using evoked magnetoencephalographic responses for the cognitive neuroscience of language
DESCRIPTION
Using Evoked Magnetoencephalographic Responses for the Cognitive Neuroscience of Language. Alec Marantz MIT KIT/MIT MEG Joint Research Lab Department of Linguistics and Philosophy. From Cog Sci to Cog Neurosci. - PowerPoint PPT PresentationTRANSCRIPT
![Page 1: Using Evoked Magnetoencephalographic Responses for the Cognitive Neuroscience of Language](https://reader035.vdocument.in/reader035/viewer/2022062521/56813e77550346895da89610/html5/thumbnails/1.jpg)
Using Evoked Magnetoencephalographic Responses for the
Cognitive Neuroscience of Language
Alec MarantzMIT
KIT/MIT MEG Joint Research LabDepartment of Linguistics and Philosophy
![Page 2: Using Evoked Magnetoencephalographic Responses for the Cognitive Neuroscience of Language](https://reader035.vdocument.in/reader035/viewer/2022062521/56813e77550346895da89610/html5/thumbnails/2.jpg)
From Cog Sci to Cog Neurosci
• Cognitive Science, including Linguistics, has used behavioral data to develop computational theories of language representation and use
• These theories play out along the dimensions of time (sequential processing stages), space (separation of processing functions) and complexity (difficulty of processing)
![Page 3: Using Evoked Magnetoencephalographic Responses for the Cognitive Neuroscience of Language](https://reader035.vdocument.in/reader035/viewer/2022062521/56813e77550346895da89610/html5/thumbnails/3.jpg)
Cognitive Neuroscience of Language
• Cognitive Science moves to Cognitive Neuroscience when the temporal, spatial, and complexity dimensions of cognitive theories are mapped onto the time course, localization, and intensity of brain activity
• However, because of the lack of temporal information, the development of Neurolinguistics with fMRI and PET techniques has tended to flatten theories of the Cognitive Neuroscience of Language
![Page 4: Using Evoked Magnetoencephalographic Responses for the Cognitive Neuroscience of Language](https://reader035.vdocument.in/reader035/viewer/2022062521/56813e77550346895da89610/html5/thumbnails/4.jpg)
Cognitive Science: Taft & Forster 1977 (traditional
articulated Cog Sci)
Affix stripping, followed by recombination of stem and affix
![Page 5: Using Evoked Magnetoencephalographic Responses for the Cognitive Neuroscience of Language](https://reader035.vdocument.in/reader035/viewer/2022062521/56813e77550346895da89610/html5/thumbnails/5.jpg)
sample prediction from model:
• -semble is a stem, since assemble, resemble, dissemble are words
• -sassin (assasin) is not a stem, since only assassin is a word
• It should take longer to reject “semble” as a non-word than “sassin,” since “semble” is a lexical item (“semble” requires looping from box 4 through box 5 in the model before reaching box 7, while “sassin” pushes directly from box 4 to box 7, “No”)
![Page 6: Using Evoked Magnetoencephalographic Responses for the Cognitive Neuroscience of Language](https://reader035.vdocument.in/reader035/viewer/2022062521/56813e77550346895da89610/html5/thumbnails/6.jpg)
Taft 2004: further behavioral support for articulated model of processing
stages
More contemporary instantiation of model -- makes predictions about RTs based, e.g., on a theory of the experimental task
![Page 7: Using Evoked Magnetoencephalographic Responses for the Cognitive Neuroscience of Language](https://reader035.vdocument.in/reader035/viewer/2022062521/56813e77550346895da89610/html5/thumbnails/7.jpg)
Flattened computational model: Gonnerman & Plaut (2000)
![Page 8: Using Evoked Magnetoencephalographic Responses for the Cognitive Neuroscience of Language](https://reader035.vdocument.in/reader035/viewer/2022062521/56813e77550346895da89610/html5/thumbnails/8.jpg)
• Masked priming experiment compares responses to Semantic sofa-COUCH Morphological hunter-HUNT Orthographic passive-PASS Unrelated award-MUNCH
• Claim: failure to find special location for the morphological condition (using fMRI) supports flat model in which morphology is an emergent property of semantic and phonological/orthographic relatedness
![Page 9: Using Evoked Magnetoencephalographic Responses for the Cognitive Neuroscience of Language](https://reader035.vdocument.in/reader035/viewer/2022062521/56813e77550346895da89610/html5/thumbnails/9.jpg)
fMRI experiment consistent with flattened computational model. Temporal/sequential processing not at issue.
