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. Author manuscript; available in PMC: 2018 Aug 21.
Published in final edited form as: Curr Biol. 2017 Aug 10;27(16):2505–2509.e2. doi: 10.1016/j.cub.2017.06.075

Face pareidolia in the rhesus monkey

Jessica Taubert 1,*, Susan G Wardle 2, Molly Flessert 1, David A Leopold 3, Leslie G Ungerleider 1
PMCID: PMC5584612  NIHMSID: NIHMS898540  PMID: 28803877

SUMMARY

Face perception in humans and non-human primates is rapid and accurate[1–4]. In the human brain, a network of visual processing regions is specialized for faces[5–7]. Although face processing is a priority of the primate visual system, face detection is not infallible. Face pareidolia is the compelling illusion of perceiving facial features on inanimate objects, such as the illusory face on the surface of the moon. Although face pareidolia is commonly experienced by humans, its presence in other species is unknown. Here we provide evidence for face pareidolia in a species known to possess a complex face processing system[8–10]: the rhesus monkey (Macaca mulatta). In a visual preference task[11, 12], monkeys looked longer at photographs of objects that elicited face pareidolia in human observers than at photographs of similar objects that did not elicit illusory faces. Examination of eye movements revealed that monkeys fixated the illusory internal facial features in a pattern consistent with how they view photographs of faces[13]. Although the specialized response to faces observed in humans[1, 3, 5–7, 14] is often argued to be continuous across primates[4, 15], it was previously unclear whether face pareidolia arose from a uniquely human capacity. For example, pareidolia could be a product of the human aptitude for perceptual abstraction, or result from frequent exposure to cartoons and illustrations that anthropomorphize inanimate objects. Instead, our results indicate that the perception of illusory facial features on inanimate objects is driven by a broadly-tuned face detection mechanism that we share with other species.

RESULTS AND DISCUSSION

To evaluate whether rhesus monkeys perceive face pareidolia, we focused on naturally occurring examples of illusory faces, as judged by human observers. We collected 15 photographs from the public domain of inanimate objects (e.g. food, appliances, tools) shown to promote the perception of illusory faces, together with 15 photographs of equivalent objects that were not perceived to contain an illusory face (e.g. Figure. 1A). We confirmed that independent human observers either perceived an illusory face or not in each stimulus by collecting behavioural ratings from ten naive observers of how ‘face-like’ each image was (Figure. 1B).

Figure 1. Experimental methods.

Figure 1

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(A). Examples of the three stimulus types used (from left to right: unfamiliar female monkeys, illusory faces, non-face objects). The non-face objects were selected from the public domain on the basis that they matched the examples of illusory faces for object content. (B). The results of the human experiment. Here, the rows represent individual subject data (N = 10) and columns represent the 45 images comprising the stimulus set. Importantly, none of the non-face objects was rated as being “face-like” (>100) on a 200-point scale (Mnon-face objects =5.24; SEM =.45). Two pairwise contrasts confirmed that the non-face objects had a significantly smaller average score than either the monkey faces (P <.01, η2 =.99) or illusory faces (P <.01, η2 =.99). (C). The trial procedure for the three conditions of interest in the monkey experiment. Each trial consisted of three time periods: fixation, free viewing, and reward after successful trial completion or time out after trial aborts.

We tested whether monkeys perceive illusory face structure in stimuli that elicited face pareidolia in humans using an established paradigm that has been successfully used to measure face detection in non-verbal subjects, including human infants[16, 17] and rhesus monkeys[11, 12]. In a free-viewing visual preference task, we presented monkeys with pairs of stimuli and measured the time they spent looking at each stimulus. It is well-established that humans and monkeys look longer at faces than at other stimuli[11, 12, 17]. Thus, if monkeys perceive illusory face structure in the same objects as humans, then they should spend longer looking at objects containing illusory faces than at similar objects that do not. A second dependent measure was the location of fixations on the experimental stimuli because both monkeys and humans[13, 18, 19] make a disproportionate number of fixations on the internal features of real faces under free-viewing conditions. Thus, if monkeys perceive the illusory faces, their pattern of fixations should focus on the illusory “eye” and “mouth” regions of these images.

