MEG, the Mental Lexicon and Morphology

MEG, the Mental Lexicon and Morphology
LP, Aug 03, Tateshina MEG, the Mental Lexicon and Morphology Liina Pylkkänen Department of Linguistics/ Center for Neuromagnetism New York University

MEG, the Mental Lexicon and Morphology
LP, Aug 03, Tateshina MEG, the Mental Lexicon and Morphology Day 1 Lexical access 1: The M350 as an MEG index of lexical activation Day 2 Lexical access 2: The M350 and mechanisms of recognition Day 3 Morphology 1: The M350 as a tool for investigating similarity and identity Day 4 Morphology 2: Electrophysiological and behavioral evidence for early effects of morphology

Acknowledgements (the yellow people have kindly allowed me to use their slides in these presentations) LP, Aug 03, Tateshina Alec Marantz (MIT) Andrew Stringfellow (UCSD) Laura Gonnerman (Lehigh University) Martin Hackl (Pomona College) David Embick (University of Pennsylvania) Meltem Kelepir (Eastern Mediterranean University) Jeanette Schaeffer (Ben Gurion University) Elissa Flagg ( U Toronto) Linnaea Stockall (MIT) Sophie Feintuch (Portsmouth High School, NH) Emily Hopkins (Portsmouth High School, NH) Eytan Zweig (NYU) Machteld van Rijsingen (NYU/ University of Amsterdam) Tony Wilson (University of Minnesota) Colin Phillips (University of Maryland, College Park) Robert Fiorentino (University of Maryland, College Park) David Poeppel (University of Maryland, College Park)

Day 1 Lexical access 1: The M350 as an MEG index of lexical activation
LP, Aug 03, Tateshina Day 1 Lexical access 1: The M350 as an MEG index of lexical activation General remarks on methodology and methodological challenges in cognitive neuroscience. Basic lexical access experiments (frequency, repetition priming): M350. Modality question. More detail on the nature of frequency effects. Pinpointing the cognitive level of the M350. Initial activation of the lexicon. Is this result compatible with evidence from other techniques? Eye-tracking, masked priming.

LP, Aug 03, Tateshina 1. Use knowledge of language to isolate neural correlates of linguistic processes Linguistic theory Psycholinguistics 2. Use neural correlates of linguistic processes as additional dependent variables in the study of language

How to isolate neural correlates of linguistic processes?
LP, Aug 03, Tateshina Method 1 Conditions differ in computational demands of linguistic function A. Method 2 Conditions differ in presence of linguistic function A. Stim 1: Stim 2: +lexical access - lexical access Intuitively: CAT KPT or, often: Task 1: Task 2: Semantic decision Phonological decision Stim 1: Stim 2: CAT CLAM Frequent Infrequent Fast lexical access Slow lexical access Task is constant so reaction times can serve as behavioral index of manipulation If the task changes there can be no behavioral index of the manipulation. Need model of cognitive functions involved.

How to isolate neural correlates of linguistic processes?
LP, Aug 03, Tateshina Method 1 Conditions differ in computational demands of linguistic function A. Same neural sources but different timing and/or magnitude Method 2 Conditions differ in presence of linguistic function A. (possibly) different sources

Why lexical access? Part of virtually all linguistic processing.
LP, Aug 03, Tateshina Part of virtually all linguistic processing. 1st processing stage that is potentially modality independent, and “linguistic”, in a narrow sense of the word. Different theories about linguistic processing and representation make contrasting predictions about lexical access  Neural correlate of lexical access a valuable additional dependent measure to behavioral processing measures.

