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Testing Unconscious Face Processing and its Specificity to Conspecifics

Nikki van Sante

11673435

Supervisor: Timo Stein

January 29, 2021

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Abstract

Unconscious processing is studied extensively with detection paradigms, such as breaking

Continuous Flash Suppression (b-CFS), where a detection difference between conditions is

assumed to be caused by differential unconscious processing preceding detection. One effect

consistently found with this approach is that faces are detected faster in an upright orientation

compared to an inverted orientation. In a b-CFS study by Stein et al. (2012) with human

participants, this inversion effect was present for human faces as well as for chimpanzee

faces, with the inversion effect for human faces being stronger. However, as b-CFS has

recently been criticized for not being able to distinguish between unconscious and conscious

factors underlying a detection difference, it is not yet clear whether the face inversion effect

occurs unconsciously. Therefore, the current study aims to replicate these previous findings

by using a novel paradigm called detection-discrimination dissociation in combination with

backward masking. This method uses an additional discrimination task to exclude conscious

processes as a cause for a detection difference. Results revealed some evidence that human

participants processed the orientation of human faces unconsciously. However, other findings

indicated that this cannot be concluded with certainty. Further, no inversion effect was

present for chimpanzee faces. These findings shed light on previous b-CFS studies and

highlight the importance of using the novel detection-discrimination dissociation paradigm in

order to determine the scope and limits of unconscious processing.

Keywords: unconscious processing, faces, inversion, conspecifics

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Testing Unconscious Face Processing and its Specificity to Conspecifics

Does the brain process information outside our awareness? And to what degree does

unconscious processing influence our behaviour? The phenomenon of blindsight clearly

demonstrates that not only conscious information influences actions. Blindsight can be

observed in patients with damage to primary visual cortex (Sanders et al., 1974; Weiskrantz,

1996). These patients have no conscious experience of seeing anything. However, when the

patients are forced to guess what they are looking at, they can accurately name objects

(Trevethan et al., 2007). Furthermore, these patients can accurately reach for objects or avoid

obstacles (de Gelder et al., 2008; Prentiss et al., 2018). This demonstrates that unconscious

processing plays an essential part in guiding behaviour in blindsight patients, which

challenged scientists to discover more about the role of unconscious processing in healthy

individuals.

However, in order to study unconscious processing in healthy participants, it is

required that stimuli can be made invisible. The rise of different experimental manipulations

made this possible. In masking paradigms, a visual stimulus is presented for a short duration

and afterwards another image, called the mask, is presented that renders the stimulus invisible

(Marcel, 1983). When the mask is not presented after the stimulus, participants will almost

always see the stimulus. Thus, by only minimally changing the task, a completely different

experience is induced, namely seeing a stimulus or being completely unaware of it. Another

experimental manipulation is interocular suppression, where a stimulus presented to one eye

is suppressed from awareness by another stimulus presented to the other eye (Izatt et al.,

2014; Lin & He, 2009). The two stimuli compete for access to awareness, such that only one

stimulus can be consciously perceived at the same time. Without changing the visual input,

the perception differs between seeing one stimulus or seeing another. These manipulations

made it possible to let all types of images and words vanish from sight and study unconscious

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processing experimentally. Consequently, researchers began to investigate the scope and

limits of unconscious processing.

One of the main approaches to study unconscious processing is to measure the time it

takes for a stimulus to be detected. In these detection paradigms, a detection difference

between stimuli is assumed to be caused by differential unconscious processing preceding

detection. However, one problem with this approach is that low-level stimulus properties

could be responsible for a detection difference between stimuli (Pournaghdali & Schwartz,

2020). A solution to this problem is inversion, where a stimulus in an upright orientation is

compared with the same stimulus in an inverted orientation. In this way, low-level properties

of both stimuli are exactly the same. One effect that is consistently found with this approach

is that faces presented in an upright orientation are detected easier than inverted faces (Jiang

et al., 2007; Stein et al., 2012, 2016; Zhou et al., 2010). In other words, upright face stimuli

break faster into awareness compared to inverted faces. However, this inversion effect is not

found for houses (Albonico et al., 2018) or lamps (Stein et al., 2012). This led some

researchers believe that face processing is special compared to the processing of other stimuli

and that there is a specific neural circuitry for the processing of faces (Haxby et al., 2000).