We recorded the eye movements of five rhesus monkeys while they were presented with pairs of stimuli on a computer screen (for details see Methods). The monkeys received a juice reward for maintaining fixation within the screen region containing the stimuli during each trial. Stimuli consisted of illusory faces, matched control objects, and monkey faces (Figure. 1C). We presented each of the 45 stimuli an equal number of times in every possible pairing (the total number of trials was 1,980 per monkey). The behavioural measure looking time (LT) was expressed as the proportion of time that the animals spent exploring each visual stimulus compared with total presentation time (5 s). Consistent with previous work, subjects spent more time looking at monkey faces than at objects (Mean difference =.31; t4 =8.08, P <.01, η2 =.94)[11, 12]. Importantly, the subjects also showed a looking preference for objects with illusory faces compared to matched objects without illusory faces (Mean difference =.33; t4 =10.23, P <.01, η2 =.96; see Figure. 2A). Interestingly, monkeys spent an even longer time looking at objects with illusory faces than at monkey faces (Mean difference =.16; t4 =5.52, P =.005, η2 =.88; see Figure. 2A), which may reflect either a response to the unusual and unexpected nature of the illusory faces[20], or an aversion to maintaining prolonged fixation on the faces of conspecifics[21], an effect frequently observed in rhesus monkeys but not well understood[22]. An analysis of the first fixation data yielded the same pattern of results; the monkeys reliably directed their initial gaze towards objects containing an illusory face, compared to either matched objects or monkey faces (see Figure. 2B). Together, the results reveal a clear viewing preference for stimuli that elicit the perception of illusory face structure in humans.

Figure 2. Experimental results.

Figure 2

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see also Figure. S1. (A). Bar graph indicates the average proportion of time spent looking at stimuli as a function of condition (error bars = +/− SEM). We found the expected advantage for monkey faces over objects, together with the hypothesized advantage for illusory faces over objects. We computed the average mean difference in each condition (monkey faces LT subtracted from illusory faces LT [I-M]; non-face objects LT subtracted from illusory faces LT [I-O]; non-face objects LT subtracted from monkey faces LT [M-O]) and performed a one-way repeated measures ANOVA (P <.01, ηp2 =.86) to confirm that illusory face paired with monkey face trials elicited the smallest stimulus preference (paired t-tests, 2-tailed; [I-M] v [I-O], P <.01, η2 =.97; [I-M] v [M-O], P =.016, η2 =.80; [I-O] v [M-O], P =.300 η2 =.24). (B). Bar graph demonstrating the distribution of first fixations in the three conditions of interest (the number of first fixations is expressed as a proportion of the total number of trials in each condition; error bars = +/− SEM). An analysis of the first fixation data indicated that in trials where monkey faces were presented with non-face objects, subjects fixated the monkey faces first, and more often (P =.01, η2 =.92). There was a similar advantage for illusory faces over non-face objects (P =.01, η2 =.96). As with the LT data, this analysis also revealed a significant preference for illusory faces over monkey faces (P =.01, η2 =.92).

To further evaluate whether the monkeys’ looking preference for objects with illusory faces reflects an experience of pareidolia similar to our own, we examined their eye gaze patterns. We divided each stimulus into 121 equally sized, square bins (1° in height and width) and tallied the distribution of fixations directed to each of the 45 stimuli across all trials. For each subject, we created a 2-dimensional density plot, normalized to the maximum number of fixations, and averaged these across all five subjects (Figure. 3A). The density plots highlight that monkeys frequently fixated the “eye” and “mouth” regions of both the monkey faces and the illusory faces, a finding consistent with human gaze behaviour when viewing real faces[23, 24]. The high density of fixations on these areas unambiguously distinguished the illusory faces from the matched object stimuli, which elicited more variable patterns of fixation (see Figure. S2 for all stimulus maps). Further, the spatial distribution of fixations showed a higher stimulus-specific correlation across subjects for the monkey face and illusory face stimuli (t14 =1.49, P =.16, η2 =.36; Figure. 3B) than for the matched objects (monkey faces v objects, t14 =8.74, P <.01, η2 =.95; illusory faces v objects, t14 =7.24, P <.01, η2 =.93; Figure. 3B).

Figure 3. Fixations calibrated in degrees of visual angle and superimposed on stimuli.

