Language
Which brain signal tracks how surprising a word's meaning is?
Open access · cc by · source: Europe PMC
Readers slowed down on words that made a mini-story implausible whether or not the word was related to the context, and this pattern matched the P600 brain response rather than the N400, as the authors' model predicted.
Study at a glance
- Design
- Computational / modelling — Recurrent neural-network model producing N400, P600 and Surprisal estimates, compared with a previously published ERP study (re-analysed with regression ERPs) and a new self-paced reading replication using the same German materials
- N
- N=31 · 31 native German speakers in the new self-paced reading experiment; the ERP data come from an earlier study (Delogu et al.) and the model was trained in 10 instances
- Population
- Native German-speaking students at Saarland University (reading experiment); simulated comprehender (model)
- Outcome
- Word-by-word reading times on the target and following word; plausibility judgements; model N400, P600 and Surprisal estimates
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Key findings
The model predicted an N400 effect only for the unrelated condition (driven by word association) but a P600 effect and higher Surprisal for both implausible conditions (driven by plausibility). Once overlap between the two ERP components was separated statistically, the ERP data matched this pattern. In the reading experiment, participants judged 91% of baseline stories plausible versus 24% and 8% of the two implausible versions, and they read both implausible target words more slowly than the baseline; plausibility predicted reading time while association did not.
Methodology
The authors built a neural-network model of word-by-word comprehension in which a retrieval stage (linked to the N400 brain response) looks up a word's meaning and an integration stage (linked to the P600) adds it to the unfolding interpretation; the model also computes how surprising each updated interpretation is. They generated predictions for an earlier German ERP study with three kinds of two-sentence stories: a plausible baseline, an implausible version where the target word was still associated with the context, and an implausible version where it was unrelated. They re-examined that ERP data with regression-based ERPs, and ran a new self-paced reading version of the same study with 31 participants.
Limitations
The model was trained on a tiny artificial world of 16 words and 160 sentences, so it demonstrates a principle rather than a realistic model of language. The link between Surprisal and the P600 is qualitative: the design had only three conditions and cannot show that the P600 grows smoothly with graded surprise, which the authors leave open. The ERP evidence depends on a regression method to undo overlap between the N400 and P600 in the raw data, and the reading-time and ERP data came from different people, so the two measures were never recorded together.
How this study connects
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