Keywords
pre-stimulus activity, anticipatory physiology, temporal processing, psychophysiology, presentiment
pre-stimulus activity, anticipatory physiology, temporal processing, psychophysiology, presentiment
The human ability to predict future events has been crucial in our evolutionary development and proliferation over epochs of time, both from a species perspective, but also, on an individual level. Our day-to-day survival is predicated on a successful marriage of experience (e.g., memory) and sensory processing (e.g., perceptual cues); for example, on a very humid heavily overcast night, our perceptions and memories inform us that a thunder storm is possible and it might be intelligent to find shelter. Such behaviour is highly adaptive as it fosters survival based strategies and is perfectly explicable in terms of current theories of biological causality. Now imagine if such prognosticating ability was possible without any sensory or other inferential cues. Such seemingly inexplicable ability would definitely hold survival advantage, if they existed. For millennia people have been reporting strange feelings of foreboding that later transpired to have significance. Over the last 36 years these phenomena have been scrutinized in the laboratory in which a subject’s physiology is monitored before a randomly presented stimulus that is designed to evoke a significant post-stimulus response. Disturbingly, moments before the stimulus is presented there are murmurings of activity, as if the body is predicting moments ahead of time. This effect is termed presentiment, or more recently, Predictive Anticipatory Activity (Mossbridge et al., 2014). By 2012 a good number of these studies had been completed and it was deemed worthwhile to conduct a meta-analysis of the extant literature at the time. Mossbridge, Tressoldi and Utts located 42 studies published from 1978 to 2010, testing the presentiment hypothesis, out of which 26 enabled a true comparison between pre and post-stimulus epochs (Mossbridge et al., 2012), that is the pre-stimulus physiological responses mirrored even if to a lesser degree, the post-stimulus responses.
Here two paradigms were used: either a randomly ordered presentation of arousing vs. neutral stimuli or guessing tasks in which the stimulus is the feedback about the participant’s guess (correct vs. incorrect). In both of these approaches it is difficult to envision mundane strategies that might explain the anomalous pre-stimulus effects observed, and indeed, Mossbridge et al, went to significant lengths in refuting the leading candidate – expectancy effects, both in the 2012 meta-analysis and in post-review exchanges with sceptical psychologists and physiologists. Regardless of the paradigm, a broad range of physiological measures were employed from skin conductance, heart rate, blood volume, respiration, electroencephalographic (EEG) activity, pupil dilation, blink rate, and/or blood oxygenation level dependent (BOLD) responses. These are recorded throughout the session, with a pre-determined anticipatory period of between 4 to 10 seconds, in which the any pre-stimulus effect is captured. The presentiment hypothesis calls for a difference between arousing and neutral pre-stimulus responses and this is calculated across sessions. Mossbridge et al. found substantive evidence in favour of a presentiment effect concatenated to over 6 sigma – extreme statistical significance. Additionally, they also found evidence of presentiment effects from mainstream research programs – something that is becoming increasingly important as these effects become more widely known.
Because of the high profile nature of Mossbridge et al, (over 93,000 views as of January 2018) there has been a good number of replications in the few years since publication. We located an additional 26 studies describing 34 effect sizes from a dozen laboratories. The most striking aspect of this fresh database is the sheer variation in experimental approaches as researchers seek to tackle more process oriented questions rather than continuing the proof-oriented work found in the earlier meta-analysis. Because expectancy effects have been forwarded to explain at least some of the presentiment effect, it is noteworthy that several experiments in this fresh cohort of studies tackle this head on by only analysing the first trial of a run. These single-trial presentiment studies are expectancy free and are becoming more dominant in this research domain. Another interesting question that is probed in these new studies is the idea of utilizing pre-stimulus physiological activity to predict future events. This provides a second objective measure of the validity of the presentiment effect. There are several studies that utilize this approach and they are discussed later on. Additionally, we also found increasing evidence of presentiment research piggybacking onto mainstream psychology programs, even informing aspects of the conventional research. Also of note we found several PhD theses describing presentiment research and a greater geographical spread than in 2012, both evidence of the increasing attention such research is garnering. Lastly, we found increasing dialogue between presentiment researchers and physicists interested in retrocausality – the idea that effects can precede their cause. This is witnessed in the recent AAAS retrocausality symposium in which several researchers participated and in which some of those papers made their way into this meta-analysis (Sheehan, 2017).
The whole procedure followed both the APA Meta-Analysis Reporting Standards (APA Publications and Communications Board Working Group on Journal Article Reporting Standards, 2008), the Preferred Reporting Items for Systematic reviews and Meta-Analyses for Protocols 2015 (Moher et al., 2015) and the reporting standards for literature searches and report inclusion (Atkinson et al., 2015). A completed PRISMA checklist can be found in Supplementary File 1.
Study inclusion criteria were the analysis of both psychophysiological or neurophysiological signals before the random presentation of whichever type of stimulus, e.g. pictures, sounds etc. Randomization could be performed by using pseudo-random algorithms e.g. like those implemented in MatLab or E-Prime® or true random sources of random digits, e.g. TrueRNG.
