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RESEARCH ARTICLE
CORRESPONDING AUTHOR:
Meike Ramon
Applied Face Cognition Lab,
Institute of Psychology,
University of Lausanne,
Quartier UNIL-Mouline –
Bâtiment Géopolis, 1015
Lausanne, Switzerland
meike.ramon@unil.ch
KEYWORDS:
Face identity processing; Face
recognition; Target Prevalence;
Expectation/context
TO CITE THIS ARTICLE:
Boudry, L., Nador, J. D.,
& Ramon, M. (2024).
Determinants of Face
Recognition: The Role of Target
Prevalence and Similarity.
Journal of Cognition, 7(1): 27,
pp. 1–15. DOI: https://doi.
org/10.5334/joc.339
ABSTRACT
Studies of facial identity processing typically assess perception (via matching) and/
or memory (via recognition), with experimental designs differing with respect to one
important aspect: Target Prevalence. Some designs include “target absent” (TA) among
“target present” (TP) trials. In visual search tasks, TA trials shift an observer’s decisional
criterion towards a stricter one, increasing misses. However, decisional biases will differ
between individuals and across an individual’s decisions as well. In this way, excluding
TA trials ensures comparable levels of expectation and thus a more controlled decisional
bias both within and between observers by not considering correct rejections and false
alarms. However, TA trials may occur, e.g., in police line-ups, where it is important to
consider observers’ face recognition ability net of the potential biases introduced by
TA and TP trials. And, while these have been investigated in numerous other stimulus
domains, their effects have not yet been extended to face recognition. We therefore
sought to fill this void by testing different versions of the previously established Models
Memory Test, which measures old/new recognition of experimentally learned facial
identities. Our study found significant expectation effects, driven by target prevalence
that persist even given prevalence changes. This implies that face recognition – even
measured with naturalistic changes – is influenced by prior perceptual decisions.
LIONEL BOUDRY
JEFFREY D. NADOR
MEIKE RAMON
*Author affiliations can be found in the back matter of this article
Determinants of Face
Recognition: The Role of
Target Prevalence and
Similarity
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DOI: 10.5334/joc.339
INTRODUCTION
Over the past two decades, scientific interest in face identity research has increased
dramatically. Searching the term “Face Processing” on pubmed.com yields 267 articles
published in the year 2000. Now, twenty years on, the same search returns seven times this
number. Several technological advances facilitating facial image creation and processing
have contributed to this growth, including proliferation of devices with cameras, alongside
rapid improvements in machine learning and artificial intelligence (AI) algorithms. These
developments have largely been benchmarked using instances where accurate processing of
facial identity is paramount, as in security and law enforcement settings (Ramon et al., 2019;
Ramon & Rjosk, 2022; Mayer & Ramon, 2023).
Assessing the extent of the benefits that these technological advances can provide requires
thorough knowledge of human performance as a benchmark. Therefore, psychological studies
over this same period have sought to characterize ability in face identity processing (FIP). These
lines of research include neuropsychological studies examining the effects of brain damage
(Ramon, Busigny, Gosselin & Rossion, 2016; for reviews see Rossion, 2022 a, b), fundamental
research investigating how real-life experience shapes measured FIP (for reviews see Ramon
& Gobbini, 2018; Meissner & Brigham, 2001), and individual differences among neurotypical
individuals (Fysh et al., 2022; Stacchi et al., 2020; Bobak et al., under review). A recent subset
of studies have focused on individuals with superior skills, so-called Super-Recognizers (Russell,
Duchaine & Nakayama, 2009; Ramon, 2021), to characterize the mechanism(s) underlying their
unique ability (Nador et al., 2021 a,b; 2022; Linka et al., 2022) and how to identify them (Mayer
& Ramon, 2023; Ramon & Rjosk, 2022; Ramon, 2021). Consequently, there has been a surge in
the development of FIP assessment tools, which typically measure specific subprocesses with
varied (at times suboptimal) reliability and precision (Fysh & Ramon, 2022; Bobak et al., under
review; Stacchi et al., 2020; Fysh et al., 2020).
TARGET PREVALENCE IN TESTS OF FIP AND VISUAL SEARCH
Across professional domains, FIP measures have been developed for several reasons (for
review, see Young & Ellis, 1989). For instance, a body of neuroscientific research aims to
understand FIP’s subprocesses and neural correlates (Rossion et al., 2020; Yovel, 2016) In law
enforcement, understanding FIP differences is important e.g., in the context of perpetrator
identification through testimony of witnesses or forensic professionals (Mayer & Ramon, 2023).
The motivation for studying FIP typically influences a range of methodological choices. These
can relate to performance measures considered, e.g., accuracy or response time for identity
matching (see. Fysh & Ramon, 2022; Nador et al., 2022), or experimental design. While some
studies seek to maximize ecological validity (e.g. Bate et al., 2018) by applying natural and
realistic changes to stimuli, others may artificially increase task difficulty by adding ambient
noise to their stimuli (e.g. Russell et al., 2009).
