The Time Course of Negative Priming

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 Key Themes

The main objective of this dissertation is to investigate the temporal dynamics of the negative priming effect, a phenomenon related to selective attention. This is achieved through a multi-faceted approach combining behavioral experiments, EEG recordings, and computational modeling. The dissertation aims to pinpoint the stage of processing where negative priming occurs and to evaluate existing theoretical accounts.

• The nature and mechanisms of negative priming
• Computational modeling of negative priming and its theoretical implications
• Electrophysiological correlates (EEG) of negative priming
• Behavioral experiments with novel paradigms to dissect the time course of negative priming
• Development of advanced signal processing techniques for EEG data analysis

Chapter Summaries

1 Introduction: This chapter provides an overview of negative priming, its computational modeling in psychology, and the structure of the thesis. It highlights the ongoing debate about the cognitive mechanisms underlying negative priming and introduces the dissertation's multi-level approach combining behavioral research, neuroimaging, theoretical psychology, and computational modeling to address this issue. The chapter also lists the original contributions of the thesis.

2 Negative Priming: This chapter offers a comprehensive review of the negative priming phenomenon. It begins by defining negative priming and introducing common experimental paradigms, demonstrating its sensitivity to subtle parameter changes. The chapter then presents and critically evaluates several prominent theories attempting to explain negative priming, including distractor inhibition theory, episodic retrieval theory, response retrieval theory, feature mismatch theory, temporal discrimination theory, dual mechanism theory, and global threshold theory. Each theory is described in detail, along with its strengths, weaknesses, and supporting/contradictory evidence.

3 Imago Semantic Action Model: This chapter details the development and testing of a computational model (ISAM) based on the global threshold theory of negative priming. The chapter begins by establishing a simplified activation dynamic from an integrate-and-fire neuron model and uses it to implement the ISAM. The model’s capacity to reproduce experimentally observed reaction times and various priming-related effects is demonstrated and discussed. This includes validating the global threshold theory's predictions about the response-stimulus interval and varying distractor saliency. Furthermore, the ISAM's predictions for single-object trials are presented, highlighting its potential for generating testable hypotheses.

4 EEG Correlates of Negative Priming: This chapter describes an EEG experiment designed to investigate the electrophysiological correlates of negative priming. It provides a thorough introduction to EEG recording techniques and data processing methods. The chapter then reviews existing literature on ERP correlates of negative priming before presenting the results of the author's experiment. These results are discussed in the context of existing research and interpreted through the lens of different theoretical accounts of negative priming, specifically focusing on the relationship between ERP components and reaction time differences between experimental conditions.

5 Interlude: Advanced EEG Analysis: This chapter serves as an interlude, presenting novel signal processing methods developed to improve the analysis of event-related potentials (ERPs) in EEG data. It addresses the limitations of traditional averaging techniques by introducing dynamic time warping and pyramidal averaging techniques to account for trial-to-trial variability in response latency. The chapter details these methods, demonstrates their improved performance compared to conventional averaging using both simulated and real data, and discusses their potential for enhancing ERP analysis in cognitive research.

6 Perception or Selection Effect: This chapter introduces a novel experimental paradigm to further investigate the temporal dynamics of negative priming. By introducing a task switch (a color cue presented after stimulus onset), the trial is divided into a stimulus identification phase and a target selection phase, allowing for separate measurement of reaction times in each phase. The chapter presents two preliminary experiments to establish the baseline and task-switching effects, followed by the main experiment incorporating the post-cue manipulation. Results are discussed in the context of different negative priming theories (distractor inhibition, episodic retrieval, and the ISAM), leading to conclusions about the temporal localization of priming effects.

7 Selection or Response Effect: This chapter employs another novel paradigm to further localize the negative priming effect, this time distinguishing between target selection and response generation phases. The experiment uses a gaze-shift paradigm, where participants must make a saccade to a separate comparison word after identifying the target object. This allows for the separation of reaction times into target selection and response generation phases. Results are analyzed to examine which phase primarily contributes to negative priming, and this is evaluated using the lens of existing negative priming theories (particularly response retrieval theory), providing further insight into the temporal dynamics of negative priming.

