Neural evidence for a single lexicogrammatical processing system - - PowerPoint PPT Presentation

Neural evidence for a single lexicogrammatical processing system

Jennifer Hughes j.j.hughes@lancaster.ac.uk

Background

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Approaches to collocation

Background

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Association measures

Background

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EEG, ERPs, and ERP components

Background

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Overview and key findings of earlier PhD Experiments

Approaches to collocation

Collocation: “co-occurrence relation between two words … [w]ords are said to collocate with one another if one is more likely to occur in the presence of the other than elsewhere” (McEnery and Hardie 2012:240) Approaches differ in terms of whether or not they propose separate systems for lexical and grammatical processing

• Idiom Principle vs. Open-Choice Principle (Sinclair 1991)
• Lexical Priming (Hoey 2005) – a single system
• Formulaic language (Wray 2002) – holistic vs. analytic system
• Construction Grammar (e.g. Goldberg 1995) – a single system

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Network model

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Approaches to collocation

• The Idiom Principle (Sinclair 1991)
• “a word becomes associated with a meaning through its

repeated occurrence in similar contexts” (Sinclair 2004:161)

• Lexical Priming (Hoey 2005)
• “collocation is fundamentally a psychological concept” (p7)
• “[a]s a word is acquired through encounters with it in speech

and writing, it becomes cumulatively loaded with the contexts and co-texts in which it is encountered” (p8)

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Approaches to collocation

• Formulaic language (Wray 2002)
• quicker access to frequently encountered sequences
• Construction Grammar
• a collocation is a particular instance of a construction
• network relationship between construction and collocation

and between constructions and other related constructions

• “[t]he collection of constructions ... constitute a highly

structured lattice of inter-related information” (Goldberg 1995)

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Association Measures

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Association Measure Type of Measure Statistic Log-likelihood Pure Significance Mutual information Pure Effect size Z-score Hybrid Effect size and significance T-score Hybrid Frequency and significance Dice coefficient Hybrid Frequency and effect size MI3 Hybrid Frequency and effect size

Statistical scores which allow us to distinguish between words which co-occur due to chance, and words which co-occur due to true statistical association (Evert 2008:32).

Defining EEG and ERPs

Electroencephalography (EEG): “a means of measuring electrical potentials in the brain by placing electrodes across the scalp” (Harley 2008) Event: experimental stimulus Event-related potentials (ERPs): “the momentary changes in electrical activity of the brain when a particular stimulus is presented to a person” (Ashcraft and Radvansky 2010)

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ERP Components

ERP component: “a scalp-recorded voltage change that reflects a specific neural or psychological process” (Kappenman and Luck 2011) N400 – lexical/semantic processing (Kutas and Hillyard 1980) P600 – syntactic processing (Osterhout and Holcomb 1992)

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(Swaab et al. 2012:422).

....... semantic error

no error

....... syntactic error

ms μV

Two processing systems… ?

The identification of the N400 and the P600 suggests that distinct neurophysiological processes are involved in semantic and syntactic processing, but…

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…more recent studies have shown that the N400 can be sensitive to syntactic violations and the P600 can be sensitive to semantic violations (e.g. Geyer et al. 2006)

… or one processing system?

“the neural systems supporting syntactic and semantic processing may be linked” (Kupperberg et al. 2006) “[r]esults such as these … raise serious and interesting questions about the relationship between semantic and syntactic processes in the brain” (Swaab et al. 2012:26) “the interaction between semantic and syntactic processes in the brain may be more dynamic than was previously suggested” (Swaab et al. 2012:28).

