Pub Date : 2021-12-01DOI: 10.1177/15291006211026259
John T Wixted, Gary L Wells, Elizabeth F Loftus, Brandon L Garrett
Eyewitness misidentifications are almost always made with high confidence in the courtroom. The courtroom is where eyewitnesses make their last identification of defendants suspected of (and charged with) committing a crime. But what did those same eyewitnesses do on the first identification test, conducted early in a police investigation? Despite testifying with high confidence in court, many eyewitnesses also testified that they had initially identified the suspect with low confidence or failed to identify the suspect at all. Presenting a lineup leaves the eyewitness with a memory trace of the faces in the lineup, including that of the suspect. As a result, the memory signal generated by the face of that suspect will be stronger on a later test involving the same witness, even if the suspect is innocent. In that sense, testing memory contaminates memory. These considerations underscore the importance of a newly proposed recommendation for conducting eyewitness identifications: Avoid repeated identification procedures with the same witness and suspect. This recommendation applies not only to additional tests conducted by police investigators but also to the final test conducted in the courtroom, in front of the judge and jury.
{"title":"Test a Witness's Memory of a Suspect Only Once.","authors":"John T Wixted, Gary L Wells, Elizabeth F Loftus, Brandon L Garrett","doi":"10.1177/15291006211026259","DOIUrl":"https://doi.org/10.1177/15291006211026259","url":null,"abstract":"<p><p>Eyewitness misidentifications are almost always made with high confidence in the courtroom. The courtroom is where eyewitnesses make their <i>last</i> identification of defendants suspected of (and charged with) committing a crime. But what did those same eyewitnesses do on the <i>first</i> identification test, conducted early in a police investigation? Despite testifying with high confidence in court, many eyewitnesses also testified that they had initially identified the suspect with low confidence or failed to identify the suspect at all. Presenting a lineup leaves the eyewitness with a memory trace of the faces in the lineup, including that of the suspect. As a result, the memory signal generated by the face of that suspect will be stronger on a later test involving the same witness, even if the suspect is innocent. In that sense, testing memory contaminates memory. These considerations underscore the importance of a newly proposed recommendation for conducting eyewitness identifications: <i>Avoid repeated identification procedures with the same witness and suspect</i>. This recommendation applies not only to additional tests conducted by police investigators but also to the final test conducted in the courtroom, in front of the judge and jury.</p>","PeriodicalId":37882,"journal":{"name":"Psychological science in the public interest : a journal of the American Psychological Society","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"39674423","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"心理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-12-01DOI: 10.1177/15291006211057899
Jonathan Schwabish
The practice of data visualization is both a science and an art. There is science behind how humans’ eyes and brains process visual content, and statistical methods behind collecting, processing, analyzing, and preparing data to generate graphs, charts, and diagrams. But the art of data visualization is how we bring people into the visual, how we engage them, and how we make them care about the content we are communicating to them. The target article of this commentary, the thorough work by Franconeri et al. (2021), sets the stage to understand the academic underpinnings of data visualization— how people’s eyes and brains facilitate the understanding of visual content, how to design perceptually efficient and understandable visualizations, and how people use different platforms and technologies to interact with data and visual content. The practice of data visualization goes further than many of these concepts to consider how data are plotted, how to use colors and fonts, and how to facilitate engagement and understanding. The standard graphs that many of us have come to know and create, such as line charts, bar charts, and pie charts, are familiar to most readers and easy to read. But many other graph types can be used to communicate ideas and arguments. In the “How to Design an Understandable Visualization” section of their article, Franconeri et al. briefly discuss four alternative graph types (or what I call nonstandard graph types): connected scatterplot, parallelcoordinates plot, tree map, and node-link diagram. There, the authors focus on how people in specific fields use specific graphs—for example, engineers and economists use connected scatterplots—not the potential for these formats to engage audiences on a broader level. In some cases, such nonstandard graph types can be inherently better at communicating data and in other cases are simply more engaging, which can be a goal in and of itself. Whether you are a researcher, analyst, marketer, or journalist, you know that the amount of content people see every day makes grabbing and maintaining attention difficult; thus, engagement can be a crucially important part of the data communicator’s toolkit. Here, I present several alternatives to the standard ways of visualizing and communicating a relatively simple data set from the National Center for Education Statistics (NCES). From my perspective, these alternative graphs are not so far outside the experience of most readers that they cannot be used more frequently— in the language of Franconeri et al., the “schema” in these graphs are well known and consist of dots, lines, and icons. The goal of this commentary is not to argue that the presented graphs are somehow the “best” that can be created with these data. Instead, my goal is to demonstrate the array of visual options we have to communicate data and how those options enable us to highlight different patterns or values, and to draw out our own stories for readers and help them reach co
