Academic journal article Memory & Cognition

The Influence of Information Redundancy on Probabilistic Inferences

Academic journal article Memory & Cognition

The Influence of Information Redundancy on Probabilistic Inferences

Article excerpt

Information redundancy affects the accuracy of inference strategies. A simulation study illustrates that under high-information redundancy simple heuristics that rely on only the most important information are as accurate as strategies that integrate all available information, whereas under low redundancy integrating information becomes advantageous. Assuming that people exercise adaptive strategy selection, it is predicted that their inferences will more often be captured by simple heuristics that focus on part of the available information in situations of high-information redundancy, especially when information search is costly. This prediction is confirmed in two experiments. The participants' task was to repeatedly infer which of two alternatives, described by several cues, had a higher criterion value. In the first experiment, simple heuristics predicted the inference process better under high-information redundancy than under low-information redundancy. In the second experiment, this result could be generalized to an inference situation in which participants had no prior opportunity to learn about the strategies' accuracies through outcome feedback. The results demonstrate that people are able to respond adaptively to different decision environments under various learning opportunities.

Decisions are often made under uncertainty. People cannot perfectly predict whether their chosen route to work will avoid the morning traffic jam or whether their favorite basketball team will win an upcoming game. Thus, they have to rely on cues-pieces of information that are imperfectly correlated with the criterion to be predicted. Such cues are sometimes highly correlated with each other and speak for the same prediction, whereas at other times they contradict each other and suggest incompatible predictions. This variance in information redundancy hi decision environments is the focus of this article.

How people's inferences are influenced by information redundancy has been addressed in the neo-Brunswikian "social judgment theory" research (Brehmer & Joyce, 1988; Cooksey, 1996; Doherty & Kurz, 1996). This is not a coincidence. One of Brunswik's (1952, 1955) core concepts is vicarious functioning. Broadly, this can be defined as "exchangeability of pathways relative to an end" (Brunswik, 1952, p. 17). More narrowly applied to the problem of probabilistic inference, vicarious functioning means that correlated cues can function as substitutes for each other. In social judgment theory research, experimenters have often applied the multiple cue probability learning (MCPL; Smedslund, 1955) paradigm. Typically in this line of research, people's judgments are described by fitting a regression model to the judgment and by comparing the model's resulting beta weights with the beta weights of the "ecological" regression model fitted to the correct criterion values (Brehmer, 1974). Information redundancy, defined as positive correlations between cues, was found to be positively correlated with judgment accuracy (Naylor & Schenck, 1968) and speed of learning (Knowles, Hammond, Stewart, & Summers, 1971). However, Armelius and Armelius (1974; see also Schmitt & Dudycha, 1975) doubted that people take correlations between cues into account when making inferences. They showed that the beta weights of regression models fitted to participants' inferences matched cue-criterion correlations rather than the ecological beta weights incorporating correlations between cues.

We argue that this skepticism about people's ability to incorporate cue redundancy into judgments has derived from using mainly one model for describing people's judgments-namely, linear regression. We suggest that people's judgments may rely on inference strategies that cannot easily be captured by a regression model. For instance, under high-information redundancy people might select strategies for their judgments that ignore information, since these strategies will still perform well. …

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