1. Introduction
Individuals differ greatly in their face memory/recognition abilities
(e.g., Wilhelm et al., 2010). For example, whereas super-recognizers
(Russell et al., 2009) remember a vast number of faces, individuals with
developmental or acquired prosopagnosia may have a hard time recognizing
even the faces of their family members (Behrmann & Avidan, 2005).
Although the general outlines of the functional neuroanatomy of face
processing are understood (Haxby et al., 2000), research on the neural
correlates of individual differences in face processing is still scarce.
In the present study, we explored whether the amplitude of the N250
component in the event-related potential (ERP) is related to individual
differences in face recognition and whether this relationship depends on
the distinctiveness of the faces to be memorized.
In their influential face processing model describing its functional
neuroanatomy, Haxby et al. (2000) distinguished between a core system
and an extended system of facial information processing. The core system
includes the fusiform face area (FFA), the occipital face area (OFA),
and the posterior superior temporal sulcus. This core system is held to
process facial features, face identity, and facial expressions,
respectively. The extended system includes brain regions that encode
biographical and semantic information gleaned from the face. In the ERP,
various components in response to faces have been described. Best known
is the N170, an occipito-temporal negativity peaking around 170 ms after
stimulus onset, which is larger to faces than to most common objects and
has therefore been interpreted as reflecting structural encoding of
faces. Following the N170 and peaking between 200 to 300 ms after
stimulus onset a repetition effect can be observed, consisting in a
temporo-occipital negativity to repeated relative to non-repeated famous
or personally familiar faces. This N250r has been related to a transient
activation of facial representations for recognition (Schweinberger &
Burton, 2003). Immediately thereafter, starting around 300 ms, a second
repetition effect for faces has been described (e.g., Schweinberger et
al., 1995). This has been related to the activation of multimodal
biographical and semantic person representation (for a review see
Schweinberger & Neumann, 2016).
Tanaka et al. (2006) investigated the N250 component in the context of
long-term repetition. A specific face was designated as a target and had
to be distinguished from several non-target faces. The target face was
repeatedly shown multiple times but widely spaced and interspersed
between other (non-target) faces. The N250 amplitude to the target face
increased from the first to the second half of the experimental session,
indicative of the build-up of face representations. The findings of
Tanaka et al. (2006) were replicated by Sommer et al. (2021). Gosling
and Eimer (2011) reported that the N250 can distinguish already familiar
faces from unfamiliar faces. Since the N250 increases after repeated
presentations of a face also when different images are used across
presentations (Andrews et al., 2017; Kaufmann et al., 2009), the
evidence suggests that the N250 reflects facial representations at a
certain level of abstraction (Wiese et al., 2021). The neural generators
of the N250 and N250r have been reported to be in or near the fusiform
gyrus (Kaufmann et al., 2009; Schweinberger et al., 2002; Schweinberger
et al., 2007).
For assessing the neural correlates of face processing-related
abilities, knowledge about the psychometric structure of these abilities
at the behavioral level becomes crucial. Fairly comprehensive
psychometric work based on multivariate test batteries applied to large
participant samples and analyzed by confirmatory factor analyses has
shown that face processing-related abilities are robustly separable from
general intelligence and are multi-faceted (Hildebrandt et al., 2010;
Wilhelm et al., 2010). In difficult (accuracy) tasks, face perception
and face memory abilities can be dissociated, but in easy (speed) tasks,
there is only a single ability of face recognition speed. The separation
of face perception and face memory has been also applied to the
single-task Cambridge face memory test (Duchaine & Nakayama, 2006)
versus the Cambridge face perception test (Duchaine et al., 2007).
Given the structure of face processing-related abilities derived from
psychometric work, research on the neural correlates of individual
differences in domain specific face processing abilities has recorded
ERPs in independent testing sessions for EEG recordings versus
psychometric measurements. Thus, in two independent large samples,
Herzmann et al. (2010) and Kaltwasser et al. (2014) showed that both the
accuracy of face perception and memory (in difficult tasks) and the
speed of face processing (in easy tasks) are negatively associated with
the latency of the N170 component and with the amplitude of the N250r.
In other words, the shorter the N170 latency of an individual and the
larger the priming effect in the N250r, the better are the mentioned
face processing abilities. The negative association between the N170
latency and the accuracy of face perception and memory was further
confirmed by Nowparast Rostami et al. (2017). These ERP studies were
concerned with explicit memory tasks where individuals attempted to
intentionally memorize and recognize faces during the EEG session as
well as in most of the tasks administered in the psychometric test
session.
Several studies with somewhat heterogeneous approaches and results were
designed to test associations between face processing performance and
fMRI data. Furl et al. (2011) compared a group of participants with
developmental prosopagnosia and normal controls (total n = 35).
Performance differences in a test battery of face identification were
mirrored in the face selectivity of the fusiform gyrus during an
incidental task; there was no relationship with repetition suppression
(often likened to priming effects) in fMRI. Huang et al. (2014) used a
localizer task in fMRI with passive viewing in two large samples. In
both samples, the face selectivity of fMRI activation in both FFA and
OFA were positively correlated with the accuracy of recognizing
previously learned faces (controlling for the recognition performance
for objects). Li et al. (2017) showed that face selectivity in the right
and left FFA correlated with two different aspects of holistic face
perception. Ramot et al. (2019) investigated resting state connectivity
and related it to performance in the Cambridge Face Memory Test. They
found no relationship for the connectivity with the face network but
with connectivity between the face network and hubs outside of this
network.
Altogether, ERP and fMRI studies show brain-behavior relationships. For
ERPs, the most consistent findings concern the N170 and N250r.
Considering that both the N170 and the N250r have been suggested to be
generated in the fusiform gyrus, these findings are largely in line with
the reports that fusiform face selectivity in fMRI is predictive of
individual differences in face memory/recognition.
An important variable in face processing tasks is the typicality or
distinctiveness of a face relative to the average of the faces
experienced by a person. This average face is viewed as a prototype in a
multidimensional face space in which any known or perceived individual
face can be located (e.g., Leopold et al., 2001; Valentine, 1991;
Valentine et al., 2016). If a face is located close to the prototype it
is “typical” and if it is far away from the prototype it is referred
to as an untypical or distinctive face. Distinctive faces are usually
easier to perceive and recognize than typical faces (e.g., Bartlett et
al., 1984; Sommer et al., 1995; Valentine & Endo, 1992).