People as Intuitive Scientists: Reconsidering Statistical Explanations of Decision Making

Mapping Mental Representations With Free Associations: A Tutorial Using the R Package associatoR

People's understanding of topics and concepts such as risk, sustainability, and intelligence can be important for psychological researchers and policymakers alike. One underexplored way of accessing this information is to use free associations to map people's mental representations. In this tutorial, we describe how free association responses can be collected, processed, mapped, and compared across groups using the R package associatoR. We discuss study design choices and different approaches to uncovering the structure of mental representations using natural language processing, including the use of embeddings from large language models. We posit that free association analysis presents a powerful approach to revealing how people and machines represent key social and technological issues.

Predecisional information search adaptively reduces three types of uncertainty

How do people search for information when they are given the opportunity to freely explore their options? Previous research has suggested that people focus on reducing uncertainty before making a decision, but it remains unclear how exactly they do so and whether they do so consistently. We present an analysis of over 1,000,000 information-search decisions made by over 2,500 individuals in a decisions-from-experience setting that cleanly separates information search from choice. Using a data-driven approach supported by a formal measurement framework, we examine how people allocate samples to options and how they decide to terminate search. Three major insights emerge. First, predecisional information search has at least three drivers that can be interpreted as reducing three types of uncertainty: structural, estimation, and computational. Second, the selection of these drivers of information search is adaptive, sequential, and guided by environmental knowledge that integrates prior expectations, task instructions, and personal experiences. Third, predecisional information search exhibits substantial interindividual heterogeneity, with individuals recruiting different drivers of information search. Together, these insights suggest that human information search is complex in ways that cannot be fully explained by monolithic accounts of information search, including proposals focused on estimation uncertainty or cost-benefit analysis. We conclude that broader theories of human information-search behavior are necessary.

Further perceptions of probability: Accurate, stepwise updating is contingent on prior information about the task and the response mode

To adapt to an uncertain world, humans must learn event probabilities. These probabilities may be stationary, such as that of rolling a 6 on a die, or changing over time, like the probability of rainfall over the year. Research on how people estimate and track changing probabilities has recently reopened an old epistemological issue. A small, mostly recent literature finds that people accurately track the probability and change their estimates only occasionally, resulting in staircase-shaped response patterns. This has been taken as evidence that people entertain beliefs about unknown, distal states of the world, which are tested against observations to produce discrete shifts between hypotheses. That idea stands in contrast to the claim that people learn by continuously updating associations between observed events. The purpose of this article is to investigate the generality and robustness of the accurate, staircase-shaped pattern. In two experiments, we find that the response pattern is contingent on the response mode and prior information about the generative process. Participants exist on continua of accuracy and staircase-ness and we only reproduce previous results when changing estimates is effortful and prior information is provided-the specific conditions of previous experiments. We conclude that explaining this solely through either hypotheses or associations is untenable. A complete theory of probability estimation requires the interaction of three components: (i) online tracking of observed data, (ii) beliefs about the unobserved "generative process," and (iii) a response updating process. Participants' overt estimates depend on how the specific task conditions jointly determine all three.

Craving money? Evidence from the laboratory and the field

Continuing to gamble despite harmful consequences has plagued human life in many ways, from loss-chasing in problem gamblers to reckless investing during stock market bubbles. Here, we propose that these anomalies in human behavior can sometimes reflect Pavlovian perturbations on instrumental behavior. To show this, we combined key elements of Pavlovian psychology literature and standard economic theory into a single model. In it, when a gambling cue such as a gaming machine or a financial asset repeatedly delivers a good outcome, the agent may start engaging with the cue even when the expected value is negative. Next, we transported the theoretical framework into an experimental task and found that participants behaved like the agent in our model. Last, we applied the model to the domain of real-world financial trading and discovered an asset-pricing anomaly suggesting that market participants are susceptible to the purported Pavlovian bias.

Local Search and the Evolution of World Models

An open question regarding how people develop their models of the world is how new candidates are generated for consideration out of infinitely many possibilities. We discuss the role that evolutionary mechanisms play in this process. Specifically, we argue that when it comes to developing a global world model, innovation is necessarily incremental, involving the generation and selection among random local mutations and recombinations of (parts of) one's current model. We argue that, by narrowing and guiding exploration, this feature of cognitive search is what allows human learners to discover better theories, without ever grappling directly with the problem of finding a "global optimum," or best possible world model. We suggest this aspect of cognitive processing works analogously to how blind variation and selection mechanisms drive biological evolution. We propose algorithms developed for program synthesis provide candidate mechanisms for how human minds might achieve this. We discuss objections and implications of this perspective, finally suggesting that a better process-level understanding of how humans incrementally explore compositional theory spaces can shed light on how we think, and provide explanatory traction on fundamental cognitive biases, including anchoring, probability matching, and confirmation bias.

