Unpredictable robots elicit responsibility attributions.

Do people hold robots responsible for their actions? While Clark and Fischer present a useful framework for interpreting social robots, we argue that they fail to account for people's willingness to assign responsibility to robots in certain contexts, such as when a robot performs actions not predictable by its user or programmer.

Human Vision Reconstructs Time to Satisfy Causal Constraints.

The goal of perception is to infer the most plausible source of sensory stimulation. Unisensory perception of temporal order, however, appears to require no inference, because the order of events can be uniquely determined from the order in which sensory signals arrive. Here, we demonstrate a novel perceptual illusion that casts doubt on this intuition: In three experiments (N = 607), the experienced event timings were determined by causality in real time. Adult participants viewed a simple three-item sequence, ACB, which is typically remembered as ABC in line with principles of causality. When asked to indicate the time at which events B and C occurred, participants' points of subjective simultaneity shifted so that the assumed cause B appeared earlier and the assumed effect C later, despite participants' full attention and repeated viewings. This first demonstration of causality reversing perceived temporal order cannot be explained by postperceptual distortion, lapsed attention, or saccades.

BARD: A Structured Technique for Group Elicitation of Bayesian Networks to Support Analytic Reasoning.

In many complex, real-world situations, problem solving and decision making require effective reasoning about causation and uncertainty. However, human reasoning in these cases is prone to confusion and error. Bayesian networks (BNs) are an artificial intelligence technology that models uncertain situations, supporting better probabilistic and causal reasoning and decision making. However, to date, BN methodologies and software require (but do not include) substantial upfront training, do not provide much guidance on either the model building process or on using the model for reasoning and reporting, and provide no support for building BNs collaboratively. Here, we contribute a detailed description and motivation for our new methodology and application, Bayesian ARgumentation via Delphi (BARD). BARD utilizes BNs and addresses these shortcomings by integrating (1) short, high-quality e-courses, tips, and help on demand; (2) a stepwise, iterative, and incremental BN construction process; (3) report templates and an automated explanation tool; and (4) a multiuser web-based software platform and Delphi-style social processes. The result is an end-to-end online platform, with associated online training, for groups without prior BN expertise to understand and analyze a problem, build a model of its underlying probabilistic causal structure, validate and reason with the causal model, and (optionally) use it to produce a written analytic report. Initial experiments demonstrate that, for suitable problems, BARD aids in reasoning and reporting. Comparing their effect sizes also suggests BARD's BN-building and collaboration combine beneficially and cumulatively.

Redressing the emperor in causal clothing.

Over-flexibility in the definition of Friston blankets obscures a key distinction between observational and interventional inference. The latter requires cognizers form not just a causal representation of the world but also of their own boundary and relationship with it, in order to diagnose the consequences of their actions. We suggest this locates the blanket in the eye of the beholder.

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.

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.

The Zero-Sum Fallacy in Evidence Evaluation.

There are many instances, both in professional domains such as law, forensics, and medicine and in everyday life, in which an effect (e.g., a piece of evidence or event) has multiple possible causes. In three experiments, we demonstrated that individuals erroneously assume that evidence that is equally predicted by two competing hypotheses offers no support for either hypothesis. However, this assumption holds only in cases in which competing causes are mutually exclusive and exhaustive (i.e., exactly one cause is true). We argue that this reasoning error is due to a zero-sum perspective on evidence, wherein people assume that evidence that supports one causal hypothesis must disconfirm its competitor. Thus, evidence cannot give positive support to both competitors. Across three experiments ( N = 49, N = 193, N = 201), we demonstrated that this error is robust to intervention and generalizes across several different contexts. We also ruled out several alternative explanations of the bias.

When causality shapes the experience of time: Evidence for temporal binding in young children.

It is well established that the temporal proximity of two events is a fundamental cue to causality. Recent research with adults has shown that this relation is bidirectional: events that are believed to be causally related are perceived as occurring closer together in time-the so-called temporal binding effect. Here, we examined the developmental origins of temporal binding. Participants predicted when an event that was either caused by a button press, or preceded by a non-causal signal, would occur. We demonstrate for the first time that children as young as 4 years are susceptible to temporal binding. Binding occurred both when the button press was executed via intentional action, and when a machine caused it. These results suggest binding is a fundamental, early developing property of perception and grounded in causal knowledge. A video abstract of this article can be viewed at https://www.youtube.com/watch?v=EQC_MqjxZQQ.

Modelling competing legal arguments using Bayesian model comparison and averaging.

Bayesian models of legal arguments generally aim to produce a single integrated model, combining each of the legal arguments under consideration. This combined approach implicitly assumes that variables and their relationships can be represented without any contradiction or misalignment, and in a way that makes sense with respect to the competing argument narratives. This paper describes a novel approach to compare and 'average' Bayesian models of legal arguments that have been built independently and with no attempt to make them consistent in terms of variables, causal assumptions or parameterization. The approach involves assessing whether competing models of legal arguments are explained or predict facts uncovered before or during the trial process. Those models that are more heavily disconfirmed by the facts are given lower weight, as model plausibility measures, in the Bayesian model comparison and averaging framework adopted. In this way a plurality of arguments is allowed yet a single judgement based on all arguments is possible and rational.