But the masked priming experimental design is confounded with respect to predictions from a Taft-style model with affix-stripping since the “orthographic” items consist of possible stems and stripable affixes (e.g., tenable/ten passive/pass)
![Page 10: Using Evoked Magnetoencephalographic Responses for the Cognitive Neuroscience of Language](https://reader035.vdocument.in/reader035/viewer/2022062521/56813e77550346895da89610/html5/thumbnails/10.jpg)
Articulated vs. Flattened Model
• Taft’s articulated affix-stripping model predicts that “tenable” and “bendable” should be processed in the same “places” (in the model/brain) and in the same temporal sequence (affix stripping followed by stem activation followed by recombination), with differences in “complexity” (measured, e.g., by level of brain activity or latency of brain events)
• Thus the cognitive science model predicts the fMRI results and makes further predictions testable with techniques that allow exploration of the latency of brain responses
![Page 11: Using Evoked Magnetoencephalographic Responses for the Cognitive Neuroscience of Language](https://reader035.vdocument.in/reader035/viewer/2022062521/56813e77550346895da89610/html5/thumbnails/11.jpg)
MEG allows cognitive neuroscience to fully embrace
cognitive science
• MEG records the magnetic fields generated by electrical activity in the brain, millisecond by millisecond
• MEG has the spatial resolution, the temporal resolution and the sensitivity necessary to test predictions from cognitive science along the space, time and complexity dimensions
![Page 12: Using Evoked Magnetoencephalographic Responses for the Cognitive Neuroscience of Language](https://reader035.vdocument.in/reader035/viewer/2022062521/56813e77550346895da89610/html5/thumbnails/12.jpg)
Plot
• Examples of MEG experiments exploiting the temporal, spatial, and intensity resolution of the technique
• A return to Taft’s stages• The future: even closer ties between experimental designs in cognitive science and cognitive neuroscience
![Page 13: Using Evoked Magnetoencephalographic Responses for the Cognitive Neuroscience of Language](https://reader035.vdocument.in/reader035/viewer/2022062521/56813e77550346895da89610/html5/thumbnails/13.jpg)
KIT/MIT MEG Lab
![Page 14: Using Evoked Magnetoencephalographic Responses for the Cognitive Neuroscience of Language](https://reader035.vdocument.in/reader035/viewer/2022062521/56813e77550346895da89610/html5/thumbnails/14.jpg)
Magnetoencephalography (MEG) =study of the brain’s magnetic fields
http://www.ctf.com/Pages/page33.html
![Page 15: Using Evoked Magnetoencephalographic Responses for the Cognitive Neuroscience of Language](https://reader035.vdocument.in/reader035/viewer/2022062521/56813e77550346895da89610/html5/thumbnails/15.jpg)
Magnetoencephalography (MEG)
Distribution of magnetic field at 93 ms (auditory M100)
Averaged epoch of activity in all sensors, overlapping wave forms, one line/sensor
Outgoing
Ingoing
Liina Pylkkänen, Aug 03, Tateshina
![Page 16: Using Evoked Magnetoencephalographic Responses for the Cognitive Neuroscience of Language](https://reader035.vdocument.in/reader035/viewer/2022062521/56813e77550346895da89610/html5/thumbnails/16.jpg)
MEG exemplified
![Page 17: Using Evoked Magnetoencephalographic Responses for the Cognitive Neuroscience of Language](https://reader035.vdocument.in/reader035/viewer/2022062521/56813e77550346895da89610/html5/thumbnails/17.jpg)
Parametric variation in letter string length and in added
visual noise
Categorical symbol vs letter manipulation
![Page 18: Using Evoked Magnetoencephalographic Responses for the Cognitive Neuroscience of Language](https://reader035.vdocument.in/reader035/viewer/2022062521/56813e77550346895da89610/html5/thumbnails/18.jpg)
M100 response varies in intensity with visual noise; M170 response varies in
intensity with string length
M100 response M170
response
Note separation in space and temporal sequence (M100 vs. M170) consistent with sequential processing model
![Page 19: Using Evoked Magnetoencephalographic Responses for the Cognitive Neuroscience of Language](https://reader035.vdocument.in/reader035/viewer/2022062521/56813e77550346895da89610/html5/thumbnails/19.jpg)
Intensity of M170 response to letters as compared to symbols confirms function of
processing at M170 time & location (“visual word form” or “letter string” area)
Reaction time to read words predicted by combination of M170 amplitude and latency
![Page 20: Using Evoked Magnetoencephalographic Responses for the Cognitive Neuroscience of Language](https://reader035.vdocument.in/reader035/viewer/2022062521/56813e77550346895da89610/html5/thumbnails/20.jpg)
Latency coding? Response latency correlates with stimulus
properties.
