Factors affecting information-seeking behaviour can be task-endogenous (e.g., probability of winning a gamble), or task-exogenous (e.g., personality trait measures). Various task-endogenous factors affecting non-instrumental information-seeking behaviour have been identified, but it is unclear how task-exogenous factors affect such behaviour, and if they interact with task-endogenous factors. In an online information seeking experiment ($N = 279$), we focus on the role that outcome probability, as a task-endogenous factor, has on information preferences. We find reliable preference for advance information on highly probable gains and low preference for highly probable losses. Comparisons with individual trait measures of information preference (e.g., intolerance of uncertainty scale, obsessive-compulsive inventory, information preferences scale) reveal minimal association between these task-exogenous factors with choice task performance. We also find minimal interaction between outcome probability and individual trait measures. Despite the choice task and trait measures purportedly tapping the same (or similar) construct, the absence of clear relationships ultimately suggests a multi-dimensional nature of information preference.

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.

We connect Conviction Narrative Theory to an account that views people as intuitive scientists who can flexibly create, evaluate, and modify representations of decision problems. We argue that without understanding how the relevant complex narratives (or indeed any representation, simple to complex) are themselves constructed, we also cannot know when and why people would rely on them to make choices.

We argue that: 1) disappointment in the effectiveness of i-frame interventions depends on realistic expectations about how they could work; 2) opportunities for system reform are rare, and i-frame interventions can lay important groundwork; 3) C&L’s evidence that i-frame interventions detract from s-frame approaches is limited; 4) nonetheless, behavioural scientists should consider what more they can contribute to systemic reforms.

The climate crisis cannot be addressed without substantial societal change. In their review article in this issue of One Earth, van Valkengoed et al. critically evaluate the long-standing claim that psychological distance presents a major barrier to achieving that change. Here, I discuss this important critique against the backdrop of the broader challenge facing the application of psychological science to policy.

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.

Standard theories suggest that humans should seek information only when it can help them make better decisions. However, recent work suggests that people choose to seek information even when it cannot influence the outcome of a choice. Across three experiments, we examined how this preference for non-instrumental information was related to the risk, regret, and rejoice associated with different choices. Experiment 1 examined how risk preference informed the appetite for non-instrumental information and tested how risk and information preference in a gamble-task related to the desire for knowledge across a range of hypothetical real-world scenarios. In Experiment 2, we tested how risk, operationalised as variance, related to non-instrumental information seeking when allowing participants to mentally simulate the potential outcomes of gambles. In Experiment 3, we provided explicit feedback about forgone options, intending to make the potential for regret or rejoice more salient. Taken together, our results show a consistent appetite for information that was robust to changes across all experimental manipulations. We found some evidence of a positive correlation between the desire for knowledge and the level of anticipated regret (Experiment 1), but overall our data appear more consistent with the idea that non-instrumental information seeking is driven by a general aversion to uncertainty than by an attempt to regulate specific future emotions.

Australian regulations strictly limit early withdrawals from retirement plan accounts. However, in 2020, the Government made otherwise illiquid plan balances temporarily liquid, offering emergency relief during the pandemic. The COVID-19 Early Release Scheme allowed participants in financial hardship easy access to up to $A20,000 of savings over two rounds. We use administrative and survey data from a large retirement plan to describe how and why participants withdrew savings under the scheme. A majority report that they needed the money immediately but around one quarter said they anticipated future needs. Most thought about the decision for less than a week, acted soon after each round opened, and withdrew as much as they could. Many people did not estimate, or appear to have mis-estimated, the impact the withdrawal could have on their retirement savings. Our findings offer insights into preferences for liquidity. They also raise questions about whether the features of the early release scheme, and their implied endorsement by the Government, influenced some withdrawers more than personal deliberations over financial welfare.

Objectives. Antibiotic prescribing in primary care contributes significantly to antibiotic overuse. Nudge interventions alter the decision-making environment to achieve behaviour change without restricting options. Our objectives were to conduct a systematic review to describe the types of nudge interventions used to reduce unnecessary antibiotic prescribing in primary care, their key features, and their effects on antibiotic prescribing overall. Methods. Medline, Embase and grey literature were searched for randomised trials or regression discontinuity studies in April 2021. Risk of bias was assessed independently by two researchers using the Cochrane Effective Practice and Organisation of Care group’s tool. Results were synthesised to report the percentage of studies demonstrating a reduction in overall antibiotic prescribing for different types of nudges. Effects of social norm nudges were examined for features that may enhance effectiveness. Results. Nineteen studies were included, testing 23 nudge interventions. Four studies were rated as having a high risk of bias, nine as moderate risk of bias and six as at low risk. Overall, 78.3% (n=18, 95% CI 58.1 to 90.3) of the nudges evaluated resulted in a reduction in overall antibiotic prescribing. Social norm feedback was the most frequently applied nudge (n=17), with 76.5% (n=13; 95% CI 52.7 to 90.4) of these studies reporting a reduction. Other nudges applied were changing option consequences (n=3; with 2 reporting a reduction), providing reminders (n=2; 2 reporting a reduction) and facilitating commitment (n=1; reporting a reduction). Successful social norm nudges typically either included an injunctive norm, compared prescribing to physicians with the lowest prescribers or targeted high prescribers. Conclusions. Nudge interventions are effective for improving antibiotic prescribing in primary care. Expanding the use of nudge interventions beyond social norm nudges could reap further improvements in antibiotic prescribing practices. Policy-makers and managers need to be mindful of how social norm nudges are implemented to enhance intervention effects.

Proposed psychological mechanisms generating noninstrumental information seeking in humans can be broadly categorized into two competing accounts: the maximization of anticipating rewards versus an aversion to uncertainty. We compare three separate formalizations of these theories on their ability to track the dependency of information-seeking behavior on increasing levels of cue-outcome delay as well as their sensitivity to outcome valence. Across three experiments using a variety of different stimuli, we observe a flat to monotonically increasing pattern of delay dependency and minimal evidence of sensitivity to outcome valence––patterns which are better predicted, qualitatively and quantitatively, by an uncertainty aversion information model.

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.

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 interruptions lags depend on the specific strategy a participant adopts, developing generic interventions to replace interruption lags is likely to be difficult.

In response to the COVID-19 pandemic, many governments around the world introduced policies aiming to provide citizens with financial relief through early access to their retirement savings. In Australia, the Early Release of Super (ERS) scheme allowed eligible citizens to withdraw up to A$20,000 in funds between April and December 2020. Using data provided by a large Australian bank, we examine the characteristics of the individuals who withdrew, how they used the withdrawn funds, and what impact this had on their financial wellbeing. We find that the scheme achieved its intended goal of providing immediate financial support to citizens in need. The scheme was primarily accessed by individuals in poorer financial circumstances, and helped withdrawers to pay down high-interest debts and avoid arrears. Based on our findings, we consider the implications for governments looking to implement similar policies in the future, as well as the opportunities to support individuals who have now withdrawn from their retirement savings.

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.

In three experiments, we sought to understand when and why people use an algorithm decision aid. Distinct from recent approaches, we explicitly enumerate the algorithm's accuracy while also providing summary feedback and training that allowed participants to assess their own skills. Our results highlight that such direct performance comparisons between the algorithm and the individual encourages a strategy of selective reliance on the decision aid; individuals ignored the algorithm when the task was easier and relied on the algorithm when the task was harder. Our systematic investigation of summary feedback, training experience and strategy-hint manipulations show that further opportunities to learn about the algorithm encourages not only increased reliance on the algorithm but also engagement in experimentation and verification of its recommendations. Together, our findings emphasise the decision-maker's capacity to learn about the algorithm providing insights for how we can improve the use of decision aids.

The effect of regulatory standards regarding the presentation of investment products on financial behavior is poorly understood. In two incentivized online experiments (N = 2,221) we examine the impact of information-based and tool-based guidance on the selection of retirement plan investment funds. Participants chose between funds that followed identical investment strategies but charged different fees. Over multiple trials, participants were instructed to identify the fund which charged the lowest fees given their hypothetical plan balance. Defaults and disclosures were found to be situationally helpful, but highlighted participants’ naivete with regard to the calculations underlying the fee structure. Advice tended to be underutilized but was beneficial when sought. Tool-based guidance in the form of a smart calculator had a moderate impact on accuracy but benefits did not persist. Together the results highlight the danger of taking a homogeneous approach to financial guidance and emphasize the need to build consumer competency.

