MINERVA‐DM and subadditive frequency judgments

There is considerable evidence that frequency (and probability) judgments are often subadditive. That is, the frequency judgment assigned to an event is often less than the sum of the frequency judgments assigned to the mutually exclusive component events that together form it. Explanations for subadditive judgments have typically relied on relatively high-level cognitive constructs such as the availability and representativeness heuristics. A lower-level explanation of subadditivity is presented in this paper through a model of memory and judgment processes, MINERVA-DM. Under MINERVA-DM, subadditivity is influenced by the similarity of the representations of the judged component events in memory to one another and by the placement of decision criteria. Results from two experiments support the model predictions. The first examines the effects of component event similarity on subadditivity. The second replicates the first and also provides support for the model's prediction of the effects of payoffs on similarity criteria. Copyright © 2004 John Wiley & Sons, Ltd.

[1]  D Kahneman,et al.  On the reality of cognitive illusions. , 1996, Psychological review.

[2]  A. Tversky,et al.  Judgment under Uncertainty: Heuristics and Biases , 1974, Science.

[3]  C. Fox,et al.  Forecasting Trial Outcomes: Lawyers Assign Higher Probability to Possibilities That Are Described in Greater Detail , 2002, Law and human behavior.

[4]  Douglas L. Hintzman,et al.  MINERVA 2: A simulation model of human memory , 1984 .

[5]  Gerd Gigerenzer,et al.  Surrogates for Theories , 1998 .

[6]  E. Hirshman,et al.  True and False Recognition in MINERVA2: Explanations from a Global Matching Perspective , 1998 .

[7]  A. Tversky,et al.  Unpacking, repacking, and anchoring: advances in support theory. , 1997 .

[8]  C. Gettys,et al.  MINERVA-DM: A memory processes model for judgments of likelihood. , 1999 .

[9]  A. Tversky,et al.  Weighing Risk and Uncertainty , 1995 .

[10]  Daniel Kahneman,et al.  Availability: A heuristic for judging frequency and probability , 1973 .

[11]  Derek J. Koehler,et al.  The enhancement effect in probability judgment , 1997 .

[12]  R. Dawes,et al.  Subadditivity in Memory for Personal Events , 1999 .

[13]  D. L. Hintzman,et al.  Effects of similarity and repetition on memory: registration without learning? , 1992, Journal of experimental psychology. Learning, memory, and cognition.

[14]  D. Krantz,et al.  A Note on Superadditive Probability Judgment , 1999 .

[15]  Glenn Shafer,et al.  A Mathematical Theory of Evidence , 2020, A Mathematical Theory of Evidence.

[16]  G. Gigerenzer How to Make Cognitive Illusions Disappear: Beyond “Heuristics and Biases” , 1991 .

[17]  A. Tversky,et al.  Options traders exhibit subadditive decision weights , 1996 .

[18]  G. Kimble,et al.  Effects of incentive on false recognition , 1973 .

[19]  A. Tversky,et al.  Support theory: A nonextensional representation of subjective probability. , 1994 .

[20]  G. Gigerenzer On Narrow Norms and Vague Heuristics: A Reply to Kahneman and Tversky (1996) , 1996 .

[21]  D. Koehler,et al.  Probability judgment in three-category classification learning. , 2000, Journal of Experimental Psychology. Learning, Memory and Cognition.

[22]  T. Wallsten,et al.  Dissociating judgment from response processes in statement verification: the effects of experience on each component. , 1999, Journal of experimental psychology. Learning, memory, and cognition.

[23]  B. Fischhoff,et al.  Fault trees: Sensitivity of estimated failure probabilities to problem representation. , 1978 .

[24]  Thomas S. Wallsten,et al.  The Theoretical Status of Judgmental Heuristics1) , 1983 .

[25]  Douglas L. Hintzman,et al.  Judgments of frequency and recognition memory in a multiple-trace memory model. , 1988 .