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Overconfidence in judgment for repea...

Sieck, Winston Ronald.

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Overconfidence in judgment for repeatable events.

Record Type:	Language materials, printed : Monograph/item
Title/Author:	Overconfidence in judgment for repeatable events./
Author:	Sieck, Winston Ronald.
Description:	69 p.
Notes:	Chair: J. Frank Yates.
Contained By:	Dissertation Abstracts International61-03B.
Subject:	Psychology, Cognitive. -
Online resource:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=9963897
ISBN:	0599681381

Overconfidence in judgment for repeatable events.
Sieck, Winston Ronald.

Overconfidence in judgment for repeatable events. - 69 p.

Chair: J. Frank Yates.

Thesis (Ph.D.)--University of Michigan, 2000.

People are overconfident in their judgments about repeatable events. As an example, suppose respondents are instructed to diagnose each of a series of hypothetical patients as having one of two diseases in the following two-stage procedure. For each patient, the respondent first indicates what disease they believe is afflicting the patient. Then the respondent states a 50% to 100% probability judgment that the patient actually has the indicated disease. Over a series of such trials, the average of the probability judgments tends to exceed the proportion of correct diagnoses (i.e. respondents tend to be overconfident). A neural network-based probability judgment (NBPJ) model and an exemplar-based probability judgment (EBPJ) model were developed for these kinds of tasks, and accounts for overconfidence were derived from each. The NBPJ asserts that people's learning of ecological probabilities is essentially veridical. However, their classification responses are fundamentally probabilistic, which results in overconfidence. The EBPJ proposes that people learn by storing past examples, and that their judgments are often based on the first example they happen to retrieve. In this model, reliance on small samples of exemplars in judgment leads to overconfidence. The models' accounts of overconfidence were compared in three experiments. The first of two key results was that eliminating the choice stage of the judgment process and directly reporting on the probability of one disease led to an increase in overconfidence. This is anticipated by the EBPJ because this procedure encourages retrieval of less information than the two-stage procedure. The NBPJ predicts the opposite result, because it locates overconfidence at the choice stage, which is eliminated in a single-stage, direct report procedure. The second principal result was that an instruction to retrieve many exemplars reduced overconfidence. This result directly supported the EBPJ's account, but was not expected by the NBPJ. Implications for current theories of likelihood judgment are discussed.

ISBN: 0599681381Subjects--Topical Terms:

1017810
Psychology, Cognitive.

Overconfidence in judgment for repeatable events.
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005 20110505
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020 $a 0599681381
035 $a (UnM)AAI9963897
035 $a AAI9963897
040 $a UnM $c UnM
100 1 $a Sieck, Winston Ronald. $3 1256412
245 1 0 $a Overconfidence in judgment for repeatable events.
300 $a 69 p.
500 $a Chair: J. Frank Yates.
500 $a Source: Dissertation Abstracts International, Volume: 61-03, Section: B, page: 1666.
502 $a Thesis (Ph.D.)--University of Michigan, 2000.
520 $a People are overconfident in their judgments about repeatable events. As an example, suppose respondents are instructed to diagnose each of a series of hypothetical patients as having one of two diseases in the following two-stage procedure. For each patient, the respondent first indicates what disease they believe is afflicting the patient. Then the respondent states a 50% to 100% probability judgment that the patient actually has the indicated disease. Over a series of such trials, the average of the probability judgments tends to exceed the proportion of correct diagnoses (i.e. respondents tend to be overconfident). A neural network-based probability judgment (NBPJ) model and an exemplar-based probability judgment (EBPJ) model were developed for these kinds of tasks, and accounts for overconfidence were derived from each. The NBPJ asserts that people's learning of ecological probabilities is essentially veridical. However, their classification responses are fundamentally probabilistic, which results in overconfidence. The EBPJ proposes that people learn by storing past examples, and that their judgments are often based on the first example they happen to retrieve. In this model, reliance on small samples of exemplars in judgment leads to overconfidence. The models' accounts of overconfidence were compared in three experiments. The first of two key results was that eliminating the choice stage of the judgment process and directly reporting on the probability of one disease led to an increase in overconfidence. This is anticipated by the EBPJ because this procedure encourages retrieval of less information than the two-stage procedure. The NBPJ predicts the opposite result, because it locates overconfidence at the choice stage, which is eliminated in a single-stage, direct report procedure. The second principal result was that an instruction to retrieve many exemplars reduced overconfidence. This result directly supported the EBPJ's account, but was not expected by the NBPJ. Implications for current theories of likelihood judgment are discussed.
590 $a School code: 0127.
650 4 $a Psychology, Cognitive. $3 1017810
650 4 $a Psychology, Experimental. $3 517106
690 $a 0623
690 $a 0633
710 2 0 $a University of Michigan. $3 777416
773 0 $t Dissertation Abstracts International $g 61-03B.
790 $a 0127
790 1 0 $a Yates, J. Frank, $e advisor
791 $a Ph.D.
792 $a 2000
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=9963897