Maximum Likelihood Threshold Hunting according to Green 1993
This library implements the threshold hunting procedure described by Green 1993.
It basically stores a set of psychometric functions with different parameters:
1. "Mean", i.e. where the function's infliction point is. In a threshold paradigm with 0% false alarm rate, this is the 50% point.
2. False alarm rate.
3. Slope of the psychometric function.
The library provides a convenient class "MLThresholdHunter" who does all the background work.
It is the job of the developer to:
1. Choose meaningful parameters.
2. Stimulate the participant (or whatever you are stimulating!) at the level provided by the current_probe_value property.
3. Provide the answer (true or false) to the instance of the instance of the class.
4. Check whether stop is true.
5. If not, repeat from 2.
6. If stop is true, current_guess is your threshold.
| Name | Type | Description |
| --- | --- | --- |
| best_function | PsychometricFunction | The most likely psychometric function |
| current_probe_value | number | The value that should be probed next |
| current_guess | number | The current guess of the value at the infliction point |
| n_trials | number | The completed number of trials |
| guesses | Array | The guesses so far |
| std_last_trials | number | The stddev of the last use_n_trials_for_stop trials |
| stop_std | number | The stddev at which to stop |
| converged | bool | True if converged |
| stop | bool | True if either converged or maximum trials exceeded. |
* MLThresholdHunter
* new MLThresholdHunter(start_value, values_range, false_alarm_range, slopes_range, min_trials, max_trials, use_n_trials_for_stop)
* [.process_response(response, [value])](#MLThresholdHunter+process_response)
| Param | Type | Default | Description |
| --- | --- | --- | --- |
| start_value | number | | The start value |
| values_range | mathjs.matrix \| Array | | The possible values |
| false_alarm_range | mathjs.matrix \| Array | | The possible false alarm rates |
| slopes_range | mathjs.matrix \| Array | | The possible slopes |
| min_trials | number | 12 | The minimum amount of trials before the algorithm can stop. |
| max_trials | number | 40 | The maximum number of trials. |
| use_n_trials_for_stop | number | 6 | Use last n trials for stop criterion. |
Kind: instance method of MLThresholdHunter
| Param | Type | Description |
| --- | --- | --- |
| response | bool | The response, true or false |
| [value] | number | The value at which the response was obtained. If undefined, use current_probe_value. |