IBSC Seminar on Priming

1. IBSC Seminar on Priming Steve Gotts CNBC and NIMH/NIH

2. Overview I) Review of empirical data on priming and related neural changes II) Discuss issues raised in last IBSC meeting: A) Are different forms of priming associated with the same neural effects? B) What might be done to evaluate the nature of representations used in connectionist models? III) Implications for connectionist models

3. I) Review of empirical data on priming and related neural changes • Behavioral priming - change in the speed, bias, or accuracy of the processing of a stimulus, following prior experience with the same or a related stimulus • indirect vs direct tasks • variety of indirect tasks (stem completion, naming, LD, etc.) • component process view: • multiple processes contribute to any given task • response time is an aggregate measure of facilitation • occurring for each component processes • priming is greatest when processes engaged at prime and • probe match

4. Repetition Suppression - decrease in hemodynamic or neural activity following repetition of the same or a related stimulus • Empirical generalizations: • regions showing suppression are normally restricted to those • that are responsive to the type of stimuli being used • suppression is observed in multiple brain regions, suggesting • that changes can be observed at multiple functional loci • (like the component process view of priming) • suppression is not observed in all regions associated with • processing stimuli in a particular task (e.g. not often observed • in primary sensory or motor regions, at least in fMRI and PET)

6. Priming as a memory phenomenon • dissociations between implicit and explicit tasks (e.g. amnesia) • explicit retrieval is often associated with enhanced rather than • decreased neural activity in medial temporal and prefrontal • regions • studied in a variety of tasks/paradigms: • - word-stem completion (implicit -> decreases) • - conceptual tasks (generally decreases in left inferior • frontal and ventral occipitotemporal) • - comparisons of implicit and explicit tasks (explicit task • on probe can reduce or reverse the decreases) • - masked priming (sometimes decreases, sometimes • increases; increases in gradual unmasking and with • backward mask of probe)

7. Priming as a tool for studying representations • fMR Adaptation (Dehaene, Grill-Spector): vary a sequence of • stimuli along a single stimulus dimension in order to measure the • sensitivity of particular cortical regions to that dimension (e.g. • vary object viewpoint, size, position, etc.) • Hyper-resolution: using the adaptation technique may afford • within-voxel discrimination by mean-activity level

9. Grill-Spector & Malach (2001)

10. Priming as a tool for studying representations • Familiar vs unfamiliar object priming: • Repetition increases are often observed for unfamiliar stimuli, whereas decreases are observed for familiar stimuli • (e.g. Henson et al., 2000; Schacter et al., 1995) • Henson's proposal: • regions that show repetition enhancement are those that subserve • a process that occurs only on the probe and not the prime • regions that show repetition suppression subserve processes • operating on both prime and probe

12. Priming as a tool for studying representations • Lag effects: • suppression (and enhancement) attenuate with lag from 10 sec to • 20 min (2 to ~ 140 intervening stimuli) (Henson et al., 2000) • both lag and intervening stimuli attenuate suppression in • occipitotemporal, with a progression to longer-lived effects as one • moves from posterior to anterior (Henson et al., in prep) • priming/suppression in object naming is greater at 30 sec • compared to 3 days, but is significant at both (van Turennout et • al., 2000)

13. Priming as a model domain for relating mind and brain • sharpening theory of Desimone (1996); Wiggs & Martin (1998) • short-term adaptation in fMR adaptation • some relevant single-cell physiology studies in monkeys: • McMahon & Olson (SFN 2003, 2004) • Baker, Behrmann, & Olson (2002) • Rainer & Miller (2000); Freedman et al. (SFN 2004) • Li, Miller, & Desimone (1993) • Miller, Li, & Desimone (1993) • long-term effects of practice are consistent with "sharpening" • short-term effects of repetition are consistent with local adaptation • or other negative feedback mechanisms

15. II) Discuss issues raised in last IBSC meeting • A) Are different forms of priming associated with the same • neural effects? • 1) Categorical (duck-chicken) vs. Associative (coat-rack) • Kotz et al. (2002): fMRI • auditory lexical decision with pairs of categorically or • associatively related words • decreases in left inferior frontal gyrus • increases in posterior middle temporal cortex • greater activity to categorical pairs than to associative pairs in • posterior medial parietal/cingulate

16. II) Discuss issues raised in last IBSC meeting • A) Are different forms of priming associated with the same • neural effects? • 2) Strategic priming effects • Mummery et al. (2002): PET • study of semantic priming using lexical decision and varying • relatedness proportion • prime word presented for 50 ms, followed by target word • trend for greater semantic priming with higher relatedness • proportion • correlated with greater decreases in left anterior temporal and • anterior cingulate (although 100% > 75% in temporal)

17. II) Discuss issues raised in last IBSC meeting • A) Are different forms of priming associated with the same • neural effects? • 3) Expectation effects • Jiang et al. (2000): fMRI • DMS task with faces; targets and distractors could repeat • the first target post-sample elicited enhanced activity in • ventral temporal and frontal/insular cortex • subsequent repetitions of the target decreased in ventral • temporal, but not in frontal/insula • repeated distractors elicited suppressed activity in ventral • temporal regions

18. II) Discuss issues raised in last IBSC meeting • B) What might be done to evaluate the nature of • representations used in connectionist models? • fMR adaptation could probably be used productively to evaluate the correspondence between distributed representations in connectionist models and real neurons • [recently used to plot detailed tuning curves for number in parietal cortex; can distinguish between log and Guassian shapes: S.Dehaene] • orthography • phonology • semantics • caveat: need to control for strategic processing to the • extent possible (masking and short delays?)

19. III) Implications for connectionist models Bottom line: Most connectionist models will NOT show repetition-related decreases in unit activity So: What is currently missing from connectionist models, and how badly does it matter?

20. What is currently missing from connectionist models? .. or alternatively, why do the changes happen in real neurons? • 1) for short-term effects (< 1 s) of repetition suppression/priming, • firing-rate adaptation? • synaptic depression? • priming could be due to residual activity • but may create difficulties for learning representations • 2) for slightly longer effects (1 s - 1 min), adaptation and synaptic • depression can still work for the activity changes, but • accounting for the priming effects gets harder • residual activity is less tenable • firing-rate decreases can be greatest for the "best" cells • changes to long-term synaptic strengths might help, though

21. Stimuli that lead to larger firing rates tend to produce larger repetition suppression effects (Miller et al., 1993; Li et al., 1993): Adaptation produces this effect in a connectionist model 2/3 of cells that were visually responsive & showed match-nonmatch differences 1/4 of cells that were visually responsive & showed match-nonmatch differences Compression of firing rates is the opposite of representational sharpening: vs.

22. What is currently missing from connectionist models? • 3) For long-term effects (minutes, hours, days, ..), need to • figure out what changes to learning rules can produce a • progressive "sharpening" of activity, while decreasing • overall activity • this would be consistent with several single-cell • recording studies (Rainer & Miller, 2000; • Baker, Behrmann, & Olson, 2002; Freedman et al., 2004) All learning rules that actually learn the patterns increase response selectivity Not all of them lead to average decreases, leaving the peak activity unchanged