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Reactions to children's faces: Males are more affected by resemblance than females are, and so are their brains

Steven M. Platekab, Danielle M. Rainesa, Gordon G. Gallup Jr.c, Feroze B. Mohamedd, Jaime W. Thomsona, Thomas E. Myersa, Ivan S. Panyavina, Sarah L. Levina, Jennifer A. Davisc, Ludivine C.M. Fonteyna, Danielle R. Arigoa

1. Introduction

2. Experiment 1: Replication of Platek et al. (2002) in color

2.1. Methods

2.1.1. Participants

2.1.2. Stimuli and procedure

2.2. Results

3. Experiment 2: Sex difference in neural substrates that process child facial resemblance

3.1. Methods

3.1.1. Participants

3.1.2. Pictures

3.1.3. fMRI imaging parameters

3.1.4. fMRI experimental design

3.1.5. Analysis of fMRI data

3.2. Results

4. Discussion

Acknowledgment

References

Copyright

1. Introduction

Kin discrimination mechanisms allow individuals to modify their behaviour with respect to genetic relatedness (Lehman & Perrin, 2002) and have been shown to affect social (DeBruine, 2002, DeBruine, 2004b, Hauber & Sherman, 2001), sexual (Lacy & Sherman, 1983, Neff & Sherman, 2002), and parental behavior (Daly & Wilson, 1982, Platek et al., 2002, Platek et al., 2003, Regalski & Gaulin, 1993, Volk & Quinsey, 2002). In humans, facial resemblance has been suggested as a kin identification mechanism (e.g., Daly & Wilson, 1998).

Because of concealed ovulation, internal fertilization, and female infidelity, parental certainty is asymmetrical: Only males are susceptible to error in identifying their young. Current estimates of extra-pair paternity in humans vary between 1% and 20%, with most around 10% (Baker & Bellis, 1995, Cerda-Flores et al., 1999, Neale et al., 2002, Sasse et al., 1994, Sykes & Irven, 2000), and the human male has apparently evolved various paternal assurance tactics to counter female infidelity (Gallup et al., 2003, Goetz et al., in press, Platek & Shackelford (in press), Shackelford, 2003, Shackelford et al., in press), thereby increasing the likelihood that paternal investment will be bestowed on one's own genetic offspring.

Men may use assessment of facial resemblance as a paternal investment strategy. Using a hypothetical investment paradigm and computerized facial morphing, Platek et al., 2002, Platek et al., 2003 found that males were more likely than females to utilize facial resemblance when asked to make hypothetical parental investments. In a recent attempt to replicate Platek et al., however, DeBruine (2004a) found that both sexes responded positively to self-resemblance in children's faces, with no apparent sex difference. The methods of DeBruine differed from the original studies of Platek et al. by using color photographs of faces, by using a different set of faces for each rating task, by using an algorithm that neotonized participants' faces (i.e., warped participants' faces to appear childlike prior to morphing) and by using images of infants aged 15–27 weeks, rather than 2 years. These modifications in methodology may account for DeBruine's failure to replicate Platek et al. and suggest that multiple factors, requiring further research, may influence the impact of facial resemblance on male parental investment decision making.

Here, we conducted two experiments to address the neurocognitive factors involved in reactions to children's faces. In Experiment 1, we replicated the procedures and slightly modified the parameters used in Platek et al. (2002). First, we used high-resolution color photographs of children and participants comparable with those in DeBruine (2004a). Second, we did not warp faces to a standardized face space, as was done previously (see Platek et al., 2002, Platek et al., 2003). In Experiment 2, we employed functional magnetic resonance imaging (fMRI) to investigate neural substrates activated by children's faces.

2. Experiment 1: Replication of Platek et al. (2002) in color

2.1. Methods

2.1.1. Participants

Forty-one right-handed college students (22 males, 19 females) volunteered for this study and received course credit for their participation. Participants were recruited to participate in a self-face recognition experiment and a childcare experiment. Males who were not clean-shaven were excluded from participation. Participants had their pictures taken and were instructed that the initial phase of their participation was a self-face recognition task. In brief, this task entailed responding to self-, famous, and several novel, unknown faces and lasted approximately 10–15 min. This procedure was used for two reasons: first, because of our ongoing interest in cognitive aspects of self-face recognition, and second, to make it less obvious to the participants that their pictures were being used in the “childcare” experiment. At the end of the study, all participants were debriefed: They were told that their pictures were used in both parts (self-face and childcare) of the study and were given the opportunity to ask any questions.

