General Commentary

published: 01 December 2011
doi: 10.3389/fnhum.2011.00156

HUMAN NEUROSCIENCE

Visual object categorization in the brain: what can we reallylearn from ERP peaks?
Guillaume A. Rousselet1*, Cyril R. Pernet2, Roberto Caldara3 and Philippe G. Schyns1
Centre for Cognitive Neuroimaging, Institute of Neuroscience and Psychology, University of Glasgow, Glasgow, UK
Brain Research Imaging Centre, SINAPSE Collaboration, University of Edinburgh, Edinburgh, UK3
Department of Psychology, University of Fribourg, Fribourg, Switzerland*Correspondence: guillaume.rousselet@glasgow.ac.uk
1
2A commentary on
Face-sensitive processes one hundred milliseconds after picture onset
by Dering, B., Martin, C. D., Moro, S.,
Pegna, A. J., and Thierry, G. (2011).
Front. Hum. Neurosci. 5:93. doi: 10.3389/
fnhum.2011.00093
In three experiments, Dering et al. (2011)
measured the amplitude and latency of themean P1 and N170 in response to faces, cars,and butterflies, cropped or morphed. The
N170 was sensitive to cropping but did not
differentiate frontal views of faces and cars.
Throughout, P1 amplitude was larger forfaces than objects. The authors concluded
that P1, not N170, is a reliable face-sensitive
event.
Although Dering et al. (2011) andThierry et al. (2007) correctly identified
problems with P1 and N170 ERP interpretations, these same problems are evident intheir own research, and, consequently, theirresults are equally inconclusive. We identify
four shortcomings in Dering et al.’s approachthat also apply to the literature they contest(e.g., Eimer, 1998; Allison et al., 1999; Carmeland Bentin, 2002; Itier and Taylor, 2004;
Rousselet et al., 2004; Rossion and Jacques,2008): categorical design, uncontrolled task
demands, group statistics, and peak measurements. ERPs are worthwhile measurements of visual processing, but only when theshortcomings reviewed below are addressed.

Beyond categorical designs
Categorical designs compare few categories
of input stimuli against brain measurements. When dealing with complex visualstimuli, categorical designs make it difficult
to attribute a brain response to specificobject categories (e.g., a face), features (e.g.,the eye) or functions, because the specificityof the response (e.g., to the face) is deter-Frontiers in Human Neurosciencemined by contrast with responses fromother categories (e.g., cars) and informal
hypotheses tested. Unfortunately, category
members share a dense correlative structureof low-level visual properties (e.g., luminance energy, main directions of orientation, spatial frequency composition), whichcannot all be controlled with a finite number of contrast categories. Consequently, thebrain response’s specificity might be due todifferences in input statistics, not to the category itself (Schyns et al., 2003).
Whereas Dering et al. (2011) controlled
total luminance, contrast, and size, otherphysical dimensions affecting P1 amplitudes and categorical judgments were notcontrolled (e.g., spectral profiles of contrasted categories: VanRullen and Thorpe,
2001; Sowden and Schyns, 2006 ; VanRullen,
2006; Honey et al., 2008; Rousselet et al.,2008a). Consequently, a significant ERP
peak difference between two input categories does not ensure that the difference
relates to the category per se, as opposed tothe uncontrolled statistics of low-level image
properties (Schyns et al., 2003; Pernet et al.,2007). Categorical designs typically use few
categories, but must still control for a muchlarger set of low-level image dimensions. By
neglecting these experimental controls, it
is almost inevitable that such designs will
deliver the type of inconclusive results thatanimate unnecessary debates in the P1 andN170 face literature. Thierry et al.’s (2007)
own results elegantly demonstrate thispoint. In their Experiment 3, they overlaid
faces with cars and instructed observers toattend to either category. Here, when the twocategories were better controlled for theirlow-level properties (though still not perfectly), the categorical modulations on theP1 component were abolished. Thus, varying levels of quality of stimulus control inThierry et al.’s work fuels their own debate:
if Dering et al. had correctly identified thewww.frontiersin.org

P1 face-sensitiveness, Thierry et al.’s (2007)
third experiment would demonstrate anincreased P1 amplitude for faces overlaid
with cars. It did not. This contradiction illustrates the pitfalls of poor stimulus control.

