Causal Models and Conditional Reasoning

2017 ◽  
Author(s):  
Mike Oaksford ◽  
Nick Chater
Keyword(s):  
Model Theory ◽  
Causal Model ◽  
Recent Model ◽  
Causal Models ◽  
Conditional Reasoning ◽  
Cognitive System ◽  
Markov Condition ◽  
Bayes Nets ◽  
Causal Bayes Nets ◽  
Conditional Statements

There are deep intuitions that the meaning of conditional statements relate to probabilistic law-like dependencies. In this chapter it is argued that these intuitions can be captured by representing conditionals in causal Bayes nets (CBNs) and that this conjecture is theoretically productive. This proposal is borne out in a variety of results. First, causal considerations can provide a unified account of abstract and causal conditional reasoning. Second, a recent model (Fernbach & Erb, 2013) can be extended to the explicit causal conditional reasoning paradigm (Byrne, 1989), making some novel predictions on the way. Third, when embedded in the broader cognitive system involved in reasoning, causal model theory can provide a novel explanation for apparent violations of the Markov condition in causal conditional reasoning (Ali et al, 2011). Alternative explanations are also considered (see, Rehder, 2014a) with respect to this evidence. While further work is required, the chapter concludes that the conjecture that conditional reasoning is underpinned by representations and processes similar to CBNs is indeed a productive line of research.

A Causal Model Theory of the Meaning ofCause,Enable, andPrevent

2009 ◽  
Vol 33 (1) ◽  
pp. 21-50 ◽  
Author(s):  
Steven Sloman ◽  
Aron K. Barbey ◽  
Jared M. Hotaling
Keyword(s):  
Model Theory ◽  
Causal Model

Causation in Legal and Moral Reasoning

2017 ◽  
Author(s):  
David A. Lagnado ◽  
Tobias Gerstenberg
Keyword(s):  
Moral Reasoning ◽  
Causal Reasoning ◽  
Causal Model ◽  
Physical World ◽  
Causal Models ◽  
Responsibility Attribution ◽  
Model Framework ◽  
The Social ◽  
Legal Concepts ◽  
Legal Judgments

Causation looms large in legal and moral reasoning. People construct causal models of the social and physical world to understand what has happened, how and why, and to allocate responsibility and blame. This chapter explores people’s common-sense notion of causation, and shows how it underpins moral and legal judgments. As a guiding framework it uses the causal model framework (Pearl, 2000) rooted in structural models and counterfactuals, and shows how it can resolve many of the problems that beset standard but-for analyses. It argues that legal concepts of causation are closely related to everyday causal reasoning, and both are tailored to the practical concerns of responsibility attribution. Causal models are also critical when people evaluate evidence, both in terms of the stories they tell to make sense of evidence, and the methods they use to assess its credibility and reliability.

On the Mental Representation of Conditional Sentences

1996 ◽  
Vol 49 (4) ◽  
pp. 1086-1114 ◽  
Author(s):  
Jonathan St. B. T. Evans ◽  
Charles E. Ellis ◽  
Stephen E. Newstead
Keyword(s):  
Mental Models ◽  
Research Methods ◽  
Mental Representation ◽  
Conditional Reasoning ◽  
Strong Tendency ◽  
Inference Rules ◽  
Counter Example ◽  
First Finding ◽  
Blue Triangle ◽  
Conditional Statements