Furthermore, to approximate real-life scenarios, some studies consider the effect of Target
Prevalence (the presence of target identities among foils during/across experimental trials) on
FIP. Thus, in 1-to-many matching, or n-alternative forced-choice recognition tasks, the target
identity signal is often absent from the possible response options on a subset of trials. (Bruce et
al., 1999; Bate et al., 2018). This is thought to serve as a model for myriad real-world scenarios,
including policing and security. For example, a mug-shot line-up created by the police may
either include the depiction of a person of interest (target present), or not (target absent).
Ideally, witnesses and professionals should not only be able to recognize persons of interest
(or “targets”) when present, but also refrain from falsely identifying others in the lineup (“foils”)
as the perpetrator, whether or not the target is absent. However, to the best of our knowledge,
no such studies to date have systematically varied Target Prevalence during face recognition
tasks, leaving substantial doubt (warranted or not) in witnesses and professionals’ judgments.
This doubt arises from more domain-general work on visual search, wherein the role of Target
Prevalence is routinely studied in diverse scenarios, such as screening baggage at airport security
for weapons (Wolfe & Van Wert, 2010; Wolfe et al., 2007), or screening radiological images to
diagnose tumors (Nakashima et al., 2013). Critically, in both fields, targets are exceedingly rare.
In mammography, for example, only 3% of scans present a tumor (Gur et al., 2004). In radiology,
low Target Prevalence has been shown to induce miss rates as high as 30% for tumors after
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DOI: 10.5334/joc.339
scan examinations (Evans et al., 2013). In airport security, baggage screeners reportedly miss
95% of weapons hidden in luggage (Fishel, Levine, & Date, 2015); some recent estimates hold
that only 10 firearms are identified per million passengers screened (Transportation Security
Administration, 2015).
Overall, researchers have shown that hit rates – the proportion of correctly identified targets
among foils – decline drastically when targets are rare (Wolfe et al., 2007). This “low prevalence
effect” is a major concern for visual search tasks in general, presumably also including FIP.
Importantly, though, this effect arises due to observers’ inherent bias towards signaling the
presence or absence of a target, such that when targets are rarer, observers are less likely to
signal their presence. It should be noted, though, that this need not necessarily imply decreased
sensitivity to targets; observers also make fewer false alarms (incorrectly identifying a foil as a
target) under such circumstances (Wolfe et al., 2007; Peltier & Becker, 2016).
EFFECTS OF TARGET PREVALENCE AND SIMILARITY ON FACE MEMORY
In practice, FIP-related tasks often – but not always – require memory of a given facial identity.
On one hand, for instance, police officers may screen CCTV footage for the presence of a
particular suspect whose photograph they have in hand. On the other, a witness may need
to identify a suspect specifically from memory. Unfortunately, false alarms in these scenarios
have serious ramifications, and eyewitness testimony is extremely prone to false alarms, to
the point that they are among the most common causes of suspect misidentifications (Wells
& Olson, 2003).
Consequently, the inclusion of Target Absent trials experimentally has become a priority (e.g.
Bate et al., 2018; Bruce et al., 1999; Matthews & Mondloch, 2018), leading researchers to
proffer many such assessments. However, no such studies have systematically varied Target
Prevalence to assess changes in hit rate, and remain prone to bias as they include Target Absent
trials.
To address this, we adapted one such assessment tool, the Models Memory Test (MMT; Bate
et al., 2018). The MMT measures recognition performance for learned face identities using
“ambient images” (Jenkins & Burton, 2011), i.e. naturally occurring variability in facial
appearance. Throughout two target recognition phases, observers are presented with triplets
of images containing two distractor and one target identity. Both phases differ in the similarity
between initially learned target images and the potential matching target stimulus. Similarity
can be high, with minor changes between the learned image of a given identity and its
matching probe, or low, i.e. entailing greater changes (see Figure 1). Additionally, the MMT
includes Target Absent trials at a constant rate of 50% of the trials throughout, and as such
cannot assess the effect of varying their prevalence on hit rates. Therefore, we extended it to
include conditions with only Target Present trials.
Practically, naturally occurring changes in facial appearance negatively impact face recognition
(Patterson & Baddeley, 1977). The MMT exploits this effect of image changes to systematically
increase Target-To-Match Similarity. That is, across the recognition phases, targets’ facial
appearance changes are initially less, and then more pronounced across Phases 1 and 2
(see Figure 1). Unfortunately, the parallel implementation of target absent trials as a second
novel feature of the original MMT is undesirable. Simply put, differences between high and
low similarity conditions could have been explained by either or both of these methodological
considerations (Target Prevalence or Similarity). And, since these factors operate in concert to
create a specific context for face recognition performance, a lack of simultaneous control over
them both limits the original MMT’s insight into face recognition memory performance. Our
inclusion of Target Present-Only conditions remedies this issue.
CLOSING THE GAP: CONTEXTUAL EFFECTS ON FACE RECOGNITION
For this study, Context comprises previously acquired experience within an ongoing situation.
Operationally, this translates to the effect that previous trials (or previous phase) have
on processing current stimuli, along with their potential consequences for future stimuli
(Zimmermann et al., 2007). In visual search tasks, context is often manipulated via priming,
through presentation of targets or foils (Kristjánsson & Campana, 2010). However, visual search
studies overlook these “implicit” contexts, wherein a given experience or percept can affect the
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following one(s). For example, an observer’s previous experience with low Target Prevalence
could bias their hit rates downwards on subsequent trials. Exploring the effect of Context in
a visual search task, Wolfe and colleagues (2007) reported that training with higher Target
Prevalence led to better subsequent performance in low and high Target Prevalence visual
search tasks.
