optimal-psychophysics 0.1.0

Python library for optimal psychophysics design and adaptive testing

Optimal Adaptive Psychometrics

This package provides a Rust implementation of different ways to obtain estimates for real-time adaptive psychometric testing. This includes parameter estimation and Expected Information Gain (EIG) calculations to select the next stimulus or item to present to the test taker.

Goals

• Speed: High perfomance for small sample sizes and real-time applications ideally using SIMD instructions where possible
• Correctness: Accurate and reliable estimates
• Simplicity: Prioritise simplicity and readability over flexibility and scalability
• Portability: Should work on most platforms (even those that don't allow JIT compilation)

Non-goals

• Scalability: Not designed for big datasets (i.e., no support for GPUs or distributed computing)
• Flexibility: Not designed for complex models or (exploratory) data analysis - you should probbaly use PyMC3 or Stan instead

(Planned) Features

• Posterior estimation
  • using Hamiltonian Monte Carlo (HMC) using no-U-turn sampling (using the nuts-rs crate)
  • using grid approximation
  • using Variational Inference methods (maybe)
• Expected Information Gain (EIG) calculations
  • using a Rao-Blackwellized Monte Carlo estimator (for cases where outcomes can be enumerated)
  • using a Nested Monte Carlo estimator
  • using grid approximation
  • using a Laplace approximation
  • using Variational Inference methods (maybe)

Short-term roadmap

• Make the base psychometric model more flexible (e.g., allow for different link functions, prior distributions, snd multivariate outcomes/designs)
• Allow better vectorisation using ndarray (will mainly be helpful when calling from Python with pyo3)
• Implement support for Arviz for posterior diagnostics on the Python side
• Use Enyme instead of manually deriving gradients (this currently requires building the Rust compiler from source)

Glossary

• Outcome: A possible result or state that can arise from an experiment or random process in a probabilistic model. For example, in a psychophysics experiment studying reaction times to visual stimuli, an outcome could be the specific time (e.g., 350 milliseconds) it takes for a participant to respond to a stimulus.
• Design: The set of deterministic inputs or predictors used in a model to explain or predict the outcome variable. These inputs are the variables that provide the framework for analyzing the relationship between them and the dependent variable in a statistical model, such as in linear regression. For example, in a psychophysics experiment, the design could include the luminance of a visual stimulus, the contrast of the stimulus, and the orientation of the stimulus.
• Parameter: A quantity that defines a statistical model. Parameters are estimated from data and used to make inferences about the population from which the data were sampled. For example, in a psychometric model, parameters could include the threshold at which a participant can detect a stimulus, the slope of the psychometric function, and the guessing rate of the participant.