Cambridge face memory test download

Do people have insight into their face recognition abilities? Quarterly Journal of Experimental Psychology , 70 2 , — Riddoch, M. Birmingham object recognition battery. Hove: Lawrence Erlbaum Associates. Ritchie, K. Viewers base estimates of face matching accuracy on their own familiarity: Explaining the photo-ID paradox. Cognition , , — Ross, E. Sensory-specific and fractional disorders of recent memory in man: I. Isolated loss of visual recent memory. Archives of Neurology , 37 4 , — Rotshtein, P.

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Oxford: Macmillan. Verhallen, R. An online version of the Mooney face test: Phenotypic and genetic associations. Neuropsychologia , 63 , 19— General and specific factors in the processing of faces. Vision Research. Warrington, E. Recognition memory test. Slough: NFER. Bury St. Edmunds: Thames Valley Test Company. White, D. Feedback training for facial image comparison. PLoS One , 9 8 , 1—8. Wilhelm, O. Individual differences in perceiving and recognizing faces: One element of social cognition.

Journal of Personality and Social Psychology , 99 , — Wilmer, J. Human face recognition ability is specific and highly heritable. Wilson, B. Behavioural assessment of the dysexecutive syndrome. Witkin, H. Young, A. Disorders of face perception. Calder, G. Rhodes, M.

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British Journal of Psychology , 76 4 , — Face perception after brain injury: selective impairments affecting identity and expression. Download references. Mike Burton. The datasets supporting the conclusions of this article are included within the article and its additional files.

Jennifer M. McCaffery, David J. Robertson, Andrew W. You can also search for this author in PubMed Google Scholar. AMB initiated and supervised all stages of design, interpretation and write-up. AWY suggested additional analyses and interpretation and wrote sections of manuscript.

All authors read and approved the final manuscript. Correspondence to A. The studies reported here received ethical approval from the Psychology Ethics Committee, acting within the College of Life Sciences and Medicine, University of Aberdeen.

All participants gave signed, informed consent for their data to be used. Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. Reprints and Permissions. McCaffery, J. Individual differences in face identity processing. Research 3, 21 Download citation. Received : 25 November Accepted : 28 March Published : 27 June Anyone you share the following link with will be able to read this content:.

Sorry, a shareable link is not currently available for this article. Provided by the Springer Nature SharedIt content-sharing initiative. Skip to main content. Search all SpringerOpen articles Search. Download PDF. McCaffery 1 , 2 , David J.

Robertson 1 , 2 , 3 , Andrew W. Significance statement Perception and recognition of face identity is critical in many real-life contexts, including the identifications made by eye-witnesses and the inspection of passports or identity cards.

Background The ability to perceive and recognise face identity is critical to real-life tasks ranging from eye-witness identification to passport control.

Procedure Participants first completed the Face Recognition Ability Questionnaire, followed by the three face-identity processing tests. Results Five participants were removed from all analyses either as a result of software failure during data collection three participants , or because they scored more than 2.

Full size image. Stimuli and tasks All participants completed five face-processing measures. Mooney face task This task Mooney, measures perceptual closure using high-contrast face images consisting of exclusively dark or light regions.

Examples of a Mooney face. Results Summary statistics for data from the measures used in Study 2 are presented in Table 2 see Additional file 1 for raw data. Table 2 Summary statistics for data from all measures used in Study 2. Discussion Our aims were to investigate the relationships between individual differences in the performance of three tasks that assessed different aspects of face-identity processing, and to investigate the relationships between these tasks and other measures of face perception and broader perceptual, cognitive, and personality measures.

Conclusions Understanding the nature of individual differences in ability to perceive and recognise face identity is of importance in real-life contexts ranging from eye-witnessing to passport control.

References Andrews, S. Article Google Scholar Barton, J. Article Google Scholar Benton, A. Google Scholar Bindemann, M. Article Google Scholar Bindemann, M. Article Google Scholar Bowles, D. Google Scholar Bruce, V. Article Google Scholar Davis, M. Chapter Google Scholar Gray, K. Article Google Scholar Hay, D. Google Scholar Kagan, J.

