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Received: 12 February 2023 Revised: 4 April 2023 Accepted: 10 April 2023
DOI: 10.1002/brb3.3026
ORIGINAL ARTICLE
Follow-up on the U.S. Central Intelligence Agency’s (CIA)
remote viewing experiments☆
Álex Escolà-Gascón1James Houran2,3Neil Dagnall4Kenneth Drinkwater4
Andrew Denovan5
1Area of Applied Mathematics and Statistics,
Ramon Llull University (Blanquerna
Foundation), Barcelona, Spain
2Laboratory for Statistics and Computation,
ISLA—Instituto Politécnico de Gestão e
Tecnologia,Vila Nova de Gaia, Portugal
3Integrated Knowledge Systems, Dallas, Texas,
USA
4Psychology Department, Faculty of Health,
Psychology and Social Care, Manchester
Metropolitan University, Manchester, UK
5Department of People and Performance,
Faculty of Business and Law, Manchester
Metropolitan University, Manchester, UK
Correspondence
Dr. ÁlexEscolà-Gascón Professor of Applied
and Data Analysis at Blanquerna Foundation,
Ramon Llull University, Barcelona, Spain.
Email: alexeg@blanquerna.url.edu
☆Special mention to Dr. Edwin May,former
Director of Central Intelligence Agency Remote
Viewing Research Program. The authors
acknowledge the declassified materials he
provided for the proper development of this
study.
Abstract
Objectives: Since 1972, the U.S. Central Intelligence Agency (CIA) commissioned sev-
eral research programs on remote viewing (RV) that were progressively declassified
from 1995 to 2003. The main objectives of this research were to statistically repli-
cate the original findings and address the question: What are the underlying cognitive
mechanisms involved in RV? The research focused on emotional intelligence (EI) theory
and intuitive information processing as possible hypothetical mechanisms.
Methods: We used a quasi-experimental design with new statistical control techniques
based on structural equation modeling, analysis of invariance, and forced-choice exper-
iments to accurately objectify results. We measured emotional intelligence with the
Mayer—Salovey–Caruso Emotional Intelligence Test. A total of 347 participants who were
nonbelievers in psychic experiences completed an RV experiment using targets based
on location coordinates. A total of 287 participants reported beliefs in psychic experi-
ences and completed another RV experiment using targets based on images of places.
Moreover, we divided the total sample into further subsamples for the purpose of repli-
cating the findings and also used different thresholds on standard deviations to test
for variation in effect sizes. The hit rates on the psi-RV task were contrasted with the
estimated chance.
Results: The results of our first group analysis were nonsignificant, but the analysis
applied to the second group produced significant RV-related effects corresponding to
the positive influence of EI (i.e., hits in the RV experiments were 19.5% predicted from
EI) with small to moderate effect sizes (between 0. 457 and 0.853).
Conclusions: These findings have profound implications for a new hypothesis of
anomalous cognitions relative to RV protocols. Emotions perceived during RV sessions
may play an important role in the production of anomalous cognitions. We propose
the Production-Identification-Comprehension (PIC) emotional model as a function of
behavior that could enhance VR test success.
This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided
the original work is properly cited.
© 2023 The Authors. Brain and Behavior published by Wiley Periodicals LLC.
Brain Behav. 2023;13:e3026. wileyonlinelibrary.com/journal/brb3 1of20
https://doi.org/10.1002/brb3.3026
ESCOLÀ-GASCÓN ET AL.2of20
KEYWORDS
anomalous cognitions, central intelligence agency, emotional intelligence, psi, remote viewing
1INTRODUCTION
In 1995, U.S. President Clinton, by order number 1995-4-17 enti-
tled “Classified National Security Information,” declassified several
research programs (among other contents) funded by the Central Intel-
ligence Agency (CIA) and Defense Intelligence Agency (DIA) of the United
States (Puthoff, 1996). These covert programs were developed over
more than 20 years at the Stanford Research Institute (SRI, now SRI Inter-
national) and the Science Applications International Corporation (SAIC)
(cf. Srinivasan, 2002). Programs addressed remote viewing (RV), that
is, determined whether certain individuals, under conditions of per-
ceptual isolation, could access information about places, buildings,
photographs, etc., from a distance using putative psi rather than con-
ventional sensory channels (Targ, 2019). The specific objective was to
explore whether RV phenomena had enough consistency and stability
for use in military espionage (McMoneagle, 2015; Puthoff, 1996). Due
to the Cold War and ensuing political-military tensions between the
United States and the former Soviet Union, American Congress classi-
fied these programs in the interests of national security (Targ, 1996).
The fact that the RV experiments were hidden or classified under-
mined transparency in scientific research practices. Specifically, other
laboratories were not given access to information and were unable to
evaluate outcomes with proper methodological or statistical rigor (see
the critique by Hyman, 1996 andNelsonetal.,1996).
1.1 What is remote viewing?
RV is an experiential technique for altered-anomalous states (see Utts,
1995,1996,2018) that allows two types of anomalous cognitions to
be subjected to empirical scrutiny (see also Schooler et al., 2018): (a)
precognition (also called anticipation of unpredictable stimuli or anoma-
lous anticipation of information, Mossbridge et al., 2012) can be defined
as the process by which a person accesses information about the
future (i.e., events that have not yet happened) without using sensory
or otherwise rational channels recognized by conventional scientific
theory (Bem, 2011); and (b) retro-cognition (also called anomalous infor-
mation reception or clairvoyance) is defined as the process by which a
person accesses content referring to the past (i.e., content that has
already happened) without using the conventional channels of biol-
ogy or logic per current scientific theory (Marwaha & May, 2016). The
expression psi phenomena or psi is a hypothetical construct that has
the same definition attributed to anomalous cognitions. However, the
term anomalous cognitions is a more neutral label, as the term psi is
often used by parapsychologists. All these concepts have been sharply
criticized on methodological, statistical, or conceptual grounds (e.g.,
Escolà-Gascón, 2022a; Houran et al., 2018; Reber & Alcock, 2020;
Wagenmakers et al., 2011).
In RV, the participant is asked to visualize the information they
intend to access (from the past or the future) (Roe et al., 2020). Then,
the participant must mentally and nonverbally represent the distant
target or targets to be guessed (May et al., 2011; Scott, 1988). The
target is often a specific place, person, or fact (May, 1996; Puthoff,
1996;Targ,1996). The targets of RV experiments (published in Nature,
see Targ & Puthoff, 1974) contained specific meanings of interest
to U.S. national security (e.g., the location of a secret military base)
(see Utts, 1995,1996,2018). The present study focused on RV rel-
ative to anomalous information reception, as it is one of the most
researched anomalous phenomena showing significant results (see
Bem et al., 2016; Tressoldi & Storm, 2021). Unfortunately, the abbre-
viation for anomalous information reception (AIR) is the same as the
abbreviation for the American Institutes for Research (also AIR) and we
wish to prevent confusion. So, henceforth, we use the terms anoma-
lous cognitions and RV to refer exclusively to anomalous information
reception.
1.2 Scientific reviews and conclusions after the
CIA declassification
Reports on the declassified SRI and SAIC experiments were evalu-
ated in 1995 by statisticians Utts (1995, 1996,2018)andHyman
(1996) for the American Institutes for Research. Although the two
authorities agreed on some points, they conflicted on several, with
the most significant disagreement being the ultimate conclusions.
Utts determined that the evidence from the SRI and SAIC experi-
ments was sufficiently consistent to accept that RV phenomena were
empirically validated. In contrast, Hyman did not consider this evi-
dence adequate, criticized some of the methodological procedures
applied by SRI, and contended that it did not support the assertion
that RV phenomena were “scientifically established.” However, they
both agreed on a critical interpretation—namely, that the effect sizes
of the experiments conducted at SAIC (which were the most rig-
orous and addressed methodological problems evident in research
conducted at SRI in May, 1996) were consistent and homogeneous.
In the words of Hyman (1996, p. 52), “At best, the results of the
SAIC experiments combined with other contemporary findings offer hope
that the parapsychologists may be getting closer to the day when they
can put something before the scientific community and challenge it to
provide an explanation.” This assertion invited further studies of RV
that attempted to replicate the observed effects (see Marwaha &
May, 2015).
3of20 ESCOLÀ-GASCÓN ET AL.
1.3 Subsequent research
Numerous experiments on anomalous cognitions have yielded results
statistically favorable (see the original experiments of Maier et al.,
2014) and unfavorable (see the replication of Ritchie et al., 2012)tothe
psi hypothesis. In the case of RV, experiments with significant results
greatly predominate (e.g., see another Nature publication, Tart et al.,
1980, and the contributions of Dunne & Jahn, 2007; Roe et al., 2020;
Schmidt et al., 2019) over unsuccessful statistical replications (e.g.,
Escolà-Gascón, 2022a; Marks & Kammann, 1978).
A curious trend and one that should be considered in this context
are sheep-goat effects. In this effect, individuals who are advocates of
parapsychology and who have had psi experiences tend to get a higher
number of hits than non-psi experiencers (Thalbourne, 2001;Thal-
bourne & Houran, 2003; Thalbourne & Storm, 2012). This trend was
obtained even in unsuccessful psi replications recently published (e.g.,
Escolà-Gascón, 2022a). Although it is not known why this effect occurs,
some evidence suggests that it may be a bias related to response rep-
etition (e.g., Brugger et al., 1990); in any case, the distinction between
believers and nonbelievers is supported by evidenceand is appropriate
to apply.