Linguistic theory Psycholinguistics What affects lexical access?
LP, Aug 03, Tateshina What affects lexical access? What brain activity is affected by those factors? Linguistic theory Psycholinguistics

CAT Lexical decision times are affected by:
LP, Aug 03, Tateshina Lexical decision times are affected by: Lexical frequency Semantic, phonological, morphological relatedness Etc. But trying to infer the cognitive level of these effects from reaction times alone is complicated. Electrophysiological data adds a dependent measure for every millisecond: CAT Time [msec] Response

LP, Aug 03, Tateshina But identifying the activity affected by any one stimulus property is not particularly informative in of itself. Need to show that some natural class of stimulus variables all affect the same neural activity, where “natural class” is defined by the predictions of a cognitive model. CAT Time [msec] Response

Assumptions/hypotheses that drive, and are tested by, the present research
LP, Aug 03, Tateshina Representation: There is a modality independent lexicon. Lexical entries connect sound and meaning – single lexicon. All word formation is syntactic. Processing: Timing of lexical access depends on the activation level of lexical entries at stimulus presentation. The activation level of lexical entries depends on Frequency Preceding context (priming) Phonological and semantic relatedness should affect the same neural activity. NB: All of these assumptions are more or less controversial so we’ll continually keep evaluating how they succeed in explaining the data.

Magnetoencephalography (MEG)
LP, Aug 03, Tateshina EEG

LP, Aug 03, Tateshina MEG EEG

MEG vs. EEG LP, Aug 03, Tateshina Source:

Right-hand rule LP, Aug 03, Tateshina Source:

LP, Aug 03, Tateshina Distribution of magnetic field at 93 ms (auditory M100) Averaged epoch of activity in all sensors overlain on each other. Outgoing Ingoing

LP, Aug 03, Tateshina

An MEG Study of Word Frequency Effects in Lexical Decision
M. Hackl1, D. Embick1,2, J. Schaeffer3, M. Kelepir1, A. Marantz1,2 1 Dept. of Linguistics and Philosophy, MIT 2 JST/MIT [Mind Articulation] Project 3 Dept. of Linguistics, Ben-Gurion University of the Negev

The frequency effect Lexical decisions to frequent words faster than decisions to infrequent words. Account in activation-based models: frequent words have a higher “resting” level.

Objective: Identification of an MEG component whose latency varies with the frequency of words, to be used as an index in further studies of lexical access and lexical organization. Primary Result: A component in the response to words at 350ms, m350, varies in latency with the frequency of words.

Stimuli: Six bins of open-class words, arranged according to frequency; Cobuild corpus, 320 million words Category n/Million Log Freq. Example number ask wheel candle clam snarl Two classes of non-words, pronounceable and non-pronounceable; ratio of words:non-words 1:1.

Task: Lexical Decision.
Subjects: n = 9; 5F, 4M; right-handed native speakers of English. Analysis: Peaks identified based on RMS analysis. A subset of 17 left-hemisphere sensors were used for identification of peaks; this set was held constant across subjects/conditions.

Three primary components

RMS analysis 1. 2. 3. M170 M250 M350 CAT 170msec 250msec 350msec
LP, Aug 03, Tateshina M M M350 170msec 250msec 350msec RMS analysis 1. 2. 3. CAT Time [msec]

Two Distinct Components
Latency of m350 response varies by log frequency of words (p < .0001) Latency of m250 response does not vary with log frequency (p = .8)

Magnetic Field and Contour Map: High Frequency

Magnetic Field and Contour Map: Low Frequency

The M350 Is the first MEG component serving as a predictor of the behavioral frequency effect. If the M350 indexes lexical access, it is also predicted to show priming effects.

A neural response sensitive to repetition
L. Pylkkänen1,2, E, Flagg1, A. Stringfellow2, A. Marantz1,2 1 Dept. of Linguistics and Philosophy, MIT 2 JST/MIT [Mind Articulation] Project

The repetition priming effect
Words are responded to more quickly on their second presentation than on their first. After a word has been accessed, its activation slowly returns to resting level – if the word is presented again while there is still residual activation, access is facilitated.

Objective: Identification of an MEG component whose latency predicts the behavioral repetition priming effect. Result: A component in the response to words and pronouceable nonwords at 350ms, M350, occurs earlier for repeated than for nonrepeated words.

