Visual Attention and the Neuroimage Bias

Several highly-cited experiments have presented evidence suggesting that neuroimages may unduly bias laypeople’s judgments of scientific research. This finding has been especially worrisome to the legal community in which neuroimage techniques may be used to produce evidence of a person’s mental state. However, a more recent body of work that has looked directly at the independent impact of neuroimages on layperson decision-making (both in legal and more general arenas), and has failed to find evidence of bias. To help resolve these conflicting findings, this research uses eye tracking technology to provide a measure of attention to different visual representations of neuroscientific data. Finding an effect of neuroimages on the distribution of attention would provide a potential mechanism for the influence of neuroimages on higher-level decisions. In the present experiment, a sample of laypeople viewed a vignette that briefly described a court case in which the defendant’s actions might have been explained by a neurological defect. Accompanying these vignettes was either an MRI image of the defendant’s brain, or a bar graph depicting levels of brain activity–two competing visualizations that have been the focus of much of the previous research on the neuroimage bias. We found that, while laypeople differentially attended to neuroimagery relative to the bar graph, this did not translate into differential judgments in a way that would support the idea of a neuroimage bias

An inclusive Research and Education Community (iREC) model to facilitate undergraduate science education reform

Funding: This work was supported by Howard Hughes Medical Institute grants to DIH is GT12052 and MJG is GT15338.Over the last two decades, there have been numerous initiatives to improve undergraduate student outcomes in STEM. One model for scalable reform is the inclusive Research Education Community (iREC). In an iREC, STEM faculty from colleges and universities across the nation are supported to adopt and sustainably implement course-based research – a form of science pedagogy that enhances student learning and persistence in science. In this study, we used pathway modeling to develop a qualitative description that explicates the HHMI Science Education Alliance (SEA) iREC as a model for facilitating the successful adoption and continued advancement of new curricular content and pedagogy. In particular, outcomes that faculty realize through their participation in the SEA iREC were identified, organized by time, and functionally linked. The resulting pathway model was then revised and refined based on several rounds of feedback from over 100 faculty members in the SEA iREC who participated in the study. Our results show that in an iREC, STEM faculty organized as a long-standing community of practice leverage one another, outside expertise, and data to adopt, implement, and iteratively advance their pedagogy. The opportunity to collaborate in this manner and, additionally, to be recognized for pedagogical contributions sustainably engages STEM faculty in the advancement of their pedagogy. Here, we present a detailed pathway model of SEA that, together with underpinning features of an iREC identified in this study, offers a framework to facilitate transformations in undergraduate science education.Peer reviewe

Adaptation and validation of the ACMG/AMP variant classification framework for MYH7-associated inherited cardiomyopathies: recommendations by ClinGen’s Inherited Cardiomyopathy Expert Panel

Purpose Integrating genomic sequencing in clinical care requires standardization of variant interpretation practices. The Clinical Genome Resource has established expert panels to adapt the American College of Medical Genetics and Genomics/Association for Molecular Pathology classification framework for specific genes and diseases. The Cardiomyopathy Expert Panel selected MYH7, a key contributor to inherited cardiomyopathies, as a pilot gene to develop a broadly applicable approach. Methods: Expert revisions were tested with 60 variants using a structured double review by pairs of clinical and diagnostic laboratory experts. Final consensus rules were established via iterative discussions. Results: Adjustments represented disease-/gene-informed specifications (12) or strength adjustments of existing rules (5). Nine rules were deemed not applicable. Key specifications included quantitative frameworks for minor allele frequency thresholds, the use of segregation data, and a semiquantitative approach to counting multiple independent variant occurrences where fully controlled case-control studies are lacking. Initial inter-expert classification concordance was 93%. Internal data from participating diagnostic laboratories changed the classification of 20% of the variants (n = 12), highlighting the critical importance of data sharing. Conclusion: These adapted rules provide increased specificity for use in MYH7-associated disorders in combination with expert review and clinical judgment and serve as a stepping stone for genes and disorders with similar genetic and clinical characteristics

