Investigating the impact of memory reactivation on the successful forgetting of negative memories Paula P. Brooks 1,3 , Justin Hulbert 4 , Arlene Lormestoire 1 , Maureen Ritchey 3 , & Kenneth Norman 1,2 1 Princeton Neuroscience Institute and 2 Department of Psychology, Princeton University; 3 Department of Psychology, Boston College; 4 Department of Psychology, Bard College Introduction Hypothesis Methodology up to 5 s (ITI = 1 s) Phase 1 : Rating Scenes 6 s (ITI = 0.5 s) Y or N? 1 2 CORRECT < 4s < 4s 2.5 s (ITI = 0.5 s) *Criterion test (70% learning criterion) not shown. Phase 2 : Study Face-Scene Pairs (incl. 3 Test-Feedback Cycles + Criterion Test) 2-hour break (± 30 min) 3 s 3 s (ITI = 1.5 – 4.5 s) Phase 3 : Think/No-Think Task one-back image detection task • negative scene • neutral scene • scrambled scene • object Phase 4 : Localizer Phase 5 : Final Cued Recall Test 41 + 41 brown house on field… 4 s 11 s beep then ITI = 3s N = 26 (target sample size = 50) Stopping an unwanted memory from coming to mind (i.e., memory suppression) might help to regulate negative memories. Memory suppression has commonly been studied using the think/no-think paradigm 1 . Although multiple studies 2 have demonstrated memory suppression effects, others have failed to replicate these findings 3,4 . What causes the suppression of negative memories to succeed or fail? DAY ONE Depression & anxiety questionnaires DAY TWO Study face-scene pairs Think/no-think (TNT) memory suppression Surprise memory test • 1-7 days apart • Detailed schematic of day two on the right Relate memory reactivation during TNT task to subsequent memory performance (using Probabilistic Curve Induction and Testing) Learning Phase TNT Phase Suppression Score = Baseline – No-Think baseline no-think think Percent Recalled TNT Conditions Anderson & Hanslmayr, 2014 We propose that differences in memory reactivation strength might lead to variability in suppression effects. Nonmonotic plasticity hypothesis (NMPH) 5,6 Some memories might be challenging to suppress because they are too strongly reactivated. In fact, individual variability (e.g., depression level) might impact memory reactivation and thus influence memory suppression effect. Specifically, we predict an increase in memory reactivation as negative valence increases. We expect participants with more depression to reactivate negative stimuli more strongly than those with lower depression. In turn, this should affect subsequent memory performance. Special thanks to Sam Nastase and Lizzie McDevitt for fMRI guidance. [1] Anderson & Green, 2001; [2] Anderson & Hanslmayr, 2014; [3] Bulevich, et al., 2006; [4] Hertel & Mahan, 2008; [5] Detre et al., 2013; [6] Ritvo, Turk-Browne, & Norman, 2019; [7] Küpper et al., 2014 * Trials repeated 12x Next Steps • Only use TNT trials that participants got correct in the criterion test, in line with prior work 7 . • Take into account individual variability in depression level, since this might impact the degree of memory reactivation. • Relate memory reactivation during the TNT trials to memory performance during the surprise cued recall task ( ). We expect to see a relationship similar to the NMPH schematic. 3 1 2 3 Preliminary Results 1 Built logistic regression (L-2 regularized, penalty = 50) scene classifiers 2 Applied scene classifier TNT data We looked at the response 3 and 4 TRs after cue onset (accounting for hemodynamic lag). As expected, there was a higher probability estimate for scenes for think versus no-think trials. Surprisingly, there were no differences in scene prediction probability across negative valence level. However, any effect might be washed out by differences in depression level. *used bilateral occipitotemporal mask* Classifier Label negative & neutral scenes scrambled scenes objects rest negative scenes scrambled scenes objects neutral scenes rest 0.0 0.2 0.4 0.6 0.8 1.0 Real Category Label Confusion Matrix 0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 Prediction Probability no-think think TNT Trial Condition p < 0.001 Average probability estimate when applying the scene classifier high medium low high medium low 0.0 0.1 0.2 0.3 0.4 0.5 0.6 Degree of Negative Valence Degree of Negative Valence Prediction Probability No-think trials Think trials We also examined the time course of the prediction probability relative to the cue onset of the think and no-think trials. We see a peak in activity 3 and 4 TRs after the cue onset. Asterisks denote significance. 0.30 0.35 0.40 0.45 0.50 0.55 0.60 -1 0 1 2 3 4 5 6 no-think trials think trials * * * Average probability estimate for scene classifier across time Prediction Probability Time (in TR, relative to cue onset)