David A. Lagnado

The advantage of timely intervention

Can people learn causal structure more effectively through intervention rather than observation? Four studies used a trial-based learning paradigm in which participants obtained probabilistic data about a causal chain through either observation or intervention and then selected the causal model most likely to have generated the data. Experiment 1 demonstrated that interveners made more correct model choices than did observers, and Experiments 2 and 3 ruled out explanations for this advantage in terms of informational differences between the 2 conditions. Experiment 4 tested the hypothesis that the advantage was driven by a temporal signal; interveners may exploit the cue that their interventions are the most likely causes of any subsequent changes. Results supported this temporal cue hypothesis.

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 peoples 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 Alickes model of culpable control.

Do we "do"?

A normative framework for modeling causal and counterfactual reasoning has been proposed by Spirtes, Glymour, and Scheines (1993; cf. Pearl, 2000). The framework takes as fundamental that reasoning from observation and intervention differ. Intervention includes actual manipulation as well as counterfactual manipulation of a model via thought. To represent intervention, Pearl employed the do operator that simplifies the structure of a causal model by disconnecting an intervened-on variable from its normal causes. Construing the do operator as a psychological function affords predictions about how people reason when asked counterfactual questions about causal relations that we refer to as undoing, a family of effects that derive from the claim that intervened-on variables become independent of their normal causes. Six studies support the prediction for causal (A causes B) arguments but not consistently for parallel conditional (if A then B) ones. Two of the studies show that effects are treated as diagnostic when their values are observed but nondiagnostic when they are intervened on. These results cannot be explained by theories that do not distinguish interventions from other sorts of events.

Time as a guide to cause

How do people learn causal structure? In 2 studies, the authors investigated the interplay between temporal-order, intervention, and covariational cues. In Study 1, temporal order overrode covariation information, leading to spurious causal inferences when the temporal cues were misleading. In Study 2, both temporal order and intervention contributed to accurate causal inference well beyond that achievable through covariational data alone. Together, the studies show that people use both temporal-order and interventional cues to infer causal structure and that these cues dominate the available statistical information. A hypothesis-driven account of learning is endorsed, whereby people use cues such as temporal order to generate initial models and then test these models against the incoming covariational data.

A General Structure for Legal Arguments About Evidence Using Bayesian Networks

A Bayesian network (BN) is a graphical model of uncertainty that is especially well suited to legal arguments. It enables us to visualize and model dependencies between different hypotheses and pieces of evidence and to calculate the revised probability beliefs about all uncertain factors when any piece of new evidence is presented. Although BNs have been widely discussed and recently used in the context of legal arguments, there is no systematic, repeatable method for modeling legal arguments as BNs. Hence, where BNs have been used in the legal context, they are presented as completed pieces of work, with no insights into the reasoning and working that must have gone into their construction. This means the process of building BNs for legal arguments is ad hoc, with little possibility for learning and process improvement. This article directly addresses this problem by describing a method for building useful legal arguments in a consistent and repeatable way. The method complements and extends recent work by Hepler, Dawid, and Leucari (2007) on object-oriented BNs for complex legal arguments and is based on the recognition that such arguments can be built up from a small number of basic causal structures (referred to as idioms). We present a number of examples that demonstrate the practicality and usefulness of the method.

Challenging the role of implicit processes in probabilistic category learning

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.

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.

Causal responsibility and counterfactuals.

How do people attribute responsibility in situations where the contributions of multiple agents combine to produce a joint outcome? The prevalence of over-determination in such cases makes this a difficult problem for counterfactual theories of causal responsibility. In this article, we explore a general framework for assigning responsibility in multiple agent contexts. We draw on the structural model account of actual causation (e.g., Halpern & Pearl, 2005) and its extension to responsibility judgments (Chockler & Halpern, 2004). We review the main theoretical and empirical issues that arise from this literature and propose a novel model of intuitive judgments of responsibility. This model is a function of both pivotality (whether an agent made a difference to the outcome) and criticality (how important the agent is perceived to be for the outcome, before any actions are taken). The model explains empirical results from previous studies and is supported by a new experiment that manipulates both pivotality and criticality. We also discuss possible extensions of this model to deal with a broader range of causal situations. Overall, our approach emphasizes the close interrelations between causality, counterfactuals, and responsibility attributions.

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 Parkinsons 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.

Probability judgment in hierarchical learning: a conflict between predictiveness and coherence.

Why are peoples judgments incoherent under probability formats? Research in an associative learning paradigm suggests that after structured learning participants give judgments based on predictiveness rather than normative probability. This is because peoples learning mechanisms attune to statistical contingencies in the environment, and they use these learned associations as a basis for subsequent probability judgments. We introduced a hierarchical structure into a simulated medical diagnosis task, setting up a conflict between predictiveness and coherence. Thus, a target symptom was more predictive of a subordinate disease than of its superordinate category, even though the latter included the former. Under a probability format participants tended to violate coherence and make ratings in line with predictiveness; under a frequency format they were more normative. These results are difficult to explain within a unitary model of inference, whether associative or frequency-based. In the light of this, and other findings in the judgment and learning literature, a dual-component model is proposed.