A systematic analysis of misleading evidence in unsafe rulings in England and Wales.

Evidence has the potential to be misleading if its value when expressing beliefs in hypotheses is not fully understood or presented. Although the knowledge base to understand uncertainties is growing, a challenge remains to prioritise research and to continuously assess the magnitude and consequences of misleading evidence in criminal cases. This study used a systematic content analysis to identify misleading evidence, drawing information from case transcripts of rulings argued unsafe by the Court of Appeal of England and Wales. In the 7-year study period, 218 applications were successful on appeal, containing 235 cases of misleading evidence. The majority (76%) of successful appeals were based upon the same materials available in the original trial, rather than the presentation of new relevant information. Witness (39%), forensic (32%), and character evidence (19%) were the most commonly observed evidence types, with the validity of witnesses (26%), probative value of forensic evidence (12%), and relevance of character evidence (10%) being the most prevalent combinations of identified issues. Additionally, the majority (66%) of misleading evidence types relate to their interpretation at activity level. The findings suggest that many of these misleading aspects could have been prevented by providing more transparency in the relationship between evidence and hypotheses. Generally, the results contribute to gaining a more complete picture of the role of misleading evidence in the criminal justice system.

Ranking the Impact of Different Tests on a Hypothesis in a Bayesian Network.

Testing of evidence in criminal cases can be limited by temporal or financial constraints or by the fact that certain tests may be mutually exclusive, so choosing the tests that will have maximal impact on the final result is essential. In this paper, we assume that a main hypothesis, evidence for it and possible tests for existence of this evidence are represented in the form of a Bayesian network, and use three different methods to measure the impact of a test on the main hypothesis. We illustrate the methods by applying them to an actual digital crime case provided by the Hong Kong police. We conclude that the Kullback-Leibler divergence is the optimal method for selecting the tests with the highest impact.

Eye-Tracking Causality.

How do people make causal judgments? What role, if any, does counterfactual simulation play? Counterfactual theories of causal judgments predict that people compare what actually happened with what would have happened if the candidate cause had been absent. Process theories predict that people focus only on what actually happened, to assess the mechanism linking candidate cause and outcome. We tracked participants' eye movements while they judged whether one billiard ball caused another one to go through a gate or prevented it from going through. Both participants' looking patterns and their judgments demonstrated that counterfactual simulation played a critical role. Participants simulated where the target ball would have gone if the candidate cause had been removed from the scene. The more certain participants were that the outcome would have been different, the stronger the causal judgments. These results provide the first direct evidence for spontaneous counterfactual simulation in an important domain of high-level cognition.

Causality in thought.

Causal knowledge plays a crucial role in human thought, but the nature of causal representation and inference remains a puzzle. Can human causal inference be captured by relations of probabilistic dependency, or does it draw on richer forms of representation? This article explores this question by reviewing research in reasoning, decision making, various forms of judgment, and attribution. We endorse causal Bayesian networks as the best normative framework and as a productive guide to theory building. However, it is incomplete as an account of causal thinking. On the basis of a range of experimental work, we identify three hallmarks of causal reasoning-the role of mechanism, narrative, and mental simulation-all of which go beyond mere probabilistic knowledge. We propose that the hallmarks are closely related. Mental simulations are representations over time of mechanisms. When multiple actors are involved, these simulations are aggregated into narratives.

Children's use of interventions to learn causal structure.

Children between 5 and 8 years of age freely intervened on a three-variable causal system, with their task being to discover whether it was a common cause structure or one of two causal chains. From 6 or 7 years of age, children were able to use information from their interventions to correctly disambiguate the structure of a causal chain. We used a Bayesian model to examine children's interventions on the system; this showed that with development children became more efficient in producing the interventions needed to disambiguate the causal structure and that the quality of interventions, as measured by their informativeness, improved developmentally. The latter measure was a significant predictor of children's correct inferences about the causal structure. A second experiment showed that levels of performance were not reduced in a task where children did not select and carry out interventions themselves, indicating no advantage for self-directed learning. However, children's performance was not related to intervention quality in these circumstances, suggesting that children learn in a different way when they carry out interventions themselves.

Probabilistic classification learning with corrective feedback is associated with in vivo striatal dopamine release in the ventral striatum, while learning without feedback is not.

