Visual Search Task

Try the Demo

In this task, participants search for a target stimulus among distractor stimuli arranged in a circular display. The task follows a classic visual search paradigm: a prime (cue) is presented first, followed by a search array where the participant must locate and identify the target using keyboard responses. Reaction times and accuracy are recorded.

The visual search paradigm is fundamental to attention research, measuring how efficiently participants can detect targets among distractors¹. Feature Integration Theory demonstrated that some visual features "pop out" automatically (parallel search), while others—particularly conjunctions of features—require serial, effortful search¹. The task also supports semantic priming designs, where text or image cues precede the search array.

Example Visual Search array on Meadows with a target stimulus (bee) among distractors (globes) arranged in a circular display.

Quick Start

1. Create or open an experiment from your Dashboard
2. Click Add task and select "Visual Search"
3. Select a stimulus set with target and distractor stimuli, as well as prime stimuli if you are using primes
4. Configure response keys and preview your experiment

New to Meadows? See the Getting Started guide for a complete walkthrough.

Alternative tasks

• Use a Timed Key Response task for simpler reaction time measurements without the circular search array.
• Use a Circular Matching task for matching stimuli to a central reference without time pressure.
• Use a Cued Imagery, another visual imagery cued by a text stimulus.

Parameters

Customize the task by changing these on the Parameters tab of the task.

General Interface settings

Customize the instruction at the top of the page, as well as toolbar buttons. These apply to most task types on Meadows.

Instruction hint

Text that you can display during the task at the top of the page.

Extended instruction

A longer instruction that only appears if the participant hovers their mouse cursor over the hint.

Hint size

Whether to display the instruction, or hide it, and what font size to use.

Fullscreen button

Whether to display a button in the bottom toolbar that participants can use to switch fullscreen mode on and off.

Prime Configuration

Configure how the cue/prime stimulus is presented before the search array.

Prime Type

What kind of stimuli to present as the prime. Options: txt, png, jpg, ogv, mp4. Default: txt.

Text Prime Variant

If using text stimuli as prime, whether to use the short or long version of the stimulus. Options: short, long. Default: short.

Text Prime Size

If a text prime is used, this is the font size as a percentage of the available width. Default: 1.0. Valid range: 0.2 to 10.0.

Text Prime Display Mode

Display a text prime in one go, or each word serially. Options:

• As a whole: Display the entire text at once
• One word after another: Display words sequentially

Default: As a whole.

Prime Duration

How long the prime should be displayed in milliseconds. Default: 1000 ms. Valid range: 1 to 10,000 ms.

Search Configuration

Configure the search array and target/distractor presentation.

Search Type

What kind of stimuli to present in the search array. Options: txt, png, jpg, ogv, mp4. Default: png.

Number Of Distractors

How many times to display the distractor stimulus in the search array. The total number of items in the array will be this value plus one (the target). Default: 5. Valid range: 0 to 8.

First Item Offset

Offset from the middle line for the first item, as a fraction of the angle between two items. This allows you to adjust the starting position of stimuli around the circle. Default: 0. Valid range: 0 to 1.

Response Configuration

Configure keyboard responses and feedback.

Response Keys

Which keyboard keys match the images, clockwise starting from 12 o'clock. The number of keys should match the number of items in the search array.

Display Response Keys

Show a key label next to each stimulus to help participants identify which key corresponds to each location. Default: checked.

Maximum Reaction Time

Amount of time the participant has to respond before the trial is considered timed out, in milliseconds. Default: 100,000 ms. Valid range: 1 to 100,000 ms.

Display Feedback

After the participant responds, a screen will display either "correct" or "wrong". Default: unchecked.

Display RT

After the participant responds, display the reaction time (e.g., "244ms"). Default: unchecked.

Feedback Duration

How long the feedback should be displayed in milliseconds. Default: 3000 ms. Valid range: 1 to 60,000 ms.

Timing

Control the temporal structure of trials.

Fixation Duration

Duration of the fixation cross between the prime and the search array, in milliseconds. Default: 500 ms. Valid range: 1 to 10,000 ms.

Inter Trial Duration

Time between trials in milliseconds. Default: 500 ms. Valid range: 1 to 10,000 ms.

Inter Trial Jitter Max

A random amount of time will be added on top of the ITI, between 0 ms and the value chosen here. Useful for preventing anticipatory responses. Default: 0 ms. Valid range: 0 to 10,000 ms.

Trial Generation

Control how trials are generated from your stimulus set.

Trial Order

How to select trial stimuli. Options:

• random: Generate random combinations of prime-target-distractor
• hardcoded: Use a predefined trial list

Default: random.

