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import JND_Radius from 'VCL-Web-Framework/scripts/experiments/jnd_radius/jnd_radius.js'
public class | source

JND_Radius

You can directly use an instance of this class. jnd_radius_exp

Constructor Summary

Public Constructor
public

constructor(params: *)

Initializes a JND_Radius experiment object.

Member Summary

Public Members
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Method Summary

Public Methods
public

calculate_adjusted_radius(constants: *): *

Calculates the adjusted radius depending on whether this is the first trial of the sub condition or not.
public

Given a JND trial data, determines whether response is correct or not.
public

Determines whether the current sub condition can end or not.
public

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

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

generate_trial(type: *): *

Generates a JND trial object for use in the JsPsych timeline.
public

Calculates the next adjusted correlation/statistic.
public

handle_data_saving(trial: *)

Handles saving the relevant data on a given trial.
public

initialize_adjusted_statistic(converge_from_above: *): *

Initializes the adjusted radius for the first time.
public

Determines whether current subcondition has converged or not.
public

prepare_experiment(balancing_type: *)

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

Public Constructors

public constructor(params: *) source

Initializes a JND_Radius experiment object.

Params:

NameTypeAttributeDescription
params *

{assoc array} Parameters passed from routing.

Public Members

public CONVERGENCE_THRESHOLD: number source

public INCORRECT_MULTIPLIER: number source

public MAX_RADIUS: number source

public MAX_TRIALS: number source

public MIN_RADIUS: number source

public MIN_TRIALS: number source

public PIXELS_PER_CM: * source

public WINDOW_INTERVAL: number source

public WINDOW_SIZE: number source

public adjusted_quantity_matrix: {} source

public balancing_type: * source

public condition_name: * source

public current_sub_condition_index: number source

public first_trial_of_sub_condition: boolean source

public graph_type: * source

public left_radius: string source

public raw_constants: * source

public right_radius: string source

public sub_condition_order: * source

public sub_conditions_constants: * source

public subject_id: * source

public subject_initials: * source

public trial_data: string source

public trial_structure: * source

Public Methods

public calculate_adjusted_radius(constants: *): * source

Calculates the adjusted radius depending on whether this is the first trial of the sub condition or not.

Params:

NameTypeAttributeDescription
constants *

{assoc array}

Return:

*

adjusted_radius {double}

public check_response(data: *): boolean source

Given a JND trial data, determines whether response is correct or not.

Params:

NameTypeAttributeDescription
data *

{JsPsych.data}

Return:

boolean

public end_sub_condition(): boolean source

Determines whether the current sub condition can end or not.

Return:

boolean

True if sub condition should end.

public export_summary_data() source

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

public export_trial_data() source

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

public generate_trial(type: *): * source

Generates a JND trial object for use in the JsPsych timeline.

Params:

NameTypeAttributeDescription
type *

{string} "test" or "practice"

Return:

*

trial {object}

public get_next_adjusted_statistic(correct: *): * source

Calculates the next adjusted correlation/statistic.

Params:

NameTypeAttributeDescription
correct *

{boolean} converge_from_above {boolean}
adjusted_quantity {double}
base_correlation {double} initial_difference {double}

Return:

*

adjusted_correlation {double}

public handle_data_saving(trial: *) source

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) trial_variables =
{type: 'jnd', run_type: '', left_radius: '', right_radius: '', };

These are variables created WITHIN the trial logic that were not present in excel (but need to be outputted to results).
export_variables = {sub_condition: '', // Chronological ordering of sub_condition [1, 2, 3 ... ] balanced_sub_condition: '', // Index of sub_condition according to balancing order jnd: '', base_radius: '', adjusted_radius: '', correct: '', };

Params:

NameTypeAttributeDescription
trial *

{object} block_type {string} "test" or "practice" constants {assoc array} index {integer} adjusted_correlation {double}

public initialize_adjusted_statistic(converge_from_above: *): * source

Initializes the adjusted radius for the first time.

Params:

NameTypeAttributeDescription
converge_from_above *

{boolean}
base_radius {double}
initial_difference {double}

Return:

*

adjusted_radius {double}

public is_converged_in_window(): boolean source

Determines whether current subcondition has converged or not.

Return:

boolean

True if converged.

public prepare_experiment(balancing_type: *) source

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

Params:

NameTypeAttributeDescription
balancing_type *

{string} Type of balancing. Currently only latin_square is supported. data_set {[{assoc array}, {assoc array}, ... ]} The data to be ordered. practice_set {[{assoc array}, {assoc array}, ... ]} The practice data.