Figure 3

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see also Figure. S2–S3. (A). Average number of fixations (≥150 ms) in 2-dimensional density plots (3 examples from each stimulus type; top row, monkey faces; middle row, illusory faces; bottom row, non-face objects). Data were normalized to each subject’s maximum fixation count, then averaged across subjects before being smoothed and superimposed on the corresponding stimulus for illustration using MATLAB’s surf function with interpolated shading. Unsmoothed data for every stimulus, together with individual subject maps (before averaging) are available in the supplementary material (Figure. S3). (B). The range of grand r-values as a function of stimulus type. After vectorizing the normalized fixation count data for each subject, we cross-correlated across subjects. This process yielded 10 r-values that were then averaged together to yield a single “grand r-value” per stimulus. The lower r values evident for the non-face objects reflect the greater variance among individual subjects. (C). Classifier performance as a function of subject; the classifier was trained with 93.33% of the data (i.e. 14 out of 15 illusory face/non-face pairs) and tested with the remaining content-matched pair. Chance performance is 50%.

Given the visible differences between the fixation patterns for illusory faces compared to matched objects (see Figure. S3 for individual subject plots), we used machine learning classification to test whether we could systematically predict the presence of an illusory face in an object from the fixation maps. A linear support vector machine was trained using yoked leave-one-exemplar-out classification on the fixation maps for viewing objects with illusory faces versus matched objects. For every subject, the classifier was able to predict whether the subject was looking at an example of pareidolia or a content-matched object from the raw (un-normalised) fixation density maps (Figure. 3C; Mean accuracy =86%, sd =1.92%; all P values <.01). As the classifier was required to generalise to new exemplars not used in the training set, the results indicate that there was a consistent difference in the pattern of eye movements monkeys made towards illusory faces versus matched non-face objects.

Collectively our results provide strong evidence that rhesus monkeys spontaneously perceive illusory faces on inanimate objects. This observation raises the fundamental question of abstraction: what prompts the primate visual system to detect any particular object or pattern as a “face”? Face detection is arguably the most fundamental process of face perception, since the system needs to know a face is present before making subsequent social judgements [3, 14, 25]. Nonetheless, nearly all studies of face processing in nonhuman primates have focused on mechanisms of individual recognition, for example providing demonstrations of the inversion effect[26–29], normative coding[30, 31], holistic processing[32–34], and perceptual narrowing[11, 35]. The few studies that have explicitly examined face detection have primarily used photographs of “real” faces drawn from a set of tightly controlled human face stimuli [11, 36–39], thus minimizing the contribution of low-level visual features.

The novel approach in the present study is to specifically exploit the false positives that arise when low-level visual features are spuriously arranged in a face-like configuration. A key element of face pareidolia, in human and nonhuman primates, is sufficient tolerance to detect faces among stimuli whose other features are clearly incongruent with a real face (e.g. on the green surface of a vegetable). Unlike faces themselves, images that elicit pareidolia are notably variable in their visual features. Similar to humans, rhesus monkeys naturally detected faces among this varied set of images, with no experimental assumptions about the optimal face category (i.e. conspecifics or human faces) nor the ideal non-face category for comparison. Notably, face detection algorithms in artificial visual systems trained on human and cartoon faces can similarly detect examples of face pareidolia as judged by human observers[40]. This is consistent with the idea that detection of illusory face structure in non-face objects, whether in a brain-based or computer-based system, requires a basic representation (or face template) that is broadly-tuned to facial features with a high degree of tolerance for local visual properties.

Our results demonstrate that the experience of face pareidolia is not restricted to humans. Instead, the underlying cause of this illusion is likely common to both humans and rhesus monkeys. This is consistent with the considerable homology between the face processing systems in the two species [10, 15]. Both humans and rhesus monkeys are known to have a complex network of visual brain areas in the inferior temporal cortex that responds preferentially to faces[5–9, 15, 41]. Perturbation of brain activity in these areas has been shown to influence behavior towards face stimuli in both species[36, 42, 43], including face detection[36, 38], which is thought to be achieved by template matching [25]. Although the precise nature of the face template is yet to be discovered, there is evidence that calibration may continue after birth, narrowing towards familiar (i.e. same species) faces with experience[35, 44]. From this perspective, one might expect large differences across species in face detection. However, the spontaneous and persistent perception of illusory facial structure in inanimate objects indicates that the face-detection system in both humans and monkeys is broadly tuned to detect facial features with a high degree of tolerance to variance in visual properties. An advantageous consequence of a broadly-tuned template is that it promotes a highly sensitive face detection system, offset with the relatively small cost of more frequent false positives. The ease with which both species perceive erroneous face structure in inanimate objects underscores the biological advantage for social animals to preferentially detect faces in their environment.

STAR Methods

CONTACT FOR REAGENT AND RESOURCE SHARING

Further information and requests for resources should be directed to and will be fulfilled by the Lead Contact Jessica Taubert (jessica.taubert@nih.gov).

