Evidence is crucial to many aspects of human cognition. We use it to update our mental models of the world, and to guide our inferences and decisions. Psychological studies of judgment and decision reveal a variety of cognitive biases in the gathering, assessment and use of evidence, but with no unifying framework. In part this is due to the lack of a comprehensive normative account. Bayesian networks provide such an account – a normative theory of belief revision and inference, dependency relations, evidence integration, and a natural link to causal models. The concept of an inference network formalizes the notion of a mental model, and the graphical representation suggests a compelling format to aid human inference, especially in complex situations.

We plan to use Bayesian networks as a normative framework against which to explore how humans use evidence. This is a necessary step towards a more unified and systematic model of human reasoning. It will also facilitate the construction of appropriate inference aids where humans deviate from the normative standard. We do not presume that people’s actual inferential practices are based on mental ‘Bayesian network’ structures (although this has been proposed by some, eg, Glymour, 2001; Gopnik et al. 2004). Current evidence suggests that when people represent and reason about uncertainty they adopt simplifying strategies and heuristics (Gilovich et al., 2002). These will sometimes approximate sound Bayesian reasoning, but can deviate in systematic ways.

The discovery, integration and use of evidence depend crucially on the prior beliefs and assumptions of the agent. This interaction will provide an integrating theme for our psychological experiments. We will examine the various ways in which people’s prior conceptions (eg, beliefs, causal models etc.) and processing mechanisms (eg, belief revision and information-integration) affect the assimilation of evidence.

Nigel Harvey
is professor of judgment and decision research at UCL. He is interested in the use of judgment to forecast and control the behaviour of systems, and in people's confidence in their judgments. Besides work on the Evidence programme his current research includes experiments in advice-taking and trust in advisors. He is associate editor of 'Thinking & Reasoning', and on the editorial board of the 'Journal of Behavioural Decision-making' and the 'International Journal of forecasting'. He has recently co-edited the 'Blackwell handbook of Judgment & Decision Making' (2004).

David Lagnado
is a post-doctoral fellow on the Evidence project. He has previously held research posts at UCL and Brown University, USA. His main research is in human learning and inference, with particular focus on models of causal and probabilistic reasoning. His work on the Evidence project will include studies on how people use evidence to make probabilistic inferences, how this fits with normative models of inference, and what can be done to improve judgment when systematic biases arise. Of particular interest is the role of causal knowledge: how do people acquire it, and how do they use it for prediction and explanation?

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