Correctness of Response in Labvanced

In many experiments, participants are asked to respond to a stimulus—for example by pressing a key, clicking a button, or typing an answer. Correctness of response refers to whether the participant’s answer matches the expected (correct) answer.

Labvanced allows you to define what counts as a correct response and automatically evaluate participant performance.

The Big Picture

In Labvanced, response correctness is typically handled using:

  • Input elements (how participants respond)
  • Variables (store responses and correct answers)
  • Conditions / logic (compare response vs. correct answer)

The general flow looks like this:

  1. Show a stimulus
  2. Collect a response
  3. Compare response to the correct answer
  4. Store whether it was correct or incorrect

Step 1: Collecting a Response

First, you need a way for participants to respond.

Common input elements:

  • Buttons (e.g., Yes / No)
  • Keyboard input (e.g., press F or J)
  • Text fields (typed answers)

Each response is automatically stored in a Variable.

Example:

  • Button click → variable named response = stores value yes

Step 2: Defining the Correct Answer

Next, you define what the correct response should be.

There are two common approaches:

Option A: Fixed Correct Answer

  • You manually set the correct answer
  • Example: Correct answer = yes

Option B: Variable-Based Correct Answer

  • The correct answer changes per trial using data frames or the Trial System
  • Stored in a variable (e.g., correct_answer)

Example:

  • Trial 1 → correct_answer = cat
  • Trial 2 → correct_answer = dog

This is useful when using multiple stimuli.

Step 3: Comparing Response to Correct Answer

Now you check whether the participant’s response is correct.

This is done using conditions (if statements):

Example logic:

if response == correct_answer, then:
Accuracy = 1
else:
Accuracy = 0

In Labvanced, this is created visually using the logic editor (no coding required).

Step 4: Storing Correctness

The result of the comparison is usually stored in a variable such as:

  • Accuracy (1 = correct, 0 = incorrect)

This allows you to:

  • Analyze accuracy later
  • Filter trials
  • Compute performance metrics

Step 5: Providing Feedback

You can show participants whether they were correct.

Common feedback:

  • “Correct!”
  • “Incorrect”
  • Visual cues (green/red screen)

This is done by:

  • Adding a feedback frame
  • Displaying different content based on the correctness variable

Example:

  • If correct → Jump to “Correct!” frame
  • If incorrect → Jump to “Incorrect” frame

Step 6: Using Correctness in Trial Flow

Correctness can also control what happens next.

Examples:

  • Repeat trial if incorrect
  • Move on only after correct response
  • Adjust difficulty dynamically

This is done using conditional branching.

Example: Simple Choice Task

Experiment flow:

  1. Show an image (e.g., a fruit)
  2. Participant presses a key:
    • F = Apple
    • J = Banana
  3. Compare response to correct answer
  4. Show feedback
  5. Next trial

In Labvanced:

  • Store correct answer per trial
  • Capture key press
  • Compare using logic
  • Save correctness

Tips for Beginners

  • Keep variable names clear (response, correct_answer, correctness)
  • Test your logic with a few trials
  • Use simple conditions before building complex ones
  • Double-check that response formats match (e.g., text vs. key codes)

Summary

Correctness of response in Labvanced involves:

  • Collecting responses (buttons, keys, input)
  • Defining correct answers (fixed or variable)
  • Comparing responses using logic
  • Storing results for analysis
  • Providing feedback to participants

Essentially, the approach you should pick depends on the task you are designing. If you need a suggestion, please reach out to our support!

Once set up, Labvanced automatically tracks accuracy, making it easy to analyze participant performance.