Prevalence Effects in Visual Search: Theoretical and Practical Implications
Trial Snapshot
- Phase
- Not Applicable
- Status
- Completed
- Sponsor
- Brigham and Women's Hospital
- Enrollment
- 1,121
- Locations
- 1
- Primary Endpoint
- Change in D' Between Pairs of Blocks.
Study Overview
Brief Summary
Imagine that a dermatologist spends the morning seeing patients who have been referred for suspicion of skin cancer. Many of them do, in fact, have skin lesions that require treatment. For this set of patients, disease 'prevalence' would be high. Suppose that the next task is to spend the afternoon giving annual screening exams to members of the general population. Here disease prevalence will be low. Would the morning's work influence decisions about patients in the afternoon? It is known from other contexts that recent history can influence current decisions and that target prevalence has an impact on decisions. In this study, decisions were decisions about skin lesions from individuals with varying degrees of expertise, using an online, medical imaging labelling app (DiagnosUs). This allowed examination of the effects of feedback history and prevalence in a single study. Blocks of trials could be of low or high prevalence, with or without feedback. Over 300,000 individual judgements were collected. (taken from Wolfe, J. M. (2022). How one block of trials influences the next: Persistent effects of disease prevalence and feedback on decisions about images of skin lesions in a large online study. . Cognitive Research: Principles and Implications (CRPI), 7, 10. doi: https://doi.org/10.1186/s41235-022-00362-0
Detailed Description
This description is based on a preregistration on the Open Science Framework site. Note that this is a "BESH" study. This type of research is not designed as a traditional clinical trial, but it is being reported here because of changes in NIH clinical trial reporting rules. This is one study from Project 2 of NE017001.
Levari et al (2018) found that people responded to a decrease in the prevalence of a stimulus by expanding their concept of it. Specifically, they asked observers to judge on each trial whether a dot, drawn from a blue-purple continuum, was blue or not. The results showed that observers were more likely to call ambiguous stimuli "blue" when blue items were less prevalent. In signal detection theory (SDT) terms, this is a liberal shift of response criterion. This is "prevalence induced concept change" (PICC). However, previous results obtained the opposite results in a long series of experiments on prevalence effects. The standard finding is that Os miss more targets at low prevalence. When blue is rare, they are less likely to call something blue. In SDT terms, this is a conservative criterion shift. This is the classic Low Prevalence Effect (LPE). In a round of earlier experiments, Lyu et al (2021) found that feedback is a critical variable. With trial-by-trial feedback, we get an LPE. With no feedback, the data usually show PICC results.
Do LPE and PICC effects show up when experts view stimuli in their expert domain? There is evidence for the LPE from search tasks (e.g. Evans, K. K., Birdwell, R. L., & Wolfe, J. M. (2013). If You Don't Find It Often, You Often Don't Find It: Why Some Cancers Are Missed in Breast Cancer Screening. . PLoS ONE 8(5): e64366. , 8(5), e64366. doi: doi:10.1371/journal.pone.0064366). However, PICC evidence has not been collected and there is no data from single item decision tasks like the "Is this dot blue?" task. This is important because criterion shifts of the sort described above can have obvious health care implications.
This study will repeat the basic "Is this dot blue" experiment using dermatology stimuli (Is this melanoma or just a nevus (a mole)?)
Hypotheses:
Study Design
- Study Type
- Interventional
- Allocation
- Na
- Intervention Model
- Single Group
- Primary Purpose
- Basic Science
- Masking
- None
Masking Description
Observers were naive about the hypothesis but could have figured out if a specific condition did or did not have feedback, for example. Once the data were collected, investigators could determine what conditions were tested on which observers
Eligibility Criteria
- Ages
- 18 Years to — (Adult, Older Adult)
- Sex
- All
- Accepts Healthy Volunteers
- Yes
Inclusion Criteria
- •All welcome to enroll on line
Exclusion Criteria
- •Under 18 yrs
Arms & Interventions
Feedback X Prevalence Using Dermatology Stimuli
In this experiment, observers (Os) completed blocks of 80 trials. On each trial, they saw an image of a spot on the skin. They classified this as a melanoma (cancer) or a nevis (benign). Blocks could be of low prevalence (20% cancer cases, 16 images) or high prevalence (50%, 40 images). Os either did received trial by trial "Feedback" about their performance accuracy, or they did not. Thus, there were four types of block.
Low prevalence, No Feedback Low prevalence, Feedback High prevalence, No Feedback High prevalence, Feedback Each of these four types of block was made available to Os on each of 6 days. Os could elect to view each of the four blocks each day. Our particular interest was in the effect of performing one block on performance on an immediately subsequent block.
Intervention: Feedback (Behavioral)
Feedback X Prevalence Using Dermatology Stimuli
In this experiment, observers (Os) completed blocks of 80 trials. On each trial, they saw an image of a spot on the skin. They classified this as a melanoma (cancer) or a nevis (benign). Blocks could be of low prevalence (20% cancer cases, 16 images) or high prevalence (50%, 40 images). Os either did received trial by trial "Feedback" about their performance accuracy, or they did not. Thus, there were four types of block.
Low prevalence, No Feedback Low prevalence, Feedback High prevalence, No Feedback High prevalence, Feedback Each of these four types of block was made available to Os on each of 6 days. Os could elect to view each of the four blocks each day. Our particular interest was in the effect of performing one block on performance on an immediately subsequent block.
Intervention: Prevalence (Behavioral)
Outcomes
Primary Outcomes
Change in D' Between Pairs of Blocks.
Time Frame: Participants could be in the study for as little as two blocks in one day up to 24 blocks collected over 6 days.
D' (d-prime) is defined as z-transform of the true positive rate - z-transform of false positive rate. True positive is when you say that a real melanoma is a melanoma. False positive is when you say that a nevis is a melanoma. A correction of 0.5 error is added to avoid calculation problems when z=0 or z=1. D' of zero indicates no ability to discriminate. D' \> zero indicates some ability to discriminate. The change of interest is the D' for Block 2 when it follows Block 1 compared to the D' for Block 2 averaged across all conditions.
Change in Criterion Between Pairs of Blocks.
Time Frame: Participants could be in the study for as little as two blocks in one day up to 24 blocks collected over 6 days.
Criterion, c, corresponds to the position of the midpoint between the z-transformed probabilities of hits (correct yes responses) and false alarms (incorrect yes responses). It is calculated as -\[z(p(h))+z(p(FA))\]/2. The criterion, c, z-score quantifies the distance away from being unbiased in units of standard deviations. A Z-score of 0 is said to be unbiased. Negative values for c indicate a more relaxed criterion for saying yes. Positive numbers indicate a more strict criterion for saying yes.
Secondary Outcomes
No secondary outcomes reported
Investigators
Jeremy M Wolfe, PhD
Professor
Brigham and Women's Hospital