![Page 21: Using Evoked Magnetoencephalographic Responses for the Cognitive Neuroscience of Language](https://reader035.vdocument.in/reader035/viewer/2022062521/56813e77550346895da89610/html5/thumbnails/21.jpg)
Auditory M100 (from auditory cortex)
![Page 22: Using Evoked Magnetoencephalographic Responses for the Cognitive Neuroscience of Language](https://reader035.vdocument.in/reader035/viewer/2022062521/56813e77550346895da89610/html5/thumbnails/22.jpg)
Frequency of tone predicts latency of M100 peak
![Page 23: Using Evoked Magnetoencephalographic Responses for the Cognitive Neuroscience of Language](https://reader035.vdocument.in/reader035/viewer/2022062521/56813e77550346895da89610/html5/thumbnails/23.jpg)
Temporal Coding?:Shape of response over time at M100 latency and
source location correlates with phonetic category of stimulus
![Page 24: Using Evoked Magnetoencephalographic Responses for the Cognitive Neuroscience of Language](https://reader035.vdocument.in/reader035/viewer/2022062521/56813e77550346895da89610/html5/thumbnails/24.jpg)
Voiced (b,d) vs. voiceless (p,t) consonant auditory evoked
response
![Page 25: Using Evoked Magnetoencephalographic Responses for the Cognitive Neuroscience of Language](https://reader035.vdocument.in/reader035/viewer/2022062521/56813e77550346895da89610/html5/thumbnails/25.jpg)
• Different ways of measuring the shape of the M100 response to voiced vs. voiceless consonants yield good computational “experts” that can classify data from a single response as either a pa/ta or a ba/da with significantly greater than chance accuracy
![Page 26: Using Evoked Magnetoencephalographic Responses for the Cognitive Neuroscience of Language](https://reader035.vdocument.in/reader035/viewer/2022062521/56813e77550346895da89610/html5/thumbnails/26.jpg)
Sequential processing of words
![Page 27: Using Evoked Magnetoencephalographic Responses for the Cognitive Neuroscience of Language](https://reader035.vdocument.in/reader035/viewer/2022062521/56813e77550346895da89610/html5/thumbnails/27.jpg)
What happens in the brain when we read words?