This hypothesis implies that there is an inborn mechanism in the brain for processing faces.

However, inversion effects are also found for bodies (Reed et al., 2003) and objects of

expertise (Stein et al., 2016). For example, greater car expertise was related to larger

inversion effects (Stein et al., 2016). The expertise hypothesis states that face processing only

seems special because people have a lot of experience with face stimuli (McKone et al.,

2007).

Up to date, it is still a question whether people are born with a face processing module

or whether rapid face detection is accomplished because of our experience with these stimuli.

One interesting approach for resolving this debate is to study looking preferences in infants.

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Newborn infants already have a preference for faces presented upright compared to inverted

(Johnson et al., 1991). This inborn face preference is probably mediated by subcortical

circuits, since cortical regions are not yet well developed in infancy (Johnson, 1990). Such an

inborn mechanism is thought to make fast detection of faces possible without conscious

awareness (Stein et al., 2011). Further, research found that there is a lot of overlap between

facial properties that attract infants’ gaze and face properties that influence the access to

awareness in adults (Stein et al., 2011). Thus, it seems that face detection is at least partly

mediated by inborn mechanisms. In order to study how experience also plays a role in face

detection it is interesting to look at the processing of faces of different species. One study

found that newborns cannot discriminate human faces from monkey faces, while they

preferred to look at monkey faces with an upright orientation compared to an inverted

orientation (Di Giorgio et al., 2012). This indicates that a face inversion effect is already

present at birth, but that is not yet specific to conspecifics. Based on these findings, it could

be hypothesized that there is an inborn face detection pathway operating unconsciously which

is not specific to conspecifics and is mediated by subcortical circuits. In contrast to infants,

adults show a conspecific advantage in attending to changes in human faces compared to

monkey faces and also visual search is faster for human faces (Neiworth et al., 2006;

Simpson et al., 2014). These findings could be a result of gaining experience with conspecific

faces. The influence of experience on face detection is hypothesized to be mediated by

cortical circuits and is thought to involve awareness (Jessen & Grossmann, 2015; Johnson,

2005). Therefore, it seems plausible that there is also an experience-dependent face detection

pathway mediated by cortical circuits which requires consciousness and is specific to

conspecifics. However, it is unclear how these two pathways influence face detection, as

inborn and experience-based factors to detection are often not dissociable. Further, conscious

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and unconscious contributions to face detection are difficult to distinguish in detection

paradigms.

Currently, it is still unclear if the face inversion effect in detection is modulated by

conscious or unconscious factors. Previous detection studies about the face inversion effect

mainly use a method called breaking Continuous Flash Suppression (b-CFS). This is a strong

form of interocular suppression where participants are presented with a stimulus of interest to

one eye and the other eye is presented with a dynamic mask of high contrast which will

dominate vision for up to several seconds (Tsuchiya & Koch, 2005). At a given moment, the

stimulus will break through into awareness and becomes visible. The time it takes for a

stimulus to break through is thought to reflect unconscious processing prior to the conscious

experience (Del Río et al., 2018). According to this assumption, the finding that upright face

stimuli break through earlier than inverted face stimuli would indicate that there is enhanced

unconscious processing prior to the detection of upright faces. However, the assumption that

unconscious processing underlies detection differences during b-CFS has recently been

criticized (Moors et al., 2019; Stein & Sterzer, 2014). It has been argued that not only

unconscious factors but also conscious factors can play a role in the detection difference

between upright and inverted faces. For instance, later conscious factors related to

identification or recognition of the stimulus can cause the difference in detection times (Stein,

2019). Also, response biases can play a role in the detection of stimuli (Stein & Peelen,

2021). There are large individual differences in how inclined people are to say that they saw

something. Some people need to be entirely sure that they saw the stimulus in order to

indicate that they saw it, while others already state they saw the stimulus even if they are not

entirely sure. A third problem with inferring that unconscious processes underlie inversion

effects in detection is that it is automatically assumed that consciousness is discrete and that

participants can accurately state whether they saw a stimulus or not. However, it is highly

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debated whether consciousness is really discrete and some studies argue that it can be better

explained as a continuous phenomenon (Fekete et al., 2018; Srinivasan, 2020; White, 2018).