2.1.2. Stimuli and procedure

High-resolution pictures of the participants were taken using a Hewlett Packard (Model 315) 2.1-megapixel digital camera under uniform lighting conditions. The participants were asked not to smile or frown, and to try to maintain a neutral unexpressive face; if a participant blinked or made a facial expression (e.g., smiled), the photo was retaken. Images were processed using a 1.9-GHz laptop computer (Dell), Adobe Photoshop Elements (Version 2.0), and Ulead MorphEditor (Version 1.0) software and were presented in color and were matched for luminance. Images were cropped (using the magnetic lasso tool in Photoshop) just under the chin, from ear to ear, and just below the hairline so that only the face was cropped. Images were then feathered (10 pt) onto a black background and mounted on a canvas of consistent size; image aspect ratio was maintained to eliminate distortion via forced warping to a standardized “face space.”

Each participant's picture was morphed (Ulead Morph Editor, Version 1.0) with three children's faces (two males, one female; age range 1.5–2.25 years), so that each stimulus image combined 50% of the participant's face and 50% of the child's face (see Fig. 1). Images were presented using SuperLab (Cedrus, Version 2.0) in an experimental design following Platek et al. (2002): Participants were asked to select one child's face out of an array of five faces in response to 10 hypothetical investment questions (see Table 1) by depressing a key on a standard computer keyboard (all responses were forced-choice). Questions were presented in random order across participants, and face positions changed across questions and participants.


View full-size image.

Fig. 1. Example facial morphs. (Color version of figure located at http://psychology.drexel.edu/ECNL/Color_morphs.tif).


Table 1.

Ten hypothetical parental investment questions and the number of participants who selected 0, 1, 2, or 3 of the self-child morphs for each question, by sex

Questions Sex Number of participants who selected self-morphs (out of three)
Male=22 Female=19 0 1 2 3
1. Which one of these children would you adopt? Male 4 5 7 6
Female 11 6 1 1
2. Which one of these children would you spend the most time with? Male 5 7 6 4
Female 12 4 2 1
3. Which one of these children would spend the least time with? Male 17 4 1 0
Female 14 4 1 0
4. Which one of these children would spend US$50 on first? Male 6 10 4 2
Female 8 10 1 0
5. Which one of these children would spend US$50 on last? Male 13 8 1 0
Female 12 5 2 0
6. Which one of these children do you think is the cutest? Male 7 9 5 1
Female 13 4 1 1
7. Which one of these children would you resent least having to pay child support for? Male 2 9 7 4
Female 11 5 2 1
8. Which one of these children would you resent most having to pay child support for? Male 15 5 2 0
Female 14 5 0 0
9. If one of these children damaged something valuable of yours, which one would you punish most? Male 19 3 0 0
Female 14 4 1 0
10. If one of these children damaged something valuable of yours, which one would you punish least? Male 3 7 7 5
Female 15 2 2 0

2.2. Results

There was no effect of sex of the child. We created composite positive (Questions 1, 2, 4, 6, 7, and 10; see Table 1) and negative (Questions 3, 5, 8, and 9) investment scores that consisted of averaging responses (selected self-child morph=1; did not=0) across faces. A Mann–Whitney U test revealed that males (mean rank=22.70) were more likely than females (13.24) to select self-morphs in response to the positive questions (p<.001). There was no sex difference in response to negative questions (male mean rank=21.73, female=20.16; p=.6). The difference between the composite positive and the composite negative scores also differed between the sexes (male mean rank=26.20, female=14.97; p<.01).

Considering individual questions, males chose self-morphs significantly more often than females in response to “which one of these children would you adopt?” (χ2=11.27, p<.01), “which one of these children would you resent least paying child support for?” (χ2=11.80, p<.01), and “if one of these children damaged something valuable of yours, which one would you punish the least?” (χ2=18.34, p<.01), and the sex difference in response to “which one of these children would you spend the most time with?” (χ2=7.32, p=.06) and “which one of these children do you think is the cutest?” (χ2=6.20, p<.1) approached significance (see Table 1). These data on individual questions need to be interpreted with caution because no statistical correction for Type I error (e.g., Bonferroni) was applied.

