1. Behavioral Analysis

It was found that when the hidden rules of idioms are violated, they're processed slower and comprehension becomes worse. This behavioral cost was the initial sign that the brain expects specific contextual prerequisites alongside idioms.

DATA.VIS // COMPREHENSION_STATSSTATUS: RENDERED
Comprehension Reaction Times
Fig. 1. Comprehension question accuracy and reaction time metrics during the task.

2. Decoding Brainwaves (EEG)

EEG allows us to look inside the brain by capturing the electrical signals generated by neurons. This experiment utilized a high-density 64-electrode montage to map activity across the entire scalp.

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Fig 2. Simplified electrode channel placement used during high-resolution capture.

3. Peaking Inside The Brain

Just a quarter of a second after seeing the first word of the idiom, a P3b component was detected. This can be interpreted as the brain's internal alarm bell flagging contextual violations.

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Scalp Topography
Fig. 3. Heatmap showing localized neural activity.

4. The Bayesian Model

A Bayesian linear mixed effects model indicated that there was a significant difference between how the brain treated the two experimental conditions. There was a larger amplitude in the IPU condition compared to the IPS condition, reflecting the brain's immediate response to the missing contextual prerequisite.

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β = 0 (Null) Mean: 0.55 0.25 0.85 95% Credible Interval
Fig 4. Posterior distribution of the voltage difference (IPU vs IPS) during the P3b window.

This is an abridged version of a peer-reviewed study.

Impact Statement

By utilizing well-established techniques in cognitive neuroscience (EEG), this study provides evidence for a novel hypothesis: Certain aspects of language (e.g., idioms) not only express conventionalized meanings but come with deeply engrained assumputions that determine their use. What is shown here is that the brain never stops predicting what's coming next in conversation, even when we're entirely unaware of this process. From a methodological perspective, this work speaks to the great utility that Bayesian modeling provides, particularly when it is necessary to compare how different factors affect a singular outcome.