Stimuli 2 x 2 design with repetition and target lexicality as factors.
Timing: + DOG Prime, 500 ms 500 ms DOG Target, real word or not?

Analysis Only correct trials were analyzed.
RMS from a minimum of 17 left hemisphere sensors showing large responses between 150 and 450 ms. The latencies and amplitudes of major RMS peaks were recorded using latency and magnetic field distribution as criteria for determining whether a peak belonged to a certain category of responses.

Effect of repetition on the M350 and RT
* * n.s n.s

M350 positive signal maximum for repeated and for nonrepeated words (single subject data)

CAT 170 msec 250 msec 350 msec 0 200 300 400 Time [msec] Sagittal view
LP, Aug 03, Tateshina 170 msec 250 msec 350 msec Sagittal view A P auditory M100 M350 CAT Time [msec]

Modality independent lexical access: MEG evidence from an auditory lexical decision task
Linnaea Stockall, Dan Wehner & Alec Marantz Dept. of Linguistics and Philosophy, MIT & KIT/MIT MEG Lab

Embick et al. (1999) M350 facilitated by high frequency stimuli in visual lexical decision experiment 1 2 3 4 5 6 Frequency Category (Frequent -- Infrequent) Latency of m350 Component Categories (n/Million): 1: 700 2: 140 3: 30 4: 6 5: 1 6: .2

M350: index of initial lexical activation
MEG activity elicited by visual words (lexical decision task): The M350 is sensitive to Lexical frequency Repetition Phonological similarity Semantic similarity Sublexical frequency Morphological Family Size The M350 is NOT sensitive to interlexical competition M350

Question: Do visual word recognition and auditory word recognition involve accessing the same mental lexicon?:

Materials & Method Stimuli: 48 High Frequency words
48 Low Frequency words 96 Non Word Fillers Matched for length, number of syllables and density Speech recorded in Soundedit and normalized for intensity

Materials & Method Subjects:
10 right handed native English speakers with normal vision gave informed consent to participate in this experiment. RT and MEG data was collected from 6 subjects, RT data only from 4 subjects.

Materials & Method Tone Test:
After the frequency experiment, subjects listened to ms long 1KHz tones.

Materials & Method MEG Data collection:
Neuromagnetic fields were recorded using an axial gradiometer whole-head system (Kanazawa Institute of Technology, Kanazawa, Japan). One subject was recorded with a 93 channel system, 3 with a 160 channel system Data were acquired in a band between DC and 200Hz, at a 1000Hz sampling frequency.

Materials & Method MEG Data analysis:
Equivalent current dipole (ECD) analysis was used to estimate the time course of activation in the cortical areas generating the M100/M350 response. Dipoles were localized for each subject for the M100 response to the tonetest using the subset of left hemisphere sensors covering the characteristic M100 field pattern The latency and intensity of the time point corresponding to the best GOF fit for the M100 dipole in the 0-500ms time window and the latency and GOF of the time point corresponding to the highest intensity for the M100 dipole in the 0-500ms time window were computed for each condition for each subject

Materials & Method Helenius, Salmelin, Service, Connolly, Leinonen & Lyytinen (2002): Cortical Activation during Spoken-Word Segmentation Stimuli: 4 sentence types

Materials & Method Left Hemisphere response locations
Find out what white vs. black bars indicate Left Hemisphere response locations 1) Same source for 100 and N400m activation 2) N400m in response to auditory lexical processing

Materials & Method M100 Field Pattern (single subject)
Location of M100 Dipole (single subject)

Results Behavior (n=10) Error Rate: Reaction Time: * ** * = p<0.005

Results MEG Data (single subject) M350 Magnetic Evoked Component
M350 Field Pattern

Results MEG Data (single subject)
Goodness of Fit of M100 dipole in 0-500ms time window Greatest GOF

