Stevens

Initializes a Stevens experiment object.

Calculates exclusion criteria using standard deviation and variance. Subcondition is flagged if:

• Standard deviation > 0.2
• Anchoring > 0.6

@ return HTML of subcondition data to print onto screen

Resets all relevant variables to now use the test version. (input_count_array, sub_conditions_constants, and current_sub_condition_index are shared variables between the practice and test trials).

This function is called once all the practice trials have run.

Determines whether the round can end or not. A round can end ONLY if there has been at least 1 input from the user on the given round

Determines whether the current sub condition can end or not.

When called, will save aggregated trial data into a CSV.

When called, will save individual trial data into a CSV.

Will generate the distractor coordinates and save them to the instance.

Generates a Stevens object for use in the JsPsych timeline.

Calculates the anchoring value for the specified subcondition. @ param {array} array of estimated mids for that trial

@ return {double} anchoring value

Retrieves the estimated midpoints of each trial for the subcondition.

@ return {array} of estimated mids

Retrieves the last stevens trial depending on block_type for a given sub condition index. If this is the first trial of a given block_type, returns null.

Calculates the standard deviation for the specified subcondition. @ param {array} array of estimated mids for that trial

@ return {double} standard deviation

Handles saving the relevant data on a given trial.

For reference, these are the helper variables created to assist in trial logic (i.e not present in excel) this.trial_variables =
{type: 'stevens', run_type: '', left_correlation: '', right_correlation: '', round_refreshes: 0, // Number of times there is a refresh for a given round high_ref_is_right: false start_ref: '' };

These are variables created WITHIN the trial logic that were not present in excel (but need to be outputted to results).
this.export_variables = {trial_num: 0, // Round index trial is currently on (aka trial_num from excel) sub_condition: '', // Chronological ordering of sub_condition [1, 2, 3 ... ] balanced_sub_condition: '', // Index of sub_condition according to balancing order estimated_mid: '', num_adjustments: 0, // Number of inputs for a given round (aka num_adjustments from excel) trials_per_round: '', };

Orders the input data according to balancing type and initializes the Stevens object's variables.

Creates the practice dataset by taking the first FOUR subconditions.

Sets the subcondition order for foundational range. Needs to balance INDIVIDUALLY the round and test type conditions, then maintain that order (e.g. all test goes first, then consistency)

Updates the estimated correlation. If : Is the first trial, will initialize the correlation and step size. Else: If there was a key press in previous trial, will calculate the the estimated correlation (depending on whether it was an inc or dec). Else if no key press in previous trial, will set estimated correlation to the previous trial's.