The impact of working memory testing on long-term associative memory.

The long-term fate of to-be-remembered information depends in part on the conditions of initial learning, including mental operations engaged via working memory. However, the mechanistic role of working memory (WM) processes in subsequent episodic memory (EM) remains unclear. Does re-exposure to word-pairs during WM recognition testing improve EM for those associations? Are benefits from WM re-exposure greater after an opportunity for retrieval practice compared to mere re-exposure to the memoranda? These questions are addressed in three experiments (N = 460) designed to assess whether WM-based recognition testing benefits long-term associative memory relative to WM-based restudying. Our results show null or negative benefits of WM recognition testing minutes later when initial WM accuracy was not considered. An EM benefit of WM recognition testing only emerges when the analyses are limited to pairs responded to correctly during WM. However, even when compared with accurate WM recognition, restudying can lead to similar associative EM benefits in specific experimental conditions. Taken together, the present results suggest that while WM re-exposure to studied pairs is beneficial to long-term associative memory, successful retrieval on initial tests may be a necessary but insufficient condition for the emergence of a "WM-based testing effect." We consider these results in relation to several hypotheses proposed to explain the testing effect in long-term memory (LTM). In view of empirical parallels with the LTM testing effect, we propose that similar processes influence the benefits of practice tests administered within the canonical boundaries of WM, suggesting continuities in memory over the short and long term.

A Specificity Principle of Memory: Evidence From Aging and Associative Memory.

The ability to remember associations among components of an event, which is central to episodic memory, declines with normal aging. In accord with the specificity principle of memory, these declines may occur because associative memory requires retrieval of specific information. Guided by this principle, we endeavored to determine whether ubiquitous age-related deficits in associative memory are restricted to specific representations or extend to the gist of associations. Young and older adults (30 each in Experiment 1, 40 each in Experiment 2) studied face-scene pairs and then performed associative-recognition tests following variable delays. Whereas both young and older adults could retrieve the gist of associations, older adults were impaired in their ability to retrieve more specific representations. Our results also show that associations can be retrieved from multiple levels of specificity, suggesting that episodic memory might be accessed on a continuum of specificity.

Are there two independent evaluative conditioning effects in relational paradigms? Dissociating the effects of CS-US pairings and their meaning.

Recent research into evaluative conditioning (EC) shows that information about the relationship between the conditioned and unconditioned stimuli can exert strong effects on the size and direction of the EC effect. Additionally, the co-occurrence of these stimuli seems to exert an orthogonal effect on evaluations. This finding has been interpreted as support for two independent types of EC effects. However, previous research devoted to this question relied on aggregated evaluative measures, allowing for alternative interpretations. In four experiments, we developed and validated a multinomial processing tree model that distinguishes effects of the pairings from effects of the meaning of the pairings. Our findings suggest that two independent EC effects contribute to overall evaluative change in a relational EC paradigm. The model that we developed offers a helpful method for future research in that it allows for an assessment of the effects of manipulations on processes rather than overall performance on an evaluative measure.

Does attitude acquisition in evaluative conditioning without explicit CS-US memory reflect implicit misattribution of affect?

Research that dissociates different types of processes within a given task using a processing tree approach suggests that attitudes may be acquired through evaluative conditioning in the absence of explicit encoding of CS-US pairings in memory. This research distinguishes explicit memory for the CS-US pairings from CS-liking acquired without encoding of CS-US pairs in explicit memory. It has been suggested that the latter effect may be due to an implicit misattribution process that is assumed to operate when US evocativeness is low. In the present research, the latter assumption was supported neither by two high-powered experiments nor by complementary meta-analytic evidence, whereas evocativeness exerted an influence on explicit memory. This pattern of findings is inconsistent with the view that CS-liking acquired without encoding of CS-US pairs in explicit memory reflects an implicit misattribution process at learning. Hence, the underlying learning process is awaiting further empirical scrutiny.

A New Model for Acquiescence at the Interface of Psychometrics and Cognitive Psychology.

When measuring psychological traits, one has to consider that respondents often show content-unrelated response behavior in answering questionnaires. To disentangle the target trait and two such response styles, extreme responding and midpoint responding, Böckenholt ( 2012a ) developed an item response model based on a latent processing tree structure. We propose a theoretically motivated extension of this model to also measure acquiescence, the tendency to agree with both regular and reversed items. Substantively, our approach builds on multinomial processing tree (MPT) models that are used in cognitive psychology to disentangle qualitatively distinct processes. Accordingly, the new model for response styles assumes a mixture distribution of affirmative responses, which are either determined by the underlying target trait or by acquiescence. In order to estimate the model parameters, we rely on Bayesian hierarchical estimation of MPT models. In simulations, we show that the model provides unbiased estimates of response styles and the target trait, and we compare the new model and Böckenholt's model in a recovery study. An empirical example from personality psychology is used for illustrative purposes.

Using recursive partitioning to account for parameter heterogeneity in multinomial processing tree models.