We reasoned that performance should be facilitated by higher vs. lower similarity between
images used during learning and recognition, leading to increased hit rates. Additionally, we
hypothesized that hit rates would be reduced when including target absent trials compared
to when excluded. A higher Target Prevalence would increase recognition performance as
discussed earlier in the context of visual search more generally. Finally, we hypothesized that
contextual effects of Target Prevalence would show carry-over within observers, such that
those who were first exposed to low target prevalence would show lower hit rates in future
perceptual decisions and vice-versa.
METHODS
All research procedures were approved by the local Ethics Committee (Approval Number 473,
University of Fribourg, Switzerland) and conducted following the tenets of the Declaration of
Helsinki (Puri, Suresh, Gogtay, & Thatte, 2009).
OBSERVERS
An a priori power analysis determined that at least 14 observers would be necessary to detect
medium-sized effects at α = .05 and β = .8 given our experimental design. Invitations for remote
participation were sent out to sixty relatives of one experimenter, all of whom participated
Figure 1 Examples of
stimuli presented in the
Model Matching Test across
test phases. Images are
reproduced from Bate et
al. (2018) under a Creative
Commons Licence (http://
creativecommons.org/
licenses/by/4.0/).
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in the experiment (half female; mean age: 40±12 years), and who had normal or corrected-
to-normal vision. The observers were unaware of the study’s purposes concerning Target
Prevalence, Target-to-Match Similarity, Context and Cultural Exposure. Observers were randomly
assigned to one of three groups, and each group completed a different version of the MMT (see
Table 1). According to their own accounts, observers were exposed to South Asian (SA), Western
Caucasian (WC), or ethnically-mixed groups (SAWC) (see below).
INTER-ETHNICITY SOCIAL CONTACT QUESTIONNAIRE (IESCQ)
We assessed our observers’ contact with/exposure to different ethnicities to ensure that any
such exposure differences would be balanced across groups. To this end, we designed a novel,
self-administered Inter-Ethnicity Social Contact Questionnaire (IESCQ), which was implemented
online beforehand and took between five and ten minutes to complete.
The IESCQ contains 10 closed-ended questions soliciting self-reports of the quality and quantity
of own-ethnicity (South Asian or Caucasian; five items) and other-ethnicity (South Asian
or Caucasian; five items) exposure and contact. IESQCQ items assess exposure within their
work setting, various social/public settings, through personal knowledge, digital media, etc.
For example, Item 2 asked, “Consider your experiences with Caucasians (White people) within
the context of various social/public settings. Approximately, what percentage of the people you
regularly interact or socialize with are Caucasians?”. The IESCQ uses the same items for own-
and other-ethnicity. All observers rated their response to each item on a percentage scale from
0 to 100 in increments of 10%, 0 being no contact/exposure at all and 100 being maximal,
daily contact. Mean percentage scores were calculated for each observer for each of the two
ethnicities. Observers with low to no exposure/contact with the other ethnicity (0–30%) or high
exposure/contact with their own ethnicity (70–100%) were assigned to a mono-ethnic group,
whereas observers with relatively similar exposure/contact to both ethnicities were placed in
the multi-ethnic group.
STIMULI AND GENERAL PROCEDURE
All experimental stimuli were taken from the original MMT (Bate et al., 2018). They depict
naturalistic, full-color, adult male faces taken under different lighting conditions and from
various viewpoints. Stimuli presented in Phase 2 included additional paraphernalia (greater
changes) (e.g., addition of reading glasses, beanies, facial hair, etc.). Target face stimuli included
14 “ambient” images (Jenkins & Burton, 2011) of 6 target identities; foil face stimuli consisted
of 300 images, each displaying a different identity. Images preserved all external features of
the face including hair and ears.
Each TP trial involved presentation of three probe stimuli: one of a target identity and two
foil identities. Each TA trial involved presentation of three probes displaying foil identities.
Observers participated online (testable.org), using their personal computers’ web browser of
choice, in full-screen mode. Prior to commencing, they were asked to make sure they could
avoid distractions and to position themselves at one arm’s length distance from the screen.
Comparable on-screen stimulus size was ensured through a default calibration procure.
Table 1 Demographic
information of observers
assigned to the three
versions of the Models
Memory Test (MMT). Groups
were exposed to different
combinations of Target
Prevalence across Phases 1
and 2, which contained only
target present trials (TP), or
included target absent trials
(TA) (as in Bate et al.’s (2018)
original study). For all groups,
Phase 1 and Phase 2 were
characterized by high and low
Target-to-Match Similarity,
respectively, due to the degree
of ambient changes among
images across phases. Each
group first completed the
first, “easier”, followed by the
second, “harder” phase (with
lesser vs. greater ambient
changes). For Group 1, Phase
1 had low Target Prevalence
(TA/TP; i.e. target absent
and target present trials)
and Phase 2 had high Target
Prevalence (TP; i.e. only target
present trials). This pattern of
Target Prevalance was reversed
in Group 2, where Phase 1
had high (TP), followed by low
Target Prevalance in Phase 2.
