Selective attention enables goal-directed behavior despite the permanent, immense input to the sensory system. Contradicting early speculations of an active attending and passive ignoring, the active nature of ignoring was revealed by the negative priming paradigm. The present thesis will describe our multi-level approach to reveal the temporal structure of negative priming. Accompanied by computational modeling, we run sophisticated psychological experiments and record and analyze EEG data.
The common denominator of all negative priming paradigms is the simultaneous presentation of targets that have to be attended to, and distractors that are to be ignored. A slowdown in the response to a formerly ignored stimulus is labeled negative priming. Because of negative priming being robust and sensitive at the same time, a variety of different theoretical accounts have been developed. But until now none of the theoretical accounts is able to explain all aspects of the negative priming effect. In order to clarify the situation, the time course of negative priming is crucial. In order to advance the debate on theoretical accounts, we build a computational model comprising most of the mechanisms suspected to play a role in negative priming tasks. The outcome is not only a meta-model for negative priming, but in itself a simplified model of the brain as a framework for action selection based on perception. We address the tradeoff between biological realism and understandability by modeling each assumed mechanism separately but keeping the internal dynamics of each of the corresponding layers very simple.
The computational implementation of theories is accompanied by a series of behavioral experiments intended to decide about the temporal localization of the negative priming effect relative to the processing of a single trial. We present an EEG experiment that replicates findings from one of the few studies on event-related potentials related to negative priming. To access the timing of the effect not only through brain recordings but behavioral measures, we design a paradigm which divides stimulus identification and target selection. The results locate negative priming also in the latter part of a trial. This remainder of a trial still contains both target selection and response generation. Therefore, another trial splitting paradigm singles out the response generation phase. We finally find the devotion of negative priming to the target selection phase.
Table of Contents
1 Introduction
1.1 Negative Priming
1.2 Computational Modeling of Negative Priming
1.3 Thesis Overview
1.4 Original Contributions
2 Negative Priming
2.1 A Paradigm to Access Selective Attention
2.2 A Showcase Negative Priming Experiment
2.3 The Negative Priming Effect
2.4 Theories of Negative Priming
2.4.1 Distractor Inhibition Theory
2.4.2 Episodic Retrieval Theory
2.4.3 Response Retrieval Theory
2.4.4 Feature Mismatch Theory
2.4.5 Temporal Discrimination Theory
2.4.6 Dual Mechanism Theory
2.4.7 Global Threshold Theory
2.5 Summary
3 Imago Semantic Action Model
3.1 Deriving Simple Activation Dynamics
3.1.1 Networks of Integrate-and-Fire Neurons
3.1.2 Network Response to Input Onset and Offset
3.1.3 Exponential Fixpoint Dynamics
3.2 Implementation of the ISAM
3.2.1 Representation Variables
3.2.2 Visual Input
3.2.3 Interference of Semantically Identical Objects
3.2.4 Adaptivity of the Threshold
3.2.5 Response Generation
3.3 Computational Results
3.3.1 Comparison to the Experimental Data
3.3.2 Dependence on the Response Stimulus Interval
3.3.3 Variation of Distractor Saliency
3.3.4 Predictions for Single-Object Trials
3.4 Discussion
3.4.1 Modeling Priming
3.4.2 Phenomenological and Neural Models
3.4.3 The Implementation of the Model
3.5 Summary
4 EEG Correlates of Negative Priming
4.1 Introduction to Electroencephalography
4.1.1 EEG Recording
4.1.2 Data Processing
4.2 Review: ERP Correlates of Negative Priming
4.2.1 N200 Component
4.2.2 P300 Component
