The Lazy Visual Word Form Area: Computational Insights into Location-Sensitivity

Thomas Hannagan and Jonathan Grainger

PLOS Computational Biology, 2013, vol. 9, issue 10, 1-12

Abstract: In a recent study, Rauschecker et al. convincingly demonstrate that visual words evoke neural activation signals in the Visual Word Form Area that can be classified based on where they were presented in the visual fields. This result goes against the prevailing consensus, and begs an explanation. We show that one of the simplest possible models for word recognition, a multilayer feedforward network, will exhibit precisely the same behavior when trained to recognize words at different locations. The model suggests that the VWFA initially starts with information about location, which is not being suppressed during reading acquisition more than is needed to meet the requirements of location-invariant word recognition. Some new interpretations of Rauschecker et al.'s results are proposed, and three specific predictions are derived to be tested in further studies.Author Summary: There is a mild form of modern “mind-reading” that involves, with heavy fMRI apparatus and software assistance, to guess from brain signals alone the locations of words that have been seen by a (consenting) subject. The recent surprise brought to us by Rauschecker et al. is not that we can currently do that, but that we can do it in a brain region that had until now been largely taken to discard information pertaining to location — the so-called Visual Word Form Area (VWFA). The contribution of our article is to explain this phenomenon in a principled manner, using computational modeling. The gist of our account is that the VWFA starts out with location information, which is indeed progressively discarded as the region maturates but only in as much as actually required to recognize words presented at different retinal locations (a necessary feat when one learns how to read). This “lazy VWFA” account captures many of the findings reported by Rauschecker et al. in a simple model with very few parameters, and it makes specific predictions that would falsify the model immediately were they to be found incorrect.

Date: 2013
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Persistent link: https://EconPapers.repec.org/RePEc:plo:pcbi00:1003250

DOI: 10.1371/journal.pcbi.1003250

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