-100 0 100 200 300 400 500 600 700 [msec]
0
200
200
[fT]
150-200ms (M170) 200-300ms (M250) 300-400ms (M350) 400-500ms
Pylkkänen and Marantz, Trends in Cognitive Sciences
Letter string processing
(Tarkiainen et al. 1999)
Lexical activation
(Pylkkänen et al. 2002)
![Page 28: Using Evoked Magnetoencephalographic Responses for the Cognitive Neuroscience of Language](https://reader035.vdocument.in/reader035/viewer/2022062521/56813e77550346895da89610/html5/thumbnails/28.jpg)
Note left lateralization of responses in standard perisylvian language areas
![Page 29: Using Evoked Magnetoencephalographic Responses for the Cognitive Neuroscience of Language](https://reader035.vdocument.in/reader035/viewer/2022062521/56813e77550346895da89610/html5/thumbnails/29.jpg)
Latency of M350 sensitive to lexical factors such as lexical
frequency and repetition
M350
(Pylkkänen, Stringfellow, Flagg, Marantz, Biomag2000 Proceedings, 2000)
Repetition Frequency
1 2 3 4 5 6
Frequency Category (Frequent -- Infrequent)
Behavioral Data: Reaction Time
Categories (n/Million):
1: 7002: 1403: 30 4: 6 5: 1 6: .2
1 2 3 4 5 6Frequency Category (Frequent -- Infrequent)
Latency of m350 Component
Categories (n/Million):
1: 7002: 1403: 30 4: 6 5: 1 6: .2
(Embick, Hackl, Shaeffer, Kelepir, Marantz, Cognitive Brain Research, 2001)
![Page 30: Using Evoked Magnetoencephalographic Responses for the Cognitive Neuroscience of Language](https://reader035.vdocument.in/reader035/viewer/2022062521/56813e77550346895da89610/html5/thumbnails/30.jpg)
M350 is (in time and place) the locus of lexical activation; lexical decision modulated by competition
among activated items occurs later and elsewhere
![Page 31: Using Evoked Magnetoencephalographic Responses for the Cognitive Neuroscience of Language](https://reader035.vdocument.in/reader035/viewer/2022062521/56813e77550346895da89610/html5/thumbnails/31.jpg)
Vitevich and Luce (1998), stages of word processing• Phonotactic probability (sub-lexical frequency of bits of words) affects lexical activation, with frequency being facilitory
• Phonological neighborhood density affects lexical decision (“after” activation), with density being inhibitory
• Phonotactic probability and neighborhood density are usually highly correlated, so the same items that facilitate activation inhibit decision
• So, words with high phonotactic probabilities from dense neighborhoods should show quicker M350 latencies but slower RTs in lexical decision
![Page 32: Using Evoked Magnetoencephalographic Responses for the Cognitive Neuroscience of Language](https://reader035.vdocument.in/reader035/viewer/2022062521/56813e77550346895da89610/html5/thumbnails/32.jpg)
Words and non-words with high probability sound sequences, from dense neighbors, show quicker
M350s and slower RTs
![Page 33: Using Evoked Magnetoencephalographic Responses for the Cognitive Neuroscience of Language](https://reader035.vdocument.in/reader035/viewer/2022062521/56813e77550346895da89610/html5/thumbnails/33.jpg)
Pylkkänen et al. (2002)
M350: M350: notnot sensitive to competition from sensitive to competition from phonological neighbors, RT phonological neighbors, RT isis
NEIGHBORHOOD COMPETITION
EFFECT
SUBLEXICAL PHON FREQUENCY
EFFECT
300
350
400
450
500
550
600
650
700
M350 RT
[msec]
High phon. prob. word (LINE) Low phon. prob. word (PAGE)
**
**
![Page 34: Using Evoked Magnetoencephalographic Responses for the Cognitive Neuroscience of Language](https://reader035.vdocument.in/reader035/viewer/2022062521/56813e77550346895da89610/html5/thumbnails/34.jpg)
Irregular Past Tense Priming:Stockall & Marantz (to appear in Mental Lexicon)
• In cross-modal priming (hear one word, make a lexical decision on a letter string presented immediately after), irregulars don’t generally prime their stems behaviorally:gave-GIVE taught-TEACH
• Allen & Badecker show that orthographic overlap in this experimental design leads to RT inhibition and that past-tense/stem pairs with higher orthographic overlap yield less priming than those with less overlap
![Page 35: Using Evoked Magnetoencephalographic Responses for the Cognitive Neuroscience of Language](https://reader035.vdocument.in/reader035/viewer/2022062521/56813e77550346895da89610/html5/thumbnails/35.jpg)
Prediction of linguistic theories (e.g., Distributed
Morphology)
• Irregular past tense/”stem” priming paradigms (gave/give, taught/teach) should yield identity priming at the stage of root/stem activation (the M350) and form competition effects among allomorphs subsequently, slowing reaction time relative to pure stem/stem identity priming.