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Overview of PhD experiments

Experiment 1: A pilot study with native speakers Experiment 2: Native speaker study Experiment 3: Non-native speaker study Experiment 4: Replication and correlation study

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Key findings in Experiments 1 & 2

Experiment 1:

• Enlarged anterior-central N400 in non-collocational

condition

• P600 results inconclusive

Experiment 2:

• Enlarged N400 in non-collocational condition, only at

midline and right hemisphere electrode sites

• No P600

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Aim 1: To see whether or not any of the results from Experiments 1 and 2 are replicable. Aim 2: To investigate the strength of the correlation between the forward transition probability of a bigram and N400 amplitude. Aim 3: To find out which association measure most closely correlates with N400 amplitude, and thus may be seen as having the most psychological validity.

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Aims of Experiment 4

Method

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Experimental stimuli

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TP band Collocational bigrams (forward transition probability in written BNC1994) Non-collocational bigrams 0.8≤b<0.9 nineteenth century (0.855) nineteenth position 0.7≤b<0.8 prime minister (0.796) prime period 0.6≤b<0.7 foreseeable future (0.678) foreseeable weeks 0.5≤b<0.6 integral part (0.509) integral thought 0.4≤b<0.5 twenty-four hours (0.429) twenty-four patients 0.3≤b<0.4 disposable income (0.353) disposable property 0.2≤b<0.3 minimum wage (0.246) minimum prize 0.1≤b<0.2 vast majority (0.182) vast opportunity 0<b<0.1 crucial point (0.017) crucial night McDonald and Shillcock (2003:648) – strong collocations have a mean forward TP of 0.01011

Experimental stimuli

Collocational condition - In the foreseeable future the new railway line will be built but the completion date has not yet been confirmed. Non-collocational condition - In the foreseeable weeks the new railway line will be built but the completion date has not yet been confirmed. True/false statement – Plans to build a new railway line have been cancelled.

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Experimental stimuli

• condition 1 sentences taken from BNC concordance lines
• all 16 participants exposed to both conditions in one of

four counterbalanced lists

• presented word-by-word at a rate of 500 ms per word

(300 ms followed by a 200 ms interstimulus interval)

• experimental effect time-locked to the second word of

the bigram

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Data analysis – Part 1

Mean amplitude (ERPLAB)

• between 350 and 500 ms for N400
• between 500 and 650 ms for P600

Repeated measures ANOVA (SPSS) with three factors:

• Experimental condition (collocational bigrams vs. non-

collocational bigrams)

• Anterior-to-posterior electrode position
• Left-to-right electrode position

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Electrode zones

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Data analysis – Part 2

Four step approach to computing a single N400 value for each bigram pair that could be correlated with association measures:

• 1. Computed a difference wave for each bigram pair
• 2. Measured the mean amplitude in the 350-500 ms latency

range from each difference wave

• 3. Extracted the N400 values for the nine representative

electrode sites

• 4. Calculated the mean of the amplitude values from these nine

electrode sites Conducted a Pearson correlation, correlating N400 amplitude with forward transition probability, and then with 8 other association measures.

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Results: Part 1

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N400 (350-500 ms)

• mean amplitude is lower in the non-collocational condition

(M = 0.146, SD = 2.999) compared to the collocational condition (M = 1.132, SD = 3.353)

• no main effect - “Because the difference between

conditions is likely to be large at a subset of the sites and small or even opposite at others, you probably won’t see a significant main effect of condition” (Luck 2014: 336)

• significant interaction between condition and anterior-to-

posterior electrode position: F(2, 820) = 7.28, p = .001

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Note that the difference between conditions is quantitative rather than qualitative; the waveforms follow the same pattern, with varying amplitudes

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Collocational condition Non-collocational condition

P600 (500-650 ms)

• mean amplitude is higher in the non-collocational condition

(M = 0.754, SD = 7.29) compared to the collocational condition (M = -0,119, SD = 9.2)

• effect is greatest at central and posterior electrode sites

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Results: Part 2

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N400 amplitude and TP

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Ranking of association measures

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Association measure Pearson's r p-value

• 1. Z-score
• 0.773

0.014*

• 2. MI3
• 0.772

0.015*

• 3. Dice coefficient
• 0.712

0.031*

• 4. T-score
• 0.679

0.044*

• 5. Backward transition probability
• 0.658

0.054

• 6. Frequency
• 0.636

0.065

• 7. Forward transition probability
• 0.621

0.074

• 8. MI
• 0.575

0.105

• 9. Log-likelihood
• 0.566

0.112

Key issue raised by the results

Why does the N400 in Experiment 4 have the classical scalp distribution, but the N400 in Experiments 1 and 2 do not? Why is a P600 elicited in Experiment 4 but not in Experiments 1 and 2?

