Interval bias in 2AFC detection tasks: sorting out the artifacts

Proportion correct in two-alternative forcedchoice (2AFC) detection tasks often varies when the stimulus is presented in the first or in the second interval.Reanalysis of published data reveals that these order effects (or interval bias) are strong and prevalent, refuting the standard difference model of signal detection theory. Order effects are commonly regarded as evidence that observers use an off-center criterion under the difference model with bias. We consider an alternative difference model with indecision whereby observers are occasionally undecided and guess with some bias toward one of the response options. Whether or not the data show order effects, the two models fit 2AFC data indistinguishably, but they yield meaningfully different estimates of sensory parameters. Under indeterminacy as to which model governs 2AFC performance, parameter estimates are suspect and potentially misleading. The indeterminacy can be circumvented by modifying the response format so that observers can express indecision when needed. Reanalysis of published data collected in this way lends support to the indecision model. We illustrate alternative approaches to fitting psychometric functions under the indecision model and discuss designs for 2AFC experiments that improve the accuracy of parameter estimates, whether or not order effects are apparent in the data.

Pipe3D, a pipeline to analyse integral Field Spectroscopy data: I. New fitting philosophy of FIT3D

We present an improved version of FIT3D, a fitting tool for the analysis of the spectroscopic properties of the stellar populations and the ionized gas derived from moderate resolution spectra of galaxies. This tool was developed to analyze integral field spectroscopy data and it is the basis of PIPE3D, a pipeline used in the analysis of CALIFA, MaNGA, and SAMI data. We describe the philosophy and each step of the fitting procedure. We present an extensive set of simulations in order to estimate the precision and accuracy of the derived parameters for the stellar populations and the ionized gas. We report on the results of those simulations. Finally, we compare the results of the analysis using FIT3D with those provided by other widely used packages, and we find that the parameters derived by FIT3D are fully compatible with those derived using these other tools.

Robust fitting of Zernike polynomials to noisy point clouds defined over connected domains of arbitrary shape

A new method for fitting a series of Zernike polynomials to point clouds defined over connected domains of arbitrary shape defined within the unit circle is presented in this work. The method is based on the application of machine learning fitting techniques by constructing an extended training set in order to ensure the smooth variation of local curvature over the whole domain. Therefore this technique is best suited for fitting points corresponding to ophthalmic lenses surfaces, particularly progressive power ones, in non-regular domains. We have tested our method by fitting numerical and real surfaces reaching an accuracy of 1 micron in elevation and 0.1 D in local curvature in agreement with the customary tolerances in the ophthalmic manufacturing industry.