Therefore, interpreting detection differences as a result of greater unconscious processing

may lead to an overestimation of unconscious processing.

These alternative explanations for inversion effects during b-CFS emphasize the

importance for developing a new, valid method that can dissociate conscious from

unconscious processing underlying detection. This would make it possible to investigate

whether the face inversion effect occurs unconsciously or whether it is only mediated by

conscious factors. One method that has recently been proposed as overcoming the problems

of b-CFS is the detection-discrimination dissociation paradigm (Stein & Peelen, 2021). This

paradigm consists of two tasks: detecting the location of the stimulus and discriminating the

stimulus on the critical stimulus dimension that underlies the detection difference. In case of

the inversion effect, this would mean that participants need to discriminate whether the face

is inverted or upright. If participants are unable to discriminate the orientation of a stimulus,

while there is still a detection difference between upright and inverted faces, this is probably

due to differential unconscious processing. This detection-discrimination dissociation method

can be used in combination with b-CFS, but it is important to find a condition where

participants perform at chance on the discrimination task and above chance on the detection

task. Because of large individual differences in breakthrough time, the b-CFS method is not

preferable (Gayet & Stein, 2017). Therefore, the current study will use the detection-

discrimination dissociation paradigm in combination with backward masking, as individual

differences are smaller and presentations times can be easily adjusted to obtain the optimal

condition.

A b-CFS study by Stein et al. (2012) found that human participants were faster in

detecting upright human faces compared to inverted human faces and that chimpanzee faces

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were also detected faster in an upright orientation compared to an inverted orientation. The

inversion effect was larger for human faces, which indicates a conspecific advantage in face

detection. Because of the problems with the b-CFS method discussed earlier, the current

study will try to replicate the b-CFS findings of Stein et al. (2012) by adopting the detection-

discrimination paradigm in combination with backward masking. Human and chimpanzee

faces will be shown with different presentation times, which can either appear left or right on

the screen and have an upright or inverted orientation. It is predicted that human upright faces

are better localized than inverted faces when discrimination is at chance performance. The

same is predicted for chimpanzee faces. For presentation times where discrimination is at

chance, it is predicted that the size of the inversion effect of chimpanzee faces compared to

human faces does not differ, because it can be hypothesized that innate, unconscious

mechanisms are not specific to conspecifics. For longer presentation times, where

discrimination is above chance, it is predicted that the inversion effect for human faces is

larger compared to the inversion effect for chimpanzee faces, because conscious mechanisms

seem to be influenced by experience. If these predictions are accurate, it would indicate that

face perception can occur unconsciously and that it is not specific to conspecifics, while

conscious face perception is specific to our own species. This will provide some evidence

that there are two distinct face detection pathways that operate together in the detection of

faces: one innate, unconscious, subcortical pathway which is not specific to our own species

and one experience-dependent, conscious, cortical pathway that is specific to our own

species.

Methods

Participants

Participants were recruited via the Behavioural Science Lab subject pool of the University of

Amsterdam and were unaware of the purpose of the study. The sample consisted of 47

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participants (35 female) and were between 18 and 45 years of age (M = 22.02, SD = 0.64).

Participants received a Psychology Research credit or 10 euros as a compensation for

participating. Inclusion criteria were normal or corrected-to-normal vision and an age of

above 16. Participants were excluded when their overall discrimination performance was

below a d’ of 0.5 at the longest presentation time. Based on this, no participants were

excluded. The study was approved by the Ethics Review Board of the Behavioural Science

Lab. Before the start of the experiment, participants were provided with an information letter

and signed informed consent. Some additional instructions about the set-up of the experiment

were provided by a researcher and participants were told to be as accurate as possible.

Practice trials with feedback were present in the experiment to make sure participants

understood the experiment.

