Performance-monitoring integrated reweighting model of perceptual learning

Informational feedback accelerates learning in multi-alternative perceptual judgements of orientation

Experience or training can substantially improve perceptual performance through perceptual learning, and the extent and rate of these improvements may be affected by feedback. In this paper, we first developed a neural network model based on the integrated reweighting theory (Dosher et al., 2013) to account for perceptual learning and performance in n-alternative identification tasks and the dependence of learning on different forms of feedback. We then report an experiment comparing the effectiveness of response feedback (RF) versus accuracy feedback (AF) or no feedback (NF) (full versus partial versus no supervision) in learning a challenging eight-alternative visual orientation identification (8AFC) task. Although learning sometimes occurred in the absence of feedback (NF), RF had a clear advantage above AF or NF in this task. Using hybrid supervision learning rules, a new n-alternative identification integrated reweighting theory (I-IRT) explained both the differences in learning curves given different feedback and the dynamic changes in identification confusion data. This study shows that training with more informational feedback (RF) is more effective, though not necessary, in these challenging n-alternative tasks, a result that has implications for developing training paradigms in realistic tasks.

Roving: The causes of interference and re-enabled learning in multi-task visual training

People routinely perform multiple visual judgments in the real world, yet, intermixing tasks or task variants during training can damage or even prevent learning. This paper explores why. We challenged theories of visual perceptual learning focused on plastic retuning of low-level retinotopic cortical representations by placing different task variants in different retinal locations, and tested theories of perceptual learning through reweighting (changes in readout) by varying task similarity. Discriminating different (but equivalent) and similar orientations in separate retinal locations interfered with learning, whereas training either with identical orientations or sufficiently different ones in different locations released rapid learning. This location crosstalk during learning renders it unlikely that the primary substrate of learning is retuning in early retinotopic visual areas; instead, learning likely involves reweighting from location-independent representations to a decision. We developed an Integrated Reweighting Theory (IRT), which has both V1-like location-specific representations and higher level (V4/IT or higher) location-invariant representations, and learns via reweighting the readout to decision, to predict the order of learning rates in different conditions. This model with suitable parameters successfully fit the behavioral data, as well as some microstructure of learning performance in a new trial-by-trial analysis.