Using data from seven microworld experiments (N = 841), we investigated how participants reacted to simulated disasters with different risk profiles in a microworld. Our central focus was to investigate how the scale of a disaster affected the choices and response times of these reactions. We find that one-off large-scale disasters prompted stronger reactions to move away from the affected region than recurrent small-scale adverse events, despite the overall risk of a disaster remaining constant across both types of events. A subset of participants are persistent risk-takers who repeatedly put themselves in harm’s way, despite having all the experience and information required to avoid a disaster. Furthermore, while near-misses prompted a small degree of precautionary movement to reduce one’s subsequent risk exposure, directly experiencing the costs of the disaster substantially increased the desire to move away from the affected region. Together, the results point to ways in which laboratory risk-taking tasks can be used to inform the kinds of communication and interventions that seek to mitigate people’s exposure to risk.

A complete policy response to Anthropocene challenges needs not only to consider how to make environmental choices easier, but also why action is necessary. We argue that motivation for action should come from a firm moral framework that might help overcome a familiar reaction: “why should I bother changing my behavior, given it will make little difference?”. We highlight the importance of changing individual behavior based on doing one’s fair share and not harming others, the immorality of imposing risk on current and future generations, and the need to target perceived inefficacy of action. Taking a step back to examine these ‘whys’ of action, rather than simply focusing on the ‘hows’ may increase our chances of not only surviving but thriving in the Anthropocene.

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.

Background: We examine how bottom-up (or stimulus-driven), and top-down (or goal-driven) processes govern the distribution of attention in risky choice. In three experiments participants chose between a certain payoff and the chance of receiving a payoff drawn randomly from an array of eight numbers. We tested the hypothesis that initial attention is driven by perceptual properties of the stimulus (e.g., font-size of the numbers), but subsequent choice is goal-driven (e.g., win the best outcome). Two experiments in which task-framing (goal-driven) and font-size (stimulus-driven) were manipulated demonstrated that payoffs with the highest values and the largest font-sizes had the greatest impact on choice. The third experiment added a number in large font to the array which could not be an outcome of the gamble (i.e., a distractor). Eye-movement and choice data indicated that although the distractor attracted attention it had no influence on option-selection. Together with computational-modelling analyses, the results suggest that perceptual salience can induce bottom-up effects of overt selection, but the perceived value of information is the crucial arbiter of intentional control over risky choice.

A persistent metaphor in decision-making research casts people as intuitive statisticians. Popular explanations based on this metaphor assume that the way in which people represent the environment is specified and fixed a priori. A major flaw in this account is that it is not clear how people know what aspects of an environment are important, how to interpret those aspects, and how to make decisions based on them. We suggest a theoretical reorientation from assuming people’s representations, to a focus on explaining how people themselves specify what is important to represent. This perspective casts decision-makers as intuitive scientists able to flexibly construct, modify, and replace the representations of the decision problems they face.

Risky inter-temporal choices involve choosing between options that can differ in outcomes, their probability of receipt, and the delay until receipt. To date, there has been no attempt to systematically test, compare and evaluate theoretical models of such choices. We contribute to theory development by generating predictions from seven models for three common manipulations- magnitude, certainty and immediacy- across six different types of risky intertemporal choices. Qualitative and quantitative comparisons of model predictions to data from an experiment involving almost 4000 individual choices revealed that an attribute comparison model, newly modified to incorporate risky inter-temporal choices, (the Risky Inter-Temporal Choice Heuristic or RITCH) provided the best account of the data. Results are consistent with growing evidence in support of attribute comparison models in the risky and inter-temporal choice literatures, and suggest that the relatively poorer fits of translation-based models reflect their inability to predict the differential impact of certainty and immediacy manipulations. Future theories of risky inter-temporal choice may benefit from treating risk and time as independent dimensions, and focusing on attribute-comparison rather than value-comparison processes.

Standardized information disclosures aim to help people compare complex financial products and make better choices. We investigate the extent to which information shown in a regulator-mandated dashboard helps retirement savers choose between alternative pension plans. We conduct incentivized experiments that collect participants’ repeated choices between two pension plans using the mandatory dashboard, and subsequently test whether an even simpler dashboard improves choices, and by how much. Participants switch quickly from a high- to a low-fee pension plan when they see explicit nominal fees but are significantly more confused by percentage fees and adjust slower. When differences between plan performance arise from gross returns, not fees, we find that complex information formats can seriously hinder participants’ recognition and reactions. We present a Bayesian updating model which estimates the relative noisiness of the signals from fees and gross returns across different treatments and use this model to show how better information presentation raises retirement savings.

When making decisions in complex environments we must selectively sample and process information with respect to task demands. Previous studies have shown that this requirement can manifest in the influence that extreme outcomes (i.e. values at the edges of a distribution) have on judgment and choice. We elucidate this influence via a task in which participants are presented, briefly, with an array of numbers and have to make one of two judgments. In 'preferential' judgments where the participants' goal was to choose between a safe, known outcome, and an unknown outcome drawn from the array, extreme-outcomes had a greater influence on choice than mid-range outcomes, especially under shorter time-limits. In 'perceptual' judgments where the participants' goal was to estimate the arrays' average, the influence of the extremes was less pronounced. A novel cognitive process model captures these patterns via a two-step selective-sampling and integration mechanism. Together our results shed light on how task goals modulate sampling from complex environments, show how sampling determines choice, and highlight the conflicting conclusions that arise from applying statistical and cognitive models to data.

Smith et al. (2014) argued that deferred feedback specifically impairs diagonal structure performance because it disables the implicit/associative system required for information-integration learning, but leaves an explicit rule-based system unhindered. Contrary to this suggestion, we showed that impairment under deferred feedback is not specific to diagonal, information-integration structures, but also occurs for conjunction structures which (on the multiple-systems view) are rule-based. These findings do not follow from Smith et al.’s multiple-systems account but follow naturally from a cognitive-demands account: The cognitive complexity and memory-demands of diagonal and conjunction tasks are greater than for the vertical task, so deferring feedback will impair both two-dimensional tasks, and may drive participants to a less-demanding unidimensional strategy.

We extended a recent model of climate change risk perception (van der Linden, 2015) to predict the risk perception of Australians and their willingness to engage in mitigation behaviours (N = 921). Affect, mitigation response inefficacy, and descriptive norms were the most important predictors of risk perception, highlighting the influence of affective, cognitive, and socio-cultural factors. Affect and mitigation response inefficacy were also important predictors of behavioural willingness, but socio-cultural influences (free-market ideology, prescriptive norms, and biospheric values) played a relatively larger role in explaining the variance of behavioural willingness. Structural equation modelling provided further evidence that risk perception and behavioural willingness are separable constructs, as some factors in the model had direct effects on willingness independent of risk perception. We discuss the need for future research to develop a comprehensive model of behavioural willingness, and the need for public communication to combat mitigation response inefficacy

We investigated previous findings suggesting a paradoxical inconsistency of people’s beliefs and choices: When making decisions under uncertainty, people seem to both overestimate the probability of rare events in their judgments and underweight the probability of the same rare events in their choices. In our reexamination, we found that people’s beliefs are consistent with their decisions, but they do not necessarily correspond with the environment. Both overestimation and underweighting of the rare event seemed to result from (most, but not all) participants’ mistaken belief that they can infer and exploit sequential patterns in a static environment. In addition, we found that such inaccurate representations can be improved through incentives. Finally, detailed analysis suggested a mixture of individual-level response patterns, which can give rise to an erroneous interpretation of group-level patterns. Our results offer an explanation for why beliefs and decisions can appear contradictory and present challenges to some current models of decisions under uncertainty.

The over-precision bias refers to the tendency for individuals to believe that their predictions are much more accurate than they really are. We investigated whether this type of overconfidence is moderated by how task-relevant information is obtained. We contrast cases in which individuals were presented with information about two options with equal average performance – one with low variance the other with high variance – in experience format (i.e., observed individual performance outcomes sequentially) or description format (i.e., presented with a summary of the outcome distribution). Across three experiments, we found that those learning from description tended to be over-precise whereas those learning from experience were under-precise. These differences were driven by a relatively better calibrated representation of the underlying outcome distribution by those presented with experience-based information. We argue that those presented with experience-based information have better learning due to more opportunities for prediction-error.