Results MEG Data (n=4) Latency of Best Fit of M100 dipole in 0-500ms time window * *= p<0.1

Results MEG data (n=4) Intensity of Best Fit of M100 dipole in 0-500ms time window n.s

Discussion: Auditory lexical decision evokes the same repsonse component evoked by visual lexical decision. component occurs at same latency has same distribution and source localization is sensitive to same stimulus manipulation (facilitation for high frequency words) Support for single lexicon model

M350 = lexical access? Not necessarily – effect could be secondary.
LP, Aug 03, Tateshina M350 = lexical access? Not necessarily – effect could be secondary. For example, we might be measuring the timing of the word/nonword decisions, which would be faster whenever lexical access is faster.  i.e. component could be task-related.

LP, Aug 03, Tateshina M350 = lexical access? Or: our assumptions about the cognitive level or frequency and repetition effects might be wrong.

What processing stage(s) are affected by lexical frequency?
LP, Aug 03, Tateshina What processing stage(s) are affected by lexical frequency? Most models: Activation – high frequency representations have a higher resting level. Balota & Chumbley (1984, 1985, 1990, etc.): Post-access decision.

Evidence that frequency effects can be “late”
LP, Aug 03, Tateshina Evidence that frequency effects can be “late” Connine et al. 1993: Lexical frequency affects consonant identification: voiced ambiguous voiceless competitor stimulus competitor BEST ?EST PEST BARK ?ARK PARK B P Would ?ARK behave like ?EST if embedded in a list of unambiguous low frequency stimuli? YES. Cynthia M. Connine, Debra Titone and Jian Wang. Auditory Word Recognition: Extrinsic and Intrinsic Effects of Word Frequency, Journal of Experimental Psychology: Learning, Memory, and Cognition, Volume 19, Issue 1, January 1993, Pages

Frequency may (and is likely) to affect multiple levels of processing.
LP, Aug 03, Tateshina Frequency may (and is likely) to affect multiple levels of processing.

Cognitive processes involved in lexical access
LP, Aug 03, Tateshina Cognitive processes involved in lexical access TURN TURNIP TURF TURTLE Activation Competition Selection time level of activation resting level Stimulus: TURN

To investigate the cognitive level of the M350:
Manipulate stimuli in such a way that Activation is facilitated Selection is slowed down Which way would the M350 move? TURN TURNIP TURF TURTLE Activation Competition Selection time level of activation resting level Stimulus: TURN

How to simultaneously facilitate activation and inhibit selection?
LP, Aug 03, Tateshina How to simultaneously facilitate activation and inhibit selection? Linguistic theory Psycholinguistics

Phonotactic probability: early facilitation
LP, Aug 03, Tateshina Phonotactic probability: early facilitation Same/different task (“low-level”) RTs to nonwords with a high phonotactic probability are speeded up. RT High probability: MIDE Sublexical frequency effect RT YUSH Low probability: (Vitevich and Luce 1998, 1999)

mile mild might migrate mike mime mine mire mind mite migraine micro
LP, Aug 03, Tateshina Phonotactic probability: later inhibition Lexical decision (“high-level”) RTs to nonwords with a high phonotactic probability are slowed down. mile mild might migrate mike mime mine mire mind mite migraine micro neighborhood activated Competition effect RT High probability: MIDE yuppie yucca yuck yum neighborhood activated RT Low probability: YUSH (Vitevich and Luce 1998, 1999)

High phonotactic probability/density induces intense competition
LP, Aug 03, Tateshina Facilitates activation slows down selection induces intense competition TURN TURNIP TURF TURTLE Activation Competition Selection time level of activation resting level Stimulus: TURN

If M350 = Selection Activation Competition Selection
LP, Aug 03, Tateshina Then high probability/ density should delay M350 latencies TURN TURNIP TURF TURTLE Activation Competition Selection time level of activation resting level Stimulus: TURN

