a quick introduction to erp in language researchucjtwyc/teaching/quick_intro_erp.pdf · a quick...
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A Quick Introduction to ERP
in Language Research
Wing-Yee Chow
University of Maryland
Fall 2011
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Why use Event-Related Potentials (ERP) in
language research?
• Allows us to investigate how language processing unfolds in real-time.
– Can monitor “covert” processing when there is no “overt” behavioral response.
– Can ask which stage is affected by a given experimental manipulation.
• Allows us to test models of cognitive processes and evaluate how these models map onto the brain.
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The General Set-Up
Luck (2005)
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Two ends of the production line
Raw Data (EEG) End Products (ERP)
?
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The production line
Raw Data (EEG)
End Products (ERP)
Isolate the data of interest
(e.g., response during the 1000ms
after seeing particular words)
Remove noise (reject bad trials;
average across trials and participants)
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The production line
Raw Data (EEG)
End Products (ERP)
Isolate the data of interest
(e.g., response during the 1000ms
after seeing particular words)
Remove noise (reject bad trials;
average across trials and participants)
![Page 7: A Quick Introduction to ERP in Language Researchucjtwyc/teaching/Quick_Intro_ERP.pdf · A Quick Introduction to ERP in Language Research Wing-Yee Chow University of Maryland Fall](https://reader036.vdocuments.us/reader036/viewer/2022080718/5f78cce90796c241cb77d789/html5/thumbnails/7.jpg)
Isolate the data of interest
EEG recorded from one electrode Epoched (sliced-up)
EEG
Luck (2005)
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The production line
Raw Data (EEG)
End Products (ERP)
Isolate the data of interest
(e.g., response during the 1000ms
after seeing particular words)
Remove noise (reject bad trials;
average across trials and participants)
![Page 9: A Quick Introduction to ERP in Language Researchucjtwyc/teaching/Quick_Intro_ERP.pdf · A Quick Introduction to ERP in Language Research Wing-Yee Chow University of Maryland Fall](https://reader036.vdocuments.us/reader036/viewer/2022080718/5f78cce90796c241cb77d789/html5/thumbnails/9.jpg)
Remove noise1
Luck (2005)
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Remove noise2 (averaging)
Individual Averages Grand Average
Luck (2005)
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The production line
Raw Data (EEG)
End Products (ERP)
Isolate the data of interest
(e.g., response during the 1000ms
after seeing particular words)
Remove noise (reject bad trials;
average across trials and participants)
![Page 12: A Quick Introduction to ERP in Language Researchucjtwyc/teaching/Quick_Intro_ERP.pdf · A Quick Introduction to ERP in Language Research Wing-Yee Chow University of Maryland Fall](https://reader036.vdocuments.us/reader036/viewer/2022080718/5f78cce90796c241cb77d789/html5/thumbnails/12.jpg)
What info is in an ERP?
Quantitative:
• Latency
• Amplitude
Qualitative:
• Polarity
• Topographic distribution
600-900ms
-4.0µV +4.0µV
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Using ERP for Language
• Violation Paradigms
– Expectations set up, then violated.
• Brian drinks coffee with a spoonful of ________
• Mary spends all her morning reading the _________
• Before Dave arrived to the breakfast session, the last
bagel had been _____
dirt
menu
eat
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N400
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N400
• Broad negative deflection of the ERP
• Latency (onset ~200-300ms post stimulus onset)
• Peak amplitude (~400ms)
• Distribution (central-parietal)
Federmeier & Laszlo (2007) Voss & Federmeier (2010)
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N400 Semantic anomaly:
I like my coffee with cream and… [sugar/socks]
Word pairs:
tire…sugar
flour…sugar
Federmeier & Laszlo (2007) Voss & Federmeier (2010)
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Case Study: N400 Federmeier & Kutas (1999):
• Does the N400 appear to be sensitive to the organization
of semantic knowledge?
They wanted to make the hotel to look more like a tropical
resort. So along the driveway they planted rows of…
(a) palms an expected exemplar
(b) pines a within-category violation
(c) tulips a between-category violation
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Case Study: N400
Predictions:
• If N400 is sensitive to the organization of semantic
knowledge
– then N400 will differ for within-category relative to
between-category violations.
• If N400 is not sensitive to the organization of
semantic knowledge
– then the same N400 for both kinds of violation.
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Federmeier & Kutas (1999)
• Yes! The N400 appear to be sensitive to the organization of semantic knowledge.
Case Study: N400
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Designing your own ERP experiments
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Design Considerations
• Minimize eye-movements and other artifacts
– Stimulus presentation
– Tasks
• Statistical power vs. Length of experiment
– Number of trials
– Number of conditions
• Fillers
– quantity & content
– Filler-Target ratio
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Filler-Target Ratio
• the proportion of trials with violation affects
different components differentially
Hahne & Friederici (1999)
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Interpreting ERP data
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Interpretation?
• Reminder:
– Scalp distribution ≠ source of activation
– Functional significance of ERP components still
work in progress
• Look out for:
– Potential component overlaps
– Baseline problem
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Interpretation?
• Variation matters
– Within-group variation differs across
populations
• e.g., non-native vs. native speakers
– Averages can be misleading
• Recommendation:
– Know your data well, very well
Luck (2005)
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Useful Reference
Luck, S. J. (2005). An Introduction to the Event-
Related Potential Technique. Cambridge, MA:
MIT Press.