In multinomial processing tree (MPT) models, individual differences between the participants in a study can lead to heterogeneity of the model parameters. While subject covariates may explain these differences, it is often unknown in advance how the parameters depend on the available covariates, that is, which variables play a role at all, interact, or have a nonlinear influence, etc. Therefore, a new approach for capturing parameter heterogeneity in MPT models is proposed based on the machine learning method MOB for model-based recursive partitioning. This procedure recursively partitions the covariate space, leading to an MPT tree with subgroups that are directly interpretable in terms of effects and interactions of the covariates. The pros and cons of MPT trees as a means of analyzing the effects of covariates in MPT model parameters are discussed based on simulation experiments as well as on two empirical applications from memory research. Software that implements MPT trees is provided via the mpttree function in the psychotree package in R.

Individual differences in use of the recognition heuristic are stable across time, choice objects, domains, and presentation formats.

The recognition heuristic (RH) is a simple decision strategy that performs surprisingly well in many domains. According to the RH, people decide on the basis of recognition alone and ignore further knowledge when faced with a recognized and an unrecognized choice object. Previous research has revealed noteworthy individual differences in RH use, suggesting that people have preferences for using versus avoiding this strategy that might be causally linked to cognitive or personality traits. However, trying to explain differences in RH use in terms of traits presupposes temporal and cross-situational stability in use of the RH, an important prerequisite that has not been scrutinized so far. In a series of four experiments, we therefore assessed the stability in RH use across (1) time, (2) choice objects, (3) domains, and (4) presentation formats of the choice objects. In Experiment 1, participants worked on the same inference task and choice objects twice, separated by a delay of either one day or one week. Experiment 2 replicated Experiment 1 using two different object sets from the same domain, whereas Experiment 3 assessed the stability of RH use across two different domains. Finally, in Experiment 4 we investigated stability across verbal and pictorial presentation formats of the choice objects. For all measures of RH use proposed so far, we found strong evidence for both temporal and cross-situational stability in use of the RH. Thus, RH use at least partly reflects a person-specific style of decision making whose determinants await further research.

Random Effects Multinomial Processing Tree Models: A Maximum Likelihood Approach.

The present article proposes and evaluates marginal maximum likelihood (ML) estimation methods for hierarchical multinomial processing tree (MPT) models with random and fixed effects. We assume that an identifiable MPT model with S parameters holds for each participant. Of these S parameters, R parameters are assumed to vary randomly between participants, and the remaining [Formula: see text] parameters are assumed to be fixed. We also propose an extended version of the model that includes effects of covariates on MPT model parameters. Because the likelihood functions of both versions of the model are too complex to be tractable, we propose three numerical methods to approximate the integrals that occur in the likelihood function, namely, the Laplace approximation (LA), adaptive Gauss-Hermite quadrature (AGHQ), and Quasi Monte Carlo (QMC) integration. We compare these three methods in a simulation study and show that AGHQ performs well in terms of both bias and coverage rate. QMC also performs well but the number of responses per participant must be sufficiently large. In contrast, LA fails quite often due to undefined standard errors. We also suggest ML-based methods to test the goodness of fit and to compare models taking model complexity into account. The article closes with an illustrative empirical application and an outlook on possible extensions and future applications of the proposed ML approach.

Testing Interactions in Multinomial Processing Tree Models.

Multinomial processing tree (MPT) models allow testing hypotheses on latent psychological processes that underlie human behavior. However, past applications of this model class have mainly been restricted to the analysis of main effects. In this paper, we adopt the interaction concept as defined in log-linear models and show why it is appropriate for MPT models. We then explain how to implement and test ordinal and disordinal two-way interaction hypotheses in MPT models. We also show how our method generalizes to higher-order interactions involving three or more factors. An empirical example from source memory and aging demonstrates the applicability of this method and allows for directly testing the associative deficit theory that age differences are larger in associative (e.g., source) memory as opposed to item memory. Throughout the paper, we explain how most analytic steps can be easily implemented in the freely available software multiTree.

Adaptive memory: Source memory is positively associated with adaptive social decision making.

The key insight behind the adaptive memory framework is that the primary function of remembering is not to help us to relive the past but to inform adaptive behavior in the future. However, the beneficial effects of memory on the individual's fitness are often difficult to study empirically. In the case of social cooperation, it is comparatively easy to derive testable predictions about the relationship between specific types of memory (e.g., source memory) and specific types of adaptive decision making (e.g., direct reciprocity). In the present study, we examined both the participants' behaviors in a Prisoner's Dilemma game and their memory performance in a source-monitoring test. Participants showed evidence of adaptive decision making. Their willingness to cooperate was strongly determined by their partners' behaviors in previous rounds. Individual parameter estimates of old-new recognition, source memory, and guessing were obtained via hierarchical multinomial processing tree modeling. Source memory was positively associated with adaptive decisions in the Prisoner's Dilemma game. The better participants' source memory, the more often they cooperated with cooperators and the less often they cooperated with cheaters. Guessing in the memory test, by contrast, was unrelated to cooperation. The results underline the importance of source memory in adaptive decision making.