If M350 = Activation Activation Competition Selection
LP, Aug 03, Tateshina Then high probability/ density should speed up M350 latencies TURN TURNIP TURF TURTLE Activation Competition Selection time level of activation resting level Stimulus: TURN

Materials (visual) Four categories of 70 stimuli: Lexical decision.
LP, Aug 03, Tateshina Four categories of 70 stimuli: High probability Low probability Word BELL, LINE PAGE, DISH Nonword MIDE, PAKE JIZE, YUSH Lexical decision. (Pylkkänen, Stringfellow, Marantz, Brain and Language, 2002)

Effect of probability/density (single subject)
High probability word M170 M250 M350 “M350-2” RT

Effect of probability/density (single subject)
High probability word Low probability word M170 M250 M350 “M350-2” RT RT

Effect of probability/density (n=10)
* Words n.s. * Nonwords (Pylkkänen, Stringfellow, Marantz, Brain and Language, 2002)

Activation Selection Competition
M350: (i) 1st component sensitive to lexical factors (ii) not affected by competition time level of activation resting level Stimulus: TURN TURN TURNIP TURF TURTLE Activation Selection Competition

M350: (i) 1st component sensitive to lexical factors
(ii) not affected by competition MEG evidence that lexical frequency affects activation.

350ms – are you KIDDING me??? That’s too LATE!!!!!!!!
LP, Aug 03, Tateshina 350ms – are you KIDDING me??? That’s too LATE!!!!!!!!

LP, Aug 03, Tateshina Eyetracking Pace of fluent reading: In text, mean reading time for words is 239ms (Carpenter and Just, 1983). But: Reading in context should be faster. 239ms is averaging over all words, function and content. We have made no claims about the processing of function words.

The gaze durations of a typical reader (from Carpenter and Just, 1983)
LP, Aug 03, Tateshina The gaze durations of a typical reader (from Carpenter and Just, 1983) Another answer to the ever-intriguing question of pyramid construction has been suggested. The Egyptian Engineer of 5,000 years ago may have used a simpled wooden device called a weightarm for handling the 2 ½ to 7 ton pyramid blocks. The weightarm is like a lever or beam pivoting on a fulcrum. Hundreds of weightarms may have been needed for each pyramid. Weightarms may have been used to lift the blocks off the barges which came from the upriver quarries. Also, they would be needed to transfer the blocks to skid roads leading to the base and for lifting the blocks onto sledges. The sledges were hauled up greased tracks to the working levels. Again, weightarms were used to pick up the blocks from the sledges and put them on skidways where workers pulled them to their placements. (Carpenter, P. and M. A. Just, 1983, What your eyes do while your mind is reading. In Rayner, K. (ed.) Eye Movements in Reading: Perceptual and Language Processes, Academic Press, p )

For the sake of argument, let’s assume that:
LP, Aug 03, Tateshina The gaze durations of a typical reader (from Carpenter and Just, 1983) In this text there are: 67 content words 70 function words For the sake of argument, let’s assume that: Processing content words in context takes 350ms. Processing function words, which are extremely frequent and often entirely predictable from the preceding syntactic context, takes 150ms, in context. Syntactic and lexical processing occur largely in parallel.

The math Time needed to process the content words:
LP, Aug 03, Tateshina The math Time needed to process the content words: 67 x 350ms = ms Time needed to process the function words: 70 x 150ms = ms Total predicted word processing time: 23 450ms ms = ms Total actual reading time of this text: 23 450ms ms = ms

LP, Aug 03, Tateshina The math The pace of normal fluent reading is perfectly compatible with the claim that lexical activation by isolated words takes ms.