Materials and stimuli

A 24-inch LCD monitor (1920 x 1080 pixels resolution) with a refresh rate of 120 Hz

was used to administer the experiment. All participants were seated in front of the screen at a

viewing distance of approximately 60 cm. The task was coded in MATLAB using

Psychtoolbox (Brainard, 1997). During the experiment six different frontal human faces and

six different frontal chimpanzee faces were used. The human face stimuli were collected via

the database of Ekman & Friesen (1976) and the chimpanzee faces were selected from the

internet (as in Stein et al., 2012). Inverted stimuli were created by turning the upright face

stimuli 180°. External facial features of the face stimuli were removed and the size was

adjusted to fit into a square image (100 × 100 pixels). A circular averaging filter was applied

to the outer edges to smooth the facial contours into the background. The contrast of all faces

was the same (SD 18.4) and the stimuli had the same luminance as the background (RGB

values [102 102 102]). Example stimuli are shown in Figure 1. The masks (284 x 284 pixels)

were constructed by creating randomly arranged circles in different shades of grey (diameter

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23-46 pixels). A total of 100 masks were generated. Stimuli were presented in a grey box

(284 × 284 pixels, RGB values [102 102 102]) in the centre of the screen surrounded by

black.

Figure 1

Example Stimuli

Note: Stimuli were human faces or chimpanzee faces in an upright or inverted orientation.

Design

The experiment used a within-subjects design. The experiment consisted of two

blocks with 384 trials with human faces and 384 trials with chimpanzee faces. Before both

blocks, participants finished 8 practice trials (with the longest presentation time) which

included feedback on their accuracy. Participants alternately viewed the human trials or the

chimpanzee trials first. The order of the localization and discrimination task was

counterbalanced and trials were presented at random. Every combination of four presentation

times, two orientations (upright/inverted), two locations (left/right) and six different face

stimuli occurred four time in each block. In between the trials there were some breaks of 10 s

included to reduce fatigue. The duration of the experiment was approximately 60 minutes.

Procedure

Before the experiment started, participants’ age, gender and handedness was noted.

The experiment began with instructions on the screen. A trial began with a screen were the

box and a fixation cross was presented for 1.5 s. After this, a blank screen was presented for

0.5 s which marked the beginning of the stimulus presentation sequence. Then, a human or

chimpanzee face was shown on the left side or the right side of the box (centre-to-centre

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distance 71 pixels) in an inverted or upright orientation. The face stimuli were presented with

four presentation times (8.3[8.3], 16.7, 25.0, and 50 ms). After the presentation of face

stimuli at the first presentation time, an additional blank screen was presented for 8.3 ms.

This value is presented in square brackets. Different presentation times were used to increase

the chance of finding a condition where discrimination is at or below chance performance.

The presentation times were determined based on pilot testing. Longer presentation times

were added to keep participants motivated to perform the task. After a face stimulus, three

backward masks were randomly selected and presented for 100 ms each.

Next, participants were asked to indicate whether the face was presented on the left or

the right side of the screen (localization task) and whether the face was in an upright or an

inverted orientation (discrimination task). Participants had to press the left or right arrow bar

on a keyboard to indicate the location of the face. To indicate if a face was presented upright

or inverted they respectively needed to press the arrow bar pointed upwards or the arrow bar

pointed downwards. The order of the localization task and the discrimination task was

counterbalanced between participants. The procedure was based on the study that introduced

the detection-discrimination dissociation paradigm (Stein & Peelen, 2021). An overview of

one trial can be found in Figure 2.

Figure 2

An Overview of a Trial

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Note. The stimulus could appear on the left or on the right side of the grey box. After the

mask, participants had to perform the localization task first and then the discrimination task

or the other way around.

Analysis

Accuracy on the localization and discrimination task was transformed into SDT

measure d’. This was done separately for human and chimpanzee faces and for each

presentation time. In the localization task, pressing the left arrow bar was classified as a hit in

trials where the face was presented left, while it was classified as a false alarm when the face

was presented right. In the discrimination task, pressing the arrow bar pointing upwards was

classified as a hit in trials with an upright face orientation and was classified as a false alarm

when the face had an inverted orientation. When hit and false alarm rates were 0 or 1, they

were converted to 1/(2N) and 1−1/(2N), respectively, where N represents the number of trials

on which the rates were based (Macmillan & Creelman, 2005). To calculate d’, the z-

transformed false alarm rate was subtracted from the z-transformed hit rate. For the

localization task, d’ was divided by the square root of two, as the localization task is a 2-AFC

task while the discrimination task is a yes/no task (Macmillan & Creelman, 2005).