Article Google Scholar Livingston, L. Article Google Scholar Megreya, A. Article Google Scholar Messick, S. Article Google Scholar Mooney, C. Article Google Scholar Mueller, J. Article Google Scholar Nowicki, S. Article Google Scholar Palermo, R. Article Google Scholar Riddoch, M. Google Scholar Ritchie, K. Article Google Scholar Shakeshaft, N.

Article Google Scholar Spearman, C. Google Scholar Verhallen, R. Google Scholar Warrington, E. Google Scholar White, D. Article Google Scholar White, D. Google Scholar Wilhelm, O. Article Google Scholar Wilson, B. Google Scholar Witkin, H. Google Scholar Young, A. Article Google Scholar Young, A. Availability of data and materials The datasets supporting the conclusions of this article are included within the article and its additional files. Robertson Authors Jennifer M. McCaffery View author publications.

View author publications. Ethics declarations Ethics approval and consent to participate The studies reported here received ethical approval from the Psychology Ethics Committee, acting within the College of Life Sciences and Medicine, University of Aberdeen.

Competing interests The authors declare that they have no competing interests. Additional file. Additional file 1: Participant-level data for studies 1 and 2.

XLSX 27 kb. About this article. Cite this article McCaffery, J. The accuracy of the normative, lab and online samples are shown in Table 1 and Fig 3. Closer inspection of performance distributions for the online samples is shown in Fig 4.

These clearly show a shift in the distribution of scores for the online samples relative to the normative group black distribution. Left: Accuracy distribution of Online Sample 1 top and Online Sample 2 bottom compared to the normative accuracy distribution black line.

Right: Sample of distribution above the super-recognition threshold 2 SDs above the mean. This confirms the test does not suffer from ceiling effects and indicates that it is sufficiently difficult for the effective upper limit of human accuracy to be below the upper bound of its measurement range. The UNSW Face Test is designed to be an online screening tool for super-recognizers, so we examined how effective it is at finding the best performers on subsequent tests.

The difficulty of the UNSW Face Test affords the opportunity to apply stricter criteria than the 2 SD cut-off that is typically used to classify super-recognizers. Does this greater resolution at the top-end of the distribution provide benefits for a screening test?

Results of this analysis are shown in Fig 5. Boxes on the right show the number of participants in Online Sample 2 represented in each distribution. For the CFMT, applying progressively stricter criteria does not select groups that perform progressively better on the other tests Fig 5 , middle row. For the GFMT, applying the strictest limit possible provides only moderate benefits Fig 5 , bottom row. These results show that the ability to set stricter screening criteria using the UNSW Face Test, compared to existing tests, provides researchers with an enhanced ability to target high performing people for follow-up testing.

Fig 6 shows the correlations between the three tests used to perform the analysis shown in Fig 5. Visual inspection of these figures suggests that the enhanced ability of the UNSW Face Test to screen for super-recognizers is due to the reduced frequency of ceiling level performance relative to the other tests.

Ceiling effects in these tests are likely caused by the recruitment methods that explicitly targeted higher performers, which is consistent with the superior accuracy we observe in our online samples relative to lab-based samples.

These results show the considerable variability in individual performance across the three tests, demonstrating the importance of repeated testing when establishing super-recognition. They also show that the UNSW Face Test does not suffer from ceiling effects, unlike existing tests and which can aid in the identification of super-recognisers. To establish test-retest reliability of the UNSW Face Test, we used a lab sample because this gave us greater control over when participants completed the two testing sessions.

Their scores at each time point are plotted in Fig 7. This might be attributable to the fact that accuracy on the UNSW Face Test is not calibrated to the midpoint of the measurement scale.

