Retrieving effectively from source memory: Evidence for differentiation and local matching processes.

The ability to distinguish between different explanations of human memory abilities continues to be the subject of many ongoing theoretical debates. These debates attempt to account for a growing corpus of empirical phenomena in item-memory judgments, which include the list strength effect, the strength-based mirror effect, and output interference. One of the main theoretical contenders is the Retrieving Effectively from Memory (REM) model. We show that REM, in its current form, has difficulties in accounting for source-memory judgments - a situation that calls for its revision. We propose an extended REM model that assumes a local-matching process for source judgments alongside source differentiation. We report a first evaluation of this model's predictions using three experiments in which we manipulated the relative source-memory strength of different lists of items. Analogous to item-memory judgments, we observed a null list strength effect and a strength-based mirror effect in the case of source memory. In a second evaluation, which relied on a novel experiment alongside two previously published datasets, we evaluated the model's predictions regarding the manifestation of output interference in item and lack of it in source memory judgments. Our results showed output interference severely affecting the accuracy of item-memory judgments but having a null or negligible impact when it comes to source-memory judgments. Altogether, these results support REM's core notion of differentiation (for both item and source information) as well as the concept of local matching proposed by the present extension.

Against naïve induction from experimental data.

This commentary argues against the indictment of current experimental practices such as piecemeal testing, and the proposed integrated experiment design (IED) approach, which we see as yet another attempt at automating scientific thinking. We identify a number of undesirable features of IED that lead us to believe that its broad application will hinder scientific progress.

Modeling choice paradoxes under risk: From prospect theories to sampling-based accounts.

Important developments in the study of decision making have been based on the establishment and testing of choice paradoxes (e.g., Allais') that reject different theories (e.g., Expected Utility Theory). One of the most popular and celebrated models in the literature, Cumulative Prospect Theory (CPT), has managed to retain its status despite a growing body of empirical evidence stemming from a collection of choice paradoxes that reject it. Two alternative models, Transfer of Attention Exchange (TAX) and an extension of Decision Field Theory (DFTe), have been proposed as possible alternatives to CPT. To date, no study has directly compared these three models within the context of a large set of lottery problems that tests different choice paradoxes. The present study accomplishes this by using a large and diverse set of lottery problems, involving both potential gains and losses. Our results support the presence and robustness of a set of 'strong' choice paradoxes that reject CPT irrespective of its parametric form. Model comparison results show that DFTe provides the best account for the present set of lottery problems, as it is able to accommodate the choice data at large in a parsimonious fashion. The success of DFTe shows that many behavioral phenomena, including paradoxes that CPT cannot account for, can be successfully captured by a simple noisy-sampling process. Overall, our results suggest that researchers should move away from CPT, and focus their efforts on alternative models such as DFTe.

Mechanisms of output interference in cued recall.

The primary aim of this paper is to elucidate the mechanisms governing output interference in cued recall. Output interference describes the phenomenon where accuracy decrease over the course of an episodic memory test. Output inference in cued recall takes the form of a decrease in correct and intrusion responses and an increase in failures to response across the test. This pattern can only be accounted for by a model with two complementary mechanisms: learning during retrieval and a response filter that prevents repeated recall of the same item. We investigate how a retrieval filter might operate by manipulating the similarity of words. The data are consistent with a retrieval filter that does not operate by a global match of a potential target to previously recalled items. Results are discussed within the search of associative memory theory.

When the Good Looks Bad: An Experimental Exploration of the Repulsion Effect.

When people are choosing among different options, context seems to play a vital role. For instance, adding a third option can increase the probability of choosing a similar dominating option. This attraction effect is one of the most widely studied phenomena in decision-making research. Its prevalence, however, has been challenged recently by the tainting hypothesis, according to which the inferior option contaminates the attribute space in which it is located, leading to a repulsion effect. In an attempt to test the tainting hypothesis and explore the conditions under which dominated options make dominating options look bad, we conducted four preregistered perceptual decision-making studies with a total of 301 participants. We identified two factors influencing individuals' behavior: stimulus display and stimulus design. Our results contribute to a growing body of literature showing how presentation format influences behavior in preferential and perceptual decision-making tasks.

