Bachelorproject Sociale Psycholgie
Belief in Supernatural Agents Explained by the Hyperactive
Intentionality Detection Device (HIDD).
Support for HIDD’ existence.
byThomas Wollersheim
University: University of Amsterdam Date: 22-05-2015
Student ID: 10320326 Words: 5047 Mentor: David Maij
Abstract
It has been hypothesized that a hyperactive intentionality detection device (HIDD) is a cognitive system responsible for belief in supernatural agents and paranormal beliefs. In the present study a geometrical figures task, in combination with a direct threat manipulation, was used to study HIDD’ possible existence. Participants (N=36) were required to correctly attribute intentions to intentional, mechanic and random animations. It was found that participants incorrectly attributed more
intentions to mechanic and random animations in the threat condition. Which indicates that people tend to see more illusory intentional agents while under threat. This finding supports the possible existence of a HIDD. However, no link was found between this possible existence and belief in the paranormal and supernatural agents.
Introduction
More than eight-in-ten people worldwide identify with a religious group according to the Pew Research Center’s Forum on Religion & Public Life. In most cases religious people believe in a supernatural agent (e.g., God) and report spiritual and religious experiences (Pollack, 2008). For the consequences of religion we would like to refer to the positive and negative outcomes (Ano & Vasconcelles, 2005; Cotton, Zebracki, Rosenthal, Tsevat, & Drotar, 2006; Krause & Wulff, 2004). In this article, we focus on the conceived psychological and cognitive basis of religious thoughts and acts, in an effort to understand the possible origin of religion.
The cognitive science of religion (CSR) tries to contribute to the understanding of the origins of religion. There are two different visions on this subject within the CSR. Religion is thought to be either an adaption for human survival or a by-product of cognitive and psychological systems (Barrett, 2000). Perceiving religion as a consequence of cognitive and psychological systems makes it, in a way, empirically testable (Atran & Norenzayan, 2004). Although the CSR cannot fully explain
religion, it may contribute to the understanding of religion’ origin. Many cognitive systems have been suggested to explain this origin. In this study we focus on the literature in which theory of mind (ToM) and the hyperactive agency detection device (HADD) are considered being able for a partial explanation of supernatural beliefs (Barrett & Lanman, 2008).
The HADD is based on the finding that humans are sensitive for seeing patterns of agency in ambiguous situations; we tend to see humanlike faces in the smoke, clouds or trees (Guthrie, 1993). A way to demonstrate the HADD is by imagining a walk through a dark forest. Every unexplained stimulus has a big chance to be perceived as the consequence of a human or animal that is present. Human survival had a better chance by incorrectly assuming another presence while there was none (positive), than assuming being safe while under threat (negative). The cost of a false-negative could mean death whereas false-positives only costs energy for the preparation of the fight or flight reaction. According the error management theory (EMT), evolution may have favored humans with a cognitive system that is more active in detecting agency, especially in a threatening situation (Barrett, 2000; Haselton & Nettle, 2006). Consequently, a supposed cognitive system that is thought to be responsible for this proneness is the HADD (Atran & Norenzayan, 2004; Barrett, 2000). The perceptual biases of the HADD towards the detection of other agents may be an explanation for the belief in supernatural agents and paranormal sensations (Atran & Norenzayan, 2004; Barrett, 2000; Guthrie, 1993).
While some theorists consider the HADD to be a cognitive system that is solely capable of the perception of agency (Atran & Norenzayan, 2004; Barrett, 2000; Guthrie, 1993), another theorist takes a different perspective. Lisdorf (2007) argues that belief in a supernatural agent, like god, seems much more complex than just perceiving one. A more extensive cognitive system seems necessary for supernatural beliefs. He proposes a system that detects agency, attributes intentions and that also generates false-positives. Besides the more parsimonious explanation for supernatural beliefs, such a cognitive systems also appears to give a better evolutionary advantage. Predicting the
intentions of other agents can put a person in control over a situation. In this way, threats can be avoided (Epley, Waytz, Akalis, & Cacioppo, 2008). In other words, where the sole perception of an agent seems unlikely to provoke a fight or flight reaction, the attribution of threatening intentions to the perceived agents does. In this study we follow the line of reasoning that attributing intentions to agents seem necessary for human survival and the belief in supernatural agents. Lisdorf titled this cognitive system the hyperactive intentionality detection device (HIDD).