Researchers addressing these issues are positioned in two groups
with conflicting stances: (a) one group includes scientists advocating
RV and anomalous cognitions (due to the cumulative empirical evi-
dence, e.g., Cardeña, 2018); and (b) the other group of researchers
who are currently not persuaded by the significant evidence for
anomalous cognitions and, due to other replications without statisti-
cal successes, reject the validity of putative psi (e.g., Reber & Alcock,
2020). Although both positions have empirical support (Escolà-Gascón,
2020a,b; Escolà-Gascón et al., 2021), the current issue for these groups
is the ideological radicalization they have undergone in the last few
decades (Carter, 2011;Leiter,2002). This extreme scientific preju-
dice resulted in the marginalization of RV and the scientific study of
anomalous cognitions (e.g., Odling-Smee, 2007).
Other researchers, who are more neutral to these polarized ide-
ologies, have emphasized the need for more research because the
statistical evidence to date is insufficient due to the extraordinary epis-
temic characteristics of RV phenomena (see Hyman, 1996). Moreover,
the significant results obtained remain a challenge to current scien-
tific knowledge (Escolà-Gascón, 2022a). It is said that epistemically,
the hypotheses of RV are extraordinary because they have no ratio-
nal or etiological foundations to explain the origin of these phenomena
(Wooffitt, 2007). When an object of study is extraordinary (or implies
anomalous phenomena), its scientific validation cannot be based on
ordinary evidence (Tressoldi, 2011). However, the lack of epistemic
foundations does not preclude or nullify the investigation of anoma-
lous cognitions (see Cardeña, 2018; Hyman & Honorton, 2018). In
fact, neither all scientific knowledge is rational, nor do all hypothe-
ses under investigation have epistemic validity as noted by Henry
(2005)andLeifer(2014). An example can be found in the mathemat-
ical theorems of incompleteness (Cheng, 2021; Visser, 2019), which
demonstrate that, mathematically, the study or acceptance of undecid-
able questions, such as anomalous cognitions, does not imply rejecting
rationality as the basis of scientific knowledge (see the current review
by Kennedy, 2022). A clearer example is in the logical principle of nonlo-
cality used in quantum mechanics (Mauri, 2021; Neppe & Close, 2015).
If science accepts objects of inquiry that are extraordinary in ques-
tions of quantum physics and in mathematics, it at least should also be
able to accept the scientific investigation (and not the scientific valid-
ity) of anomalous cognitions (Henry, 2005). We further contend that
investigations of anomalous phenomena must adopt the principles of
objectivity, confrontation, and the mutability of the scientific process
(Bunge, 2013). Not applying this approach to the study of seemingly
divergent or undecidable objects of study would otherwise result in
the Aristotelian fallacy of the negation of the consequent and prevent
the exercise of scientific falsification (Escolà-Gascón, 2020a, 2020b).
Moreover, assuming this conclusion without the contrast or applica-
tion of the method would also have serious ethical consequences and
promote scientific prejudice and pseudo-skepticism that characterizes
“scientism” (Houran & Bauer, 2022;Leiter,2002; Truzzi, 1987).
1.4 The signaling theory of emotions
In his report, the former director of the SAIC RV research program
mentions the role of emotions as a potential factor that could influence
participants’ performance (see May, 1996). The possible influence of
emotions on RV testing was also mentioned in other subsequent pub-
lications (e.g., May & Marwaha, 2018). Recently, Escolà-Gascón et al.
(2022b) published with Cell Press a report on anomalous cognitions
showing a quadratic relationship between the use of emotions and hits
on precognition tests. Although the hits on precognition tests were
unsuccessful, the significant relationship between perceived emotional
intelligence (EI) and hits supports the possibility that EI may be an influ-
ential cognitive factor in the use of anomalous cognitions. One of the
criticisms the authors received was that they measured perceived EI
using self-report questionnaires and not as a formal cognitive ability
(see Escolà-Gascón et al., 2022b). Therefore, one possibility for extend-
ing research on RV would be to include the assessment of EI as a
cognitive attribute mediating the outcomes of anomalous cognitions.
In the following paragraph, we propose a possible theoretical approach
that could justify this association.
Salovey and Mayer (1990) developed a theoretical model of emo-
tions and the meaning of EI. They viewed emotions as behaviors that
emit signals with psychological meanings that are decoded by the
receiving individuals or the environment (cf. Mayer & Geher, 1996).
This decoding usually involves the activation of a rational-strategic
reasoning and cognitive reasoning based on intuition and experience
(Mayer et al., 2000); both are grounded in dual models of cognitive pro-
cessing (Evans, 2003; Osman, 2004). Similarly,the contents of decoding
vary according to multiple factors ranging from sociocultural variables
to more biological issues or individual differences (Mayer & Salovey,
1995). Within this model, EI is understood as a skill set to identify, dis-
criminate, generate, and apply one’s own emotions and those of others,
ESCOLÀ-GASCÓN ET AL.4of20
as well as to use them for redirecting one’s own thoughts or behav-
iors (Salovey & Mayer, 1990). Therefore, EI is not a personality trait
but a cognitive attribute that is independent of the classical construct
of general intelligence (Mayer et al., 2002). Mayer et al. (2016)cre-
ated a cognitive assessment instrument (with hits and misses) to test EI,
which was called the Mayer—Salovey–Caruso Emotional Intelligence Test
(MSCEIT).
The rationale for linking EI to RV outcomes draws on the proposal
that anomalous cognitions function as a crawl by an individual in search
of distant information (May,1996; Utts, 1995, 1996,2018). In this case,
the targets (e.g., the locations of places) that the RV participant must
ascertain might—like emotions—have signals unknown to current sci-
entific knowledge, yet detectable by certain people. An assumption
of anomalous signals is based on the logical axiom of nonlocality (e.g.,
Lucadou et al., 2007); that they are detectable is the main hypothetical
model tested here. Similarly, the nonlocality principle is also considered
in MSCEIT indirectly; the original authors did not cite this principle in
their theoretical justification, but it was deducible at the time that they
employed the experiential and intuitive areas to measure EI.
More specifically, the signals that Mayer et al. (2016) attributed to
emotions are not assumed to be a wave function equivalent to signals
emitted by other physical systems (e.g., a cell phone antenna). The sig-
nal is a stimulus that contains key information (meanings); the stimulus
or emotion is modeled as a signal because it communicates a message
or state and not because the signal is a wave function. Understanding
this point is vital, as anomalous cognitions also cannot be assumed to
be physical signals measurable as wave functions. In fact, the targets
used in RV are not rationally connected to sensory perception (through
the conventional senses). The same is true for the meanings attributed
to emotions (which remain undetermined until the individual makes an
observation): the same emotion can have different meanings, and there
is no logical chain of rational interpretations. For example, a person
could interpret their experience of the “fear” emotion as feeling per-
sonally threatened. In the case of EI, the meaning of “feels threatened”
is not exclusively the product of a logical-strategic procedure, it also
includes a dimension, that is, irrational and intuitive. This is the aspect
that our study is interested in measuring.
1.5 The present study
Research on RV is useful and necessary for two essential reasons.
First, it represents one of the frontiers of current knowledge. Sci-
ence does not advance only by investigating what we already know; it
must also confront uncertainty and transform the unknown into some-
thing operative and accessible to human knowledge (Leifer, 2014).
Second, theorists currently lack knowledge of many of the regulating
mechanisms of human perception and cognition (Khrennikov, 2015).
Indeed, we should not exclude RV phenomena from the study of sen-
sory and cognitive processes because there is evidence that indicates
that anomalous cognitions ontologically represent more than method-
ological or statistical artifacts, perceptual disturbances, or clinical
symptoms (Cardeña, 2018).
FIGURE 1 Hypothetical mechanistic model that relates emotional
intelligence to the application of remote viewing. This figure also
includes the logic of how the experiments were executed (see “Section
2” for more information).
This study does not a priori affirm or deny the ontological existence
of psi, instead the authors scrutinize anomalous phenomena in statis-
tical and falsificationist terms (cf. Popper, 1959; Schooler et al., 2018).
More concretely,we analyze differences between observed results and
estimated expectations to verify the findings of the SAIC experiments
as per Hyman’s (1996) recommendations. Strictly speaking, any signif-
icant results would not validate the existence of anomalous processes
in RV phenomena, but would strengthen the hypothesis in favor of psi-
related RV. Such an outcome would provide an important update on the
status of these phenomena.
Furthermore, the authors analyzed the association between
experiential-based emotional processes and RV outcomes—
particularly, the relationship between the experiential area of EI
and the participant’s hit rate. If the targets were to function analo-
gously to the experiential facet of EI, this would lend credence to the
hypothesis that emotions play a key role in generating anomalous RV
phenomena. The main difference with the MSCEIT model of EI is that
in RV the strategic facet would not be used because there would be
no sensory contact between the participant and the target. This would
suggest the hypothetical model illustrated in Figure 1.
The model in Figure 1is explained as follows: First, the target to be
guessed is fixed (both for the coordinates and for the images). Next,
the RV technique is used, and the participant is asked to visualize the
type of place to which the target belongs. When applying RV, the par-
ticipant is asked to close their eyes, take several deep breaths, and
concentrate on their thoughts. Then, the participant activates their
cognitive schemas and establishes an abstract thought-representation
of the supposed place. After this thought-representation, an emotion
5of20 ESCOLÀ-GASCÓN ET AL.
should follow (this is based on the stimulus-thought-emotion-behavior
logic, see Lazarus, 1982). According to the dual process (see Evans,
2003; Osman, 2004) of EI as a cognition, the perceived emotion will
be used by the participant as an experiential or intuitive procedure to
make anomalous cognition decisions. Our exploratory hypothesis is to
find out whether EI acts as a mediating variable between belief systems
and psi test scores.
2METHODS
2.1 Description of the sample
The sample consisted of 634 participants between 20 and 63 years
of age (M=41.25; SD =12.45). Of these, 62% identified as women
and 38% as men. All of them declared no prior psychiatric history and
signed their informed consent to this research. Participants data were
recorded anonymously.
