Rumelhart Symposium: Language as a Dynamical System: In Honor of Jeff Elman
Bibliographic record
Abstract
Rumelhart Symposium: Language as a Dynamical System: In Honor of Jeff Elman Symposium Organizer: Ping Li (pli@richmond.edu) Department of Psychology, University of Richmond Richmond, VA 23173 USA Symposium Presenters: Gerry Altmann (g.altmann@psych.york.ac.uk) Department of Psychology, University of York Heslington, York Y010 5DD UK Mary Hare (hare@crl.ucsd.edu) Department of Psychology, Bowling Green State University Bowling Green, OH 43403 USA Ping Li (pli@richmond.edu) Department of Psychology, University of Richmond Richmond, VA 23173 USA Ken McRae (kenm@uwo.ca) Department of Psychology, University of Western Ontario London, ON, N6A 5C2 Canada Kim Plunkett (kim.plunkett@psy.ox.ac.uk) Department of Experimental Psychology, Oxford University Oxford, OX1 3UD UK Keywords: Dynamical systems; Jeff Elman; Language acquisition; Language processing; Rumelhart prize. Data will be drawn from studies in which participants’ eye movements are monitored as they hear a sentence describing an event that could unfold within a visual scene that they are either concurrently viewing, or have viewed in the past. Parallels will be drawn with equivalent studies on reading sentences in context, suggesting that the mental representations of the visual world, and of the world experienced through reading, are remarkably similar. Altmann concludes that incrementality in language comprehension is a by-product of the process by which we acquire information about both language and the visual world with which we interact. Introduction Language as a dynamical system, a proposal championed by Jeff Elman (Elman, 1990, 1995; Elman et al., 1996), has had a profound impact on our thinking of the relationship between language and cognition. This perspective distinguishes itself from the view of cognition based on static building blocks in the form of symbols and rules. Recent advances in developmental psychology and cognitive neuroscience provide further support for the dynamical perspective and further evidence on the neural and computational mechanisms underlying the dynamic changes that occur in the language learner and the speaker. In this symposium, several colleagues who have worked with Jeff Elman in the past decade or so will present their data and theory that exemplify the view of language as a dynamical system. Mary Hare Hare will argue that fundamental issues in the representation and processing of language have to do with the interface among lexical, conceptual, and syntactic structure. Meaning and structure are related, and one view of this relationship is that lexical meaning determines structure. A contrasting view adopted here is that the relevant generalizations are not based on lexical knowledge, but on the language user’s interpretation of generalized events in the world. A set of priming studies will demonstrate that nouns denoting salient elements of events prime event participants. In addition, corpus analyses and self-paced reading studies will show that different senses of a verb reflect variations on the types of event that the verb refers to, and that this knowledge leads to expectations about subsequent arguments or structure during sentence comprehension. Overview of Presentations Gerry Altmann Altmann will begin by describing a variety of data demonstrating how incrementality in language comprehension is intimately tied to dynamically changing predictions in respect of what is likely to be coming next.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.003 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".