Another cognitive system that seems to attribute intentions to perceived agents is ToM (Barrett & Lanman, 2008). Despite their similarities it is not clear whether ToM and the HIDD are two different cognitive systems that operate individually, if they are one and the same or whether the HIDD is a cognitive system that uses ToM somewhere along the way. A difference between the two systems considers the empirical evidence that supports their existence. While empirical evidence supports the existence of ToM (Abell, Happe & Frith, 2000(Baron-Cohen, 1997), there is uncertainty about the existence of the HIDD. No study directly tested the existence of a HIDD (Barnes & Gibson, 2013). This requires a manipulation of threat or ambiguity (Barrett & Lanman, 2008; Haselton & Nettle, 2006). Consequently, it is not clear yet whether humans possess this cognitive system that would give us an evolutionary advantage and which may provoke supernatural beliefs.
Some studies support the possible existence of a HIDD, however, indirectly. One study used functional magnetic resonance imaging (fMRI) to investigate the relationship between ToM and believing in the supernatural (Riekki, Lindeman, & Raij, 2014). Supernatural believers and skeptics had to rate the intentionality of randomly and intentionally moving animations of geometric objects in the Geometrical Figures Task (GFT). Supernatural believers reported more false-positives in attributing intentionality to random movements than skeptics. They also showed stronger activation of ToM-related circuitries. Yet, it remained unclear if the HIDD could be hold accountable for this difference.
Another study investigated the relationship between awe, uncertainty and intentionality detection (Valdesolo & Graham, 2014). Results showed that a feeling of uncertainty mediated the relationship between awe and intentionality detection. The existence of a HIDD is not directly tested in this study because it lacked a direct threat (Haselton & Nettle, 2008). However, results indicate that situational factors, like uncertainty, may influence the hyperactivity of the intentionality detection device. Consequently, situational factors that directly act upon the hyperactivity of the system determine a possible existence. According the EMT, a direct threatening or an ambiguous situation are such situational factors (Haselton & Nettle, 2006). Therefore in the current study, a possible existence of the HIDD is investigated by creating a situation that is directly threatening. A situation in which people see more illusionary intentional agents while being under threat favors HIDD’ existence.
Sounds will be used in the current study to simulate a threatening situation for multiple reasons. First of all, nonlinear sounds are able of generating threatening situations (Blumstein, Davitian, & Kaye, 2010). Besides, these threatening situations can be generated without priming agency. A final argument is that sounds can be presented together with the visual task (Treisman & Davies, 2012). In this way a threatening situation can be simulated while performing an agency detection task. Intentional agency detection will be measured by a GFT (Heider & Simmel, 1944). The task either shows intentional, mechanical or random movements of 2D figures over a screen. The clips are presented for a short period of time. Only a fast detection of intentionality seems to give an evolutionary advantage. A slow detection could mean it’s too late.
The HIDD is empirically supported when people produce more false-positives in detecting intentionality while experiencing threat. In this case, false-positives represent a detection of
intentionality at the moment that figures actually make random or mechanic movements. Following the HIDD, the prediction is that an experience of threat leads to more false-positives in intentionality detection. Consequently, we expect that participants in the threat condition will produce more
false-positives in attributing intentions to random or mechanic movements than participants in the control condition.
The significance of this study becomes clear by looking at the great number of studies that cite the HADD or the HIDD as being the cognitive system responsible for believing in the
supernatural (Barrett, 2000; Barrett & Lanman, 2008; Epley et al., 2008; Lisdorf, 2007; Riekki,
Lindeman, Aleneff, Halme, & Nuortimo, 2013; Riekki et al., 2014; Valdesolo & Graham, 2014; van Elk, 2013; Willard & Norenzayan, 2013). These studies build upon a theory that has yet to be empirically tested. By empirically testing the HIDD its existence can be supported or falsified. This would not necessarily mean that the HADD does not exist. Perhaps they operate individually, cooperate or are one and the same. However, in the light of the existing literature, proof of the existence of the HIDD gives advantage over the HADD because of the more parsimonious explanation for human survival and supernatural beliefs.
Method Participants
A total of 45 participants signed up for this study. Due to human error and technical difficulties seven participants were excluded1. Most included participants (38) were acquaintances
(21 woman, mean age = 24,3 years) and recruited by personal requests. They didn’t receive a reward for their participation. The rest of the participants were recruited from the student population of the University of Amsterdam. They participated for course credits.
Data check
1 The first participant of the GFT was excluded because of an error in the program. A second was excluded due
to the computer shutting down. The rest of the participants were excluded because of errors in coding (3) or substantial missing values (2); indicating they did not follow the instructions.
