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.

Managing Interruptions to Improve Diagnostic Decision-Making: Strategies and Recommended Research Agenda.

Interruptions are an inevitable occurrence in health care. Interruptions in diagnostic decision-making are no exception and can have negative consequences on both the decision-making process and well-being of the decision-maker. This may result in inaccurate or delayed diagnoses. To date, research specific to interruptions on diagnostic decision-making has been limited, but strategies to help manage the negative impacts of interruptions need to be developed and implemented. In this perspective, we first present a modified model of interruptions to visualize the interruption process and illustrate where potential interventions can be implemented. We then consider several empirically tested strategies from the fields of health care and cognitive psychology that can lay the groundwork for additional research to mitigate effects of interruptions during diagnostic decision-making. We highlight strategies to minimize the negative impacts of interruptions as well as strategies to prevent interruptions altogether. Additionally, we build upon these strategies to propose specific research priorities within the field of diagnostic safety. Identifying effective interventions to help clinicians better manage interruptions has the potential to minimize diagnostic errors and improve patient outcomes.

MEM-EX: An exemplar memory model of decisions from experience.

Many real-world decisions must be made on basis of experienced outcomes. However, there is little consensus about the mechanisms by which people make these decisions from experience (DfE). Across five experiments, we identified several factors influencing DfE. We also introduce a novel computational modeling framework, the memory for exemplars model (MEM-EX), which posits that decision makers rely on memory for previously experienced outcomes to make choices. Using MEM-EX, we demonstrate how cognitive mechanisms provide intuitive and parsimonious explanations for the effects of value-ignorance, salience, outcome order, and sample size. We also conduct a cross-validation analysis of several models within the MEM-EX framework. We compare these to three alternative models; two baseline models built on the principle of expected value maximization, and another employing a suite of choice methods previously shown to perform well in prediction tournaments. We find that MEM-EX consistently outperforms these competitors, demonstrating its value as a tool for making quantitative predictions without overfitting. We discuss the implications of these findings for our understanding of the interplay between attention, memory, and experience-based choice.

Observing effects in various contexts won't give us general psychological theories.

Generalization does not come from repeatedly observing phenomena in numerous settings, but from theories explaining what is general in those phenomena. Expecting future behavior to look like past observations is especially problematic in psychology, where behaviors change when people's knowledge changes. Psychology should thus focus on theories of people's capacity to create and apply new representations of their environments.

Metastudies for robust tests of theory.

We describe and demonstrate an empirical strategy useful for discovering and replicating empirical effects in psychological science. The method involves the design of a metastudy, in which many independent experimental variables-that may be moderators of an empirical effect-are indiscriminately randomized. Radical randomization yields rich datasets that can be used to test the robustness of an empirical claim to some of the vagaries and idiosyncrasies of experimental protocols and enhances the generalizability of these claims. The strategy is made feasible by advances in hierarchical Bayesian modeling that allow for the pooling of information across unlike experiments and designs and is proposed here as a gold standard for replication research and exploratory research. The practical feasibility of the strategy is demonstrated with a replication of a study on subliminal priming.

How to Change the Weight of Rare Events in Decisions From Experience.

When people make risky choices, two kinds of information are crucial: outcome values and outcome probabilities. Here, we demonstrate that the juncture at which value and probability information is provided has a fundamental effect on choice. Across four experiments involving 489 participants, we compared two decision-making scenarios: one in which value information was revealed during sampling (standard) and one in which value information was revealed after sampling (value ignorance). On average, participants made riskier choices when value information was provided after sampling. Moreover, parameter estimates from a hierarchical Bayesian implementation of cumulative-prospect theory suggested that participants overweighted rare events when value information was absent during sampling but did not overweight such events in the standard condition. This suggests that the impact of rare events on choice relies crucially on the timing of probability and value integration. We provide paths toward mechanistic explanations of our results based on frameworks that assume different underlying cognitive architectures.

How well do women understand and remember information in labour versus in late pregnancy? A pilot randomised study.

