Well, nearly… Since FrACT₁₀ has an API based on HTML messages, it can be observed and controlled fully programmatically. Consequently, an “Ideal Observer” (Geisler 1989, Geisler 2011) can easily be implemented.
What is an “Ideal Observer”? A quote from Kersten & Mammassian 2007 [free PDF] may help a little:
Ideal observer models are applications of Bayesian Statistical Decision Theory to problems of neural information transduction, transmission and utilization. A basic motivation is that because sensory inputs provide noisy or ambiguous information about states of the world, probabilistic methods are required to understand how reliable decisions can be made. Thus the focus is first on modeling the information for a task, independent of the observer under study, and second on comparisons of that model with a test observer, such as a human or neuron. A key rationale for such comparisons is that the ideal observer can be used to normalize performance for task difficulty…
Using FrACT₁₀’s messages API, the Ideal Observer is demonstrated →here. The observer currently follows a psychometric function with adjustable “acuity”=flex, slope and lapse rate. As acuity tasks you can choose Sloan letters, Landolt Cs, Tumbling Es and Vernier lines. The guessing probability is automatically adjusted to the task (e.g., Sloan letter: 10%, tumbling E: 25%).
For anyone interested, you can look that the JavaScript code: sim.js. The main response code is in runAutoResponding(). As of now, it’s little more than a demo, highlighting the power of FrACT₁₀’s messages API, and can serve as kickoff for experiments proper.