Comparing single‐ and dual‐process models of memory development

Abstract

This experiment examined single-process and dual-process accounts of the development of visual recognition memory. The participants, 6-7-year-olds, 9-10-year-olds and adults, were presented with a list of pictures which they encoded under shallow or deep conditions. They then made recognition and confidence judgments about a list containing old and new items. We replicated the main trends reported by Ghetti and Angelini () in that recognition hit rates increased from 6 to 9 years of age, with larger age changes following deep than shallow encoding. Formal versions of the dual-process high threshold signal detection model and several single-process models (equal variance signal detection, unequal variance signal detection, mixture signal detection) were fit to the developmental data. The unequal variance and mixture signal detection models gave a better account of the data than either of the other models. A state-trace analysis found evidence for only one underlying memory process across the age range tested. These results suggest that single-process memory models based on memory strength are a viable alternative to dual-process models for explaining memory development.

Sources of Interference in Memory across Development

“…The results from the item recognition task show a similar pattern compared to previous studies in that memory accuracy increases across development 15,16 , accuracy for low-frequency words were better than high-frequency words 4,25,26 , and that there was no evidence for a strong list-length or list-strength effect using words as stimuli 18,19 . Results from the source recognition task showed a developmental increase in accuracy 27 with a numerical advantage for low-frequency words and no list-strength effect.…”

“…Therefore, it is important to ask: how the sources of interference change across development and how they affect memory development? Previous studies examining recognition memory development show that memory accuracy increases and interference decreases throughout early development [12][13][14][15][16][17] . For example, Hayes and colleagues used signal detection theory to measure how the ability to discriminate the to-be-remembered memories from other memories changes across development 15,16 .…”

“…Previous studies examining recognition memory development show that memory accuracy increases and interference decreases throughout early development [12][13][14][15][16][17] . For example, Hayes and colleagues used signal detection theory to measure how the ability to discriminate the to-be-remembered memories from other memories changes across development 15,16 . However, most of the studies only support the fact that the signal-to-noise ratio in recognition memory increases with development while being agnostic about the underlying sources of interference and how they affect memory performance across development 16 .…”

“…For example, Hayes and colleagues used signal detection theory to measure how the ability to discriminate the to-be-remembered memories from other memories changes across development 15,16 . However, most of the studies only support the fact that the signal-to-noise ratio in recognition memory increases with development while being agnostic about the underlying sources of interference and how they affect memory performance across development 16 . Moreover, as studies of memory development have mainly focused on examining the change in memory strength and binding abilities across development [15][16][17] , examining the sources of interference provides new insights into memory development.…”

A test of two processes: The effect of training on deductive and inductive reasoning

“…However, important evidence for such accounts is often based on functional dissociations. Moving beyond this kind of evidence, we have added to the growing body of claims that single-process accounts have been prematurely discounted (e.g., in recognition memory: Dunn, 2008;Hayes, Dunn, Joubert, & Taylor, 2017;face perception: Loftus, Oberg, & Dillon, 2004;category learning: Newell, formally instantiating the competing theories, identifying critical ordinal data patterns that can distinguish them, and then performing targeted experiments to see whether single-process models really can be rejected.…”