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We present motivation and practical steps necessary to find parameter estimates of joint models of behavior and neural electrophysiological data. This tutorial is written for researchers wishing to build joint models of human behavior and scalp and intracranial electroencephalographic (EEG) or magnetoencephalographic (MEG) data, and more specifically those researchers who seek to understand human cognition. Although these techniques could easily be applied to animal models, the focus of this tutorial is on human participants. Joint modeling of M/EEG and behavior requires some knowledge of existing computational and cognitive theories, M/EEG artifact correction, M/EEG analysis techniques, cognitive modeling, and programming for statistical modeling implementation. This paper seeks to give an introduction to these techniques as they apply to estimating parameters from neurocognitive models of M/EEG and human behavior, and to evaluate model results and compare models. Due to our research and knowledge on the subject matter, our examples in this paper will focus on testing specific hypotheses in human decision-making theory. However, most of the motivation and discussion of this paper applies across many modeling procedures and applications. We provide Python (and linked R) code examples in the tutorial and appendix. Readers are encouraged to try the exercises at the end of the document. [ABSTRACT FROM AUTHOR]

Computer-vision-based gaze estimation refers to techniques that estimate gaze direction directly from video recordings of the eyes or face without the need for an eye tracker. Although many such methods exist, their validation is often found in the technical literature (e.g., computer science conference papers). We aimed to (1) identify which computer-vision-based gaze estimation methods are usable by the average researcher in fields such as psychology or education, and (2) evaluate these methods. We searched for methods that do not require calibration and have clear documentation. Two toolkits, OpenFace and OpenGaze, were found to fulfill these criteria. First, we present an experiment where adult participants fixated on nine stimulus points on a computer screen. We filmed their face with a camera and processed the recorded videos with OpenFace and OpenGaze. We conclude that OpenGaze is accurate and precise enough to be used in screen-based experiments with stimuli separated by at least 11 degrees of gaze angle. OpenFace was not sufficiently accurate for such situations but can potentially be used in sparser environments. We then examined whether OpenFace could be used with horizontally separated stimuli in a sparse environment with infant participants. We compared dwell measures based on OpenFace estimates to the same measures based on manual coding. We conclude that OpenFace gaze estimates may potentially be used with measures such as relative total dwell time to sparse, horizontally separated areas of interest, but should not be used to draw conclusions about measures such as dwell duration. [ABSTRACT FROM AUTHOR]

Over the past four decades, point-light displays (PLD) have been integrated into psychology and psychophysics, providing a valuable means to probe human perceptual skills. Leveraging the inherent kinematic information and controllable display parameters, researchers have utilized this technique to examine the mechanisms involved in learning and rehabilitation. However, classical PLD generation methods (e.g., motion capture) are difficult to apply for behavior analysis in real-world situations, such as patient care or sports activities. Therefore, there is a demand for automated and affordable tools that enable efficient and real-world-compatible generation of PLDs for psychological research. In this paper, we propose SmartDetector, a new artificial intelligence (AI)-based tool for automatic PLD creation from RGB videos. To evaluate humans’ perceptual skills for processing PLD building with SmartDetector, 126 participants were randomly assigned to recognition, discrimination, or detection tasks. Results demonstrated that, irrespective of the task, PLDs generated by SmartDetector exhibited commendable perceptual performance in terms of accuracy and response times compared to literature findings. Moreover, to enhance usability and broaden accessibility, we developed an intuitive web interface for our method, making it available to a wider audience. The resulting application is available at https://plavimop.prd.fr/index.php/en/automatic-creation-pld. SmartDetector offers interesting possibilities for using PLD in research and makes the use of PLD more accessible for nonacademic applications. [ABSTRACT FROM AUTHOR]