Modeling Neural Correlates of Selective Attention

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1 Modeling Neural Correlates of Selective Attention Hecke Schrobsdorff Bernstein Center for Computational Neuroscience Göttingen DPG Frühjahrstagung 2007 Regensburg BP12: Neuroscience 03/27/2007

2 Introduction to Negative Priming Introduction How do humans ignore irrelevant perceptual input? Accessing Selective Attention via Negative Priming (NP) response to a previously ignored stimulus is slowed NP as a drawback of perceptual optimization NP acts on an abstract, semantic level NP effects are universal but sensitive Negative Priming Experiments prime-probe pairs of target-distractor presentation variation of stimuli, response stimulus interval changing paradigm by intermittent absence of distractors EEG-recordings of negative priming experiments

3 A Negative Priming Experiment Negative Priming Experiment PP PP2 time NP NP2 stimulus onset reaction response stimulus interval reaction time time continuous identity priming paradigm with speech reaction green target, red distractor reaction times vary significantly with priming condition in general: NP2 > NP > > PP > PP2

4 The Imago Semantic Action Model Imago Semantic Action Model (ISAM) Semantic Analysis Semantic Transcoding Adaptive Threshold Space of Possible Actions Sensory Input Pattern Recognition Automatic Rating of Relevance Post Hoc Rating of Relevance A Model for Action Selection automatic rating of input by relevance determination of possible actions by an adaptive adaptation according to overall possibility of rearrangement by a semantic feedback loop action selection if exactly one object is super negative priming is produced by a forced decay of remaining activity B. Kabisch (2003) Negatives Priming und Schizophrenie - Formulierung und empirische Untersuchung eines neuen theoretischen Ansatzes, PhD thesis, Friedrich-Schiller-Universität, Jena

5 The Imago Semantic Action Model Dynamical Behavior of the ISAM 1 target input average target sensitivity distractor 0 RT RSI RT time [ms] HS, M. Ihrke, B. Kabisch, J. Behrendt, M. Hasselhorn, J. M. Herrmann A Computational Approach to Negative Priming, Connection Science, submitted

6 The Imago Semantic Action Model Dynamical Behavior of the ISAM by input 1 target input average target sensitivity distractor 0 RT RSI RT time [ms] HS, M. Ihrke, B. Kabisch, J. Behrendt, M. Hasselhorn, J. M. Herrmann A Computational Approach to Negative Priming, Connection Science, submitted

7 The Imago Semantic Action Model Dynamical Behavior of the ISAM by input 1 target input average target sensitivity distractor adaptation 0 RT RSI RT time [ms] HS, M. Ihrke, B. Kabisch, J. Behrendt, M. Hasselhorn, J. M. Herrmann A Computational Approach to Negative Priming, Connection Science, submitted

8 The Imago Semantic Action Model Dynamical Behavior of the ISAM by input 1 target input average target decision making sensitivity distractor adaptation 0 RT RSI RT time [ms] HS, M. Ihrke, B. Kabisch, J. Behrendt, M. Hasselhorn, J. M. Herrmann A Computational Approach to Negative Priming, Connection Science, submitted

9 The Imago Semantic Action Model Dynamical Behavior of the ISAM by input 1 target input average target decision making sensitivity distractor sensitivity cutoff adaptation 0 RT RSI RT time [ms] HS, M. Ihrke, B. Kabisch, J. Behrendt, M. Hasselhorn, J. M. Herrmann A Computational Approach to Negative Priming, Connection Science, submitted

10 The Imago Semantic Action Model Dynamical Behavior of the ISAM by input negative priming condition positive priming condition decision making 1 sensitivity cutoff adaptation 0 RT RSI RT time [ms] HS, M. Ihrke, B. Kabisch, J. Behrendt, M. Hasselhorn, J. M. Herrmann A Computational Approach to Negative Priming, Connection Science, submitted