Before analyzing the data the participants were checked whether they reported ‘normal’ values. Extreme values2 that did not match the pattern in a plot were excluded from the analysis.
Materials
Sounds in combination with a darkened room were used in this study to induce a
threatening feeling. The design was counterbalanced and within subjects. The two conditions were divided into a total of four blocks to control for learning effects. The experimental condition used a nonlinear sounds fragment of Polymorphia by Krzysztof Penerecki to induce the threatening feeling. This fragment is chosen because of its nonlinearity, disturbing effect and the absence of agency. In the control condition so called ‘elevator music’ was used. The fragment Elevator Music 1 hour of Antoine B was chosen. These linear sounds are widely used because of its neutrality. The sounds of the experimental and control condition were both retrieved from youtube.com.
The current study used the GFT to measure intentionality (Riekki et al., 2014). There were three kinds of animations in this task. These animations performed random, mechanical or intentional movements. The mechanical movements are an addition to an earlier study (Heider & Simmel, 1944). The intentional animations acted in a humanlike-story setting. Playing a tag-game is an example of one of the stories (Riekki et al., 2014). In the mechanical animations figures influenced each other’s movements by following the laws of nature. The random animations moved around randomly, with the only rule that they could not touch each other; otherwise they would appear to run through each other. Each clip lasted six seconds. They were followed by a maximum of two response tasks. The first task was to decide if the movements were intentional. Participant had four seconds to complete this task. Subscribing intentions to random or mechanical movements was considered to be a false-positive. The second task was to decide if the subscribed intentions were
2 Extreme values were considered to be three times the interquartile (difference between upper and lower
positive or negative. Participants had 10 seconds to fulfill this task. This second task served two functions. First, it confirmed the manipulation. Second, this task controlled for ‘easy answering’. By asking what the participants saw, they had to pay attention and think about their choice. Both tasks went on as soon as the decision was made. Each participant assessed the 36 clips, of which 12 intentional, 12 random and 12 mechanical, twice and spread out over two blocks because of the within-subjects design. There was a two-second break between the assessment of the movements and the following clip. Completing the GFT took 18 minutes, excluding breaks. The manipulation was verified by means of the Galvanick Skin Response (GSR), a shortened positive and negative affect schedule (PANAS) and questions about the perceived control of the participants. Education, age, gender and the use of stimulating substances were also measured.
The level of arousal was measured with the GSR. This device (BioSystem) measures a change in the electrical properties of the skin, which varies depending on the amount of sweat-induced moisture on the skin. This is related to anxiety (Chattopadhyay, Bond, & Lader, 1975).
The level of anxiety was measured with a shortened PANAS (alpha was .87), designed by Watson and Clark (1991). Seven items tested the positive and negative affect. Participants had to rate to which extend emotional states were applicable to them. Items such as, ‘’tensed, calm and worried’’ were evaluated. Scores ranged from 7 to 35 per block. Participants completed this questionnaire four times in total. This test proved to be sufficiently reliable anxiety.
Self-constructed items tested religiosity and spiritualism. Four items tested religious belief (alpha was .77) and two items considered spiritualism (alpha was .50), was too low for being included in the analysis). Participants had to answer religious items such as, ‘’To which degree do you believe in the existence of a god?’’ For religion the scores ranged from 4 to 28. For spiritualism the scores ranged from 2 to 14.
The belief in supernatural agency was measured with the Revised Paranormal Belief Scale (RPBS) (alpha was .93), designed by Tobacyk (2004). The test consisted of 15 items assessing different aspects of belief in the paranormal. Items that seemed too absurd were excluded. Participants had to rate the items by indicating to which extend the statement was applicable to them on a seven point Likert-scale. In which 1 represents a strong disagree and 7 a strong agree. An example of an item: ‘’Reincarnation exists’’. Scores ranged from 15 to 105. A higher score
represented a higher paranormal belief.
The negativity bias was measured with the negativity bias scale (alpha was .62, after deleting item 4 and 6), designed by Fessler, Pisor and Navarrete (2014). Participants had to rate seven items by indicating to which extend the statement was applicable to them on a nine point Likert-scale. A score of 1 represented a total disagree, rating a 5 mend neutral and a 9 was a total agree with the statement. Participants had to rate statements such as, ‘’I often fear for my own safety’’. Scores ranged from seven to 63. The higher the score the more a participant was biased to negativity.
