Abstract
Dual-process models of recognition memory typically assume that independent familiarity and recollection signals with distinct temporal profiles can each lead to recognition (enabling two routes to recognition), whereas single-process models posit a unitary “memory strength” signal. Using multivariate classifiers trained on spectral EEG features, we quantified neural evidence for recognition decisions as a function of time. Classifiers trained on a small portion of the decision period performed similarly to those also incorporating information from previous time points indicating that neural activity reflects an integrated evidence signal. We propose a single-route account of recognition memory that is compatible with contributions from familiarity and recollection signals, but relies on a unitary evidence signal that integrates all available evidence.
Footnotes
Christoph T. Weidemann, Department of Psychology, Swansea University, Wales, UK and Department of Psychology, University of Pennsylvania, PA, USA; Michael J. Kahna, Department of Psychology, University of Pennsylvania, PA, USA. The authors would like to thank Jesse Pazdera and other members of the Computational Memory Laboratory at the University of Pennsylvania for their assistance with data collection and preprocessing as well as Youssef Ezzyat, James Kragel, Nora Herweg, Ethan Solomon, and Rivka Cohen, for helpful comments on a draft of this paper. This work was supported by grant NIMH RO1 55687 to MJK. This manuscript has been published as a preprint on BioRxiv (https://doi.org/10.1101/165225) and parts of this work have been presented at the 2017 Context and Episodic Memory Syposium and the 2017 Annual Meeting of the Society for Mathematical Psychology. Correspondence concerning this article should be addressed to Christoph T. Weidemann, ctw{at}cogsci.info.