Analyzing distributional properties of interference effects across modalities: chances and challenges.

In research investigating Stroop or Simon effects, data are typically analyzed at the level of mean response time (RT), with results showing faster responses for compatible than for incompatible trials. However, this analysis provides only limited information as it glosses over the shape of the RT distributions and how they may differ across tasks and experimental conditions. These limitations have encouraged the analysis of RT distributions using delta plots. In the present review, we aim to bring together research on distributional properties of auditory and visual interference effects. Extending previous reviews on distributional properties of the Simon effect, we additionally review studies reporting distributional analyses of Stroop effects. We show that distributional analyses of sequential effects (i.e., taking into account congruency of the previous trial) capture important similarities and differences of interference effects across tasks (Simon, Stroop) as well as across sensory modalities, despite some challenges associated to this approach.

Lifespan trajectories of risk preference, impulsivity, and self-control: A dataset containing self-report, informant-report, behavioral, hormone and functional neuroimaging measures from a cross-sectional human sample.

This paper describes data collected from a cross-sectional convenience sample of 200 healthy human volunteers between 16 and 81 years of age. We assembled an extensive battery of measures of risk preference, impulsivity, and self-control, as well as a range of demographic and cognitive measures, Crucially, we adopted different measure categories, including self-reports, informant reports, behavioral measures, and biological measures (hormones, brain function) to capture individual differences, and adopted a within-participant design. Data collection took place over multiple sessions. First, participants completed a laboratory session at the university during which we collected computer-assisted self-report measures (i.e., standardized questionnaires) as well as behavioral measures using computerized tasks. Second, participants independently completed a home session that included the completion of self-report measures, and the collection of saliva samples. In parallel, we acquired informant reports from up to three individuals nominated by the study participants. Third, participants completed a final session at the local hospital during which we collected structural and functional neuroimaging data, as well as further self-report measures. The data was collected to address questions concerning the developmental trajectories of risk preference and related constructs while assessing the impact of the assessment method; however, we invite fellow researchers to benefit from and further explore the data for research on decision-making under risk and uncertainty in general, and to apply novel analytical approaches (e.g., machine-learning applications to the neuroimaging data). Combining a large set of measures with a within-participant design affords a wealth of opportunities for further insights and a more robust evidence base supporting current theorizing on (age-related) differences in risk preference, impulsivity, and self-control.

Absolute and relative stability of loss aversion across contexts.

Individuals' decisions under risk tend to be in line with the notion that "losses loom larger than gains." This loss aversion in decision making is commonly understood as a stable individual preference that is manifested across different contexts. The presumed stability and generality, which underlies the prominence of loss aversion in the literature at large, has been recently questioned by studies reporting how loss aversion can disappear, and even reverse, as a function of the choice context. The present study investigated whether loss aversion reflects a trait-like attitude of avoiding losses or rather individuals' adaptability to different contexts. We report three experiments investigating the within-subject context sensitivity of loss aversion in a two-alternative forced-choice task. Our results show that the choice context can shift people's loss aversion, though somewhat inconsistently. Moreover, individual estimates of loss aversion are shown to have a considerable degree of stability. Altogether, these results indicate that even though the absolute value of loss aversion can be affected by external factors such as the choice context, estimates of people's loss aversion still capture the relative dispositions toward gains and losses across individuals. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Convergence of Age Differences in Risk Preference, Impulsivity, and Self-Control: A Multiverse Analysis.