The individual differences in the tendency to anthropomorphize were measured with the anthropomorphize scale (alpha was .82), designed by Waytz, Cacioppo and Epley (2010). Participants had to rate 14 items by indicating to which degree they agreed with the statements on a nine point Likert-scale. A score of 1 represented a total disagree, rating a 5 mend neutral and a 9 was a total agree with the statement. Participants had to rate statements such as, ‘’the ocean has a conscious’’. Scores ranged from 14 to 126. A higher score represented a higher tendency of the participant to anthropomorphize.
The intolerance of uncertainty was measured by a short version of the Intolerance of Uncertainty scale (IOU) (alpha was .78), designed by Carleton, Norton and Asmundson (2007). Participants had to rate 11 items by indicating to which degree a statement was applicable to them on a five point Likert-scale. Answer options varied from 1 (total disagree) to 5 (total agree).
Participants had to rate statements such as, ‘’I can’t stand being surprised’’. Scores ranged from 11 to 55. The higher the score the less a participant was able to tolerate uncertainty.
Procedure
The participants were tested individually in a room with only a desk, chair and computer. Each participant got accompanied by one of the two instructors. Both instructors executed their own designed study. These instructors took place in a room next to the participants. Subsequently, the participants were connected to the GSR. After this the instructor installed the headphone, checked the volume to be on 20 and informed the participant to watch the screen and follow the
instructions. The instructor then left the room. Next the instructions were shown. Participant had the chance to practice in three training sessions in which they saw one intentional clip, random clip and one mechanical clip. After each clip the participants had to decide if the animations were intentional. The participant received immediate feedback after each practice trial. The participants were instructed to connect to the headphone after the last practice trial. The darkening of the room depended on the condition. The participants then started with rating the 36 clips spread over two blocks. The clips were randomly shown. An addition to the practice trials was that intentional clips were emotionally evaluated by indicating if the movements were positive, neutral or negative. The participants had a mandatory break after each block. During these mandatory breaks the
participants had to fill in two short (10 questions in total) questionnaires at qualtrics.com about their level of anxiety and control. They did this four times in total. The music was muted and the lights went on during these breaks. Depending on the following block the manipulations started again at the beginning of the subsequent task. The participants completed the third and fourth block in the same manner as the first two blocks. The same 36 clips were shown again but in a different order. As soon as the last block was done, the instructor entered the room, disabled the GSR and asked the participants to fill in the questionnaires on qualitrics.com. The participants were debriefed by
reading a final document on qualtrics.com, in which their rights were stated. Each session lasted approximately 40 minutes. Participants red and signed the informed consent prior to the experiment.
Results Data
Two participants were left out of the analysis due to their extreme values3 on the dependent
variable in the control condition4. After plotting, they proved to be inconsequent with the pattern as
well. These values only appeared after merging the mechanic and random variables in both conditions. Where the exclusion the first outlier (participant number 120) had a positive impact on the test results, the exclusion of the second outlier (participant number 211) had a negative effect 5.
Either way, both were excluded because of their extreme values in the control condition. An extra seven participants were excluded for measurements considering the anxiety due to missing values.
Manipulation check
In the first analysis, two repeated measures ANOVA were executed over anxiety and level of control during the test with condition (horror / control) as the independent variable and the
sequence of the blocks as between-subjects variable. In the case of anxiety, the non-parametric test
3 Two participants (number 120 and number 211) were three times the interquartile (difference between
upper and lower quartile) away from the upper quartile.
4 Participant number 120 reported a high number of false-positives in the control condition (17) opposed to
the horror condition (11). This participant proved to be the only one who did not followed the normal pattern. This might be explained with the note that the sounds of the control condition were experienced as very annoying. Participant 211 reported a mild outlier in the horror condition (19) and an extreme outlier in the control condition (16). This indicated that this participant did not understand or refused to follow the instructions.
5 By excluding participant number 120, a bigger main effect of condition was found, F(1, 31) = 5.43, p=.029, η2=.60. By excluding participant number 211, no main effect of condition was found F(1,31) = 1.87, p=.181, η2 =.057.
Pillai’s Trace was used6. A main effect of condition, F (1, 23) = 53.76, p<.001, η2=.70 indicated that
participants experienced more anxiety in the horror condition (M=29.30, SD=1.52) than the control condition (M=20.75, SD=1.09). This indicated that the manipulation had the effect that was
expected. A marginal significance interaction F(5, 23) = 2.57, p=.054, η2=.36, indicated that the
sequence of the blocks did had a marginal effect on the level of anxiety participants experienced. Though post-hoc testing revealed no significant difference between sequences of the blocks. This indicated that no specific sequence proved to be of a significance influence on the level of anxiety.