Research on risky and intertemporal decision-making often focuses on descriptive models of choice. This class of models sometimes lack a psychological process account of how different cognitive processes give rise to choice behavior. Here, we attempt to decompose these processes using sequential accumulator modeling (i.e., the linear ballistic accumulator model). Participants were presented with pairs of gambles that either involve different levels of probability or delay (Experiment 1) or a combination of these dimensions (both probability and delay; Experiment 2). Response times were recorded as a measure of preferential strength. We then combined choice data and response times, and utilized variants of the linear ballistic accumulator to explore different assumptions about how preferences are formed. Specifically, we show that a model that allows for the subjective evaluation of a fixed now/certain option to change as a function of the delayed/risky option with which it is paired provides the best account of the combined choice and response time (RT) data. The work highlights the advantages of using cognitive process models in risky and intertemporal choice, and points toward a common framework for understanding how people evaluate time and probability.

Past research indicates that individuals respond adaptively to contextual factors in multiattribute choice tasks. Yet it remains unclear how this adaptation is cognitively governed. In this article, empirically testable implementations of two prominent competing theoretical frameworks are developed and compared across two multiattribute choice experiments: the adaptive toolbox framework assuming discrete choice strategies and the adjustable spanner framework assuming one comprehensive adaptive strategy. Results from two experiments indicate that in the environments we tested, in which all cue information was presented openly, the toolbox makes better predictions than the adjustable spanner both in- and out-of-sample. Follow-up simulation studies indicate that it is difficult to discriminate the models based on choice outcomes alone but allowed the identification of a small subset of cases where the predictions of both models diverged. Our results suggest that people adapt their decision strategies by flexibly switching between using as little information as possible and use of all of the available information.

Recent experimental evidence in experience based decision-making suggests that people are more risk seeking in the gains domain relative to the losses domain. This critical result is at odds with the standard reflection effect observed in description-based choice and explained by Prospect Theory. The so-called reversed-reflection effect has been predicated on the extreme-outcome rule, which suggests that memory biases affect risky choice from experience. To test the general plausibility of the rule, we conducted two experiments examining how the magnitude of prospective outcomes impacts risk preferences.We found that while the reversed-reflection effect was present with small-magnitude payoffs, using payoffs of larger magnitude brought participants’ behavior back in line with the standard reflection effect. Our results suggest that risk preferences in experience-based decision-making are not only affected by the relative extremeness but also by the absolute extremeness of past events.

Despite overwhelming scientific evidence, many members of the public remain skeptical about anthropogenic global warming. Hence, we examined how the presentation of factual scientific evidence affects lay evaluations of scientific claims. Taking inspiration from cognitive research on inductive reasoning, two studies examined the impact of evidential diversity on acceptance of claims in the domains of climate change and public health. Participants were presented with scientific claims based on competing evidence options and were asked to choose the best and worst form of evidence for each claim. The diversity of the available evidence was manipulated across three dimensions; geographical (evidence from two geographically near or far nations), socio-cultural (evidence from two culturally similar or dissimilar nations), and temporal (evidence drawn from two different periods or the same period). In both studies, diverse evidence on the geographical and socio-cultural dimension increased perceived support for scientific claims, but the relative impact of these dimensions differed between domains; geographical diversity had a larger effect on claims about climate change; socio-cultural diversity had a larger effect on claims about health. On the temporal dimension, recent non-diverse evidence (i.e. from the same recent period) increased perceived support for scientific claims more than diverse evidence. These results may have important implications for the communication of complex scientific evidence to a lay audience.

This study develops a structural dynamic life-cycle model to examine the behavior of members of an industry-wide pension fund to assess both the prevalence of defaults and their impact on retirement savings. We estimate the model using the simulated method of moments on administrative data from a large Australian pension fund. Our results show that default settings strongly influence wealth accumulation. Such settings are also highly persistent, both over time and across decisions. Overall, the findings suggest that if defaults (particularly the irreversible ones) are not carefully designed, retirement savings can be severely affected.

Can judgments be biased via passive monitoring of eye-gaze? We examined this question using a perceptual discrimination task (Experiment 1) and a complex moral judgment task (Experiment 2). Information about the location of participants’ gaze at particular time-points in a trial was used to prompt responses. When there was no objective perceptual information available to decision-makers, the timing of the prompt had a small, but detectable effect on judgments (Experiment 1). However, this small effect did not scale up to more complex judgments about moral issues (Experiment 2). Our results are consistent with the well-established idea that participants’ judgments are reflected in their eye-gaze, but do not support the recent bold claim of a causal link wherein the timing of a gaze-contingent response-prompt influences complex judgments.

There is growing interest in modelling how people make choices which involve both risks and delays, i.e. risky inter-temporal choices. Here we investigate an untested assumption underlying several proposed risky inter-temporal choice models: that pure risky choices and pure inter-temporal choices are special cases of risky inter-temporal choice. We test this assumption by presenting a single group of participants with risky choices and inter-temporal choices. We then compare the performance of a model which is fit to both choice types simultaneously, with the performance of separate models fit to the risky choice and intertemporal choice data. We find, using Bayesian model comparison, that the majority of participants are best fit by a single model which incorporates both risky and inter-temporal choices. This result supports the assumption that risky choices and inter-temporal choices may be special cases of risky inter-temporal choice. Our results also suggest that, under the conditions of our experiment, interpretation of monetary value is very similar in risky choices and inter-temporal choices.

Using behavioural and social science to inform government policy has the potential to benefit society – provided good scientific practice is adopted. We argue that there are threats to scientific practice in the current Behavioural Insights environment that potentially undermine the validity and usefulness of such work. We discuss what constitutes good science and why this is important, and examine threats to scientific practice from the perspective of scientists conducting experiments on policy initiatives. We aim to assist researchers, governments and policy makers identify conditions where the fit between science and government policy is ‘just right’ (i.e., the ‘Goldilocks Zone’). We discuss potential pathways for developing appropriate infrastructure and procedures to achieve this goal. In particular, we suggest that the early engagement of all parties is necessary to ensure projects incorporate sound science and deliver societal benefit.

It is sometimes supposed that category learning involves competing explicit and procedural systems, with only the former reliant on working memory capacity (WMC). In two experiments participants were trained for three blocks on both filtering (often said to be learned explicitly) and condensation (often said to be learned procedurally) category structures. Both experiments (total N = 160) demonstrated that participants with higher WMC tended to be more accurate in condensation tasks, but not less accurate in filtering tasks. Further, state-trace analysis did not find a differential influence of WMC on performance in these tasks. Finally, inspection of the mixture of response strategies at play across the two conditions and three blocks showed only a minor influence of WMC, and then only on later training blocks. The results provide no support for the existence of a ‘system’ of category learning that is independent of working-memory and are instead consistent with most single-system interpretations of category learning.

Learning to choose adaptively when faced with uncertain and variable outcomes is a central challenge for decision makers. This study examines repeated choice in dynamic probability learning tasks in which outcome probabilities changed either as a function of the choices participants made or independently of those choices. This presence/absence of sequential choice–outcome dependencies was implemented by manipulating a single task aspect between conditions: the retention/withdrawal of reward across individual choice trials. The study addresses how people adapt to these learning environments and to what extent they engage in 2 choice strategies often contrasted as paradigmatic examples of striking violation of versus nominal adherence to rational choice: diversification and persistent probability maximizing, respectively. Results show that decisions approached adaptive choice diversification and persistence when sufficient feedback was provided on the dynamic rules of the probabilistic environments. The findings of divergent behavior in the 2 environments indicate that diversified choices represented aresponse to the reward retention manipulation rather than to the mere variability of outcome probabilities. Choice in both environments was well accounted for by the generalized matching law, and computational modeling-based strategy analyses indicated that adaptive choice arose mainly from reliance on reinforcement learning strategies.

Probability matching is a robust and common failure to adhere to normative predictions in sequential decision making. We show that this choice anomaly is nearly eradicated by gathering individual decision makers into small groups and asking the groups to decide. The group choice advantage emerged both when participants generated responses for an entire sequence of choices without outcome feedback (Exp.1a) and when participants made trial-by-trial predictions with outcome feedback after each decision (Exp.1b). We show that the dramatic improvement observed in group settings stands in stark contrast to a complete lack of effective solitary deliberation. These findings suggest a crucial role of group discussion in alleviating the impact of hasty intuitive responses in tasks better suited to careful deliberation.

