Severity influences categorical likelihood communications: A case study with Southeast Asian weather forecasters.

Risk assessments are common in multiple domains, from finance to medicine. They require evaluating an event's potential severity and likelihood. We investigate the possible dependence of likelihood and severity within the domain of impact-based weather forecasting (IBF), following predictions derived from considering asymmetric loss functions. In a collaboration between UK psychologists and partners from four meteorological organisations in Southeast Asia, we conducted two studies (N = 363) eliciting weather warnings from forecasters. Forecasters provided warnings denoting higher likelihoods for high severity impacts than low severity impacts, despite these impacts being described as having the same explicit numerical likelihood of occurrence. This 'Severity effect' is pervasive, and we find it can have a continued influence even for an updated forecast. It is additionally observed when translating warnings made on a risk matrix to numerical probabilities.

Drawing conclusions: Representing and evaluating competing explanations.

Despite the increase in studies investigating people's explanatory preferences in the domains of psychology and philosophy, little is known about their preferences in more applied domains, such as the criminal justice system. We show that when people evaluate competing legal accounts of the same evidence, their explanatory preferences are affected by whether they are required to draw causal models of the evidence. In addition, we identify 'mechanism' as an explanatory feature that people value when evaluating explanations. Although previous research has shown that people can reason correctly about causality, ours is one of the first studies to show that generating and drawing causal models directly affects people's evaluations of explanations. Our findings have implications for the development of normative models of legal arguments, which have so far adopted a singularly 'unified' approach, as well as the development of modelling tools to support people's reasoning and decision-making in applied domains. Finally, they add to the literature on the cognitive basis of evaluating competing explanations in new domains.

Coordination and expertise foster legal textualism.

A cross-cultural survey experiment revealed a dominant tendency to rely on a rule's letter over its spirit when deciding which behaviors violate the rule. This tendency varied markedly across (k = 15) countries, owing to variation in the impact of moral appraisals on judgments of rule violation. Compared with laypeople, legal experts were more inclined to disregard their moral evaluations of the acts altogether and consequently exhibited stronger textualist tendencies. Finally, we evaluated a plausible mechanism for the emergence of textualism: in a two-player coordination game, incentives to coordinate in the absence of communication reinforced participants' adherence to rules' literal meaning. Together, these studies (total n = 5,794) help clarify the origins and allure of textualism, especially in the law. Within heterogeneous communities in which members diverge in their moral appraisals involving a rule's purpose, the rule's literal meaning provides a clear focal point-an identifiable point of agreement enabling coordinated interpretation among citizens, lawmakers, and judges.

Motive on the mind: Explanatory preferences at multiple stages of the legal-investigative process.

Much work has investigated explanatory preferences for things like animals and artifacts, but how do explanation preferences manifest in everyday life? Here, we focus on the criminal justice system as a case study. In this domain, outcomes critically depend on how actors in the system (e.g., lawyers, jurors) generate and interpret explanations. We investigate lay preferences for two difference classes of information: information that appeals to opportunistic aspects of a crime (i.e., how the culprit could have committed the crime) vs. motivational aspects of that crime (i.e., the purpose for committing the crime). In two studies, we demonstrate that people prefer 'motive' accounts of crimes (analogous to a teleology preference) at different stages of the investigative process. In an additional two studies we demonstrate that these preferences are context-sensitive: namely, we find that 'motive' information tends to be more incriminating and less exculpatory. We discuss these findings in light of a broad literature on the cognitive basis of explanatory preferences; specifically, we draw analogy to preferences for teleological vs. mechanistic explanations. We also discuss implications for the criminal justice system.

Are There Cross-Cultural Legal Principles? Modal Reasoning Uncovers Procedural Constraints on Law.

