An introduction to the Objective Psychophysics Toolbox (o_ptb)

The Psychophysics Toolbox (PTB) is one of the most popular toolboxes for the development of experimental paradigms. It is a very powerful library, providing low-level, platform independent access to the devices used in an experiment such as the graphics and the sound card. While this low-level design results in a high degree of flexibility and power, writing paradigms that interface the PTB directly might lead to code that is hard to read, maintain, reuse and debug. Running an experiment in different facilities or organizations further requires it to work with various setups that differ in the availability of specialized hardware for response collection, triggering and presentation of auditory stimuli. The Objective Psychophysics Toolbox (o_ptb) provides an intuitive, unified and clear interface, built on top of the PTB that enables researchers to write readable, clean and concise code. In addition to presenting the architecture of the o_ptb, the results of a timing accuracy test are presented. Exactly the same Matlab code was run on two different systems, one of those using the VPixx system. Both systems showed sub-millisecond accuracy.

The timing mega-study: comparing a range of experiment generators, both lab-based and online

Many researchers in the behavioral sciences depend on research software that presents stimuli, and records response times, with sub-millisecond precision. There are a large number of software packages with which to conduct these behavioral experiments and measure response times and performance of participants. Very little information is available, however, on what timing performance they achieve in practice. Here we report a wide-ranging study looking at the precision and accuracy of visual and auditory stimulus timing and response times, measured with a Black Box Toolkit. We compared a range of popular packages: PsychoPy, E-Prime®, NBS Presentation®, Psychophysics Toolbox, OpenSesame, Expyriment, Gorilla, jsPsych, Lab.js and Testable. Where possible, the packages were tested on Windows, macOS, and Ubuntu, and in a range of browsers for the online studies, to try to identify common patterns in performance. Among the lab-based experiments, Psychtoolbox, PsychoPy, Presentation and E-Prime provided the best timing, all with mean precision under 1 millisecond across the visual, audio and response measures. OpenSesame had slightly less precision across the board, but most notably in audio stimuli and Expyriment had rather poor precision. Across operating systems, the pattern was that precision was generally very slightly better under Ubuntu than Windows, and that macOS was the worst, at least for visual stimuli, for all packages. Online studies did not deliver the same level of precision as lab-based systems, with slightly more variability in all measurements. That said, PsychoPy and Gorilla, broadly the best performers, were achieving very close to millisecond precision on several browser/operating system combinations. For response times (measured using a high-performance button box), most of the packages achieved precision at least under 10 ms in all browsers, with PsychoPy achieving a precision under 3.5 ms in all. There was considerable variability between OS/browser combinations, especially in audio-visual synchrony which is the least precise aspect of the browser-based experiments. Nonetheless, the data indicate that online methods can be suitable for a wide range of studies, with due thought about the sources of variability that result. The results, from over 110,000 trials, highlight the wide range of timing qualities that can occur even in these dedicated software packages for the task. We stress the importance of scientists making their own timing validation measurements for their own stimuli and computer configuration.

The timing mega-study: comparing a range of experiment generators, both lab-based and online

Many researchers in the behavioral sciences depend on research software that presents stimuli, and records response times, with sub-millisecond precision. There are a large number of software packages with which to conduct these behavioural experiments and measure response times and performance of participants. Very little information is available, however, on what timing performance they achieve in practice. Here we report a wide-ranging study looking at the precision and accuracy of visual and auditory stimulus timing and response times, measured with a Black Box Toolkit. We compared a range of popular packages: PsychoPy, E-Prime®, NBS Presentation®, Psychophysics Toolbox, OpenSesame, Expyriment, Gorilla, jsPsych, Lab.js and Testable. Where possible, the packages were tested on Windows, MacOS, and Ubuntu, and in a range of browsers for the online studies, to try to identify common patterns in performance. Among the lab-based experiments, Psychtoolbox, PsychoPy, Presentation and E-Prime provided the best timing, all with mean precision under 1 millisecond across the visual, audio and response measures. OpenSesame had slightly less precision across the board, but most notably in audio stimuli and Expyriment had rather poor precision. Across operating systems, the pattern was that precision was generally very slightly better under Ubuntu than Windows, and that Mac OS was the worst, at least for visual stimuli, for all packages. Online studies did not deliver the same level of precision as lab-based systems, with slightly more variability in all measurements. That said, PsychoPy and Gorilla, broadly the best performers, were achieving very close to millisecond precision on a number of browser configurations. For response times (using a high-performance button box), most of the packages achieved precision at least under 10 ms in all browsers, with PsychoPy achieving a precision under 3.5 ms in all. There was considerable variability between operating systems and browsers, especially in audio-visual synchrony which is the least precise aspect of the browser-based experiments. Nonetheless, the data indicate that online methods can be suitable for a wide range of studies, with due thought about the sources of variability that result.The results, from over 110,000 trials, highlight the wide range of timing qualities that can occur even in these dedicated software packages for the task. We stress the importance of scientists making their own timing validation measurements for their own stimuli and computer configuration.

Can Change Probability Contextual Information Improve the Change Identification Process?

The visual world is extremely complex, so unconscious mechanisms exist to autonomously direct attention to objects with behavioral importance. One such mechanism – contextual cueing – utilizes the visual context of a scene to focus attention. Therefore, because contextual information unconsciously influences human visual perception, its role in enabling individuals to process scenes is of great interest. This study examined whether contextual information regarding change probability can facilitate the process of change identification. MATLAB and Psychophysics Toolbox Version 3 were used to present abstract scenes in a one-shot change blindness paradigm. Two types of scenes were presented: one in which context was predictive of change likelihood, the other in which context was non-predictive of change likelihood. The accuracy with which subjects detected and localized changes in both scene types was compared, but no significant difference in accuracy was found. This observation suggests that contextual information regarding change probability alone is insufficient to improve the change identification accuracy. Subsequently, it may be that even when individuals are aware that a visual scene is likely to change, they still require additional contextual cues to improve change identification.