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.

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.

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.

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.

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.

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.

Categorical Updating in a Bayesian Propensity Problem.

We present three experiments using a novel problem in which participants update their estimates of propensities when faced with an uncertain new instance. We examine this using two different causal structures (common cause/common effect) and two different scenarios (agent-based/mechanical). In the first, participants must update their estimate of the propensity for two warring nations to successfully explode missiles after being told of a new explosion on the border between both nations. In the second, participants must update their estimate of the accuracy of two early warning tests for cancer when they produce conflicting reports about a patient. Across both experiments, we find two modal responses, representing around one-third of participants each. In the first, "Categorical" response, participants update propensity estimates as if they were certain about the single event, for example, certain that one of the nations was responsible for the latest explosion, or certain about which of the two tests is correct. In the second, "No change" response, participants make no update to their propensity estimates at all. Across the three experiments, the theory is developed and tested that these two responses in fact have a single representation of the problem: because the actual outcome is binary (only one of the nations could have launched the missile; the patient either has cancer or not), these participants believe it is incorrect to update propensities in a graded manner. They therefore operate on a "certainty threshold" basis, whereby, if they are certain enough about the single event, they will make the "Categorical" response, and if they are below this threshold, they will make the "No change" response. Ramifications are considered for the "categorical" response in particular, as this approach produces a positive-feedback dynamic similar to that seen in the belief polarization/confirmation bias literature.

A counterfactual simulation model of causal judgments for physical events.

How do people make causal judgments about physical events? We introduce the counterfactual simulation model (CSM) which predicts causal judgments in physical settings by comparing what actually happened with what would have happened in relevant counterfactual situations. The CSM postulates different aspects of causation that capture the extent to which a cause made a difference to whether and how the outcome occurred, and whether the cause was sufficient and robust. We test the CSM in several experiments in which participants make causal judgments about dynamic collision events. A preliminary study establishes a very close quantitative mapping between causal and counterfactual judgments. Experiment 1 demonstrates that counterfactuals are necessary for explaining causal judgments. Participants' judgments differed dramatically between pairs of situations in which what actually happened was identical, but where what would have happened differed. Experiment 2 features multiple candidate causes and shows that participants' judgments are sensitive to different aspects of causation. The CSM provides a better fit to participants' judgments than a heuristic model which uses features based on what actually happened. We discuss how the CSM can be used to model the semantics of different causal verbs, how it captures related concepts such as physical support, and how its predictions extend beyond the physical domain. (PsycInfo Database Record (c) 2021 APA, all rights reserved).

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.

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.

Causality influences children's and adults' experience of temporal order.

Although it has long been known that time is a cue to causation, recent work with adults has demonstrated that causality can also influence the experience of time. In causal reordering (Bechlivanidis & Lagnado, 2013, 2016) adults tend to report the causally consistent order of events rather than the correct temporal order. However, the effect has yet to be demonstrated in children. Across four preregistered experiments, 4- to 10-year-old children (N = 813) and adults (N = 178) watched a 3-object Michotte-style "pseudocollision." While in the canonical version of the clip, object A collided with B, which then collided with object C (order: ABC), the pseudocollision involved the same spatial array of objects but featured object C moving before object B (order: ACB), with no collision between B and C. Participants were asked to judge the temporal order of events and whether object B collided with C. Across all age groups, participants were significantly more likely to judge that B collided with C in the 3-object pseudocollision than in a 2-object control clip (where clear causal direction was lacking), despite the spatiotemporal relations between B and C being identical in the two clips (Experiments 1-3). Collision judgments and temporal order judgments were not entirely consistent, with some participants-particularly in the younger age range-basing their temporal order judgments on spatial rather than temporal information (Experiment 4). We conclude that in both children and adults, rather than causal impressions being determined only by the basic spatial-temporal properties of object movement, schemata are used in a top-down manner when interpreting perceptual displays. (PsycINFO Database Record (c) 2020 APA, all rights reserved).

Temporal Binding, Causation, and Agency: Developing a New Theoretical Framework.

In temporal binding, the temporal interval between one event and another, occurring some time later, is subjectively compressed. We discuss two ways in which temporal binding has been conceptualized. In studies showing temporal binding between a voluntary action and its causal consequences, such binding is typically interpreted as providing a measure of an implicit or pre-reflective "sense of agency." However, temporal binding has also been observed in contexts not involving voluntary action, but only the passive observation of a cause-effect sequence. In those contexts, it has been interpreted as a top-down effect on perception reflecting a belief in causality. These two views need not be in conflict with one another, if one thinks of them as concerning two separate mechanisms through which temporal binding can occur. In this paper, we explore an alternative possibility: that there is a unitary way of explaining temporal binding both within and outside the context of voluntary action as a top-down effect on perception reflecting a belief in causality. Any such explanation needs to account for ways in which agency, and factors connected with agency, has been shown to affect the strength of temporal binding. We show that principles of causal inference and causal selection already familiar from the literature on causal learning have the potential to explain why the strength of people's causal beliefs can be affected by the extent to which they are themselves actively involved in bringing about events, thus in turn affecting binding.

The developmental profile of temporal binding: From childhood to adulthood.

