The effects of mnemonic variability and spacing on memory over multiple timescales.

The memory benefit that arises from distributing learning over time rather than in consecutive sessions is one of the most robust effects in cognitive psychology. While prior work has mainly focused on repeated exposures to the same information, in the real world, mnemonic content is dynamic, with some pieces of information staying stable while others vary. Thus, open questions remain about the efficacy of the spacing effect in the face of variability in the mnemonic content. Here, in two experiments, we investigated the contributions of mnemonic variability and the timescale of spacing intervals, ranging from seconds to days, to long-term memory. For item memory, both mnemonic variability and spacing intervals were beneficial for memory; however, mnemonic variability was greater at shorter spacing intervals. In contrast, for associative memory, repetition rather than mnemonic variability was beneficial for memory, and spacing benefits only emerged in the absence of mnemonic variability. These results highlight a critical role for mnemonic variability and the timescale of spacing intervals in the spacing effect, bringing this classic memory paradigm into more ecologically valid contexts.

Causal learning with delays up to 21 hours.

Considerable delays between causes and effects are commonly found in real life. However, previous studies have only investigated how well people can learn probabilistic relations with delays on the order of seconds. In the current study we tested whether people can learn a cause-effect relation with delays of 0, 3, 9, or 21 hours, and the study lasted 16 days. We found that learning was slowed with longer delays, but by the end of 16 days participants had learned the cause-effect relation in all four conditions, and they had learned the relation about equally well in all four conditions. This suggests that in real-world situations people may still be fairly accurate at inferring cause-effect relations with delays if they have enough experience. We also discuss ways that delays may interact with other real-world factors that could complicate learning.

Cognitive perspectives on maintaining physicians' medical expertise: V. Using a motivational framework to understand the benefits and costs of testing.

We apply a motivational perspective to understand the implications of physicians' longitudinal assessment. We review the literature on situated expectancy-value theory, achievement goals, mindsets, anxiety, and stereotype threat in relation to testing and assessment. This review suggests several motivational benefits of testing as well as some potential challenges and costs posed by high-stakes, standardized tests. Many of the motivational benefits for testing can be understood from the equation of having the perceived benefits of the test outweigh the perceived costs of preparing for and taking the assessment. Attention to instructional framing, test purposes and values, and longitudinal assessment frameworks provide vehicles to further enhance motivational benefits and reduce potential costs of assessment.

Cognitive perspectives on maintaining physicians' medical expertise: III. Strengths and weaknesses of self-assessment.

Is self-assessment enough to keep physicians' cognitive skills-such as diagnosis, treatment, basic biological knowledge, and communicative skills-current? We review the cognitive strengths and weaknesses of self-assessment in the context of maintaining medical expertise. Cognitive science supports the importance of accurately self-assessing one's own skills and abilities, and we review several ways such accuracy can be quantified. However, our review also indicates a broad challenge in self-assessment is that individuals do not have direct access to the strength or quality of their knowledge and instead must infer this from heuristic strategies. These heuristics are reasonably accurate in many circumstances, but they also suffer from systematic biases. For example, information that feels easy to process in the moment can lead individuals to overconfidence in their ability to remember it in the future. Another notable phenomenon is the Dunning-Kruger effect: the poorest performers in a domain are also the least accurate in self-assessment. Further, explicit instruction is not always sufficient to remove these biases. We discuss what these findings imply about when physicians' self-assessment can be useful and when it may be valuable to supplement with outside sources.

Cognitive perspectives on maintaining physicians' medical expertise: IV. Best practices and open questions in using testing to enhance learning and retention.

Although tests and assessments-such as those used to maintain a physician's Board certification-are often viewed merely as tools for decision-making about one's performance level, strong evidence now indicates that the experience of being tested is a powerful learning experience in its own right: The act of retrieving targeted information from memory strengthens the ability to use it again in the future, known as the testing effect. We review meta-analytic evidence for the learning benefits of testing, including in the domain of medicine, and discuss theoretical accounts of its mechanism(s). We also review key moderators-including the timing, frequency, order, and format of testing and the content of feedback-and what they indicate about how to most effectively use testing for learning. We also identify open questions for the optimal use of testing, such as the timing of feedback and the sequencing of complex knowledge domains. Lastly, we consider how to facilitate adoption of this powerful study strategy by physicians and other learners.

Cognitive perspectives on maintaining physicians' medical expertise: I. Reimagining Maintenance of Certification to promote lifelong learning.

Until recently, physicians in the USA who were board-certified in a specialty needed to take a summative test every 6-10 years. However, the 24 Member Boards of the American Board of Medical Specialties are in the process of switching toward much more frequent assessments, which we refer to as longitudinal assessment. The goal of longitudinal assessments is to provide formative feedback to physicians to help them learn content they do not know as well as serve an evaluation for board certification. We present five articles collectively covering the science behind this change, the likely outcomes, and some open questions. This initial article introduces the context behind this change. This article also discusses various forms of lifelong learning opportunities that can help physicians stay current, including longitudinal assessment, and the pros and cons of each.

