This article addresses methodological concerns in research on grammatical aspects of code-switching. Data from code-switching have the potential for a unique contribution to linguistics by giving us access to combinations of linguistic features that may be difficult (or impossible) to observe in monolingual data. Nonetheless, the use of code-switching data for linguistic inquiry is not without issues. In this paper, we focus on three methodological questions specific to code-switching research: (i) project design, (ii) experimental procedure and (iii) participant selection. Drawing on experimental data from both published works and in-progress projects, we highlight potential solutions to each methodological challenge, concluding that several solutions are often required to mitigate the impact of confounding variables. In line with previous work (e.g. Grosjean 1998, Gullberg, Indefrey & Muysken 2009), we suggest that researchers clearly report on their methodology. Our overall goal is to contribute to a dialogue on best practices in code-switching research.

Successive relearning involves repeated retrieval practice of the same information (with feedback) over multiple, spaced sessions. We implemented successive relearning in an introductory psychology class to explore potential learning benefits. After each weekly lecture, students were sent links via email to engage in three learning practice sessions, each separated by two days. Half the students engaged in successive relearning (relearn condition), answering 20 fill-in-the-blank questions with corrective feedback. Within each session, correctly answered questions were dropped, whereas incorrectly answered questions were presented up to 2 more times. The other half of students restudied the same 20 sentences without blanks twice per session (restudy condition). Unlike previous research, we controlled the exposure duration of the learning materials between the relearn and restudy conditions.Learning practice sessions continued throughout the remaining 10 weeks of the semester, with students alternating each week between the relearning and restudying tasks. Recall of course material at the end of the semester was better for relearning compared to restudying.Increased recall during relearning sessions was associated with further learning benefits including improved metacognition, increased self-reported sense of mastery, increased attentional control, and reduced anxiety. Individual differences were not associated with the benefit of relearning over restudying in the retention tests. Qualitative feedback indicated that students found successive relearning to be enjoyable and valuable. Our research indicates that successive relearning is a valuable addition to any university course and is easy to implement using digital resources.

Scientific discovery is a driving force for progress involving creative problem-solving processes to further our understanding of the world. The process of scientific discovery has historically been intensive and time-consuming; however, advances in computational power and algorithms have provided an efficient route to make new discoveries. Complex tools using artificial intelligence (AI) can efficiently analyze data as well as generate new hypotheses and theories. Along with AI becoming increasingly prevalent in our daily lives and the services we access, its application to different scientific domains is becoming more widespread. For example, AI has been used for the early detection of medical conditions, identifying treatments and vaccines (e.g., against COVID-19), and predicting protein structure. The application of AI in psychological science has started to become popular. AI can assist in new discoveries both as a tool that allows more freedom to scientists to generate new theories and by making creative discoveries autonomously. Conversely, psychological concepts such as heuristics have refined and improved artificial systems. With such powerful systems, however, there are key ethical and practical issues to consider. This article addresses the current and future directions of computational scientific discovery generally and its applications in psychological science more specifically.

The motion aftereffect (MAE) is the perception of illusory motion following extended exposure to a moving stimulus. The MAE has been used to probe the role of attention in motion processing. Many studies have reported that MAEs are reduced if attention is diverted from the adaptation stimulus, but others have argued that motion adaptation is independent of attention. We explored several factors that might modulate the attention-adaptation relationship and therefore explain apparent inconsistencies, namely (a) adaptation duration, (b) motion type: translating versus complex, and (c) response bias. Participants viewed translating (Experiments 1a and 2) or rotating (Experiment 1b) random dot patterns while fixating a central letter stream. During adaptation, participants reported brief changes in the adaptor speed (attention-focused) or the presence of white vowels within the letter stream (attention-diverted). Trials consisted of multiple adaptation-test cycles, and the MAE was measured after each adaptation period. Across experiments, focused attention produced significantly larger MAEs than did diverted attention (15% change, Cohen's = .41). Attention affected the MAE asymptote, rather than its accumulation rate, and had larger effects for translational than for complex motion. The effect of attention remained evident after controlling for response bias. Our results suggest that attention affects multiple levels of the motion-processing hierarchy: not only higher level motion processing, as seen with apparent motion, but also low-level motion processing, as evidenced by the MAE. (PsycINFO Database Record (c) 2018 APA, all rights reserved).