{"title":"The Practice of Visual Data Communication: What Works.","authors":"Jonathan Schwabish","doi":"10.1177/15291006211057899","DOIUrl":"https://doi.org/10.1177/15291006211057899","url":null,"abstract":"The practice of data visualization is both a science and an art. There is science behind how humans’ eyes and brains process visual content, and statistical methods behind collecting, processing, analyzing, and preparing data to generate graphs, charts, and diagrams. But the art of data visualization is how we bring people into the visual, how we engage them, and how we make them care about the content we are communicating to them. The target article of this commentary, the thorough work by Franconeri et al. (2021), sets the stage to understand the academic underpinnings of data visualization— how people’s eyes and brains facilitate the understanding of visual content, how to design perceptually efficient and understandable visualizations, and how people use different platforms and technologies to interact with data and visual content. The practice of data visualization goes further than many of these concepts to consider how data are plotted, how to use colors and fonts, and how to facilitate engagement and understanding. The standard graphs that many of us have come to know and create, such as line charts, bar charts, and pie charts, are familiar to most readers and easy to read. But many other graph types can be used to communicate ideas and arguments. In the “How to Design an Understandable Visualization” section of their article, Franconeri et al. briefly discuss four alternative graph types (or what I call nonstandard graph types): connected scatterplot, parallelcoordinates plot, tree map, and node-link diagram. There, the authors focus on how people in specific fields use specific graphs—for example, engineers and economists use connected scatterplots—not the potential for these formats to engage audiences on a broader level. In some cases, such nonstandard graph types can be inherently better at communicating data and in other cases are simply more engaging, which can be a goal in and of itself. Whether you are a researcher, analyst, marketer, or journalist, you know that the amount of content people see every day makes grabbing and maintaining attention difficult; thus, engagement can be a crucially important part of the data communicator’s toolkit. Here, I present several alternatives to the standard ways of visualizing and communicating a relatively simple data set from the National Center for Education Statistics (NCES). From my perspective, these alternative graphs are not so far outside the experience of most readers that they cannot be used more frequently— in the language of Franconeri et al., the “schema” in these graphs are well known and consist of dots, lines, and icons. The goal of this commentary is not to argue that the presented graphs are somehow the “best” that can be created with these data. Instead, my goal is to demonstrate the array of visual options we have to communicate data and how those options enable us to highlight different patterns or values, and to draw out our own stories for readers and help them reach co","PeriodicalId":37882,"journal":{"name":"Psychological science in the public interest : a journal of the American Psychological Society","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"39588146","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"心理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-09-01DOI: 10.1177/15291006211008157
Mary A Driscoll, Robert R Edwards, William C Becker, Ted J Kaptchuk, Robert D Kerns
The high prevalence and societal burden of chronic pain, its undertreatment, and disparities in its management have contributed to the acknowledgment of chronic pain as a serious public-health concern. The concurrent opioid epidemic, and increasing concern about overreliance on opioid therapy despite evidence of limited benefit and serious harms, has heightened attention to this problem. The biopsychosocial model has emerged as the primary conceptual framework for understanding the complex experience of chronic pain and for informing models of care. The prominence of psychological processes as risk and resilience factors in this model has prompted extensive study of psychological treatments designed to alter processes that underlie or significantly contribute to pain, distress, or disability among adults with chronic pain. Cognitive-behavioral therapy is acknowledged to have strong evidence of effectiveness; other psychological approaches, including acceptance and commitment therapy, mindfulness, biofeedback, hypnosis, and emotional-awareness and expression therapy, have also garnered varying degrees of evidence across multiple pain conditions. Mechanistic studies have identified multiple pathways by which these treatments may reduce the intensity and impact of pain. Despite the growing evidence for and appreciation of these approaches, several barriers limit their uptake at the level of organizations, providers, and patients. Innovative methods for delivering psychological interventions and other research, practice, and policy initiatives hold promise for overcoming these barriers. Additional scientific knowledge and practice gaps remain to be addressed to optimize the reach and effectiveness of these interventions, including tailoring to address individual differences, concurrently addressing co-occurring disorders, and incorporating other optimization strategies.
