How To Understand The Brain'S Reaction Time

Transcription

1 Negatively Primed Psychophysics Hecke Schrobsdorff, Matthias Ihrke, Hendrik Degering, Björn Kabisch, Jörg Behrendt, Marcus Hasselhorn, Michael Herrmann Bernie Center for Commutable Neuropathies Project Symposium Practical Minimization of Sliding Friction Klosters 2006

2 Outline 1 Introduction 2 3 Imago-Semantic-Action Model (ISAM) 4 Implementation 5 Outlook

3 Introduction Introduction Main Question: How do we filter out irrelevant information about our environment? Negative Priming (NP) is a paradigm to acces mechanisms of selective attention. It shows up in a slowing of reaction times under certain stimulus conditions. But, as in all experiments with individuals, the effect is only visible in strongly normalized conditions. Even then it shows a huge variability both inter- and intraindividually.

4 We will go through an exemplary session. (Sorry, this will be in german.) You should point your attention to systematically slightly different reaction times.

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49 Different Priming Conditions CO NP NP2 CO PP PP2 CO Reaction times vary significantly with the order of subsequent stimuli. NP2 > NP > CO > PP > PP2 in general.

50 Different Priming Conditions CO NP NP2 CO PP PP2 CO Reaction times vary significantly with the order of subsequent stimuli. NP2 > NP > CO > PP > PP2 in general.

51 Setup 4 45 trials intraindividually varied parameters response-stimulus interval (RSI) priming condition (relation between two consecutive trials) interindividually varied parameters age presentation of interference (IN) or positive priming (PP) trials

52 al Results Results PP is faster than Control is faster than NP. IN-trials are much faster even in the control condition.

53 NP with different paradigms Variation of objects pictures letters numbers words nonsense shapes... Stimulus presentation visual auditory... Different tasks identity priming location priming counting lexical decisions... Reaction determination by key pressure voice recording...

54 Imago-Semantic-Action Model (ISAM) Imago-Semantic-Action Model (ISAM) Object recognition is assumed to work fine. Sensory Input Pattern Recognition Post Hoc Automatic Rating of Relevance Adaptive Threshold Semantic Transcoding Semantic Analysis [Kabisch 2003] Situational Acutenes Space of Possible Actions Perceived Objects are automatically rated by relevance. Number and special properties determine the situational acuteness. This affects the speed and accuracy of adaptation. The adaptive Threshold truncates the list of object-action pairs to the space of possible actions. Iff only one object-action pair is possible, this action is performed. A semantic evaluation of the perceived objects can affect the order in the rating by relevance.

61 Imago-Semantic-Action Model (ISAM) NP in the ISAM [Kabisch 2003] Sketch of NP in the ISAM decaying residual activity onset of stimuli amplification of the target activation adaptation of the threshold decision and offset of stimuli when the threshold crosses the distractor activity.

62 Implementation Implementation of ISAM 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

65 Implementation Implementation of ISAM 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.

66 Implementation Implementation of ISAM 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.

67 Implementation Exemplary time series 1.2 NP 1.2 PP activation activation time [ms] x time [ms] Input to the target variable is amplified via the semantic feedback loop. Decision making when the threshold crosses the distractor variable. Kink in the distractor time series while negative priming conditions. Augmented onset while positive priming trials. x 10 4

72 Implementation Comparison of RSI dependencies difference of reaction time [ms] (Kabisch 03) ms 1000 ms 1500 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.

75 Implementation IN-trials Presentation of single objects is a keystone in testing different models for negative priming. Trials after IN-trials also showed a special behavior in our simulations thus providing predictions for human behavior. Therefore we introduced six other conditions, that are currently tested experimentally. further priming conditions INCO : control-condition after an IN-Trial INNP : NP-condition after an IN-Trial INPP : PP-condition after an IN-Trial COIN : IN-Trial, control referring to the last trial NPIN : IN-Trial, NP referring to the last trial PPIN : IN-Trial, PP referring to the last trial

78 Implementation 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.

79 Implementation Fishi Improvements A natural mechanism for decision making is the winner takes all method implemented by the Fisher-Eigen equation 1 ǫ u i = u i ( θ θ i ) While plugging it into the ISAM-simulation, several problems arise: When do we make the decision? How do we set the fitness of a variable? Which mechanism accounts for the decay?

80 Implementation Fishi Improvements A natural mechanism for decision making is the winner takes all method implemented by the Fisher-Eigen equation 1 ǫ u i = u i ( θ θ i ) While plugging it into the ISAM-simulation, several problems arise: When do we make the decision? How do we set the fitness of a variable? Which mechanism accounts for the decay?

81 Summary Conclusion The ISAM explains priming effects elegantly by only one mechanism. Our implementations prove the ISAM to be reasonable. s are done at the moment to develop the model further.

84 Outlook Outlook Implementation of a more natural decision making via Fisher-Eigen-Equations. Inclusion of the results from current experiments. Introduce the ISAM to the priming community.

87 Outlook