Work

Surprisal-based comparison between a symbolic and a connectionist model of sentence processing

Year: 2009

Type: article

Abstract: The 'unlexicalized surprisal' of a word in sentence context is defined as the negative logarithm of the probability of the word's part-of-speech given the sequence of previous partsof-speech of the sentence.Unlexicalized surprisal is known to correlate with word reading time.Here, it is shown that this correlation grows stronger when surprisal values are estimated by a more accurate language model, indicating that readers make use of an objectively accurate probabilistic language model.Also, surprisals as estimated by a Simple Recurrent Network (SRN) were found to correlate more strongly with reading-time data than surprisals estimated by a Probabilistic Context-Free Grammar (PCFG).This suggests that the SRN forms a more accurate psycholinguistic model.

Source: eScholarship (California Digital Library)

Authors: Frank, S.L., Taatgen, N.A., van Rijn, H.

Institution: University of Amsterdam

Language: English

Topic: Neurobiology of Language and Bilingualism

Subfield: Cognitive Neuroscience

Field: Neuroscience

Domain: Life Sciences

Open Access status: green

Funder: Nederlandse Organisatie voor Wetenschappelijk Onderzoek

OpenAlex ID: W1486038082

FWCI: 3.229

Cites: 13

Cited by: 66

Related to: 10