Cognitive perspectives on maintaining physicians' medical expertise: II. Acquiring, maintaining, and updating cognitive skills.

Over the course of training, physicians develop significant knowledge and expertise. We review dual-process theory, the dominant theory in explaining medical decision making: physicians use both heuristics from accumulated experience (System 1) and logical deduction (System 2). We then discuss how the accumulation of System 1 clinical experience can have both positive effects (e.g., quick and accurate pattern recognition) and negative ones (e.g., gaps and biases in knowledge from physicians' idiosyncratic clinical experience). These idiosyncrasies, biases, and knowledge gaps indicate a need for individuals to engage in appropriate training and study to keep these cognitive skills current lest they decline over time. Indeed, we review converging evidence that physicians further out from training tend to perform worse on tests of medical knowledge and provide poorer patient care. This may reflect a variety of factors, such as specialization of a physician's practice, but is likely to stem at least in part from cognitive factors. Acquired knowledge or skills gained may not always be readily accessible to physicians for a number of reasons, including an absence of study, cognitive changes with age, and the presence of other similar knowledge or skills that compete in what is brought to mind. Lastly, we discuss the cognitive challenges of keeping up with standards of care that continuously evolve over time.

Pseudoscientific Health Beliefs and the Perceived Frequency of Causal Relationships.

Beliefs about cause and effect, including health beliefs, are thought to be related to the frequency of the target outcome (e.g., health recovery) occurring when the putative cause is present and when it is absent (treatment administered vs. no treatment); this is known as contingency learning. However, it is unclear whether unvalidated health beliefs, where there is no evidence of cause-effect contingency, are also influenced by the subjective perception of a meaningful contingency between events. In a survey, respondents were asked to judge a range of health beliefs and estimate the probability of the target outcome occurring with and without the putative cause present. Overall, we found evidence that causal beliefs are related to perceived cause-effect contingency. Interestingly, beliefs that were not predicted by perceived contingency were meaningfully related to scores on the paranormal belief scale. These findings suggest heterogeneity in pseudoscientific health beliefs and the need to tailor intervention strategies according to underlying causes.

Motivated Reasoning in an Explore-Exploit Task.

The current research investigates how prior preferences affect causal learning. Participants were tasked with repeatedly choosing policies (e.g., increase vs. decrease border security funding) in order to maximize the economic output of an imaginary country and inferred the influence of the policies on the economy. The task was challenging and ambiguous, allowing participants to interpret the relations between the policies and the economy in multiple ways. In three studies, we found evidence of motivated reasoning despite financial incentives for accuracy. For example, participants who believed that border security funding should be increased were more likely to conclude that increasing border security funding actually caused a better economy in the task. In Study 2, we hypothesized that having neutral preferences (e.g., preferring neither increased nor decreased spending on border security) would lead to more accurate assessments overall, compared to having a strong initial preference; however, we did not find evidence for such an effect. In Study 3, we tested whether providing participants with possible functional forms of the policies (e.g., the policy takes some time to work or initially has a negative influence but eventually a positive influence) would lead to a smaller influence of motivated reasoning but found little evidence for this effect. This research advances the field of causal learning by studying the role of prior preferences, and in doing so, integrates the fields of causal learning and motivated reasoning using a novel explore-exploit task.

The Accuracy of Causal Learning Over Long Timeframes: An Ecological Momentary Experiment Approach.

The ability to learn cause-effect relations from experience is critical for humans to behave adaptively - to choose causes that bring about desired effects. However, traditional experiments on experience-based learning involve events that are artificially compressed in time so that all learning occurs over the course of minutes. These paradigms therefore exclusively rely upon working memory. In contrast, in real-world situations we need to be able to learn cause-effect relations over days and weeks, which necessitates long-term memory. 413 participants completed a smartphone study, which compared learning a cause-effect relation one trial per day for 24 days versus the traditional paradigm of 24 trials back- to- back. Surprisingly, we found few differences between the short versus long timeframes. Subjects were able to accurately detect generative and preventive causal relations, and they exhibited illusory correlations in both the short and long timeframe tasks. These results provide initial evidence that experience-based learning over long timeframes exhibits similar strengths and weaknesses as in short timeframes. However, learning over long timeframes may become more impaired with more complex tasks.

Expectancy and Utilisation of Reflexology among Women with Advanced Breast Cancer.

