The dynamics of fidelity over the time course of long-term memory.
Bayesian models of cognition assume that prior knowledge about the world influences judgments. Recent approaches have suggested that the loss of fidelity from working to long-term (LT) memory is simply due to an increased rate of guessing (e.g. Brady, Konkle, Gill, Oliva, & Alvarez, 2013). That is, recall is the result of either remembering (with some noise) or guessing. This stands in contrast to Bayesian models of cognition while assume that prior knowledge about the world influences judgments, and that recall is a combination of expectations learned from the environment and noisy memory representations. Here, we evaluate the time course of fidelity in LT episodic memory, and the relative contribution of prior category knowledge and guessing, using a continuous recall paradigm. At an aggregate level, performance reflects a high rate of guessing. However, when aggregate data is partitioned by lag (i.e., the number of presentations from study to test), or is un-aggregated, performance appears to be more complex than just remembering with some noise and guessing. We implemented three models: the standard remember-guess model, a three-component remember-guess model, and a Bayesian mixture model and evaluated these models against the data. The results emphasize the importance of taking into account the influence of prior category knowledge on memory.
Rutgers University, Department of Psychology, 152 Frelinghuysen Rd, Piscataway, NJ 08854, USA. Electronic address: kimele.persaud@rutgers.edu.
Kimele
K
Persaud
Rutgers University, Department of Psychology, 152 Frelinghuysen Rd, Piscataway, NJ 08854, USA. Electronic address: pernille.hemmer@rutgers.edu.
Pernille
P
Hemmer
D001499:Bayes Theorem; D003118:Color Perception; D006801:Humans; D061212:Memory, Episodic; D057567:Memory, Long-Term; D011939:Mental Recall; D008960:Models, Psychological; D010775:Photic Stimulation; D013997:Time Factors
Bayesian mixture models; Episodic memory; Prior knowledge and expectations; Remember-guess models