8 The General Model for Negative Priming: This chapter introduces a comprehensive computational model to integrate and compare various negative priming theories. The model encompasses multiple processing layers (feature, binding, semantic, action, and episodic memory), each with a biologically plausible dynamic. The chapter details the model’s architecture, dynamics, and how it incorporates different theoretical mechanisms. Through simulations of the voicekey and word-picture comparison paradigms, the model's ability to generate predictions under different theoretical assumptions is demonstrated, providing a framework for quantitatively comparing competing theories of negative priming.

Keywords

Negative priming, selective attention, computational modeling, EEG, ERP, event-related potentials, Imago Semantic Action Model (ISAM), distractor inhibition, episodic retrieval, response retrieval, temporal discrimination, task switching, reaction time, cognitive control, working memory, dynamic time warping, recurrence plots.

Frequently Asked Questions: A Comprehensive Study of Negative Priming

What is the main focus of this dissertation?

This dissertation investigates the temporal dynamics of the negative priming effect, a phenomenon related to selective attention. It uses a multi-faceted approach combining behavioral experiments, EEG recordings, and computational modeling to pinpoint the stage of processing where negative priming occurs and evaluate existing theoretical accounts.

What are the key themes explored in this research?

Key themes include the nature and mechanisms of negative priming; computational modeling of negative priming and its theoretical implications; electrophysiological correlates (EEG) of negative priming; behavioral experiments with novel paradigms to dissect the time course of negative priming; and the development of advanced signal processing techniques for EEG data analysis.

What are the different theoretical accounts of negative priming discussed?

The dissertation examines several prominent theories attempting to explain negative priming, including distractor inhibition theory, episodic retrieval theory, response retrieval theory, feature mismatch theory, temporal discrimination theory, dual mechanism theory, and global threshold theory. Each theory is critically evaluated based on its strengths, weaknesses, and supporting/contradictory evidence.

What computational model is developed and tested?

A computational model called the Imago Semantic Action Model (ISAM) is developed and tested. Based on the global threshold theory, the ISAM simulates experimentally observed reaction times and various priming-related effects, allowing for the validation of the theory's predictions.

What EEG experiments were conducted?

EEG experiments were designed to investigate the electrophysiological correlates of negative priming. The experiments involved a thorough introduction to EEG recording techniques and data processing methods, reviewing existing literature on ERP correlates of negative priming before presenting the results of the author's experiment.

What novel signal processing methods were developed and applied?

Novel signal processing methods, such as dynamic time warping and pyramidal averaging, were developed to improve the analysis of event-related potentials (ERPs) in EEG data. These methods address the limitations of traditional averaging techniques by accounting for trial-to-trial variability in response latency.

What novel experimental paradigms were used to investigate negative priming?

Novel paradigms, including a task switch paradigm (dividing trials into stimulus identification and target selection phases) and a gaze-shift paradigm (separating reaction times into target selection and response generation phases), were employed to further investigate the temporal dynamics of negative priming and better localize the effect.

What is the "General Model for Negative Priming"?

A comprehensive computational model, the "General Model for Negative Priming," was developed to integrate and compare various negative priming theories. This model encompasses multiple processing layers and allows for quantitative comparison of competing theories.

What are the main conclusions of the dissertation?

The dissertation concludes by discussing the implications of computational modeling in psychology, EEG correlates of negative priming, behavioral paradigms beyond response latencies, the time course of negative priming, and providing an outlook for future research.

What are the keywords associated with this research?

Keywords include: Negative priming, selective attention, computational modeling, EEG, ERP, event-related potentials, Imago Semantic Action Model (ISAM), distractor inhibition, episodic retrieval, response retrieval, temporal discrimination, task switching, reaction time, cognitive control, working memory, dynamic time warping, recurrence plots.