Benefits of multinomial processing tree models with discrete and continuous variables in memory research: an alternative modeling proposal to Juola et al. (2019)

Signal detection theory (SDT) and two-high threshold models (2HT) are often used to analyze accuracy data in recognition memory paradigms. However, when reaction times (RTs) and/or confidence levels (CLs) are also measured, they usually are analyzed separately or not at all as dependent variables (DVs). We propose a new approach to include these variables based on multinomial processing tree models for discrete and continuous variables (MPT-DC) with the aim to compare fits of SDT and 2HT models. Using Juola et al.'s (2019, Memory & Cognition, 47[4], 855-876) data we have found that including CLs and RTs reduces the standard errors of parameter estimates and accounts for interactions among accuracy, CLs, and RTs that classical versions of SDT and 2HT models do not. In addition, according to the simulations, there is an increase in the proportion of correct model selections when relevant DV are included. We highlight the methodological and substantive advantages of MPT-DC in the disentanglement of contributing processes in recognition memory.

Memory error speed predicts subsequent accuracy for recognition misses but not false alarms

The current study aims to test whether faster recognition memory errors tend to result from stronger misleading retrieval, making them harder to correct in subsequent decisions than slower errors, and whether this pattern holds for both miss and false-alarm errors. We used a paradigm in which each single-item Old/New recognition decision was followed by a two-alternative forced-choice (2AFC) test between a target and a lure. Each 2AFC trial had one item that had just been tested for an Old/New judgment and one item that had not been previously tested. Across 183 participants, the RTs for single-item recognition errors were used to predict accuracy in the 2AFC test using a hierarchical logistic regression model. The results showed a relationship between error RT and subsequent 2AFC accuracy that was qualified by an interaction with error type. Slower miss responses were more likely to be corrected than faster misses, but no accuracy differences were observed between slower and faster false alarms. The implications of these findings are discussed as they relate to assumptions about memory processes underlying inaccurate retrieval, using the diffusion model and the two-high-threshold model as examples of accounts that explain errors in terms of misleading retrieval and failed retrieval, respectively.

Experimental validation of a multinomial processing tree model for analyzing eyewitness identification decisions

To improve police protocols for lineup procedures, it is helpful to understand the processes underlying eyewitness identification performance. The two-high threshold (2-HT) eyewitness identification model is a multinomial processing tree model that measures four latent cognitive processes on which eyewitness identification decisions are based: two detection-based processes (the detection of culprit presence and absence) and two non-detection-based processes (biased and guessing-based selection). The model takes into account the full 2 × 3 data structure of lineup procedures, that is, suspect identifications, filler identifications and rejections in both culprit-present and culprit-absent lineups. Here the model is introduced and the results of four large validation experiments are reported, one for each of the processes specified by the model. The validation experiments served to test whether the model's parameters sensitively reflect manipulations of the processes they were designed to measure. The results show that manipulations of exposure duration of the culprit's face at encoding, lineup fairness, pre-lineup instructions and ease of rejection of culprit-absent lineups were sensitively reflected in the parameters representing culprit-presence detection, biased suspect selection, guessing-based selection and culprit-absence detection, respectively. The results of the experiments thus validate the interpretations of the parameters of the 2-HT eyewitness identification model.

Closing the door to false memory: the effects of levels-of-processing and stimulus type on the rejection of perceptually vs. semantically dissimilar distractors

False recognition memory for nonstudied items that share features with targets can be reduced by retrieval monitoring mechanisms. The recall-to-reject process, for example, involves the recollection of information about studied items that disqualifies inconsistent test probes. Monitoring for specific features during retrieval may be enhanced by an encoding orientation that is recapitulated during retrieval. In two experiments, we used concrete words or door scenes as materials and manipulated the level of processing at study and the type of distractors presented at test. We showed that for the verbal material, semantic level of processing at study results in an effective rejection of semantically inconsistent distractors. However, for the pictorial material, the perceptual level of processing leads to an effective rejection of perceptually inconsistent distractors. For targets, the effect of levels of processing was observed for words but not for pictures. The results suggest that retrieval monitoring mechanisms depend on interactions between encoding orientation, study materials, and differentiating features of distractors.