* NB: This is the original MMT
reported by Bate et al. (2018).
TA/TP: indicates that a phase
contains both target absent
(TA) and target present (TP)
trials. TP: indicates that a phase
contains only target present
(TP) trials.
CONTEXT (PHASE1–PHASE2) CULTURAL EXPOSURE OF PARTICIPANTS ASSIGNED
TO CONTEXTS
N
(MALE/FEMALE); AGE ± SD
WESTERN
CAUCASIAN (WC)
SOUTH ASIAN
(SA)
MIXED (SAWC)
Group 1: Low-to-high Target
Prevalence (TA/TP—TP)
7 (3/4); 35 ± 6 7 (3/4); 45 ± 13 6 (4/2); 41 ± 11
Group 2: High-to-low Target
Prevalence (TP—TA/TP)
7 (3/4); 40 ± 12 6 (3/3); 43 ± 14 6 (2/4); 34 ± 9
Group 3: Low-to-low Target
Prevalence* (TA/TP—TA/TP*)
6 (4/2); 43 ± 18 7 (3/4); 39 ± 12 7 (5/2); 39 ± 11
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PROCEDURES & DIFFERENT VERSIONS OF THE MMT USED
The original MMT’s design was delivered to Group 3, where an initial Target Learning (encoding
and target test) was followed by two Recognition Phases of 45 trials each with low Target
Prevalence (equal proportion of TP and TA trials in each Recognition Phase). Recognition phases
differed in terms of Target-To-Match Similarity (i.e., similarity between the learned targets and
probes presented during recognition phases). As demonstrated in Figure 1, Phases 1 and 2
involved lesser vs. greater changes (change of lighting or viewpoint, vs. change of hairstyle,
addition of a beard, glasses, etc), respectively.
To assess the effect of target-absent trials on face recognition, we created two modified versions
(Group 1, Group 2; see Table 1) of the original MMT (Bate et al., 2018). At base, all versions
contain two phases schematically represented in Figure 2: (1) Target Learning (consistent across
versions) and (2) Target Recognition (differing across test versions). Target Recognition consists
of two phases (45 trials each), which differ in terms of Target-to-Match Similarity. Similarity
between learned targets and to-be-matched probes is higher in Target Recognition Phase 1
(“easy” trials), compared to Target Recognition Phase 2 (“difficult” trials), where paraphernalia
and external facial information differ between target and probe images. Target-To-Match
Similarity differed in the same manner across phases for all groups as described above.
Stimulus aspects aside, the three MMT versions differ in terms of Target Prevalence across trials,
with fixed order of Target-to-Match Similarity (low, followed by high). In the original MMT, both
Target Recognition Phases include equal numbers of target-present (TP) and target-absent (TA)
trials; its setup is therefore referred to as TA/TP—TA/TP (see Table 1). This “original” version of
the MMT was delivered to Group 3 as described above. Our two modified MMT versions both
involved the same response modalities and comprised the same number of TP trials as the
original. However, they differed in terms of whether the “easy” and “difficult” Target Recognition
Phases contained TA trials.
For Group 1, the (easier) Target Recognition Phase 1 was identical to the original MMT
(containing TA and TP trials), while the (more difficult) Target Recognition Phase 2 involved
only TP trials (with a doubled number to ensure equal number of trials across phases/blocks).
Group 1 is therefore referred to as TA/TP—TP. For Group 2 on the other hand, the experiment is
structured as the opposite as Group 1, with a TP—TA/TP structure: its Target Recognition Phase
1 contained only TP trials (but doubled compared to the MMT), followed by the original MMT
Target Recognition Phase 2.
To summarize, across Target Recognition Phases the three test versions used (Groups 1–3)
contain the same (decreasing) Target-to-Match Similarity (high; low), with varied Target
Prevalence (TA/TP; TP). For all versions, observers provided their responses by button press,
indicating whether any of the probes matches a target identity (by pressing 1, 2 or 3), or
not (by pressing 0). They were aware of the type of manipulation (High or Low Target-
to-Match Similarity/High or Low Target Prevalence) before each Testing Phase. When a
phase contained TP trials only (High Target Prevalence), observers could not respond by
pressing button 0, only buttons 1,2 or 3 could validate a response and pass to the next
stimuli.
Figure 2 Experimental design.
The experiment starts with
Target Learning followed by
Target Recognition. During
Target Learning, observers
sequentially encode three
images of a given target
identity, followed by a
3-alternative forced-choice
(3AFC) target test of the
encoded images. Target
learning of all six target
identities finishes with a final
20s review of target identities
using novel images. Target
Recognition comprises two
phases, which differ in their
Target-to-Match Similarity
(Phase 1: high; Phase 2:
low; see Methods). Target
Recognition Phases can differ
in terms of Target Prevalence,
i.e., they can either contain
only trials depicting targets
(Target-Present; TP), or
mixed trials (Target-Absent/
Target-Present; TA/TP). Our
three groups (see Table 1)
were subjected to different
experimental Contexts,
which represent our possible
combinations of Target
Prevalence (TP; TA/TP), across
Target Recognition Phases
with fixed order of Target-to-
Match Similarity (low, followed
by high).
