Computational [Version of the] Imago Semantic Action Model CISAM

Transcription

1 Computational [Version of the] Imago Semantic Action Model CISAM Hecke Schrobsdorff BCCN Göttingen C4 Forschungsseminar morgen früh Wed, May 3th, 27

2 Outline 1 Introduction 2 The Imago-Semantic Action Model 3 4 RSI Dependency 5 IN-trials

3 Introduction Introduction Main Goal Get a detailed description of mechanisms that produce priming effects. Identify possible sources of variance. Have a framework to interprete single trial data (e.g. from EEG recordings). Concrete Steps Investigate the usefulness of the ISAM by Kabisch by computational modeling in direct interaction with experiments.

4 Introduction Introduction Main Goal Get a detailed description of mechanisms that produce priming effects. Identify possible sources of variance. Have a framework to interprete single trial data (e.g. from EEG recordings). Concrete Steps Investigate the usefulness of the ISAM by Kabisch by computational modeling in direct interaction with experiments.

5 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 threshold threshold adaptation according to overall rearrangement by a semantic feedback loop action selection if only one object surpasses the threshold negative priming by a forced decay of concurring activity B. Kabisch (23) Negatives Priming und Schizophrenie - Formulierung und empirische Untersuchung eines neuen theoretischen Ansatzes, PhD thesis, Friedrich-Schiller-Universität, Jena

6 Implementation of CISAM Two variables for each object i. τ i if it is target, δ i for the distractor case. Natural adaptation to external input: (µ {τ,δ}) 1 dµ i γ dt 1 dµ i ǫ dt = I i,µ µ i if µ i < I i,µ = I i,µ µ i if µ i > I i,µ Interference between distractor and target variable of one and the same object: 1 dδ i ǫ dt = δ i ζ τ i

7 Implementation of CISAM Two variables for each object i. τ i if it is target, δ i for the distractor case. Natural adaptation to external input: (µ {τ,δ}) 1 dµ i γ dt 1 dµ i ǫ dt = I i,µ µ i if µ i < I i,µ = I i,µ µ i if µ i > I i,µ Interference between distractor and target variable of one and the same object: 1 dδ i ǫ dt = δ i ζ τ i

8 Implementation of CISAM Two variables for each object i. τ i if it is target, δ i for the distractor case. Natural adaptation to external input: (µ {τ,δ}) 1 dµ i γ dt 1 dµ i ǫ dt = I i,µ µ i if µ i < I i,µ = I i,µ µ i if µ i > I i,µ Interference between distractor and target variable of one and the same object: 1 dδ i ǫ dt = δ i ζ τ i

9 Implementation of CISAM Adaptation of the threshold to a global activity level: 1 dθ = µ θ, with α dt ( ( µ = n 2 1 n r τ + r δ + )) n (τ i + δ i ) i=1 Iff only one variable is above threshold level, a decision is made.

10 Implementation of CISAM Adaptation of the threshold to a global activity level: 1 dθ = µ θ, with α dt ( ( µ = n 2 1 n r τ + r δ + )) n (τ i + δ i ) i=1 Iff only one variable is above threshold level, a decision is made.

11 Dynamical Behavior of the CISAM 1 target input average target threshold sensitivity distractor 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, accepted

12 Dynamical Behavior of the CISAM 1 target input average target threshold sensitivity distractor threshold adaptation 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, accepted

13 Dynamical Behavior of the CISAM by input 1 target input average target threshold sensitivity distractor threshold adaptation 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, accepted

14 Dynamical Behavior of the CISAM by input 1 target input average target threshold decision making sensitivity distractor threshold adaptation 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, accepted

15 Dynamical Behavior of the CISAM by input 1 target input average target threshold decision making sensitivity distractor sensitivity cutoff threshold adaptation 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, accepted

16 Dynamical Behavior of the CISAM negative priming condition positive priming condition 1 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, accepted

17 RSI Dependency Comparison of RSI dependencies difference of reaction time [ms] Experiment (Kabisch 3) ms 1 ms 15 ms (RSI) [ms] NP2 NP PP PP2 Simulation of the ISAM control NP NP2 PP PP Interesting strange effects imply multiple mechanisms. The ISAM also shows reversal of effects.

18 RSI Dependency Comparison of RSI dependencies difference of reaction time [ms] Experiment (Kabisch 3) ms 1 ms 15 ms (RSI) [ms] NP2 NP PP PP2 Simulation of the ISAM control NP NP2 PP PP Interesting strange effects imply multiple mechanisms. The ISAM also shows reversal of effects.

19 RSI Dependency Comparison of RSI dependencies difference of reaction time [ms] Experiment (Kabisch 3) ms 1 ms 15 ms (RSI) [ms] NP2 NP PP PP2 Simulation of the ISAM control NP NP2 PP PP Interesting strange effects imply multiple mechanisms. The ISAM also shows reversal of effects.

20 IN-trials Simulational Results for IN-Trials control NP NP2 PP PP2 INCO INNP INPP COIN NPIN PPIN Main results of the simulation Priming effects still exist in IN-trials Trials that follow IN trials are systematically slower.

21 IN-trials Conclusion Take Home Message Outlook The CISAM proves the ISAM to be useful. A global threshold mechanism can account for priming effects. A lot of abitrarity has to be addressed. Complete remodeling for the Tastaturvariante. Include a flexible feature binding mechanism. Sepecificity of Stimulus features?!