Discrimination d’ was analysed for different presentation times using a repeated-

measures ANOVA. To analyse localization d’ a repeated-measures ANOVA with the factors

presentation time and orientation was conducted. Further, with one-sample t-tests it was

investigated for different presentation times whether participants discriminated at chance

performance. After this, inversion effects were analysed for interesting presentation times by

conducting one-tailed paired-sample t-tests. If the face inversion effect is independent of

discrimination, we would expect that localization d’ follows the same pattern for trials with

correct vs. incorrect discrimination response. This was analysed by conducting a repeated-

measures ANOVA with the two factors orientation and presentation for correct

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discrimination trials. After this, the same was done for incorrect discrimination trials. For

these two analysis, participants were included when they had at least five trials per condition

(upright-human, inverted-human, upright-chimpanzee and inverted-chimpanzee) at the three

shortest presentation times with correct and incorrect discrimination response. The longest

presentation time was not used for this analysis because of a ceiling effect. To compare the

inversion effects of human faces and chimpanzee faces, a repeated-measures ANOVA was

conducted with the factors species, orientation and presentation time. Normality was not

tested because it can be assumed that t-tests and ANOVA’s are robust for normality with a

sample size of above 30 (Kwak & Kim, 2017; Pituch & Stevens, 2016). For each ANOVA,

Mauchly’s test for sphericity was performed. Greenhouse-Geisser corrections were applied to

the degrees of freedom, when the assumption of sphericity was violated and corrected p-

values are reported. For all tests a significance level of .05 was used.

All analyses were performed in JASP (JASP Team, 2020) with standard frequentist

statistics and Bayesian statistics. For the Bayesian statistics, default prior scales were used.

BF10 represents the evidence for the alternative hypothesis, while BF01 can be interpreted as

the evidence in favour of the null hypothesis. BF0+ or BF+0 are reported in one-sided tests.

For multi-factorial ANOVAs, the inclusion BF is reported which can be interpreted as the

evidence for all models with a particular effect in comparison to all models without that

effect. The analysis of the current study was based on the study by Stein and Peelen (2021).

Results

Human faces

For human faces, discrimination performance was almost equal for the two shortest

presentation times and then increased, F(1.84, 83.58) = 247.65, p < .001, ηp2 = 0.85; BF10 =

1.67x1057. This is shown in Figure 3. Localization increased with presentation times, F(2.23,

100.47) = 284.61, p < .001, ηp2 = 0.86; BF10 = 1.39x10118. Human upright faces (M = 1.43,

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SD = 1.13) were localized better than inverted human faces (M = 1.25, SD = 1.12), F(1, 45) =

32.13, p < .001, ηp2 = 0.42; BF10 = 152.41. Further, there was a significant interaction

between orientation and presentation time, F(3, 135) = 5.99, p < .001, ηp2 = 0.12; but BF01 =

2.79. As shown in Figure 3, the inversion effect was larger at the two intermediate

presentation times. There seems to be no inversion effect at the longest presentation time,

probably because performance was at ceiling.

Figure 3

Discrimination, Localization for Upright Faces and Localization for Inverted Faces for

Human and Chimpanzee Faces

Note. Mean localization d’ for upright faces (orange line) and localization d’ for inverted

faces (blue line) for different presentation times (a value in square brackets refers to the

duration of a blank box presented after the face stimulus) for human faces (left) and

chimpanzee faces (right). Error bars represent 95% CIs for the difference between upright

and inverted faces. For comparison, mean discrimination d’ (black line) with 95% CIs is also

shown in both panels.