11 TP9 FT7 T7 TP7 F7 P7 FC5 C5 CP5 AF7 F5 P5 C3 PO7 F3 FC3 CP3 P3 Fpz Fp2 AF3 AFz AF4 O1 F1 FC1 C1 CP1 PO3 P1 Fz Cz CPz Pz POz Oz F2 FC2 C2 CP2 P2 O2 F4 P4 PO4 FC4 C4 CP4 AF8 F6 P6 PO8 F8 FC6 C6 P8 FT8 T8 CP6 TP8 Modeling Neural Correlates of Selective Attention EEG Correlates of Negative Priming ERP Differences While Negative Priming 10µV Fp1 1s + NP PP EEG Recordings 64-channel EEG recordings state-of-the-art post processed event related potentials of NP, PP, grand average over 16 subjects J. Behrendt, H. Gibbons, HS, M. Ihrke, J. M. Herrmann, M. Hasselhorn Event-Related Brain Potential Correlates of Semantic Negative Priming, Journal of Psychophysiology, in preparation

12 TP9 FT7 T7 TP7 F7 P7 FC5 C5 CP5 AF7 F5 P5 C3 PO7 F3 FC3 CP3 P3 Fpz Fp2 AF3 AFz AF4 O1 F1 FC1 C1 CP1 PO3 P1 Fz Cz CPz Pz POz Oz F2 FC2 C2 CP2 P2 O2 F4 P4 PO4 FC4 C4 CP4 AF8 F6 P6 PO8 F8 FC6 C6 P8 FT8 T8 CP6 TP8 Modeling Neural Correlates of Selective Attention EEG Correlates of Negative Priming ERP Differences While Negative Priming 10µV Fp1 P5 P300 1s s 2µV NP PP P300 P6 NP PP + 0.2s 2µV NP PP Lateralized Smaller P300 P300 for NP and PP are reduced less effort for stimulus evaluation for NP and PP no persisting inhibition but persisting ISAM: of the prime is reused in NP, PP J. Behrendt, H. Gibbons, HS, M. Ihrke, J. M. Herrmann, M. Hasselhorn Event-Related Brain Potential Correlates of Semantic Negative Priming, Journal of Psychophysiology, in preparation

13 TP9 FT7 T7 TP7 F7 P7 FC5 C5 CP5 AF7 F5 P5 C3 PO7 F3 FC3 CP3 P3 Fpz Fp2 AF3 AFz AF4 O1 F1 FC1 C1 CP1 PO3 P1 Fz Cz CPz Pz POz Oz F2 FC2 C2 CP2 P2 O2 F4 P4 PO4 FC4 C4 CP4 AF8 F6 P6 PO8 F8 FC6 C6 P8 FT8 T8 CP6 TP8 Modeling Neural Correlates of Selective Attention EEG Correlates of Negative Priming ERP Differences While Negative Priming 10µV Fp1 1s + FpZ PSW 2µV + 0.2s NP PP NP PP Frontal Positive Slow Wave PSW: NP > > PP PSW amplitude reflects coordination complexity most control in NP trials, least in PP-trials ISAM: semantic feedback loop is sensitive to priming J. Behrendt, H. Gibbons, HS, M. Ihrke, J. M. Herrmann, M. Hasselhorn Event-Related Brain Potential Correlates of Semantic Negative Priming, Journal of Psychophysiology, in preparation

14 EEG Correlates of Negative Priming Conclusion Take Home Message first identification of neural correlates of semantic NP ISAM is a reasonable framework to consider EEG results Outlook identifying sources of variance in the EEG-data adding these as new parameters to the ISAM finding the age dependency in the ISAM

15 EEG Correlates of Negative Priming Thanks to the Experts ISAM: Björn Kabisch EEG Recordings: Torsten Wüstenberg EEG Data Analysis: Henning Gibbons Ralph Meier Miguel Valencia Ustárroz... to the BCCN People Heads: Theo Geisel Michael Herrmann Marcus Hasselhorn Coordination: Tobias Niemann Project: Jörg Behrendt Matthias Ihrke... and to You!

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