The recent increase in the frequency and impact of natural disasters highlights the need to provide the public with accurate information concerning disaster prevalence. Most approaches to this problem assume that providing summaries of the nature and scale of disasters will lead people to reduce their exposure to risk. Herewe present experimental evidence that such ex post ‘news reports’ of disaster occurrences can increase the tolerance for risk-taking (which implies that rare events are underweighted). This result is robust across several hundred rounds of choices in a simulated microworld, persists even when the long-run expected value of risky choices is substantially lower than safe choices, and is contingent on providing risk information about disasters that have been (personally) experienced and those that have been avoided (‘forgone’ outcomes). The results suggest that augmenting personal experience with information summaries of the number of adverse events (for example, storms, floods) in different regions may, paradoxically, increase the appeal of a disaster-prone region. This finding implies a need to communicate long-term trends in severe climatic events, thereby reinforcing the accumulation of events, and the increase in their associated risks, across time.

Using a laboratory experiment, we study how retirement plan members choose investmentoptions using five information items prescribed by regulators. We found that asset alloca-tion information for pre-mixed investment options – normally presented as a pie chart ora table – had the largest impact on choices. Participants preferred investment options withmore, and more evenly weighted, asset class allocations. This novel application of a 1/nstrategy differs significantly from the existing findings of naïve diversification in ‘mix-it-yourself’ conditions where participants spread resources evenly across funds or categories.When asset allocation information was included, coefficients on return and risk informa-tion had unexpected signs, but when asset allocation was omitted, participants preferredoptions with high Sharpe ratios. We also demonstrate that none of the five prescribedinformation items was significant in explaining individual choices of more than 35% ofparticipants. These findings highlight that information contained in prescribed investmentdisclosures might not be used in the manner intended by the regulator. The results raiseimportant methodological questions about the way ‘user-friendly’ information prescribedby regulators is validated before being legislated.

Studies examining personal experiences of climate change-related events highlight the potential to encourage climate action by framing it as happening now, in your neighborhood, and affecting people like you e that is, psychologically close. We compare this literature to studies that examine psychological distance. The review reveals a disconnect: while studies of personal experience suggest merits of reducing psychological distance, other studies present a more nuanced picture in which psychological proximity does not always lead to more concern about or action on climate change. Despite its emphasis, psychological distance has not been widely studied in experimental work in the climate change context, and there is a need for more systematic examination of its effects across a range of mitigation and adaptation actions. Further, our review identifies potential pitfalls associated with decreasing psychological distance, such as fear and avoidance. Finally, we provide preliminary recommendations for optimal ways to bring climate change 'home.'

Countless decisions, from the trivial to the crucial, are made in complex social contexts while facing uncertain consequences. Yet a large portion of decision making research focuses on either the effects of social interaction or the effects of environmental uncertainty by examining strategic games against others or individual games against nature. Drawing a connection between these approaches, the authors extend a standard individual choice paradigm to include social interaction with 1 other person. In this paradigm, 2 competing decision makers repeatedly select among 2 options, each offering a particular probability of a fixed payoff. When both players choose the same, correct option, the payoff is evenly split; when they choose different options, the player choosing the correct option receives the full payoff. The addition of this social dimension gives players an opportunity to fully exploit an uncertain environment via cooperation—by consistently choosing opposite options. We present 2 experiments that manipulate environmental (Experiment 1) and social (Experiment 2) aspects of the paradigm. In Experiment 1, the outcome probabilities were either known or unknown to participants; in Experiment 2, participants’ attention was drawn to individual or group gains by introducing either within- or between-groups competition. Efficient cooperation did not emerge spontaneously in Experiment 1. Instead, most people probability maximized, mirroring the behavior observed in individual choice. By contrast, betweengroups competition in Experiment 2 facilitated efficient— but not always equitable— exploitation of uncertain environments. This work links the concepts of individual risky choice and strategic decision making under both environmental and social uncertainty.

Ambiguity is often characterized with unknown probability distributions (for potential outcomes), which people avoid while making decisions. Existing literature shows that ambiguity aversion often persists even when these probability distributions are explicitly described to decision makers. We test the hypothesis that exposure to these probability distributions via sampling experience, rather than description, will lead to a reduction in ambiguity aversion. We used the classic two-colour Ellsberg task in which the participants were asked to choose to bet on either a risky bet (i.e. probabilities were known) or (versions of) an ambiguous bet (i.e. exact probabilities were unknown). Different probability distributions, each providing subtly different information about the underlying properties of the ambiguous bet, were either experienced through sampling or described to participants prior to choice. Overall, the results indicated that people demonstrated ambiguity-neutral attitudes when the underlying probability distributions were experienced. In contrast, when described, attitudes toward ambiguity changed as a function of the type of probability distribution. Additional analyses confirmed that decision makers were less likely to be ambiguity-averse when their individual experiences were positive during sampling trials (i.e. observing distributions with winning being more likely or more frequent).

The strength of conclusions about the adoption of different categorization strategies—and their implications for theories about the cognitive and neural bases of category learning— depend heavily on the techniques for identifying strategy use. We examine performance in an often-used 'informationintegration' category structure and demonstrate that strategy identification is affected markedly by the range of models under consideration, the type of data collected, and model-selection techniques. We use a set of 27 potential models that represent alternative rule-based and information-integration categorization strategies. Our experimental paradigm includes the presentation of nonreinforced transfer stimuli that improve one’s ability to discriminate among the predictions of alternative models. Our modelselection techniques incorporate uncertainty in the identification of individuals as either rule-based or information-integration strategy users. Based on this analysis we identify 48% of participants as unequivocally using an information-integration strategy. However, adopting the standard practice of using a restricted set of models, restricted data, and ignoring the degree of support for a particular strategy, we would typically conclude that 89% of participants used an information-integration strategy. We discuss the implications of potentially erroneous strategy identification for the security of conclusions about the categorization capabilities of various participant and patient groups

Deep cuts in greenhouse gas emissions are required to mitigate climate change. However, there is low willingness amongst the public to prioritise climate policies for reducing emissions. Here we show that the extent to which Australians are prepared to reduce their country’s CO2 emissions is greater when the costs to future national income are framed as a ‘‘foregone-gain’’—incomes rise in the futurebut not by as much as in the absence of emission cuts—rather than as a ‘‘loss’’— incomes decrease relative to the baseline expected future levels (Studies 1 & 2). The provision of a normative message identifying Australia as one of the world’s largest CO2 emitters did not increase the amount by which individuals were prepared to reduce emissions (Study 1), whereas a normative message revealing the emission policy preferences of other Australians did (Study 2). The results suggest that framing the costs of reducing emissions as a smaller increase in future income and communicating normative information about others’ emission policy preferences are effective methods for leveraging public support for emission cuts.

Humanity faces an unprecedented set of global environmental problems. We argue that to promote pro-environmental decisions and to achieve public consensus on the need for action we must address individual and collective understanding (cognition) of environmental problems, as well as individual and collective commitments to take action to mitigate or prevent those problems. We review literature pertaining to psychological predispositions, mental models, framing, psychological distance, and the social context of decisions that help elucidate how these goals of cognition and commitment can be achieved. This article reveals the complex and multiply determined nature of environmental decisions. However, we argue that this complexity points to opportunities to reduce the inherent uncertainty surrounding global environmental challenges via appeals to the psychological mechanisms that underpin our decisions.

To what extent do we know our own minds when making decisions? Variants of this question have preoccupied researchers in a wide range of domains, from mainstream experimental psychology (cognition, perception, social behavior) to cognitive neuroscience and behavioral economics. A pervasive view places a heavy explanatory burden on an intelligent cognitive unconscious, with many theories assigning causally effective roles to unconscious influences. This article presents a novel framework for evaluating these claims and reviews evidence from three major bodies of research in which unconscious factors have been studied: multiple-cue judgment, deliberation without attention, and decisions under uncertainty. Studies of priming (subliminal and primes-to-behavior) and the role of awareness in movement and perception (e.g., timing of willed actions, blindsight) are also given brief consideration. The review highlights that inadequate procedures for assessing awareness, failures to consider artifactual explanations of 'landmark' results, and a tendency to uncritically accept conclusions that fit with our intuitions have all contributed to unconscious influences being ascribed inflated and erroneous explanatory power in theories of decision making. The review concludes by recommending that future research should focus on tasks in which participants’ attention is diverted away from the experimenter’s hypothesis, rather than the highly reflective tasks that are currently often employed.

