Do item-dependent context representations underlie serial order in cognition?:Commentary on Logan (2021)

Logan (2021) presented an impressive unification of serial order tasks including whole report, typing, and serial recall in the form of the context retrieval and updating (CRU) model. Despite the wide breadth of the model's coverage, its reliance on encoding and retrieving context representations that consist of the previous items may prevent it from being able to address a number of critical benchmark findings in the serial order literature that have shaped and constrained existing theories. In this commentary, we highlight three major challenges that motivated the development of a rival class of models of serial order, namely positional models. These challenges include the mixed-list phonological similarity effect, the protrusion effect, and interposition errors in temporal grouping. Simulations indicated that CRU can address the mixed-list phonological similarity effect if phonological confusions can occur during its output stage, suggesting that the serial position curves from this paradigm do not rule out models that rely on interitem associations, as has been previously been suggested. The other two challenges are more consequential for the model's representations, and simulations indicated the model was not able to provide a complete account of them. We highlight and discuss how revisions to CRU's representations or retrieval mechanisms can address these phenomena and emphasize that a fruitful direction forward would be to either incorporate positional representations or approximate them with its existing representations

What are the boundary conditions of differentiation in episodic memory?

Scan of slide of of workers at Huntington Plant from abov

What are the boundary conditions of differentiation in episodic memory?

One of the critical findings in recognition memory is the null list-strength effect (LSE), which states that repeating study items does not hurt the performance of other studied items. Episodic memory models were able to predict the null LSE by using the principle of differentiation, in which repetitions of an item accumulate into a single strong memory trace. A hypothesized boundary of differentiation is that repetitions of an item in different contexts will create new traces. Two experiments tested this hypothesis by repeating words across different study-test cycles rather than within a single list followed by a test on all of the studied lists. Results indicated that as the proportion of strong items increased, there was both a null LSE and a non-significant decrease in the FAR, which is contrary to the predicted strength-based mirror effect. These two results in tandem provide a challenge for differentiation models

A diffusion decision model analysis of evidence variability in the lexical decision task

The lexical-decision task is among the most commonly used paradigms in psycholinguistics. In both the signal-detection theory and Diffusion Decision Model (DDM; Ratcliff, Gomez, & McKoon, Psychological Review, 111, 159–182, 2004) frameworks, lexical-decisions are based on a continuous source of word-likeness evidence for both words and non-words. The Retrieving Effectively from Memory model of Lexical-Decision (REM–LD; Wagenmakers et al., Cognitive Psychology, 48(3), 332–367, 2004) provides a comprehensive explanation of lexical-decision data and makes the prediction that word-likeness evidence is more variable for words than non-words and that higher frequency words are more variable than lower frequency words. To test these predictions, we analyzed five lexical-decision data sets with the DDM. For all data sets, drift-rate variability changed across word frequency and non-word conditions. For the most part, REM–LD’s predictions about the ordering of evidence variability across stimuli in the lexical-decision task were confirmed

Characterizing Belief Bias in Syllogistic Reasoning: A Hierarchical Bayesian Meta-Analysis of ROC Data

The belief-bias effect is one of the most-studied biases in reasoning. A recent study of the phenomenon using the signal detection theory (SDT) model called into question all theoretical accounts of belief bias by demonstrating that belief-based differences in the ability to discriminate between valid and invalid syllogisms may be an artifact stemming from the use of inappropriate linear measurement models such as analysis of variance (Dube et al., Psychological Review, 117(3), 831–863, 2010). The discrepancy between Dube et al.’s, Psychological Review, 117(3), 831–863 (2010) results and the previous three decades of work, together with former’s methodological criticisms suggests the need to revisit earlier results, this time collecting confidence-rating responses. Using a hierarchical Bayesian meta-analysis, we reanalyzed a corpus of 22 confidence-rating studies (N = 993). The results indicated that extensive replications using confidence-rating data are unnecessary as the observed receiver operating characteristic functions are not systematically asymmetric. These results were subsequently corroborated by a novel experimental design based on SDT’s generalized area theorem. Although the meta-analysis confirms that believability does not influence discriminability unconditionally, it also confirmed previous results that factors such as individual differences mediate the effect. The main point is that data from previous and future studies can be safely analyzed using appropriate hierarchical methods that do not require confidence ratings. More generally, our results set a new standard for analyzing data and evaluating theories in reasoning. Important methodological and theoretical considerations for future work on belief bias and related domains are discussed