EXPERIMENTAL MODELS AND SUBJECT DETAILS

Human data

We presented 10 adult subjects (randomly selected undergraduate psychology students at the University of Sydney, 7 female, average age =22.5 years, sd =2.2; reported normal or corrected-to-normal vision) with every stimulus, 10 times, in a pseudorandomised order (450 trials in total). Sample size was determined prior to data collection and was based on previous pilot study with a much larger stimulus set. The research protocol was approved by the Human Research Ethics Committee of the University of Sydney (Project No. 2015/336).

On each trial, the subject was asked to indicate how “face-like” each stimulus appeared on a sliding scale (200 pixels in length; Figure. 1b) using a cursor. The initial cursor position was determined at random (i.e. at any location along the scale) and the subject controlled the final position using the arrow keys on a USB keyboard. Human subjects were told that the speed of their response was not important, but were encouraged to respond reasonably quickly (mean reaction time after stimulus onset = 1.85 s). There was an average inter-trial interval of 600 ms (± 20 ms). Stimulus presentation and data collection were controlled using psychtoolbox (version 3) for MATLAB.

Monkey data

Five adult male rhesus monkeys (monkeys JJ, Tm, Sm, Fd, and Kb; Macaca mulatta; 10–17 years old) participated in the study. A sixth monkey, monkey Ik, started the experiment but did not finish the trials in the allocated time, thus the incomplete dataset for this monkey was excluded from analysis. We note that these were all the monkeys available for testing at the time. All procedures were approved by the National Institute of Mental Health (NIMH) Animal Care and Use Committee and conformed to all to all NIH guidelines. The monkeys were acquired from a breeding facility in the United States where they were housed in large groups until their transfer to the National Institute of Mental Health (NIMH) for quarantine at the age of ~4 years. After that, they were individually caged with auditory and visual contact with the 20 other rhesus monkeys in the same colony room. To our knowledge, these subjects had never been intentionally exposed to examples of face pareidolia prior to participating in this experiment.

METHOD DETAILS

Stimuli

We selected photographs of female rhesus monkeys from the private library of J. Taubert. The examples of face pareidolia and matched non-face objects were collected from the public domain using the Google image search engine (Figure 1A). All photographs and images were cropped to a square size (368 pixels wide) but were otherwise not changed. The number of images used to represent each condition was limited by the final number of trials that would be required is all possible pairs were to be presented to each subject reliably.

Procedure

Eye position was monitored throughout with an infrared pupil tracking system (ISCAN, Inc.) with a 4-ms sampling rate. During the experiment the subjects initiated a trial by fixating a central fixation point (.4°) for 500 ms before two stimuli were presented simultaneously for 5s (Figure 1C). The square stimuli were viewed from approximately 57 cm and subtended a visual angle of 10.2° in width and height. All 45 stimuli appeared on the left of the screen (center of image horizontally displaced by 8° of visual angle from the center of the screen) with every other stimulus on the right (center of image also horizontally displaced by 8° of visual angle from the center of the screen), and vice versa. This allowed for each stimulus in a unique pair to be presented once on the left and once on the right. In total, each subject successfully completed 1980 trials in a pseudorandom order. Successfully completed trials were rewarded with juice. If saccadic eye movements were made outside the screen for more than 500 ms, the trial was aborted without reward, and was repeated at a later time. If a trial was aborted, the next trial began after the 5s time out; otherwise the inter-trial interval was only 1s. Each monkey underwent a number of daily sessions to complete the experiment. For the 5 monkeys included in the analysis, the average was 3 sessions (min = 2 sessions, max = 4 sessions). Stimulus presentation and randomization of trials was determined by vCortex software (version 2.2).

The primary behavioural measure of interest was looking time (LT), expressed as the proportion of time that the animals spent exploring each of the stimuli relative to total presentation time. For each monkey, we summed the amount of time spent looking at each stimulus during the three conditions of interest relative to the amount of time each stimulus appeared on the screen (see Figure 1b).

