Consequences of Confirmation and Disconfirmation in a Simulated Research Environment

Mynatt, Clifford R.; Doherty, Michael E.; Tweney, Ryan D.

doi:10.1080/00335557843000007

Cited by 217 publications

(94 citation statements)

References 7 publications

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“…14 Confirmation bias is well documented in the behavioral and economic literatures. [15][16][17] It empirically is even stronger when information is presented sequentially, as it is in clinical emergency care, compared with when all the information is available up front. 18 Variability in the temporal processing and receipt of information may influence decision making because the longer a person holds onto a decision or approach, the more difficult it becomes for him or her to break from that thinking.…”

Section: Discussionmentioning

confidence: 99%

Profiles in Patient Safety: Confirmation Bias in Emergency Medicine

Pines

2006

Academic Emergency Medicine

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Confirmation bias is a pitfall in emergency care and may lead to inaccurate diagnoses and inappropriate treatments and care plans. Because of the increasing severity and volume of emergency care, emergency physicians often must rely on heuristics, such as rule-out protocols, as a guide to diagnosing and treating patients. The use of heuristics or protocols can be potentially misleading if the initial diagnostic impression is incorrect. To minimize cognitive dissonance, clinicians may accentuate confirmatory data and ignore nonconfirmatory data. Clinicians should recognize confirmation bias as a potential pitfall in medical decision making in the emergency department. Reliance on the scientific method, Bayesian reasoning, metacognition, and cognitive forcing strategies may serve to improve diagnostic accuracy and improve patient care.ACADEMIC EMERGENCY MEDICINE 2006; 13:90-94 ª

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Section: Discussionmentioning

confidence: 99%

Profiles in Patient Safety: Confirmation Bias in Emergency Medicine

Pines

2006

Academic Emergency Medicine

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“…In fact, Mynatt et al (1978) note that many hypotheses were minor variations on previous hypothesisindicating investigation of a frame -and that there were also occassional large differences in adjacent hypotheses -indicating a switch to a new frame.…”

Section: Hypothesis Formation and Scientific Discoverymentioning

confidence: 97%

“…Finally, we will use a context in which prior knowledge and the semantics of the situation play a role in the content, form and plausibility of initial hypotheses and in the criteria for revising hypotheses. (The "simulated universe" tasks used by Mynatt, Doherty & Tweney [1977, 1978 include similar extensions, although their analysis is focused on the logic of confirmation and disconfirmation. )…”

Section: Laboratory Studies Of Scientific Reasoning: Two Exemplarsmentioning

confidence: 99%

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Dual Space Search During Scientific Reasoning

Klahr¹,

Dunbar²

1988

Cognitive Science

848

455

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The purpose of the two studies reported here was to develop an integrated model of the scientific reasoning process. Subjects were placed in a simulated scientific discovery context by first teaching them how to use an electronic device and then asking them to discover how a hitherto unencountered function worked. To do this task, subjects had to formulate hypotheses based on their prior knowledge, conduct experiments, and evaluate the results of their experiments. In the first study, using 20 adult subjects, we identified two main strategies that subjects used to generate new hypotheses. One strategy was to search memory and the other was to generalize from the results of previous experiments. We described the former group as searching an hypothesis space, and the latter as searching an experiment space. In a second study, with 10 adults, we investigated how subjects search the hypothesis space by instructing them to state all the hypotheses that they could think of prior to conducting any experiments. Following this phase, subjects were then allowed to conduct experiments. Subjects who could not think of the correct rule in the hypothesis generation phase discovered the correct rule only by generalizing from the results of experiments in the experimental phase. Both studies provide support for the view that scientific reasoning can be characterized as search in two problem spaces. By extending Simon and Lea's (1974) Generalized Rule Inducer, we present a general model of Scientific Discovery as Dual Search (SDDS) that shows how search in two problem spaces (an hypothesis space and an experiment space) shapes hypothesis generation, experimental design, and the evaluation of hypotheses. The model also shows how these processes interact with each other. Finally, we interpret earlier findings about the psychology of scientific reasoning in terms of the SDDS model.

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“…Efforts to improve performance on the 2-4-6 task by instructional manipulations that have emphasized falsification have generally proved unsuccessful Gorman, Gorman, Latta & Cunningham, 1984;Gorman, Stafford & Gorman, 1987;Kareev, Halberstadt & Shafir, 1993;Mynatt, Doherty & Tweney, 1977;Mynatt, Doherty & Tweney, 1978;Tweney, Doherty, Worner, Pliske, Mynatt, Gross & Arkkelin, 1980). With the benefit of hindsight, this finding may not be so surprising, in view of Poletiek's (1996) proposal that falsification in hypothesis testing is psychologically implausible because of the paradoxical relationship between testing behavior and the correctness of one's current hypothesis.…”

mentioning

confidence: 99%

Contrast class cues and performance facilitation in a hypothesis-testing task: evidence for an iterative counterfactual model

Gale

Ball

2011

Mem Cogn

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Hypothesis-testing performance on Wason's (Quarterly Journal of Experimental Psychology 12:129-140, 1960) 2-4-6 task is typically poor, with only around 20% of participants announcing the to-be-discovered "ascending numbers" rule on their first attempt. Enhanced solution rates can, however, readily be observed with dualgoal (DG) task variants requiring the discovery of two complementary rules, one labeled "DAX" (the standard "ascending numbers" rule) and the other labeled "MED" ("any other number triples"). Two DG experiments are reported in which we manipulated the usefulness of a presented MED exemplar, where usefulness denotes cues that can establish a helpful "contrast class" that can stand in opposition to the presented 2-4-6 DAX exemplar. The usefulness of MED exemplars had a striking facilitatory effect on DAX rule discovery, which supports the importance of contrast-class information in hypothesis testing. A third experiment ruled out the possibility that the useful MED triple seeded the correct rule from the outset and obviated any need for hypothesis testing. We propose that an extension of Oaksford and Chater's (European Journal of Cognitive Psychology 6:149-169, 1994) iterative counterfactual model can neatly capture the mechanisms by which DG facilitation arises.

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Consequences of Confirmation and Disconfirmation in a Simulated Research Environment

Cited by 217 publications

References 7 publications

Profiles in Patient Safety: Confirmation Bias in Emergency Medicine

Profiles in Patient Safety: Confirmation Bias in Emergency Medicine

Dual Space Search During Scientific Reasoning

Contrast class cues and performance facilitation in a hypothesis-testing task: evidence for an iterative counterfactual model

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