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Published on: January 23, 2017
Time-course analysis of temporal preparation on central processes.
Tanja Leonhard1, Daniel Bratzke, Hannes Schröter
1Abteilung für Allgemeine und Biologische Psychologie, Fachbereich Psychologie, Universität Tübingen, Friedrichstr. 21, 72072, Tübingen, Germany. tanja.leonhard@uni-tuebingen.de
Psychological Research
|July 26, 2011
Summary
This study investigated temporal preparation effects on reaction times. Findings suggest preparation influences more than just central processing, challenging existing models.
Area of Science:
- Cognitive Psychology
- Psychophysics
- Human Information Processing
Background:
- Warning stimuli typically facilitate faster responses to a subsequent response signal (RS).
- The precise locus of this temporal preparation effect within cognitive processing remains debated.
- Los and Schut (2008) proposed preparation acts on central processes during RS perception.
Purpose of the Study:
- To test the central preparation model (CPM) proposed by Los and Schut (2008).
- To investigate the time-course of temporal preparation by manipulating warning signal and RS timing.
- To examine whether temporal preparation exclusively affects central processing stages.
Main Methods:
- Developed a stochastic model (CPM) based on the central preparation hypothesis.
- Conducted three experiments manipulating the temporal relationship between warning stimuli and response signals.
- Tracked the complete time-course of temporal preparation effects.
Main Results:
- Observed systematic deviations between experimental data and CPM predictions.
- The results question the assumption that temporal preparation solely impacts central processing.
- Data suggest temporal preparation may influence earlier or parallel processing stages.
Conclusions:
- The central preparation model (CPM) does not fully account for observed temporal preparation effects.
- Temporal preparation likely involves mechanisms beyond solely acting on central processes.
- An alternative explanation based on the parallel grains model is considered.
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