Medical informed consent is the process by which a 'competent', non-coerced individual receives sufficient information including risks of a medical procedure and gives permission for it to occur. The capacity to give an informed consent might be impaired during labour. This study aimed to examine women's abilities to understand and remember during labour. Women were prospectively recruited at 36 weeks of gestation and randomised to undertake questionnaires which assessed their ability to understand and remember information. They were randomised to: (1) information given in labour only, written format (2) information in labour, verbal (3) information at 36 weeks plus labour, written (4) information at 36 weeks plus labour, verbal. Immediate comprehension and retention was assessed at 36 weeks, in labour, and 24-72 hours after birth. Forty-nine women completed the questionnaires regarding understanding and retention of information at 36 weeks, six intrapartum, and five postpartum (90% attrition). Women receiving information at 36 weeks and in labour versus in labour had a higher comprehension of pregnancy-related information, its retention, and total score. Women receiving information in late pregnancy and labour may comprehend and retain it better than women only receiving information during labour. Given small sample size, further research is needed to support these preliminary findings. Impact statement What is already known on this subject? The evidence regarding the capacity of labouring women to give informed consent is largely based on women's self-reported experiences or expert opinions and has mixed findings. Existing guidelines recommend that an informed consent should be given antenatally for both clinical practice and research. Studies show that obtaining an informed consent antenatally is neither feasible nor widely implemented. What do the results of this study add? A novel approach to providing empirical evidence regarding women's capacity to comprehend and retain information during labour. Our study confirms the difficulty with antenatal recruitment for intrapartum research. What are the implications of these findings for clinical practice and/further research? This raises ethical concerns regarding the current intrapartum research in which consent is largely sought at the time of the study. Emphasises the need to explore the question 'Do labouring women have the capacity to consent to research?' in order to ensure that women are protected during labour.

The elimination of positive priming with increasing prime duration reflects a transition from perceptual fluency to disfluency rather than bias against primed words.

With immediate repetition priming of forced choice perceptual identification, short prime durations produce positive priming (i.e., priming the target leads to higher accuracy, while priming the foil leads to lower accuracy). Many theories explain positive priming following short duration primes as reflecting increased perceptual fluency for the primed target (i.e., decreased identification latency). However, most studies only examine either accuracy or response times, rather than considering the joint constraints of response times and accuracy to properly address the role of decision biases and response caution. This is a critical oversight because several theories propose that the transition to negative priming following a long duration prime reflects a decision strategy to compensate for the effect of increased perceptual fluency. In contrast, the nROUSE model of Huber and O'Reilly (2003) explains this transition as reflecting perceptual habituation, and thus a change to perceptual disfluency. We confirmed this prediction by applying a sequential sampling model (the diffusion race model) to accuracy and response time distributions from a new single item same-different version of the priming task. In this way, we measured strategic biases and perceptual fluency in each condition for each subject. The nROUSE model was only applied to accuracy from the original forced-choice version of the priming task. This application of nROUSE produced separate predictions for each subject regarding the degree of fluency and disfluency in each condition, and these predictions were confirmed by the drift rate parameters (i.e., fluency) from the response time model in contrast to the threshold parameters (i.e., bias).

Unconscious information changes decision accuracy but not confidence.

The controversial idea that information can be processed and evaluated unconsciously to change behavior has had a particularly impactful history. Here, we extend a simple model of conscious decision-making to explain both conscious and unconscious accumulation of decisional evidence. Using a novel dichoptic suppression paradigm to titrate conscious and unconscious evidence, we show that unconscious information can be accumulated over time and integrated with conscious elements presented either before or after to boost or diminish decision accuracy. The unconscious information could only be used when some conscious decision-relevant information was also present. These data are fit well by a simple diffusion model in which the rate and variability of evidence accumulation is reduced but not eliminated by the removal of conscious awareness. Surprisingly, the unconscious boost in accuracy was not accompanied by corresponding increases in confidence, suggesting that we have poor metacognition for unconscious decisional evidence.

Measuring Intuition: Nonconscious Emotional Information Boosts Decision Accuracy and Confidence.

