2. Visual Processing in Fingerprint Experts and Novices Tom Busey Indiana University, Bloomington John Vanderkolk Indiana State Police, Fort Wayne Expertise with fingerprint examiners was tested in behavioral and EEG studies. Experts show greater tolerance for noise, are unaffected by longer memory delays, and show evidence of configural processing. This last finding was confirmed in an EEG study where experts show a reliable delay of the N170 component when fingerprints were inverted, while novices did not. Configural processing may be one element that underlies perceptual expertise. www.indiana.edu/~busey/

3. How Do Experts Make Identifications? Easy Match Hard Match

4. An Experiment

5. Study Fragment About a second

6. Mask Either about a second or 3 seconds

7. Test Images Until Response

8. Testing Fingerprint Expertise: X-AB Sequential Matching Task example stimulus pairs:

9. Reduce Matching based on Low- Level Features Overall Brightness change Study image is rotated up to 90° in either direction Two image manipulations designed to simulate latent prints –Added noise –Partial masking

10. Added Noise

11. Partial Masking

12. Semi-Transparent Masks FingerprintPartially Masked Fingerprints Logical Combination Recovers Original Fingerprint original inverse

13. Includes combinations:

14. Image Degradations at Test

15. Partial Masking Semi-Transparent Masks FingerprintPartially Masked Fingerprints Summation Recovers Original Fingerprint original inverse

16. Behavioral Data Full ImagesPartial Images Full Images in Noise Partial Images in Noise Experts: No effect of delay, interaction between noise and partial masking.

17. Evidence for Configural Processing Full Image (Both Halves)Partial Image (One Half) Question: What is the relation between d b and d o ? if d b = d o : One half doesn't influence information acquired from other half if d b < d o : Get less information from one half when second is present if d b > d o : Get more information from one half when second is present (consistent with configural or gestalt processing) info from first half? no (1-d b ) yes (d b ) no (1-d b ) yes (d b ) info from second half? no (1-g) yes (g) info from guessing? Correct DecisionWrong Decision no (1-d o ) yes (d o ) info from first half? no (1-g) yes (g) info from guessing? Correct DecisionWrong Decision

18. Multinomial Modeling Conclusions To test for configural processing, fit a reduced model with d b = d o. If we can reject this model, then we know that the two are not the same. Fit the full model to see the relation between d b and d o. Experts: No noise: we reject reduced model, so d b and d o are significantly different Full model: d b =.841, d o =.944 wrong direction for configural processing In noise: we reject reduced model, so d b and d o are significantly different Full model: d b =.50, d o =.30 Consistent with configural processing Novices: No noise: we reject reduced model, so d b and d o are significantly different Full model: d b =.40, d o =.54 wrong direction for configural processing In noise: we can't reject reduced model, so d b and d o are not significantly different Full model: d b =.19, d o =.13 No evidence for configural processing

19. Evidence for Configural Processing: Multinomial Modeling To test for configural processing, we can use the accuracy rate in the partial image condition to make a prediction for the full image condition, assuming no configural processing. If performance in the full image condition exceeds the prediction, we have evidence that is consistent with configural processing.

20. Evidence for Configural Processing: Multinomial Modeling To test for configural processing, we can use the accuracy rate in the partial image condition to make a prediction for the full image condition, assuming no configural processing. If performance in the full image condition exceeds the prediction, we have evidence that is consistent with configural processing. Experts in noise: We predict performance in the full image condition to be about 75% correct. Instead it is around 90%. Experts are doing better with the whole image than we predict they would do based on partial- image performance. This is configural processing at work.

21. Evidence for Configural Processing: Multinomial Modeling To test for configural processing, we can use the accuracy rate in the partial image condition to make a prediction for the full image condition, assuming no configural processing. If performance in the full image condition exceeds the prediction, we have evidence that is consistent with configural processing. Experts: No noise: No evidence for configural processing In noise: Consistent with configural processing Novices: No noise: No evidence for configural processing In noise: No evidence for configural processing

22. Configural Processing in Faces: The ‘Thatcher Illusion’ (Thomson, 1980) Features are perceived individually, image looks ok. Features are perceived in context, image looks grotesque.

23. EEG Recording Basics Record from the surface of the scalp Amplify 20,000 times Electrical signals are related to neuronal firing, mainly in post- synaptic potentials in cortex. Very small signals, very noisy data.

24. EEG Recording Basics Average over lots of trials (200 trials per condition)

25. EEG and Configural Processing Faces produce a strong component over the right hemisphere at about 170 ms after stimulus onset, which is called the N170. Inverted faces cause a delay of 10-20 ms in the N170. Trained objects (Greebles) show a delay in the N170 component with inversion, but only in the left hemisphere (channel T5). Data from Rossion, Gauthier, Goffaux, Tarr & Crommelinck (2002) Data from Rossion, Gauthier, Tarr, Despland, Bruyer, Linotte & Crommelinck (2000) Coupled with behavioral data suggesting configural processing with faces, an advanced N170 to an upright stimulus suggests that the N170 latency differences indicate configural processing.

26. An Obvious Experiment: Show upright and inverted fingerprints to Fingerprint examiners and novices. If experts process fingerprints configurally, we should see a delayed N170 to inverted fingerprints. Also test faces to replicate the face inversion effect in our subjects. Test both identification and categorization tasks.

27. Expert Data- Identification Task Amplitude (µV) Time (ms) Upright Fingerprint Inverted Fingerprint Upright Face Inverted Face Delayed Experts: delayed N170 with inverted fingerprints and inverted faces. Electrode T6

28. Novice Data- Identification Task Upright Fingerprint Inverted Fingerprint Upright Face Inverted Face Amplitude (µV) Time (ms) Delayed No Delay Novices: no delayed N170 with inverted fingerprints, but see with faces. Electrode T6

29. Expert Data- Categorization Task Amplitude (µV) Time (ms) Upright Fingerprint Inverted Fingerprint Upright Face Inverted Face Delayed Experts: delayed N170 with inverted fingerprints and inverted faces. Electrode T6

30. Novice Data- Categorization Task Upright Fingerprint Inverted Fingerprint Upright Face Inverted Face Amplitude (µV) Time (ms) Delayed No Delay Novices: no delayed N170 with inverted fingerprints, but see with faces. Electrode T6

31. Summary and Conclusions Fingerprint experts demonstrate strong performance in an X-AB matching task, robustness to noise and evidence for configural processing when stimuli are presented in noise. This latter finding was confirmed using upright and inverted fingerprints in an EEG experiment. Experts showed a delayed N170 component for inverted fingerprints in the same channel that they show a delayed N170 for inverted faces. Thus they appear to be processing upright fingerprints in part using configural or holistic processing, which stresses relational information and implies dependencies between individual features. In the case of fingerprints, this may come from idiosyncratic feature elements instead of well-defined features such as eyes and mouths. www.indiana.edu/~busey/