How Do Cognitive Biases Influence Reasoning?

2021 ◽  
pp. 77-96
Author(s):  
Gale M. Sinatra ◽  
Barbara K. Hofer
Keyword(s):  
Reflective Thinking ◽  
Cognitive Biases ◽  
Confirmation Bias ◽  
Availability Heuristic ◽  
Turn On

Individuals like to think of themselves as rational actors, careful and considered in their thinking and capable of sound and reliable judgments. Yet people often engage in automatic, reflexive thinking. It takes effort to turn on the reflective, deliberative mind; and humans are basically cognitive misers. In Chapter 4, “How Do Cognitive Biases Influence Reasoning?,” the authors explain how particular cognitive biases such as confirmation bias, the availability heuristic, illusions of understanding, and the appeal of intuitive theories, influence reasoning about scientific issues. They explain how difficult it is to stay open to new perspectives and to fairly evaluate information that challenges what one thinks one knows—or wants to believe is true. They offer suggestions for what individuals and educators can do to engage in and promote the effortful work of reflective thinking and how to check one’s own biases when interpreting complex scientific topics.

Confirmation bias in decision making for fingerprints, DNA and eyewitness evidence cannot be explained by cognitive style or thinking dispositions.

2019 ◽  
Author(s):  
Daniel Edgcumbe
Keyword(s):  
Decision Making ◽  
Cognitive Style ◽  
Cognitive Biases ◽  
Confirmation Bias ◽  
Criminal Case ◽  
Court Cases ◽  
Legal Proceedings ◽  
Thinking Dispositions ◽  
Evaluation Of Evidence ◽  
Facial Composite

Pre-existing beliefs about the background or guilt of a suspect can bias the subsequent evaluation of evidence for forensic examiners and lay people alike. This biasing effect, called the confirmation bias, has influenced legal proceedings in prominent court cases such as that of Brandon Mayfield. Today many forensic providers attempt to train their examiners against these cognitive biases. Nine hundred and forty-two participants read a fictional criminal case and received either neutral, incriminating or exonerating evidence (fingerprint, eyewitness, or DNA) before providing an initial rating of guilt. Participants then viewed ambiguous evidence (alibi, facial composite, handwriting sample or informant statement) before providing a final rating of guilt. Final guilt ratings were higher for all evidence conditions (neutral, incriminating or exonerating) following exposure to the ambiguous evidence. This provides evidence that the confirmation bias influences the evaluation of evidence.

scholarly journals Expanding the Weighted Updating Model

2016 ◽  
Author(s):  
Jesse Aaron Zinn
Keyword(s):  
Cognitive Biases ◽  
Bayesian Updating ◽  
Irrelevant Information ◽  
Base Rate ◽  
Order Effects ◽  
Availability Heuristic ◽  
Base Rate Neglect ◽  
Attribution Bias

This work casts light upon a pair of restrictions inherent to the basic weighted updating model, which is a generalization of Bayesian updating that allows for biased learning. Relaxing the restrictions allows for the study of individuals who discriminate between observations or who treat information in a dynamically inconsistent manner. These generalizations augment the set of cognitive biases that can be studied using new versions of the weighted updating model to include the availability heuristic, order effects, self-attribution bias, and base-rate neglect in light of irrelevant information.

The Efficacy of ACH in Mitigating Serial Position Effects and Confirmation Bias in an Intelligence Analysis Scenario1

2020 ◽  
pp. 107-144
Author(s):  
Martha Whitesmith
Keyword(s):  
Serial Position ◽  
Cognitive Biases ◽  
Confirmation Bias ◽  
Scoring Systems ◽  
Diagnostic Value ◽  
Intelligence Community ◽  
Intelligence Analysis ◽  
Position Effects ◽  
Serial Position Effects ◽  
The Us

Chapter three provides details of an experimental study conducted in 2016 to provide an evaluation of the efficacy of ACH in mitigating the cognitive biases of serial position effects and confirmation bias using the scoring systems of credibility of information and diagnostic value of information. The study is based on a disguised version of the intelligence case for both the biological and nuclear weapons capabilities of Saddam Hussein’s regime that was used to support the US decision to invade Iraq in 2003. The study shows that the version of ACH taught by the PHIA to the UK’s intelligence community between 2016-2017 has no statistically significant mitigating effect on the occurrence of serial position effects or confirmation bias.