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STATISTICAL ANALYSES
MEASURES
As across MMT versions, not all Target Recognition Phases included TA trials. These versions were
the result of the combination of Target Recognition Phases with different Target Prevalence.
Therefore, to compare performance between contexts and phases (by considering Hit Rate,
rather than Accuracy as the dependent variable), our analyses were conducted only on TP trials,
while TA trials (False Alarms or Correct Rejections) were not considered.
REPLICATION OF THE ORIGINAL MMT
First, to ensure construct validity, we compared our data obtained using the original MMT
version (which is equivalent to Group 3 regarding the experimental conditions: TA/TP—
TA/TP Target Prevalence combination) to those reported by Bate et al. (2018). To this end,
we compared Accuracy and Sensitivity (d’), as well as hit and correct rejection rates between
studies. Note that since, unfortunately, the original MMT’s authors could not provide their
observers’ individual data, we could only compare data between studies at the mean
Accuracy level via one-sample t-tests (wherein Bate et al.’s (2018) across-observer means
represent μ0).
LINEAR MIXED-EFFECTS MODELLING
To assess potential effects of Target-to-Match Similarity, Target Prevalence and Cultural Exposure
on observers’ hit rates, we successively fitted linear mixed effects models to observer-level
data (R, Version 4.0.5; R Core Team, 2013; lme4 package; Bates, Maechler, Bolker, & Walker,
2015), allowing us to compute each factor’s Bayesian Information Criterion (BIC). As a general
strategy, we began by fitting Hit Rate data to a null model (Model 0, including an intercept
term only). Subsequently, we compared it against more complex models, successively adding
a single fixed effect (i.e., Target-to-Match Similarity, or Target Prevalence) to each one, then
calculating the Bayes Factor (BF) between Models n and n+1 (where n denotes the last
favored—and least complex—model). We would then retain whichever model the BF favored
for subsequent comparisons. In any case where multiple models of rank n+1 were equally
favored over model n, the AIC was used to adjudicate between them by selecting the most
parsimonious among them. Finally, we added Cultural Exposure (and associated interactions)
to the most favored fixed-effects model as a random factor (since this was neither controlled
nor assigned) in the same iterative manner.
CONTEXT EFFECTS: TARGET-TO-MATCH SIMILARITY AND TARGET PREVALENCE
The models described above tested for effects of Target-to-Match Similarity and Target
Prevalence across the two modified versions of the MMT (Groups 1 and 2), and the original
MMT (Group 3). To assess the influence of Target Prevalence Context across Target-to-Match
Similarity, we compared each of our 3MT cohorts to our MMT cohort using the same strategy
outlined above, with two more sets of linear mixed-effects models. Effectively, these tested
the effects of changing Target Prevalence from Low to High or vice versa on performance in the
Low Target-to-Match Similarity. In all cases, following model selection, we compared relevant
marginal conditions for the significant factors with t-tests.
RESULTS
REPLICATION OF THE ORIGINAL MODELS MEMORY TEST (MMT)
First, comparing the mean performance of observers who completed the original MMT (in our
replication through Group 3) against results from Bate and colleagues (2018), we obtain similar
results overall. Despite obtaining generally higher means for overall Accuracy, Hit Rate, Correct
Rejection Rate as well as Sensitivity, t-tests comparing the samples’ means between studies
yielded no significant differences (see Table 2). Additionally, we confirmed the absence of a
statistical difference by calculating per measure the effect size (Cohen’s D, in Table 2) of the
difference across cohorts. Overall, these results suggest that (at the group level) observers in
Group 3 achieved comparable performance to the mean reported by Bate et al. (2018).
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SEPARATING EFFECTS OF TARGET-TO-MATCH SIMILARITY AND TARGET
PREVALENCE ON HIT RATE
We modeled the effects of hit rate as described in the Statistical Analyses section. Bayes
Factors comparing the models (see Figure 3) decisively support Model 2 compared to Models 0
or 1. Thus, we retained Model 2 (including Target-to-Match Similarity and Target Prevalence as
main effects on hit rate). A paired samples t-test yielded a significant difference between hit
rates observed in the high (Mean = .70, SD = .20) vs. low (Mean = .53, SD = .20) Target-to-Match
Similarity conditions (t(59) = 6.76, p < .05). As anticipated, observers’ hit rates were generally
lower during Target Recognition Phase 2, thereby confirming increased task difficulty via more
extreme ambient changes. An independent-samples t-test investigating the two conditions of
the Target Prevalence yielded a significant difference between high (Mean = .71 ± .19) and low
(Mean = .57 ± .21) Target Prevalence (t(118) = –3.55, p < .05): observers generally performed
better under high Target Prevalence scenarios.
CONTEXTUAL EFFECTS: TARGET-TO-MATCH SIMILARITY AND TARGET
PREVALENCE
We conducted separate model comparisons between the cohorts who completed modified
versions of the MMT (Group 1 and 2), and our original MMT cohort (Group 3) (for details, see
Statistical Analyses). Figure 4a displays groups’ mean hit rates for Target Recognition Phases
1 and 2 (across which Target-to-Match Similarity decreased); Figure 4b displays the results of
the multi-level models detailed below. Specifically, here we sought to determine whether the
effect of Context on hit rate depends on the presence of TA trials in Phase 1 or Phase 2.