Uncertainty forms an integral part of climate science, and it is often used to argue against mitigative action. This article presents an analysis of uncertainty in climate sensitivity that is robust to a range of assumptions. We show that increasing uncertainty is necessarily associated with greater expected damages from warming, provided the function relating warming to damages is convex. This constraint is unaffected by subjective or cultural risk-perception factors, it is unlikely to be overcome by the discount rate, and it is independent of the presumed magnitude of climate sensitivity. The analysis also extends to 'second-order' uncertainty; that is, situations in which experts disagree. Greater disagreement among experts increases the likelihood that the risk of exceeding a global temperature threshold is greater. Likewise, increasing uncertainty requires increasingly greater protective measures against sea level rise. This constraint derives directly from the statistical properties of extreme values. We conclude that any appeal to uncertainty compels a stronger, rather than weaker, concern about unabated warming than in the absence of uncertainty.

Abstract In most decision-making situations, there is a plethora of information potentially available to people. Deciding what information to gather and what to ignore is no small feat. How do decision makers determine in what sequence to collect information and when to stop? In two experiments, we administered a version of the German cities task developed by Gigerenzer and Goldstein (1996), in which participants had to decide which of two cities had the larger population. Decision makers were not provided with the names of the cities, but they were able to collect different kinds of cues for both response alternatives (e.g., 'Does this city have a university?') before making a decision. Our experiments differed in whether participants were free to determine the number of cues they examined. We demonstrate that a novel model, using hierarchical latent mixtures and Bayesian inference (Lee & Newell, 2011) provides a more complete description of the data from both experiments than simple conventional strategies, such as the take–the–best or the Weighted Additive heuristics.

The description-experience "gap" refers to the observation that choices are influenced by whether information about potential alternatives is learnt from a summary description or from the experience of sequentially sampling individual outcomes. In this chapter, we traverse the cognitive steps required to make a decision-information acquisition, storage, representation, and then choice-and at each step briefly review the evidence for sources of discrepancy between these two formats of choice. We conclude that description- and experience-based choice formats lie along a continuum of uncertainty and share important core features, including the explicit representation of probability, the combining of this probability information with outcome information, and utility maximization. The implication of this conclusion is that the differences between description- and experience-based choices emerge from how uncertainty information is acquired and stored rather than how it is represented or used.

Probability matching in sequential decision making is a striking violation of rational choice that has been observed in hundreds of experiments. Recent studies have demonstrated that matching persists even in described tasks in which all the information required for identifying a superior alternative strategy—maximizing—is present before the first choice is made. These studies have also indicated that maximizing increases when (1)the asymmetry in the availability of matching and maximizing strategies is reduced and (2)normatively irrelevant outcome feedback is provided. In the two experiments reported here, we examined the joint influences of these factors, revealing that strategy availability and outcome feedback operate on different time courses. Both behavioral and modeling results showed that while availability of the maximizing strategy increases the choice of maximizing early during the task, feedback appears to act more slowly to erode misconceptions about the task and to reinforce optimal responding. The results illuminate the interplay between 'topdown' identification of choice strategies and 'bottom-up' discovery of those strategies via feedback.

Hierarchical Bayesian methods offer a principled and comprehensive way to relate psychological models to data. Here we use them to model the patterns of information search, stopping and deciding in a simulated binary comparison judgment task. The simulation involves 20 subjects making 100 forced choice comparisons about the relative magnitudes of two objects (which of two German cities has more inhabitants). Two worked-examples show how hierarchical models can be developed to account for and explain the diversity of both search and stopping rules seen across the simulated individuals. We discuss how the results provide insight into current debates in the literature on heuristic decision making and argue that they demonstrate the power and flexibility of hierarchical Bayesian methods in modeling human decisionmaking.

Analyses of multi-attribute decision problems are dominated by accounts which assume people select from a repertoire of cognitive strategies to make decisions. This paper explores an alternative account based on sequential sampling and evidence accumulation. Two experiments varied aspects of a decision environment to examine competing models of decision behavior. The results highlighted the intra-participant consistency but inter-participant differences in the amount of evidence considered in decisions. This pattern was best captured by a sequential evidence accumulation model (SEQ) which treated pure Take-The-Best (TTB) and pure ‘‘rational’’ (RAT) models as special cases of a single model. The SEQ model was also preferred by the minimum description length (MDL) criterion to a naive strategy-selection model (NSS) which assumed that TTB or RAT could be selected with some probability for each decision.

The diversity, ingenuity and differences of opinion displayed in the articles of the recent special issues on the recognition heuristic are testament to the power and theoretical fertility of a simple idea about the role of recognition in decision making. In this brief comment I mention a number of these papers, but my focus is on points of agreement and disagreement with the conclusions drawn by Gigerenzer and Goldstein (2011) in their review of a decade’s worth of research on the recognition heuristic.

Two paradigms are commonly used to examine risky choice based on experiential sampling. The feedback paradigm involves a large number of repeated, consequential choices with feedback about the chosen (partial feedback) or chosen and foregone (full feedback) payoffs. The sampling paradigm invites cost-free samples before a single consequential choice. Despite procedural differences, choices in both experience-based paradigms suggest underweighting of rare events relative to their objective probability. This contrasts with overweighting when choice options are described, thereby leading to a ‘gap’ between experience and description-based choice. Behavioural data and modelbased analysis from an experiment comparing choices from description, sampling, and partial- and full-feedback paradigms replicated the ‘gap’, but also indicated significant differences between feedback and sampling paradigms. Our results suggest that mere sequential experience of outcomes is insufficient to produce reliable underweighting. We discuss when and why underweighting occurs, and implicate repeated, consequential choice as the critical factor.

Two studies examined multiple category reasoning in property induction with cross-classified foods. Pilot tests identified foods that were more typical of a taxonomic category (e.g., 'fruit'; termed ‘taxonomic primary’) or a script based category (e.g., 'snack foods'; termed ‘script primary’). They also confirmed that taxonomic categories were perceived as more coherent than script categories. In Experiment 1 participants completed an induction task in which information from multiple categories could be searched and combined to generate a property prediction about a target food. Multiple categories were more often consulted and used in prediction for script primary than for taxonomic primary foods. Experiment 2 replicated this finding across a range of property types but found that multiple category reasoning was reduced in the presence of a concurrent cognitive load. Property type affected which categories were consulted first and how information from multiple categories was weighted. The results show that multiple categories are more likely to be used for property predictions about cross-classified objects when an object is primarily associated with a category that has low coherence.

In four experiments, we investigated how people make feature predictions about objects whose category membership is uncertain. Artificial visual categories were presented and remained in view while a novel instance with a known feature, but uncertain category membership was presented. All four experiments showed that feature predictions about the test instance were most often based on feature correlations (referred to as feature conjunction reasoning). Experiment 1 showed that feature conjunction reasoning was generally preferred to category-based induction in a feature prediction task. Experiment 2 showed that people used all available exemplars to make feature conjunction predictions. Experiments 3 and 4 showed that the preference for predictions based on feature conjunction persisted even when category-level information was made more salient and inferences involving a larger number of categories were required. Little evidence of reasoning based on the consideration of multiple categories (e.g., Anderson, (Psychological Review, 98:409–429, 1991)) or the single, most probable category (e.g., Murphy & Ross, (Cognitive Psychology, 27:148–193, 1994)) was found.

When people are uncertain about the category membership of an item (e.g., Is it a dog or a dingo?), research shows that they tend to rely only on the dominant or most likely category when making inductions (e.g., How likely is it to befriend me?). An exception has been reported using speeded induction judgments where participants appeared to use information from multiple categories to make inductions (Verde, Murphy, & Ross, 2005). In two speeded induction studies, we found that participants tended to rely on the frequency with which features co-occurred when making feature predictions, independently of category membership. This pattern held whether categories were considered implicitly (Experiment 1) or explicitly (Experiment 2) prior to feature induction. The results converge with other recent work suggesting that people often rely on feature conjunction information, rather than category boundaries, when making inductions under uncertainty.

Successful retrieval on a test compared to just re-studying material improves long-term retention—a phenomenon called the ‘testing effect’. This study investigated the role of feedback and collaborative testing on the retention of course material in a tertiary educational setting. Tested material was better retained relative to non-tested material (testing effect), and feedback facilitated correction of errors. Group testing produced higher performance on the initial, but not final test performance, compared to individual testing. This set of findings suggests that to encourage long-term retention, educators should utilise individual formative testing with feedback; theoretical implications are also discussed.

