Abstract
This contribution reviews (some of) the history of analysis by synthesis, an approach to perception and comprehension articulated in the 1950s. Whereas much research has focused on bottom-up, feed-forward, inductive mechanisms, analysis by synthesis as a heuristic model emphasizes a balance of bottom-up and knowledge-driven, top-down, predictive steps in speech perception and language comprehension. This idea aligns well with contemporary Bayesian approaches to perception (in language and other domains), which are illustrated with examples from different aspects of perception and comprehension. Results from psycholinguistics, the cognitive neuroscience of language, and visual object recognition suggest that analysis by synthesis can provide a productive way of structuring biolinguistic research. Current evidence suggests that such a model is theoretically well motivated, biologically sensible, and becomes computationally tractable borrowing from Bayesian formalizations.
Highlights
This contribution reviews the history of analysis by synthesis, an approach to perception and comprehension articulated in the 1950s
Biolinguistics 4.2–3: 174–200, 2010 http://www.biolinguistics.eu enriches this model, and outline a set of research questions that are becoming salient, in part answerable today, and that set an agenda for future research
Why should a discussion of this algorithm be of any interest for biolinguistics? The biolinguistic program is rooted in the desire to unify the theoretical foundations of linguistic research with the material infrastructure provided by biology, and especially neurobiology
Summary
It is a commonplace that perception is in part constructive (e.g., James 1890). The computational mind takes imperfect, blurred, and continuously varying input and reports out discrete representations. A critical feature of the AxS architecture is that it combines statistical pattern recognition, symbolic generative processes and hypothesis confirmation (for example, of the form ‘compare the predicted pattern to the actual input, calculate the error, iterate the process until the error is minimized’) These different subroutines that jointly constitute the AxS architecture are gaining support in various areas of language research (Poeppel & Monahan 2010) as well as other areas of perception, notably vision (Hochstein & Ahissar 2002, Yuille & Kersten 2006), and we are optimistic that pursuing AxS (an approach that is broadly consistent with current approaches to Bayesian inference in perception) as a research strategy might be fruitful in studying biolinguistics in a real, practical sense — that is, merging biology and linguistics in the service of one particular problem in perception and comprehension
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