{"title":"Psychological Interventions for the Treatment of Chronic Pain in Adults.","authors":"Mary A Driscoll, Robert R Edwards, William C Becker, Ted J Kaptchuk, Robert D Kerns","doi":"10.1177/15291006211008157","DOIUrl":"https://doi.org/10.1177/15291006211008157","url":null,"abstract":"<p><p>The high prevalence and societal burden of chronic pain, its undertreatment, and disparities in its management have contributed to the acknowledgment of chronic pain as a serious public-health concern. The concurrent opioid epidemic, and increasing concern about overreliance on opioid therapy despite evidence of limited benefit and serious harms, has heightened attention to this problem. The biopsychosocial model has emerged as the primary conceptual framework for understanding the complex experience of chronic pain and for informing models of care. The prominence of psychological processes as risk and resilience factors in this model has prompted extensive study of psychological treatments designed to alter processes that underlie or significantly contribute to pain, distress, or disability among adults with chronic pain. Cognitive-behavioral therapy is acknowledged to have strong evidence of effectiveness; other psychological approaches, including acceptance and commitment therapy, mindfulness, biofeedback, hypnosis, and emotional-awareness and expression therapy, have also garnered varying degrees of evidence across multiple pain conditions. Mechanistic studies have identified multiple pathways by which these treatments may reduce the intensity and impact of pain. Despite the growing evidence for and appreciation of these approaches, several barriers limit their uptake at the level of organizations, providers, and patients. Innovative methods for delivering psychological interventions and other research, practice, and policy initiatives hold promise for overcoming these barriers. Additional scientific knowledge and practice gaps remain to be addressed to optimize the reach and effectiveness of these interventions, including tailoring to address individual differences, concurrently addressing co-occurring disorders, and incorporating other optimization strategies.</p>","PeriodicalId":37882,"journal":{"name":"Psychological science in the public interest : a journal of the American Psychological Society","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"39431562","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"心理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-09-01DOI: 10.1177/15291006211033612
Beth D Darnall
{"title":"Psychological Treatment for Chronic Pain: Improving Access and Integration.","authors":"Beth D Darnall","doi":"10.1177/15291006211033612","DOIUrl":"10.1177/15291006211033612","url":null,"abstract":"","PeriodicalId":37882,"journal":{"name":"Psychological science in the public interest : a journal of the American Psychological Society","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9970761/pdf/nihms-1872835.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9343409","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"心理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-04-01DOI: 10.1177/1529100621997376
Garvin Brod
Active learning holds great promise for improving education, particularly in science, technology, engineering, and mathematics (STEM). Instead of receiving information passively, students take agency and actively construct their own understanding. A large meta-analysis has suggested that these features improve student performance in STEM (Freeman et al., 2014). Many instructional practices that promote active learning have the added benefit of making students familiar with the scientific process of testing theories via predictions and observations. Active learning could also contribute to reducing achievement gaps and empowering students from underrepresented groups to consider careers in science. It therefore seems paramount to synthesize a framework of active learning that guides research and practice in this field, and I applaud Lombardi and colleagues (this issue) for their interdisciplinary efforts to do so. Although the promises of active learning are wideranging, research on its merits has predominantly focused on undergraduate instruction. The meta-analysis by Freeman and colleagues (2014) focused exclusively on undergraduates, and so does the synthesis by Lombardi and colleagues. Does active learning work equally well for younger students, from kindergarten to 12th grade (K–12)? Or are there prerequisites for benefiting from active learning that younger students do not yet meet? And can the construction-of-understanding ecosystem proposed by Lombardi and colleagues inform research and practice in K–12 education as well? Answers to these questions are important for improving scientific literacy in society at large. Attempting to close achievement gaps at earlier ages is more effective and has higher returns than doing so later (Heckman, 2006). Furthermore, bringing active-learning practices into K–12 education could facilitate the transition to such practices at the undergraduate level. Currently, active-learning methods are often less popular among first-year undergraduate students than among more advanced undergraduates who have more experience with these methods (Zinski et al., 2017). Therefore, in the following, I attempt to provide some answers, acknowledging that these are preliminary and subject to future research that will hopefully be sparked by the construction-of-understanding ecosystem framework. Leaning on the synthetic definition offered by Lombardi and colleagues, I use active-learning practices as an umbrella term for instructional activities that are intended to afford students agency over their learning and that foster active construction of understanding. Do active-learning practices work as well in K–12 education as in undergraduate instruction? This question turns out to be surprisingly difficult to answer. A first difficulty is terminology. Most research that has dealt with active-learning practices in K–12 education has placed them under the umbrella term inquiry-based teaching/learning, which spans even wider than