Temporal binding refers to a phenomenon whereby the time interval between a cause and its effect is perceived as shorter than the same interval separating two unrelated events. We examined the developmental profile of this phenomenon by comparing the performance of groups of children (aged 6-7, 7-8, and 9-10 years) and adults on a novel interval estimation task. In Experiment 1, participants made judgements about the time interval between (a) their button press and a rocket launch, and (b) a non-causal predictive signal and rocket launch. In Experiment 2, an additional causal condition was included in which participants made judgements about the interval between an experimenter's button press and the launch of a rocket. Temporal binding was demonstrated consistently and did not change in magnitude with age: estimates of delay were shorter in causal contexts for both adults and children. In addition, the magnitude of the binding effect was greater when participants themselves were the cause of an outcome compared with when they were mere spectators. This suggests that although causality underlies the binding effect, intentional action may modulate its magnitude. Again, this was true of both adults and children. Taken together, these results are the first to suggest that the binding effect is present and developmentally constant from childhood into adulthood.

Causation without realism.

Current theories of causality from visual input predict causal impressions only in the presence of realistic interactions, sequences of events that have been frequently encountered in the past of the individual or of the species. This strong requirement limits the capacity for 1-shot induction and, thus, does not sit well with our abilities for rapid creative causal learning, as illustrated, for example, by the effortless way we adapt to new technology. We present 4 experiments (N = 720) that reveal strong causal impressions upon first encounter with collision-like sequences that the literature typically labels "noncausal." Our stimuli include both the commonly used computer-based animations and edited video sequences. Besides direct reports, we present evidence based on goal-oriented behavior that makes sense only in the presence of strong causal assumptions. Finally, we document impressions of causality in highly unrealistic sequences involving, for example, instantaneous shape or size change. In the case of the more realistic clips used in the past, causal ratings abruptly decline and approach the findings of previous work, only after a canonical collision (launch event) is presented. We argue that previously used experimental procedures conceal order effects because of participants adapting to the task and reinterpreting its demands. We discuss ways to account for this adaptation whereby people either focus on experiences of perceptual causation or take realism into account even when asked for impressions of causality. (PsycINFO Database Record (c) 2019 APA, all rights reserved).

The effect of feedback on non-motor probabilistic classification learning in Parkinson's disease.

It has been proposed that procedural learning is mediated by the striatum and, it has been reported that patients with Parkinson's disease (PD) are impaired on the weather prediction task (WPT) which involves probabilistic classification learning with corrective feedback (FB). However, PD patients were not impaired on probabilistic classification learning when it was performed without corrective feedback, in a paired associate (PA) manner; suggesting that the striatum is involved in learning with feedback rather than procedural learning per se. In Experiment 1 we studied FB- and PA-based learning in PD patients and controls and, as an improvement on previous methods, used a more powerful repeated measures design and more equivalent test phases during FB and PA conditions (including altering the FB condition to remove time limits on responding). All participants (16 PD patients, H&Y I-III and 14 matched-controls) completed the WPT under both FB and PA conditions. In contrast to previous results, in Experiment 1 we did not find a selective impairment in the PD group on the FB version of the WPT relative to controls. In Experiment 2 we used a between groups design and studied learning with corrective FB in 11 PD patients (H&Y I.5-IV) and 13 matched controls on a more standard version of the WPT similar to that used in previous studies. With such a between groups design for comparison of FB and PA learning on the WPT in PD, we observed impaired learning in PD patients relative to controls across both the FB and PA versions of the WPT. Most importantly, in Experiment 2 we also failed to find a selective impairment on the FB version of the WPT coupled with normal learning on the PA version in PD patients relative to controls. Our results do not support the proposal that the striatum plays a specific role in probabilistic classification learning with feedback.

Judgments of cause and blame: the effects of intentionality and foreseeability.

What are the factors that influence everyday attributions of cause and blame? The current studies focus on sequences of events that lead to adverse outcomes, and examine people's cause and blame ratings for key events in these sequences. Experiment 1 manipulated the intentional status of candidate causes and their location in a causal chain. Participants rated intentional actions as more causal, and more blameworthy, than unintentional actions or physical events. There was also an overall effect of location, with later events assigned higher ratings than earlier events. Experiment 2 manipulated both intentionality and foreseeability. The preference for intentional actions was replicated, and there was a strong influence of foreseeability: actions were rated as more causal and more blameworthy when they were highly foreseeable. These findings are interpreted within two prominent theories of blame, [Shaver, K. G. (1985). The attribution of blame: Causality, responsibility, and blameworthiness. New York: Springer-Verlag] and [Alicke, M. D. (2000). Culpable control and the psychology of blame. Psychological Bulletin, 126, 556-574]. Overall, it is argued that the data are more consistent with Alicke's model of culpable control.

The impact of discredited evidence.

How do people revise their beliefs when evidence is discredited? In three studies, mock jurors read simplified criminal cases and then judged the probability that a suspect was guilty on the basis of sequentially presented evidence. Study 1 showed an extension effect: When two items of incriminating evidence were presented, a subsequent discrediting of the second item also lessened belief in the first item, irrespective of whether it was directly related to the discredited item. Study 2 showed that this effect depended on the order of evidence presentation: When the discrediting evidence was presented early, rather than late, in the sequence, there was no extension to unrelated items. Study 3 showed that the extension effect held only when items of evidence were both incriminating or both exonerating, but not when they were mixed. To explain these findings, we draw on coherence-based models of juror reasoning and propose that people group evidence according to its direction with respect to the guilt hypothesis.