OBJECTIVES: Numerous theories exist regarding age differences in risk preference and related constructs, yet many of them offer conflicting predictions and fail to consider convergence between measurement modalities or constructs. To pave the way for conceptual clarification and theoretical refinement, in this preregistered study we aimed to comprehensively examine age effects on risk preference, impulsivity, and self-control using different measurement modalities, and to assess their convergence. METHODS: We collected a large battery of self-report, informant report, behavioral, hormone, and neuroimaging measures from a cross-sectional sample of 148 (55% female) healthy human participants between 16 and 81 years (mean age = 46 years, standard deviation [SD] = 19). We used an extended sample of 182 participants (54% female, mean age = 46 years, SD = 19) for robustness checks concerning the results from self-reports, informant reports, and behavioral measures. For our main analysis, we performed specification curve analyses to visualize and estimate the convergence between the different modalities and constructs. RESULTS: Our multiverse analysis approach revealed convergent results for risk preference, impulsivity, and self-control from self- and informant reports, suggesting a negative effect of age. For behavioral, hormonal, and neuroimaging outcomes, age effects were mostly absent. DISCUSSION: Our findings call for conceptual clarification and improved operationalization to capture the putative mechanisms underlying age-related differences in risk preference and related constructs.

Better Accuracy for Better Science . . . Through Random Conclusions.

Conducting research with human subjects can be difficult because of limited sample sizes and small empirical effects. We demonstrate that this problem can yield patterns of results that are practically indistinguishable from flipping a coin to determine the direction of treatment effects. We use this idea of random conclusions to establish a baseline for interpreting effect-size estimates, in turn producing more stringent thresholds for hypothesis testing and for statistical-power calculations. An examination of recent meta-analyses in psychology, neuroscience, and medicine confirms that, even if all considered effects are real, results involving small effects are indeed indistinguishable from random conclusions.

Evaluating the robustness of parameter estimates in cognitive models: A meta-analytic review of multinomial processing tree models across the multiverse of estimation methods.

Researchers have become increasingly aware that data-analysis decisions affect results. Here, we examine this issue systematically for multinomial processing tree (MPT) models, a popular class of cognitive models for categorical data. Specifically, we examine the robustness of MPT model parameter estimates that arise from two important decisions: the level of data aggregation (complete-pooling, no-pooling, or partial-pooling) and the statistical framework (frequentist or Bayesian). These decisions span a multiverse of estimation methods. We synthesized the data from 13,956 participants (164 published data sets) with a meta-analytic strategy and analyzed the magnitude of divergence between estimation methods for the parameters of nine popular MPT models in psychology (e.g., process-dissociation, source monitoring). We further examined moderators as potential sources of divergence. We found that the absolute divergence between estimation methods was small on average (<.04; with MPT parameters ranging between 0 and 1); in some cases, however, divergence amounted to nearly the maximum possible range (.97). Divergence was partly explained by few moderators (e.g., the specific MPT model parameter, uncertainty in parameter estimation), but not by other plausible candidate moderators (e.g., parameter trade-offs, parameter correlations) or their interactions. Partial-pooling methods showed the smallest divergence within and across levels of pooling and thus seem to be an appropriate default method. Using MPT models as an example, we show how transparency and robustness can be increased in the field of cognitive modeling. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Validating a two-high-threshold measurement model for confidence rating data in recognition.

Signal Detection models as well as the Two-High-Threshold model (2HTM) have been used successfully as measurement models in recognition tasks to disentangle memory performance and response biases. A popular method in recognition memory is to elicit confidence judgements about the presumed old/new status of an item, allowing for the easy construction of ROCs. Since the 2HTM assumes fewer latent memory states than response options are available in confidence ratings, the 2HTM has to be extended by a mapping function which models individual rating scale usage. Unpublished data from 2 experiments in Bröder and Schütz (2009) validate the core memory parameters of the model, and 3 new experiments show that the response mapping parameters are selectively affected by manipulations intended to affect rating scale use, and this is independent of overall old/new bias. Comparisons with SDT show that both models behave similarly, a case that highlights the notion that both modelling approaches can be valuable (and complementary) elements in a researcher's toolbox.

MPTinR: analysis of multinomial processing tree models in R.