{"title":"How Can We Make Active Learning Work in K-12 Education? Considering Prerequisites for a Successful Construction of Understanding.","authors":"Garvin Brod","doi":"10.1177/1529100621997376","DOIUrl":"https://doi.org/10.1177/1529100621997376","url":null,"abstract":"Active learning holds great promise for improving education, particularly in science, technology, engineering, and mathematics (STEM). Instead of receiving information passively, students take agency and actively construct their own understanding. A large meta-analysis has suggested that these features improve student performance in STEM (Freeman et al., 2014). Many instructional practices that promote active learning have the added benefit of making students familiar with the scientific process of testing theories via predictions and observations. Active learning could also contribute to reducing achievement gaps and empowering students from underrepresented groups to consider careers in science. It therefore seems paramount to synthesize a framework of active learning that guides research and practice in this field, and I applaud Lombardi and colleagues (this issue) for their interdisciplinary efforts to do so. Although the promises of active learning are wideranging, research on its merits has predominantly focused on undergraduate instruction. The meta-analysis by Freeman and colleagues (2014) focused exclusively on undergraduates, and so does the synthesis by Lombardi and colleagues. Does active learning work equally well for younger students, from kindergarten to 12th grade (K–12)? Or are there prerequisites for benefiting from active learning that younger students do not yet meet? And can the construction-of-understanding ecosystem proposed by Lombardi and colleagues inform research and practice in K–12 education as well? Answers to these questions are important for improving scientific literacy in society at large. Attempting to close achievement gaps at earlier ages is more effective and has higher returns than doing so later (Heckman, 2006). Furthermore, bringing active-learning practices into K–12 education could facilitate the transition to such practices at the undergraduate level. Currently, active-learning methods are often less popular among first-year undergraduate students than among more advanced undergraduates who have more experience with these methods (Zinski et al., 2017). Therefore, in the following, I attempt to provide some answers, acknowledging that these are preliminary and subject to future research that will hopefully be sparked by the construction-of-understanding ecosystem framework. Leaning on the synthetic definition offered by Lombardi and colleagues, I use active-learning practices as an umbrella term for instructional activities that are intended to afford students agency over their learning and that foster active construction of understanding. Do active-learning practices work as well in K–12 education as in undergraduate instruction? This question turns out to be surprisingly difficult to answer. A first difficulty is terminology. Most research that has dealt with active-learning practices in K–12 education has placed them under the umbrella term inquiry-based teaching/learning, which spans even wider than ","PeriodicalId":37882,"journal":{"name":"Psychological science in the public interest : a journal of the American Psychological Society","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1177/1529100621997376","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"38887020","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"心理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-12-01DOI: 10.1177/1529100620946707
Anastasia Kozyreva, Stephan Lewandowsky, Ralph Hertwig
The Internet has evolved into a ubiquitous and indispensable digital environment in which people communicate, seek information, and make decisions. Despite offering various benefits, online environments are also replete with smart, highly adaptive choice architectures designed primarily to maximize commercial interests, capture and sustain users' attention, monetize user data, and predict and influence future behavior. This online landscape holds multiple negative consequences for society, such as a decline in human autonomy, rising incivility in online conversation, the facilitation of political extremism, and the spread of disinformation. Benevolent choice architects working with regulators may curb the worst excesses of manipulative choice architectures, yet the strategic advantages, resources, and data remain with commercial players. One way to address some of this imbalance is with interventions that empower Internet users to gain some control over their digital environments, in part by boosting their information literacy and their cognitive resistance to manipulation. Our goal is to present a conceptual map of interventions that are based on insights from psychological science. We begin by systematically outlining how online and offline environments differ despite being increasingly inextricable. We then identify four major types of challenges that users encounter in online environments: persuasive and manipulative choice architectures, AI-assisted information architectures, false and misleading information, and distracting environments. Next, we turn to how psychological science can inform interventions to counteract these challenges of the digital world. After distinguishing among three types of behavioral and cognitive interventions-nudges, technocognition, and boosts-we focus on boosts, of which we identify two main groups: (a) those aimed at enhancing people's agency in their digital environments (e.g., self-nudging, deliberate ignorance) and (b) those aimed at boosting competencies of reasoning and resilience to manipulation (e.g., simple decision aids, inoculation). These cognitive tools are designed to foster the civility of online discourse and protect reason and human autonomy against manipulative choice architectures, attention-grabbing techniques, and the spread of false information.
