As requested by some, here are the slides from my 2015 CUNY Sentence Processing Conference plenary last week:
I’m posting them here for discussion purposes only. During the Q&A several interesting points were raised. For example Read the rest of this entry »
We hope to see y’all at CUNY in a few weeks. In the interest of hopefully luring to some of our posters, here’s an overview of the work we’ll be presenting. In particular, we invite our reviewers, who so boldly claimed (but did not provide references for the) triviality of our work ;), to visit our posters and help us mere mortals understand.
- Articulation and hyper-articulation
- Unsupervised and supervised learning during speech perception
- Syntactic priming and implicit learning during sentence comprehension
- Uncovering the biases underlying language production through artificial language learning
Interested in more details? Read on. And, as always, I welcome feedback. (to prevent spam, first time posters are moderated; after that your posts will always directly show)
The summer conference season is coming up and HLP Lab, friends, and collaborators will be presenting their work at CMCL (Baltimore, joint with ACL), ACL (Baltimore), CogSci (Quebec City), and IWOLP (Geneva). I wanted to take this opportunity to give an update on some of the projects we’ll have a chance to present at these venues. I’ll start with three semi-randomly selected papers. Read the rest of this entry »
Presentation at CNS symposium on “Prediction, adaptation and plasticity of language processing in the adult brain”
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:
- What is prediction and why do we predict?
- What is adaptation and why do we adapt?
- 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).
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.
A few days ago, I posted a summary of some recent work on syntactic alignment with Kodi Weatherholtz and Kathryn Campell-Kibler (both at The Ohio State University), in which we used the WAMI interface to collect speech data for research on language production over Amazon’s Mechanical Turk.
I’ll be giving a plenary presentation at the 15th Texas Linguistic Society conference to be held in October in Austin, TX. Philippe Schlenker from NYU and David Beaver from Austin will be giving plenaries, too. The special session will be on the “importance of experimental evidence in theories of syntax and semantics, and focus on research that highlights the unique advantages of the experimental environment, as opposed to other sources of data” (from their website). Submit an abstract before May 1st and I see you there.
A few days ago, I presented at the Gradience in Grammar workshop organized by Joan Bresnan, Dan Lassiter , and Annie Zaenen at Stanford’s CSLI (1/17-18). The discussion and audience reactions (incl. lack of reaction in some parts of the audience) prompted a few thoughts/questions about Gradience, Grammar, and to what extent the meaning of generative has survived in the modern day generative grammar. I decided to break this up into two posts. This summarizes the workshop – thanks to Annie, Dan, and Joan for putting this together!
The stated goal of the workshop was (quoting from the website):
For most linguists it is now clear that most, if not all, grammaticality judgments are graded. This insight is leading to a renewed interest in implicit knowledge of “soft” grammatical constraints and generalizations from statistical learning and in probabilistic or variable models of grammar, such as probabilistic or exemplar-based grammars. This workshop aims to stimulate discussion of the empirical techniques and linguistic models that gradience in grammar calls for, by bringing internationally known speakers representing various perspectives on the cognitive science of grammar from linguistics, psychology, and computation.
Apologies in advance for butchering the presenters’ points with my highly subjective summary; feel free to comment. Two of the talks demonstrated