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Mean TP is much higher in Experiment 4 (0.452) than in Experiments 1 and 2 (0.231). Higher TP = stronger collocation = stronger expectation = greater increase in cognitive load when expectation is broken. The N400 and P600 could only be elicited ‘fully’ when the cognitive load is exceptionally high.

Implications and conclusions

• The difference between conditions is quantitative rather than

qualitative, suggesting a scale of collocationality rather than a dichotomy, i.e. one processing system rather than two.

• Both the N400 and P600 are elicited in response to reading a non-

collocation, suggesting that the voltage deflections which are typically known to be associated with lexical/semantic and grammatical processing do not work completely independently…

• … instead, they work together as part of a single processing system
• The two most widely used association measures seem to have the

least psychological validity, suggesting that corpus-based studies of lexicogrammar do not always use the optimal association measure for their purposes.

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References (1)

Ashcraft, M. H. and Radvansky, G. A. (2010). Cognition (5th edn.). London: Pearson. Evert, S. (2008). Corpora and collocations. In A. Lüdeling and M. Kytö (Eds.). Corpus Linguistics: An International Handbook. Berlin: Mouton de Gruyter, pp. 1212-1248. Geyer, A., Holcomb, P., Kuperberg, G., and Pearlmutter, N. (2006). Plausibility and sentence comprehension: An ERP Study. Cognitive Neuroscience Supplement. Goldberg, A. E. (1995). A construction grammar approach to argument

• structure. Chicago: University of Chicago Press.

Harley, T. A. (2008). The psychology of language: From data to theory (3rd edn.). New York: Psychology Press. Hoey, M. (2005). Lexical priming: A new theory of words and language. London: Routledge.

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References (2)

Kappenman, E. S. and Luck, S. J. (2011). ERP components: The ups and downs of brainwave recordings. In E. S. Kappenman and S.

• J. Luck (Eds.). The Oxford handbook of event related potentials.

New York: Oxford University Press, pp. 3-30.

Kuperberg, G. R., Caplan, D., Sitnikova, T., Eddy, M., & Holcomb, P. J. (2006). Neural correlates of processing syntactic, semantic, and thematic relationships in sentences. Language and Cognitive Processes, 21:489–530. Kutas, M. and Hillyard, S. A. (1980). Reading senseless sentences: Brain potentials reflect semantic incongruity. Science, 207(4427):203- 205. Luck, S. J. (2014). An introduction to the event-related potential technique (2nd edn.). Cambridge, MA: MIT Press. McEnery, T. and Hardie, A. (2012). Corpus linguistics: Methods, theory and practice. Cambridge: Cambridge University Press.

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References (3)

Osterhout, L. and Holcomb, P. (1992). Event-related brain potentials elicited by syntactic anomaly. Language and Cognitive Processes, 8(4):785-806. Sinclair, J. (1991). Corpus, concordance, collocation. Oxford: Oxford University Press. Sinclair, J. (2004). Trust the text: Language, corpus, and discourse. London: Routledge. Swaab, T. Y., Ledoux, K., Camblin, C. C. and Boudewyn, M. (2012). Language-related ERP components. In S. J. Luck and E. S. Kappenman (Eds.). The Oxford handbook of event related

• potentials. New York: Oxford University Press, pp. 397-439.

Wray, A. (2002). Formulaic language and the lexicon. Cambridge: Cambridge University Press.

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