Despite pervasive variation in the content of laws, legal theorists and anthropologists have argued that laws share certain abstract features and even speculated that law may be a human universal. In the present report, we evaluate this thesis through an experiment administered in 11 different countries. Are there cross-cultural principles of law? In a between-subjects design, participants (N = 3,054) were asked whether there could be laws that violate certain procedural principles (e.g., laws applied retrospectively or unintelligible laws), and also whether there are any such laws. Confirming our preregistered prediction, people reported that such laws cannot exist, but also (paradoxically) that there are such laws. These results document cross-culturally and -linguistically robust beliefs about the concept of law which defy people's grasp of how legal systems function in practice.

Propensities and Second Order Uncertainty: A Modified Taxi Cab Problem.

The study of people's ability to engage in causal probabilistic reasoning has typically used fixed-point estimates for key figures. For example, in the classic taxi-cab problem, where a witness provides evidence on which of two cab companies (the more common 'green'/less common 'blue') were responsible for a hit and run incident, solvers are told the witness's ability to judge cab color is 80%. In reality, there is likely to be some uncertainty around this estimate (perhaps we tested the witness and they were correct 4/5 times), known as second-order uncertainty, producing a distribution rather than a fixed probability. While generally more closely matching real world reasoning, a further important ramification of this is that our best estimate of the witness' accuracy can and should change when the witness makes the claim that the cab was blue. We present a Bayesian Network model of this problem, and show that, while the witness's report does increase our probability of the cab being blue, it simultaneously decreases our estimate of their future accuracy (because blue cabs are less common). We presented this version of the problem to 131 participants, requiring them to update their estimates of both the probability the cab involved was blue, as well as the witness's accuracy, after they claim it was blue. We also required participants to explain their reasoning process and provided follow up questions to probe various aspects of their reasoning. While some participants responded normatively, the majority self-reported 'assuming' one of the probabilities was a certainty. Around a quarter assumed the cab was green, and thus the witness was wrong, decreasing their estimate of their accuracy. Another quarter assumed the witness was correct and actually increased their estimate of their accuracy, showing a circular logic similar to that seen in the confirmation bias/belief polarization literature. Around half of participants refused to make any change, with convergent evidence suggesting that these participants do not see the relevance of the witness's report to their accuracy before we know for certain whether they are correct or incorrect.

Strategies for selecting and evaluating information.

Within the domain of psychology, Optimal Experimental Design (OED) principles have been used to model how people seek and evaluate information. Despite proving valuable as computational-level methods to account for people's behaviour, their descriptive and explanatory powers remain largely unexplored. In a series of experiments, we used a naturalistic crime investigation scenario to examine how people evaluate queries, as well as outcomes, in probabilistic contexts. We aimed to uncover the psychological strategies that people use, not just to assess whether they deviated from OED principles. In addition, we explored the adaptiveness of the identified strategies across both one-shot and stepwise information search tasks. We found that people do not always evaluate queries strictly in OED terms and use distinct strategies, such as by identifying a leading contender at the outset. Moreover, we identified aspects of zero-sum thinking and risk aversion that interact with people's information search strategies. Our findings have implications for building a descriptive account of information seeking and evaluation, accounting for factors that currently lie outside the realm of information-theoretic OED measures, such as context and the learner's own preferences.

Does hippocampal volume explain performance differences on hippocampal-dependant tasks?

Marked disparities exist across healthy individuals in their ability to imagine scenes, recall autobiographical memories, think about the future and navigate in the world. The importance of the hippocampus in supporting these critical cognitive functions has prompted the question of whether differences in hippocampal grey matter volume could be one source of performance variability. Evidence to date has been somewhat mixed. In this study we sought to mitigate issues that commonly affect these types of studies. Data were collected from a large sample of 217 young, healthy adult participants, including whole brain structural MRI data (0.8 mm isotropic voxels) and widely-varying performance on scene imagination, autobiographical memory, future thinking and navigation tasks. We found little evidence that hippocampal grey matter volume was related to task performance in this healthy sample. This was the case using different analysis methods (voxel-based morphometry, partial correlations), when whole brain or hippocampal regions of interest were examined, when comparing different sub-groups (divided by gender, task performance, self-reported ability), and when using latent variables derived from across the cognitive tasks. Hippocampal grey matter volume may not, therefore, significantly influence performance on tasks known to require the hippocampus in healthy people. Perhaps only in extreme situations, as in the case of licensed London taxi drivers, are measurable ability-related hippocampus volume changes consistently exhibited.

