Diagnostic Reasoning

2017 ◽  
Author(s):  
Björn Meder ◽  
Ralf Mayrhofer
Keyword(s):  
Causal Inference ◽  
Conditional Probability ◽  
Information Acquisition ◽  
Value Of Information ◽  
Probabilistic Inference ◽  
Diagnostic Value ◽  
Diagnostic Reasoning ◽  
Computational Framework ◽  
Single Effect ◽  
Causal Structures

This chapter discusses diagnostic reasoning from the perspective of causal inference. The computational framework that provides the foundation for the analyses—probabilistic inference over graphical causal structures—can be used to implement different models that share the assumption that diagnostic inferences are guided and constrained by causal considerations. This approach has provided many critical insights, with respect to both normative and empirical issues. For instance, taking into account uncertainty about causal structures can entail diagnostic judgments that do not reflect the empirical conditional probability of cause given effect in the data, the classic, purely statistical norm. The chapter first discusses elemental diagnostic inference from a single effect to a single cause, then examines more complex diagnostic inferences involving multiple causes and effects, and concludes with information acquisition in diagnostic reasoning, discussing different ways of quantifying the diagnostic value of information and how people decide which information is diagnostically relevant.

scholarly journals Bayesian Comparison of Explicit and Implicit Causal Inference Strategies in Multisensory Heading Perception

2017 ◽  
Author(s):  
Luigi Acerbi ◽  
Kalpana Dokka ◽  
Dora E. Angelaki ◽  
Wei Ji Ma
Keyword(s):  
Causal Inference ◽  
Discrimination Task ◽  
Model Comparison ◽  
Inference Process ◽  
Computational Framework ◽  
Bayesian Model Comparison ◽  
Vestibular Cues ◽  
Multisensory Cues ◽  
The Brain ◽  
Rule Out

AbstractThe precision of multisensory heading perception improves when visual and vestibular cues arising from the same cause, namely motion of the observer through a stationary environment, are integrated. Thus, in order to determine how the cues should be processed, the brain must infer the causal relationship underlying the multisensory cues. In heading perception, however, it is unclear whether observers follow the Bayesian strategy, a simpler non-Bayesian heuristic, or even perform causal inference at all. We developed an efficient and robust computational framework to perform Bayesian model comparison of causal inference strategies, which incorporates a number of alternative assumptions about the observers. With this framework, we investigated whether human observers’ performance in an explicit cause attribution and an implicit heading discrimination task can be modeled as a causal inference process. In the explicit inference task, all subjects accounted for cue disparity when reporting judgments of common cause, although not necessarily all in a Bayesian fashion. By contrast, but in agreement with previous findings, data from the heading discrimination task only could not rule out that several of the same observers were adopting a forced-fusion strategy, whereby cues are integrated regardless of disparity. Only when we combined evidence from both tasks we were able to rule out forced-fusion in the heading discrimination task. Crucially, findings were robust across a number of variants of models and analyses. Our results demonstrate that our proposed computational framework allows researchers to ask complex questions within a rigorous Bayesian framework that accounts for parameter and model uncertainty.

scholarly journals A Teaching Resource for a Computational Framework of the Value of Information in Origin of Life

2018 ◽  
Author(s):  
soumya banerjee ◽  
joyeeta ghose
Keyword(s):  
Complex Systems ◽  
Origin Of Life ◽  
Value Of Information ◽  
Critical Role ◽  
Computational Framework ◽  
Teaching Resource ◽  
Processing Information ◽  
The Origin Of Life ◽  
Role Of Information

Information plays a critical role in complex biological systems. Complex systems like immune systems andant colonies co-ordinate heterogeneous components in a decentralized fashion. How do these distributeddecentralized systems function? One key component is how these complex systems efficiently processinformation. These complex systems have an architecture for integrating and processing information comingin from various sources and points to the value of information in the functioning of different complexbiological systems. This paper is a teaching resource that explains the role of information processing inquestions around the origin of life and suggests how computational simulations may yield insights intoquestions related to the origin of life.