![Page 36: Using Evoked Magnetoencephalographic Responses for the Cognitive Neuroscience of Language](https://reader035.vdocument.in/reader035/viewer/2022062521/56813e77550346895da89610/html5/thumbnails/36.jpg)
MEG irregular past-tense priming experiment
Design:Visual-visual immediate priming, lexical decision on the target
(see Pastizzo and Feldman 2002 )
+prime
target
450 50 200 0 …2500ms
Duration of trial (ms)
![Page 37: Using Evoked Magnetoencephalographic Responses for the Cognitive Neuroscience of Language](https://reader035.vdocument.in/reader035/viewer/2022062521/56813e77550346895da89610/html5/thumbnails/37.jpg)
MEG Results: M350 Priming for Past Tense/Stem equivalent to identity
priming
Significant priming for
Identity condition (*p=0.01)
TAUGHT-TEACH vs.SMACK-TEACH (*p=0.04)
GAVE-GIVE vs. PLUM-GIVE (*p=0.05)
No reliable effect for: STIFF-STAFF vsGRAB-STAFF (p=0.13)
Amount of PriingAmount of Priming n=8
![Page 38: Using Evoked Magnetoencephalographic Responses for the Cognitive Neuroscience of Language](https://reader035.vdocument.in/reader035/viewer/2022062521/56813e77550346895da89610/html5/thumbnails/38.jpg)
RT Results: Competition effects; no significant priming for TAUGHT-TEACH
62.4
-27.6 -13.018.7
-40
-20
0
20
40
60
80
1Amount of priming (ms)
Identity stiff-staff taught-teach gave-give
**
**
n.s.
Significant priming for Identity condition (**p=0.0009)
GAVE-GIVE (*p=0.03)
Significant inhibition for STIFF-STAFF (*p=0.01)
No reliable effect for TAUGHT-TEACH (p=0.21) (but trend towards inhibition)
![Page 39: Using Evoked Magnetoencephalographic Responses for the Cognitive Neuroscience of Language](https://reader035.vdocument.in/reader035/viewer/2022062521/56813e77550346895da89610/html5/thumbnails/39.jpg)
MEG & RT Results:MEG taps stem activation; RT reflects decision in the face of competition
**
**
n.s.
-50
-20
10
40
70
gave-give ident stiff-staff taught-teach
M350 Latency RT
![Page 40: Using Evoked Magnetoencephalographic Responses for the Cognitive Neuroscience of Language](https://reader035.vdocument.in/reader035/viewer/2022062521/56813e77550346895da89610/html5/thumbnails/40.jpg)
Follow-up: Add regulars and ritzy/glitzy condition
• Regulars walk-walked
• Orthographic & Semantic Overlap:
boil-broil• Reverse order, stem before past tense
![Page 41: Using Evoked Magnetoencephalographic Responses for the Cognitive Neuroscience of Language](https://reader035.vdocument.in/reader035/viewer/2022062521/56813e77550346895da89610/html5/thumbnails/41.jpg)
ritzy-glitzy items
drop~drip clash~clangflip~flop blossom~bloom pet~pat ghost~ghoulgloom~glum shrivel~shrinksquish~squash crumple~rumpleboil~broil screech~screamstrain~sprain converge~mergemangle~tangle scald~scorchslim~trim crinkle~wrinkle bump~lump attain~gainburst~bust scrape~scratch
![Page 42: Using Evoked Magnetoencephalographic Responses for the Cognitive Neuroscience of Language](https://reader035.vdocument.in/reader035/viewer/2022062521/56813e77550346895da89610/html5/thumbnails/42.jpg)
-20
-10
0
10
20
30
40
50
give-gave teach-taught date-dated boil-broilAmount of Priming (ms/fT)
MEG RT
-50
-20
10
40
70
gave-give ident stiff-staff taught-teach
Amount of Priming (ms/fT)
M350 Latency RT
Order effect on RT; i.e., on form competition
![Page 43: Using Evoked Magnetoencephalographic Responses for the Cognitive Neuroscience of Language](https://reader035.vdocument.in/reader035/viewer/2022062521/56813e77550346895da89610/html5/thumbnails/43.jpg)
Linguistic Computational Models of Morphology fully supported
• Relation between irregular past tense form and stem is like that between regular past tense form and stem (or between identical stems), not like that between words phonologically/orthographically and semantically related (boil - broil)
• Root priming separates from form competition (between allomorphs of stem) in time course of lexical access
![Page 44: Using Evoked Magnetoencephalographic Responses for the Cognitive Neuroscience of Language](https://reader035.vdocument.in/reader035/viewer/2022062521/56813e77550346895da89610/html5/thumbnails/44.jpg)
Taft (2004), “Morphological Decomposition and the Reverse Base
Frequency Effect.”
