Two models of causal and scientific reasoning, Humean and Bayesian, have been used as the basis for asking questions about how individual people reason. Both models are idealized or formal in that they are treated as being applicable to any situation, regardless of the topic area. However, by itself, the Humean model provides no strategy for deciding whether, for example, a covariation reflects genuine cause or simple coincidence. The Bayesian model, by itself, provides no strategy for determining which related events, or “priors”, ought to be treated as relevant.
Since neither approach is sufficient, by itself, to fully model causal and scientific reasoning, what is missing? Part of the answer, which researchers have been investigating, is the importance of background information, especially explanation (or mechanism or process). Rather than being an idealized model, the emphasis on explanation takes account of actual scientific practice. In actual practice, scientists aim to explain phenomena, not merely to amass a list of covariations or priors. Furthermore, they often rely on explanation to distinguish covariations that are genuinely causal rather than merely specious, as well as to decide which related events or Bayesian priors should be treated as relevant.
However, a reliance on explanation raises the question of how to decide whether a particular explanation is credible. Abduction, or inference to the best explanation, suggests a possible strategy based on scientific practice, namely, that one explanation for an event is preferred to another because it provides the more plausible account of the data that is also more causally coherent with what else we know about the world, that is, with the broader web of information related to the explanation. This description suggests that one way of examining people’s reasoning is to ask how it approximates, not an idealized formal model, but what scientists actually do.
The emphasis on the web of information shifts the focus from simply identifying a causal event to the broader aim of making sense of a constellation of information. Some specific questions it suggests include the following:
1. What else we know about the world can encompass a large body of information. Thus, one explanation might be preferable to another, not because of a single piece of information or data, but because the explanation is more causally coherent with the cumulative weight of the evidence. Do people take account of this?
2. Relative plausibility suggests that amassing evidence that confirms one explanation might well disconfirm a competing explanation because the competitor fails to account for the evidence. Do people treat confirmation and disconfirmation as two sides of the same coin?
3. The broader web of information includes information about events that are anomalous to an explanation as well as information about competing for alternative explanations. How do people reason about anomalies and alternative accounts?
4. Finally, when an explanation is able to account for information, do people find the explanation itself to be increasingly credible?
Such questions can be relevant not only to the psychological study of how people reason but also to studies of people’s reasoning about explanations in educational as well as cross-cultural (including historical) settings.
Two models of causal and scientific reasoning, Humean and Bayesian, have been used as the basis for asking questions about how individual people reason. Both models are idealized or formal in that they are treated as being applicable to any situation, regardless of the topic area. However, by itself, the Humean model provides no strategy for deciding whether, for example, a covariation reflects genuine cause or simple coincidence. The Bayesian model, by itself, provides no strategy for determining which related events, or “priors”, ought to be treated as relevant.
Since neither approach is sufficient, by itself, to fully model causal and scientific reasoning, what is missing? Part of the answer, which researchers have been investigating, is the importance of background information, especially explanation (or mechanism or process). Rather than being an idealized model, the emphasis on explanation takes account of actual scientific practice. In actual practice, scientists aim to explain phenomena, not merely to amass a list of covariations or priors. Furthermore, they often rely on explanation to distinguish covariations that are genuinely causal rather than merely specious, as well as to decide which related events or Bayesian priors should be treated as relevant.
However, a reliance on explanation raises the question of how to decide whether a particular explanation is credible. Abduction, or inference to the best explanation, suggests a possible strategy based on scientific practice, namely, that one explanation for an event is preferred to another because it provides the more plausible account of the data that is also more causally coherent with what else we know about the world, that is, with the broader web of information related to the explanation. This description suggests that one way of examining people’s reasoning is to ask how it approximates, not an idealized formal model, but what scientists actually do.
The emphasis on the web of information shifts the focus from simply identifying a causal event to the broader aim of making sense of a constellation of information. Some specific questions it suggests include the following:
1. What else we know about the world can encompass a large body of information. Thus, one explanation might be preferable to another, not because of a single piece of information or data, but because the explanation is more causally coherent with the cumulative weight of the evidence. Do people take account of this?
2. Relative plausibility suggests that amassing evidence that confirms one explanation might well disconfirm a competing explanation because the competitor fails to account for the evidence. Do people treat confirmation and disconfirmation as two sides of the same coin?
3. The broader web of information includes information about events that are anomalous to an explanation as well as information about competing for alternative explanations. How do people reason about anomalies and alternative accounts?
4. Finally, when an explanation is able to account for information, do people find the explanation itself to be increasingly credible?
Such questions can be relevant not only to the psychological study of how people reason but also to studies of people’s reasoning about explanations in educational as well as cross-cultural (including historical) settings.