Next, we looked for the longest presentation time where discrimination was at chance

performance. At the shortest presentation time, discrimination d’ was greater than zero, t(45)

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= 2.99, p = .002 (one-tailed), Cohen’s d = 0.44; BF+0 = 15.53, while at the second shortest

presentation time discrimination d’ was not significantly different from zero, t(45) = 1.59, p

= .06 (one-tailed), Cohen’s d = 0.23; but BF0+ = 1.05. However, this finding could also be

interpreted as borderline significant (Tshikuka et al., 2016). Further, the Bayes Factor shows

that the null and the alternative hypothesis are almost equally likely. Therefore, it is

inconclusive whether discrimination d’ is at chance or above chance performance at the

second presentation time. At the two longest presentation times, discrimination d’ was above

chance performance, both t(45) ≥ 6.91, p < .001 (one-tailed), d ≥ 1.02; BF+0 > 1.84x106.

Next, it was tested whether an inversion effect was present at the three shortest presentation

times. For the second shortest presentation time, where discrimination was not significantly

different from 0, a significant inversion effect was found, which might indicates an

unconscious origin of this effect, t(45) = 3.07, p = .004, d = 0.45; BF10 = 9.26. This finding is

shown in Figure 4. To demonstrate that unconscious processing underlies the detection

difference between upright and inverted faces it is also important to perform an additional

analysis where the inversion effect is directly compared with the discrimination measure in

the same metric (Meyen et al., 2020; Schmidt & Vorberg, 2006). This analysis showed that

there was no significant difference between the inversion effect at the second presentation

time and discrimination at the second presentation time, t(45) = 1.22, p = .227, Cohen’s d =

0.18; BF01 = 3.11. Therefore, it is not clear whether the inversion effect at the second

presentation time is due to differential unconscious processing. At the shortest presentation

time, there was no significant inversion effect, t(45) = 1.24, p = .221, d = 0.18; BF01 = 3.05.

Further, a large inversion effect was present at the third presentation time, t(45) = 5.23, p

< .001, d = 0.77; BF10 = 4.33x103.

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Figure 4

Inversion Effect at the Critical Presentation Time of 16.7 ms

Note. Mean discrimination d’, localization d’ for upright faces and localization d’ for inverted

faces at the critical presentation time of 16.7 ms. While discrimination did not significantly

differ from zero (p = .06), upright faces were localized better than inverted faces. Error bars

represent 95% CIs.

** p < .01

To further test whether the inversion effect occurs unconsciously, we analysed

whether the inversion effect shows a different pattern in trials were participants discriminated

correctly versus incorrectly. Localization performance was not influenced by correctness of

discrimination, F(1, 37) < 0.01, p = .96, ηp2 < 0.01; BF01 = 9.17 (see Figure 5). There was no

significant interaction between correctness of discrimination, orientation and presentation

time, F(2,74) = 0.78, p = .46, ηp2 = 0.02; BF01 = 6.58. Further, there was no significant

interaction between correctness of discrimination and orientation, F(1,37) = 0.02, p = .90, ηp2

< 0.01; BF01 = 6.50. The interaction between correctness of discrimination and presentation

time was also not significant, F(2, 74) = 0.90, p = .40, ηp2 = 0.02; BF01 = 12.53. Further, for

trials with a correct discrimination response there was an effect for orientation, F(1,37) =

12.53, p = .001, ηp2 = 0.25; BF10 = 5.47. For trials with an incorrect discrimination response

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there was an effect for orientation as well, F(1, 37) = 9.93, p = .003, ηp2 = 0.21; BF10 = 9.82.

Thus, the inversion effect is independent of the discrimination performance, which could be

interpreted as some evidence for an unconscious face inversion effect.

Figure 5

Localization d’ for Trials with Correct and Incorrect Discrimination

Note. Mean localization d’ for upright (orange line) and inverted (blue line) faces for

different presentation times (a value in square brackets refers to the duration of a blank box

presented after the face stimulus) for trials with correct discrimination (left) and incorrect

discrimination (right). The inversion effect was not dependent on the correctness of

discrimination. Error bars represent 95% CIs for the difference between upright and inverted

faces.