The researchers formed two groups that had different experimen-
tal and sampling conditions: (a) Group 1 consisted of people who
reported no previous “psychic” experiences (nonbelievers, n=347),
and RV experiments based on coordinates of specific locations were
applied (see Subsection 2.2.1. for more information); and (b) Group 2
consisted of people who previously reported having “psychic” experi-
ences (believers, n=287), and RV experiments based on images of the
locations identified by the coordinates were applied.
2.1.1 Why participants were classified as
“nonbelievers with coordinates” and “believers with
photographs”
This classification and distribution of participants was based on previ-
ously published evidence found by other researchers. On one hand, the
distinction between believers and nonbelievers was based on sheep-
goat effects, which show that experienced individuals have favorable
attitudes toward parapsychology and perform better on experimental
psi tests than nonbelievers (Thalbourne, 2001; Thalbourne & Houran,
2003; Thalbourne & Storm, 2012). This trend was recently observed
in the replication by Escolà-Gascón et al. (2022); although no signifi-
cant effects in favor of anomalous cognitions were obtained, believing
participants scored higher than nonbelievers on the RV tests.
On the other hand, CIA declassified reports from the SRI and SAIC
revealed that participants tended to obtain better matches or hits
when they applied RV with targets that were graphical representa-
tions (e.g., photographs). In fact, considering this pattern, May and
Marwaha (2018) speculated that participants applying RV with pho-
tographs might be describing the characteristics of the contents of the
photographs rather than the actual physical locations depicted in the
photographs. If the previous evidence was correct, generatingbeliever-
photographs and nonbeliever-coordinate groups should maximize the
observed statistical differences in scores between both.
Therefore, the criterion concerning why these two groups were
formed, was supported by the previous statistical evidence, and we
aimed to find out whether the previous evidence remained stable in the
present replication.
2.2 Procedures and materials used
2.2.1 Explanation and conditions of the new RV
experiment
A RV experiment model was designed based on the techniques used
in the SAIC, as well as forced-choice designs. The interjudge design
(applied in the original RV experiments) was discarded due to the asso-
ciated methodological problems detected in the last decades and for
being highly unstable (Kruth, 2021). Additionally, Hyman (1996)and
other skeptical researchers criticized this design because the judges
who evaluated participants’ responses in the original experiments
(determining to what degree participants’ RV responses matched or
not matched the targets) were not external to, or independent of, the
SRI and SAIC research centers.
In our case, RV targets (i.e., targets to be guessed) corresponded
to the locations of four types of places: (a) military bases, (b) hospi-
tals, (c) schools (or education centers), and (d) cemeteries. The authors
selected sites for their government interest and status as strategic
locations in the event of conflict or outright war. Thirty-two targets
were registered (eight each of military bases, hospitals, schools, and
cemeteries). The numbers were equivalent to ensure equiprobability of
target type. The registration of the targets was applied via two means:
(a) the geographical coordinates of their location were taken; and (b)
exact images of the point indicating the coordinates were extracted
from Google Maps. Even if the participants had no perceptual connec-
tion or access to the information of each target, this was important
to evaluate whether the target’s “presentation type” (i.e., coordinate-
based presentation versus picture-based presentation) affected the
experimental outcomes.
Each participant performed 32 trials: in each trial, one of the 32
locations was randomly selected beforehand. Specifically, the random
selections were made taking into account the category of each of the
locations: first, one location from each of the categories was randomly
selected; second, after one location from each category had been ran-
domly selected, one location category was also randomly selected from
the four typologies. This chosen location is the one that the participant
was expected to hit by supposedly employing anomalous cognitions.
In the first random selection, there was replenishment of the loca-
tions for each trial; that is, after a location had been chosen from a
specific category and for a specific trial, said location was available
again to be randomly selected in the next trial. Participants were only
informed that there were four types of locations and that they had
to guess which of them had been previously selected. Participants in
both groups also knew that in each selected category, a location was
assigned.
ESCOLÀ-GASCÓN ET AL.6of20
FIGURE 2 Graphical summary of the steps performed in the
remote viewing experiment. These steps are in accordance with the
proposed hypothesis in Figure 1.
For Group 1, each coordinate was printed on a micropaper that
was stored in a small envelope, with this envelope then placed in an
A5-sized envelope (like matryoshka dolls). The envelopes were sealed,
and both researchers and participants were blinded to their contents.
An external technician assistant, independent of the researchers
handled this process, and another support technician checked that
the envelopes had no marks, transparencies, or otherwise showed
evidence of tampering to ensure the internal validity of the protocol.
In each trial, the participant was shown an envelope containing the
location coordinates of a place, which could not be opened. The partic-
ipant could see the envelope but not physically touch it or manipulate
it. The RV protocol was then implemented, participants were asked to
close their eyes, take up to four deep breaths, and instructed to visu-
alize, at least, to which type of place the randomly specified location
within the two envelopes belonged. For up to 15 minutes, participants
had to determine whether the target location was a military establish-
ment, hospital, school, or cemetery. If the participant’s choice matched
the target category, +1 point (hit) was scored. When there was no
match, 0 points were scored. At the end of each trial, although the cor-
rect answers were not shown to the participant, there was a margin of
time for the participants to share with the experiment technician their
first impressions. One month after the experiment, the participant
could request to discuss their results with a researcher.
For Group 2, the same envelope procedure used for concealing the
coordinates of target locations was used to conceal the photographs
of the target locations. The participants then followed the same trial
procedure as Group 1, with the exception mentioned above. Finally,
selection of the location in both the coordinates and photograph exper-
iments was random and different for each trial and for each participant.
Thus, the correction template or stimulus sequence was different
across participants. Figure 2summarizes in an operational manner and
considering the contents of Figure 1, the steps of the experiments.
In total, 32 hits were possible, with an average of eight hits expected
by chance (32/4 =8). In each trial, the participant could also ver-
bally describe the contents they individually visualized about the target
location. This information was used for subsequent qualitative stud-
ies. The experimenter (vs. the study investigators) collated data and
responses for each experiment.
2.2.2 Specifications on the type of design used
In our research, we used the qualitative RV protocol originally
employed by the researchers at the SAIC institute. However, if we were
to use only these protocols, our study would be solely qualitative (with
the limitations that this represents). To use quantitative measures, we
included a forced-choice design, in which the participant had to choose
one of four specific alternatives (as explained in the previous subsec-
tion). Forced-choice designs are more robust and valid than any other
qualitative design. It is possible to combine the experimental tasks of
the original RV protocols with the forced-choice designs, generating a
more complete and extended protocol than the original RV protocols.
For this reason, this research is a protocol replication of what the
SAIC researchers did, but it is also an extension, as we integrate the
forced-choice protocols as outlined in the previous subsection. Clari-
fying this issue is crucial to avoid confusion and to better substantiate
why we consider the present study a replication and also an improved
extension of the original investigations that the CIA commissioned. By
employing a forced-choice design and quantifying the measurements,
we can also employ more robust predictive models such as the struc-
tural equation models (SEM) that we explain in the statistical analysis
subsection.
We hope to provide qualitative analyses in future reports; the
present research focuses on the quantitative and forced version
attributable to RV.
2.2.3 Experimental controls
The controls for the experiment addressed the major methodological
limitations of the SRI and SAIC experiments. Below outlines how the
critical points highlighted by Utts (1995, 1996,2018) were resolved in
the present study:
1. One of the problems with the CIA-funded SRI experiments was ran-
dom selection of targets without replacement, such that, when a
target was chosen, it was precluded from being chosen in the other
trials. Utts (1995, 1996,2018)andHyman(1996) both noted that
this practice could provide clues to the participant about the cat-
egory to which the targets belonged. Thus, the design employed
target replacement.
2. Another criticism of the SAIC experiments related to the coor-
dinates of the targets. In the original experiments, the target’s
coordinates were shown to the participant; hence, participants
knew the coordinates of the target that they were to describe. If
7of20 ESCOLÀ-GASCÓN ET AL.
any participant knew how to rationally interpret the coordinates of
a function, this could reveal an approximate location and facilitate a
guess. Accordingly,we concealed the coordinates from each partici-
pant using the envelope procedure, as outlined in Section 2.2.1., and
participants could neither handle nor manipulate the envelopes.
3. Hyman (1996) noted that the lack of double-blind conditions with
the participants and researchers in the original experiments could
have led to unwitting cuing of correct targets. Therefore, our par-
ticipants had no contact with the researcher during the execution of
the experiment. Instead, an experiment technician oversaw the pro-
tocol. Also, the technician and the participant were unaware of the
random target selections. The computerized random selections and
envelopes were prepared by an assistant independent of the exper-
imenters and investigators, stored in a locked cabinet, and given
to the experiment technician only at the time of the investigation.
Additionally, the researcher had no contact with the independent
assistant who made the random selections. This triple-blind tech-
nique guaranteed methodological rigor with respect to conscious
or unconscious cuing. Finally, participants did not have access to
the computer that made the selections or to the envelopes with the
coordinates.
2.3 The Mayer—Salovey–Caruso Emotional
Intelligence Test (MSCEIT)
The MSCEIT was developed based on the model of dual information
processing (Mayer et al., 2016). This measure consists of eight dimen-
sions (or tests) that, when grouped together,are amenable to structural
equation modeling of EI theory. These dimensions, and their associ-
ated activities and means of assessment, are: (a) faces—a task that
determines whether the participant knows how to correctly recognize
emotions in other people’s faces; (b) drawings—an activity in which the
participant must identify what emotions are being depicted in differ-
ent representations of art, music, and activities in the environment;
(c) facilitation—a cognitive task that examines the degree to which the
participant is able to understand how moods influence behavior and
thinking; (d) sensations—measures the degree to which the individual
is able to correctly relate the emotions he or she feels to primitive sen-
sations such as light, color, and temperature; (e) changes—assesses the
degree to which the subject understands a chain of emotions and how
emotions develop; (f) combinations—examines the participant’s ability
to classify and organize emotions into complex sets that define feelings;
(g) emotional management—analyzes the individual’s ability to employ
their emotions and use them in decision-making processes; and (h) emo-
tional relationship—measures the same as the previous task, but instead
of using their own emotions, the individual works with the emotions of
others.
