Combination Rules in Information Integration

The decisions that people make often show peculiar biases, especially when combining information together. Let's say that you are a juror in a murder trial. The prosecution presents you with two pieces of evidence: an eyewitness who claims to have seen the defendant at the scene of the crime and an expert who matched the fingerprints on the murder weapon to the defendant. Both pieces of evidence strongly implicate the defendant, but neither is perfect. Surprisingly we've found, using a more controlled laboratory task, that the impression of the guilt of the defendant does not increase with the second piece of evidence. If a participant in the experiment thought that there was a 75% chance of guilt based on one pieces of evidence, they still believed that there was a 75% chance of guilt based on two pieces of evidence.

However, we do not always combine information incorrectly -- the rule we use to combine information is malleable. An example of this comes if we reframe the murder case as a perceptual task. Instead of determining guilt or innocence, let's say we are concerned with whether someone made a "ba" or "pa" sound. And if instead of evaluating two witnesses, let's say that we are presented with a video of a mouth moving and an audio clip. This is the same problem as the murder case example, but here people combine information correctly. If the information is presented to people "cognitively", as separate pieces of text, they combine it incorrectly. If the information is presented "perceptually", as different parts of the same image, they combine it correctly.

Other researchers have looked at when people combine information differently, but have used different manipulations in different tasks with different stimuli. In our pilot work, we made the murder case and sound identification task as similar as possible using the same set of stimuli and still found that people combined information very differently. Our overall goal is to develop a unified understanding of in what drives people to combine information correctly or incorrectly by exploring a variety of tasks using a common set of stimuli and manipulations.

In addition to our overall goal, we have some specific aims for this research. Our first aim is to investigate why changing the presentation between the extremes of the murder trial and sound identification tasks affects how we combine information. These manipulations should tell us whether the difference is due to having a better perceptual rather than cognitive system, whether it is due to having more experience with the perceptual task, or whether people have particular assumptions about where the information comes from.

Our second aim is to understand how people combine information about how risky an investment is and how much potential reward could be gained. Though investors often have to choose between investments, researchers do not know whether people combine risk and reward correctly in this task. Once we have determined what type of rule people are using, we see if the murder trial/sound identification task manipulations have the same effect here - whether people have common mechanisms for choosing combination rules in the two tasks.

Although people often do not integrate information correctly, their strategy might in fact be optimal given their uncertainty about the correct integration rule. Statistically, if one is unsure of the correct rule, the best strategy to get close to the true answer is to average estimates from candidate rules. Our third aim is to test whether people are in fact behaving optimally in the face of uncertainty about integration rules.

Our research into what induces people to integrate information normatively is interesting to business leaders and public policy makers, has implications for how we train people to understand gambling, and is of general interest to the public. We have identified several pathways for the research to make an impact.

Business leaders and public policy makers can use the results of the research to encourage people to integrate information correctly. Our predictions of economic behaviour depend on the assumption that people make normative decisions in their subjective evaluation of the evidence. We can make an impact on future business leaders and public policy makers through the University of Warwick's joint MScs in decision science and behavioural economics. Our findings for this project will be incorporated into the material for the courses, which will expose those going into business and public policy to the theories that we develop.

Reducing problem gambling is a goal of the charity GamCare, which has recommended that risk and probability be taught in schools in order to make young people aware of the actual probabilities associated with gambling. They are working to develop materials for this purpose, as probability training has shown to reduce incidence of problem gambling. Better understanding how to get people to combine information correctly would be useful for training purposes and we plan to distribute the results of our findings to GamCare in order to point the way to more effective materials for gambling education, which could then be used to reduce the incidence of problem gambling.

Finally, our results will be interesting to the general public, who are understandably curious about when decisions can be faulty. Unlike the popular conception that we are often completely unable to reason accurately, this research instead emphasises how good choices may instead be induced by presenting the problem in a perceptual format and how these decisions reflect more complex normative strategies than they first appear to be. To make the research publicly available, Stewart will make himself available to media outlets for discussing the implications of our work on the flexibility and reasons behind why people make judgments that appear to be poor.