Tasks

Digit Span

import neuropsydia as n  # Load neuropsydia
import numpy as np  # For generation of random sequence


n.start()  # Initialize neuropsydia
n.instructions("Listen to the experimenter.")  # Display instructions

# Initialize values
number_of_fails = 0  # Initial number of errors
span = 2  # Initial span

while number_of_fails < 3:  # Do it while the number of errors is smaller than 3
    sequence = np.random.randint(10, size=span)  # Generate sequence of size span with ints ranging from 0 to 9
    good_answer = ""  # Initiate an empty good_answer

    for digit in sequence:  # For every digit in the sequence...
        good_answer = good_answer + str(digit)  # Add the current stimulus to the good answer
        n.newpage("grey")  # Load a grey background
        n.time.wait(250)  # Display an empty screen for 250 ms
        n.newpage("grey")  # Load a grey background
        n.write(digit, size=3)  # Load the stimulus
        n.refresh()  # Display the stimulus on screen
        n.time.wait(1000)  # Wait 1000 ms

    # Get answer
    n.newpage("white")
    answer = n.ask("Answer:")

    # Manage result
    if answer == good_answer:
        span = span + 1  # Increase span
        number_of_fails = 0  # Reset value
    else:
        number_of_fails = number_of_fails + 1

n.newpage()  # Load a white background
n.write("Max span: " + str(span-1))  # Write task result
n.refresh()  # Render it on screen
n.time.wait(3000)  # Wait for 3s

n.close()  # Close neuropsydia

Hint

Try to run this task!

• Can you change the sequence generation so it contains letters rather than digits?
• Can you change the rules so the sequence length increases every two good answers?
• Can you store the results and save them?

Go/No Go

import neuropsydia as n  # Load neuropsydia
import random  # Import the random module
import pandas as pd  # To manipulate and save data
import numpy as np  # To do some maths

n.start()  # Start neuropsydia
n.instructions("Goal: Hit SPACE whenever a GREEN circle appears. \nIf RED, don't press anything!")  # Display instructions and break line with \n
n.newpage("grey")  # Fill the screen
n.countdown()  # Display countdown

# Initialize the data storage with a dictionary containing empty lists
data = {"Trial": [],
        "Stimulus": [],
        "ISI":[],
        "RT":[],
        "Response":[]}

n_trials = 10  # Number of trials
for trial in range(n_trials):  # Iterate over the number of trials
    stimulus = random.choice(["green", "green", "green", "red"])  # Select a stimulus type
    ISI = random.randrange(start=250, stop=1250, step=250)  # Select the inter-stimuli interval (ISI)

    n.newpage("grey")  # Fill the screen
    n.write("+")  # Fixation cross
    n.refresh()  # Diplay it on screen
    n.time.wait(ISI)  # Wait

    n.circle(size=2, fill_color=stimulus)  # Display the stimulus (filled with the color selected above)
    n.refresh()  # Diplay it on screen
    response, RT = n.response(time_max=1000)  # Wait until 1 s and collect the response and its time

    # Categorize the response
    if response == "SPACE" and stimulus == "green":
        response_type = "HIT"  # Hit
    if response != "SPACE" and stimulus == "green":
        response_type = "MISS"  # Miss
    if response == "SPACE" and stimulus == "red":
        response_type = "FA"  # False Alarm
    if response != "SPACE" and stimulus == "red":
        response_type = "CR"  # Correct Rejection

    # Store data by appending each item to its list
    data["Trial"].append(trial)
    data["Stimulus"].append(stimulus)
    data["ISI"].append(ISI)
    data["RT"].append(RT)
    data["Response"].append(response_type)

# Data saving
df = pd.DataFrame.from_dict(data)  # Transform the data dictionary into a proper and savable dataframe
df.to_csv("data.csv")  # Save it

# Quick analysis
RTs = df[df['Response']=="HIT"]["RT"]  # Select the Hits' RTs
print("Mean RT: " + str(round(RTs.mean(), 2)))   # Print the mean
print("SD RT: " + str(round(RTs.std(), 2)))  # Print the standard deviation
print("Number of False Alarms: " + str(len(df[df['Response']=="FA"])))  # Print the number of intrusions (false alarms)

n.close()  # Close neuropsydia

Hint

Try to run this task!