The basal ganglia (BG) mediate certain types of procedural learning, such as probabilistic classification learning on the 'weather prediction task' (WPT). Patients with Parkinson's disease (PD), who have BG dysfunction, are impaired at WPT-learning, but it remains unclear what component of the WPT is important for learning to occur. We tested the hypothesis that learning through processing of corrective feedback is the essential component and is associated with release of striatal dopamine. We employed two WPT paradigms, either involving learning via processing of corrective feedback (FB) or in a paired associate manner (PA). To test the prediction that learning on the FB but not PA paradigm would be associated with dopamine release in the striatum, we used serial (11) C-raclopride (RAC) positron emission tomography (PET), to investigate striatal dopamine release during FB and PA WPT-learning in healthy individuals. Two groups, FB, (n = 7) and PA (n = 8), underwent RAC PET twice, once while performing the WPT and once during a control task. Based on a region-of-interest approach, striatal RAC-binding potentials reduced by 13-17% in the right ventral striatum when performing the FB compared to control task, indicating release of synaptic dopamine. In contrast, right ventral striatal RAC binding non-significantly increased by 9% during the PA task. While differences between the FB and PA versions of the WPT in effort and decision-making is also relevant, we conclude striatal dopamine is released during FB-based WPT-learning, implicating the striatum and its dopamine connections in mediating learning with FB.

When 'neutral' evidence still has probative value (with implications from the Barry George Case).

The likelihood ratio (LR) is a probabilistic method that has been championed as a 'simple rule' for evaluating the probative value of forensic evidence in court. Intuitively, if the LR is greater than one then the evidence supports the prosecution hypothesis; if the LR is less than one it supports the defence hypothesis, and if the LR is equal to one then the evidence favours neither (and so is considered 'neutral'-having no probative value). It can be shown by Bayes' theorem that this simple relationship only applies to pairs of hypotheses for which one is the negation of the other (i.e. to mutually exclusive and exhaustive hypotheses) and is not applicable otherwise. We show how easy it can be - even for evidence experts - to use pairs of hypotheses that they assume are mutually exclusive and exhaustive but are not, and hence to arrive at erroneous conclusions about the value of evidence using the LR. Furthermore, even when mutually exclusive and exhaustive hypotheses are used there are extreme restrictions as to what can be concluded about the probative value of evidence just from a LR. Most importantly, while the distinction between source-level hypotheses (such as defendant was/was not at the crime scene) and offence-level hypotheses (defendant is/is not guilty) is well known, it is not widely understood that a LR for evidence about the former generally has no bearing on the LR of the latter. We show for the first time (using Bayesian networks) the full impact of this problem, and conclude that it is only the LR of the offence level hypotheses that genuinely determines the probative value of the evidence. We investigate common scenarios in which evidence has a LR of one but still has significant probative value (i.e. is not neutral as is commonly assumed). As illustration we consider the ramifications of these points for the case of Barry George. The successful appeal against his conviction for the murder of Jill Dando was based primarily on the argument that the firearm discharge residue (FDR) evidence, assumed to support the prosecution hypothesis at the original trial, actually had a LR equal to one and hence was 'neutral'. However, our review of the appeal transcript shows numerous examples of the problems with the use of hypotheses identified above. We show that if one were to follow the arguments recorded in the Appeal judgement verbatim, then contrary to the Appeal conclusion, the probative value of the FDR evidence may not have been neutral as was concluded.

Does the "why" tell us the "when"?

Traditional approaches to human causal reasoning assume that the perception of temporal order informs judgments of causal structure. In this article, we present two experiments in which people followed the opposite inferential route: Perceptual judgments of temporal order were instead influenced by causal beliefs. By letting participants freely interact with a software-based "physics world," we induced stable causal beliefs that subsequently determined participants' reported temporal order of events, even when this led to a reversal of the objective temporal order. We argue that for short timescales, even when temporal-resolution capabilities suffice, the perception of temporal order is distorted to fit existing causal beliefs.

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.

Making a positive difference: Criticality in groups.

How critical are individual members perceived to be for their group's performance? In this paper, we show that judgments of criticality are intimately linked to considering responsibility. Prospective responsibility attributions in groups are relevant across many domains and situations, and have the potential to influence motivation, performance, and allocation of resources. We develop various models that differ in how the relationship between criticality and responsibility is conceptualized. To test our models, we experimentally vary the task structure (disjunctive, conjunctive, and mixed) and the abilities of the group members (which affects their probability of success). We show that both factors influence criticality judgments, and that a model which construes criticality as anticipated credit best explains participants' judgments. Unlike prior work that has defined criticality as anticipated responsibility for both success and failures, our results suggest that people only consider the possible outcomes in which an individual contributed to a group success, but disregard group failure.

Not so simple! Causal mechanisms increase preference for complex explanations.

Mechanisms play a central role in how we think about causality, yet not all causal explanations describe mechanisms. Across five experiments, we find that people evaluate explanations differently depending on whether or not they include mechanisms. Despite common wisdom suggesting that explanations ought to be simple in the sense of appealing to as few causes as necessary to explain an effect, the literature is divided over whether people adhere to this principle. Our findings suggest that the presence of causal mechanisms in an explanation is one factor that reduces adherence. While competing explanations are often judged based on their probability of being correct, mechanisms afford a different way of evaluating explanations: They describe the underlying nature of causal relations. Complex explanations (appealing to multiple causes) contain more causal relations and thus allow for more mechanistic information, providing a fuller account of the causal network and promoting a greater sense of understanding.