The long-held popular notion of intuition has garnered much attention both academically and popularly. Although most people agree that there is such a phenomenon as intuition, involving emotionally charged, rapid, unconscious processes, little compelling evidence supports this notion. Here, we introduce a technique in which subliminal emotional information is presented to subjects while they make fully conscious sensory decisions. Our behavioral and physiological data, along with evidence-accumulator models, show that nonconscious emotional information can boost accuracy and confidence in a concurrent emotion-free decision task, while also speeding up response times. Moreover, these effects were contingent on the specific predictive arrangement of the nonconscious emotional valence and motion direction in the decisional stimulus. A model that simultaneously accumulates evidence from both physiological skin conductance and conscious decisional information provides an accurate description of the data. These findings support the notion that nonconscious emotions can bias concurrent nonemotional behavior-a process of intuition.

Response-time evidence for mixed memory states in a sequential-presentation change-detection task.

Response-time (RT) and choice-probability data were obtained in a rapid visual sequential-presentation change-detection task in which memory set size, study-test lag, and objective change probabilities were manipulated. False "change" judgments increased dramatically with increasing lag, consistent with the idea that study items with long lags were ejected from a discrete-slots buffer. Error RTs were nearly invariant with set size and lag, consistent with the idea that the errors were produced by a stimulus-independent guessing process. The patterns of error and RT data could not be explained in terms of encoding limitations, but were consistent with the hypothesis that long retention lags produced a zero-stimulus-information state that required guessing. Formal modeling of the change-detection RT and error data pointed toward a hybrid model of visual working memory. The hybrid model assumed mixed states involving a combination of memory and guessing, but with higher memory resolution for items with shorter retention lags. The work raises new questions concerning the nature of the memory representations that are produced across the closely related tasks of change detection and visual memory search.

Resources masquerading as slots: Flexible allocation of visual working memory.

Whether the capacity of visual working memory is better characterized by an item-based or a resource-based account continues to be keenly debated. Here, we propose that visual working memory is a flexible resource that is sometimes deployed in a slot-like manner. We develop a computational model that can either encode all items in a memory set, or encode only a subset of those items. A fixed-capacity mnemonic resource is divided among the items in memory. When fewer items are encoded, they are each remembered with higher fidelity, but at the cost of having to rely on an explicit guessing process when probed about an item that is not in memory. We use the new model to test the prediction that participants will more often encode the entire set of items when the demands on memory are predictable.

Location-based errors in change detection: A challenge for the slots model of visual working memory.

The slots model of visual working memory, despite its simplicity, has provided an excellent account of data across a number of change detection experiments. In the current research, we provide a new test of the slots model by investigating its ability to account for the increased prevalence of errors when there is a potential for confusion about the location in which items are presented during study. We assume that such location errors in the slots model occur when the feature information for an item in one location is swapped with the feature information for an item in another location. We show that such a model predicts two factors that will influence the extent to which location errors occur: (1) whether the test item changes to an "external" item not presented at study, or to an "internal" item presented at another location during study, and (2) the number of items in the study array. We manipulate these factors in an experiment, and show that the slots model with location errors fails to provide a satisfactory account of the observed data.

A quantum of truth? Querying the alternative benchmark for human cognition.

We focus on two issues: (1) an unusual, counterintuitive prediction that quantum probability (QP) theory appears to make regarding multiple sequential judgments, and (2) the extent to which QP is an appropriate and comprehensive benchmark for assessing judgment. These issues highlight how QP theory can fall prey to the same problems of arbitrariness that Pothos & Busemeyer (P&B) discuss as plaguing other models.

Toward nonprobabilistic explanations of learning and decision-making.

Referring to probabilistic concepts (such as randomness, sampling, and probability distributions among others) is commonplace in contemporary explanations of how people learn and make decisions in the face of environmental unknowns. Here, we critically evaluate this practice and argue that such concepts should only play a relatively minor part in psychological explanations. To make this point, we provide a theoretical analysis of what people need to do in order to deal with unknown aspects of a typical decision-making task (a repeated-choice gamble). This analysis reveals that the use of probabilistic concepts in psychological explanations may and often does conceal essential, nonprobabilistic steps that people need to take to attempt to solve the problems that environmental unknowns present. To give these steps a central role, we recast how people solve these problems as a type of hypothesis generation and evaluation, of which using probabilistic concepts to deal with unknowns is one of many possibilities. We also demonstrate some immediate practical consequences of our proposed approach in two experiments. This perspective implies a shift in focus toward nonprobabilistic aspects of psychological explanations. (PsycInfo Database Record (c) 2023 APA, all rights reserved).

