{"title":"A Framework for Understanding Cognitive Biases in Technical Communication","authors":"Quan Zhou","doi":"10.55177/tc131231","DOIUrl":null,"url":null,"abstract":"Purpose: The communication of technical information is often susceptible to cognitive biases. Technical communicators need to understand cognitive biases and know how to tackle them accordingly. This article devises a framework of principles that provides technical communicators an operable affordance and a vocabulary to approach cognitive biases and to communicate empathetically. Method: I review a vast body of literature in technical communication with a focus on problems caused by cognitive biases. This work reveals significant problems in information visualization that can provide for a nuanced discussion on cognitive biases in technical communication. Using these problems as a guide, I draw upon cognitive theories in how people use information, the prospect theory about how people make decisions, and the self-determination theory about how such decisions are influenced by the social context. I then assemble a framework of principles that illuminates the workings of cognitive biases. I extrapolate sample questions that technical communicators can use to examine cognitive biases in information visualization and technical communication. Results: The framework of principles explains how cognitive biases affect technical communication. These principles are useful for gaining a deeper understanding of users from a cognitive bias perspective and optimizing for empathetic communication. Conclusion: Technical communicators and users are prone to cognitive biases. The framework of principles, cognitive biases, and sample questions presented in this article provide technical communicators a new lens to examine their work and improve user experience.","PeriodicalId":46338,"journal":{"name":"Technical Communication","volume":" ","pages":""},"PeriodicalIF":1.5000,"publicationDate":"2023-02-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Technical Communication","FirstCategoryId":"98","ListUrlMain":"https://doi.org/10.55177/tc131231","RegionNum":4,"RegionCategory":"文学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"COMMUNICATION","Score":null,"Total":0}
引用次数: 0
Abstract
Purpose: The communication of technical information is often susceptible to cognitive biases. Technical communicators need to understand cognitive biases and know how to tackle them accordingly. This article devises a framework of principles that provides technical communicators an operable affordance and a vocabulary to approach cognitive biases and to communicate empathetically. Method: I review a vast body of literature in technical communication with a focus on problems caused by cognitive biases. This work reveals significant problems in information visualization that can provide for a nuanced discussion on cognitive biases in technical communication. Using these problems as a guide, I draw upon cognitive theories in how people use information, the prospect theory about how people make decisions, and the self-determination theory about how such decisions are influenced by the social context. I then assemble a framework of principles that illuminates the workings of cognitive biases. I extrapolate sample questions that technical communicators can use to examine cognitive biases in information visualization and technical communication. Results: The framework of principles explains how cognitive biases affect technical communication. These principles are useful for gaining a deeper understanding of users from a cognitive bias perspective and optimizing for empathetic communication. Conclusion: Technical communicators and users are prone to cognitive biases. The framework of principles, cognitive biases, and sample questions presented in this article provide technical communicators a new lens to examine their work and improve user experience.