OBJECTIVE: Little is understood about patient expectations and use of complementary therapies (CT) during cancer treatment. A secondary analysis of an 11-week reflexology trial among women with breast cancer was conducted. We examined factors that predicted women's expectations about reflexology for symptom relief, factors that predicted utilisation of reflexology, and whether by the end of the trial they believed that reflexology had helped with symptom management. METHODS: Women (N = 256) were interviewed at baseline and week 11. Friend or family caregivers in the reflexology group were trained to deliver standardised sessions to patients at least once a week for 4 weeks. Baseline and week-11 reflexology expectations were analysed using general linear models. Reflexology utilisation was analysed with generalised linear mixed effects models. RESULTS: Patients who expected benefits from reflexology ("higher expectancy") at baseline were younger, had lower anxiety, higher education, higher spirituality, and greater CT use. Worsening symptoms over time were associated with greater utilisation of reflexology, but only when baseline expectancy was low. At week 11, expectancy was higher for those with greater symptom improvement. CONCLUSIONS: Assessing patterns of patient factors, expectancy, and change in symptoms can help determine who is likely to use reflexology, and when.

Distinguishing causation and correlation: Causal learning from time-series graphs with trends.

Time-series graphs are ubiquitous in scientific and popular communications and in mobile health tracking apps. We studied if people can accurately judge whether there is a relation between the two variables in a time-series graph, which is especially challenging if the variables exhibit temporal trends. We found that, for the most part, participants were able to discriminate positive vs. negative relations even when there were strong temporal trends; however, when there is a positive causal relation but opposing temporal trends (one variable increases and the other decreases over time), people have difficulty inferring the positive causal relation. Further, we found that a simple dynamic presentation can ameliorate this challenge. The present finding sheds light on when people can and cannot accurately learn causal relations from time-series data and how to present graphs to aid interpretability.

Causal strength induction from time series data.

One challenge when inferring the strength of cause-effect relations from time series data is that the cause and/or effect can exhibit temporal trends. If temporal trends are not accounted for, a learner could infer that a causal relation exists when it does not, or even infer that there is a positive causal relation when the relation is negative, or vice versa. We propose that learners use a simple heuristic to control for temporal trends-that they focus not on the states of the cause and effect at a given instant, but on how the cause and effect change from one observation to the next, which we call transitions. Six experiments were conducted to understand how people infer causal strength from time series data. We found that participants indeed use transitions in addition to states, which helps them to reach more accurate causal judgments (Experiments 1A and 1B). Participants use transitions more when the stimuli are presented in a naturalistic visual format than a numerical format (Experiment 2), and the effect of transitions is not driven by primacy or recency effects (Experiment 3). Finally, we found that participants primarily use the direction in which variables change rather than the magnitude of the change for estimating causal strength (Experiments 4 and 5). Collectively, these studies provide evidence that people often use a simple yet effective heuristic for inferring causal strength from time series data. (PsycINFO Database Record

Do people reason rationally about causally related events? Markov violations, weak inferences, and failures of explaining away.

Making judgments by relying on beliefs about the causal relationships between events is a fundamental capacity of everyday cognition. In the last decade, Causal Bayesian Networks have been proposed as a framework for modeling causal reasoning. Two experiments were conducted to provide comprehensive data sets with which to evaluate a variety of different types of judgments in comparison to the standard Bayesian networks calculations. Participants were introduced to a fictional system of three events and observed a set of learning trials that instantiated the multivariate distribution relating the three variables. We tested inferences on chains X1→Y→X2, common cause structures X1←Y→X2, and common effect structures X1→Y←X2, on binary and numerical variables, and with high and intermediate causal strengths. We tested transitive inferences, inferences when one variable is irrelevant because it is blocked by an intervening variable (Markov Assumption), inferences from two variables to a middle variable, and inferences about the presence of one cause when the alternative cause was known to have occurred (the normative "explaining away" pattern). Compared to the normative account, in general, when the judgments should change, they change in the normative direction. However, we also discuss a few persistent violations of the standard normative model. In addition, we evaluate the relative success of 12 theoretical explanations for these deviations.

Searching for the best cause: Roles of mechanism beliefs, autocorrelation, and exploitation.

When testing which of multiple causes (e.g., medicines) works best, the testing sequence has important implications for the validity of the final judgment. Trying each cause for a period of time before switching to the other is important if the causes have tolerance, sensitization, delay, or carryover (TSDC) effects. In contrast, if the outcome variable is autocorrelated and gradually fluctuates over time rather than being random across time, it can be useful to quickly alternate between the 2 causes, otherwise the causes could be confounded with a secular trend in the outcome. Five experiments tested whether individuals modify their causal testing strategies based on beliefs about TSDC effects and autocorrelation in the outcome. Participants adaptively tested each cause for longer periods of time before switching when testing causal interventions for which TSDC effects were plausible relative to cases when TSDC effects were not plausible. When the autocorrelation in the baseline trend was manipulated, participants exhibited only a small (if any) tendency toward increasing the amount of alternation; however, they adapted to the autocorrelation by focusing on changes in outcomes rather than raw outcome scores, both when making choices about which cause to test as well as when making the final judgment of which cause worked best. Understanding how people test causal relations in diverse environments is an important first step for being able to predict when individuals will successfully choose effective causes in real-world settings. (PsycINFO Database Record

Do capuchin monkeys (Cebus apella) diagnose causal relations in the absence of a direct reward?