Control of task demands
Dering et al.’s (2011) participants discriminated between faces and cars or butterflies.
Assuming that the stimuli were properly
controlled, observers in Dering et al.’s task
could still discriminate a face from any
car or butterfly using only the presenceor absence of one of many face features(global appearance, texture, left or righteye, eyelashes, nose) or their combinations.
This weakness highlights the broader (andoften neglected) role of task demands in
cognitive neuroimaging studies of recognition. A face can be categorized as “John,” aWestern Caucasian, who is 40, is handsome,in good health and currently has a happy
face. Suggestion that “the P1 (or the N170)
is categorically sensitive to faces as opposed
to cars or butterflies” leaves unresolved theissue of whether the N170 is sensitive to theface, as an object category, or to the categorization task that is used to test the face with.
If the P1 effects of Dering et al. arose fromprocessing categorization-specific facial
information, changing task demands (bychanging the categorization performed on
the same face) should differentially influence the brain response (Schyns, 1998;Pernet et al., 2007). The task demands ofDering et al. are not sufficiently controlled
to inform the underlying information processing function of the P1 or N170.

Problems with group statistics
One of the main results of Dering et al.
(2011) is that the N170 peak amplitudes do
not differ between different stimulus categories. This result is only valid if the data
conform to the assumptions of ­normality

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Rousselet et al.

and heteroscedasticity that underlie theanalyses. Standard linear statistics areextremely sensitive to departure fromthese assumptions leading to low statisticalpower (Wilcox, 2005). A convincing demonstration that the N170 is not a marker
of face processing would require detailed
investigation of the data distribution using
robust statistics and actually showing thedata. Moreover, effects present in individual
observers might be washed out by grandaverage group statistics (Rousselet andPernet, 2011; Rousselet et al., 2011). Effects
might also be distributed across electrodes,
as multivariate multi-electrode analyses
can reveal (Philiastides and Sajda, 2006;
Philiastides et al., 2006; Ratcliff et al., 2009).
Without systematic univariate or multivariate analyses in single-subjects, null effects asreported for the N170 by Dering et al. areinconclusive.
Compared to group analyses, singletrial parametric analyses also provide
more powerful tools to infer function
because they exploit the richness of singletrial data to examine how parametricallymanipulated input information co-varies
with brain response variability (Hubeland Wiesel, 1979; Freedman et al., 2002,2003; Ratcliff et al., 2009; Schyns et al.,2009a; Schyns, 2010; Pernet et al., 2011b;
Rousselet and Pernet, 2011). In contradiction to Dering et al. (2011), suchanalyses have revealed sensitivity to categorical differences from about 100 ms
to about 300 ms, encompassing the N170
(Philiastides and Sajda, 2006; Philiastides
et al., 2006; Rousselet et al., 2008b, 2011;
Schyns et al., 2009b; van Rijsbergen andSchyns, 2009). Critically, parametric studies demonstrate that mean ERPs cannot
elucidate the information content of single-trial fluctuations around the central
tendency of the distribution (see Smithet al., 2004, Figure 1, for a graphicaldemonstration).

Extending from peakmeasurements
Finally, by restricting analyses to peak ERP
differences between groups, Dering et al.’s(2011) results fall short of providing a complete picture of the relevant brain responses.
Important effects occur between peaks,
which are quantifiable by applying analyses
at all time points and electrodes (Delormeet al., 2007; Schyns et al., 2007, 2009b, 2011;

Frontiers in Human Neuroscience

ERPs and visual categorization

Rousselet et al., 2008b; Vizioli et al., 2010a,b;Pernet et al., 2011a). Peaks can index the endof a process rather than the specific information processing mechanism leading tothis endpoint (Schyns et al., 2007).
In sum, Dering et al. (2011) applied
a pervasive but now arguably out-dated
approach to an otherwise useful brain
measurement. The limitations of theirapproach apply as equally to their own
results as to those they criticize. The issue
of “face specificity” of the N170 should be
addressed with more powerful methods
such as using parametric manipulationsof well-controlled stimuli, addressing task
demands, and systematic single-subject statistics. Otherwise, numerous inconclusive
data will continue to fuel unnecessary andopinionated controversies.

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Received: 17 October 2011; accepted: 14 November 2011;
published online: 01 December 2011.
Citation: Rousselet GA, Pernet CR, Caldara R and Schyns
PG (2011) Visual object categorization in the brain: what
can we really learn from ERP peaks? Front. Hum. Neurosci.
5:156. doi: 10.3389/fnhum.2011.00156
Copyright © 2011 Rousselet, Pernet, Caldara and Schyns.
This is an open-access article distributed under the termsof the Creative Commons Attribution Non Commercial
License, which permits non-commercial use, distribution,and reproduction in other forums, provided the original
authors and source are credited.

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