Chimpanzee faces

For chimpanzee faces, iscrimination d’ increased with higher presentation times,

F(1.38, 61.98) = 165.61, p < .001, ηp2 = 0.79; BF10 = 1.04x1049. Localization d’ also

increased with higher presentation times, F(1.92, 86.34) = 239.53, p < .001, ηp2 = 0.84; BF10

= 2.77x10112. This is shown in figure 3. Further, there was no significant inversion effect for

chimpanzee faces, as participants were not better in localizing upright chimpanzee faces (M =

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1.14, SD = 1.14) compared to inverted chimpanzee faces (M = 1.17, SD = 1.15), F(1, 45) =

1.71, p = .197, ηp2 = 0.04; BF01= 7.55. Moreover, there was no significant interaction

between orientation and presentation time, F(2.43, 109,34) = 0.476, p = .66, ηp2 = 0.01; BF01

= 25.71. As no inversion effect was found, no further analyses were performed.

Human vs. chimpanzee faces

Finally, it was tested whether the inversion effect was larger for human faces

compared to chimpanzee faces. Overall, localization d’ was higher for human faces compared

to chimpanzee faces, F(1,45) = 27.56, p < .001, ηp2 = 0.38; BF10 = 5.33x109. Further, there

was a significant interaction of species by orientation, F(1, 45) = 30.31, p < .001, ηp2 = 0.40;

BF10 = 7.61x1012. In other words, the inversion effect is larger for human faces compared to

chimpanzee faces. Furthermore, there was a significant three-way interaction of species by

orientation by presentation time, F(3, 135) = 3.74, p = .013, ηp2 = 0.08; BF10 = 5.73x1016. To

directly test our hypothesis, we compared the inversion effect at the potentially unconscious

presentation time of 16.7 ms between human and chimpanzee faces. Contrary to our

expectations, the inversion effect at the second presentation time was larger for human faces

compared to chimpanzee faces, t(45) = 3.05, p = .004, Cohen’s d = 0.45; BF10 = 8.91. The

inversion effects for human and chimpanzee faces for different presentation times are shown

in Figure 6.

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Figure 6

Inversion Effects for Human and Chimpanzee Faces

Note. The inversion effect (localization d’ upright faces – localization d’ inverted faces) for

different presentation times (a value in square brackets refers to the duration of a blank box

presented after the face stimulus) for human faces (left panel) and chimpanzee faces (right

panel). Grey dots represent individual data points. Horizontal black lines represent the mean

and error bars represent 95% CIs for the difference between upright and inverted faces.

Discussion

The aim of this study was to replicate the findings by Stein et al. (2012) about the face

inversion effect by adopting the novel detection-discrimination dissociation paradigm in

combination with backward masking. Results revealed that there was some evidence that

human participants processed the orientation of human faces unconsciously. However, other

findings indicated that this cannot be concluded with certainty. Further, we did not find any

inversion effect for chimpanzee faces. Thus, the current study demonstrated that unconscious

processing of face orientation might be specific to conspecifics. These results are partially

consistent with the findings of Stein et al. (2012).

The previous study conducted with b-CFS by Stein et al. (2012) found that human and

chimpanzee faces were detected faster when presented in an upright orientation compared to

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an inverted orientation. Further, they found that this inversion effect was stronger for human

faces compared to chimpanzee faces. This is inconsistent with the current finding that there is

no inversion effect for chimpanzee faces. One explanation for this is that the previous study

used b-CFS, while the current study used backward masking in combination with the

detection-discrimination dissociation paradigm. Although both paradigms intent to measure

unconscious processing, invisibility is induced differently. Because of this, the underlying

neural mechanisms differ which could explain the different findings between the two

paradigms. Furthermore, b-CFS uses reaction times to measure a detection difference, which

could be influenced by decisional processes, while the current study uses a criterion-free

measure based on accuracy. It is possible that the inversion effect for chimpanzee faces in the

study of Stein et al. (2012) occurred because participants overall pressed a bit later for

inverted faces to indicate detection, just because those faces are less common. Thus, the

inversion effect for chimpanzee faces in the study of Stein et al. (2012) could have been

caused by conscious factors which are ruled out by the detection-discrimination dissociation

paradigm.