We introduce MPTinR, a software package developed for the analysis of multinomial processing tree (MPT) models. MPT models represent a prominent class of cognitive measurement models for categorical data with applications in a wide variety of fields. MPTinR is the first software for the analysis of MPT models in the statistical programming language R, providing a modeling framework that is more flexible than standalone software packages. MPTinR also introduces important features such as (1) the ability to calculate the Fisher information approximation measure of model complexity for MPT models, (2) the ability to fit models for categorical data outside the MPT model class, such as signal detection models, (3) a function for model selection across a set of nested and nonnested candidate models (using several model selection indices), and (4) multicore fitting. MPTinR is available from the Comprehensive R Archive Network at http://cran.r-project.org/web/packages/MPTinR/ .

Metabolic Profiles Point Out Metabolic Pathways Pivotal in Two Glioblastoma (GBM) Cell Lines, U251 and U-87MG.

Glioblastoma (GBM) is the most lethal central nervous system (CNS) tumor, mainly due to its high heterogeneity, invasiveness, and proliferation rate. These tumors remain a therapeutic challenge, and there are still some gaps in the GBM biology literature. Despite the significant amount of knowledge produced by research on cancer metabolism, its implementation in cancer treatment has been limited. In this study, we explored transcriptomics data from the TCGA database to provide new insights for future definition of metabolism-related patterns useful for clinical applications. Moreover, we investigated the impact of key metabolites (glucose, lactate, glutamine, and glutamate) in the gene expression and metabolic profile of two GBM cell lines, U251 and U-87MG, together with the impact of these organic compounds on malignancy cell features. GBM cell lines were able to adapt to the exposure to each tested organic compound. Both cell lines fulfilled glycolysis in the presence of glucose and were able to produce and consume lactate. Glutamine dependency was also highlighted, and glutamine and glutamate availability favored biosynthesis observed by the increase in the expression of genes involved in fatty acid (FA) synthesis. These findings are relevant and point out metabolic pathways to be targeted in GBM and also reinforce that patients' metabolic profiling can be useful in terms of personalized medicine.

Toward a more comprehensive modeling of sequential lineups.

Sequential lineups are one of the most commonly used procedures in police departments across the USA. Although this procedure has been the target of much experimental research, there has been comparatively little work formally modeling it, especially the sequential nature of the judgments that it elicits. There are also important gaps in our understanding of how informative different types of judgments can be (binary responses vs. confidence ratings), and the severity of the inferential risks incurred when relying on different aggregate data structures. Couched in a signal detection theory (SDT) framework, the present work directly addresses these issues through a reanalysis of previously published data alongside model simulations. Model comparison results show that SDT modeling can provide elegant characterizations of extant data, despite some discrepancies across studies, which we attempt to address. Additional analyses compare the merits of sequential lineups (with and without a stopping rule) relative to showups and delineate the conditions in which distinct modeling approaches can be informative. Finally, we identify critical issues with the removal of the stopping rule from sequential lineups as an approach to capture within-subject differences and sidestep the risk of aggregation biases.

The repulsion effect in preferential choice and its relation to perceptual choice.

People rely on the choice context to guide their decisions, violating fundamental principles of rational choice theory and exhibiting phenomena called context effects. Recent research has uncovered that dominance relationships can both increase or decrease the choice share of the dominating option, marking the two ends of an attraction-repulsion continuum. However, empirical links between the two opposing effects are scarce and theoretical accounts are missing altogether. The present study (N = 55) used eye tracking alongside a within-subject design that contrasts a perceptual task and a preferential-choice analog in order to bridge this gap and uncover the underlying information-search processes. Although individuals differed in their perceptual and preferential choices, they generally engaged in alternative-wise comparisons and a repulsion effect was present in both conditions that became weaker the more predominant the attribute-wise comparisons were. Altogether, our study corroborates the notion that repulsion effects are a robust and general phenomenon that theoretical accounts need to take seriously.

Bias effects in a two-stage recognition paradigm: A challenge for "pure" threshold and signal detection models.