The scores for these six dimensions are converted to EI quotients
(EQ) per respective normative groups. In this research, we used the
sex-differentiated normative groups belonging to the general Span-
ish population (see Mayer et al., 2016). These dimensions are grouped
into four categories (i.e., perception, facilitation, comprehension, and
management), which form two large “areas” (corresponding to the dual
models of cognition): the strategic area (analytical type reasoning) and
the experiential area (intuitive reasoning). Both areas collectively pro-
duce a total score of EI. The second-order factors can be combined to
form more summarized structures. For example, the eight dimensions
can be used directly to estimate the two areas of intelligence that are
of interest to our research, that is, the strategic and experiential areas
(Mayer et al., 2003). Finally, we should emphasize that the reliability
coefficients and internal consistency of MSCEIT scores in the present
samples were acceptable across all dimensions (alpha coefficient >0.8
and McDonald’s omega >0.8).
2.4 Sampling
The sample selection was nonprobabilistic (meaning that participants
were not chosen randomly). Participants were chosen from respon-
dents to specific announcements in academic organizations (profes-
sional associations and colleges) and informal groups of believing
individuals claiming to have had psychic experiences (these indepen-
dent groups have a presence in social networks). Collaboration with
these groups and organizations enabled the participation of the sample
described in Subsection 2.1. Prior to the RV experiments, the partici-
pant was asked to respond to the MSCEIT and to specify, on a 10-level
semantic differential scale, their attitude toward parapsychology and
psychic phenomena (see Figure 3).
Values or positions close to (−5) indicated a rejection of the possible
existence of psychic phenomena and positions close to (+5) reflected
an acceptance of the existence of such phenomena. Responses were
coded from 0 to 10 to measure the degree of favorable attitude
toward psychic phenomena. At the end of the MSCEIT and having
answered this question, the participants were given a 15-to-20-minute
rest period before starting the RV experiment.
2.5 Statistical analysis
We processed the data with the JASP software (based on the R pro-
gramming language, see JASP Team, 2023; The R Core Team, 2022),
and the AMOS expansion of the SPSS statistical package was used for
the SEM. Parameter estimation of the SEM analysis was based on the
maximum likelihood criterion. This criterion was used to obtain a wide
range of fit indices and to be able to perform the invariance analysis
comparing Groups 1 and 2 (see Putnick & Bornstein, 2016). The invari-
ance analysis is a method that allows us to know if the differences
observed between the two groups are attributable to the conditions of
the experiment or if, on the contrary, they are due to problems related
to measurement bias.
Applied to the theoretical model of this study, this method has six
levels of invariance that are set by establishing different restrictions:
(a) configuration invariance (equality restrictions on the configuration
of the theoretical model); (b) first-order factorial invariance (equal-
ity restrictions are set on the factor loadings of the first-order latent
ESCOLÀ-GASCÓN ET AL.8of20
FIGURE 3 Example of semantic differential scale used in this study. Responses were coded from 0 to 10.
variables); (c) second-order factorial invariance (equality restrictions
are set on the effects or loadings of the second-order latent variables);
(d) scalar invariance (restrictions are set on the parameters related to
the mediation effects involving the observed variables “hits” and “atti-
tude toward psychic phenomena”); (e) residual invariance of the latent
variables (equality restrictions are set on the latent variables receiv-
ing effects from others); and (f) residual invariance of the observable
variables (equality restrictions are set on the errors attributed to the
observable variables receiving effects).
Following Brown’s (2015) criteria, we complied with at least the
configuration invariance and the factorial invariance (although it is
advisable to also comply with the scalar invariance in order to be able to
carry out a contrast of the intercepts or latent means). The last two lev-
els (i.e., residual invariance) are usually not fulfilled because the errors
have a completely random statistical behavior. To check which levels
of invariance are met and which are not, the changes or variability of
three fit indices must be analyzed: the chi-square statistic, the compar-
ative fit index (CFI), and the root mean square error of approximation
(RMSEA). In the case of chi-square, the variation between the above
levels should not be significant (p>.05). For the CFI and RMSEA, the
variation should not be greater than 0.01 (Brown, 2015).
Analysis also determined whether participants’ responses in the
RV tests exceeded the expected statistical chance. For this purpose,
a right-handed one-sided contrast was applied using the one-tailed t
test. We also calculated the Bayes Factor in favor of the alternative
hypothesis (BF10 ) as an alternative estimator and set the a priori prob-
abilities distributions at 50%; thus, there was equiprobability1among
alternative and null hypotheses. To avoid confusion, here we spec-
ify our statistical hypotheses: the null hypothesis was that the hits in
RV experiments are not higher than expected chance; the alternative
hypothesis (unilateral), is that the hits in RV experiments are higher
than expected chance. The confidence level used in these analyses was
99% or higher.
3RESULTS
Prior to the analysis of the contrast of means and the check as
to whether the hits in the RV experiments exceeded the estimated
chance, the authors wanted to analyze the theoretical validity relating
EI to anomalous cognitions. In addition, we also wanted to statistically
analyze whether the answers given to us by the participants and the
scores obtained could be attributable to conditions related to the con-
tents of the questions and the design of the experiment. If this were
the case, there would be a bias problem in the MSCEIT and RV exper-
iments. This analysis is carried out by studying the invariances that
we explained in Subsection 2.4. Previously, Figure 4showed the lin-
ear correlation models between the variables, which should allow the
application of a mediation effects model among the variables. On the
one hand, the fit indices for Group 1 were as follows: χ2=37.838;
Normed χ2=1.221; RMSEA =0.025 (0.001–0.050); AGFI (adjusted
goodness of fit index) =0.961; CFI =0.995; TLI (Tucker–Lewis coef-
ficient) =0.993; IFI (incremental fit index) =0.995; RFI (relative fit
index) =0.964; NFI (normed fit index) =0.975. On the other hand,
the fit indices for Group 2 were as follows: χ2=33.110; Normed
χ2=1.068; RMSEA =0.015 (0.000–0.071); AGFI =0.962; CFI =0.998;
TLI =0.997; IFI =0.998; RFI =0.962; NFI =0.974. Due to the pos-
itive values of the fit indices in both groups, we were able to apply
and analyze the fixed effects in Figure 5. Concretely, Figure 5shows
the theoretical models with the standardized parameter estimates
(effects). Parameters that were not significant are bolded. These analy-
ses were applied for both Groups 1 and 2. Similarly, invariance analysis
was applied to the model in Figure 5.
The unmediated direct effects of “Attitudes”on “RV hits” were 0.302
(p<.001) for Group 1 and 0.244 (p<.001) for Group 2. As a first
conclusion, we can infer that the mediation effects of the variable
“Experiential” only reduced 15.1% 2of the variance of the direct effects
of Group 1 and 14.2% of the variance of Group 2. However, the effects
of the “Experiential” variable on hits were significant; which allowed us
to focus on the interpretation of these statistical effects.
The experiential area of the EI has small effects on the hits in the
RV experiments. Although the effects are small (0.3 to 0.4), it was pos-
sible to calculate what proportion of the variance of the hits could
be predicted by the experiential area: 15.8% for Group 1 and 9.1%
for Group 2. However, in Group 2, the strategic area also contributed
some information that, in total, predicts RV hits by 19.5%. In addition,
the experience area acts as a mediating variable between attitudes
towards psychic phenomena and RV hits. This observation comple-
ments the Sheep-Goat effect, where greater belief in the paranormal
positively correlates with greater sensitivity to internal and external
9of20 ESCOLÀ-GASCÓN ET AL.
FIGURE 4 Correlational theoretical model that would justify the application of a fixed effects model (the model in Figure 5). Standardized
effect parameters are shown with non-significant parameters highlighted in bold. Discontinuous lines indicate a mediation effect between the
variables “Attitudes” and “RV hits.”
stimuli (Thalbourne, 2001; Thalbourne & Houran, 2003; Thalbourne &
Storm, 2012).
Tab l e 1shows the results of the invariance analysis. The goodness-
of-fit indices provide insight to the theoretical validity of the model, as
well as the presence or absence of bias in the responses and the design
of the experiment.
All fit indices (including the chi-square index, which is usually very
sensitive in this type of analysis) supported the validity of the model
in both Groups 1 and 2. This result indicates a robust relationship
between EI and hits in the RV experiments. The invariance analysis was
also positive, which suggests the residual invariance of the latent vari-
ables and all prior levels. This implies that there were no biases in the
participants’ scores or in the design of the RV experiments.
Taken altogether,we conclude that the experiential area of EI clearly
and positively influenced the hit rate in the RV responses documented
here. We also surmise that no obvious biases altered and distorted
the research outcomes. Table 2begins the contrast of the mean val-
ues between Groups 1 and 2. Also included is the analysis of the latent
mean of the EI score and the contrast of the intercepts of the SEMs in
Figure 5.
Analysis found significant differences and small-to-moderate effect
sizes. In general, Group 2 scores exceeded those of Group 1. Specifi-
cally, RV hits increased in Group 2 by almost one SD over Group 1. The
highest effects were found for the attitude toward psychic phenom-
ena in Table 2. This increase in effect size could be explained by the
fact that participants in Group 1 reported no prior psychic experiences,
whereas those in Group 2 did. The same logic applies to increases in
scores on the EI variables. The intercepts were clearly significant and
represent the average value that would be obtained on the dependent
variables when the value of “x=0” in the function. Finally, the latent
mean revealed that the Group 2 EI mean differed from the Group 1
mean by up to three SDsappliedtothestandardizedfactorscores(z).
