PsychoPy2: Experiments in behavior made easy.

PsychoPy is an application for the creation of experiments in behavioral science (psychology, neuroscience, linguistics, etc.) with precise spatial control and timing of stimuli. It now provides a choice of interface; users can write scripts in Python if they choose, while those who prefer to construct experiments graphically can use the new Builder interface. Here we describe the features that have been added over the last 10 years of its development. The most notable addition has been that Builder interface, allowing users to create studies with minimal or no programming, while also allowing the insertion of Python code for maximal flexibility. We also present some of the other new features, including further stimulus options, asynchronous time-stamped hardware polling, and better support for open science and reproducibility. Tens of thousands of users now launch PsychoPy every month, and more than 90 people have contributed to the code. We discuss the current state of the project, as well as plans for the future.

Combined eyetracking and keystroke-logging methods for studying cognitive processes in text production.

Writers typically spend a certain proportion of time looking back over the text that they have written. This is likely to serve a number of different functions, which are currently poorly understood. In this article, we present two systems, ScriptLog+ TimeLine and EyeWrite, that adopt different and complementary approaches to exploring this activity by collecting and analyzing combined eye movement and keystroke data from writers composing extended texts. ScriptLog+ TimeLine is a system that is based on an existing keystroke-logging program and uses heuristic, pattern-matching methods to identify reading episodes within eye movement data. EyeWrite is an integrated editor and analysis system that permits identification of the words that the writer fixates and their location within the developing text. We demonstrate how the methods instantiated within these systems can be used to make sense of the large amount of data generated by eyetracking and keystroke logging in order to inform understanding of the cognitive processes that underlie written text production.