Models of classroom assessment for course-based research experiences

Course-based research pedagogy involves positioning students as contributors to authentic research projects as part of an engaging educational experience that promotes their learning and persistence in science. To develop a model for assessing and grading students engaged in this type of learning experience, the assessment aims and practices of a community of experienced course-based research instructors were collected and analyzed. This approach defines four aims of course-based research assessment—(1) Assessing Laboratory Work and Scientific Thinking; (2) Evaluating Mastery of Concepts, Quantitative Thinking and Skills; (3) Appraising Forms of Scientific Communication; and (4) Metacognition of Learning—along with a set of practices for each aim. These aims and practices of assessment were then integrated with previously developed models of course-based research instruction to reveal an assessment program in which instructors provide extensive feedback to support productive student engagement in research while grading those aspects of research that are necessary for the student to succeed. Assessment conducted in this way delicately balances the need to facilitate students’ ongoing research with the requirement of a final grade without undercutting the important aims of a CRE education

Visual attention and the neuroimage bias.

Several highly-cited experiments have presented evidence suggesting that neuroimages may unduly bias laypeople's judgments of scientific research. This finding has been especially worrisome to the legal community in which neuroimage techniques may be used to produce evidence of a person's mental state. However, a more recent body of work that has looked directly at the independent impact of neuroimages on layperson decision-making (both in legal and more general arenas), and has failed to find evidence of bias. To help resolve these conflicting findings, this research uses eye tracking technology to provide a measure of attention to different visual representations of neuroscientific data. Finding an effect of neuroimages on the distribution of attention would provide a potential mechanism for the influence of neuroimages on higher-level decisions. In the present experiment, a sample of laypeople viewed a vignette that briefly described a court case in which the defendant's actions might have been explained by a neurological defect. Accompanying these vignettes was either an MRI image of the defendant's brain, or a bar graph depicting levels of brain activity-two competing visualizations that have been the focus of much of the previous research on the neuroimage bias. We found that, while laypeople differentially attended to neuroimagery relative to the bar graph, this did not translate into differential judgments in a way that would support the idea of a neuroimage bias

Making Sense of Research on the Neuroimage Bias

Both academic and legal communities have cautioned that laypersons may be unduly persuaded by images of the brain and may fail to interpret them appropriately. While early studies confirmed this concern, a second wave of research was repeatedly unable to find evidence of such a bias. The newest wave of studies paints a more nuanced picture in which, under certain circumstances, a neuroimage bias reemerges. To help make sense of this discordant body of research, we highlight the contextual significance of understanding how laypersons’ decision making is or is not impacted by neuroimages, provide an overview of findings from all sides of the neuroimage bias question, and discuss what these findings mean to public use and understanding of neuroimages

Fixation Count and Dwell Time for Caption Interest Area.

<p>(a) Mean Number of Fixations (per 10,000 pixels) on the Caption Interest Area. Participants fixated on the image caption significantly more often when it was paired with the bar graph than when it was paired with the neuroimage. (b) Mean Dwell Time (per 10,000 pixels) on the Caption Interest Area. Participants dwelled on the image caption significantly longer when it was paired with the bar graph than when it was paired with the neuroimage. (c) Mean Time (per 10,000 pixels) until First Fixation on the Caption Interest Area. The amount of time participants took to initially direct their gaze on the caption was not significantly different between the bar graph and neuroimage.</p

Tests of the indirect effect of Image Type on Recidivism through various measures of visual attention.

<p>Tests of the indirect effect of Image Type on Recidivism through various measures of visual attention.</p

Fixation Count and Dwell Time for Image Interest Area.

<p>(a) Mean Number of Fixations (per 10,000 pixels) on the Scenario’s Image Interest Area. Participants in the bar graph condition fixated on the image significantly more often than those in the neuroimage condition. (b) Mean Dwell Time (per 10,000 pixels) on the Scenario’s Image Interest Area. Participants in the bar graph condition tended to dwell longer on the image than participants in the neuroimage condition, however this result was not significant.</p

Mean Recidivism Scores For Each Image Type.

<p>Participants who viewed the neuroimage reported a stronger belief that the defendant in the scenario was likely to recidivate than participants who viewed the bar graph.</p