The non-unitary nature of information preference

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.

Active inductive inference in children and adults: A constructivist perspective

A defining aspect of being human is an ability to reason about the world by generating and adapting ideas and hypotheses. Here we explore how this ability develops by comparing children's and adults' active search and explicit hypothesis generation patterns in a task that mimics the open-ended process of scientific induction. In our experiment, 54 children (aged 8.97±1.11) and 50 adults performed inductive inferences about a series of causal rules through active testing. Children were more elaborate in their testing behavior and generated substantially more complex guesses about the hidden rules. We take a 'computational constructivist' perspective to explaining these patterns, arguing that these inferences are driven by a combination of thinking (generating and modifying symbolic concepts) and exploring (discovering and investigating patterns in the physical world). We show how this framework and rich new dataset speak to questions about developmental differences in hypothesis generation, active learning and inductive generalization. In particular, we find children's learning is driven by less fine-tuned construction mechanisms than adults', resulting in a greater diversity of ideas but less reliable discovery of simple explanations.

Is Conviction Narrative Theory a theory of everything or nothing?

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.

Determinants of early-access to retirement savings: Lessons from the COVID-19 pandemic

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.

All Models Are Wrong, and Some Are Religious: Supernatural Explanations as Abstract and Useful Falsehoods about Complex Realities

Many cognitive and evolutionary theories of religion argue that supernatural explanations are byproducts of our cognitive adaptations. An influential argument states that our supernatural explanations result from a tendency to generate anthropomorphic explanations, and that this tendency is a byproduct of an error management strategy because agents tend to be associated with especially high fitness costs. We propose instead that anthropomorphic and other supernatural explanations result as features of a broader toolkit of well-designed cognitive adaptations, which are designed for explaining the abstract and causal structure of complex, unobservable, and uncertain phenomena that have substantial impacts on fitness. Specifically, we argue that (1) mental representations about the abstract vs. the supernatural are largely overlapping, if not identical, and (2) when the data-generating processes for scarce and ambiguous observations are complex and opaque, a naive observer can improve a bias-variance trade-off by starting with a simple, underspecified explanation that Western observers readily interpret as "supernatural." We then argue that (3) in many cases, knowledge specialists across cultures offer pragmatic services that involve apparently supernatural explanations, and their clients are frequently willing to pay them in a market for useful and effective services. We propose that at least some ethnographic descriptions of religion might actually reflect ordinary and adaptive responses to novel problems such as illnesses and natural disasters, where knowledge specialists possess and apply the best available explanations about phenomena that would otherwise be completely mysterious and unpredictable.

Sensitivity to Evidential Dependencies in Judgments Under Uncertainty

According to Bayesian models of judgment, testimony from independent informants has more evidential value than dependent testimony. Three experiments investigated learners' sensitivity to this distinction. Each experiment used a social version of the balls-and-urns task, in which participants judged which of two urns was the most likely source of evidence presented by multiple informants. Informants either provided independent testimony based solely on their own observations or dependent-sequential testimony that considered the testimonies of previous informants. Although participants updated their beliefs with additional evidence, this updating was generally insensitive to evidential dependency (Experiments 1 and 2). A notable exception was when individuals were separated according to their beliefs about the relative value of independent and sequential evidence. Those who viewed independent evidence as having greater value subsequently gave more weight to independent testimony in the balls-and-urns task (Experiment 3), in line with the predictions of a Bayesian model. Our findings suggest that only a minority of individuals conform to Bayesian predictions in the relative weighting of independent and dependent evidence in judgments under uncertainty.

The day after the disaster: Risk-taking following large- and small-scale disasters in a microworld

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 Generative View of Rationality and Growing Awareness†