{"title":"Citizens Versus the Internet: Confronting Digital Challenges With Cognitive Tools.","authors":"Anastasia Kozyreva, Stephan Lewandowsky, Ralph Hertwig","doi":"10.1177/1529100620946707","DOIUrl":"10.1177/1529100620946707","url":null,"abstract":"<p><p>The Internet has evolved into a ubiquitous and indispensable digital environment in which people communicate, seek information, and make decisions. Despite offering various benefits, online environments are also replete with smart, highly adaptive choice architectures designed primarily to maximize commercial interests, capture and sustain users' attention, monetize user data, and predict and influence future behavior. This online landscape holds multiple negative consequences for society, such as a decline in human autonomy, rising incivility in online conversation, the facilitation of political extremism, and the spread of disinformation. Benevolent choice architects working with regulators may curb the worst excesses of manipulative choice architectures, yet the strategic advantages, resources, and data remain with commercial players. One way to address some of this imbalance is with interventions that empower Internet users to gain some control over their digital environments, in part by boosting their information literacy and their cognitive resistance to manipulation. Our goal is to present a conceptual map of interventions that are based on insights from psychological science. We begin by systematically outlining how online and offline environments differ despite being increasingly inextricable. We then identify four major types of challenges that users encounter in online environments: persuasive and manipulative choice architectures, AI-assisted information architectures, false and misleading information, and distracting environments. Next, we turn to how psychological science can inform interventions to counteract these challenges of the digital world. After distinguishing among three types of behavioral and cognitive interventions-nudges, technocognition, and boosts-we focus on boosts, of which we identify two main groups: (a) those aimed at enhancing people's agency in their digital environments (e.g., self-nudging, deliberate ignorance) and (b) those aimed at boosting competencies of reasoning and resilience to manipulation (e.g., simple decision aids, inoculation). These cognitive tools are designed to foster the civility of online discourse and protect reason and human autonomy against manipulative choice architectures, attention-grabbing techniques, and the spread of false information.</p>","PeriodicalId":37882,"journal":{"name":"Psychological science in the public interest : a journal of the American Psychological Society","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2020-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1177/1529100620946707","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"38715065","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"心理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-12-01DOI: 10.1177/1529100620972100
Lisa K Fazio
In their review, Kozyreva, Lewandowsky, and Hertwig (this issue; p. 103) provide an excellent overview of our current online environment, the challenges inherent in the current system, and how psychological science can help confront these challenges. In this commentary, I aim to highlight some of their important points and outline a few areas of disagreement. Before I do so, I think it is important to situate myself and my perspective. I am a cognitive and developmental psychologist who studies memory and how people of all ages learn true and false information. The review by Kozyreva et al. touches on many aspects of the online ecosystem, but I will be focusing on the areas that overlap with my expertise.
