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Purpose: Measuring accurate thresholds in children can be challenging. A typical psychophysical experiment is usually too long to keep children engaged. However, a reduction in the number of trials decreases the precision of the threshold estimate. We evaluated the efficiency of forced-choice paradigms with 2 or 4 alternatives (2-AFC, 4-AFC) in a disparity detection experiment. 4-AFC paradigms are statistically more efficient, but also more cognitively demanding, which might offset their theoretical advantage in young children. Methods: We ran simulations evaluating bias and precision of threshold estimates of 2-AFC and 4-AFC paradigms. In addition, we measured disparity thresholds in 43 children (aged 6 to 17 years) with a 4-AFC paradigm and in 49 children (aged 4 to 17 years) with a 2-AFC paradigm, both using an adaptive weighted one-up one-down staircase. Results: Simulations indicated a similar bias and precision for a 2-AFC paradigm with double the number of trials as a 4-AFC paradigm. On average, estimated threshold of the simulated data was equal to the model threshold, indicating no bias. The precision was improved with an increasing number of trials. Likewise, our data showed a similar bias and precision for a 2-AFC paradigm with 60 trials as for a 4-AFC paradigm with 30 trials. Trials in the 4-AFC paradigm took slightly longer as participants scanned more alternatives. However, the 4-AFC task still ended up faster for a given precision. Conclusion: Bias and precision were similar in a 4-AFC task compared to a 2-AFC task with double the number of trials. However, a 4-AFC paradigm was more time efficient and is therefore recommended. [ABSTRACT FROM AUTHOR]

In order to extract retinal disparity from a visual scene, the brain must match corresponding points in the left and right retinae. This computationally demanding task is known as the stereo correspondence problem. The initial stage of the solution to the correspondence problem is generally thought to consist of a correlation-based computation. However, recent work by Doi et al suggests that human observers can see depth in a class of stimuli where the mean binocular correlation is 0 (half-matched random dot stereograms). Half-matched random dot stereograms are made up of an equal number of correlated and anticorrelated dots, and the binocular energy model—a well-known model of V1 binocular complex cells—fails to signal disparity here. This has led to the proposition that a second, match-based computation must be extracting disparity in these stimuli. Here we show that a straightforward modification to the binocular energy model—adding a point output nonlinearity—is by itself sufficient to produce cells that are disparity-tuned to half-matched random dot stereograms. We then show that a simple decision model using this single mechanism can reproduce psychometric functions generated by human observers, including reduced performance to large disparities and rapidly updating dot patterns. The model makes predictions about how performance should change with dot size in half-matched stereograms and temporal alternation in correlation, which we test in human observers. We conclude that a single correlation-based computation, based directly on already-known properties of V1 neurons, can account for the literature on mixed correlation random dot stereograms. [ABSTRACT FROM AUTHOR]

Bayesian staircases are widely used in psychophysics to estimate detection thresholds. Simulations have revealed the importance of the parameters selected for the assumed subject's psychometric function in enabling thresholds to be estimated with small bias and high precision. One important parameter is the slope of the psychometric function, or equivalently its spread. This is often held fixed, rather than estimated for individual subjects, because much larger numbers of trials are required to estimate the spread as well as the threshold. However, if this fixed value is wrong, the threshold estimate can be biased. Here we determine the optimal slope to minimize bias and maximize precision when measuring stereoacuity with Bayesian staircases. We performed 2- and 4AFC disparity detection stereo experiments in order to measure the spread of the disparity psychometric function in human observers assuming a Logistic function. We found a wide range, between 0.03 and 3.5 log10 arcsec, with little change with age. We then ran simulations to examine the optimal spread using the empirical data. From our simulations and for three different experiments, we recommend selecting assumed spread values between the percentiles 60–80% of the population distribution of spreads (these percentiles can be extended to other type of thresholds). For stereo thresholds, we recommend a spread around the value σ = 1.7 log10 arcsec for 2AFC (slope β = 4.3 /log10 arcsec), and around σ = 1.5 log10 arcsec for 4AFC (β = 4.9 /log10 arcsec). Finally, we compared a Bayesian procedure (ZEST using the optimal σ) with five Bayesian procedures that are versions of ZEST-2D, Psi, and Psi-marginal. In general, for the conditions tested, ZEST optimal σ showed the lowest threshold bias and highest precision. [ABSTRACT FROM AUTHOR]