For constant Phase 1 Context: Group 1 (TA/TP—TP) vs 3 (TA/TP—TA/TP)
To begin with, we considered the scenario where Phase 1 was identical between groups,
changing only during Phase 2 for Group 1. Having confirmed a general effect of target similarity
(Figure 3), we treated this as our zero-order model, and compared it against models including
a main effect of Context (Figure 4b; model 1a) and a Context by Similarity interaction (Figure
4b; model 1b). While Model 1a provides no better explanation than Model 0, we find decisive
evidence favoring Model 1b over Model 0. Overall, this suggests that the interaction between
Target Prevalence and Target-to-Match Similarity best explains observers’ pattern of hit rates.
Post-hoc independent-samples t-tests revealed no difference in hit rates for Phase 1 (High
Target-to-Match Similarity; t(37) = –.53, p > .05; M1 = .62 ± .20 vs. M3 = .66 ± .18) when Groups
initially experienced the same initial conditions (target absent on half of trials). However, in
Table 2 Comparison of
behavioral performance
between the cohort reported
for the original Models
Memory Test (Bate et
al., 2018) and our sample
(Group 3).
ORIGINAL
MMT N = 40
(33Y)
GROUP 3
N = 20
(37Y)
DIFFERENCE BETWEEN
MMT VERSIONS
EFFECT SIZE OF
DIFFERENCE
COHEN’S D
Mean ACC ± SD .54 ± .14 .57 ± .16 t(19) = 0.72, p = .4777 > .05 .16
Hit Rate ACC ± SD .51 ± .20 .53 ± .16 t(19) = 0.62, p = .5422 > .05 .14
Correct Rejection
Rate ACC ± SD
.57 ± .23 .60 ± .21 t(19) = 0.61, p = .5474 > .05 .14
d’ ± SD .26 ± .84 .38 ± .93 t(19) = 0.58, p = .38 > .05 .13
Figure 3 Multi-level models
per factor independently
of group. Numbers below
each model name represent
(in order) the Bayesian
information criterion (BIC),
the Bayes Factor (BF), and the
BF’s logarithmic expression
(Log10). Black font indicates
models showing better
evidence of explaining the
variance among participants
in comparison with the inferior
level’s model. The highest
model in black is gathered for
further analyses.
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Phase 2 (Low Target-to-Match Similarity), we find significantly higher hit rates for Group 1
(M = .60, SD = .14) than Group 3 (M = .41 ± .18), (t(37) = 3.56, p < .05). Overall, the observed
interaction effect suggests that completing Phase 1 including TA trials differentially influenced
Phase 2 Hit Rate, such that the change in context (i.e. when only TP trials were shown in Phase
2) influenced Hit Rate in Phase 2.
Phase 1 Context varied between Groups: Group 2 (TP—TA/TP) vs 3 (TA/TP—TA/TP)
Next, we sought to examine whether the effect of Similarity also depended on Context: would
hit rates in fact decrease following exposure to low (versus high) target prevalence during
Phase 1. The Bayes Factor between models including versus excluding Context as a factor
(either alone or interactively, while accounting for Similarity) favored neither one. However,
comparison of AIC between models suggests that the model including only the Context main
effect is the most parsimonious (ΔAIC = –7.3). Within Model 1aA, we find a main effect of
Target Prevalence Context. A post-hoc independent samples t-test of this effect revealed that
observers assigned to Group 2 performed significantly better than those in Group 3 ((t(80) =
3.39, p < .01); M2 = .70 ± .22 vs. M3 = .54 ± .216; t(80) = 3.39, p < .05).
Figure 4 Groups’ performance
and multi-level model results.
a. Mean hit rates per group
and Target Recognition Phase.
Multi-level model results
for b. Group 1 vs. Group 3,
and c. Group 2 vs. Group 3.
Numbers below each model
name represent (in order) the
Bayesian information criterion
(BIC), the Bayes Factor (BF),
and the BF’s logarithmic
expression (Log10). Black font
indicates models showing
better evidence of explaining
the variance among
participants in comparison
with the inferior level’s model.
The highest model in black is
gathered for further analyses.
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DISCUSSION
Our systematic investigation of Target Prevalence, Target-to-Match Similarity, and Context as
factors influencing neurotypical face recognition performance finds that TA trial prevalence
influences observers’ hit rates regardless of the levels of other factors. Overall, their inclusion
in MMT-type tasks deteriorates observers’ hit rates, which ought to be expected given the
results of visual search studies for non-face stimuli (Wolfe & Van Wert, 2010; Wolfe et al.,
2007). Practically speaking, our results suggest that, while inclusion of TA trials are important
in gauging sensitivity in FIP measures, these are likely prone to intra-observer bias. As such,
care should be taken to interpret performance measures while either controlling for TA trial
prevalence, or systematically varying it.
Aside from that, we did not find any interaction with Target Prevalence. As discussed below, this
suggests that response bias is unaffected by other factors that can cause variation in hit rate.