Number Of Trials

Only applies in random mode. Unique combinations of stimuli will be either removed or repeated to reach this number. Default: 50. Valid range: 1 to 10,000.

Repeat Failed Trials

If the trial timed out, or the response was incorrect, it will be repeated after the last trial. This ignores the "number of trials" setting. Default: unchecked.

Breaks

Configure rest breaks during the task.

Trials Between Breaks

A break will be shown and data stored every time the participant has finished this many trials. Default: 5000. Valid range: 1 to 5,000.

Break Text

This text will be shown during a break. Default: "You've just finished one block.\nTake a break, and press continue when ready."

Catch Trials

Configure optional catch trials to verify participant attention to the prime.

Catch Trial Fraction

What proportion of trials should be followed by a question about the prime. Set to 0 to disable catch trials. Default: 0.0. Valid range: 0 to 1.

Catch Text

The text shown during the catch prompt. Default: "Does this image match the sentence? (y/n)".

Catch Response Keys

Valid keyboard keys for the response to the catch question. If feedback is used, these are assumed to correspond to [match] and [not a match], in that order.

Display Catch Feedback

After the participant responds to a catch trial, display whether the response was correct or wrong. Default: unchecked.

Data

For general information about the various structures and file formats that you can download for your data see Downloads.

Annotations

Trial-wise "annotations" (table rows), with one row per trial. Columns:

• trial - numerical index of the trial
• time_trial_start - timestamp when the search array was displayed (seconds since 1/1/1970)
• time_trial_response - timestamp when the participant responded (seconds since 1/1/1970)
• stim1_id - meadows internal id of the prime stimulus
• stim1_name - filename of the prime stimulus as uploaded
• stim2_id - meadows internal id of the target stimulus
• stim2_name - filename of the target stimulus as uploaded
• stim3_id - meadows internal id of the distractor stimulus
• stim3_name - filename of the distractor stimulus as uploaded
• label - the response in format key_targetLocation (e.g., j_2), or timedout if no response

Analysis

Calculate Reaction Times and Accuracy

Reaction time (RT) is calculated as the difference between time_trial_response and time_trial_start. Accuracy is determined by comparing the response key to the target location.

PythonR

import pandas as pd
import matplotlib.pyplot as plt

# Load the annotations data
df = pd.read_csv('Meadows_myExperiment_v1_annotations.csv')

# Calculate reaction time in milliseconds
df['rt_ms'] = (df['time_trial_response'] - df['time_trial_start']) * 1000

# Parse the label to extract key pressed and target location
df[['key_pressed', 'target_location']] = df['label'].str.split('_', expand=True)
df['target_location'] = pd.to_numeric(df['target_location'], errors='coerce')

# Filter out timed-out trials
df_valid = df[df['label'] != 'timedout'].copy()

# Basic statistics
print(f"Mean RT: {df_valid['rt_ms'].mean():.2f} ms")
print(f"Median RT: {df_valid['rt_ms'].median():.2f} ms")
print(f"Response rate: {len(df_valid) / len(df) * 100:.1f}%")

# Plot RT distribution
plt.figure(figsize=(10, 6))
plt.hist(df_valid['rt_ms'], bins=50, edgecolor='black')
plt.xlabel('Reaction Time (ms)')
plt.ylabel('Frequency')
plt.title('Visual Search Reaction Time Distribution')
plt.show()

library(tidyverse)

# Load the annotations data
df <- read_csv('Meadows_myExperiment_v1_annotations.csv')

# Calculate reaction time and parse label
df <- df %>%
  mutate(
    rt_ms = (time_trial_response - time_trial_start) * 1000
  ) %>%
  filter(label != 'timedout') %>%
  separate(label, into = c('key_pressed', 'target_location'), 
           sep = '_', remove = FALSE) %>%
  mutate(target_location = as.numeric(target_location))

# Basic statistics
df %>%
  summarise(
    mean_rt = mean(rt_ms),
    median_rt = median(rt_ms),
    sd_rt = sd(rt_ms),
    n_trials = n()
  )

# Plot RT distribution
ggplot(df, aes(x = rt_ms)) +
  geom_histogram(bins = 50, fill = 'steelblue', color = 'black') +
  labs(x = 'Reaction Time (ms)', y = 'Frequency',
       title = 'Visual Search Reaction Time Distribution') +
  theme_minimal()

References

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1. Treisman, A. M., & Gelade, G. (1980). A feature-integration theory of attention. Cognitive Psychology, 12(1), 97-136. doi:10.1016/0010-0285(80)90005-5 ↩↩