4.2.3 Positive Slow Wave Component
4.2.4 Summary of ERP Correlates
4.3 Hypotheses
4.4 Experimental Setup
4.5 Data Analysis
4.6 Behavioral Results
4.7 ERP Results
4.8 Discussion
4.9 Conclusion
4.10 Summary
5 Interlude: Advanced EEG Analysis
5.1 EEG Analysis in Cognitive Research
5.2 Models for Event-Related Potentials
5.3 Dynamic Time Warping
5.4 Pyramidal Averaging Dynamic Time Warping
5.5 Trial Clustering for Cleaner Averages
5.6 Enhancing Averaging by Integrating Time Markers
5.7 Recurrence Plots to Obtain the Warping Function
5.7.1 Recurrence Plots
5.7.2 Phase-Space Reconstruction
5.7.3 Line-of-Synchrony Detection in Cross-Recurrence Plots
5.7.4 An Algorithm for Line-of-Synchrony Detection
5.7.5 Results
5.8 Summary
6 Perception or Selection Effect
6.1 Task Switch Paradigm
6.1.1 Sequence of Experiments
6.1.2 Task Switch and Negative Priming
6.1.3 Condition Set
6.2 Task Switch and the ISAM
6.2.1 Extension of the ISAM
6.2.2 Calibration
6.2.3 Pre-Cue Simulation
6.2.4 Post-Cue Simulation
6.3 Hypotheses
6.4 Preparatory Task Switch Experiments
6.4.1 Design
6.4.2 Participants
6.4.3 Procedure
6.4.4 Data Analysis
6.4.5 Results, Baseline Experiment
6.4.6 Results, Pre-Cue Experiment
6.4.7 Discussion
6.5 Post-Cue Task Switch Experiment
6.5.1 Design
6.5.2 Participants
6.5.3 Data Analysis
6.5.4 Results, Stimulus Identification Phase
6.5.5 Results, Target Selection Phase
6.5.6 Results, Comparison of Partial Reaction Times
6.5.7 Discussion
6.6 General Discussion
6.7 Summary
7 Selection or Response Effect
7.1 Gaze Shift Paradigm
7.2 Hypotheses
7.3 Gaze Shift Experiment
7.3.1 Design
7.3.2 Participants
7.3.3 Procedure
7.3.4 Extraction of Partial Reaction Times
7.3.5 Analysis of Behavioral Data
7.3.6 EEG Data Analysis
7.4 Results
7.4.1 Response-Repetition Effect
7.4.2 Partial Reaction Times
7.4.3 EEG Correlates
7.5 Discussion
7.6 Summary
8 The General Model for Negative Priming
8.1 A Framework to Test all Negative Priming Theories
8.1.1 Different Paradigms
8.1.2 Inclusion of Theories
8.2 Characterizing System Components
8.2.1 Feature Layers
8.2.2 Semantic Representations
8.2.3 Episodic Memory
8.2.4 Memory Retrieval
8.2.5 Central Executive
8.3 Implementation of the General Model
8.3.1 Feature Variables
8.3.2 Feature Binding Mechanism
8.3.3 Semantic Variables
8.3.4 Short-Term Modulation of Connectivity
8.3.5 The Adaptive Threshold in the Semantic Layer
8.3.6 Action Variables
8.3.7 Memory Processes
8.3.8 Connectivity Modulation
8.3.9 Generating Real World Reaction Times
8.4 Defining Setscrews for the Theories
8.5 Voicekey Paradigm
8.6 Word Picture Comparison Task
8.7 Discussion
8.8 Summary
8.9 Simulation Plots
9 Conclusion
9.1 Computational Modeling in Psychology
9.2 EEG Correlates
9.3 Behavioral Paradigms Beyond Response Latencies
9.4 The Time Course of Negative Priming
9.5 Summary and Outlook
Objectives and Topics
This thesis investigates the temporal structure of the negative priming effect—a phenomenon where responses to previously ignored stimuli are slowed—through an interdisciplinary approach combining behavioral research, EEG studies, and computational modeling. The central research question focuses on identifying the specific stage of trial processing (e.g., stimulus identification, target selection, or response generation) at which the negative priming effect arises and resolving the ongoing debate between competing theoretical accounts like distractor inhibition, episodic retrieval, and response retrieval.
- Mechanisms of selective attention and the negative priming phenomenon.
- Development and validation of the Imago Semantic Action Model (ISAM) and the subsequent General Model for negative priming.
- Electrophysiological (EEG) correlates of negative priming using event-related potentials (ERPs).
- Experimental splitting of trial processing phases using task-switching and gaze-shift paradigms.
- Advanced signal processing techniques, specifically dynamic time warping and recurrence plots for EEG analysis.
