Cambridge Car Memory Test¶

This pre-configured paradigm lets you collect data using the Cambridge Car Memory Test - a variant of the Cambridge Face Memory Test (CFMT) that uses cars as stimuli instead of faces. With this "preset", you can quickly assess object recognition abilities and determine whether face recognition deficits are specific to faces or extend to other object categories. This page explains how to use the preset, what data you'll collect, and how to analyze your results.

Cars are particularly interesting stimuli because they require detailed visual discrimination between similar exemplars, similar to faces, but they are non-living objects without social significance.

Example Cambridge Car Memory Test stimulus — Example stimulus from the Cambridge Car Memory Test showing cars that participants must learn to recognize and discriminate.

Quick Start

Create or open an experiment from your Dashboard
Click Add pre-configured tasks
Choose "Cambridge Car Memory Test"
Preview your experiment

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

Background¶

The Cambridge Car Memory Test was developed by Dennett and colleagues as part of a battery of tests to evaluate visual recognition abilities across different stimulus categories¹². Like the CFMT, the test requires participants to learn to recognize specific exemplars and discriminate them from similar distractors. By following the identical structure and procedure as the CFMT, the test ensures that any performance differences between faces and cars can be attributed to category-specific processing rather than task demands.

Research using car recognition tests has helped distinguish between face-specific deficits and broader visual agnosia. Some individuals with prosopagnosia show selective impairment for faces while maintaining normal car recognition, suggesting specialized face processing mechanisms¹.

Test Structure¶

The preset includes a single task (cmt_cars) that follows the standard Cambridge Memory Test format. Participants typically complete the test in 10-15 minutes.

Practice Phase - Familiarization with the task
Introduction Phase - Learn target car identities (shown from 3 viewpoints each)
Review Phase - Review all target cars
Novel Test - Identify targets among novel distractor cars
Noise Test - Identify targets with visual noise added (more difficult)

In each test trial, participants view 3 cars and press 1, 2, or 3 to indicate which car is one of the target identities.

Tasks Included¶

This preset contains the following task:

Task Name	Task Type	Description
`cmt_cars`	`cmt`	The car memory test with all phases

Data¶

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

Annotations

As stimulus-wise "annotations" (table rows), with columns:

trial - numerical index of the trial
time_trial_start - timestamp when the test images were 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 stimulus
stim1_name - filename encoding trial information
label - the key pressed by the participant (1, 2, or 3)

Analysis¶

Scoring¶

The primary outcome is the total score (number of correct responses). Score calculation is identical to the CFMT.

SpreadsheetPythonR

In Google Sheets or Microsoft Excel, you can score the test:

Load Data: Download the annotations data in Table format from Meadows, then import the Meadows_myExperiment_v1_cmt_cars1_annotations.csv file into your spreadsheet application.
Create Correct Column: Add a formula to check if the response was correct. In a new column (e.g., column D if label is in column C):
```
=IF(C2="1", 1, 0)
```
This assumes the correct answer is always "1" (the target in the first position). Drag this formula down for all rows.
Calculate Total Score: Sum the correct responses:
```
=SUM(D:D)
```
Calculate Percentage: Divide by the total number of trials:
```
=SUM(D:D)/COUNTA(C:C)*100
```
Calculate Reaction Time: Create a formula to calculate RT in milliseconds:
```
=(time_trial_response - time_trial_start) * 1000
```
Then calculate the mean RT across all trials.

import pandas as pd

# Load the annotations data
df = pd.read_csv('Meadows_myExperiment_v1_cmt_cars1_annotations.csv')

# For standard CMT, the target is typically in position 1
df['correct'] = df['label'] == '1'

# Calculate total score
total_score = df['correct'].sum()
max_score = len(df)
percentage = (total_score / max_score) * 100

print(f"Total Score: {total_score}/{max_score} ({percentage:.1f}%)")

# Calculate reaction time
df['rt_ms'] = (df['time_trial_response'] - df['time_trial_start']) * 1000
print(f"Mean RT: {df['rt_ms'].mean():.0f} ms")

library(tidyverse)

# Load the annotations data
df <- read_csv('Meadows_myExperiment_v1_cmt_cars1_annotations.csv')

# Calculate accuracy
df <- df %>%
  mutate(
    correct = label == '1',
    rt_ms = (time_trial_response - time_trial_start) * 1000
  )

# Calculate total score
results <- df %>%
  summarise(
    total_score = sum(correct),
    max_score = n(),
    percentage = mean(correct) * 100,
    mean_rt = mean(rt_ms)
  )

print(results)

Interpreting Scores¶

Performance on the Cambridge Car Memory Test is typically compared with performance on the CFMT to assess the specificity of recognition deficits. Key interpretations include:

Face-specific deficit: Impaired face recognition but normal car recognition suggests specialized face processing
Domain-general deficit: Impaired performance on both tests suggests broader visual recognition problems
Car expertise effects: Individuals with car expertise may show enhanced performance on this test

Individual Differences

Performance on car recognition tests can be influenced by expertise with cars. Consider collecting information about participants' interest in or experience with cars when interpreting results.

References¶

Dennett, H. W., McKone, E., Tavashmi, R., Hall, A., Pidcock, M., Edwards, M., & Duchaine, B. (2012). The Cambridge Car Memory Test: A task matched in format to the Cambridge Face Memory Test, with norms, reliability, sex differences, dissociations from face memory, and expertise effects. Behavior Research Methods, 44(2), 587–605. doi:10.3758/s13428-011-0160-2 ↩↩
McKone, E., Hall, A., Pidcock, M., Palermo, R., Wilkinson, R. B., Rivolta, D., Yovel, G., Davis, J. M., & O'Connor, K. B. (2011). Face ethnicity and measurement reliability affect face recognition performance in developmental prosopagnosia: Evidence from the Cambridge Face Memory Test-Australian. Cognitive Neuropsychology, 28(2), 109–146. doi:10.1080/02643294.2011.616880 ↩