Because our aim was to produce a challenging test with a greater resolution at the upper tail of the distribution, we were not concerned about compression of variance towards the lower end of the scale, but this is likely to limit the strength of test-retest correlations.

Nonetheless, as demonstrated in Fig 5 , the test is very effective at identifying high performers on subsequent tests. This 1. Next, we sought to establish convergent validity using a lab sample.

Accuracy scores and correlations between the tests are shown in Table 2. This pattern of correlation supports those shown by overall accuracy, in that the UNSW Face Test is more strongly associated with face memory than matching ability. These correlations are consistent with previous reports of an association between standardized tests of face identification ability [ 11 , 25 , 27 , 30 , 49 , 50 ] and provide evidence of high convergent validity.

Their accuracy scores are shown in Table 3 , along with correlation coefficients between each of the tests. This pattern confirms that the UNSW Face Test has discriminant validity and is measuring domain-specific face identification abilities.

To investigate demographic effects, we examined the effects of age, ethnicity, and gender on accuracy by combining Online Samples 1 and 2. We used our Online Samples for this analysis because large online cohorts are typically more diverse and heterogeneous than university student samples. The effects of age on accuracy were striking and described in detail below, whereas effects of ethnicity and gender were more subtle and so these are reported in S1 Appendix.

All data is available in S2 Datasheet for this purpose, but to facilitate this process in Table 4 we provide the age-specific data from our online samples see [ 51 ]. Previous research shows that face identification ability increases markedly from childhood to adulthood—peaking at around age 31 —before slowly declining with further aging [ 52 ]; see also [ 53 ].

Visual inspection of test accuracy in Fig 8 shows a strikingly similar pattern. We computed estimated peak accuracy and standard error by fitting a quadratic function to the logarithm of age and then using a bootstrapping resampling procedure, resampling from the data with replacement times. In both the CFMT and UNSW Face Test, the ages of the faces were young adults and so these results may reflect an own age bias whereby younger participants benefited from the choice of stimuli [ 54 , 55 ].

However, because the average age of faces in the UNSW Face Test is almost 10 years younger than the peak performance age, this explanation alone cannot account for this age effect. Average accuracy for each participant age on the UNSW Face Test separated for overall left , memory task middle and sorting task right. Size and shade of each data point show the number of participants in that age group. Visual inspection of Fig 8 suggests that accuracy on the memory sub-task is modulated more by participant age in comparison to the sorting sub-task.

The divergence of aging effects for memory and sorting tasks is consistent with previous work showing that face identification accuracy is less sensitive to aging in the perceptual matching of face images, compared to recognition memory tasks [ 27 ]. Unlike existing face identification tests, the UNSW Face Test is designed to be administered online and delivered en masse to large cohorts of participants.

Because super-recognizers are rare, a mass screening tool enables researchers to identify larger groups of super-recognizers for follow-up confirmatory testing than is possible with existing tests. Despite testing over 24, participants, no participant has achieved a perfect score at the time of writing. It is, therefore, an open question as to whether the limits of human ability in face identification tasks fall below the upper bound of the measurement scale used in this test.

Moreover, because the accuracy of super-recognizers is below this upper-bound, it enables researchers to discriminate between super-recognizers that achieve, for example, a score that is 2 SDs vs 4 SDs. Although the accuracy threshold used to define super-recognizers varies across studies, we have shown here that stricter recruitment criteria translate to higher performance in participant groups.

So from a pragmatic viewpoint, if the goal is to study the highest performing participants on face identification tasks, adopting stricter criteria on screening tests will yield a greater proportion of high performing participants in follow up confirmatory tests. A great deal of effort is required to create a standardized psychometric test that is well-calibrated, reliable and provides a valid measure of ability [ 30 ].

And yet, many standardized tests are available online as initial screening tools, which means participants could practice the tests repeatedly. This is problematic because it reduces the legitimacy of these tests in scientific use and also, perhaps more concerningly, where these tests have been used in recruitment for security and policing roles [ 13 , 18 , 28 ].

