Psiexperiment is a plugin-based experiment controller that facilitates the process of writing experiments. There are four core plugins: context, experiment, controller and data.

A context item provides information about a value that is managed by the context plugin. When defining a context item in one of your plugin manifests, you will provide basic information about the item (e.g., a GUI label, compact GUI label and numpy dtype). This information will be used by plugins that interact with the context plugin (for example, the name and dtype of the context item will be used by the HDF5Store plugin to set up the table that stores acquired trial data).

There are currently three specific types of context items. A result is a value provided by a plugin. It cannot be defined by the user. Common use cases may include the values computed after a trial is acquired (e.g., one can compute the reaction_time and provide it as a result).

A parameter is a value that can be configured by the user before and during an experiment. While the value of the parameter can be modified by the user during the experiment, it cannot be roved. There are some parameters that do not make sense as roving parameters. For example, if we define a go_probability parameter that determines the probability that the next trial is a GO trial instead of a NOGO, it does not make sense to rove this value from trial-to-trial. It, however, may make sense to change this during the couse of an experiemnt (e.g., during training).

A roving parameter is like a parameter, except that it can be roved from trial to trial. When selected for roving, the next value of the parameter is provided by a selector.

A selector maintains a sequence of expressions for one or more roving parameters. In some experiments, you'll only have a single selector. In other experiments, you may want multiple selectors (e.g., one for go trials, one for remind trials and one for nogo trials). Right now, the only difference between different types of selectors will be the GUI that's presented to the user for configuring the sequence of values. Internally, all of them maintain a list of values that should be presented on successive trials.

Manages the context items and selectors (see terminology). Every experiment has a set of values, or parameters, that define the course of the experiment. This plugin provides a central registry of all context items, manages their current values (i.e., when notified, the current values will be cleared and re-computed for the next trial). A subset of these context items can be specified as roving parameters (i.e., their value varies from trial to trial in a predefined way). Values for roving parameters are managed by one or more selectors. Selectors are objects that provide a mechanism for specifying the sequence of values that the context plugin draws from on each trial.

Provides basic management of the experiment workspace by managing GUI elements provided by the loaded plugins (e.g., the context plugin provides several GUI widgets) as well as providing methods to save/restore the layout of the workspace as well as save/restore the preferences for each plugin.

Provides basic management and analysis of data acquired during an experiment.

TODO

TODO

This is a significant rewrite of Neurobehavior that leverages the strengths of the Atom/Enaml framework. The key change is that Psiexperiment is plugin-based. Writing experiments in Neurobehavior involved subclassing several classes (e.g., paradigm, experiment, controller, data) and incorporating various mixins (e.g., for the pump controller and pump data). Often this required some cumbersome hacks to get the GUI and experiment to behave the way you want. There were also some significant limitations with the context management system (i.e., adding new parameters required subclassing the paradigm class and possibly incorporating mixins for pumps, etc.).

In contrast, setting up new experiments in psiexperiment should be simpler. You decide the types of plugins you want loaded and write a manifest file that defines the specific extensions you want. If you have a specific type of analysis that you need done on the acquired data, you can write a new plugin and load it.