Is all mental effort equal? The role of cognitive demand-type on effort avoidance.

Humans are often termed "cognitive misers" for their aversion to mental effort. Both in and outside the laboratory people often show preferences for low-effort tasks and are willing to forgo financial reward to avoid more demanding alternatives. Mental effort, however, does not seem to be ubiquitously avoided: people play crosswords, board games, and read novels, all as forms of leisure. While such activities undoubtedly require effort, the type of cognitive demands they impose appear markedly different from the tasks typically used in psychological research on mental effort (e.g., N-Back, Stroop Task, vigilance tasks). We investigate the effect disparate demands, such as tasks which require problem solving (e.g., solve the missing number: 1, 3, 7, 15, 31,?) compared to those which require rule-implementation (e.g., N-Back task), have on people's aversion to or preference for increased mental effort. Across four experiments using three different tasks, and a mixture of online and lab-based settings, we find that aversion to effort remains largely stable regardless of the types of cognitive demands a task imposes. The results are discussed in terms of other factors that might induce the pursuit of mental effort over and above the type of cognitive demands imposed by a task.

The mazing race: Effects of interruptions and benefits of interruption lags in a novel maze-like decision-making paradigm.

Interruptions are an inevitable, and often negative, part of everyday life that increase both errors and the time needed to complete even menial tasks. However, existing research suggests that being given time to prepare for a pending interruption-a lag time-can mitigate some of the interruption costs. To understand better why interruption lags are effective, we present a series of three experiments in which we develop and test a novel sequential decision-making paradigm, the mazing race. We find that interruption lags were only beneficial when participants had a clear strategy for how to complete the task, allowing them to avoid specific errors. In the final experiment, we attempted to use what we learned about the kinds of errors introduced by interruptions to develop a feedback-based intervention, aimed at dealing with situations in which interruption lags are not possible. We found that feedback was, only in certain situations, an effective replacement for an interruption lag. Overall, however, because the usefulness of interruption lags depend on the specific strategy a participant adopts, developing generic interventions to replace interruption lags is likely to be difficult. (PsycInfo Database Record (c) 2023 APA, all rights reserved).

A power-law model of psychological memory strength in short- and long-term recognition.

A classic law of cognition is that forgetting curves are closely approximated by power functions. This law describes relations between different empirical dependent variables and the retention interval, and the precise form of the functional relation depends on the scale used to measure each variable. In the research reported here, we conducted a recognition task involving both short- and long-term probes. We discovered that formal memory-strength parameters from an exemplar-recognition model closely followed a power function of the lag between studied items and a test probe. The model accounted for rich sets of response time (RT) data at both individual-subject and individual-lag levels. Because memory strengths were derived from model fits to choices and RTs from individual trials, the psychological power law was independent of the scale used to summarize the forgetting functions. Alternative models that assumed different functional relations or posited a separate fixed-strength working memory store fared considerably worse than the power-law model did in predicting the data.

Arrested Theory Development: The Misguided Distinction Between Exploratory and Confirmatory Research.

Science progresses by finding and correcting problems in theories. Good theories are those that help facilitate this process by being hard to vary: They explain what they are supposed to explain, they are consistent with other good theories, and they are not easily adaptable to explain anything. Here we argue that, rather than a lack of distinction between exploratory and confirmatory research, an abundance of flexible theories is a better explanation for the current replicability problems of psychology. We also explain why popular methods-oriented solutions fail to address the real problem of flexibility. Instead, we propose that a greater emphasis on theory criticism by argument might improve replicability.

Consensus-based guidance for conducting and reporting multi-analyst studies.

Any large dataset can be analyzed in a number of ways, and it is possible that the use of different analysis strategies will lead to different results and conclusions. One way to assess whether the results obtained depend on the analysis strategy chosen is to employ multiple analysts and leave each of them free to follow their own approach. Here, we present consensus-based guidance for conducting and reporting such multi-analyst studies, and we discuss how broader adoption of the multi-analyst approach has the potential to strengthen the robustness of results and conclusions obtained from analyses of datasets in basic and applied research.