Context: Models of cognitive processes require very large datasets to be fitted. Sadly, it is difficult for a single laboratory to achieve this alone. I propose that we work collectively and collaboratively in realizing a 1 million RT experiment. This initiative is described on my ResearchGate page, www.researchgate.net/profile/Denis_Cousineau.
Beyond model fitting, such a dataset will be beneficial for other purposes. Examples: Are parameters describing performances normally distributed across subjects? Does training effect --resulting in faster RT-- imply that the parameters are all evolving in one direction? Does Bayesian estimation more apt than Maximum Likelihood at characterizing the parameters? How can we estimate non-decision times? What is the shape of the RT distributions and how training effect alter its shape? In Bayes again, after how many trials does priors have no longer an impact on the estimates? etc.
Task Requirements: The task must be behavioral only (response times and response choices) as it is not possible to move EEG or fMRI material all around the world. The precise task used is not a critical constraint because the current models of cognition (sampling models such as LBA or Diffusion) are very flexible; they aim at predicting performance in a wide variety of situations.
The tasks proposed will be evaluated based on the following criteria criteria:
- small number of response alternatives
- few conditions
- low error rates
- stimuli that can be characterized for modeling purposes
- possibility of extended training on that task.
Possible candidate tasks could include: A redundant target detection task (Miller, 1982) or a same-different task (e.g., Bamber, 1969). These tasks are simple and accuracy is typically high (above 90% correct); they are possibly performed using some form of parallel processing; response times are generally well below 700 ms.
So in summary:
What task and experimental design would be most suitable given the above constraints?
Please let's see what are the possible tasks, comment on others' proposals, and if you find the perfect task, upvote it. Have your colleagues make comments or propose new tasks.