Effects of Salient News Items on Information Acquisition and Issue Perception

2002 ◽  
Vol 14 (1) ◽  
pp. 14-22 ◽  
Author(s):  
Silvia Knobloch ◽  
Dolf Zillmann ◽  
Rhonda Gibson ◽  
James A. Karrh
Keyword(s):  
Information Acquisition ◽  
Relevant Information ◽  
Diagnostic Value ◽  
Issue Salience ◽  
Qualitative Information ◽  
News Report ◽  
Target Region ◽  
The Public ◽  
Medical News ◽  
Issue Perception

Abstract. A medical news report was manipulated to project either Alabama or Texas as the target region for the outbreak of a new (fictitious) disease. Residents of Alabama and Texas responded to these reports, making the report of the threat to their respective territories relevant to them, while rendering the report of the threat to other regions of the country comparatively irrelevant. Regionally defined issue salience was found to foster superior acquisition of both quantitative and qualitative information of diagnostic value. Issue salience also led to estimates of greater danger to the public and self. It increased the perceived newsworthiness and usefulness of the reports as well. These findings suggest that issue salience motivates attention to, and the acquisition of, diagnostically relevant information that tends to be poorly processed or ignored under conditions of insufficient relevance.

Using trade show information to enhance company success: an empirical investigation

2012 ◽  
Vol 27 (5) ◽  
pp. 384-391 ◽  
Author(s):  
Harriette Bettis‐Outland ◽  
Wesley J. Johnston ◽  
R. Dale Wilson
Keyword(s):  
Regression Model ◽  
Multiple Regression ◽  
Information Acquisition ◽  
Value Of Information ◽  
Multiple Regression Model ◽  
Content Type ◽  
Independent Variables ◽  
Trade Show ◽  
Trade Shows ◽  
The Relationship

PurposeThis paper seeks to provide an exploratory empirical study of the variables that are part of the return on trade show information (RTSI) concept, which is based on the use and value of information gathered at a trade show.Design/methodology/approachThe research is designed to explore relationships and identify those variables that are a particularly important part of the RTSI concept. The paper provides an exploratory test of the relationship between a series of variables that are related to the value of information gathered at trade shows. Data were collected from trade show attendees approximately 60 days after the trade show. A multiple regression model was developed that explores the relationship between the dependent variable that focuses on information value and the independent variables on various aspects of information acquisition, information dissemination, and information use.FindingsThe final multiple regression model found a significant relationship for several variables and has an adjusted R2 value of 0.552. Four significant independent variables were identified – one each in the information use and the shared information categories and two in the information acquisition category. These findings present an interesting picture of how information is used within an organization after it is acquired at a trade show.Research limitations/implicationsThe research is limited by the multiple regression model used to explore the relationships in the data. Also, data from only one trade show were used in the model.Practical implicationsThis paper focuses on the intangible, longer‐term benefits as important considerations when determining the value of new trade show information to the firm. The evaluation of trade show information also should include these intangible benefits, such as improved interdepartmental relations or interactions as well as discussions with other trade show participants in finding new uses for information that impacts the company's future success, as well as shorter‐term benefits such as booth activity.Originality/valueThe paper offers a unique approach for determining the value of information acquired at trade shows. Though information gathering has been included as an outcome variable in previous trade show studies, no other research has studied the value of this new trade show information to the company.

scholarly journals What makes a good query? Prospects for a comprehensive theory of human information acquisition

2021 ◽  
Author(s):  
Björn Meder ◽  
Vincenzo Crupi ◽  
Jonathan D. Nelson
Keyword(s):  
Information Acquisition ◽  
Value Of Information ◽  
Psychological Research ◽  
Good Question ◽  
Comprehensive Theory ◽  
Open Questions ◽  
New Concepts ◽  
Formal Framework ◽  
Short And Long Term ◽  
Advance Theory