We explored a two-stage recognition memory paradigm in which people first make single-item "studied"/"not studied" decisions and then have a chance to correct their errors in forced-choice trials. Each forced-choice trial included one studied word ("target") and one nonstudied word ("lure") that received the same previous single-item response. For example, a studied-studied trial would have a target that was correctly called "studied" and a lure that was incorrectly called "studied." The two-high-threshold (2HT) model and the unequal-variance signal detection (UVSD) model predict opposite effects of biasing the initial single-item responses on subsequent forced-choice accuracy. Results from two experiments showed that the bias effect is actually near zero and well out of the range of effects predicted by either model. Follow-up analyses suggested that the model failures were not a function of experiment artifacts like changing memory states between the two types of recognition trials. Follow-up analyses also showed that the dual process signal detection model made better predictions for the forced-choice data than 2HT and UVSD models. (PsycInfo Database Record (c) 2022 APA, all rights reserved).

A conceptual model of alcohol use and adherence to antiretroviral therapy: systematic review and theoretical implications for mechanisms of action.

Alcohol consumption is one of the most prevalent correlates of antiretroviral therapy (ART) adherence, yet causal processes underlying this association remain largely unexplored. The goal of this systematic review was to develop a conceptual model that describes the causal effect of alcohol consumption on ART nonadherence. We reviewed 230 studies that examined the association between alcohol consumption and ART adherence with three primary aims: (1) to replicate and extend previous reviews of the literature, (2) to summarize and critique study designs capable of answering questions about temporal overlap and (3) to summarize potential mechanisms of action. A model of alcohol-associated ART nonadherence was proposed to guide future work, integrating general theories of ART adherence and theory on the psychological and behavioral effects of alcohol intoxication. The conceptual model describes two mechanistic processes-prospective memory impairment and interactive toxicity beliefs/avoidance behaviors-involved in alcohol-associated intentional and unintentional nonadherence, respectively. This model can be used to guide future research on the causal processes involved in the frequently observed correlation between alcohol consumption and adherence.

Developmental Regulation of Transcription in Touriga Nacional Berries under Deficit Irrigation.

Grapevine (Vitis vinifera L.) is one of the most economically important crops worldwide, especially due to the economic relevance of wine production. Abiotic stress, such as drought, may contribute to low yield, shifts in quality, and important economic loss. The predicted climate change phenomena point to warmer and dryer Mediterranean environmental conditions; as such, it is paramount to study the effects of abiotic stress on grapevine performance. Deficit irrigation systems are applied to optimize water use efficiency without compromising berry quality. In this research, the effect of two deficit irrigation strategies, sustained deficit irrigation (SDI) and regulated deficit irrigation (RDI), in the grape berry were assessed. The effects of different levels of drought were monitored in Touriga Nacional at key stages of berry development (pea size, véraison, and full maturation) through RNA-Seq transcriptome analysis and by specific differentially expressed genes (DEGs) monitoring through RT-qPCR. Handy datasets were obtained by bioinformatics analysis of raw RNA-Seq results. The dominant proportion of transcripts was mostly regulated by development, with véraison showing more upregulated transcripts. Results showed that primary metabolism is the functional category more severely affected under water stress. Almost all DEGs selected for RT-qPCR were significantly upregulated in full maturation and showed the highest variability at véraison and the lowest gene expression values in the pea size stage.

Normative accounts of illusory correlations.

When learning about the joint occurrence of different variables, individuals often manifest biases in the associations they infer. In some cases, they infer an association when none is present in the observed sample. In other cases, they infer an association that is contrary to the one that is in fact observed. These illusory correlations are often interpreted as being the byproduct of selective processing or as the outcome of an "illogical" pseudocontingency heuristic. More recently, a normative account of illusory correlations has been proposed, according to which they can be given a normative underpinning in terms of an application of Laplace's Rule of Succession. The present work will discuss the empirical and theoretical limitations associated with this normative account, and argue for its dismissal. As an alternative, we propose a normative account that casts illusory correlations as the expected outcome of a Bayesian reasoner relying on marginal frequencies. We show that this account succeeds in capturing the qualitative patterns found in a corpus of published studies. (PsycInfo Database Record (c) 2021 APA, all rights reserved).