3. Experiment 2: Sex difference in neural substrates that process child facial resemblance

3.1. Methods

3.1.1. Participants

Nine right-handed students (four males, five females), none of whom had participated in Experiment 1, volunteered for participation. Handedness was assessed using a modified version of the Edinburgh Handedness Inventory (Oldfield, 1971).

3.1.2. Pictures

Each participant's picture was taken and morphed with a child's face (50%, Ulead Morph Editor version 1.0), as in Experiment 1. Additionally, one novel and one famous face were also morphed with the same child's face and used as control stimuli. At the completion of the study, participants were asked whether they recognized any of the faces, and none did. Each of the three stimulus classes was created independently for each participant. The stimuli were of the same visual quality as in Experiment 1, that is, presented in color and at the same size and resolution.

3.1.3. fMRI imaging parameters

Images were collected by a Siemens Magnetom Vision 1.5 Tesla scanner with echoplanar capability (25 mT/m, rapid switching gradients). The participants were instructed to lie still throughout the scanning procedure and focus on the center of the field of view while looking into the goggles (see below). Additionally, foam pads within the head coil helped secure head fixation and prevent motion during scanning.

Scanning began with the collection of high-resolution T1-weighted imaging sequence acquired in the axial plane to locate the positions for in-plane structural images. Twenty-six (whole brain) contiguous (no gap) 5-mm axial high-resolution T1-weighted structural images (matrix size=256×256; TR=600; TE=15 ms; FOV=21 cm; NEX=1; and slice thickness=5 mm) were collected for spatial normalization procedures and overlay of functional data. Precise localization-based standard anatomic markers (AC-PC line) were used for all participants (Talairach and Tournoux, 1988). Next, functional images were acquired with a gradient-echo echo planar free induction decay (EPI-FID) sequence (T2* weighted: 128×128 matrix; FOV=21 cm; slice thickness=5 mm; TR=4 s; and TE=54 ms) in the same plane as the structural images. The size of the imaging voxel was 1.72×1.72×5 mm.

3.1.4. fMRI experimental design

The study was designed to measure blood oxygenation level dependent (BOLD) responses to self- versus nonself morphs. A boxcar design was used, in which participants were shown a stimulus for 1500 ms every 2 s during the activation period. During the rest period, a radial checkerboard pattern was presented in the same fashion to collect the baseline MRI signal. The checkerboard has been used as a rest stimulus in other face-processing studies and serves to control for visual cortex activation (Henson, Shallice, Gorno-Tempini, & Dolan, 2002). Participants were asked to look at the images and think about whether the image made them feel positive, neutral, or negative. Each of the three stimuli (self-child, stranger-child, and famous-child morphs) was presented in blocks of six activation and six rest epochs (20 s) to maximize BOLD signal response and minimize habituation. The stimuli were delivered through stereoscopic goggles designed for use within the fMRI environment (Resonance Technology, Inc) and Neurobehavioral Systems Presentation software (www.neurobs.com).

3.1.5. Analysis of fMRI data

The postacquisition image preprocessing and statistical analysis were performed using SPM'2 (Statistical Parametric Mapping, Wellcome Department of Cognitive Neurology, University College of London, UK), run under the Matlab® (The Mathworks, Natick, MA) environment. Images were converted from the Siemens format into the ANALYZE (AnalyzeDirect, Lenexa, KY) format adopted in the SPM package. Slice timing correction was performed to compensate for delays associated with acquisition time differences among slices during the sequential imaging. A 3D automated image registration routine (six-parameter rigid body, sinc interpolation; second-order adjustment for movement) was applied to the volumes to realign them with the first volume of the first series used as a spatial reference. All functional and anatomical volumes were then transformed into the standard anatomical space using the T2 EPI template and the SPM normalization procedure (Ashburber & Friston, 1999). This procedure uses a sinc interpolation algorithm to account for brain size and position with a 12-parameter affine transformation, followed by a series of nonlinear basic function transformations seven, eight, and seven nonlinear basis functions for the x, y, and z directions, respectively, with 12 nonlinear iterations to correct for morphological differences between the template and given brain volume. Next, all volumes underwent spatial smoothing by convolution with a Gaussian kernel of 3.44×3.44×10 mm3 (two times the voxel size) full width at half maximum (FWHM), to increase the signal-to-noise ratio (SNR) and account for residual intersession differences.

