How inappropriate high‐pass filters can produce artifactual effects and incorrect conclusions in ERP studies of language and cognition

Tanner, Darren; Morgan‐Short, Kara; Luck, Steven J.

doi:10.1111/psyp.12437

Cited by 300 publications

(231 citation statements)

References 46 publications

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“…They were re-referenced to the average of the left and right mastoids and filtered using a 0.1 -40 Hz band-pass filter offline. This highpass filter value was tested in a recent study and found to not introduce artefactual effects (Tanner, Morgan-Short, & Luck, 2015). A 15Hz low-pass filter was applied for the presentation of grand averages only, but was not used in the analyses.…”

Section: Methodsmentioning

confidence: 99%

Wait a second! delayed impact of argument roles on on-line verb prediction

Chow

Lau

Wang

et al. 2018

Language, Cognition and Neuroscience

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Comprehenders can use rich contextual information to anticipate upcoming input on the fly, but recent findings suggest that salient information about argument roles may not impact verb prediction. We took advantage of the word order properties of Mandarin Chinese to examine the time course with which argument role information impacts verb prediction. We isolated the contribution of argument role information by manipulating the order of pre-verbal noun phrase arguments while holding lexical information constant, and we examined its effects on accessing the verb in long-term semantic memory by measuring the amplitude of the N400 component.Experiment 1 showed when the verb appeared immediately after its arguments, even strongly constraining argument role information failed to modulate the N400 response to the verb. An N400 effect emerged in Experiment 2 when the verb appeared at a greater delay. Experiment 3 corroborated the contrast between the first two experiments through a within-participants manipulation of the time interval between the arguments and the verb, by varying the position of an adverbial phrase. These results suggest time is a key factor governing how diverse contextual information contributes to predictions. Here argument role information is shown to impact verb prediction, but its effect is not immediate.3

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Section: Methodsmentioning

confidence: 99%

Wait a second! delayed impact of argument roles on on-line verb prediction

Chow

Lau

Wang

et al. 2018

Language, Cognition and Neuroscience

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“…replicating the strong and statistically significant effect of cloze on noun-N400s 4,6 . The effect yielded strong to extremely strong evidence for the null hypothesis, whereas our noun data 103 yielded extremely strong evidence for the alternative hypothesis (upper graphs).…”

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confidence: 99%

“…Current theories of language comprehension [1][2][3] posit prediction, or 3 context-based pre-activation, as an essential mechanism occurring at all levels of linguistic 4 representation (semantic, morpho-syntactic and phonological/orthographic) and facilitating 5 the integration of words into the unfolding discourse representation. The strongest evidence 6 to date for phonological pre-activation comes from DeLong, Urbach and Kutas 4 , who 7 monitored participants' electrophysiological brain responses as they read sentences, presented 8 one word at a time, with expected/unexpected indefinite article + noun combinations like,…”

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confidence: 99%

“…The sentences varied 10 expectations ('cloze' probability) for a consonant-or vowel-initial noun, as determined in a 11 sentence-completion task using other participants. Expectedly, the amplitude of the N400 12 event-related potential (ERP) decreased (became less negative) with increasing cloze 13 reflecting ease of processing [5][6] . Whereas the decreased N400 at the noun could be due to its 14 pre-activation or because high-cloze nouns are easier to integrate, crucially, N400s at the 15 immediately-preceding article a or an showed the same relationship with cloze, i.e.,…”

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confidence: 99%

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Limits on prediction in language comprehension: A multi-lab failure to replicate evidence for probabilistic pre-activation of phonology

Nieuwland

Politzer-Ahles

Heyselaar

et al. 2017

Preprint

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In current theories of language comprehension, people routinely and implicitly predict upcoming words by pre-activating their meaning, morpho-syntactic features and even their specific phonological form. To date the strongest evidence for this latter form of linguistic prediction comes from a 2005 Nature Neuroscience landmark publication by DeLong, Urbach and Kutas, who observed a graded modulation of article- and noun-elicited electrical brain potentials (N400) by the pre-determined probability that people continue a sentence fragment with that word ('cloze'). In a direct replication study spanning 9 laboratories (N=334), we failed to replicate the crucial article-elicited N400 modulation by cloze, while we successfully replicated the commonly-reported noun-elicited N400 modulation. This pattern of failure and success was observed in a pre-registered replication analysis, a pre-registered single-trial analysis, and in exploratory Bayesian analyses. Our findings do not support a strong prediction view in which people routinely pre-activate the phonological form of upcoming words, and suggest a more limited role for prediction during language comprehension.

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“…However, baseline correction has been studied in the context of EEG/ MEG data, as shown by a recent debate about whether or not baseline correction of EEG/ MEG data should be abandoned in favor of high-pass filtering (Maess, Schröger, & Widmann, 2016;Tanner, Morgan-Short, & Luck, 2015;Tanner, Norton, Morgan-Short, & Luck, 2016). However, pupil-size data is different from EEG/ MEG data.…”

Section: Of 25mentioning

confidence: 99%

Safe and sensible baseline correction of pupil-size data

Mathôt

Fabius

Heusden

et al. 2017

Preprint

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Measurement of pupil size (pupillometry) has recently gained renewed interest from psychologists, but there is little agreement on how pupil-size data is best analyzed. Here we focus on one aspect of pupillometric analyses: baseline correction, that is, analyzing changes in pupil size relative to a baseline period. Baseline correction is useful in experiments that investigate the effect of some experimental manipulation on pupil size. In such experiments, baseline correction improves statistical power by taking into account random fluctuations in pupil size over time. However, we show that baseline correction can also distort data if unrealistically small pupil sizes are recorded during the baseline period, which can easily occur due to eye blinks, data loss, or other distortions. Divisive baseline correction (corrected pupil size = pupil size / baseline) is affected more strongly by such distortions than subtractive baseline correction (corrected pupil size = pupil size - baseline). We make four recommendations for safe and sensible baseline correction of pupil-size data: 1) use subtractive baseline correction; 2) visually compare your corrected and uncorrected data; 3) be wary of pupil-size effects that emerge faster than the latency of the pupillary response allows (within ±220 ms after the manipulation that induces the effect); and 4) remove trials on which baseline pupil size is unrealistically small (indicative of blinks and other distortions).

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How inappropriate high‐pass filters can produce artifactual effects and incorrect conclusions in ERP studies of language and cognition

Cited by 300 publications

References 46 publications

Wait a second! delayed impact of argument roles on on-line verb prediction

Wait a second! delayed impact of argument roles on on-line verb prediction

Limits on prediction in language comprehension: A multi-lab failure to replicate evidence for probabilistic pre-activation of phonology

Safe and sensible baseline correction of pupil-size data

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