scholarly journals A Functional Model of Sensemaking in a Neurocognitive Architecture

2013 ◽  
Vol 2013 ◽  
pp. 1-29 ◽  
Author(s):  
Christian Lebiere ◽  
Peter Pirolli ◽  
Robert Thomson ◽  
Jaehyon Paik ◽  
Matthew Rutledge-Taylor ◽  
...  
Keyword(s):  
Human Performance ◽  
Cognitive Biases ◽  
Probability Distributions ◽  
Cognitive Architecture ◽  
Functional Model ◽  
Cognitive Model ◽  
Intelligence Analysis ◽  
Probability Matching ◽  
Making Sense ◽  
Dual Constraints

Sensemaking is the active process of constructing a meaningful representation (i.e., making sense) of some complex aspect of the world. In relation to intelligence analysis, sensemaking is the act of finding and interpreting relevant facts amongst the sea of incoming reports, images, and intelligence. We present a cognitive model of core information-foraging and hypothesis-updating sensemaking processes applied to complex spatial probability estimation and decision-making tasks. While the model was developed in a hybrid symbolic-statistical cognitive architecture, its correspondence to neural frameworks in terms of both structure and mechanisms provided a direct bridge between rational and neural levels of description. Compared against data from two participant groups, the model correctly predicted both the presence and degree of four biases: confirmation, anchoring and adjustment, representativeness, and probability matching. It also favorably predicted human performance in generating probability distributions across categories, assigning resources based on these distributions, and selecting relevant features given a prior probability distribution. This model provides a constrained theoretical framework describing cognitive biases as arising from three interacting factors: the structure of the task environment, the mechanisms and limitations of the cognitive architecture, and the use of strategies to adapt to the dual constraints of cognition and the environment.

Errors in Surgical Neuropathology and the Influence of Cognitive Biases: The Psychology of Intelligence Analysis

2006 ◽  
Vol 130 (5) ◽  
pp. 613-616 ◽  
Author(s):  
Roger E. McLendon
Keyword(s):  
Decision Making ◽  
Cognitive Psychology ◽  
Medical Decision Making ◽  
Cognitive Biases ◽  
Correct Diagnosis ◽  
Data Sources ◽  
Medical Decision ◽  
Intelligence Analysis ◽  
First Impression ◽  
Decision Making Processes

Abstract Context.—A significant difficulty that pathologists encounter in arriving at a correct diagnosis is related to the way information from various sources is processed and assimilated in context. Objective.—These issues are addressed by the science of cognitive psychology. Although cognitive biases are the focus of a number of studies on medical decision making, few if any focus on the visual sciences. Data Sources.—A recent publication authored by Richards Heuer, Jr, The Psychology of Intelligence Analysis, directly addresses many of the cognitive biases faced by neuropathologists and anatomic pathologists in general. These biases include visual anticipation, first impression, and established mindsets and subconsciously influence our critical decision-making processes. Conclusions.—The book points out that while biases are an inherent property of cognition, the influence of such biases can be recognized and the effects blunted.

Cognitive Bias in Intelligence Analysis

2020 ◽  
Author(s):  
Martha Whitesmith
Keyword(s):  
Social Sciences ◽  
Cognitive Bias ◽  
Cognitive Biases ◽  
Risk Mitigation ◽  
Meta Analysis ◽  
Intelligence Analysis ◽  
Experimental Setting ◽  
Alternative Approach ◽  
The Social ◽  
Underlying Causes

Belief, Bias and Intelligence outlines an approach for reducing the risk of cognitive biases impacting intelligence analysis that draws from experimental research in the social sciences. It critiques the reliance of Western intelligence agencies on the use of a method for intelligence analysis developed by the CIA in the 1990’s, the Analysis of Competing Hypotheses (ACH). The book shows that the theoretical basis of the ACH method is significantly flawed, and that there is no empirical basis for the use of ACH in mitigating cognitive biases. It puts ACH to the test in an experimental setting against two key cognitive biases with unique empirical research facilitated by UK’s Professional Heads of Intelligence Analysis unit at the Cabinet Office, includes meta-analysis into which analytical factors increase and reduce the risk of cognitive bias and recommends an alternative approach to risk mitigation for intelligence communities. Finally, it proposes alternative models for explaining the underlying causes of cognitive biases, challenging current leading theories in the social sciences.

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.

Theoretical perspectives and approaches

2015 ◽  
Author(s):  
Ian P. Albery ◽  
Dinkar Sharma ◽  
Asli Niazi ◽  
Antony C. Moss
Keyword(s):  
Social Learning ◽  
Drug Use ◽  
Research Methods ◽  
Cognitive Biases ◽  
Cognitive Model ◽  
Addictive Behaviour ◽  
Incentive Sensitization ◽  
Theoretical Perspectives

This chapter explores the role of cognition and cognitive biases in the understanding of concepts related to addiction, such as craving, from a number of theoretical stances. These include the dual-affect model, incentive sensitization theory, social learning and expectancy approaches, and finally the cognitive model of drug urges and drug-use behaviour. It also explores methodologies and research methods that have been used to test these various cognitive accounts of addictive behaviour.

scholarly journals Towards a quantum-like cognitive architecture for decision-making

2020 ◽  
Vol 43 ◽  
Author(s):  
Catarina Moreira ◽  
Lauren Fell ◽  
Shahram Dehdashti ◽  
Peter Bruza ◽  
Andreas Wichert
Keyword(s):  
Decision Making ◽  
Quantum Mechanics ◽  
Cognitive Biases ◽  
Cognitive Architecture ◽  
Decision Models ◽  
Classical Models ◽  
The Mind ◽  
Computational Resources

Abstract We propose an alternative and unifying framework for decision-making that, by using quantum mechanics, provides more generalised cognitive and decision models with the ability to represent more information compared to classical models. This framework can accommodate and predict several cognitive biases reported in Lieder & Griffiths without heavy reliance on heuristics or on assumptions of the computational resources of the mind.

scholarly journals Spiking Neural Networks for early prediction in human–robot collaboration

2019 ◽  
Vol 38 (14) ◽  
pp. 1619-1643
Author(s):  
Tian Zhou ◽  
Juan P Wachs
Keyword(s):  
Human Performance ◽  
State Of The Art ◽  
Cognitive Model ◽  
Surgical Instruments ◽  
Spiking Neural Network ◽  
Turn Taking ◽  
Multiple State ◽  
Implicit And Explicit ◽  
Human Robot Collaboration ◽  
Prediction Capability

This article introduces the Turn-Taking Spiking Neural Network (TTSNet), which is a cognitive model to perform early turn-taking prediction about a human or agent’s intentions. The TTSNet framework relies on implicit and explicit multimodal communication cues (physical, neurological and physiological) to be able to predict when the turn-taking event will occur in a robust and unambiguous fashion. To test the theories proposed, the TTSNet framework was implemented on an assistant robotic nurse, which predicts surgeon’s turn-taking intentions and delivers surgical instruments accordingly. Experiments were conducted to evaluate TTSNet’s performance in early turn-taking prediction. It was found to reach an [Formula: see text] score of 0.683 given 10% of completed action, and an [Formula: see text] score of 0.852 at 50% and 0.894 at 100% of the completed action. This performance outperformed multiple state-of-the-art algorithms, and surpassed human performance when limited partial observation is given (<40%). Such early turn-taking prediction capability would allow robots to perform collaborative actions proactively, in order to facilitate collaboration and increase team efficiency.

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