• Can you change the number of trials?
• Can you change the ratio of no go trials?

Flanker

import neuropsydia as n  # Load neuropsydia
import pandas as pd  # To manipulate and save data
import numpy as np  # To do some maths

n.start()  # Start neuropsydia
n.instructions("Hit RIGHT or LEFT arrow according to the direction of the CENTRAL arrow.")  # Display instructions

# Initialize cache
cache = {}
for possible_angle in [0, 90, 180]:
    cache = n.preload("arrow-left.png", size=2, rotate=possible_angle, cache=cache)  # Preload images

# Initialize the data storage with a dictionary containing empty lists
data = {"Trial": [],
        "Trial_Type": [],
        "Stimulus_Orientation": [],
        "RT":[],
        "Response":[]}


n.newpage("grey")  # Fill the screen
n.countdown()  # Display countdown

n_trials = 10  # Number of trials
for trial in range(n_trials):  # Iterate over the number of

    n.newpage("grey")  # Fill the screen
    n.write("+")  # Fixation cross
    n.refresh()  # Diplay it on screen
    n.time.wait(500)  # Wait

    # Trial characteristics
    stimulus_angle = np.random.choice([0, 180])  # select target orientation
    trial_type = np.random.choice(["Congruent", "Neutral", "Incongruent"])  # select trial type
    if trial_type == "Congruent":
        distractors_angle = stimulus_angle
    if trial_type == "Incongruent":
        if stimulus_angle == 0:
            distractors_angle = 180
        else:
            distractors_angle = 0
    if trial_type == "Neutral":
        distractors_angle = 90


    n.image("arrow-left.png", x=-5, size=2, cache=cache, rotate=distractors_angle)  # Distractor
    n.image("arrow-left.png", x=-2.5, size=2, cache=cache, rotate=distractors_angle)  # Distractor
    n.image("arrow-left.png", x=0, size=2, cache=cache, rotate=stimulus_angle)  # Target
    n.image("arrow-left.png", x=2.5, size=2, cache=cache, rotate=distractors_angle)  # Distractor
    n.image("arrow-left.png", x=5, size=2, cache=cache, rotate=distractors_angle)  # Distractor
    n.refresh()
    response, RT = n.response(time_max=1000)  # Wait until 1 s and collect the response and its time


    # Response check
    if (response == "LEFT" and stimulus_angle == 0) or (response == "RIGHT" and stimulus_angle == 180):
        response = 1
    else:
        response = 0

    # Store data by appending each item to its list
    data["Trial"].append(trial)
    data["Trial_Type"].append(trial_type)
    data["Stimulus_Orientation"].append(stimulus_angle)
    data["RT"].append(RT)
    data["Response"].append(response)

# Data saving
df = pd.DataFrame.from_dict(data)  # Transform the data dictionary into a proper and savable dataframe
df.to_csv("data.csv")  # Save it

# Quick analysis
mean_cong = df[(df["Trial_Type"]=="Congruent") & (df["Response"]==1)]["RT"].mean()
mean_neu = df[(df["Trial_Type"]=="Neutral") & (df["Response"]==1)]["RT"].mean()
mean_incong = df[(df["Trial_Type"]=="Incongruent") & (df["Response"]==1)]["RT"].mean()
print("Mean RT Congruent: " + str(round(mean_cong, 2)))  # Print the mean of congruent
print("Mean RT Neutral: " + str(round(mean_neu, 2)))  # Print the mean of neutral
print("Mean RT Incongruent: " + str(round(mean_incong, 2)))  # Print the mean of incongruent

n.close()  # Close neuropsydia

Hint

Try to run this task!

• Can you count the number of errors?
• Can you ask for the participant name at the beginning, and save data using it?