The influence of cue unreliability on judgment in a multiple cue probability learning task

York, Kenneth M.; Doherty, Michael E.; Kamouri, Joseph

doi:10.1016/0749-5978(87)90026-4

Cited by 26 publications

(27 citation statements)

References 18 publications

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“…In general, as the cues became less reliable, navigation time estimates became less accurate. This finding replicates many studies examining the effects of cue reliability on judgment (Brehmer, 1970;Doherty & Sullivan, 1989;York, Doherty, & Kamouri, 1987). Reliability feed-forward information, on average, facilitated judgment task performance when compared to the no-reliability feed-forward condition.…”

Section: Discussionsupporting

confidence: 76%

“…There has been substantial research suggesting that loss of control in the execution of multi-cue knowledge can be attenuated by feedback (Balzer et al, 1989;Brehmer, 1970;Doherty & Sullivan, 1989;York et al, 1987). Further, the amount and nature of feedback needed to maintain control become less demanding as the task becomes more analytical (see Hammond, 1990;Brehmer, 1978;Searcy, 1994).…”

Section: Iconic Feed-forward Display and Cognitionmentioning

confidence: 99%

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Iconic Representation and Dynamic Information Fidelity: Implications for Decision Support

Mahan¹,

Wang²,

Yanchus³

et al. 2006

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Public reporting burden for this collection of information is estimated to average 1 hour per response, including the time for reviewing instructions, searching existing data sources, gathering and maintaining the data needed, and completing and reviewing the collection information. Send comments regarding this burden estimate or any other aspect of this collection of information, including suggestions for reducing the burden, to Department of Defense, Washington Headquarters Services, Directorate for Information Operations and Reports (0704-0188), 1215 Jefferson Davis Highway, Suite 1204, Arlington, VA 22202-4302. Respondents should be aware that notwithstanding any other provision of law, no person shall be subject to any penalty for failing to comply with a collection of information if it does not display a currently valid OMB control number. PLEASE DO NOT RETURN YOUR FORM TO THE ABOVE ADDRESS. 1. REPORT DATE (DD-MM-YYYY)October 2006 5f. WORK UNIT NUMBER PERFORMING ORGANIZATION NAME(S) AND ADDRESS(ES)University of Georgia AHRP Laboratory Department of Psychology Athens, GA PERFORMING ORGANIZATION REPORT NUMBER ARL-CR-0580 SPONSOR/MONITOR'S ACRONYM(S) 9. SPONSORING/MONITORING AGENCY NAME(S) AND ADDRESS(ES) U.S. Army Research Laboratory Human Research and Engineering Directorate Aberdeen Proving Ground, MD 21005-5425 SPONSOR/MONITOR'S REPORT NUMBER(S) DISTRIBUTION/AVAILABILITY STATEMENTApproved for public release; distribution is unlimited. SUPPLEMENTARY NOTESThe contracting officer's representative (COR) is Linda Elliott, U.S. Army Research Laboratory, ATTN: AMSRD-ARL-HR-MW, Bldg 459, Aberdeen Proving Ground, MD 21005-5425. ABSTRACTThis report addresses the fitness of using icon-based systems to support decision making within a domain where the quality and reliability of the information are dynamically changing. Specifically, the study examined the use of iconic and noniconic display forms to communicate information fidelity levels to decision makers for the purpose of supporting land navigation judgments. The results indicated that graphic and animated icons, as well as traditional digital display formats, produced accurate navigation judgments when information fidelity was high. In contrast, graphic and animated icon formats produced highest performance when information fidelity was moderate and/or low. These results are evaluated in the context of creating useful iconic display principles that may be applied to complex and uncertain decision environments where the fidelity of the information used to make decisions is in flux.

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Section: Discussionsupporting

confidence: 76%

Section: Iconic Feed-forward Display and Cognitionmentioning

confidence: 99%

Iconic Representation and Dynamic Information Fidelity: Implications for Decision Support

Mahan¹,

Wang²,

Yanchus³

et al. 2006

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“…Balzer et al (1989) conclude that it is TI versus cognitive (subjective) information that positively influences performance. In addition, York, Doherty and Kamouri (1987) suggest that outcome feed-back may interact with cue reliability to influence judgment. Other studies, however, suggest that decision-making biases (Brehmer, 1980), randomness or "noise" in outcomes (Hammond, Summers & Deane, 1973), and expertise of the decision maker (Benson & Onkal, 1992;Fischer, 1982) make outcome feedback unnecessary or harmful for making decisions.…”