Searching for information in a goal-directed manner is central for learning, diagnosis, and prediction. Children continuously ask questions to learn new concepts, doctors do medical tests to diagnose their patients, and scientists perform experiments to test their theories. But what makes a good question? What principles govern human information acquisition and how do people decide which query to conduct to achieve their goals? What challenges need to be met to advance theory and psychology of human inquiry? Addressing these issues, we introduce the conceptual and mathematical ideas underlying different models of the value of information, what purpose these models serve in psychological research, and how they can be integrated in a unified formal framework. We also discuss the conflict between short- and long-term efficiency of prominent methods for query selection, and the resulting normative and methodological implications for studying human sequential search. A final point of discussion concerns the relations between probabilistic (Bayesian) models of the value of information and heuristic search strategies, and the insights than can be gained from bridging different levels of analysis and types of models. We conclude by discussing open questions and challenges that research needs to address to build a comprehensive theory of human information acquisition.

Predicting Confirmation Bias

2020 ◽  
pp. 184-206
Author(s):  
Martha Whitesmith
Keyword(s):  
Serial Position ◽  
Hypothesis Testing ◽  
Value Of Information ◽  
Confirmation Bias ◽  
Diagnostic Value ◽  
Position Effects ◽  
Serial Position Effects ◽  
Initial Information ◽  
Focal Hypothesis ◽  
Meta Analyses

Chapter five provides details of the meta-analyses into confirmation bias. It will show that the analytical conditions of diagnostic weighting of initial information, consistency of information, hypothesis testing instructions and type of information likely have an impact on confirmation bias. It will also show that the results undermine key assumptions in predominant predictions models the inability to identify diagnostic value of information theory concerning confirmation bias (Koslowski and Maqueda 1993, and Kuhn et al. 1988). The chapter proposes alternative models for predicting serial position effects and confirmation bias. These models argue that whilst the risk of occurrence of serial position effects and confirmation bias are impacted by different analytical conditions, they share an underlying cognitive process: a force towards forming a focal hypothesis early on in belief acquisition.

scholarly journals Divide et impera : subgoaling reduces the complexity of probabilistic inference and problem solving

2015 ◽  
Vol 12 (104) ◽  
pp. 20141335 ◽  
Author(s):  
Domenico Maisto ◽  
Francesco Donnarumma ◽  
Giovanni Pezzulo
Keyword(s):  
Problem Solving ◽  
Memory Load ◽  
Probabilistic Inference ◽  
Mechanistic Explanation ◽  
Cognitive Effort ◽  
Computational Framework ◽  
Descriptive Complexity ◽  
Control Programs ◽  
Controlling Behaviour ◽  
And Control

It has long been recognized that humans (and possibly other animals) usually break problems down into smaller and more manageable problems using subgoals. Despite a general consensus that subgoaling helps problem solving, it is still unclear what the mechanisms guiding online subgoal selection are during the solution of novel problems for which predefined solutions are not available. Under which conditions does subgoaling lead to optimal behaviour? When is subgoaling better than solving a problem from start to finish? Which is the best number and sequence of subgoals to solve a given problem? How are these subgoals selected during online inference? Here, we present a computational account of subgoaling in problem solving. Following Occam's razor, we propose that good subgoals are those that permit planning solutions and controlling behaviour using less information resources, thus yielding parsimony in inference and control. We implement this principle using approximate probabilistic inference: subgoals are selected using a sampling method that considers the descriptive complexity of the resulting sub-problems. We validate the proposed method using a standard reinforcement learning benchmark (four-rooms scenario) and show that the proposed method requires less inferential steps and permits selecting more compact control programs compared to an equivalent procedure without subgoaling. Furthermore, we show that the proposed method offers a mechanistic explanation of the neuronal dynamics found in the prefrontal cortex of monkeys that solve planning problems. Our computational framework provides a novel integrative perspective on subgoaling and its adaptive advantages for planning, control and learning, such as for example lowering cognitive effort and working memory load.

Sign in / Sign up

Export Citation Format

Share Document