Discrete-state versus continuous models of the confidence-accuracy relationship in recognition memory

The relationship between confidence and accuracy in recognition memory is important in real-world settings (e.g., eyewitness identification) and is also important to understand at a theoretical level. Signal detection theory assumes that recognition decisions are based on continuous underlying memory signals and therefore inherently predicts that the relationship between confidence and accuracy will be continuous. Almost invariably, the empirical data accord with this prediction. Threshold models instead assume that recognition decisions are based on discrete-state memory signals. As a result, these models do not inherently predict a continuous confidence-accuracy relationship. However, they can accommodate that result by adding hypothetical mapping relationships between discrete states and the confidence rating scale. These mapping relationships are thought to arise from a variety of factors, including demand characteristics (e.g., instructing participants to distribute their responses across the confidence scale). However, until such possibilities are experimentally investigated in the context of a recognition memory experiment, there is no sense in which threshold models adequately explain confidence ratings at a theoretical level. Here, we tested whether demand characteristics might account for the mapping relationships required by threshold models and found that confidence was continuously related to accuracy (almost identically so) both in the presence of strong experimenter demands and in their absence. We conclude that confidence ratings likely reflect the strength of a continuous underlying memory signal, not an attempt to use the confidence scale in a manner that accords with the perceived expectations of the experimenter.

Familiarity, recollection, and receiver-operating characteristic (ROC) curves in recognition memory

The Atkinson-Shiffrin theory describes and explains some of the processes involved in storing and retrieving information in human memory. Here we examine predictions of related models for search and decision processes in recognizing information in long-term memory. In some models, recognition is presumably based on a test item's familiarity judgment, and subsequent decisions follow from the sensitivity and decision parameters of signal detection theory. Other models dispense with the continuous notion of familiarity and base recognition on discrete internal states such as relative certainty that an item has or has not been previously studied, with an intermediate state of uncertainty that produces guesses. Still others are hybrid models with two criteria located along a familiarity continuum defining areas for rapid decisions based on high or low familiarities. For intermediate familiarity values, the decision can be delayed pending the results of search for, and occasional recollection of, relevant episodic information. Here we present the results from a study of human recognition memory for lists of words using both response time and error data to construct receiver-operating characteristic (ROC) curves derived from three standard methods based on the same data set. Models are evaluated against, and parameters estimated from, group as well as individual subjects' behavior. We report substantially different ROC curves when they are based on variations in target-word frequency, confidence judgments, and response latencies. The results indicate that individual versus group data must be used with caution in determining the appropriate theoretical interpretation of recognition memory performance.

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.

The speed of memory errors shows the influence of misleading information: Testing the diffusion model and discrete-state models

In this report, we evaluate single-item and forced-choice recognition memory for the same items and use the resulting accuracy and reaction time data to test the predictions of discrete-state and continuous models. For the single-item trials, participants saw a word and indicated whether or not it was studied on a previous list. The forced-choice trials had one studied and one non-studied word that both appeared in the earlier single-item trials and both received the same response. Thus, forced-choice trials always had one word with a previous correct response and one with a previous error. Participants were asked to select the studied word regardless of whether they previously called both words "studied" or "not studied." The diffusion model predicts that forced-choice accuracy should be lower when the word with a previous error had a fast versus a slow single-item RT, because fast errors are associated with more compelling misleading memory retrieval. The two-high-threshold (2HT) model does not share this prediction because all errors are guesses, so error RT is not related to memory strength. A low-threshold version of the discrete state approach predicts an effect similar to the diffusion model, because errors are a mixture of responses based on misleading retrieval and guesses, and the guesses should tend to be slower. Results showed that faster single-trial errors were associated with lower forced-choice accuracy, as predicted by the diffusion and low-threshold models.

Recognition Decisions From Visual Working Memory Are Mediated by Continuous Latent Strengths

Making recognition decisions often requires us to reference the contents of working memory, the information available for ongoing cognitive processing. As such, understanding how recognition decisions are made when based on the contents of working memory is of critical importance. In this work we examine whether recognition decisions based on the contents of visual working memory follow a continuous decision process of graded information about the correct choice or a discrete decision process reflecting only knowing and guessing. We find a clear pattern in favor of a continuous latent strength model of visual working memory-based decision making, supporting the notion that visual recognition decision processes are impacted by the degree of matching between the contents of working memory and the choices given. Relation to relevant findings and the implications for human information processing more generally are discussed.