This is obviated by considering the effect of eliminating TA trials on response bias: observers’
decisions are then forced to be between target locations or response keys (not between signal
and noise), thus any residual bias no longer corresponds to a shift in criterion (preference for
reporting signal or noise), but purely in preference for one or another response button, or image
location. Consequently, studies including TA trials underestimate hit rates, and likely do not (or
cannot) control for this by any other manipulation (e.g., similarity between learned targets and
probe stimuli), since these effects are separable. Rather, systematic variation or control over
target prevalence are necessary to minimize criterion changes.
TARGET-TO-MATCH SIMILARITY
One of our goals was to investigate the effect of similarity between an encoded target identity
and its matching probe items in a modified MMT. We find increased hit rates between Target
Recognition Phases 1 and 2 (which shift from high to low Target-to-Match Similarity), closely
replicating Bate and colleagues’ (2018) originally reported results. The conditions experienced
by Group 1 of our study tightly mirror those of the original MMT, so it seems unlikely that the
between-group Context effects we find here are attributable to methodological differences
between studies.
We further find strong evidence for an effect of Target-To-Match Similarity; participants’
performance was better in the first Target Recognition Phase, where similarity was higher
compared to the second one. This aligns with previous studies suggesting that more pronounced
changes in the appearance of recently learned target identities negatively affect recognition
(Ellis, 1975; Patterson & Baddeley, 1977). Note, however, that the MMT’s experimental design
(Bate et al., 2018) involved a fixed order, i.e., higher followed by lower similarity across Target
Recognition Phases (vs. a potentially fully randomized trials order with respect to their Target-
to-Match Similarity).
TARGET PREVALENCE
Previous studies investigating the effect of Target Prevalence outside the domain of face
processing have reliably shown that the frequency of target (or signal) occurrence strongly
influences response bias, such that reporting of signals is commensurate with their prevalence
(Wolfe & Van Wert, 2010; Wolfe et al., 2007). We sought to determine the impact of TA trials
on face recognition performance by modifying the MMT. Its Target Recognition Phases involve
only TP trials, so we devised versions including equal proportions of TP and TA trials in either or
both Phases. Mirroring previous findings from the visual search literature mentioned above, we
observe reduced hit rates when TP trials are embedded among TA trials.
Overall, we believe that our findings concerning the negative impact of low Target Prevalence
on hit rates during face recognition extends to other FIP reliant (including applied) scenarios,
we anticipate observing inter-individual differences in the expression of this effect. Once
confirmed, this would support our view that task-specific training (e.g., of radiologists, luggage
screeners, law enforcement professionals) should include a combination of TP and TA trials, as
well as characterize the effect of target prevalence variations on individuals’ performance. In
addition to approximating real-world conditions, this could aid observers in guarding against
the decision biases they express during recognition tasks, and ideally reduce their impact in
applied settings.
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CONTEXT EFFECTS
Wolfe and colleagues (2007) have previously reported that visual search performance varies
with the frequency of a target’s occurrence and position. Specifically, initial training with high
(vs. low) Target Prevalence affected performance in a subsequent low prevalence phase. Our
final aim was to characterize this contextual carryover effect, in the context of face recognition
memory.
First, we found that Group 1 (TA/TP—TP) achieved higher Hit Rates than Group 3 (TA/TP—
TA/TP; original MMT) on average. Having been exposed to the identical procedure in the first
Target Recognition Phase (with low Target Prevalence), the only between-groups factor that
could explain this performance difference is Target Prevalence in the second Target Recognition
Phase, characterized by lower Target-to-Match Similarity. While Group 3 experienced low Target
Prevalence throughout both phases, Group 1 only experienced high Target Prevalence during
the second Target Recognition Phase. The effect of Target Prevalence between Groups 1 and 3
suggests that high Target Prevalence counteracts the increased difficulty due to lower Target-
to-Match Similarity across phases.
Second, to investigate a potential contextual effect, we compared the performance of Group
2 (TP—TA/TP) and Group 3 (TA/TP—TA/TP). Unlike the previous comparison, the favored model
accounted for only the Context, but not the interaction between Context and Target-to-Match
Similarity. This was explained by a main effect of Context, due to Group 2 exhibiting significantly
better performance than Group 3. If Group 2’s observers had highest Hit Rates for both phases
compared to those from Group 3, we cannot account for any specific contextual effects
regarding Phase 1 or Phase 2. This is because of the relative non-significance of the model 1b
including the interaction between Context and Target-to-Match Similarity (Figure 4b; model 1b).
Consequently, we can only talk about a general contextual effect between both groups on the
Hit Rate.
Here, in line with our expectation, and similar to the aforementioned findings (Wolfe et al., 2007),
Target Prevalence in the first Target Recognition Phase affected performance in the second
phase. We observed a behavioral advantage for initial high (TP) vs. low (TA/TP) prevalence, with
the prior leading to better performance at a second low prevalence (TA/TP) phase. Thus, when
varying Target Prevalence dichotomously, we observed a systematic response bias related to
the target’s occurrence.
LIMITATIONS
The present study was designed to explore factors potentially affecting face recognition
performance in the MMT (Bate et al., 2018), which was recently introduced as a more ecological
alternative to the well-established CFMT+ (Russell et al., 2009). While our observations support
the notion of important contextually determined biases, these findings arose in the context of
a restricted set of experimental conditions.