This is a more robust effect to consider rather than the direct differ-
ence observed for this variable. With these intercepts, this logic cannot
be applied, as it requires setting the Group 1 intercepts to “0,” which
would mathematically nullify the analysis because there is more than
one “0” involved.
Tab l e 3provides the most important analysis of whether the
hits were able to exceed the estimated mathematical expectation.
Because these analyses are provocative to the skeptical approach of
the authors, we wanted to include a division of the sample by sys-
tematically differentiating between participants with high levels of
experiential EI and those with low levels. This differentiation was made
according to two independent criteria: (a) we considered the original
criteria based on the EI quotients of Mayer et al. (2016). In this case,
scores equal to or above 110 would serve as a threshold to discrim-
inate between highly competent participants, from those within the
intervals of the mean (between 90 and 109 points) and against those
ESCOLÀ-GASCÓN ET AL.10 of 20
TAB L E 1 Analysis of the validity of the proposed theoretical model and analysis of invariance for bias control
Indices of goodness of fit
Invariance of
configuration
First order factorial
invariance
Second order
factorial invariance Scalar invariance
Residual invariance
(latent variables)
Residual invariance
(observable
variables)
χ274.670 (p=.170) 87.238 (p=.107) 92.697 (p=.070) 95.946 (p=.061) 101.876 (p=.036) 126.601 (p=.004*)
Δχ2–12.568 (p=.128) 5.459 (p=.065) 3.249 (p=.197) 5.930 (p=.052) 24.725 (p=.003*)
Normed χ21.167 1.212 1.253 1.262 1.306 1.455
RMSEA (Threshold: <0.05) 0.016 (∼0-0.030) 0.018 (∼0–0.031) 0.020 (∼0–0.032) 0.020 (∼0–0.032) 0.022 (0.006–0.033 0.027 (0.016–0.037)
ΔRMSEA (Threshold <0.01) – 0.002 0.002 0 0.002 0.005
CFI (Threshold >0.95) 0.996 0.994 0.993 0.993 0.991 0.985
ΔCFI (Threshold <0.01) – 0.002 0.001 0 0.002 0.006
AGFI (Threshold >0.90) 0.961 0.960 0.959 0.958 0.958 0.953
TLI (Threshold >0.95) 0.994 0.993 0.992 0.991 0.990 0.985
IFI (Threshold >0.95) 0.996 0.994 0.993 0.993 0.991 0.985
RFI (Threshold >0.95) 0.963 0.961 0.960 0.959 0.958 0.953
NFI (Threshold >0.95) 0.973 0.969 0.967 0.966 0.964 0.955
Note.χ2, Chi square; AGF, adjusted goodness of fit index; CFI, Comparative Fit Index; IFI, incremental fit index; NFI, normed fit index, RFI, relative fit index; RMSEA, Root Mean Square Error of Approximation;
TLI, Tucker-Lewis coefficient .
11 of 20 ESCOLÀ-GASCÓN ET AL.
TAB L E 2 Descriptive statistics, latent means of factor scores, and intercepts
Group 1 (using coordinate-based
targets, n=347)
Group 2 (using image-based targets,
n=287) Latent mean of the factor scores of group
2 of the EI factor (setting the mean of
group 1 to 0) and latent means of the
intercepts assuming scalar invariance.
Student’s t-test (using
Welch’s correction) and
Hedges’ g-tests
Mean
Standard
deviation Mean
Standard
deviation
Hits 8.31 1.768 10.09 1.889 7.172p<.001 −12.194**
0.975
Faces 98.12 17.45 105.95 16.479 85.380p<.001 −5.798**
0.461
Drawings 99.08 17.712 105.43 17.142 86.026p<.001 −4.567**
0.363
Facilitation 97.54 17.358 104.35 16.785 84.647p<.001 −5.009**
0.399
Sensations 98.69 16.890 105.25 17.874 86.409p<.001 −4.715**
0.377
Changes 100.55 17.901 104.32 16.984 102.247p<.001 −2.715*
0.216
Combinations 98.79 17.142 106.31 16.911 102.135p<.001 −5.539**
0.441
Emotional management 100.54 16.667 105.86 17.390 102.939p<.001 −3.906**
0.312
Emotional relationships 99.21 17.305 105.81 16.417 102.328p<.001 −4.915**
0.391
Strategic area 98.36 15.521 105.24 15.124 0 (equality restriction required) −5.638**
0.449
Experiential area 99.77 13.341 105.58 13.025 0 (equality restriction required) −5.522**
0.440
Total E I
(MSCEIT)
99.07 11.688 105.41 11.279 3.619
(latent mean of
factor scores) p<.001
−6.934**
0.552
Attitude towards
psychic phenomena
3.71 1.752 5.94 1.937 It represents an observable variable of
exogenous type; it has no intercepts or
latent mean of the factor scores.
−15.099**
1.209
Note.
*p<.01 .
**p<.001.
EI, emotional intelligence.
The contrast of direct means between groups 1 and 2 is also included.
ESCOLÀ-GASCÓN ET AL.12 of 20
TAB L E 3 Analysis of the statistical significance of the hits in relation to what would be expected by chance
Group definition criteria Groups
t-test
Unilateral contrast
μSample >μExp. chance
BF10 (error %)
μSample >μExp. chance
Descriptive and Gross
differences regarding
expected chance (≤8)
Effect sizes (Cohen’s d-criterion)
σlevels.
(σ=2.45) d(μ=8)
Nonpsychics, using coordinates
as a target and lowest EI levels
Group 1
N=347
3.25** Bilateral =10.428
(∼0%)
Unilateral =20.842
(∼0%)
Mean =8.31
S.D. =1.768
Difference =0.31
0.31/2.45 =0.126a
Psychics, using pictures as a
target and highest EI levels
Group 2
N=287
18.8** 60.477
(∼0%)
Mean =10.09
S.D. =1.889
Difference =2.09
2.09/2.45 =0.853
Criterion of Mayer et al. (2016).
Participants with experiential
EI ≥110
Group A
N=193
16.474** 44.837
(∼0%)
Mean =9.79
S.D. =1.507
Difference =1.79
1.79/2.45 =0.730
Criterion of Mayer et al. (2016).
Participants with experiential
EI between 89–109
Group B
N=294
15.392** 45.291
(∼0%)
Mean =9.62
S.D. =1.807
Difference =1.62
1.62/2.45 =0.661
Criterion of Mayer et al. (2016).
Participants with experiential
EI <89
Group C
N=147
-4.975
(p∼1)
0.015
(∼0%)
Mean =7.22
S.D. =1.890
Difference =−0.78
The contrast was unilateral and the
mean observed was less than 8.
Criterion according to the
experiential EI median (>102)
Median
Group 1
N=312
20.925** 65.497
(∼0%)
Mean =9.86
S.D. =1.577
Difference =1.86
1.86/2.45 =0.759
Criterion according to the
experiential EI median (≤102)
Median
Group 2
N=322
3.040** 11.496
(∼0%)
Mean =8.40
S.D. =2.155
Difference =0.40
0.40/2.45 =0.163a
All N participants Total hits
N=634
13.9** 39.920
(∼0%)
Mean =9.12
S.D. =2.028
Difference =1.12
1.12/2.45 =0.457
Note.
*p<.01.
**p<.001.
Average effect size of SAIC experiments =0.447 (Average established only with significant effect sizes).
aThe effect size is less than 0.2, which is null and allows us to infer that these differences have no applied value and are not interpretable.
13 of 20 ESCOLÀ-GASCÓN ET AL.
FIGURE 5 Theoretical models applied in groups 1 and 2 relating EI to hits in RV experiments. Standardized effect parameters are shown with
nonsignificant parameters highlighted in bold. Discontinuous lines indicate a mediation effect between the variables “Attitudes” and “RV hits.”
with insufficient experiential EI levels (below 89 points). And (b), we
also took into account the median of the EI levels of the “Experiential”
dimension, which was 102. With these subdivisions of the total sam-
ple, we aimed to perform replications using a split-sample approach
to analyze the consistency of the results versus mere statistical sig-
nificance (cf. Cohen, 1994; Dixon & Glover, 2020; Earp & Trafimow,
2015; Houran et al., 2018; Kornbrot et al., 2018; Tressoldi, 2012). We
do not intend to replicate the contrast of the latent means because this
is only a complement to analyze whether or not the average scores
of the hits on the RV tests also exceed the expected chance in these
new samples. In total, eight groups are presented: the first two were
the two main groups analyzed above, the other three follow the cri-
teria of Mayer et al. (2016), the next two were established according
to the median and the last one provides the averages over the total
sample.
The average value expected by chance was 8 hits. The effect sizes
considered the difference between this average value and the average
total hits of each group and the limits based on the standard deviation.
The theoretical standard deviation expected by chance was also used.
This deviation was calculated as follows:
𝜎∼
𝜎=32 ×1
4×3
4=6=2.45,
Therefore, the standard deviation, which is the average of the
expected theoretical variability, was 2.45. Table 3shows the compar-
isons between the means of the observed hits in each group and the
theoretical mean expected by chance. Significant differences would
indicate that the theoretical mean expected by chance was exceeded.
Effect sizes would reveal the strength of the observed effect.