The non-parametric test Pillai’s Trace was also used over the level of control during the test7.
A main effect of condition, F (1, 30) = 20.43, p<.001, η2=.41 indicated that participants experienced
less control in the horror condition (M=9.40, SD=.65) over the control condition (M=11.12, SD=.62). This indicated that the manipulation had the effect that was expected. The sequence of blocks proved not to interact with this found main effect F(1, 30) = 1.47, p = .23, η2=.20.
Test results
A repeated measures ANOVA was conducted over the false-positives in perceiving
intentionality. Within subject variables were the type of clip (mechanic / random) and the condition (horror / control) with the sequence of the blocks as the between-subject variable. Intentional clips were not included in this analysis because false-positives in intentionality detection could not be generated with these clips, only false-negatives. False-negatives in intentionality detection represent the opposite outcome.
6 Reason for using a non-parametric test: a significant value of the Box’s test, F (15, 746.75) = 46.69, p =.005,
and the significant value of the total anxiety in the horror condition of the Levene’s test, F (5, 23) = 6.88, p <.001.
7 Reason for using a non-parametric test: the Box’s test proved to be significant, F (15, 2372.32) = 1.848, p
A non-parametric test was used due to the significant values of Levene’s test in the
mechanic clips, F (5, 30) = 4.51, p=.004, and random clips, F (5, 30) = 4.99, p=.002, within the control condition. These significant values were not to be found in the mechanic clips, F (5, 30) = 1.95, p=.115, and random clips, F (5, 30) = 1.77, p=.15, within the horror condition.
As expected, a main effect of condition, F (1, 30) = 4.56, p= .041, η2= .13, suggested that
participants in the horror condition (M=1.48; SD=.33), reported more false-positives in the detection of intentionality when rating the mechanic and random clips, over the control condition, (M=1.10; SD; .27), see table 1. This indicated that they saw more intentionality when there actually was none. A main effect of type of clip, F (1, 30) = 17.33, p<.001, η2= .37, indicated that participants incorrectly
saw more intentionality in random clips (M=2.10, SD=.45) over mechanic clips (M=.48; SD=.19), see table 1.
Table 1
Averages and standard deviations of false-positives. Different columns represent the different type of clips. Different rows represents the different conditions.
Condition Mechanic Random Total
Horror .63 (.22) 2.33(.50) 1.48(.33)
Control .34(.19) 1.87(.43) 1.10(.27)
Total .48(.19) 2.10(.45)
The sequence of the blocks proved not to influence the found main effect of condition, F (5, 30) = .19, p=.96, η2=.03 . This indicated that the number of false-positives didn’t depend on
sequence of the blocks. Finally, against our expectation, the two main effects of type of clip and condition did not interact, F (1, 30) = .51, p=.48, η2=.02. The type of clip showed the same kind of
pattern over the two conditions, see figure 1. This indicated that participants did not differ in the way they experienced increased difficulty, over condition, with rating the intentionality of both clips.
Figure 1. Averages of false-positives in intentionality detection. Different lines represent the different type of clips spread out over two conditions.
Another repeated measures ANOVA was conducted over the false-negatives of the
intentionality clips. While condition (horror / control) was the within subject variable, the sequence of block served as the between subject variable. The non-parametric test Pillai’s Trace was used. Results indicated that the condition did not have an effect on the number of false alarms of in the rating of intentional clips F(1,30)=.94, p=.339.
To investigate if possible covariates could explain the found results, a regression analysis was executed over the contracted score between the horror and control condition in false-positives. Covariates as age, education, gender, negativity bias (M=29.89), RPBS (M=30.08), IOU (M=27.82), anthropomorphism (M=46.08), and religiosity (M=11.68) were included in this analysis. Not a single covariate proved to be a significant predictor of false-positives.
