Are you sure? The relationship between response certainty and performance in visual detection using a perimetry-style task

Abstract

Conventional psychophysical methods ignore the degree of confidence associated with each response. We compared the psychometric function for detection with that for "absolute certainty" in a perimetry-style task, to explore how knowledge of response certainty might aid the estimation of detection thresholds. Five healthy subjects performed a temporal 2-AFC detection task, indicating on each trial whether they were "absolutely certain." The method of constant stimuli was used to characterize the shape of the two psychometric functions. Four eccentricities spanning central and peripheral vision were tested. Where possible, conditions approximated those of the Humphrey Field Analyzer (spot size, duration, background luminance, test locations). Based on the empirical data, adaptive runs (ZEST) were simulated to predict the likely improvement in efficiency obtained by collecting certainty information. Compared to detection, threshold for certainty was 0.5 to 1.0 dB worse, and slope was indistinguishable across all eccentricities tested. A simple two-stage model explained the threshold difference; under this model, psychometric functions for detection and for certainty-given-detection are the same. Exploiting this equivalence is predicted to reduce the number of trials required to achieve a given level of accuracy by approximately 30% to 40%. The chances of detecting a spot and the chances of certainty-givendetection were approximately the same in young, healthy subjects. This means, for example, that a spot detected at threshold was labeled as "certainly" detected approximately half the time. The collection of certainty information could be used to improve the efficiency of estimation of detection thresholds.

Scaling the size of perimetric stimuli reduces variability and returns constant thresholds across the visual field

“…FOS curves have been described by a cumulative normal transition (1-Φ). For grouped data, psychometric functions have been fit to all data after thresholds were equalized between individuals by shifting curves along the x-axis ( Bedggood, Ahmad, Chen, Lim, Maqsudi, & Metha, 2020 ). This “equalization” step is necessary to avoid underestimation of the slope, which is expected with threshold differences between sessions (or in this case, individuals; Wallis, Baker, Meese, & Georgeson, 2013 ).…”

Digital body preservation: Technique and applications

“…The experts were asked to identify the features on the optimal model and mark them with “A” or “P”, depending on their absence or presence, respectively. In addition, based on psychophysical principles (Bedggood et al, 2020), the experts were asked to indicate their level of certainty they could identify the feature as: (1) “absolutely certain,” and (2) “uncertain,” This was done to exclude the possible bias that the accurate anatomical area was delineated, but the feature itself was not clearly visible. In this way, an optimal model with favorable physical fidelity and a limited number of polygons was defined.…”

“…(1) Before starting the scan process, the body part was positioned; (2) After positioning, the body part was scanned from different angles; (3) This resulted in multiple rough scans; (4) To clean the scans, redundant points were removed by an outlier removal algorithm; (5) Scans were manually aligned by assigning landmarks on the scans; (6) After successful alignment, a global registration algorithm was run to convert all one-frame surfaces to a single coordinate system; (7) After the global registration step, a sharp fusion was run to combine all frames into one 3D mesh; (8) In a final step, texture mapping was done. psychophysical principles (Bedggood et al, 2020), the experts were asked to indicate their level of certainty they could identify the feature as: (1) "absolutely certain," and (2) "uncertain," This was done to exclude the possible bias that the accurate anatomical area was delineated, but the feature itself was not clearly visible. In this way, an optimal model with favorable physical fidelity and a limited number of polygons was defined.…”