SPM'2 General Linear Model (GLM) and random effects analysis (RFX) procedures were used to identify voxels associated with significant activation. To test for sex differences in activation to self-morphs, we performed two primary contrasts: overall child face versus null (nonface stimulus) and self-child morph versus non-self-child morph (by sex: independent samples t test in SPM'2). Statistical parametric maps (SPM{t}) were obtained reflecting significantly activated voxels for each contrast and the model used (p<.005; cluster=four voxels).

3.2. Results

When responding to children's faces, independent of whether the child face was a self- or non-self-morph, we observed significant (p<.005, four-voxel cluster detection) activation in right middle (BA 11) and left inferior frontal (BA 45) gyri (see Fig. 2a and Table 2) for both sexes and no significant sex difference.


View full-size image.

Fig. 2. (a) Significant activation for overall effect of looking at children's faces. (b) Significant activation in males when contrasting self-child morphs with non-self-child morphs. (c) Significant activation in females when contrasting self-child morphs with non-self-child morphs. All contrasts are set to a threshold of p<.01 with a spatial detection cluster threshold set at four voxels. (Color version of figure located at http://psychology.drexel.edu/ECNL/Color_Figs1–3.tif).


Table 2.

Local maxima of cerebral blood flow (CBF) change for each experimental contrast (height threshold p<.01, minimum spatial extent=4 voxels)

Region BA HEM x y z Z score p (nmax>k)
All child faces
Middle frontal gyrus 11 R 38 48 −7 2.68 <.01
Inferior frontal gyrus 45 L −20 21 −3 2.65 <.01
Males (self-child morph–non-self-child morph)
Middle frontal gyrus 10 L −28 44 18 2.93 <.01
Superior frontal gyrus 10 L −30 54 14 2.39 <.01
Medial frontal gyrus 9 L −2 40 26 3.09 <.01
Females (self-child morph–non-self-child morph)
Superior frontal gyrus 10 R 40 4 16 4.64 <.01
Insula R 36 36 31 3.61 <.01
Medial frontal gyrus 10 R 12 50 10 3.53 <.01
Medial superior frontal gyrus 10 L –18 60 8 2.47 <.01

BA=Brodmann's area; HEM=cerebral hemisphere; coordinates in Talairach space (Talairach & Tournoux, 1988).

There were no significant patterns of activation when comparing morphs made with famous faces and those made with stranger faces, hence, these conditions were collapsed for comparison to the self-morphs. When comparing activation associated with the self-morphs minus the non-self-morphs, males showed different activation when viewing self-child morphs than did females. In males, cluster analysis for self-morphs minus non-self-morphs pointed to significant activation (p<.005, cluster=4) in the left superior, middle, and medial frontal gyri (BA 10,9; see Fig. 2b and Table 2), whereas in females, cluster analysis for self-child morph minus non-self-child morph revealed significant activation in the right superior and medial frontal gyri (BA 10) and insula, as well as the left medial superior frontal gyrus (see Fig. 2c and Table 2). Males and females did not differ in patterns of activation associated with viewing non-self morphs. However, the right hemisphere frontal lobe activation observed in females to self-morphs minus non-self-morphs was in the same region as that observed in the overall child face condition, which was not true for males.

4. Discussion

These data show that the sex difference in reactions to facial resemblance of children (Burch & Gallup, 2000, Platek, 2002, Platek et al., 2002, Platek et al., 2003, Volk & Quinsey, 2002) is replicable using high-resolution color images and may be associated with a sex difference in neural processing.

