MétaCan
Menu
Back to cohort
Record W6894266353 · doi:10.5283/epub.51267

Professional Vision and Visual Expertise

2020· article· en· W6894266353 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

aboutThe title or abstract carries a Canadian signal from the geographic lexicon.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenueUniversity of Regensburg Publication Server (University of Regensburg) · 2020
Typearticle
Languageen
FieldPsychology
TopicVisual and Cognitive Learning Processes
Canadian institutionsnot available
Fundersnot available
KeywordsHabilitationGazeVisual literacyDomain (mathematical analysis)Work (physics)

Abstract

fetched live from OpenAlex

For a broader readership, what was this habilitation about? This habilitation focused on the gaze of professionals. When a radiologist, for example, looks at an x-ray scan of a patient’s lung, she sees very quickly if the patient has lung cancer—while somebody without expertise in radiology does not see the same symptoms in an x-ray scan, or might look at irrelevant parts of the visualization. Consider a second example: When a teacher looks at his students in the classroom or lecture hall, he “reads” his students and sees very quickly who pays attention, who is daydreaming, who needs support, and who does not follow the instructions. These two examples from the fields of medicine and education show how important the gaze is in some professions. To become expert in such a domain requires training one’s gaze and developing a “professional vision”. How the professional vision of experts differs from non-experts in a domain is, however, far from evident. What are the underlying mechanisms? How does an expert look at a visual scene compared to, say, a novice or a student in training? And how can novices be supported instructionally to develop visual expertise? These were the overarching questions that guided the work reported here. To answer these questions, the research performed in the habilitation was interdisciplinary and international. First, it was interdisciplinary because theories and methods from educational science, psychology, and medicine were needed to answer our questions on professional vision and visual expertise. For example, we used receiver operating characteristics (ROC) analysis from medicine to measure how accurate the diagnoses of radiologists and medical students were; we used eye tracking from psychology to measure where experts and novices looked at and for how long; and we designed learning environments to contribute to the education of novices. We used a range of methods, from quantitative statistical calculations to qualitative conversation analysis, largely because the research questions we had required a set of different methodologies used in the medical, psychological, and educational sciences. Still, the notion of “professional vision”, interestingly, had its origins neither in education, psychology, nor medicine—but was coined by Chuck Goodwin (†), who was a linguist and anthropologist. In addition to being interdisciplinary, the research performed in the habilitation was international because colleagues from (in alphabetic order) Australia, Canada, Finland, France, Germany, the Netherlands, and Sweden collaborated to answer our joint research questions in the 16 individual journal articles that together form this cumulative, publication-based habilitation. What we learned was that experts compared to non-experts are faster in solving visual tasks; they are more accurate and precise in their solutions; their reasoning processes are more knowledge-based; they use a repertoire of visual practices; they look more and longer on task-relevant information and tend to ignore task-irrelevant information; they look at visual scenes holistically; and they metacognitively monitor their information processing. Seeing the eye movements of an expert during task solution is a useful educational intervention for students and for professionals in training. This is the essence of the 16 manuscripts that we published in peer-reviewed educational, psychological, and medical journals.. The habilitation includes some pioneering work. We were the first to perform a systematic meta-analysis of eye-tracking research on expertise differences. We were the first to use eye movement modeling examples with dynamic, three-dimensional visualizations. We were the first to use eye tracking in the domain of emergency medicine. We were the first to examine the professional vision of school principals. We were the first to apply the gaze relational index as a measure of visual expertise. And we were the first to develop the frameworks of horizontal transition of expertise and the cognitive theory of visual expertise. Ultimately, we hope these new kinds of evidence, measures, and theories are useful for other researchers in the field of expertise and professional vision, and we hope the findings inspire educational practitioners who wish to support their learners in the early development of visual expertise. We close this habilitation’s summary paper with a quote from Goodwin that we also used to open the summary paper—indicating how important it is to be aware and to consider different disciplinary perspectives when we seek to understand (a) why an expert teacher and a teacher student spot different cues in the same classroom or (b) why a radiologist and a medical student see different things when they look at the same chest x-ray scan, simply because, as Goodwin (1994, p. 606) said: “All vision is perspectival”.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.375
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.022
GPT teacher head0.270
Teacher spread0.249 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it