Taxonomy and Epidemiology of Error in Radiology

2019 ◽  
pp. 29-74
Author(s):  
Michael A. Bruno
Keyword(s):  
Working Memory ◽  
Cognitive Biases ◽  
Confirmation Bias ◽  
Diagnostic Error ◽  
Attentional Focus ◽  
Relative Contribution ◽  
Anchoring Bias ◽  
Classification Of Error

This chapter provides an overview of the prevalence and classification of error types in radiology, including the frequency and types of errors made by radiologists. We will review the relative contribution of perceptual error—in which findings are simply not seen—as compared to other common types of error. This error epidemiology will be considered in the light of the underlying variability and uncertainties present in the radiological process. The role of key cognitive biases will also be reviewed, including anchoring bias, confirmation bias, and availability bias. The role of attentional focus, working memory, and problems caused by fatigue and interruption will also be explored. Finally, the problem of radiologist error will be considered in the context of the overall problem of diagnostic error in medicine.

Confirmation and Cognitive Bias in Design Cognition

2012 ◽  
Author(s):  
Gregory M. Hallihan ◽  
Hyunmin Cheong ◽  
L. H. Shu
Keyword(s):  
Information Processing ◽  
Analogical Reasoning ◽  
Design Research ◽  
Cognitive Biases ◽  
Confirmation Bias ◽  
Psychological Research ◽  
Human Information Processing ◽  
Concept Generation ◽  
Design Cognition ◽  
Concept Evaluation

The desire to better understand design cognition has led to the application of literature from psychology to design research, e.g., in learning, analogical reasoning, and problem solving. Psychological research on cognitive heuristics and biases offers another relevant body of knowledge for application. Cognitive biases are inherent biases in human information processing, which can lead to suboptimal reasoning. Cognitive heuristics are unconscious rules utilized to enhance the efficiency of information processing and are possible antecedents of cognitive biases. This paper presents two studies that examined the role of confirmation bias, which is a tendency to seek and interpret evidence in order to confirm existing beliefs. The results of the first study, a protocol analysis involving novice designers engaged in a biomimetic design task, indicate that confirmation bias is present during concept generation and offer additional insights into the influence of confirmation bias in design. The results of the second study, a controlled experiment requiring participants to complete a concept evaluation task, suggest that decision matrices are effective tools to reduce confirmation bias during concept evaluation.

Overcoming Confirmation and Blind Spot Biases When Communicating Science

2017 ◽  
Author(s):  
Kate Kenski
Keyword(s):  
Decision Making ◽  
Cognitive Biases ◽  
Confirmation Bias ◽  
Blind Spot ◽  
Future Research ◽  
Costs And Benefits ◽  
Process Information ◽  
Bias Blind Spot

This chapter focuses on two biases that lead people away from evaluating evidence and scientific studies impartially—confirmation bias and bias blind spot. The chapter first discusses different ways in which people process information and reviews the costs and benefits of utilizing cognitive shortcuts in decision making. Next, two common cognitive biases, confirmation bias and bias blind spot, are explained. Then the literature on “debiasing” is explored. Finally, the implications of confirmation bias and bias blind spot in the context of communicating about science are examined, and an agenda for future research on understanding and mitigating these biases is offered.

scholarly journals The Measurement of Individual Differences in Cognitive Biases: A Review and Improvement

2021 ◽  
Vol 12 ◽  
Author(s):  
Vincent Berthet
Keyword(s):  
Decision Making ◽  
Individual Differences ◽  
Cognitive Biases ◽  
Empirical Work ◽  
Confirmation Bias ◽  
Generic Measures