In Experiment 1, using software comparable to DeBruine (2004a), we replicated and improved upon Platek et al. (2002) by demonstrating that males respond favorably to children as a function of facial resemblance when making hypothetical investment decisions. In Experiment 2, we extended these results and found a sex difference in neural substrates activated by seeing children's faces that resembled the viewer. Unlike females, males showed significant neural activation in the left frontal cortex, which has been hypothesized to be involved in the inhibition of negative responses (Collette et al., 2001, Davidson, 1997, Harmon-Jones & Sigelman, 2001). In another recent investigation of the neural correlates of facial resemblance and face recognition in child and adult faces, using fast event-related fMRI, Platek, Keenan, & Mohamed (in preparation) also found a sex difference in activation associated with facial resemblance—specifically when responding to child faces that were morphed to resemble the participant. It would appear, therefore, that facial resemblance in children may activate neural substrates associated with the inhibition of negative responses in males. Thus, sex differences in reactions to facial resemblance may be driven by neurocognitive processes that are recruited when facing specific adaptive problems, such as making determinations about paternity, which supports the hypothesis put forth by Daly and Wilson (1998) that males may use self-resemblance in allocating paternal investment.

Although not directly tested in this study, the right lateralized frontal and medial frontal activation observed in females may be part of a mentalizing (e.g., theory of mind) module. In our behavioral studies (e.g., Platek et al., 2002), participants were asked how they made their choices: Most males denied adopting any particular strategy, but females often reported attributing personality characteristics when responding to hypothetical investment questions (e.g., trying to find the ‘nicest’ child to give money to, spend time with, or adopt). In other words, females may have consulted psychological mechanisms that infer personality characteristics to make their hypothetical investment decisions. This hypothesis is supported by recent neuropsychological and neuroimaging data on mentalizing (see Castelli et al., 2000, Fletcher et al., 1995, Gallagher et al., 2000, Ishii et al., 2002, Klin et al., 2003, Platek et al., 2004, Stuss et al., 2001, Vogeley et al., 2001).

There are obvious limitations to these studies. Experiment 1 relies on responses to hypothetical questions about morphed child faces, which may not adequately model real-world scenarios. Additionally, participating in a self-face recognition task prior to making responses about self-child morphs may have introduced an unconscious self-face priming bias. However, there is no obvious reason why males should be more affected by self-face recognition priming than females are; indeed, one might expect that females would be more affected because they perform better on unconscious self-recognition tasks (Keenan et al., personal communication; Platek, Burch, & Gallup, 2001). Although we used a small sample in Experiment 2, large sample sizes are not typically needed to achieve adequate statistical power in functional imaging studies. The sample size was adequate to demonstrate group effects but insufficient to examine correlates of individual differences in the BOLD response to self-morph presentations. In addition, the boxcar experimental design may not be as sensitive to subtle changes in BOLD response as event-related fMRI is (D'Esposito, Zarahn, & Aguirre, 1999). However, to account for the possibility of low activation levels and habituation, we used six blocks that have been shown in the Drexel Functional Neuroimaging Laboratory to be a reliable number of epochs to produce maximum activation associated with stimulus exposure, while limiting habituation effects.

Despite these limitations, this study demonstrates a sex difference in hypothetical investment decision making to high-resolution color child morphs, as well as in the underlying neural activation associated with viewing self-resembling child morphs. These findings suggest that selection produced a neurocognitive response module in males that is specific to detecting, responding, and favoring facial resemblance in children.

Acknowledgments

The authors thank Todd Shackelford, Lisa DeBruine, Julian Paul Keenan, Samuel Critton, Gordon Bear, David Smith, Robert Haskell, and Farzin Irani for helpful discussion concerning this line of investigation.

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a Department of Psychology, Drexel University, 3141 Chestnut Street, Philadelphia, PA 19104, United States

b School of Biomedical Engineering, Science, and Health Systems, Drexel University, Philadelphia, PA 19104, United States

c Department of Psychology, University at Albany, The State University of New York, Albany, NY 12222, United States

d Department of Radiology, Temple University, Hospital, 3401 Broad St, Philadelphia, PA 19140, United States

Corresponding author. Department of Psychology, Drexel University, 3141 Chestnut Street, Philadelphia, PA 19104, United States. Tel.: +1 215 895 6105; fax: +1 215 895 4930

 This paper is based, in part, upon the Best Postdoctoral Paper award-winning presentation by S.M. Platek at HBES2003.

PII: S1090-5138(04)00061-3

doi:10.1016/j.evolhumbehav.2004.08.007



2007:11:13