Because the people pictured in the test have agreed for their images to be used, it can be linked to popular media content, and the interactive nature of the task is suited to engaging consumers of popular media.

Anecdotal accounts from super-recognizers and our students suggest that they find the task enjoyable and are motivated to perform well. We attribute this to the difficulty of the test, and its strong face validity —stemming from the fact it was created using the type of images that people typically encounter on the internet.

Given that super-recognizers are increasingly being deployed to perform important real-world tasks, there are strong theoretical and practical motivations for researchers to rectify this in the years ahead [ 17 ]. We hope the UNSW Face Test can support the initial recruitment phase on which these research activities are based, enabling researchers to find more individuals with this intriguing ability. All participants gave their informed consent either digitally or in writing.

The faces of the individuals depicted in Fig 1 and those included in the test have given written informed consent as outlined in PLOS consent form to publish and use their faces for these purposes. In the GFMT [ 27 ] participants decide whether pairs of images show the same person or two different people. Participants completed the short version of the GFMT, which contains 40 image pairs 20 match, 20 non-match.

GFMT images were captured minutes apart in studio-conditions with different cameras. The CFMT [ 26 ] is a standardized test of face memory. In this test, there are 3 blocks of 24 increasingly difficult trials. Participants memorize 6 novel faces study phase and then attempt to identify them in a three-person lineup test phase.

Images in block 1 are the same as those shown in the study phase. Images in block 2 are novel images of the study faces captured in untrained views and lighting conditions. Finally, images in block 3 are novel images that have been degraded with visual noise.

In this block, participants learn and are tested on novel images that contain more extensive visual noise and variability in the pose, expression, and visible features.

The CCMT [ 44 ] was created as a measure of individual differences in object discrimination. Using the same trial structure as the CFMT, this test provides a measure of object recognition ability that is independent of face recognition.

The MFFT [ 45 ] is a task that measures cognitive style, impulsivity versus reflexivity. Participants decide whether a target drawing is identical to one of six variants, or absent. Participants complete 20 trials. Thirty-one participants met the exclusion criteria and were removed from analyses. Because this is an online test our sample likely contains multiple attempts by some participants. When we were able to link multiple attempts to the same email address we only included their first attempt.

There were 1, participants who met the exclusion criteria and were removed from analyses. The sample consisted of 17, individuals of European descent These participants volunteered to complete the tests online by clicking an advertisement for research participation with The University of Greenwich between March 7, , and August 1, There were 30 participants met the exclusion criteria and were removed from analyses.

Participants in Lab Sample 1 completed two test sessions, one week apart. Participants took between 45—60 minutes to complete the test battery at Time 1 and 15—20 minutes to complete Time 2. No participants met the exclusion criteria. There were 46 individuals of Asian descent Due to time constraints, one participant did not complete the GFMT but did complete the other tests, resulting in a final sample of 79 for the GFMT and 80 for the remaining tests.

Participants took between 75—90 minutes to complete the test battery. There were 62 individuals of Asian descent Thanks to Daniel Noble and Natalija Pavleski for assistance with stimuli selection and preparation, to Christel Macdonald, Daniel Guilbert, Albert Lin, and Monika Durova for assistance with data collection, and to Richard Kemp for his thoughtful commentary. Browse Subject Areas? Click through the PLOS taxonomy to find articles in your field.

Introduction People show a surprising degree of variation in their ability to identify faces, ranging from chance-level to perfect accuracy. Test design and procedure The UNSW Face Test consists of two tasks completed in a fixed order: a recognition memory task and a match-to-sample sorting task see Fig 1. Download: PPT. We have no evidence that their face recognition processes are qualitatively different than normal.

It seems more likely that super-recognizers are simply the high end of a broad distribution of face recognition ability. Though the cut-off is likely arbitrary, we believe it is relevant that the people we tested arrived independently at the conclusion that they are much better than average. How much better than normal are the super-recognizers described here, and is it a meaningful difference? One approach to addressing this question is to compare the difference between control and super-recognizer performance with the difference between control and developmental prosopagnosic performance.

