Periodic Reporting for period 1 - DecodePL (Perceptual learning as optimized decoding: from maps to mechanisms)
Reporting period: 2017-01-01 to 2018-12-31
We usually think that as we emerge from childhood, our brains become less plastic, making learning effortful and highly specific. Recent findings however challenge this view, suggesting that even adult perceptual learning, often considered the most specific form of learning, has the potential to generalize across training conditions. This questions classical theories positing that perceptual learning changes encoding in early sensory areas, as the functional properties of these areas cannot account for generalization. Building on recent computational models, I propose instead that PL relates to decoding, that is, how information from sensory areas is communicated and read out by higher areas to make decisions. Decoding accounts are theoretically attractive yet technically challenging to test, as they require a multiscale brain investigation, i.e. tracking perceptual learning across networks, areas, and single neurons. In this project, we address these challenges by combining functional magnetic resonance imaging and electrophysiological recordings during learning tasks. This allows us to test where perceptual learning takes place in the brain (in sensory areas and/or in higher-order brain areas), and what computations the neurons in these areas perform. This project, at the intersection of neuroscience, psychology and computational theory, will set forth the foundations for a mechanistic investigation of perceptual learning at an unprecedented level of detail, bridging multiple scales from whole-brain networks down to single neurons. Ultimately, this innovative framework will help us understand the building blocks of adult brain plasticity, and how to optimize rehabilitation and educational applications.
We have conducted several studies to address the two major aims of this project, namely to identify where perceptual learning takes place and which neural computations underlie these learning effects. To this end, we have used neuroimaging techniques that provide whole brain coverage to answer the localization question, and electrophysiology to address the computational aspect of the project. Across our studies, we find that learning effects do indeed occur in sensory areas themselves, but critically also involve brain areas not traditionally considered to be the locus of perceptual learning, e.g. the prefrontal cortex. Even within sensory cortex, learning involves interactions between brain areas and is not limited to local changes in the functional properties of neurons.
We carried out the following dissemination activities so far:
- Five empirical papers published in international peer-reviewed journals.
- Two data sets were published in online repositories.
- Talks or poster presentations at ten international conferences, workshops or symposia.
- Public engagement, including press releases, social media activity, tour through research site.
We carried out the following dissemination activities so far:
- Five empirical papers published in international peer-reviewed journals.
- Two data sets were published in online repositories.
- Talks or poster presentations at ten international conferences, workshops or symposia.
- Public engagement, including press releases, social media activity, tour through research site.
Our results challenge traditional theories of perceptual learning by expanding the focus of investigation beyond sensory cortices. These insights provide starting points to develop new training programs that lead to robust and generalizable learning on the basis of neurophysiological principles. If successful, these may help to develop new applications, e.g. for the rehabilitation of sensory deficits after stroke.