Judgment Biases in a Simulated Classroom—A Cognitive-Environmental Approach

Fiedler, Klaus; Walther, Eva; Freytag, Peter; Plessner, Henning

doi:10.1006/obhd.2001.2981

Cited by 94 publications

(53 citation statements)

References 64 publications

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“…For instance, although positive outcomes were constantly frequent and negative outcomes constantly infrequent across all providers, this difference was more clearly recognized for the most frequent provider but often missed and underestimated for the rarest provider. Thus, the regressive tendency to underestimate real frequency differences, which characterizes all memory-based frequency estimates, was most apparent where samples were most impoverished, consistent with many previous demonstrations of differential regression in confirmation-bias studies (Fiedler, 1996;Fiedler et al, 2002;Fiedler & Walther, 2003;Zuckerman, Knee, Hodgins & Miyake, 1995). In any case, natural sampling did not provide a useful remedy at all, because unreliability and regression biases came in through the back door.…”

Section: Discussionsupporting

confidence: 86%

The ultimate sampling dilemma in experience-based decision making.

Fiedler

2008

Journal of Experimental Psychology: Learning, Memory, and Cogni

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Computer simulations and two experiments are reported to delineate the ultimate sampling dilemma, which constitutes a serious obstacle to inductive inferences in a probabilistic world. Participants were asked to take the role of a manager who is to make purchasing decisions based on positive versus negative feedback about three providers in two different product domains. When information sampling (from a computerized data base) was over, they had to make inferences about actual differences in the data base from which the sample was drawn (e.g., about the actual superiority of different providers, or about the most likely origins of negatively valenced products). The ultimate sampling dilemma consists in a forced choice between two search strategies that both have their advantages and their drawbacks: natural sampling and deliberate sampling of information relevant to the inference task. Both strategies leave the sample unbiased for specific inferences but create errors or biases for other inferences. Many everyday decision problems rely on direct environmental-learning experience.Teachers' grading decisions are informed by observations of students' performance in different disciplines. Personnel selection relies on applicants' reactions to various tasks and interview topics. Or, consumer choices reflect the information acquired about brands or providers in different product domains. There appears to be a simple and straightforward way of optimizing such experience-based decisions: If only the learning process relies on a sufficiently large sample of observations, it must be possible to discern the optimal decision through optimal data selection (Oaksford & Chater, 2003).The learning task seems to have a clear-cut structure. For a consumer to make an optimal choice between alternative providers, it is only necessary to compare the quality feedback that is available for different providers in specific product domains. Granting that the feedback is reliable and accurately reflects the contingency between providers and product quality, figuring out the best provider, with the highest rate of positive evaluations, should be straightforward. The consumer's task should be easy to solve if only the differences between providers are strong enough and sufficient observations are available.The aim of the present investigation is to contest this seemingly plausible sketch of simple experience-based decision making. In fact, finding a generally correct solution to such clearly structured problems is fraught with huge difficulties. It is actually impossible, because every sample of observations about mundane decision problems entails the potential to be misleading under certain conditions. I refer to the "ultimate sampling dilemma" to highlight the fact that any reasonable sampling strategy, which serves to optimize one decision, produces a sampling bias with regard to other decisions informed by the same data. Illustration of the Ultimate Sampling DilemmaThat judgments and decisions depend crucially on the samples of...

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Section: Discussionsupporting

confidence: 86%

The ultimate sampling dilemma in experience-based decision making.

Fiedler

2008

Journal of Experimental Psychology: Learning, Memory, and Cogni

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“…The school class scenario we have used for illustration throughout this paper provides a prominent example (cf. Fiedler et al, 2002). Fair grading and accurate achievement assessment are of interest to everybody; it is crucial for valid feedback and selection decisions.…”

Section: Discussionmentioning

confidence: 99%

“…Sample size intrudes into the assessment of a proportion within the sample (Estes, 1976). For example, teachers' estimates of students' correct response rates are biased toward the frequency of responses they have provided (Fiedler, Walther, Freytag, and Plessner, 2002). Frequently presented statements tend to be considered true (Dechêne, Stahl, Hansen, and Wänke, 2010).…”

Section: Confusion Of Intensional and Extensional Informationmentioning

confidence: 99%

Anomalies in the detection of change: When changes in sample size are mistaken for changes in proportions

et al. 2015

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Detecting changes, in performance, sales, markets, risks, social relations, or public opinions, constitutes an important adaptive function. In a sequential paradigm devised to investigate detection of change, every trial provides a sample of binary outcomes (e.g., correct vs. incorrect student responses). Participants have to decide whether the proportion of a focal feature (e.g., correct responses) in the population from which the sample is drawn has decreased, remained constant, or increased. Strong and persistent anomalies in change detection arise when changes in proportional quantities vary orthogonally to changes in absolute sample size. Proportional increases are readily detected and nonchanges are erroneously perceived as increases when absolute sample size increases. Conversely, decreasing sample size facilitates the correct detection of proportional decreases and the erroneous perception of nonchanges as decreases. These anomalies are however confined to experienced samples of elementary raw events from which proportions have to be inferred inductively. They disappear when sample proportions are described as percentages in a normalized probability format. To explain these challenging findings, it is essential to understand the inductivelearning constraints imposed on decisions from experience.

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“…Cogent evidence stems from several experiments conducted in a virtual school-class environment (Fiedler, Walther, Freytag, & Plessner, 2002), in which participants play the role of a teacher whose task is to assess the performance of 16 students in a school class represented on the computer screen, corroborate this assumption. When asked to test the hypothesis that boys are better in science and girls are better in language, they normally engage in positive testing (Klayman & Ha, 1987), gathering predominantly information that speaks to the hypothesis to be tested.…”

Section: Revisiting the Confirmation Biasmentioning

confidence: 94%

From intrapsychic to ecological theories in social psychology: Outlines of a functional theory approach

Fiedler

2014

Euro J Social Psych

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Social psychology is facing a major developmental task, which is not primarily one of achieving larger data samples and stricter significance testing. What is needed, rather, is an improvement in logic of science and powerful theorizing. The starting point of this article is a critical appraisal that the intrapsychic concepts (motives, attitudes, and explicit and implicit goals) that are the major focus of social-psychological theories lack in explanatory power because they are too close to the effects (actions, judgments, and decisions) they are intended to explain. To overcome this apparent weakness (compared with neuroscience or genetics), it is proposed that more powerful theories should relate social cognition and behavior to more distant variables in the environment. A functional analysis reveals that intrapsychic processes are multiply predetermined by extrapsychic, environmental constraints. The theoretical value and fertility of the proposed ecological functionalism are illustrated with reference to three areas of empirical research: sampling approaches to understanding biases in judgment and decision making; strategic influences on priming effects within a functional analysis of adaptive behavior; and the impact of verbal and symbolic communication constraints on the construction of attributions, stereotypes, and culture.

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Judgment Biases in a Simulated Classroom—A Cognitive-Environmental Approach

Cited by 94 publications

References 64 publications

The ultimate sampling dilemma in experience-based decision making.

The ultimate sampling dilemma in experience-based decision making.

Anomalies in the detection of change: When changes in sample size are mistaken for changes in proportions

From intrapsychic to ecological theories in social psychology: Outlines of a functional theory approach

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