Auszug aus dem Buch
1.1 Negative Priming
Selective attention enables goal-directed behavior despite the permanent, immense input to the sensory system. The downside of this ability involves the problem of how information is ignored. Contradicting early speculations of an active attending and passive ignoring, a special situation revealed the active nature of ignoring. In the original experiment by Dalrymple-Alford and Budayr (1966), subjects had to process lists of Stroop tasks. While in the original Stroop task no systematic repetition of color and color words was implemented, these authors composed the stimulus cards in a special way, namely the ignored meaning of a color word always became the to be named color in which the next word was shown on some of the lists, on others there was no relation between two succeeding words. The experiment showed that people were slower in responding to the related lists compared to unrelated stimulus colors. Even if the semantic meaning of the words has been ignored, it must have entered the cognitive system as it showed the characteristic interference.
Summary of Chapters
1 Introduction: Provides an overview of the negative priming phenomenon, the research approach, and the thesis structure.
2 Negative Priming: Introduces the negative priming phenomenon, terminology, experimental paradigms, and theoretical accounts.
3 Imago Semantic Action Model: Details the computational implementation of a model based on global threshold theory to simulate and analyze negative priming.
4 EEG Correlates of Negative Priming: Presents a study on electrophysiological correlates using EEG to reveal processing differences in trial conditions.
5 Interlude: Advanced EEG Analysis: Discusses the development of signal processing methods, such as dynamic time warping, to improve EEG analysis.
6 Perception or Selection Effect: Examines the localization of negative priming effects by splitting trial processing into identification and selection phases.
7 Selection or Response Effect: Investigates whether negative priming occurs during target selection or response generation using a gaze-shift paradigm.
8 The General Model for Negative Priming: Introduces a comprehensive computational meta-model integrating various theories into a unified framework.
9 Conclusion: Synthesizes the empirical and computational results and provides an outlook on future research directions.
Keywords
Negative priming, selective attention, computational modeling, EEG, event-related potentials, episodic retrieval, distractor inhibition, response retrieval, trial processing, task switching, gaze shift, action selection, Imago Semantic Action Model, cognitive control, temporal structure
Frequently Asked Questions
What is the core subject of this thesis?
The thesis investigates the temporal structure of the "negative priming effect," a psychological phenomenon where individuals show slowed reaction times to stimuli they have previously ignored. The author explores at what point in the cognitive trial process this slowing occurs.
What are the primary thematic areas covered?
The work integrates psychological behavioral research, electroencephalography (EEG) studies, and sophisticated computational modeling to explain the cognitive mechanisms behind how we ignore irrelevant information.
What is the main objective or research question?
The goal is to provide a unified explanation for negative priming by identifying whether the effect arises during the perceptual identification phase, the target selection phase, or the final response generation phase.
Which scientific methods are applied?
The research uses behavioral experiments, including task-switch and gaze-shift paradigms, high-resolution EEG recording, and the development of non-linear differential equation models to simulate cognitive trial dynamics.
What is covered in the main body of the work?
The main body moves from theoretical reviews to the creation of the Imago Semantic Action Model (ISAM), progresses through EEG empirical studies, utilizes advanced signal processing algorithms for noise reduction, and culminates in a comprehensive "General Model" that encapsulates multiple competing theories.
Which keywords characterize this research?
The research is characterized by terms such as negative priming, selective attention, computational modeling, EEG, event-related potentials (ERPs), and episodic retrieval.
What specifically does the ISAM model demonstrate?
The Imago Semantic Action Model (ISAM) demonstrates that a single, adaptive threshold mechanism can quantitatively reproduce reaction time patterns for both positive and negative priming, providing a biologically plausible dynamic system for action selection.
How does the author resolve the debate between conflicting priming theories?
The author uses empirical results from time-marker experiments to falsify rigid assumptions of existing theories and proposes a flexible "General Model." This meta-model allows different theoretical mechanisms to be tested as "setscrews" within a unified framework, shifting the debate toward a more integrated understanding.
- Arbeit zitieren
- Hendrik Schrobsdorff (Autor:in), 2009, The Time Course of Negative Priming, München, GRIN Verlag, https://www.hausarbeiten.de/document/165942