The propensity interpretation of probability and diagnostic split in explaining away.

Causal judgements in explaining-away situations, where multiple independent causes compete to account for a common effect, are ubiquitous in both everyday and specialised contexts. Despite their ubiquity, cognitive psychologists still struggle to understand how people reason in these contexts. Empirical studies have repeatedly found that people tend to 'insufficiently' explain away: that is, when one cause explains the presence of an effect, people do not sufficiently reduce the probability of other competing causes. However, the diverse accounts that researchers have proposed to explain this insufficiency suggest we are yet to find a compelling account of these results. In the current research we explored the novel possibility that insufficiency in explaining away is driven by: (i) some people interpreting probabilities as propensities, i.e. as tendencies of a physical system to produce an outcome and (ii) some people splitting the probability space among the causes in diagnostic reasoning, i.e. by following a strategy we call 'the diagnostic split'. We tested these two hypotheses by manipulating (a) the characteristics of cover stories to reflect different degrees to which the propensity interpretation of probability was pronounced, and (b) the prior probabilities of the causes which entailed different normative amounts of explaining away. Our results were in line with the extant literature as we found insufficient explaining away. However, we also found empirical support for our two hypotheses, suggesting that they are a driving force behind the reported insufficiency.

Widening Access to Bayesian Problem Solving.

Bayesian reasoning and decision making is widely considered normative because it minimizes prediction error in a coherent way. However, it is often difficult to apply Bayesian principles to complex real world problems, which typically have many unknowns and interconnected variables. Bayesian network modeling techniques make it possible to model such problems and obtain precise predictions about the causal impact that changing the value of one variable may have on the values of other variables connected to it. But Bayesian modeling is itself complex, and has until now remained largely inaccessible to lay people. In a large scale lab experiment, we provide proof of principle that a Bayesian network modeling tool, adapted to provide basic training and guidance on the modeling process to beginners without requiring knowledge of the mathematical machinery working behind the scenes, significantly helps lay people find normative Bayesian solutions to complex problems, compared to generic training on probabilistic reasoning. We discuss the implications of this finding for the use of Bayesian network software tools in applied contexts such as security, medical, forensic, economic or environmental decision making.

Manipulating the temporal locus and content of mind-wandering.

The human brain has a tendency to drift into the realm of internally-generated thoughts that are unbound by space and time. The term mind-wandering (MW) is often used describe such thoughts when they are perceptually decoupled. Evidence suggests that exposure to forward and backward illusory motion skews the temporal orientation of MW thoughts to either the future or past respectively. However, little is known about the impact of this manipulation on other features of MW. Here, using a novel experimental paradigm, we first confirmed that our illusory motion method facilitated the generation of MW thoughts congruent with the direction of motion. We then conducted content analyses which revealed that goal orientation and temporal distance were also significantly affected by the direction of illusory motion. We conclude that illusory motion may be an effective means of assaying MW and could help to elucidate this ubiquitous, and likely critical, component of cognition.

Identifying the cognitive processes underpinning hippocampal-dependent tasks.

Autobiographical memory, future thinking, and spatial navigation are critical cognitive functions that are thought to be related and are known to depend upon a brain structure called the hippocampus. Surprisingly, direct evidence for their interrelatedness is lacking, as is an understanding of why they might be related. There is debate about whether they are linked by an underlying memory-related process or, as has more recently been suggested, because they each require the endogenous construction of scene imagery. Here, using a large sample of participants and multiple cognitive tests with a wide spread of individual differences in performance, we found that these functions are indeed related. Mediation analyses further showed that scene construction, and not memory, mediated (explained) the relationships between the functions. These findings offer a fresh perspective on autobiographical memory, future thinking, navigation, and also on the hippocampus, where scene imagery appears to play an influential role. (PsycINFO Database Record (c) 2019 APA, all rights reserved).