LP, Aug 03, Tateshina Eyetracking Lexical effects can be seen in eyetracking way before 350ms: Dahan, Magnuson & Tanenhaus (2001): Lexical frequency affects eye movements at 250ms. If saccadic programming time is taken into account, this actually means that lexical frequency affects processing already at 100ms! (Dahan, D., Magnuson, J.S., & Tanenhaus, M.K. (2001). Time course of frequency effects in spoken-word recognition: Evidence from eye movements. Cognitive Psychology, 42, )

Eyetracking 1. 500 ms of seeing the pictures Target
LP, Aug 03, Tateshina Eyetracking Dahan, Magnuson & Tanenhaus paradigm (my reconstruction): High frequency competitor ms of seeing the pictures Low frequency competitor 2. “Pick up the bench” 3. “and put it below the circle”. Target (Dahan, D., Magnuson, J.S., & Tanenhaus, M.K. (2001). Time course of frequency effects in spoken-word recognition: Evidence from eye movements. Cognitive Psychology, 42, )

LP, Aug 03, Tateshina Eyetracking Dahan, Magnuson & Tanenhaus paradigm (my reconstruction): High frequency competitor Result: More fixations to high frequency competitor at ms after target onset. Low frequency competitor Target (Dahan, D., Magnuson, J.S., & Tanenhaus, M.K. (2001). Time course of frequency effects in spoken-word recognition: Evidence from eye movements. Cognitive Psychology, 42, )

LP, Aug 03, Tateshina Eyetracking Dahan, Magnuson & Tanenhaus paradigm (my reconstruction): Result: More fixations to high frequency competitor at ms after target onset. (Dahan, D., Magnuson, J.S., & Tanenhaus, M.K. (2001). Time course of frequency effects in spoken-word recognition: Evidence from eye movements. Cognitive Psychology, 42, )

Eyetracking 1. 500 ms of seeing the pictures
LP, Aug 03, Tateshina Eyetracking Dahan, Magnuson & Tanenhaus paradigm (my reconstruction): ms of seeing the pictures Dahan, Magnuson & Tanenhaus: “With the current procedure, the delay between the presentation of the pictures and the spoken instruction was only 500 ms, making it less likely that participants would have time to implicitly name the pictures” 2. “Pick up the bench” 3. “and put it below the circle”. (Dahan, D., Magnuson, J.S., & Tanenhaus, M.K. (2001). Time course of frequency effects in spoken-word recognition: Evidence from eye movements. Cognitive Psychology, 42, )

LP, Aug 03, Tateshina Eyetracking Dahan, Magnuson & Tanenhaus paradigm (my reconstruction): ms of seeing the pictures What’s relevant is the delay between the presentation of the pictures and target onset, not the delay between the presentation of the pictures and the spoken instruction.  Participants, in fact, have well over a second to activate the names of the objects before target onset. 2. “Pick up the bench” 3. “and put it below the circle”. (Dahan, D., Magnuson, J.S., & Tanenhaus, M.K. (2001). Time course of frequency effects in spoken-word recognition: Evidence from eye movements. Cognitive Psychology, 42, )

LP, Aug 03, Tateshina Eyetracking Dahan, Magnuson & Tanenhaus paradigm (my reconstruction): ms of seeing the pictures It’s not a matter of “implicit naming of the pictures”. Pictures activate their names completely automatically, even if the pictures aren’t consciously perceived (e.g. Dell'Acqua & Grainger, Cognition, 1999). 2. “Pick up the bench” 3. “and put it below the circle”. (Dahan, D., Magnuson, J.S., & Tanenhaus, M.K. (2001). Time course of frequency effects in spoken-word recognition: Evidence from eye movements. Cognitive Psychology, 42, )

LP, Aug 03, Tateshina Eyetracking In this type of eye-tracking paradigm all the object names are likely to be already accessed at the onset of the target word. Further, it’s not even the usual type of priming paradigm as the prime (i.e. the picture) continues to be in the visual field during target processing. Data from this type of a paradigm do not challenge the claim that lexical activation by isolated words takes ms.

Final note on masked priming
LP, Aug 03, Tateshina Final note on masked priming Interval between prime and target does not need to be 350ms in order to obtain priming effects. Semantic priming has been reported for SOA’s as short as 50ms. Can SOA manipulations, or masked priming, give us precise information about the timing of lexical access?

