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

JND

You can directly use an instance of this class. jnd_exp

Constructor Summary

Public Constructor
public

constructor(Parameters: assoc array)

Initializes a JND experiment object.

Member Summary

Public Members
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Method Summary

Public Methods
public

calculate_adjusted_value(constants: *): *

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

check_response(data: JsPsych.data): boolean

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_adjusted_distribution(constants: *, adjusted_value: *): object

Generates the adjusted distribution depending on the type of task.
public

generate_trial(type: *): *

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

get_next_adjusted_correlation(last_JND_trial: object): double

Calculates the next adjusted correlation (the traditional way).
public

get_next_adjusted_statistic(last_JND_trial: object): double

Calculates the next adjusted value.
public

handle_data_saving(trial: *)

Handles saving the relevant data on a given trial.
public

Initializes the adjusted value for the first time.
public

Determines whether current subcondition has converged or not.
public

Checks whether we have just started a subcondition.
public

prepare_experiment(balancing_type: *)

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

public

prepare_practice(dataset: *)

Orders the input dataset by randomizing it, and initializes the practice variables.

Public Constructors

public constructor(Parameters: assoc array) source

Initializes a JND experiment object.

Params:

NameTypeAttributeDescription
Parameters assoc array

passed from routing.

Public Members

public CONVERGENCE_THRESHOLD: number source

public INCORRECT_MULTIPLIER: number source

public MAX_CORRELATION: number source

public MAX_TRIALS: number source

public MIN_CORRELATION: 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_group: * source

public condition_name: * source

public current_practice_condition_index: number source

public current_sub_condition_index: number source

public distractor_coordinates: string source

public first_trial_of_sub_condition: boolean source

public graph_type: * source

public left_coordinates: string source

public practice_conditions_constants: * source

public raw_constants: * source

public right_coordinates: 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_value(constants: *): * source

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

Params:

NameTypeAttributeDescription
constants *

{assoc array}

Return:

*

adjusted_value {double}

public check_response(data: JsPsych.data): boolean source

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

Params:

NameTypeAttributeDescription
data JsPsych.data

@param. {object} constants

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_adjusted_distribution(constants: *, adjusted_value: *): object source

Generates the adjusted distribution depending on the type of task. If no task is specified, defaults to using correlation.

Params:

NameTypeAttributeDescription
constants *
adjusted_value *

Return:

object

Adjusted coordinates

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_correlation(last_JND_trial: object): double source

Calculates the next adjusted correlation (the traditional way).

Params:

NameTypeAttributeDescription
last_JND_trial object

. {object} constants

Return:

double

next_adjusted_statistic

public get_next_adjusted_statistic(last_JND_trial: object): double source

Calculates the next adjusted value.

Params:

NameTypeAttributeDescription
last_JND_trial object

. {object} constants

Return:

double

next_adjusted_statistic

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_correlation: '', right_correlation: '', };

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_correlation: '', adjusted_value: '', correct: '', };

Params:

NameTypeAttributeDescription
trial *

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

public initialize_adjusted_statistic(constants: object): double source

Initializes the adjusted value for the first time.

Params:

NameTypeAttributeDescription
constants object

Return:

double

adjusted_value

public is_converged_in_window(): boolean source

Determines whether current subcondition has converged or not.

Return:

boolean

True if converged.

public is_start_of_subcondition(): boolean source

Checks whether we have just started a subcondition.

Return:

boolean

True if sub condition has just started.

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. dataset {[{assoc array}, {assoc array}, ... ]} The data to be ordered.

public prepare_practice(dataset: *) source

Orders the input dataset by randomizing it, and initializes the practice variables.

Params:

NameTypeAttributeDescription
dataset *

{[{assoc array}, {assoc array}, ... ]} The data to be ordered.