Presentation at CNS symposium on “Prediction, adaptation and plasticity of language processing in the adult brain”

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Earlier this week, Dave Kleinschmidt and I gave a presentation as part of a mini-symposium at Cognitive Neuroscience Conference  on “Prediction, adaptation and plasticity of language processing in the adult brain” organized by Gina Kuperberg.  For this symposium we were tasked to address the following questions:

  1. What is prediction and why do we predict?
  2. What is adaptation and why do we adapt?
  3. How do prediction and adaptation relate?

Although we address these questions in the context of language processing, most of our points are pretty general. We aim to provide intuitions about the notions of distribution, prediction, distributional/statistical learning and adaptation. We walked through examples of belief-updating, intentionally keeping our presentation math-free. Perhaps some of the slides are of interest to some of you, so I attached them below. A more in-depth treatment of these questions is also provided in Kleinschmidt & Jaeger (under review, available on request).

Comments welcome. (sorry – some of the slides look strange after importing them and all the animations got lost but I think they are all readable).

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It was great to see these notions discussed and related to ERP, MEG, and fMRI research in the three other presentations of the symposium by Matt Davis, Kara Federmeier and Eddy Wlotko, and Gina Kuperberg. You can read their abstracts following the link to the symposium I included above.

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One thought on “Presentation at CNS symposium on “Prediction, adaptation and plasticity of language processing in the adult brain”

    tiflo responded:
    April 11, 2014 at 11:43 am

    Just to follow up on some questions I’ve gotten by email about this presentation.

    1) Couldn’t one substitute “learning” for “adaptation” and still everything you’d say is true?

    Yes. As a matter of fact, part of what we argued for in our presentation is that what is generally referred to as adaptation typically is learning, specifically distributional learning. We mean this in the sense that adaptation is mostly not just due to ‘simple’ priming (whatever that is, but some people seem to have clear intuitions about it).

    2) How does adaptation relate to priming, e.g., syntactic priming?

    See Jaeger and Snider (2013-Cognition) and Fine et al (2013-PLoS One) for our opinion on this issue. Manabu Arai and Reiko Mazuka also have an interesting recent paper that speaks to this issue (2013-QJEP) and Manabu and I have been re-analyzing the data further to get a clearer idea of the relation between a) prediction errors, b) the magnitude of priming effects on the next trial, and c) cumulative effects on shifted expectations/beliefs (see our 2014 CUNY poster, which I might post some time soon).

    3) Do you use the term adaptation to refer to one specific type of learning or could it be multiple mechanisms?

    We think that this is one of the open questions. It seems likely given existing evidence that there’s similar distributional learning at multiple levels of linguistic representations, but it’s also rather likely –though less well studied– that there’s learning at multiple time scales. For example, Matt Davis talked about studies that show changes in behavior only after consolidation (e.g., sleep). I think that this is an area that deserves further attention. One possibility is that the type of rapid adaptation we observe in some experiments (and presumably in many real-life situations) occurs when the (statistics of the) observed signal can be reasonably well captured by an existing (i.e., previously learned) generative model or a generative model that is similar to a previously learned generative model. I understood part of Matt Davis’s presentation to suggest that perhaps slower learning is observed when the signal requires a generative learning that starkly deviates from those previously learned. This would also be the case in certain (but not all) second language learning scenarios.

    Further questions and comments welcome — Just post them here.

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