Predicting Behavior With Implicit Measures: Disillusioning Findings, Reasonable Explanations, and Sophisticated Solutions.

Two decades ago, the introduction of the Implicit Association Test (IAT) sparked enthusiastic reactions. With implicit measures like the IAT, researchers hoped to finally be able to bridge the gap between self-reported attitudes on one hand and behavior on the other. Twenty years of research and several meta-analyses later, however, we have to conclude that neither the IAT nor its derivatives have fulfilled these expectations. Their predictive value for behavioral criteria is weak and their incremental validity over and above self-report measures is negligible. In our review, we present an overview of explanations for these unsatisfactory findings and delineate promising ways forward. Over the years, several reasons for the IAT's weak predictive validity have been proposed. They point to four potentially problematic features: First, the IAT is by no means a pure measure of individual differences in associations but suffers from extraneous influences like recoding. Hence, the predictive validity of IAT-scores should not be confused with the predictive validity of associations. Second, with the IAT, we usually aim to measure evaluation ("liking") instead of motivation ("wanting"). Yet, behavior might be determined much more often by the latter than the former. Third, the IAT focuses on measuring associations instead of propositional beliefs and thus taps into a construct that might be too unspecific to account for behavior. Finally, studies on predictive validity are often characterized by a mismatch between predictor and criterion (e.g., while behavior is highly context-specific, the IAT usually takes into account neither the situation nor the domain). Recent research, however, also revealed advances addressing each of these problems, namely (1) procedural and analytical advances to control for recoding in the IAT, (2) measurement procedures to assess implicit wanting, (3) measurement procedures to assess implicit beliefs, and (4) approaches to increase the fit between implicit measures and behavioral criteria (e.g., by incorporating contextual information). Implicit measures like the IAT hold an enormous potential. In order to allow them to fulfill this potential, however, we have to refine our understanding of these measures, and we should incorporate recent conceptual and methodological advancements. This review provides specific recommendations on how to do so.

Assumptions behind scoring source versus item memory: Effects of age, hippocampal lesions and mild memory problems.

Source monitoring paradigms have been used to separate: 1) the probability of recognising an item (Item memory) and 2) the probability of remembering the context in which that item was previously encountered (Source memory), conditional on it being recognised. Multinomial Processing Tree (MPT) models are an effective way to estimate these conditional probabilities. Moreover, MPTs make explicit the assumptions behind different ways to parameterise Item and Source memory. Using data from six independent groups across two different paradigms, we show that one would draw different conclusions about the effects of age, age-related memory problems and hippocampal lesions on Item and Source memory, depending on the use of: 1) standard accuracy calculation vs MPT analysis, and 2) two different MPT models. The MPT results were more consistent than standard accuracy calculations, and furnished additional parameters that can be interpreted in terms of, for example, false recollection or missed encoding. Moreover, a new MPT structure that allowed for separate memory representations (one for item information and one for item-plus-source information; the Source-Item model) fit the data better, and provided a different pattern of significant differences in parameters, than the more conventional MPT structure in which source information is a subset of item information (the Item-Source model). Nonetheless, there is no theory-neutral way of scoring data, and thus proper examination of the assumptions underlying the scoring of source monitoring paradigms is necessary before theoretical conclusions can be drawn.

Using a multinomial tree model for detecting mixtures in perceptual detection.

In the area of memory research there have been two rival approaches for memory measurement-signal detection theory (SDT) and multinomial processing trees (MPT). Both approaches provide measures for the quality of the memory representation, and both approaches provide for corrections for response bias. In recent years there has been a strong case advanced for the MPT approach because of the finding of stochastic mixtures on both target-present and target-absent tests. In this paper a case is made that perceptual detection, like memory recognition, involves a mixture of processes that are readily represented as a MPT model. The Chechile (2004) 6P memory measurement model is modified in order to apply to the case of perceptual detection. This new MPT model is called the Perceptual Detection (PD) model. The properties of the PD model are developed, and the model is applied to some existing data of a radiologist examining CT scans. The PD model brings out novel features that were absent from a standard SDT analysis. Also the topic of optimal parameter estimation on an individual-observer basis is explored with Monte Carlo simulations. These simulations reveal that the mean of the Bayesian posterior distribution is a more accurate estimator than the corresponding maximum likelihood estimator (MLE). Monte Carlo simulations also indicate that model estimates based on only the data from an individual observer can be improved upon (in the sense of being more accurate) by an adjustment that takes into account the parameter estimate based on the data pooled across all the observers. The adjustment of the estimate for an individual is discussed as an analogous statistical effect to the improvement over the individual MLE demonstrated by the James-Stein shrinkage estimator in the case of the multiple-group normal model.