimental data in a number of other applications (Dosher et al, 2013; Lu et al 2010; Liu et al 2010, 2012) (see the Appendix for a discussion). Similarly, the initial weights before learning were set in proportion to the preferred orientation of the units: w i = ( θ i /30) w init , reflecting general prior knowledge about orientation given initial task instructions in the target experiments.…”

Mechanisms for Extracting a Signal from Noise as Revealed through the Specificity and Generality of Task Training

“…Nevertheless, as both signal-in-noise and feature difference tasks are likely to involve pooling signals across space, the extent to which spatial integration can explain the differential training effects we observe is somewhat limited. Lu et al (2010) implemented a Hebbian reweighting process (Petrov et al, 2005) to provide a different level of explanation for learning under high and low noise. Under their model, training in low noise promotes optimal weights for relevant features and down-weights irrelevant features.…”