First, as mentioned previously, we did not implement all possible contexts, thereby lacking the
TP–TP condition. Second, following the original MMT design, across Target Recognition Phases,
Target-to-Match Similarity always decreased (high followed by low); the opposite direction
was never tested. A complete experimental design involving all possible combinations would
entail four different contexts (TP—TP, TP—TA/TP, TA/TP—TP and TA/TP—TA/TP) as well as
counterbalanced orders of the Target-to-Match Similarity and the Target Prevalence. This would
preclude the possibility of a cohort or confounding effect. Third and finally, we treated Target
Prevalence dichotomously, and further studies are needed that systematically vary the ratio
of TP:TA trails within TA/TP contexts. Target Prevalence effects in FIP and other visual search
tasks are relatively ubiquitous and domain-general. To determine why target recognition
performance is negatively affected by low Target Prevalence, Wolfe and colleagues’ (Wolfe &
Van Wert, 2010; Wolfe et al., 2007) analyzed signal sensitivity (d’), as well as response times for
visual search tasks. They concluded that prevalence influences decision criterion and, therefore,
the perceptual decisions about an item.
Our findings were obtained in the context of a recognition task, which is analogous to a 3
items visual search. As such, the presently observed effects would be likely to differ in tasks
where simultaneous matching is probed, i.e., those devoid of a memory component. Of note,
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a learning effect across the trials cannot be excluded. Indeed, observers assigned to Group 2
were exposed to 45 TP trials (high Target Prevalence) during Phase 1. As such they had roughly
twice the amount of exposure to target identities as compared to observers in Group 3 (exposed
to TA/TP trials in the low Target Prevalence Phase 1). To eliminate such a potential effect, a more
complex design would be required to ensure comparable TP exposure.
Finally, our analyses focused on the only metric comparable given the between-group variation
in Target Prevalence across Target Recognition Phases: hit rate. Future studies could include an
extended experimental design, via introduction of a fourth TP––TP context, in combination with
a within-observer approach, whereby participants complete different contexts (e.g. TP—TA/TP
and TA/TP—TP), and sensitivity analyses.
CONCLUSION
Inspired by visual search studies reporting variations in decisional biases and error rates related
to varied Target Prevalence, the present study sought to address generally acknowledged, but
empirically under-investigated factors assumed to influence face recognition performance:
Target Prevalence, Target-to-Match Similarity and contextual effects. We reasoned that all are
crucial across various applied visual tasks, including radiology, baggage screening, and suspect
identification in law enforcement. Our findings suggest that all three factors influence visual
recognition performance, but not necessarily interactively. This has general implications for test
development and training of professionals performing visual tasks in more realistic situations.
With the mentioned factors, we propose means in which this work could be extended to allow
a more fine-grained investigation of the reported effects, including an individual differences
approach.
ABBREVIATIONS
Face identity processing (FIP); Artificial intelligence (AI); Target present (TP); Target absent (TA).
DATA ACCESSIBILITY STATEMENT
All data and code can be found on the accompanying OSF project for this publication (https://
osf.io/3swhx/).
ETHICS AND CONSENT
All procedures were approved by the Ethics Committee of the University of Fribourg (Switzerland)
(approval number 473); participants were healthy volunteers who did not receive financial
compensation for participation. Experiments were completed online (administered on the AFC
Lab’s bespoke testing platform, or Testable). Each participant was provided a digital version of
the informed consent document and given unlimited time to read the document and provide
their consent prior to beginning each of the two sets of behavioral experiments.
ACKNOWLEDGEMENTS
We thank Caren (Sasha) Lasrado for her support in designing and running the experiments, all
volunteers for their participation, and Sarah Bate for providing the original MMT.
FUNDING INFORMATION
MR is supported by a Swiss National Science Foundation PRIMA (Promoting Women in Academia)
grant (PR00P1_179872).
COMPETING INTERESTS
The authors have no competing interests to declare.
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AUTHOR CONTRIBUTIONS
LB: Data curation, Formal analysis, Validation, Writing – original draft, Writing – review &
editing. JN: Formal analysis, Validation, Writing – review & editing. MR: Conceptualization,
Data curation, Funding acquisition, Methodology, Project administration, Resources, Software,
Supervision, Visualization, Writing – original draft, Writing – review & editing.
AUTHOR AFFILIATIONS
Lionel Boudry orcid.org/0000-0002-7061-3369
Applied Face Cognition Lab, University of Lausanne, Lausanne, Switzerland
Jeffrey D. Nador orcid.org/0000-0002-6502-7226
Applied Face Cognition Lab, University of Lausanne, Lausanne, Switzerland
Meike Ramon orcid.org/0000-0001-5753-5493
Applied Face Cognition Lab, University of Lausanne, Lausanne, Switzerland
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TO CITE THIS ARTICLE:
Boudry, L., Nador, J. D.,
& Ramon, M. (2024).
Determinants of Face
Recognition: The Role of Target
Prevalence and Similarity.
Journal of Cognition,
7(1):
27, pp. 1–15. DOI: https://doi.
org/10.5334/joc.339
Submitted: 28 June 2023
Accepted: 13 December 2023
Published: 21 February 2024
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