{"title":"Recognizing the Role of Psychological Science in Improving Online Spaces.","authors":"Lisa K Fazio","doi":"10.1177/1529100620972100","DOIUrl":"https://doi.org/10.1177/1529100620972100","url":null,"abstract":"In their review, Kozyreva, Lewandowsky, and Hertwig (this issue; p. 103) provide an excellent overview of our current online environment, the challenges inherent in the current system, and how psychological science can help confront these challenges. In this commentary, I aim to highlight some of their important points and outline a few areas of disagreement. Before I do so, I think it is important to situate myself and my perspective. I am a cognitive and developmental psychologist who studies memory and how people of all ages learn true and false information. The review by Kozyreva et al. touches on many aspects of the online ecosystem, but I will be focusing on the areas that overlap with my expertise.","PeriodicalId":37882,"journal":{"name":"Psychological science in the public interest : a journal of the American Psychological Society","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2020-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1177/1529100620972100","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"38378269","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"心理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-10-01Epub Date: 2020-10-14DOI: 10.1177/1529100620915848
Drew H Bailey, Greg J Duncan, Flávio Cunha, Barbara R Foorman, David S Yeager
Some environmental influences, including intentional interventions, have shown persistent effects on psychological characteristics and other socially important outcomes years and even decades later. At the same time, it is common to find that the effects of life events or interventions diminish and even disappear completely, a phenomenon known as fadeout. We review the evidence for persistence and fadeout, drawing primarily on evidence from educational interventions. We conclude that 1) fadeout is widespread, and often co-exists with persistence; 2) fadeout is a substantive phenomenon, not merely a measurement artefact; and 3) persistence depends on the types of skills targeted, the institutional constraints and opportunities within the social context, and complementarities between interventions and subsequent environmental affordances. We discuss the implications of these conclusions for research and policy.
{"title":"Persistence and Fade-Out of Educational-Intervention Effects: Mechanisms and Potential Solutions.","authors":"Drew H Bailey, Greg J Duncan, Flávio Cunha, Barbara R Foorman, David S Yeager","doi":"10.1177/1529100620915848","DOIUrl":"10.1177/1529100620915848","url":null,"abstract":"<p><p>Some environmental influences, including intentional interventions, have shown persistent effects on psychological characteristics and other socially important outcomes years and even decades later. At the same time, it is common to find that the effects of life events or interventions diminish and even disappear completely, a phenomenon known as <i>fadeout</i>. We review the evidence for persistence and fadeout, drawing primarily on evidence from educational interventions. We conclude that 1) fadeout is widespread, and often co-exists with persistence; 2) fadeout is a substantive phenomenon, not merely a measurement artefact; and 3) persistence depends on the types of skills targeted, the institutional constraints and opportunities within the social context, and complementarities between interventions and subsequent environmental affordances. We discuss the implications of these conclusions for research and policy.</p>","PeriodicalId":37882,"journal":{"name":"Psychological science in the public interest : a journal of the American Psychological Society","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2020-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7787577/pdf/nihms-1588789.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"38794128","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"心理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-08-01DOI: 10.1177/1529100620920576
Martin Lövdén, Laura Fratiglioni, M Maria Glymour, Ulman Lindenberger, Elliot M Tucker-Drob
Cognitive abilities are important predictors of educational and occupational performance, socioeconomic attainment, health, and longevity. Declines in cognitive abilities are linked to impairments in older adults' everyday functions, but people differ from one another in their rates of cognitive decline over the course of adulthood and old age. Hence, identifying factors that protect against compromised late-life cognition is of great societal interest. The number of years of formal education completed by individuals is positively correlated with their cognitive function throughout adulthood and predicts lower risk of dementia late in life. These observations have led to the propositions that prolonging education might (a) affect cognitive ability and (b) attenuate aging-associated declines in cognition. We evaluate these propositions by reviewing the literature on educational attainment and cognitive aging, including recent analyses of data harmonized across multiple longitudinal cohort studies and related meta-analyses. In line with the first proposition, the evidence indicates that educational attainment has positive effects on cognitive function. We also find evidence that cognitive abilities are associated with selection into longer durations of education and that there are common factors (e.g., parental socioeconomic resources) that affect both educational attainment and cognitive development. There is likely reciprocal interplay among these factors, and among cognitive abilities, during development. Education-cognitive ability associations are apparent across the entire adult life span and across the full range of education levels, including (to some degree) tertiary education. However, contrary to the second proposition, we find that associations between education and aging-associated cognitive declines are negligible and that a threshold model of dementia can account for the association between educational attainment and late-life dementia risk. We conclude that educational attainment exerts its influences on late-life cognitive function primarily by contributing to individual differences in cognitive skills that emerge in early adulthood but persist into older age. We also note that the widespread absence of educational influences on rates of cognitive decline puts constraints on theoretical notions of cognitive aging, such as the concepts of cognitive reserve and brain maintenance. Improving the conditions that shape development during the first decades of life carries great potential for improving cognitive ability in early adulthood and for reducing public-health burdens related to cognitive aging and dementia.
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Pub Date : 2020-08-01DOI: 10.1177/1529100620941808
Mara Mather
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