Group 2′s hit rate did significantly exceed chance expectations. In
fact, the effect size of 0.853 is a comparatively high value given that
the average effect size in the SAIC experimentswas 0.447. This result—
derived from the use of recommended improvements to the original
protocols (cf. Hyman, 1996; Utts, 1995, 1996,2018)—statistically sug-
gests the presence of RV effect. In the remaining samples, the contrasts
were significant in five of the eight samples. It should be noted that in
groups A, B, and C, the significant contrasts coincide with significant
increases in the experiential EI quotients. It is also true that in Group
ESCOLÀ-GASCÓN ET AL.14 of 20
B, the experiential EI quotients were within the limits of normality, and
the minimum effect size was 0.661, which is in line with what is sug-
gested by the results of the previous SEMs. Finally, considering the
significant results in the three groups that coincide with increases in
EI levels, we have more statistical evidence that implicates the role of
EI in producing RV hits.
3.1 On the thresholds according to expected
randomness
Following classical logic in considering whether or not RV occurs, the
average hits should be greater than 8. The crucial question here is
how many hits greater than 8 are necessary to support the hypoth-
esis that the anomalous cognitions have occurred. If we were to
apply frequentist logic to a single person’s responses, the most con-
servative threshold that the hits should exceed would be 10.45 hits
(8 +2.45 =10.45). As the observed hits are discrete values, the value of
10 or 11 should be taken. This would be the case if we wanted to apply
the rules of frequentist probability to the hits of a single person, but it is
not the case when this threshold is applied to average values observed
in different individuals and in different samples.
In our case, we are working with groups of people and, therefore,
we use averages of hits with a margin of error or change. Specifically,
the margin of variation of these averages is assumed to be the stan-
dardized average variability (i.e., the standard deviation). Therefore,
upper and lower limits could be defined based on the mean ±the
observed standard deviation, which would form the confidence inter-
val. Confidence intervals represent the space of the most plausible
probability of finding the observed mean. Therefore, an observed point
mean would have two limits (minima and maxima), within which there
would be fluctuations or average changes that would summarize all
the hits of a particular group of individuals (within-subject variability).
The main implication of this is that it would not be entirely correct
to apply the rule in the previous paragraph directly to the averages
observed in each group. If the upper limit of the interval of the mean
of expected hits by chance is 10.45, the comparative element should
NOT be the observed mean as a point estimate (which in this case the
highest would be 10.09, belonging to group two of Table 3), but should
be the upper limit of the interval of the observed mean (which would
be 10.09 +1.889 =11.979). Therefore, the comparison between the
direct observed mean (10.09) and the average upper limit of what is
expected by chance (10.45) is not appropriate. The comparisons should
be made at the same level of inference and, consequently, we obtain
that 11.979 is more than 10.45. We reassert that this would not be
applicable to the total hits of a single case; as we are analyzing sets
of cases and samples, we must take into account such average varia-
tions based on standard deviations and attributable to the observed
mean.
Finally, the evidence (11.979 >10.45) allows us to conclude that
in this study certain significant results were obtained in favor of
RV. Moreover, considering the sample characteristics of this group
(high EI and favorable attitudes toward experiencing anomalous cogni-
tions), we have further reason to infer that these are favorable sample
conditions for openers in RV tests.
4DISCUSSION
Our research had two objectives: (a) to test RV in quasi-experimental
conditions and in an updated manner, following the proposals of the
research initially commissioned by the CIA and conducted at SRI/SAIC;
and (b) to seek an alternative approach to the affirmation-denial
dichotomy on whether RV effects are scientifically verified. We, there-
fore, divide our commentary into two parts. One section proposes our
interpretations and implications of the results, and the other addresses
the question of whether RV phenomena are scientifically established.
4.1 The use of EI in anomalous cognitions
The SEMs in Figure 4and the fit indices strongly suggest a valid link
between EI and RV hit rate. Of course, these correlations did not corre-
spond to very strong effects and so should be interpreted with caution.
We suspect here that EI is primary; that is, higher experiential EI leads
to higher RV hit rates. An analogous hypothetical interpretation is that
increasing the levels of EI also increases the likelihood of correct RV
“guesses.” The difference between the first interpretation and the sec-
ond is in methodology. Outside the purely experimental realm, yes, we
can say that EI levels influence increases in RV hit rate. However, if
we consider the strict application of the experimental methodology,
the above affectations could not be stated in causal terms because
there was no random assignment of the participants to the experimen-
tal conditions.3Within the framework of statistical (and not empirical)
causality, we can consider the fixed effects of the exogenous EI variable
on the RV hit rate (endogenous variable) as statistically occurring. This
means that, within the statistical framework, at least, increases in RV
effects do occur when EI increases. This link allows us to explore what
role emotions play in the production of anomalous cognitions. The fol-
lowing section outlines one speculative process model that should be
tested in future research.
4.1.1 The emotional
Production-Identification-Comprehension (PIC) model
for anomalous cognitions
Much research outside the RV literature indicates that emotions
play an essential role in the production of behaviors (Lazarus, 1982).
The ABC behavioral model of psychology (Antecedent-Behavior-
Consequence: Iwata et al., 1994) asserts that emotion is a response or
a consequence of thought, which is preceded by an antecedent stim-
ulus (Zajonc, 1980). Emotion promotes consequently other behaviors
or responses that become part of the ABC loop, interacting with other
stimuli and restarting the whole process chain. The ABC model could be
applicable in the case of RV, if we include emotions as one of the most
15 of 20 ESCOLÀ-GASCÓN ET AL.
essential variables in this process. While other behaviors do not require
emotions to be executed, some highly complex behaviors do require
emotional perception. In these situations, emotions act as precipitat-
ing factors or “precursors” of the behavior. We believe that something
similar might happen with psi-related functioning.
The stimulus would be the target that the participants must perceive
or ascertain, the thought would be the cognitive reasoning that the par-
ticipant establishes to mentally represent the target (the RV technique
is applied here). The cognitive reasoning and mental representation
would have an emotional impact on the participant. Upon perceiving
an emotion (or, even simply, a sensation), the participant connects with
the mental representation and makes a cognitive judgment. This judg-
ment is a consequent or behavior that might correspond to the hit-miss
results in the RV experiments. Within this context, it seems plausi-
ble that individuals with high emotion production, identification, and
understanding can more effectively leverage their emotional reaction
to find the correct response in RV experiments. Indeed, in everydaylife,
the functional use of emotions has been shown to be a decisive factor
in behavioral modifications (Brackett et al., 2004). These reasons col-
lectively lead us to posit that individuals with high EI should exhibit
higher hit rates on RV tests (and perhaps other types of psi-related
experiments or outcomes).
By way of further explanation, within the stimulus-thought-
emotion-response loop, the part that interests us most in this research
is emotion. If we pay attention to the parameter estimates in Figure 5,
specifically in the experiential area variable, we observe that the strate-
gic area predicts very little variance in RV hit rate (in fact, these
parameters were nonsignificant). The fact that only the experiential area
of the MSCEIT is significant implicates emotional processing in the pro-
duction of anomalous cognitions. Consequently, the statement in the
previous paragraph could be modified as follows—individuals capable of
producing or processing emotions with ease, that is, know how to identify
them and their meanings, will be those who perform better on RV tasks.
This hypothetical process is called the “Production-Identification-
Comprehension (PIC) Model.” It predicts that RV hit rates should be
modifiable if we assume it is possible to train individuals to increase
their EI abilities. However, PIC is for now only a statistically (and not
empirically) valid model, which means that it will be necessary to apply
it in further research and to investigate it strictly under experimen-
tal conditions. That said, our interpretations and proposals seem to
agree with independent research showing that people with higher lev-
els of transliminality (or the similar constructs of thin mental boundary
functioning or heightened sensory processing sensitivity) also score
higher on various measures of putative psi (Thalbourne & Houran,
2003; Thalbourne & Storm, 2012; Ventola et al., 2019).
4.1.2 PIC as both a complement and uncertainty
The findings inherent to the PIC Model represent a crucial corrobora-
tion of previous research correlating alterations in consciousness with
anomalous cognitions (e.g., Krippner et al., 1972;Luke,2011). When
consciousness does not remain in its “ordinary” state, it produces emo-
tional responses that can interact with the contents of phenomenology
of trance states (Polito et al., 2010). A similar analogy could be made
with so-called “haunt or poltergeist” episodes, which are related to
psychophysiological “dis-ease” states (e.g., Laythe et al., 2021). We do
not intend here to explain the theoretical basis of altered states of
consciousness but merely emphasize that our results align to previ-
ous evidence, and, for this reason, the PIC framework complements
prior findings and insights about psi-related experiences. We even sug-
gest that the negative correlations that Escolà-Gascón (2022a) found
between the results of RV experiments and altered states of con-
sciousness might be due to the difficulty of applying EI in trance states
and, consequently, could also be along the same lines as this proposal.
Indeed, Utts (1995, 1996,2018) likewise emphasized that it is easier
to find participants who can easily produce anomalous cognitions than
it is to train them (cf. Tart, 1976). This assertion may well be correct,
in that it is only EI (specifically PIC) that would be trainable versus psi-
functioning. Future research should explore whether PIC is a trainable
component and, thus, a possible catalyst for anomalous phenomena.
However, we acknowledge the uncertainties of the proposed PIC
Model. The nonlocality hypothesis still holds in the PIC model for sev-
eral reasons. This study did not address which connector allows for the
relation or translation of emotions to anomalous cognitions. Similarly,
because of the speed at which the stimulus-thought-emotion-response
cycle occurs, we also do not know to what degree emotional production
(and not mental representation) is the precipitating factor in success-
ful RV responses. Moreover, that emotions are related to RV does not
mean that this correlation is stable with the other anomalous phe-
nomena we highlighted in the introduction. Therefore, this reinforces
the need for additional studies on anomalous cognitions, and specif-
ically on identifying the underlying mechanisms for these types of
phenomena.
4.2 Are the anomalous phenomena scientifically
established?