From an evolutionary perspective, it seems predictable that an inversion effect is

present for conspecific faces and not for faces of other species. For humans, detecting or

localizing other human faces efficiently is ecologically and socially relevant, for example for

communicating, reading other’s emotions and for connecting with each other. As we see

human faces throughout life almost always in an upright orientation, it is not surprising that

we are better in localizing upright faces compared to inverted ones. However, faces of other

species are not as relevant for humans which could explain why there is no inversion effect

for those stimuli. Support for this idea is provided by a study that showed that chimpanzees

also show an inversion effect for faces of conspecifics but not for human faces (Parr, 2011).

21

A related question is whether this conspecific inversion effect is innate or whether it is

due to experience and what brain circuits play a role. As discussed earlier, one idea is that

there is an innate subcortical face detection pathway that operates unconsciously and is not

specific to conspecifics while there is another experience-dependent cortical face detection

circuit which is specific to conspecifics where awareness is involved (Jessen & Grossmann,

2015; Mark H. Johnson, 2005; Stein et al., 2011). However, this hypothesis is contradictory

with the current results, as we found some evidence that face processing occurs

unconsciously, but that this was indeed specific to faces of our own species. Therefore,

another speculation could be that these unconsciously operating subcortical circuits that serve

face detection in infancy, are inhibited by cortical circuits during maturation (Johnson et al.,

2015; Pascalis & Kelly, 2009; Stein et al., 2011). It has become clear that a lot of cortical

regions also play a role in the processing of unconscious information (Dehaene, 2014). A

cortical brain region that is important in the processing of faces is the fusiform face area

(FFA). It is found that this brain area activates when faces are presented unconsciously

(Lehmann et al., 2004). Further, the FFA also activates for objects of expertise (Bilalić et al.,

2011; Gauthier et al., 2000; McGugin et al., 2012, 2014; Tarr & Gauthier, 2000). Thus, the

activation pattern of the FFA can be influenced by experience. If during adulthood the

subcortical circuit that served face detection during infancy is suppressed by the cortical brain

circuit involving the FFA, it is plausible that there is an unconscious inversion effect for

human faces and not for chimpanzee faces, as humans have far more experience with faces of

their own species. However, this is only a speculation and future studies should examine this

idea, as the current study did not look at any brain circuits.

A general methodological issue with this study is that the presentation times of the

face stimuli were not optimal. An important issue for this study was to find a presentation

time where subjects performed at chance on the discrimination task. However, results with

22

the human faces showed that at the shortest presentation time, subjects already performed

above chance at the discrimination task. The second shortest presentation time seemed to not

differ from 0 at discrimination significantly, but a p-value of 0.06 can also be interpreted as

borderline significant and Bayesian statistics showed that it was almost equally likely that

participants performed at chance versus above chance (Tshikuka et al., 2016). Further, a

direct comparison between the inversion effect and discrimination showed no evidence for an

unconscious inversion effect. However, analysing the inversion patterns for trials with correct

discrimination response separately from trials with incorrect discrimination response showed

some evidence for an unconscious origin of the inversion effect for human faces. Because of

these contradictory results, the conclusion that there is an unconscious inversion effect for

human faces should be treated with caution and future studies should try to find optimal

presentation times to determine whether the face inversion effect occurs unconsciously.

The current study shows that it is important to re-examine the b-CFS literature by

adopting the detection-discrimination dissociation paradigm, as the findings are inconsistent

with previous b-CFS results of Stein et al. (2012). As previously indicated, findings with b-

CFS cannot only be based on unconscious processing, but also on conscious influences.

Therefore, in order to clarify the scope and limits of unconscious processing, the detection-

discrimination dissociation paradigm should be used instead of b-CFS, as this paradigm

excludes conscious contributions to detection. Besides re-examining the b-CFS literature,

future research could also build upon this study by examining whether other facial properties

are processed unconsciously. For example, it would be interesting to study whether facial

expressions are processed unconsciously or whether familiarity of faces influence detection

unconsciously.

In conclusion, this study provided some evidence for an unconscious face inversion

effect specific to conspecifics. However, it cannot be concluded with certainty that this effect

23

is unconscious. This study points out that the extent of unconscious processing may be

somewhat limited and sheds light on previous b-CFS studies. Further, the current findings

highlight the importance of using the novel detection-discrimination dissociation paradigm in

research about unconscious processing. Adopting this new method in future research will

help unravelling the scope and limits of unconscious processing.

24

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