Section: Prior Research and Hypothesismentioning

confidence: 99%

Outcome Feedback Effects on Risk Propensity in an MCPLP Task

Taylor

Hall²,

Cosier

et al. 1996

Journal of Management

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In this experimental analysis, the effects of outcome feedback on risk propensity were assessed within the multiple-cue-probability-learning-paradigm (MCPLP). The individual decision maker in this taskDecision makers have to make decisions which can be characterized by uncertainty, incomplete information, complexity, novelty, iteration, and the use of judgment (e.g., Taylor, 1987). Decision making in uncertain environments, without complete information about relevant variables and the relationships among these variables, has been a topic of research in the study of strategic decision making (Ungson, Braunstein & Hall, 1981). For some time, the Brunswik (1955) lens model has been promoted for examining this and other related issues (e.g., Cosier, 1978; Taylor, Cosier & Ganster, 1979). In a laboratory setting (e.g., Cosier, 1978), this perspective has provided insight into decision-making processes and the effects of variables on decision outcomes (Schwenk, 1982).The Brunswik lens model is a paradigm with broad applications to the study of decision making. Since its introduction nearly forty years ago, the lens model has been the basis for hundreds of scientific studies examining a multitude of decision-making variables. The lens model assumes that there are three basic elements in a decision-making event: a decision maker, information, and a decision criterion.

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“…Another curious feature in this literature is that judges tend to identify as relevant cues that they did not use but rarely use cues that they did not name (Harries, Evans, Dennis, & Dean, 1996). There is evidence that judges report the cues to which they attend but are unaware that some of this attended information is not actually used in their judgments (Harries et al, 2000;York, Doherty, & Kamouri, 1987).…”

mentioning

confidence: 99%

Explicit and implicit processes in multicue judgment

et al. 2003

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Expert judgment is often exercised in situations in which multiple pieces of information are available and relevant in varying degrees. For example, a doctor may be attempting to diagnose the likely illness of a patient on the basis of symptoms, medical history, clinical examination, and results of imperfectly diagnostic tests, as well as relevant demographic information, such as gender, age, and occupation. To take another example, a personnel manager might be judging the suitability of a candidate for a position, taking into account work experience, qualifications, interview performance, and results of psychometric tests. These are examples of what are termed multicue judgments, where the various dimensions of information are the cues, whose values have particular and distinct instantiations for individual cases.What makes multicue judgment of psychological interest, as well as of practical importance, is the potential for bias when such judgments are made intuitively. If experts lack self-insight into the processes underlying these judgments, they may be unconsciously biased. For example, it may be contrary to law or company policy for the personnel manager to take into account age or gender in his or her decision making. If no explicit algorithm is followed, however, we may have nothing better than self-report from the decision maker to establish which cues were actually used and with what relative weighting. For this reason, a tradition of research based on social judgment theory was developed some years ago to capture the tacit policies that decision makers use when making multicue judgments. Social judgment theory has its origins in the psychology of Egon Brunswick and is rooted in the ecological approach to human cognition-specifically, by relating the way in which the available information is modeled by the environment, on the one hand, and by the judge, on the other. For the history of the theory, the reader is referred to Doherty and Kurz (1996) and, for details of the associated lens model methodology, to Cooksey (1996).Social judgment theory uses multiple linear regression to measure policies that are implicit in the judgments that people make-a technique known as policy capture. In such studies, participants-or judges-are asked to make wholistic judgments about a series of cases in which information about the same set of multiple cues is available. The implicit policy can then be inferred by performing a multiple linear regression analysis from the cues onto the judgments. The resulting beta weights tell us which cues influenced the judge and to what relative extent. In some studies, an objective criterion of correct performance is also available. In the case of personnel selection decisions, for example, we may be able to assess job performance at a later date. In a full lens model analysis, a second multiple regression is performed from the cues onto the criterion. A simplified (ignoring nonlinear and nonadditive aspects) version of the lens model equation is then given as follows: achievement = ...

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The influence of cue unreliability on judgment in a multiple cue probability learning task

Cited by 26 publications

References 18 publications

Iconic Representation and Dynamic Information Fidelity: Implications for Decision Support

Iconic Representation and Dynamic Information Fidelity: Implications for Decision Support

Outcome Feedback Effects on Risk Propensity in an MCPLP Task

Explicit and implicit processes in multicue judgment

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