Starting in 1995 and after declassification, the American Congress,
through the organizations that had developed the experiments on RV,
commissioned Professors Hyman (1996) from the University of Ore-
gon and Utts (1995, 1996,2018) from the University of California to
prepare a review report on the results obtained in the research pro-
grams that the CIA originally funded and conducted. Reviews should
answer the question of whether “psi” phenomena are scientifically
established. However, the expression “being scientifically established”
(the original expression used in the reviews by Utts and Hyman) can
have at least two meanings that would not be mutually exclusive but do
have logically conflicting features.
On the one hand, the expression could be interpreted exclusively
from a statistical or probabilistic judgment. In fact, the approach and
statistical judgment used by SRI and SAIC consisted of the application
of hypothesis testing based on statistical scrutiny. Specifically, these
tests analyzed the statistical significance of the discrepancies between
the observed measurements (obtained in the trials and experiments)
ESCOLÀ-GASCÓN ET AL.16 of 20
and the estimated mathematical expectation (see the Mathematics
Handbook published by Escolà-Gascón, 2022c for a major revision).
Consequently, this kind of statistical judgment would entail interpret-
ing the occurrence of a given phenomenon as a set of significant
deviations that may be above or below the estimated mathemati-
cal expectation. This probability inference would make it possible to
ensure that the measurements of the deviations are not explained by
the set of random (or chance) fluctuations.
However, this interpretation does not allow empirical assurance
of when the supposed measured phenomenon is occurring (Escolà-
Gascón, 2022a, 2022b). Therefore, within the statistical-probabilistic
approach, concluding that a phenomenon is “scientifically established”
should mean that only sufficient significant deviations were obtained
(quantified by effect size tests), which were consistent and stable in
relation to their measurements. If we focus on this approach, the con-
clusion that a phenomenon happens consistently and is statistically
stable should not imply acknowledging or admitting that such a phe-
nomenon is empirically real. However, the fact that the deviations are
significant and are not explained by random fluctuations does rep-
resent statistical evidence supporting the hypothesis associated with
RV.
On the other hand, in science, from a strictly factual approach, when
an object of study is “scientifically established,” it means that sufficient
evidence has been obtained to justify the real and functional existence
of that object of study. Given the justification based on the burden
of proof (or proofs), the object is formally accepted and established
within the corpus of scientific knowledge. Unlike the probabilistic and
statistical approach, empirical scrutiny would allow us to specify when
a given phenomenon does or does not occur (if the scrutiny complies
with experimental conditions and controls). These two interpretations
based on different paradigms or approaches are crucial to an accurate
understanding of the conclusions of the theoretical evaluations pre-
sented by the two professors cited above. The question that arises from
these two interpretations is: can we consider that Jessica Utts’ judg-
ment was centered on the first interpretation and Ray Hyman’s on the
second? If so, both professors would be correct in their conclusions
because they used different perspectives on scientific inference.
From a thorough review of declassified SRI and SAIC reports and
publications, Utts (1995, 2018). concluded that anomalous phenomena
(or psi-functioning) were scientifically established. She also argued that
the scientific challenge would not be in rereplicating the SRI and SAIC
experiments, but in conducting research that would address the under-
lying mechanisms involved in producing the anomalous phenomena.
An important note here is that Utts acknowledged the methodologi-
cal limitations with the SRI experiments and explained how these were
remedied in experiments subsequently conducted at SAIC. Utts’ sta-
tistical and methodological explanation suggests that her conclusion
refers to the statistical (versus empirical) approach. In the same vein,
Utts did not mention the word “empirical” and does not use expressions
referring to possible evidence beyond the statistical judgment itself.
Therefore, her conclusions based on effect sizes of deviations should
not be incorrect if taken within the framework of statistical scrutiny.
In contrast, Hyman (1996) concluded that there was insufficient
evidence to accept RV as a scientifically established phenomenon. He
criticized that, for a phenomenon to occur, it is not necessary to resort
to estimated mathematical hope (i.e., chance). His argument referenced
the phenomenon relative to the psychophysical study of memory. This
suggests that Hyman interpreted Utts’ conclusions from an empirical
and not a statistical approach, which could explain why there were so
many discrepancies between the two authors’ assessments. Further-
more, we must also bear in mind that not all phenomena are empirically
observable and, consequently, only mathematical representation and
statistical judgment would be scientifically available in decision-making
(Escolà-Gascón, 2022c). Many phenomena have no direct observation
in the physical sciences (e.g., the state of temperature and variations
over time). In this sense, the fact that a phenomenon is not empirically
observable and recordable does not make it a “pseudoscientific con-
cept” (i.e., that it does not have sufficient epistemic foundations, see
e.g., Fasce et al., 2021).
There is another essential nuance in that both professors agreed
on several points and interpretations. Here, we will highlight the main
agreement, as it is one of the reasons supporting a replication such
as the present study. Hyman and Utts concurred that the significant
effect sizes of the multiple SAIC experiments were statistically consis-
tent or very similar to each other. Likewise, Hyman added that these
nonrandomly attributable coincidences were not conclusive in them-
selves and that, only with further research replications could obtain
more information on whether these sizes remain stable. This means
that new replications should be carried out with the maximum con-
ditions of experimental control and rigor. Ultimately, both evaluation
reports provided helpful appraisals of the scientific value of the CIA
and DIA’s RV experiments. However, our narrative analysis suggests
that both Utts and Hyman were correct from empirical versus statis-
tical points of view and that their contributions, thus, have different
impacts and implications.
4.2.1 Do our results show statistical evidence of
an anomalous effect?
Tab l e 3shows some effect sizes with provocative implications. To clar-
ify, the effect size indicates the degree to which the “hits” (i.e., correct
responses or information) exceededchance expectations. The most rel-
evant results to highlight at this point reference mediation effects and
involve Group 2 (high levels of experiential EI, Cohen’s d=0.853),
Group A (high levels of experiential EI, Cohen’s d=0.730), Group B
(moderate levels of experiential EI, Cohen’s d=0.661), and Group 1
(high levels of experiential EI, Cohen’s d=0.759). The total effect size
(including the 634 cases) was 0.457.
There are two criteria for interpreting these results. First, we could
use the classic Cohen (1988) criterion. This is rather arbitrary, but it
continues to be widely used and accepted as valid. Cohen (1988)sug-
gested that values below 0.20 indicate no effect; between 0.21 and
0.49, the effects are small; between 0.50 and 0.70, the effects are
moderate; and values greater than 0.70 are large effects. Applying
these criteria to Table 3, we find that those groups with high scores
on EI showed large effects. The effect sizes likewise decrease as EI
decreases in the groups. We lack sufficient data for a correlational
17 of 20 ESCOLÀ-GASCÓN ET AL.
analysis but can tentatively confirm this trend via visual inspection,
which certainly should be tested in new and future research. As Truzzi
(1987) suggested, extraordinary objects of study require analyses and
interpretations that go beyond the canonical.
Second, we could apply Ferguson’s (2009) statistical criterion.
Unlike Cohen’s (1988), Ferguson’s (2009) approach is based on what
effect sizes should be obtained in order to be able to make consis-
tent statistical inferences. Following this principle, a minimum value
of 0.4 is needed to assume a small effect. Values equal to or greater
than 1.15 indicate moderate effects, and those above 2.70 are strong
effects. Using these thresholds, our results can be interpreted as small
rather than moderate or large. This implies a lower level of consistency
of the inferences, and therefore more original research is needed to
make firm conclusions.
However, a critical point is that effect sizes are only minimally
acceptable (greater than 0.4) when individuals score high on EI. This
coincidence and the significant differences obtained with SEM analy-
ses of invariance do support a possible direction of scientific research
regarding the explanation of why anomalous cognitions occur—that is,
it is necessary to understand the role of emotions and how participants
manage them (per the level of EI). This does not mean to defend that EI
is real or not real; we simply propose that, in the same way that there
are skills (referred to as intelligence) that allow us to regulate certain
decisions and actions, these skills could also be applicable to the reg-
ulation and use of emotions. We strive to address this point in our RV
research.
Taken altogether, we contend that our results certainly constitute
“statistical anomalies,” as they clearly defy the expectations of prob-
ability theory. Along these lines, it is crucial to assess to what degree
these statistical anomalies are evidence for anomalous cognition. An
anomaly represents just that: something strange that should not hap-
pen in statistical terms but does occur. And this occurrence is not
one-off, because similar observations are documented across other,
independent studies that we previously cited. Such findings do not
equate to explanations, so they do not establish the ontological reality
of putative psi. That said, we must concede that the effect sizes of these
statistical anomalies are consistent with the hypothesis that human
cognition is not limited to known scientific knowledge and orthodox
theories. Our results certainly highlight that the hypothesis proposal
of the first scientists to address RV is not necessarily incompatible with
scientific knowledge (see e.g., Nature publications Targ & Puthoff, 1974;
Tart et al., 1980). Nevertheless, the statistical anomalies observed here
and elsewhere add to the growing body of empirical literature that
justifies continued research in this area of consciousness studies.
4.3 Limitations and conclusions
Although the preceding discussion highlighted major limitations of
our study, arguably the most relevant of these to consider in future
research are: (a) the methodology was quasi-experimental versus
strictly experimental, which limits causal statements; (b) the posi-
tive and significant association between EI and RV hit rates does not
imply that emotions are necessarily the underlying mechanism for
RV effects; and (c) following Hyman (1996), Group 2′s above-chance
scoring only implies a statistical versus empirical verification of RV
phenomena. We should also underscore that our study was not pre-
registered, so new research should be conducted in ways that can
be externally verified. Describing hypotheses, methods, and analyses
before a study is conducted helps to foster transparency and, thus,
reduce publication bias, especially with respect to controversial topics
like RV phenomena (for a discussion, see Rabeyron, 2020).