We also analysed LT for the within category trials (for example, trials that presented a monkey face with another monkey face; see Figure S1). We calculated the amount of time each monkey spent looking at each individual monkey face stimulus during within category trials (i.e. when it was paired against another monkey face). Looking time was summarized as a proportion relative to the total time each stimulus was present on the screen and then averaged across the five subjects. A one-way repeated measures ANOVA revealed a significant effect of exemplar indicating that, on average, the monkeys spent a differential amount of time looking at the specific individual monkey faces (F14,56 =4.733, P <.001, ηp2 =.54). The monkey faces with the longest and shortest average LT are depicted in Figure S1A. We tested for a relationship between average proportion LT and first fixations in the monkey face trials. There was no evidence of a relationship between these dependent measures (Pearson’s r =.23, P =.40; Figure S1B). A one-way ANOVA on the first fixation data found no evidence of a significant effect of exemplar (F14,26 =.661, P =.80, ηp2 =.14). In sum, although the results imply that the monkeys looked for longer periods of time at certain monkey faces, there was no indication that this LT advantage depended on the first fixation. We performed the same analysis on the average LT data from the illusory face pair condition, which also revealed evidence of significant preferences among the stimuli (F14,56 =9.494, P <.001, ηp2 =.70; Figure S1C). There was no evidence of a significant preference in the corresponding first fixation data (F14,56 =1.293, P =.24, ηp2 =.24). Likewise, there was no evidence of a relationship between these two dependent measures for illusory face stimuli (Pearson’s r =.29, P =.29; Figure S1D). Finally, we ran a repeated measures ANOVA on the average LT data from the non-face object pair condition which yielded a result consistent with the monkeys spending differential amounts of time looking at each non-face stimulus (F14,56 =6.595, P <.001, ηp2 =.62; Figure S1E). However, unlike the other within category conditions, there was some evidence of a significant preference in the corresponding first fixation data, although the effect size was small (F14,56 =1.882, P =.05, ηp2 =.32; Fig. S1e). Additionally, there was evidence of a relationship between average LT and first fixation measurements for within-category non-face trials, implying that the non-face objects that captured spatial attention in the first instance were also the objects that the monkeys tended to look at the longest (Pearson’s r =.62, P =.013).

Additionally, we measured the spatial location of the first fixation (≥150 ms) on each completed trial (see Figure 2B) and the location of the first and every subsequent fixation that was directed towards a visual stimulus (for the fixation density map associated with each Monkey Face stimulus before we averaged across subject see Figure S3A; maps associated with the Illusory Face stimuli are provided in Figure S3B; and for the maps associated with the Nonface Objects see Figure S3C). In Figures 3A and S2 we averaged the fixation density maps across the five subjects.

Classifier

Leave-one-exemplar-out classification was used to prevent the classifier from exploiting any systematic differences in eye movements for particular stimuli. A linear support vector machine (SVM) was trained for each subject using MATLAB’s fitcsvm function on the fixation maps for N-1 stimuli for each class (pareidolia images vs. matched objects), with the remaining stimulus in each class used as the test data. We used yoked cross validation, so that for each cross validation fold the exemplar left out of the pareidolia set matched the object left out of the non-face set, maintaining equal N in each class. Cross validation was repeated 15 times for each of the 5 subjects, thus each stimulus was used once in the test set.

QUANTIFICATION AND STATISTICAL ANALYSIS

Group statistics on looking time preferences and the number of first fixations (repeated measures ANOVAs and subsequent paired t-tests) were performed using SPSS statistical data package, version 24. The analysis of cross-subject reliability was performed using custom made software in the mathworks MATLAB environment (version R2016a). For each monkey, a linear SVM was trained with the fixation density data elicited by 14 pairs of stimuli (14 pareidolia images and their object matched counterparts) and tested against the remaining 15th pair. This procedure was repeated 15 times giving each stimulus pair a chance to serve as the test stimuli. The associated processing pipeline was coded in the MATLAB environment using the fitcsvm function.

Supplementary Material

SuppMaterial

Acknowledgments

This work was supported by the National Institute of Mental Health Intramural Research Program (to L.G.U.). The authors would also like to thank Robert Keys for his continued assistance with the project, and the members of the Laboratory of Neuropsychology (NIMH) for their critical feedback during the early stages of analysis. Finally, we thank the anonymous reviewers for their invaluable comments that helped shape the final publication.

Footnotes

AUTHOR CONTRIBUTIONS

Conceptualization, J.T., S.G.W., D.A.L., and L.G.U.; Methodology, J.T., M.F., D.A.L., and L.G.U.; Formal Analysis, J.T. and S.G.W.; Resources, J.T. and L.G.U.; Data Curation, J.T., S.G.W., and M.F.; Writing – Original Draft, J.T. and S.G.W.; Writing – Review & Editing, J.T., S.G.W., D.A.L., and L.G.U.; Visualization, J.T., S.G.W., and D.A.L.; Project Administration, J.T. and M.F.; Funding acquisition, L.G.U.

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