Example: Semantic priming.
LP, Nov 00, Tokyo Can we infer the timing of lexical access by measuring reaction times (RT) only? Example: Semantic priming. Real word or not? RT (yes or no) DOCTOR NURSE Time RT DRIVER NURSE Time

Is the effect lexical or post-lexical? I.e. automatic or conscious?
LP, Nov 00, Tokyo Can we infer the timing of lexical access by measuring reaction times (RT) only? Is the effect lexical or post-lexical? I.e. automatic or conscious? RT DOCTOR NURSE Time RT DRIVER NURSE Time

If lexical (= automatic)
LP, Nov 00, Tokyo If lexical (= automatic) DOCTOR activates NURSE RT DOCTOR NURSE Time RT DRIVER NURSE Time

If lexical (= automatic)
LP, Nov 00, Tokyo If lexical (= automatic) NURSE is accessed faster due to residual activation RT DOCTOR NURSE Time RT DRIVER NURSE Time

If post-lexical (= conscious)
LP, Nov 00, Tokyo If post-lexical (= conscious) NURSE is responded to faster since it fits the preceding context (e.g. Neely 1991) RT DOCTOR NURSE Time RT DRIVER NURSE Time

Masking: ####### NURSE DOCTOR ####### ####### NURSE DRIVER #######
LP, Nov 00, Tokyo If post-lexical, effect should dissappear if we make the “preceding context” invisible to conscious recognition Masking: ####### NURSE DOCTOR ####### Time ####### NURSE DRIVER ####### Time

Effect remains, i.e. is automatic
LP, Nov 00, Tokyo If post-lexical, effect should dissappear if we make the “preceding context” invisible to conscious recognition Effect remains, i.e. is automatic (e.g. Deacon et al 2000). RT ####### NURSE DOCTOR ####### Time RT ####### NURSE DRIVER ####### Time

NURSE is accessed faster because DOCTOR already activated it
LP, Nov 00, Tokyo NURSE is accessed faster because DOCTOR already activated it activation activation RT DOCTOR NURSE nurse nurse Time activation RT DRIVER NURSE nurse Time

NURSE is accessed faster because DOCTOR already activated it
LP, Nov 00, Tokyo NURSE is accessed faster because DOCTOR already activated it When does lexical access occur? activation activation RT DOCTOR NURSE nurse nurse Time activation RT DRIVER NURSE nurse Time

When does the activation of NURSE occur?
LP, Nov 00, Tokyo When does the activation of NURSE occur? How much can we shorten the interval between the 1st and the 2nd word until the effect dissappears? activation activation RT DOCTOR NURSE nurse nurse Time activation RT DRIVER NURSE nurse Time

When does the activation of NURSE occur?
LP, Nov 00, Tokyo When does the activation of NURSE occur? How much can we shorten the interval between the 1st and the 2nd word until the effect dissappears? 200ms activation RT DOCTOR NURSE nurse Time activation RT DRIVER NURSE nurse Time

LP, Nov 00, Tokyo Conclusions: (i) the effect on RTs is lexical. (ii) it takes at least 200 ms for DOCTOR to activate NURSE (by semantic association). 200ms activation RT DOCTOR NURSE nurse Time activation RT DRIVER NURSE nurse Time

What we can’t conclude:
LP, Nov 00, Tokyo that the activation of the semantic associate happens in some specific time window (the activation of NURSE by semantic association could happen after the onset of the target). anything about the activation time of the stimulus that the subject is performing the task on (except that it’s faster or slower than in some other condition). With MEG we can do both, and more... activation activation RT DOCTOR NURSE nurse nurse Time

Summary of Day 1 M350: Sensitive to stimulus factors that we’d expect to affect lexical access. Not task-related as is not sensitive to competition. Index of early automatic lexical activation. An early dependent measure for testing hypotheses about language processing and represenations.

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