Therefore, this updated report on RV and the experiments com-
missioned by the CIA and DIA allow us to state the following: (a) RV
experiments (investigated under RV conditions and discarding the sur-
vival hypothesis) yield above-chance results. (b) The fact that statistical
chance has been overcome does not empirically validate RV but rather
provides statistical verification of a robust anomaly that suggests
anomalous cognition might be ontologically “real.” (c) EI and specifi-
cally PIC skills significantly predict RV scores between 9 and 19.5%.
This raises the possibility that emotions could directly or indirectly
precipitate anomalous cognitions (and perhaps even other psi-related
cognitions). (d) Anomalous cognitions should only be regarded as sci-
entifically established phenomena within statistical and mathematical
contexts but not be accepted as empirically validated phenomena due
to the lack of tangential evidence causally linking physical mechanisms
to the observed effects.
Finally,our previous publications have echoed Hyman’s (1986)skep-
ticism about the ontological reality of psi (e.g., Dagnall et al., 2016;
Drinkwater et al., 2021; Escolà-Gascón, 2020a,b; Houran et al., 2017,
2018; Irwin et al., 2012a,b; Lange et al., 2019). But we also defend
the principles of neutrality, intellectual humility, and falsification in
scientific research. Thus, the present results compel the authors to
voice an updated position statement, that is, our skeptically oriented
team obtained ample evidence supporting the existence of robust statis-
tical anomalies that currently lack an adequate scientific explanation and
therefore are consistent with the hypothesis of psi. This outcome stands in
stark contrast to the literature on experimenter and observer effects,
which are often cited as substantial hindrances to psi effects (Kennedy,
2003). Our findings certainly undermine this view as a blanket state-
ment. We accordingly recommend that new studies both welcome
and leverage the participation of proper skeptics in “adversarial col-
laborations.” These exercises are rarely used in parapsychology but
involve researchers with differing views who jointly construct and
implement studies that fairly address controversial issues while con-
trolling for obvious ideological biases or methodological artifacts (e.g.,
Hyman & Honorton, 2018; Lange et al., 2004; Laythe & Houran, 2022;
LeBel et al., 2022; Schlitz et al., 2006). Indeed, we agree with Cowan
et al.’s (2020) assertion that this approach might be the most pro-
ductive way to change current scientific views on highly controversial
topics.
DATA AVAILABILITY STATEMENT
The raw data supporting the conclusions of this article will be made
available by the authors, without undue reservation.
ORCID
Álex Escolà-Gascón https://orcid.org/0000-0002-3086-4024
ESCOLÀ-GASCÓN ET AL.18 of 20
PEER REVIEW
The peer review history for this article is available at https://publons.
com/publon/10.1002/brb3.3026.
ENDNOTES
1The a priori distributions that were adjusted were the default distributions
configured in the JASP software (based on the R programming language).
These a priori distributions are set such that the probability that the null
hypothesis is true is 50%, and the probability that the alternative hypoth-
esis is true is also 50%. These a priori probabilities (based on the default a
priori distributions) are configured in this way when a neutral position is
assumed with respect to the certainty of the hypotheses. This means that
one does not pretend to be either for or against any hypothesis. For the
default a priori distributions in JASP, the reader and reviewers are referred
to Heo et al. (2020).
2The proportions of variance explained are obtained from the linear com-
bination of the squared observed standardized effects. This value can
be interpreted as the amount of fluctuations (or variance) that an inde-
pendent variable (or exogenous variable) predicts in another variable
considered dependent or endogenous (see Brown, 2015).
3Because no strictly experimental controls were applied, we must also high-
light the hypothesis that individuals with RV learned to have more EI and,
therefore, psi test successes should also affect EI scores. We acknowl-
edge that this could be a possibility, but as a hypothetical interpretation,
it is weak because we cannot scientifically verify which individuals have
psi and which do not. To be sure which individuals have psi and which do
not implies accepting that RV is a real cognition and this has serious impli-
cations for research. We (the authors of this manuscript) do not accept
a priori that psi is real, and the hypothetical model posed in the intro-
duction is falsificationist (not verificationist), which precludes applying this
hypothetical interpretation to the findings.
REFERENCES
Bem, D., Tressoldi, P. E., Rabeyron, T., & Duggan, M. (2016). Feeling the
future: A meta-analysis of 90 experiments on the anomalous anticipa-
tion of random future events. F1000Research,4,1188. https://doi.org/10.
12688/f1000research.7177.2
Bem, D. J. (2011). Feeling the future: Experimental evidence for anomalous
retroactive influences on cognition and affect. Journal of Personality and
Social Psychology,100(3), 407–425. https://doi.org/10.1037/a0021524
Brackett, M., Mayer, J., & Warner, R. (2004). Emotional intelligence and
its relation to everyday behaviour. Personality and Individual Differences,
36(6), 1387–1402. https://doi.org/10.1016/s0191-8869(03)00236-8
Brown, T. A. (2015).Confirmatory factor analysis for applied research. Guilford
Press.
Brugger, P., Landis, T.,& Regard, M. (1990). A ‘sheep-goat effect’ in repetition
avoidance: Extra-sensory perception as an effect of subjective proba-
bility? British Journal of Psychology,81(4), 455–468. https://doi.org/10.
1111/j.2044-8295.1990.tb02372.x
Bunge, M. (2013). La ciencia, su método y su filosofía. Laetoli.
Cardeña, E. (2018). The experimental evidence for parapsychological phe-
nomena: A review. American Psychologist,73(5), 663–677. https://doi.
org/10.1037/amp0000236
Carter, J. (2011). Radical skepticism, closure, and robust knowledge. Jour-
nal of Philosophical Research,36, 115–133. https://doi.org/10.5840/jpr_
2011_2
Cheng, Y. (2021). Current research on Gödel’s incompleteness theorems.
Bulletin of Symbolic Logic,27(2), 113–167. https://doi.org/10.1017/bsl.
2020.44
Cohen, J. (1988). Statistical power analysis for the behavioral sciences.
Lawrence Erlbaum Associates.
Cohen, J. (1994). The earth is round (p<.05). American Psychologist,49, 997–
1003. https://doi.org/10.1037/0003-066X.49.12.997
Cowan, N., Belletier, C., Doherty, J. M., Jaroslawska, A. J., Rhodes, S.,
Forsberg, A., Naveh-Benjamin, M., Barrouillet, P., Camos, V., & Logie, R. H.
(2020). How do scientific views change? Notes from an extended adver-
sarial collaboration. Perspectives on Psychological Science,15, 1011–1025.
https://doi.org/10.1177/1745691620906415
Dagnall, N., Denovan, A., Drinkwater, K., Parker, A., & Clough, P. (2016).
Toward a better understanding of the relationship between belief in the
paranormal and statistical bias: The potential role of schizotypy. Fron-
tiers in Psychology,7, Article 1045. https://doi.org/10.3389/fpsyg.2016.
01045
Dixon, P., & Glover, S. (2020). Assessing evidence for replication: A
likelihood-based approach. Behavior Research Methods,52, 2452–2459.
https://doi.org/10.3758/s13428-020-01403-6
Drinkwater, K. G., Dagnall, N., Denovan, A., & Williams, C. (2021). Paranor-
mal belief, thinking style and delusion formation: A latent profileanalysis
of within-individual variations in experience-based paranormal facets.
Frontiers in Psychology,12, 670959. https://doi.org/10.3389/fpsyg.2021.
670959
Dunne, B. J., & Jahn, R. G. (2007). Information and uncertainty in remote
perception research. EXPLORE,3(3), 254–269. https://doi.org/10.1016/
j.explore.2007.03.010
Earp, B. D., & Trafimow, D. (2015). Replication, falsification, and the crisis
of confidence in social psychology. Frontiers in Psychology,6, Article 621.
https://doi.org/10.3389/fpsyg.2015.00621
Escolà-Gascón, A. (2020a). Researching unexplained phenomena:
Empirical-statistical validity and reliability of the Multivariable Multi-
axial Suggestibility Inventory-2 (MMSI-2). Heliyon,6, Article e04291.
https://doi.org/10.1016/j.heliyon.2020.e04291
Escolà-Gascón, Á. (2020b). Researching unexplained phenomena II: New
evidences for anomalous experiences supported by the Multivari-
able Multiaxial Suggestibility Inventory-2 (MMSI-2). Current Research in
Behavioral Sciences,1, Article 100005. https://doi.org/10.1016/j.crbeha.
2020.100005
Escolà-Gascón, Á. (2022a). Forced-choice experiment on Anomalous
Information Reception and correlations with states of consciousness
using the Multivariable Multiaxial Suggestibility Inventory-2 (MMSI-
2). EXPLORE,18(2), 170–178. https://doi.org/10.1016/j.explore.2020.
11.009
Escolà-Gascón, Á. (2022b). Handbook of statistics: Step by step mathematical
solutions. McGraw-Hill.
Escolà-Gascón, Á., Dagnall, N., & Gallifa, J. (2021). The multivariable mul-
tiaxial suggestibility inventory-2 (MMSI-2): A psychometric alternative
to measure and explain supernatural experiences. Frontiers in Psychology,
12,https://doi.org/10.3389/fpsyg.2021.692194
Escolà-Gascón, Á., Wright, A. C., & Houran, J. (2022). ‘Feeling’ or ‘sensing’
the future? testing for anomalous cognitions in clinical versus healthy
populations. Heliyon,8(11), Article e09188. https://doi.org/10.1016/j.
heliyon.2022.e11303
Evans, J. (2003). In two minds: Dual-process accounts of reasoning. Trends in
Cognitive Sciences,7(10), 454–459. https://doi.org/10.1016/j.tics.2003.
08.012
Fasce, A., Avendaño,