• Claim: Base frequency effects (RT to complex word correlates with freq of stem) reflect access of the stem of morphological complex forms whereas surface frequency effects (RT to complex word correlates with freq of complex word) reflect stage of checking recombination of stem and affix for existence and/or well-formedness.
• “The suggestion being made, then, is that the advantage at the early stages of processing of having a relatively high base frequency could be potentially obscured by counterbalancing factors happening at later stages of processing.” [750-1]
![Page 45: Using Evoked Magnetoencephalographic Responses for the Cognitive Neuroscience of Language](https://reader035.vdocument.in/reader035/viewer/2022062521/56813e77550346895da89610/html5/thumbnails/45.jpg)
Lexical Decision Task
• non-word foils consisting of existing words with ungrammatical affixes (mirths, kettled, joying, redly, iratest) (just like the Devlin “orthographic” cases)
• three classes of words “mending” class: low surface frequency
low base frequency “seeming” class: low surface frequency
high base frequency “growing” class: mid surface frequency
high base frequency
![Page 46: Using Evoked Magnetoencephalographic Responses for the Cognitive Neuroscience of Language](https://reader035.vdocument.in/reader035/viewer/2022062521/56813e77550346895da89610/html5/thumbnails/46.jpg)
• Claim: advantage of high base frequency for “seem” at stem access stage (indexed by the M350) is offset in RT by a disadvantage for the low-frequency of the use of the –ing with the “seem” stem, i.e., at the post-affix recombination stage, indexed by RT
• (For Taft, manipulating the foils in lexical decision attenuated the surface frequency effect, arguing for two stages of processing in the indirect fashion typical of good cognitive science )
![Page 47: Using Evoked Magnetoencephalographic Responses for the Cognitive Neuroscience of Language](https://reader035.vdocument.in/reader035/viewer/2022062521/56813e77550346895da89610/html5/thumbnails/47.jpg)
Reilly and Holt 2004, with the KIT/MIT MEG Team
• Replicate Taft’s experiment in the MEG Lab
• Predict: base frequency affects root access and thus M350 latency
surface frequency affects post-M350 recombination stage and thus RT
![Page 48: Using Evoked Magnetoencephalographic Responses for the Cognitive Neuroscience of Language](https://reader035.vdocument.in/reader035/viewer/2022062521/56813e77550346895da89610/html5/thumbnails/48.jpg)
Results: M350 Latency tracks Base Frequency, RT tracks Surface
FrequencyMending classlow surfacelow base
Seeming classlow surfacehigh base
Growing classmid-surfacehigh base
Surface frequency 7.8 7.7 75.9Base frequency 36.5 460.3 456.9RT Taft 687 701 653RT MIT 780 805 746M350 MIT 375 362 356
Surface Frequency effect at RT (significant at .05 level), Mending and Seeming slower than GrowingBase Frequency effect at M350 Latency (significant at .05 level), Mending slower than Seeming and Growing
>>
>
![Page 49: Using Evoked Magnetoencephalographic Responses for the Cognitive Neuroscience of Language](https://reader035.vdocument.in/reader035/viewer/2022062521/56813e77550346895da89610/html5/thumbnails/49.jpg)
Conclusion
• MEG serves as a tool to upgrade cognitive science (& linguistics) to cognitive neuroscience without losing the empirically motivated richness of cognitive computational theories
• Cog Sci notions of space, time, and complexity map onto brain space, latency and magnitude of neural activity
![Page 50: Using Evoked Magnetoencephalographic Responses for the Cognitive Neuroscience of Language](https://reader035.vdocument.in/reader035/viewer/2022062521/56813e77550346895da89610/html5/thumbnails/50.jpg)
What’s the next step?
• Traditional approaches to MEG analysis involve averaging together many responses (repeated from an experimental “bin”) prior to computing differences in responses by condition within each subject
• This contrasts with standard cognitive science practice (e.g., with RT) of including a dependent measure from each trial in the ANOVA.
• To fully incorporate cognitive theories into cognitive neuroscience, including the correlation of continuous variables with continuous response measures and the use of item analyses in complex designs, we need to include single trial MEG data in our analyses
![Page 51: Using Evoked Magnetoencephalographic Responses for the Cognitive Neuroscience of Language](https://reader035.vdocument.in/reader035/viewer/2022062521/56813e77550346895da89610/html5/thumbnails/51.jpg)
Why not single trial MEG?