Consider the following question: If a bat and a ball cost $1.10 in total and the bat costs $1 more than the ball, how much does the ball cost? If you are like many people, your immediate answer would be 10¢ (Kahneman and Frederick 2002). You would be wrong. Think a little and you will see why. This is a simple question and the answer is easy, but people still often get it wrong. Contrast the simplicity of this question with the complexity of global warming. Is there any hope of communicating such complex issues if many people fall foul on a simple mental arithmetic problem? Yes. These simple examples and the resulting errors can illuminate how we make decisions. Knowledge of these mechanisms can help us understand how to communicate climate science to fit with the way humans process information. Our aim in this paper is to provide some suggestions for improving this fit.

Inferences about target variables can be achieved by deliberate integration of probabilistic cues or by retrieving similar cue-patterns (exemplars) from memory. In tasks with cue information presented in on-screen displays, rule-based strategies tend to dominate unless the abstraction of cue-target relations is unfeasible. This dominance has also been demonstrated — surprisingly — in experiments that demanded the retrieval of cue values from memory (M. Persson & J. Rieskamp, 2009). In three modified replications involving a fictitious disease, binary cue values were represented either by alternative symptoms (e.g., fever vs. hypothermia) or by symptom presence vs. absence (e.g., fever vs. no fever). The former representation might hinder cue abstraction. The cues were predictive of the severity of the disease, and participants had to infer in each trial who of two patients was sicker. Both experiments replicated the rule-dominance with present-absent cues but yielded higher percentages of exemplar-based strategies with alternative cues. The experiments demonstrate that a change in cue representation may induce a dramatic shift from rule-based to exemplar-based reasoning in formally identical tasks.

The effects of memory constraints upon information acquisition and decision making were examined in two experiments using binary prediction tasks, where participants observe outcomes for two options before deciding which one to bet upon. Our studies extend previous investigations to the case where participants learn the structure of the task through observation, but where information acquisition is separated from the task of prediction. Participants with higher cognitive capacity (larger memory span or higher intelligence) were more likely to adopt the 'maximizing' strategy (always selecting the more frequent alternative). This finding conflicts with some recent investigations of similar tasks, a contrast that implies that the presence of feedback on choices may be important in determining the strategic actions of high-capacity individuals. Participants selecting the optimal strategy were in turn more efficient in their data acquisition. The behaviour of participants adopting suboptimal choice strategies was consistent with prediction based upon a 'narrow window of experience'—that is, seeking to match the characteristics of small samples of observations.

State-trace analysis was used to investigate the effect of concurrent working memory load on perceptual category learning. Initial reanalysis of Zeithamova and Maddox (2006, Experiment 1) revealed an apparently two-dimensional state-trace plot consistent with a dual-system interpretation of category learning. However, three modified replications of the original experiment found evidence of a single resource underlying the learning of both rule-based and information integration category structures. Follow-up analyses of the Zeithamova and Maddox data, restricted to only those participants who had learned the category task and performed the concurrent working memory task adequately, revealed a one-dimensional plot consistent with a single-resource interpretation and the results of the three new experiments. The results highlight the potential of state-trace analysis in furthering our understanding of the mechanisms underlying category learning.

Many theorists propose two types of processing: heuristic and analytic. In conflict tasks, in which these processing types lead to opposing responses, giving the analytic response may require both detection and resolution of the conflict. The ratio bias task, in which people tend to treat larger numbered ratios (e.g., 20/100) as indicating a higher likelihood of winning than do equivalent smaller numbered ratios (e.g., 2/10), is considered to induce such a conflict. Experiment 1 showed response time differences associated with conflict detection, resolution, and the amount of conflict induced. The conflict detection and resolution effects were replicated in Experiment 2 and were not affected by decreasing the influence of the heuristic response or decreasing the capacity to make the analytic response. The results are consistent with dual-process accounts, but a single-process account in which quantitative, rather than qualitative, differences in processing are assumed fares equally well in explaining the data.

A striking finding has emerged recently in the literature: When decision makers are faced with essentially the same choice, their preferences differ as a function of whether options are described or are ‘‘experienced’’ via observation and feedback. For example, when presented the described choice: (A) A 90% chance of $0 and a 10% chance of $10 or (B) $1 for sure, people tend to prefer (A). But when those same two options are experienced through observation of ‘‘draws’’ from two payoff distributions that match the described options, the modal preference reverses. Why? This is just one question that the papers in this special issue address. In addition, they address the rich repertoire of issues that arise when one considers experience-based choices. The decisions-from experience paradigm—with its focus on the acquisition and integration of information prior to choice, as well the choice itself—taps many of the fundamentals of psychology (learning, memory, encoding, knowledge representation, modelling) thus inspiring novel and fruitful avenues for research. This paper reviews recent research on experience-based choice, and highlights the contribution of the papers in the special issue. The paper introduces a framework that places different types of decisions along a continuum of uncertainty about what one is choosing between, which emphasizes the rich and varied role of ‘‘experience’’ in decision making. It ends by identifying important unsolved questions that are ripe for future research.

Recently it has been observed that different choices can be made about structurally identical risky decisions depending on whether information about outcomes and their probabilities is learned by description or from experience. Current evidence is equivocal with respect to whether this choice 'gap' is entirely an artefact of biased samples. The current experiment investigates whether a representational bias exists at the point of encoding by examining choice in light of decision makers’ mental representations of the alternatives, measured with both verbal and nonverbal judgment probes. We found that, when estimates were gauged by the nonverbal probe, participants presented with information in description format (as opposed to experience) had a greater tendency to overestimate rare events and underestimate common events. The choice gap, however, remained even when accounting for this judgment distortion and the effects of sampling bias. Indeed, participants’ estimation of the outcome distribution did not mediate their subsequent choice. It appears that experience-based choices may derive from a process that does not explicitly use probability information.

Traditional decision research shows that when people are told the probability of a rare event (e.g. the chance of side effects), they generally treat this event as if it were more likely than its objective probability (overweighting). In contrast, recent studies indicate that when outcomes are experienced firsthand, people tend to underweight the probability of rare events. In this paper, we suggest that the distinction between described and experienced information can play a significant role in shared decision making, and can provide a plausible explanation for some discrepancies between the perspectives of doctors and patients. We highlight some of the advantages and disadvantages of experiential and description-based information, and how knowledge of these might be used to improve risk communication.

Koehler and Macchi (2009) criticize the experiments presented in Newell, Mitchell, and Hayes (2008) as being ‘‘virtually irrelevant’’ to exemplar cuing theory. This reply addresses that interpretation and argues that the experiments dealt with issues at the heart of the theory and provided evidence highly relevant to understand how people think about low-probability events. The role of the ‘target’ in probabilistic statements is examined, highlighting the need for further theoretical and empirical clarification of the concept. The remaining specific criticisms raised in the commentary are discussed as well.

Mitchell et al. present a lucid and provocative challenge to the claim that links between mental representations are formed automatically. However, the propositional approach they offer requires clearer specification, especially with regard to how propositions and memories interact. A definition of a system would also clarify the debate, as might an alternative technique for assessing task 'dissociations.'

How effective are different types of feedback in helping us to learn multiple contingencies? This article attempts to resolve a paradox whereby, in comparison to simple outcome feedback, additional feedback either fails to enhance or is actually detrimental to performance in nonmetric multiple-cue probability learning (MCPL), while in contrast the majority of studies of metric MCPL reveal improvements at least with some forms of feedback. In three experiments we demonstrate that if feedback assists participants to infer cue polarity then it can in fact be effective in nonmetric MCPL. Participants appeared to use cue polarity information to adopt a linear judgement strategy, even though the environment was nonlinear. The results reconcile the paradoxical contrast between metric and nonmetric MCPL and support previous findings of people’s tendency to assume linearity and additivity in probabilistic cue learning.

This paper examines controversial claims about the merit of 'unconscious thought' for making complex decisions. In four experiments, participants were presented with complex decisions and were asked to choose the best option immediately, after a period of conscious deliberation, or after a period of distraction (said to encourage 'unconscious thought processes'). In all experiments the majority of participants chose the option predicted by their own subjective attribute weighting scores, regardless of the mode of thought employed. There was little evidence for the superiority of choices made 'unconsciously', but some evidence that conscious deliberation can lead to better choices. The final experiment suggested that the task is best conceptualized as one involving 'online judgement' rather than one in which decisions are made after periods of deliberation or distraction. The results suggest that we should be cautious in accepting the advice to 'stop thinking' about complex decisions.

