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.

Fitting model-based psychometric functions to simultaneity and temporal-order judgment data: MATLAB and R routines

Research on temporal-order perception uses temporal-order judgment (TOJ) tasks or synchrony judgment (SJ) tasks in their binary SJ2 or ternary SJ3 variants. In all cases, two stimuli are presented with some temporal delay, and observers judge the order of presentation. Arbitrary psychometric functions are typically fitted to obtain performance measures such as sensitivity or the point of subjective simultaneity, but the parameters of these functions are uninterpretable. We describe routines in MATLAB and R that fit model-based functions whose parameters are interpretable in terms of the processes underlying temporal-order and simultaneity judgments and responses. These functions arise from an independent-channels model assuming arrival latencies with exponential distributions and a trichotomous decision space. Different routines fit data separately for SJ2, SJ3, and TOJ tasks, jointly for any two tasks, or also jointly for the three tasks (for common cases in which two or even the three tasks were used with the same stimuli and participants). Additional routines provide bootstrap p-values and confidence intervals for estimated parameters. A further routine is included that obtains performance measures from the fitted functions. An R package for Windows and source code of the MATLAB and R routines are available as Supplementary Files.