Individual differences have been neglected in decision-making research on heuristics and cognitive biases. Addressing that issue requires having reliable measures. The author first reviewed the research on the measurement of individual differences in cognitive biases. While reliable measures of a dozen biases are currently available, our review revealed that some measures require improvement and measures of other key biases are still lacking (e.g., confirmation bias). We then conducted empirical work showing that adjustments produced a significant improvement of some measures and that confirmation bias can be reliably measured. Overall, our review and findings highlight that the measurement of individual differences in cognitive biases is still in its infancy. In particular, we suggest that contextualized (in addition to generic) measures need to be improved or developed.

scholarly journals CONFIRMATION BIAS & PSYCHOEDUCATION WITH RELATION TO MENTAL ILLNESS

2021 ◽  
Vol 9 (10) ◽  
pp. 677-679
Author(s):  
Shambhavi Prathap ◽  
Jyoti Das
Keyword(s):  
Mental Illness ◽  
Cognitive Biases ◽  
Confirmation Bias ◽  
Mental Healthcare ◽  
Mental Illnesses ◽  
Data Driven ◽  
Present Understanding

The article aims to review the present understanding of cognitive biases and how they play a role in the understanding of mental illnesses. The paper explores the effect of conformation bias in a collectivistic society and how psychoeducation can play a role in forwarding research backed and data-driven mental healthcare.

scholarly journals Accounting for Confirmation Bias in Crowdsourced Label Aggregation

2021 ◽  
Author(s):  
Meric Altug Gemalmaz ◽  
Ming Yin
Keyword(s):  
Large Scale ◽  
Cognitive Biases ◽  
Synthetic Data ◽  
Confirmation Bias ◽  
Ground Truth ◽  
Machine Intelligence ◽  
Human In The Loop ◽  
Subjective Beliefs ◽  
Beliefs And Values ◽  
Label Aggregation

Collecting large-scale human-annotated datasets via crowdsourcing to train and improve automated models is a prominent human-in-the-loop approach to integrate human and machine intelligence. However, together with their unique intelligence, humans also come with their biases and subjective beliefs, which may influence the quality of the annotated data and negatively impact the effectiveness of the human-in-the-loop systems. One of the most common types of cognitive biases that humans are subject to is the confirmation bias, which is people's tendency to favor information that confirms their existing beliefs and values. In this paper, we present an algorithmic approach to infer the correct answers of tasks by aggregating the annotations from multiple crowd workers, while taking workers' various levels of confirmation bias into consideration. Evaluations on real-world crowd annotations show that the proposed bias-aware label aggregation algorithm outperforms baseline methods in accurately inferring the ground-truth labels of different tasks when crowd workers indeed exhibit some degree of confirmation bias. Through simulations on synthetic data, we further identify the conditions when the proposed algorithm has the largest advantages over baseline methods.

scholarly journals Experimental Evaluation of a Debiasing Method for Analysis in Engineering Design

2019 ◽  
Vol 1 (1) ◽  
pp. 489-498 ◽  
Author(s):  
Thomas Nelius ◽  
Sven Matthiesen
Keyword(s):  
Engineering Design ◽  
Experimental Evaluation ◽  
Design Education ◽  
Engineering Students ◽  
Cognitive Biases ◽  
Technical Problem ◽  
Confirmation Bias ◽  
Supporting Evidence ◽  
Decision Matrix ◽  
Design Engineers

AbstractDuring analysis in engineering design, systematic thinking errors - so-called cognitive biases - can lead to inaccurate understanding of the design problem. With a simplified version of the Analysis of Competing Hypotheses - ACH method and a simplified decision matrix, the confirmation bias in particular can be minimized. To evaluate this method, it was taught to experienced design engineers and mechanical engineering students. During the experimental evaluation the participants analysed a real technical problem. The procedures and results were compared with a previously conducted study with the same task. The design engineers have not changed their approaches and could not further improve their analysis success. The students profited considerably from the training. They have mentioned twice as much supporting evidence and six times as much contradicting evidence through the training indicating a more extensive analysis. As a result, the students showed significantly fewer signs of confirmation bias than without training. The findings suggest that debiasing strategies should be introduced early in engineering design education.

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