Which statement conveys greater risk: '100 people die from cancer every day' or '36,500 people die from cancer every year'? In statistics where both frequencies and temporal information are used to convey risk, two theories predict opposite answers to this question. Construal level theory predicts that '100 people die from cancer every day' will be judged as more risky, while the ratio bias predicts that the equivalent '36,500 people die from cancer every year' will result in higher risk judgments. An experiment investigated which format produces higher risk ratings, and whether ratings are influenced by increasing the salience of the numerical or temporal part of the statistic. Forty-eight participants were randomly assigned to a numerical, temporal or control salience condition, and rated risk framed as number of deaths per day or per year. The year format was found to result in higher perceived risk, indicating that the ratio bias effect is dominant, but there was no effect of salience.

Most experimental investigations of risky choice provide participants with a description of the probabilities and outcomes for each option, and observe that small probabilities are overweighted. However, when payoffs are learned from repeated experience of outcomes (as in many real-world decisions), different patterns of choice are observed—consistent with underweighting rare events. We re-examined this phenomenon to determine whether biased sampling and recency effects in experience-based choice could account for this description–experience gap. Two hundred and forty paid participants made choices for 12 pairs of simple gambles. In the objective description condition, probabilities and outcomes were specified. In the free sampling condition, participants observed repeated plays of each gamble before choosing. Participants in four yoked conditions received the same information as the free sampling participants—either described or experienced. Differences between objective description and free sampling were consistent with underweighting rare events in experience-based choice. However, consistent with a biased sampling account, patterns of choice in the yoked conditions barely differed from the free sampling condition: given identical information, presentation mode has no effect. Recency effects in choice occurred only when outcomes were actively sampled, and were unaffected by working memory capacity. The absence of recency for passive observation implies actor–observer differences in forming expectations or testing hypotheses. The results provide no support for the claim that decisions from description and decisions from experience require separate descriptive theories.

In four experiments, we examined the effect of manipulating study phase attention in a Stroop task on the extent of repetition priming in the lexical decision task (LDT). Experiment 1 replicated the immunity of the LDT to division of attention reported by Szymanski and MacLeod (1996), using a standard Stroop configuration. Response times to previously encountered words were identical regardless of whether the participants were required to read the words or name the color in which they were presented. Experiment 2 demonstrated that implementing the Stroop manipulation across separate visual objects reduced but did not eliminate priming of unattended words, provided the words remained in the attended region of the stimulus display. When this constraint was removed in Experiment 3, priming of unattended words disappeared. Experiment 4 demonstrated statistically equivalent priming for attended and unattended words when the Stroop manipulation remained in the same visual object but attention was directed to a single letter of the word. In all four experiments, the Stroop manipulation had a clear effect on recognition. These results qualify claims that the LDT might be immune to manipulations of study phase attention and suggest that the LDT has a lower threshold level of attention at encoding than do other standard implicit tests of memory.

Three experiments tested the exemplar cuing and frequency format accounts of how the ‘imaginability’ of low-probability events is enhanced. The experiments manipulated imaginability by varying the statistics used to describe negative (e.g. being scarred as a result of laser surgery) and positive (e.g. winning a lottery) low-probability events. The results strongly supported the frequency format account, whereby imaginability is enhanced through the use of frequency formats for conveying statistical information (e.g. 20 out of 2000 as opposed to 0.01%). However, only limited support was found for exemplar cuing (EC) theory. Overall the results support the claim that the imaginability of outcomes plays a key role in thinking about low-probability events, but question the mechanisms specified by EC theory for mediating such effects.

Decision research in psychology has traditionally been influenced by the homo oeconomicus metaphor with its emphasis on normative models and deviations from the predictions of those models. In contrast, the principal metaphor of cognitive psychology conceptualizes humans as ‘information processors’, employing processes of perception, memory, categorization, problem solving and so on. Many of the processes described in cognitive theories are similar to those involved in decision making, and thus increasing cross-fertilization between the two areas is an important endeavour. A wide range of models and metaphors has been proposed to explain and describe ‘information processing’ and many models have been applied to decision making in ingenious ways. This special issue encourages cross-fertilization between cognitive psychology and decision research by providing an overview of current perspectives in one area that continues to highlight the benefits of the synergistic approach: cognitive modeling of multi-attribute decision making. In this introduction we discuss aspects of the cognitive system that need to be considered when modeling multi-attribute decision making (e.g., automatic versus controlled processing, learning and memory constraints, metacognition) and illustrate how such aspects are incorporated into the approaches proposed by contributors to the special issue. We end by discussing the challenges posed by the contrasting and sometimes incompatible assumptions of the models and metaphors.

The authors review their own empirical work inspired by the adaptive toolbox metaphor. The review examines factors influencing strategy selection and execution in multi-attribute inference tasks (e.g., information costs, time pressure, memory retrieval, dynamic environments, stimulus formats, intelligence). An emergent theme is the re-evaluation of contingency model claims about the elevated cognitive costs of compensatory in comparison with non-compensatory strategies. Contrary to common assertions about the impact of cognitive complexity, the empirical data suggest that manipulated variables exert their influence at the meta-level of deciding how to decide (i.e., which strategy to select) rather than at the level of strategy execution. An alternative conceptualisation of strategy selection, namely threshold adjustment in an evidence accumulation model, is also discussed and the difficulty in distinguishing empirically between these metaphors is acknowledged.

Considerable interest in the hypothesis that different cognitive tasks recruit qualitatively distinct processing systems has led to the proposal of separate explicit (declarative) and implicit (procedural) systems. A popular probabilistic category learning task known as the weather prediction task is said to be ideally suited to examine this distinction because its two versions, 'observation' and 'feedback,' are claimed to recruit the declarative and procedural systems, respectively. In two experiments, we found results that were inconsistent with this interpretation. In Experiment 1, a concurrent memory task had a detrimental effect on the implicit (feedback) version of the task. In Experiment 2, participants displayed comparable and accurate insight into the task and their judgment processes in the feedback and observation versions. These findings have important implications for the study of probabilistic category learning in both normal and patient populations.

The perceptual fluency/attributional model of the mere-exposure effect proposed by R. F. Bornstein and P. D’Agostino (1992) predicts that when recognition of a previously presented stimulus is above chance, feelings of fluency associated with that stimulus are discounted and thus the amount of fluency (mis)attributed to liking is reduced. This correction process results in smaller mere-exposure effects for supraliminal stimuli than for ‘‘subliminal’’ stimuli because when recognition is below chance participants are unaware of the source of fluency and they do not engage in correction. We tested this prediction in three experiments by presenting photographs of faces (Experiments 1 and 2) and polygons (Experiment 3) at varying exposure frequencies for 40 ms and 400 ms durations. Contrary to the prediction of the model, a significant mere-exposure effect was only found when recognition performance was at its highest level. Furthermore, across the three experiments liking and recognition were positively correlated.

The recognition heuristic is claimed to be distinguished from notions of availability and fluency through its categorical or ‘‘binary’’ treatment of information and the ‘‘inconsequentiality’’ of further knowledge to inferences based on recognition. Using the citysize task of Goldstein and Gigerenzer (2002) we demonstrate that: (1) increasing the validity of other information in the environment decreases the reliance on recognition; (2) cities that are both recognized and have other information known about them (e.g. they have a soccer team) are chosen more often than those which are simply recognized; and (3) there is a negative correlation between the time taken to identify a city and the proportion of times it is selected as the larger of a pair. None of these results is predicted by the process model of the recognition heuristic. The implication of the results for the distinction between the recognition, availability and fluency heuristics is discussed.

In multiple-cue learning (also known as probabilistic category learning) people acquire information about cue-outcome relations and combine these into predictions or judgments. Previous researchers claimed that people can achieve high levels of performance without explicit knowledge of the task structure or insight into their own judgment policies. It has also been argued that people use a variety of suboptimal strategies to solve such tasks. In three experiments the authors reexamined these conclusions by introducing novel measures of task knowledge and self-insight and using 'rolling regression' methods to analyze individual learning. Participants successfully learned a four-cue probabilistic environment and showed accurate knowledge of both the task structure and their own judgment processes. Learning analyses suggested that the apparent use of suboptimal strategies emerges from the incremental tracking of statistical contingencies in the environment.

Previous studies have suggested better learning when people actively intervene rather than when they passively observe the stimuli in a judgment task. In 4 experiments, the authors investigated the hypothesis that this improvement is associated with a shift from exemplar memory to cue abstraction. In a multiple-cue judgment task with continuous cues, the data replicated the improvement with intervention and participants who experimented more actively produced more accurate judgments. In a multiple-cue judgment task with binary cues, intervention produced poorer accuracy and participants who experimented more actively produced poorer judgments. These results provide no support for a representational shift but suggest that the improvement with active intervention may be limited to certain tasks and environments.