Four experiments are reported which attempt to externalize subjects’ mental representation of conditional sentences, using novel research methods. In Experiment 1, subjects were shown arrays of coloured shapes and asked to rate the degree to which they appeared to be true of conditional statements such as “If the figure is green then it is a triangle”. The arrays contained different distributions of the four logically possible cases in which the antecedent or consequent is true or false: TT, TF, FT, and FF. For example, a blue triangle would be FT for the conditional quoted above. In Experiments 2 to 4, subjects were able to construct their own arrays to make conditionals either true or false with any distribution of the four cases they wished to choose. The presence and absence of negative components was varied, as was the form of the conditional, being either “if then” as above or “only if”: “The figure is green only if it is a triangle”. The first finding was that subjects represent conditionals in fuzzy way: conditionals that include some counter-example TF cases (Experiment 1) may be rated as true, and such cases are often included when subjects construct an array to make the rule true (Experiments 2 to 4). Other findings included a strong tendency to include psychologically irrelevant FT and FF cases in constructed arrays, presumably to show that conditional statements only apply some of the time. A tendency to construct cases in line with the “matching bias” reported on analogous tasks in the literature was found, but only in Experiment 4, where the number of symbols available to construct each case was controlled. The findings are discussed in relation to the major contemporary theories of conditional reasoning based upon inference rules and mental models, neither of which can account for all the results.

scholarly journals Norms and high-level cognition: Consequences, trends, and antidotes

2011 ◽  
Vol 34 (5) ◽  
pp. 260-261
Author(s):  
Simon McNair ◽  
Aidan Feeney
Keyword(s):  
Bayes Nets ◽  
Psychological Science ◽  
Causal Bayes Nets ◽  
High Level

AbstractWe are neither as pessimistic nor as optimistic as Elqayam & Evans (E&E). The consequences of normativism have not been uniformly disastrous, even among the examples they consider. However, normativism won't be going away any time soon and in the literature on causal Bayes nets new debates about normativism are emerging. Finally, we suggest that to concentrate on expert reasoners as an antidote to normativism may limit the contribution of research on thinking to basic psychological science.

Leaning Causal Models with Conditional Causal Probabilities from Data

2002 ◽  
Vol 6 (1) ◽  
pp. 25-32
Author(s):  
Koichi Yamada ◽  
Keyword(s):  
Numerical Experiments ◽  
Causal Model ◽  
Causal Models ◽  
Conditional Probabilities ◽  
Prior Probabilities ◽  
Counting Data

We propose a way to lean probabilistic causal models using conditional causal probabilities (CCPs) to represent uncertainty of causalities. The CCP is a probability devised by Peng and Reggia representing the uncertainty that a cause actually causes an effect given the cause. The main advantage of using CCPs is that they represent exact probabilities of causalities that people recognize mentally, and that the number of probabilities used in the causal model is far smaller than that of conditional probabilities by all combinations of possible causes. Thus, Peng and Reggia assumed that CCPs are given by human experts as subjective ones, and did not discuss how to calculate them from data when a dataset was available. We address this problem, starting from a discussion about properties of data frequently given in practical problems, and shows that prior probabilities that should be learned may differ from those derived by counting data. We then discuss and propose how to learn prior probabilities and CCPs from data, and evaluate the proposed method through numerical experiments and analyze results to show that the precision of leaned models is satisfactory.

Hindsight Bias and the Activation of Counterfactual Mind-Sets

2008 ◽  
Vol 55 (5) ◽  
pp. 342-349 ◽  
Author(s):  
Steffen Nestler ◽  
Gernot von Collani
Keyword(s):  
Model Theory ◽  
Priming Effect ◽  
Causal Model ◽  
Hindsight Bias ◽  
Hypothetical Scenario ◽  
Mind Set ◽  
Causal Strength

Previous research has shown that conditional counterfactuals are positively related to the magnitude of creeping determinism. Unlike previous experiments which show this increased hindsight bias to occur after exceptional antecedents, we investigated another possible factor, namely a prior activation of a counterfactual mind-set. We investigated our prediction using a hypothetical scenario. Prior to reading the hindsight scenario some participants were asked to solve a scrambled-sentence test including conditional counterfactual sentences. Results of two experiments were consistent with our predictions: Participants that solved the scrambled-sentence test perceived the outcome to be more inevitable than participants in a no-outcome control condition and participants in a no-prime control condition. Furthermore, we found that this increase in creeping determinism was mediated by the perceived causal strength of the target antecedent for the occurrence of the outcome, and that the priming-effect did not occur when an unconditional counterfactual mind-set was activated before. The results are interpreted as supporting a causal-model theory of the hindsight bias.

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