We adapted a method from developmental psychology to explore whether capuchin monkeys (Cebus apella) would place objects on a "blicket detector" machine to diagnose causal relations in the absence of a direct reward. Across five experiments, monkeys could place different objects on the machine and obtain evidence about the objects' causal properties based on whether each object "activated" the machine. In Experiments 1-3, monkeys received both audiovisual cues and a food reward whenever the machine activated. In these experiments, monkeys spontaneously placed objects on the machine and succeeded at discriminating various patterns of statistical evidence. In Experiments 4 and 5, we modified the procedure so that in the learning trials, monkeys received the audiovisual cues when the machine activated, but did not receive a food reward. In these experiments, monkeys failed to test novel objects in the absence of an immediate food reward, even when doing so could provide critical information about how to obtain a reward in future test trials in which the food reward delivery device was reattached. The present studies suggest that the gap between human and animal causal cognition may be in part a gap of motivation. Specifically, we propose that monkey causal learning is motivated by the desire to obtain a direct reward, and that unlike humans, monkeys do not engage in learning for learning's sake.

Children use temporal cues to learn causal directionality.

The ability to learn the direction of causal relations is critical for understanding and acting in the world. We investigated how children learn causal directionality in situations in which the states of variables are temporally dependent (i.e., autocorrelated). In Experiment 1, children learned about causal direction by comparing the states of one variable before versus after an intervention on another variable. In Experiment 2, children reliably inferred causal directionality merely from observing how two variables change over time; they interpreted Y changing without a change in X as evidence that Y does not influence X. Both of these strategies make sense if one believes the variables to be temporally dependent. We discuss the implications of these results for interpreting previous findings. More broadly, given that many real-world environments are characterized by temporal dependency, these results suggest strategies that children may use to learn the causal structure of their environments.

Causal systems categories: differences in novice and expert categorization of causal phenomena.

We investigated the understanding of causal systems categories--categories defined by common causal structure rather than by common domain content--among college students. We asked students who were either novices or experts in the physical sciences to sort descriptions of real-world phenomena that varied in their causal structure (e.g., negative feedback vs. causal chain) and in their content domain (e.g., economics vs. biology). Our hypothesis was that there would be a shift from domain-based sorting to causal sorting with increasing expertise in the relevant domains. This prediction was borne out: the novice groups sorted primarily by domain and the expert group sorted by causal category. These results suggest that science training facilitates insight about causal structures.

Causal structure learning over time: observations and interventions.

Seven studies examined how people learn causal relationships in scenarios when the variables are temporally dependent - the states of variables are stable over time. When people intervene on X, and Y subsequently changes state compared to before the intervention, people infer that X influences Y. This strategy allows people to learn causal structures quickly and reliably when variables are temporally stable (Experiments 1 and 2). People use this strategy even when the cover story suggests that the trials are independent (Experiment 3). When observing variables over time, people believe that when a cause changes state, its effects likely change state, but an effect may change state due to an exogenous influence in which case its observed cause may not change state at the same time. People used this strategy to learn the direction of causal relations and a wide variety of causal structures (Experiments 4-6). Finally, considering exogenous influences responsible for the observed changes facilitates learning causal directionality (Experiment 7). Temporal reasoning may be the norm rather than the exception for causal learning and may reflect the way most events are experienced naturalistically.

What matters in scientific explanations: effects of elaboration and content.

Given the breadth and depth of available information, determining which components of an explanation are most important is a crucial process for simplifying learning. Three experiments tested whether people believe that components of an explanation with more elaboration are more important. In Experiment 1, participants read separate and unstructured components that comprised explanations of real-world scientific phenomena, rated the components on their importance for understanding the explanations, and drew graphs depicting which components elaborated on which other components. Participants gave higher importance scores for components that they judged to be elaborated upon by other components. Experiment 2 demonstrated that experimentally increasing the amount of elaboration of a component increased the perceived importance of the elaborated component. Furthermore, Experiment 3 demonstrated that elaboration increases the importance of the elaborated information by providing insight into understanding the elaborated information; information that was too technical to provide insight into the elaborated component did not increase the importance of the elaborated component. While learning an explanation, people piece together the structure of elaboration relationships between components and use the insight provided by elaboration to identify important components.