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ERPs in Deception, Malingering, and False Memory

ERPs in Deception, Malingering, and False Memory. J. Peter Rosenfeld Psychology Department Northwestern University Evanston Illinois,USA. Principal Collaborators. Joel Ellwanger Tuti Reinhart Miller Archana Rao Matt Soskins Greg Bosh Many of the original ideas here were theirs.

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ERPs in Deception, Malingering, and False Memory

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  1. ERPs in Deception, Malingering, and False Memory J. Peter Rosenfeld Psychology Department Northwestern University Evanston Illinois,USA

  2. Principal Collaborators • Joel Ellwanger • Tuti Reinhart Miller • Archana Rao • Matt Soskins • Greg Bosh • Many of the original ideas here were theirs.

  3. A simple neural code

  4. Event-related potentials

  5. P300 Attributes: • An Endogenous, Event-Related Potential (ERP) • Positive polarity (down in Illinois). • Latency range: 300-1000 msec • varies with stimulus complexity/evaluation time • Typical Scalp Amplitude(Amp) Map • Pz > Cz > Fz • Amp = f(stim. probability, meaning)

  6. P300 at 3 scalp sites

  7. P300 amplitude as recognition index • Autobiographical items (previous slide) • Guilty Knowledge test items (Rosenfeld et al., 1988) • Antisocial/illegal acts in employee screening (Rosenfeld et al., 1991). • Matches to samples in tests of malingered cognitive deficits

  8. Normals: autobiog. oddball

  9. CHI patients: autobiog. oddball

  10. Individual detection rates for various stimuli (normal simulators).

  11. E-Name forgetters(oddball is dark line)

  12. Screening example

  13. Autobiographical paradigm has limitations in detecting malingerers • Most malingerers are not so unsophisticated as to verbally state that they don’t recall, say, their birthdate, when in fact they may have just filled out a card in which they provided that information.

  14. Continuation… • The behavioral “MDMT” was developed as an entrapment test to catch these people. It’s a simple matching-to-sample test: A sample 3-digit number is presented followed either by a match or mismatch.

  15. Simple MDMT paradigm: • There is a 5-15 second interval between sample and probe. This is an easy task, yielding 100% performance even in patients with moderate head injury--unless, oddly enough, they happen to be in litigation ! • Where does one set the threshold for diagnosis of malingering? 90%? (Some non-litigating malingerers score well below 90%, as we’ll see.)

  16. Behavioral MDMT not reliable: Some non-litigating pts. fail

  17. Souped-up MDMT: simple version • “Simple” means only one probe stimulus per sample. • P300 is recorded as soon as the probe --match or mismatch-- is presented. • Match probability is kept low. • RESULTS------------>

  18. Match-To-Sample example

  19. Computer-plotted data:

  20. What would 75%-HITTING plaintiff’s lawyer say? • “Sure, my client scores 75% correct and his P300 to matches is bigger than to mismatches. But that’s because he mostly DOES make the correct discrimination--but 75% is still less than normal. Therefore, give us the money (me, one-third).”

  21. Continuation… • We did 2 experiments: 1) If a malingerer aims to score 75% correct, whither P300? 2) What happens to P300 with a really tough discrimination?

  22. Manipulated 75% “hit” rate produces a larger P300…. 100% 100%

  23. Experiment 2: Difficult tasks: 7 and 9 digit numbers, match to sample.

  24. P300 wiped out in difficult task, at 75%, even at accuracy> 90%

  25. Simple P3-MDMT summary: • If one fakes 75% hits, one’s P300 gets bigger(or doesn’t change). • If one has genuine difficulty--honest 75%--then P300 is totally removed. • These findings should allow discrimination of normals, malingerers, real deficit(pts). • BUT…diagnostic hit rate only 70% !!

  26. Scalp Distribution • For P300, Pz > Cz > Fz, usually, but… • There are many ways that this can be so:

  27. SITE AMP Pz Fz Cz

  28. Cz Pz Fz lie SITES truth AMP Fz Pz Cz

  29. Scaling: • McCarthy & Wood (1985) recommend the vector length method. • fz(s)=fz/[FZ^2+CZ^2+PZ^2] • Some scaling is required to make the interpretation of differentially located neurogenerator neuron sets.

  30. Match-to-Sample Test: advanced version • 386 sample • 212 • 457 • 386 (*) • 789 • 325 • 123

  31. Stimulus-Response Types • Match(R) probe • “Match” (RR--honest/correct) • “Mismatch” (RW--dishonest/error) • Mismatch(W) probe • “Mismatch” (WW--honest/correct) • “Match” (WR--dishonest/error)

  32. ERPs in Liar Group to R and W

  33. Deception swamps out R/W effect

  34. “Profiles” of Deception

  35. Truth vs Lie Groups

  36. Deception overcomes paradigm effects

  37. Specificity (“Pinnochio”) • Simple Truth vs. Lie Groups differ in task demands. • This is not relevant for practical field detection. • It is relevant for claims pertaining to a specific lie response. • How do you make a “perfect” control group?

  38. An imperfect(but not bad) control Two groups run in two trial blocks of autobiog. oddball: [1. Phone #, 2. Bday] • Lie Group • Block 1 : Respond truthfully, repeat forwards. • Block 2: Lie 50% of time, repeat forwards. • Control Group • Block 1: Respond truthfully, repeat forwards. • Block 2: Respond truthfully, repeat backwards(50%).

  39. Only lying liars stick out.

  40. Same result with simple truth control

  41. Lie Response<>Truth Response; Psychopathy is irrelevant(swamped).

  42. Psychopathy Effect is frontal,late(?)

  43. What’s next? • We have done pretty well with three(one)sites. • The next step is to utilize 32 sites. • This may adequately sample the head... • But then, we may have too many sites to manage. • So we will utilize principal component analysis in space to find what Donchin calls “virtual” sites. • We also need to perfect within-subject analysis.

  44. The data: Are these curves parallel? AMP “Normal” Distribution Fz Cz Pz

  45. Individual Profile Diagnostics: • 1. Srebro,R. EEG Journal,Vol. 100,(1996) 25-32. This is a cross correlation/bootstrap method. • 2. Our present, to-be-explored method: • Bootstrap distributions of average P300 values for each site/condition point. • Do ANOVA to obtain condition(known truth and test)-by-site interaction value of F-statistic. See how this value fits into a distribution of Fs based on iterated, randomly shuffled truth and test values.

  46. Individual Analysis • Cross correlation takes care of scaling, so Srebro’s method is used for “pure” profile effects. • For unscaled data, which confounds amplitude and distribution(but could possibly better discriminate truth-tellers and liars), we use the ANOVA method, particularly to see the F for interaction.

  47. Site List • 1. F7 6. T3 11. T5 • 2. F3 7. C3 12. P3 • 3. FZ 8. CZ 13. PZ • 4. F4 9. C4 14. P4 • 5. F8 10. T4 15. T6

  48. Different profiles(15 sites, one guilty subject), T and L blocks. Truth r = +.17 Lie

  49. Real vs shuffled z-scores Shuffled Real

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