Most developmental prosopagnosics we have tested in our laboratories score around 2—3 standard deviations below normal on the CFMT short form.

In comparison, three super-recognizers scored around 2 standard deviations above the mean on the CFMT long form. Similarly, on the CFPT, effect sizes were very similar for the comparisons between prosopagnosics and controls, and between super-recognizers and controls.

In both face recognition and face perception, the super-recognizers are about as good as many developmental prosopagnosics are bad. This suggests the possibility that many cases of developmental prosopagnosia represent the low end of normal face recognition ability rather than a qualitatively different kind of face processing.

Whether one or more distributions underlie the range of face recognition ability, the existence of super-recognizers indicates that this range is larger than has been appreciated. On one end of this range lie developmental prosopagnosics, some of whom even have difficulty recognizing members of their nuclear family. On the other end of this range lie super-recognizers, who frequently recognize complete strangers out of context after many years. The discovery that the range of face recognition ability is wide rather than narrow is relevant in applied and theoretical contexts.

Various social institutions are premised upon the false assumption that all people have similar face recognition ability. Many occupations rely critically on face recognition. Given the estimated prevalence of prosopagnosia, it is likely that thousands of prosopagnosic security personnel are checking ID photographs daily.

It would be preferable for people in these occupations to have average or even superior face recognition ability. Face recognition ability could be assessed as part of the selection process for such jobs. Eyewitness identification accuracy is significantly correlated with performance on standardized face recognition tests Morgan et al.

Our third experiment, on face perception and the face inversion effect, illustrates how comparing the super-recognizers and developmental prosopagnosics can be used to investigate basic mechanisms of face processing. The super-recognizers were better at perceiving differences between faces than control subjects, who in turn were better than developmental prosopagnosics. This suggests that variation in face recognition ability is not solely a function of memory, but also of perception.

The current work replicates this finding and extends it by showing that super-recognizers, with exceptionally good face recognition, have larger face inversion effects than normal controls. The continuous distribution of inversion decrements Figure 5 further supports the notion of quantitative rather than qualitative differences between prosopagnosic and normal face processing.

Our discovery of super-recognizers demonstrates that people can not only be much worse than average at face recognition as in developmental prosopagnosia , but also much better than average. This provides support for the possibility that developmental prosopagnosia—in some cases—represents a low-functioning version of normal face processing rather than an impaired version. National Center for Biotechnology Information , U. Psychon Bull Rev. Author manuscript; available in PMC Jan Author information Copyright and License information Disclaimer.

Copyright notice. See other articles in PMC that cite the published article. Abstract We tested four people who claimed to have significantly better than ordinary face recognition ability. SUBJECTS Following recent media coverage of our work on developmental prosopagnosia, several people contacted us because they believe that they are the opposite of prosopagnosic—that they are exceptionally good at recognizing faces.

Method Tests Before They Were Famous BTWF This is a famous face recognition test, in which subjects view 56 photographs of famous individuals and attempt to name them or provide uniquely identifying descriptions.

Open in a separate window. Figure 1. Figure 2. Figure 3. Methods Participants In addition to the four super-recognizers, we tested 26 new control subjects 15 female, mean age 33 years, S. Figure 4. Figure 5. Footnotes 1 The faces in Fig. Developmental Prosopagnosia: A study of three patients. Brain and Cognition. Congenital prosopagnosia: face-blind from birth.

Configural face processes in acquired and developmental prosopagnosia: evidence for two separate face systems? Family resemblance: Ten family members with prosopagnosia and within-class object agnosia.

Cognitive Neuropsychology. The Cambridge Face Memory Test: Normal performance and an investigation of its validity using inverted performance and prosopagnosic subjects. Developmental prosopagnosia: a window to content-specific face processing.

Current Opinion in Neurobiology.