• For the type of experiment discussed in this talk, we would need to extract response amplitude and latency information from each trial, given a “response” defined in terms of source localization
• So, we would look at each single response for dipole source activation (latency of peak response, amplitude of response) for a source identified from grand averaged data for a subject
![Page 52: Using Evoked Magnetoencephalographic Responses for the Cognitive Neuroscience of Language](https://reader035.vdocument.in/reader035/viewer/2022062521/56813e77550346895da89610/html5/thumbnails/52.jpg)
M100 Latency, Single Trials(Marantz, in preparation)
• Left hemisphere M100 source computed via single dipole model from grand averaged response to 60 tones, 30 at 200Hz, 30 at 1KHz
• Weight matrix from dipole source used as spatial filter over raw data to derive dipole activation latency for each tone individually
![Page 53: Using Evoked Magnetoencephalographic Responses for the Cognitive Neuroscience of Language](https://reader035.vdocument.in/reader035/viewer/2022062521/56813e77550346895da89610/html5/thumbnails/53.jpg)
Single trial M100 latencies
Latency of left hemisphere M100 latency as a function of stimulus tone frequency
80
90
100
110
120
130
140
150
160
6 8 10 12 14 16 18Tone Frequency 200Hz vs. 1KHz
M100 activation latency (ms)
Series1
200Hz 1 KHz
![Page 54: Using Evoked Magnetoencephalographic Responses for the Cognitive Neuroscience of Language](https://reader035.vdocument.in/reader035/viewer/2022062521/56813e77550346895da89610/html5/thumbnails/54.jpg)
Single trial analysis as in behavioral studies is possible using only normal
MEG techniques and tools
• No fancy pre-processing• No fancy localization or statistical tools
• For responses less automatic than the M100, expect overlap in scatter plots to be greater (approaching that for RTs in e.g. lexical decision experiments)
![Page 55: Using Evoked Magnetoencephalographic Responses for the Cognitive Neuroscience of Language](https://reader035.vdocument.in/reader035/viewer/2022062521/56813e77550346895da89610/html5/thumbnails/55.jpg)
Taft & Forster re-visited
• Is RT slow-down for -semble (bound stem) over -sassin (pseudo-stem) attributable to lexical access for “semble” but not for “sassin,” as Taft claims, or to response competition from words (resemble, dissemble, assemble vs. assassin)?
• Prediction: slow-down at lexical access should show up at M350 while slow-down for response competition should occur after (as shown by neighborhood density and past tense studies)
![Page 56: Using Evoked Magnetoencephalographic Responses for the Cognitive Neuroscience of Language](https://reader035.vdocument.in/reader035/viewer/2022062521/56813e77550346895da89610/html5/thumbnails/56.jpg)
Brown & Marantz (in preparation)
• 3 subjects• 20 real stems, 20 pseudo stems (matched by Taft & Forster along various dimensions) per condition
• Single trial analysis of MEG data: M350 dipole activation peak analysis, with M350 dipole fitted over left-hemisphere sensors on the grand average to all stimuli in the experiment
![Page 57: Using Evoked Magnetoencephalographic Responses for the Cognitive Neuroscience of Language](https://reader035.vdocument.in/reader035/viewer/2022062521/56813e77550346895da89610/html5/thumbnails/57.jpg)
Slow-down is observed at M350: for 3 subjects and 108 observations, difference is significant over the single trial MEG data but not yet for
RT
Real Stems(-semble)
Pseudo Stems(-sassin)
Reaction time
784ms 719ms p=0.16
M350 Latency (over single trials)
356ms 339ms p=0.005
![Page 58: Using Evoked Magnetoencephalographic Responses for the Cognitive Neuroscience of Language](https://reader035.vdocument.in/reader035/viewer/2022062521/56813e77550346895da89610/html5/thumbnails/58.jpg)
• Taft theory of decomposition in which bound stems have lexical entries is fully supported by the MEG data
• Single trial MEG data is at least as consistent as reaction time data
• MEG can be used on par with RT to add additional dependent variables to experiments testing computational theories within cognitive neuroscience