In this paper we contrast bounded and ecological rationality with a proposed alternative, generative rationality. Ecological approaches to rationality build on the idea of humans as "intuitive statisticians" while we argue for a more generative conception of humans as "probing organisms." We first highlight how ecological rationality's focus on cues and statistics is problematic for two reasons: (a) the problem of cue salience, and (b) the problem of cue uncertainty. We highlight these problems by revisiting the statistical and cue-based logic that underlies ecological rationality, which originate from the misapplication of concepts in psychophysics (e.g., signal detection, just-noticeable-differences). We then work through the most popular experimental task in the ecological rationality literature-the city size task-to illustrate how psychophysical assumptions have informally been linked to ecological rationality. After highlighting these problems, we contrast ecological rationality with a proposed alternative, generative rationality. Generative rationality builds on biology-in contrast to ecological rationality's focus on statistics. We argue that in uncertain environments cues are rarely given or available for statistical processing. Therefore we focus on the psychogenesis of awareness rather than psychophysics of cues. For any agent or organism, environments "teem" with indefinite cues, meanings and potential objects, the salience or relevance of which is scarcely obvious based on their statistical or physical properties. We focus on organism-specificity and the organism-directed probing that shapes awareness and perception. Cues in teeming environments are noticed when they serve as cues-for-something, requiring what might be called a "cue-to-clue" transformation. In this sense, awareness toward a cue or cues is actively "grown." We thus argue that perception might more productively be seen as the presentation of cues and objects rather than their representation. This generative approach not only applies to relatively mundane organism (including human) interactions with their environments-as well as organism-object relationships and their embodied nature-but also has significant implications for understanding the emergence of novelty in economic settings. We conclude with a discussion of how our arguments link with-but modify-Herbert Simon's popular "scissors" metaphor, as it applies to bounded rationality and its implications for decision making in uncertain, teeming environments.

Leveraging artificial intelligence to improve people's planning strategies

SignificanceMany bad decisions and their devastating consequences could be avoided if people used optimal decision strategies. Here, we introduce a principled computational approach to improving human decision making. The basic idea is to give people feedback on how they reach their decisions. We develop a method that leverages artificial intelligence to generate this feedback in such a way that people quickly discover the best possible decision strategies. Our empirical findings suggest that a principled computational approach leads to improvements in decision-making competence that transfer to more difficult decisions in more complex environments. In the long run, this line of work might lead to apps that teach people clever strategies for decision making, reasoning, goal setting, planning, and goal achievement.

Violations of economic rationality due to irrelevant information during learning in decision from experience

According to normative decision-making theories, the composition of a choice set should not affect people’s preferences regarding the different options. This assumption contrasts with decades of research that have identified multiple situations in which this principle is violated, leading to context effects. Recently, research on context effects has been extended to the domain of experience-based choices, where it has been shown that forgone outcomes from irrelevant alternatives affect preferences — an accentuation effect. More specifically, it has been shown that an option presented in a situation in which its outcomes are salient across several trials is evaluated more positively than in a context in which its outcomes are less salient. In the present study, we investigated whether irrelevant information affects preferences as much as relevant information. In two experiments, individuals completed a learning task with partial feedback. We found that past outcomes from non-chosen options, which contain no relevant information at all, led to the same accentuation effect as did counterfactual outcomes that provided new and relevant information. However, if the information is entirely irrelevant (from options that could not have been chosen), individuals ignored it, thus ruling out a purely perceptual account of the accentuation effect. These results provide further support for the influence of salience on learning and highlight the necessity of mechanistic accounts in decision-making research.

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

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

The intuitive conceptualization and perception of variance

Statistical concepts (e.g., mean, variance, correlation) offer powerful ways to characterize the structure of the environment. To what extent do statistical concepts also play a role for people assessing the environment? Previous work on the mind as "intuitive statistician" has mainly focused on the judgment of means and correlations (Peterson & Beach, 1967). Much less is known about how people conceptualize and judge variance. In a survey and three experimental studies, we explored people's intuitive understanding of variance as a concept and investigated the factors affecting people's judgments of variance. The survey findings showed that most people hold concepts of variance that they can articulate; these concepts, however, reflect not only statistical variance (i.e., deviations from the average) but also the pairwise distance between stimuli, their range, and their variety. The experimental studies revealed that although people's judgments of variance are sensitive to the statistical variance of stimuli, variety and range also play an important role. The results can inform psychological models of judgments of variance.

Ethnoscientific expertise and knowledge specialisation in 55 traditional cultures

People everywhere acquire high levels of conceptual knowledge about their social and natural worlds, which we refer to as ethnoscientific expertise. Evolutionary explanations for expertise are still widely debated. We analysed ethnographic text records (N = 547) describing ethnoscientific expertise among 55 cultures in the Human Relations Area Files to investigate the mutually compatible roles of collaboration, proprietary knowledge, cultural transmission, honest signalling, and mate provisioning. We found relatively high levels of evidence for collaboration, proprietary knowledge, and cultural transmission, and lower levels of evidence for honest signalling and mate provisioning. In our exploratory analyses, we found that whether expertise involved proprietary vs. transmitted knowledge depended on the domain of expertise. Specifically, medicinal knowledge was positively associated with secretive and specialised knowledge for resolving uncommon and serious problems, i.e. proprietary knowledge. Motor skill-related expertise, such as subsistence and technological skills, was positively associated with broadly competent and generous teachers, i.e. cultural transmission. We also found that collaborative expertise was central to both of these models, and was generally important across different knowledge and skill domains.