A Single Trial Analysis of EEG in Recognition Memory: Tracking the Neural Correlates of Memory Strength
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Summary
This work applied single-trial regressor analysis to EEG and behavioral data from two recognition memory experiments to test whether these two components contribute to the recognition decision process and provides strong evidence that only a single EEG component reflects evidence being used in the recegnition decision process.
- Type
- article
- Published
- 2016-09-29
- Cited by
- 39
- References
- 59
- Access
- Open access
- OpenAlex
- https://openalex.org/W2525285987
- Semantic Scholar
- https://api.semanticscholar.org/CorpusID:26245132
Keywords
Psychology, Recognition memory, Electroencephalography, Neural correlates of consciousness, Cognitive psychology
References
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Cited by
- The Electrophysiological Signature of Remember–Know Is Confounded with Memory Strength and Cannot Be Interpreted as Evidence for Dual-process Theory of Recognition
- Mechanisms involved in the assessment of cumulative long-term familiarity of object concepts: An ERP study
- Internal and External Sources of Variability in Perceptual Decision-Making
- Modeling Numerosity Representation with an Integrated Diffusion Model
- Single-Trial EEG Analysis Predicts Memory Retrieval and Reveals Source-Dependent Differences
- Comparing the confidence calculation rules for forced-choice recognition memory: A winner-takes-all rule wins
- Individual Differences in Cortical Processing Speed Predict Cognitive Abilities: a Model-Based Cognitive Neuroscience Account
- Artificial Intelligence
- Decision Making on Spatially Continuous Scales
- Advances in techniques for imposing reciprocity in brain-behavior relations
- A Model of Relational Reasoning Through Selective Attention
- Dynamics of brain activity reveal a unitary recognition signal
- MELD: Mixed effects for large datasets
- Late positive complex in event-related potentials tracks memory signals when they are decision relevant
- Usefulness of a familiarity signal during recognition depends on test format: Neurocognitive evidence for a core assumption of the CLS framework
- Evidence accumulation models with R: A practical guide to hierarchical Bayesian methods
- Modeling the Interaction of Numerosity and Perceptual Variables with the Diffusion Model
- Sequential sampling models without random between-trial variability: the racing diffusion model of speeded decision making
- Characterizing the brain’s dynamical response from scalp-level neural electrical signals: a review of methodology development
- Usefulness of familiarity signals during recognition depends on test format: Neurocognitive evidence for a core assumption of the CLS framework.
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