Discussion 0 0,5 1 1,5 2 2,5 control horror Fa lse -p os iti ves (a ver ag e) Condition mechanic clip random clip
The goal of this study was to investigate a possible existence of a HIDD. Two findings support this existence. First, it was found that participants made more mistakes by attributing intentionality to random or mechanic movements in a threatening situation. This supports our hypothesis that the experience of threat leads to more false-positives in the detection of intentionality. Whereas
previous studies focused on individual differences (Riekki et al., 2013; Riekki et al., 2014; van Elk, 2013), indirect threatening situation (Epley et al., 2008; Valdesolo & Graham, 2014) or only used tasks that challenged the participants to perceive agency, without attributing intentionality (Barrett, 2000; Guthrie, 1993; van Elk, 2013), our finding suggests that direct threat influences our perception by perceiving more intentional agents that are not there. This finding is in line with the EMT; it is better being safe than sorry. In turn, these false perceptions of intentionality under threat gives support to a possible existence of a HIDD.
Second, it was found that the variances considering false-positives in the control condition were distributed unevenly. The majority reported no false-positives whereas a substantial number reported a high number of false-positives. Average results were rare. This indicates that there is individual difference in the way people perceive intentionality in a non-threatening situation. We could speak of a difference in sensitivity of the HIDD, would it exist. What speaks in favor of this existence is the disappearance of the individual differences in the horror condition. Threat seems to level out the individual differences in intentionality detection. In other words, people react the same in a threatening situation (more false-positives) because this triggers our intentionality detection device to be hyperactive.
An interesting, unexpected, outcome was the insignificance of the interaction between the type of clip and the condition, see figure 1. We expected that the number of false-positives of the random clips would increase over condition, we did not expect the same pattern with the mechanic clips. We anticipated that the mechanic clips were clear and easy to interpret, and thus expected that a difference of condition would be of no influence. Obviously, the absolute number of
false-positives was higher in the random clips; the more ambiguous clips. In light of a possible existence of a HIDD, this indicates that people see more intentions in ambiguous situations, which is in line with Barrett and Lanman (2008). But, the addition of threat makes it evenly more likely to detect intentions in ambiguous as in situations where it is clear that no intentions are present. This seems odd.
A shortcoming of this study that might explain this odd result is associated with the manipulation of light. Multiple participants mentioned that they had trouble finding the right keys on the keyboard after rating the emotion of the intention (other keys) in the horror condition. These ‘mishits’ might have explained the difference in false-positives between the conditions. However, participants reported no difference in false-negatives with rating intentional clips over condition. This indicates that the manipulation of light is only a theoretical confound.
A second shortcoming of this study was the used sample. By the use of convenience
sampling a great majority of our sample included acquaintances. Despite a strict procedure, working with acquaintances could have had a negative effect on the manipulation. There was a less
professional relationship. It is assumable that they felt more relaxed, felt less threatened or lost focus because they knew the experimenter. This could explain the low effect-size we found. They even might have tried to please the experimenter by answering what they thought the study hoped for. Which could explain the effect of condition. However, answers of the participants on concluding question in the survey indicated that the purpose of this study was not clear to them.
Besides these methodological shortcomings, the theoretical discussion between the HADD and the HIDD is not resolved by this study. It is still not clear if the HADD and the HIDD are two separate systems, if they work together or if they are one and the same. The support of this study to the HIDD does not imply anything about the HADD. It does give support to a system that is
supernatural (Lisdorf, 2007). Further research should examine the role of the HIDD opposed to the HADD in an effort to end this discussion.
A concluding finding of this study considers the insignificance of the covariates. The level of religiosity, paranormal belief or anthropomorphism proved no significance predictor of number of false-positives. While some theories and studies claim a relationship between supernatural belief and false-positives in the detection of intentional agents, results from this study indicate no such direct relationship exist. A possible explanation for this finding might lie within our sample, in which a high percentage had an university background. They reported low levels on the covariates. Further research might use a sample that report higher scores on aspect of supernatural beliefs to check if such relationship exists.
In sum, the present study indicates that people may have an intentionality detection device that gets hyperactive in a threatening situation; producing more false-positives. Despite the low effect-size and the methodological shortcoming this study still supports the assumption of
researchers, whom operate within the CSR, of the existence of such system. Yet, it remains unclear what the boundaries are of the HIDD, ToM and HADD. In remains unclear if and how they differ or if they are one and the same. Anyway, this study is not capable of determining if the HIDD is
responsible for belief in the supernatural or that it is responsible for the origin of religious thought. No such relationship has been found. Besides, these beliefs seem too complex to be explained by such a simple task. It withholds beliefs, rituals and group processes that are determined by different factors as upbringing, education and culture (van Elk, 2013). Not by a HIDD alone.
Acknowledgements
We thank Reikki et al., (2014) for the use of their GFT; which included the addition of the mechanic animations.
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