The authors identify and provide an integration of 3 criteria for establishing cue-search hierarchies in inferential judgment. Cues can be ranked by information value according to expected information gain (Bayesian criterion), cue-outcome correlation (correlational criterion), or ecological validity (accuracy criterion). All criteria significantly predicted information acquisition behavior; however, in 3 experiments, the most successful predictor was the correlational criterion (followed by the Bayesian). Although participants showed sensitivity to task constraints, searching for less information when it was more expensive (Experiment 1) and when under time constraints (Experiment 2), concomitant changes in the relative frequency of acquisition of cues with different information values were not observed. A rational analysis illustrates why such changes in the frequency of acquisition would be beneficial, and reasons for the failure to observe such behavior are discussed.

Fractals have experienced considerable success in quantifying the complex structure exhibited by many natural patterns and have captured the imagination of scientists and artists alike. With ever widening appeal, they have been referred to both as "fingerprints of nature" and "the new aesthetics." Our research has shown that the drip patterns of the American abstract painter Jackson Pollock are fractal. In this paper, we consider the implications of this discovery. We first present an overview of our research from the past five years to establish a context for our current investigations of human response to fractals. We discuss results showing that fractal images generated by mathematical, natural and human processes possess a shared aesthetic quality based on visual complexity. In particular, participants in visual perception tests display a preference for fractals with mid-range fractal dimensions. We also present recent preliminary work based on skin conductance measurements that indicate that these mid-range fractals also affect the observer's physiological condition and discuss future directions based on these results.

In 2 experiments, the authors sought to distinguish between the claim that recognition of an object is treated simply as a cue among others for the purposes of decision making in a cue-learning task from the claim that recognition is attributed a special status with fundamental, noncompensatory properties. Results of both experiments supported the former interpretation. When recognition had a high predictive validity, it was relied on (solely) by the majority of participants; however, when other cues in the environment had higher validity, recognition was ignored, and these other cues were used. The results provide insight into when, where, and why recognition is used in decision making and also question the elevated status assigned to recognition in some frameworks (e.g., D. G. Goldstein & G. Gigerenzer, 2002).

Examination of search strategies has tended to focus on choices determined by decision makers’ personal preferences among relevant cues, and not on learning cue-criterion relationships. We present an empirical and rational analysis of cue search for environments with objective criteria. In such environments, cues can be evaluated on the basis of three properties: validity (the probability that a cue identifies the correct choice if cue values differ between alternatives); discrimination rate (the proportion of occasions on which a cue has differing values); and success (the expected proportion of correct choices when only that cue can be used). Our experiments show that though there is a high degree of individual variability, success is a key determinant of search. Furthermore, a rational analysis demonstrates why success-directed search is the most adaptive strategy in many circumstances.

One interpretation of levels of processing effects (LOP) on priming in implicit tests of memory is in terms of deficits in lexical processing during shallow study tasks. In two experiments the extent of lexical processing engaged in during standard shallow encoding tasks was manipulated by placing the encoding question either before or after the target stimulus. Clear evidence was found in explicit memory tasks that placing the question after the target stimulus increased the depth of processing of words presented in shallow encoding tasks. In contrast, there was no evidence of such an effect on the priming observed in implicit memory tasks. The results suggest that the role of lexical processing in LOP effects on priming requires further specification.

The fast-and-frugal heuristics approach to decision making under uncertainty advocated by Gigerenzer and colleagues (e.g., Gigerenzer & Goldstein,1996) has achieved great popularity despite a relative lack of empirical validation. We report two experiments that examine the use of one particular heuristic—‘‘take-the-best’’ (TTB). In both experiments the majority of participants adopted frugal strategies,but only one-third (33%) behaved in a manner completely consistent with TTB
s search,stopping and decision rules. Furthermore,a significant proportion of participants in both experiments adopted a non-frugal strategy in which they accumulated more information than was predicted by TTB
s stopping rule. The results provide an insight into the conditions under which different heuristics are used,and question the predictive power of the fast-and-frugal approach.

Aspects of an experimental environment were manipulated in 3 experiments to examine the parameters under which the 'take-the-best' (TTB) heuristic (e.g., G. Gigerenzer & D. G. Goldstein, 1996) operates. Results indicated TTB use to be more prevalent when the cost of information was high, when validities of the cues were known, and when a deterministic environment was used. However, large individual variability in strategy use was observed as well as a significant proportion of behavior inconsistent with TTB, expecially its stopping rule. The results demarcate some of the heuristic’s boundary conditions and also question the validity of TTB as a psychologically plausible and pervasive model of behavior.

R.F. Bornstein (1994) questioned whether subliminal mere exposure effects might generalize to structurally related stimuli, thereby providing evidence for the existence of implicit learning. Two experiments examined this claim using letter string stimuli constructed according to the rules of an artificial grammar. Experiment 1 demonstrated that brief, masked exposure to grammatical strings impaired recognition but failed to produce a mere exposure effect on novel structurally related strings seen at test. Experiment 2 replicated this result but also demonstrated that a reliable mere exposure effect could be obtained, provided the same grammatical strings were presented at test. The results suggest that the structural relationship between training and test items prevents the mere exposure effect when participants are unaware of the exposure status of stimuli, and therefore provide no evidence for the existence of implicit learning.

Since their discovery by Mandelbrot (The Fractal Geometry of Nature, Freeman, New York, 1977), fractals have experienced considerable success in quantifying the complex structure exhibited by many natural patterns and have captured the imaginations of scientists and artists alike. With ever-widening appeal, they have been referred to both as ‘‘fingerprints of nature’’ (Nature 399 (1999) 422) and ‘‘the new aesthetics’’ (J. Hum. Psychol. 41 (2001) 59). Here, we show that humans display a consistent aesthetic preference across fractal images, regardless of whether these images are generated by nature’s processes, by mathematics, or by the human hand.

Four experiments examined the claim that cross-format transfer in invariant learning is reliant solely on the presence of repetition structure in study and teststrings (Stadler, Warren, & Lesch, 2000).Experiments 1, 2, and 3 used strings with no repetitions and found significant cross-format transfer in combination with a non-significant transfer decrement—no significant difference between same- and changed-format conditions. Further investigation of the basis of the role of repetition structure revealed an emphasis on the perceptual salience of test stimuli (Experiment 4). Our results contrast with those of Stadler et al. and suggest that under the conditions we employed invariant learning is not highly sensitive to changes in the perceptual characteristics of stimuli and therefore is inaccurately described as hyperspecific. We suggest that the term hyperspecific be reserved for cases in which minor format changes result in significant perfor- mance impairments—for example, typographical effects in implicit memory.

Three experiments are reported that examine the nature of knowledge underlying performance in the invariant learning task. Previous research (Bright & Burton, 1994; McGeorge & Burton, 1990) has supported an account of perfor- mance based on the implicit abstraction and application of a rule pertaining to the invariant feature. In contrast, we found effects in both the digit and clock invariant tasks that are difficult to explain solely in terms of subjects acquiring the experimenters’ rule. In all three experiments, manipulation of test item properties that are independent of the invariant feature led to a detriment in performance that is not predicted by an account based on the experimenters’ rules. Furthermore, the use of an on-line measure of awareness (confidence ratings) provided some evidence that performance is mediated by low confidence expli- cit knowledge.

Three experiments are reported that investigate the relationship between the structural mere exposure effect (SMEE) and implicit learning in an artificial grammar task. Subjects were presented with stimuli generated from a finite-state grammar and were asked to memorize them. In a subsequent test phase subjects were required first to rate how much they liked novel items, and second whether or not they thought items conformed to the rules of the grammar. A small but consistent effect of grammaticality was found on subjects’ liking ratings (a 'structural mere exposure effect') in all three experiments, but only when encoding and testing conditions were consistent. A change in the surface representation of stimuli between encoding and test (Experiment 1), memorizing fragments of items and being tested on whole items (Experiment 2), and a mismatch of processing operations between encoding and test (Experiment 3) all removed the SMEE. In contrast, the effect of grammaticality on rule judgements remained intact in the face of all three manipulations. It is suggested that rule judgements reflect attempts to explicitly recall information about training items, whereas the SMEE can be explained in terms of an attribution of processing fluency.