It is important to point out that these eligibility criteria are different from those used by Mossbridge et al. Those authors selected only studies were the anticipatory signals mirrored the post-stimulus ones. Differently we included all studies that used anticipatory signals to predict future events independently of the presence of post-stimulus physiological signals. For example, some authors, e.g. Mossbridge (2015) used heart rate variability to predict winning i.e. $4, versus losing outcomes. Our inclusion criteria are consequently more comprehensive than those used by Mossbridge et al.
Both co-authors who are experts in this type of investigations, searched for studies through Google Scholar and PubMed by using the keywords: “presentiment” OR “anticipation” OR “precognition”. Furthermore, we emailed a request of the data of completed studies to all authors we knew were involved in this type of investigations. Even if Mossbridge et al. included all studies available up to 2010, we also searched studies that could have been missed in that meta-analysis. We searched all completed studies, both peer reviewed and non-peer reviewed, e.g. Ph.D dissertations, from January 2008 to October 2017.
Study selection is illustrated in the flow-diagram presented in Figure 1
Excluded records were studies were the psychophysiological variables were analysed only after and not before the stimuli presentations (Jin et al., 2013) and with an unusual procedure (Tressoldi et al., 2015), i.e. using heart rate feedback to inform a voluntary decision to predict random positive or negative events.
Records excluded after the screening were studies where authors did not agree to share their data for different reasons (Baumgart et al., 2017; Modestino et al., 2011). Excluded studies revealed either statistically significant or trending evidence in support of the anticipation effect in most cases, thus reducing the concerns surrounding biased removal.
The references of the included studies are reported in Supplementary File 2.
The two co-authors agreed on the following coding variables: Authors; year of publication; participant selection: yes = selected according to specific criteria; no = selected without specific criteria; number of participants; number of trials; stimuli type; type of randomisation: pseudo or true random; psychophysiological signals, e.g. EEG, Heart Rate, etc.; anticipatory period; type of statistics; value of statistics and independently extracted them from the eligible studies. After the comparison, they discussed how to solve the inter-coder’ differences.
On the database we have added a note for each effect size, describing where we extracted the corresponding statistics in the original papers. The database along with all 18 papers are available from Tressoldi (2017). A summary of the selected studies along with their corresponding effect sizes, variance and standard error, is reported on Table S1 in the Supplementary File 3.
Apart from the overall effect, we chose to compare the following moderator variables, peer review (PeerRev, yes vs no) as a control of study quality. Given the low number of studies no further moderator analyses were carried out.
The standardized effect size d of each dependent variable, was estimated from the descriptive statistics (means, standard deviation and number of participants) when available. In all other cases, it was estimated by using the available summary statistics, i.e. paired t-test; Stouffer’s Z; etc. by using Lakens’ software (Lakens, 2013) and the function escalc () of the R package metaphor (Viechtbauer, 2017).
All effect sizes were then converted into the Hedges’ g and the corresponding variance by using the formulae suggested by Borenstein et al. (2009) estimating an average correlation of 0.5 between the dependent variables.
Given our choice of keeping (not averaging) all effect sizes when multiple dependent variables were analysed, we estimated the overall random model weighted effect size by using the robumeta package (Fischer et al., 2017) which implement a Robust Variance Estimation method when there are dependent effect sizes (Tanner-Smith & Tipton, 2014).
In order to control the reliability of the results, a second analysis was carried out by using a multilevel approach as suggested by (Assink & Wibbelink, 2016) implemented with the metafor package (Viechtbauer, 2010) and reported in the Table S2 in the Supplementary File 3.
The Bayesian meta-analysis was implemented with the brms package (Bürkner, 2017).
A copy of the syntax is available here: https://doi.org/10.6084/m9.figshare.5661070.v1 (Tressoldi, 2017)
Even if with our search activity we are quite sure to have reduced to a minimum the problem of publication bias, we performed a statistical estimation by using the Copas selection model which is recommended by Jin et al. (2015).
Studies: Peer review papers: 8; Non -Peer review papers:10. Number of experiments: 26 contributed by 13 authors. Number of effect sizes: 34. Average number of participants: 97.5. Average anticipatory period: 3.5 seconds. Four studies were preregistered (see database).
The group analyses for males and females reported in three papers (Mossbridge, 2014; Mossbridge, 2015; Singh, 2009), were considered independent effect sizes.
The forest plot is presented in Figure 2. The summary of the frequentist multilevel random model analysis is presented in Table 1 compared with the results obtained by Mossbridge et al., whereas the summary of the Bayesian multilevel random model meta-analysis is presented in Table 2.
n | ES | 95% Conf. Int. | p | I2 | τ2 | |
---|---|---|---|---|---|---|
Mossbridge et al. | 26 | 0.21 | 0.13 – 0.29 | 5.7×10-8 | 27.4 | 0.012 |
Overall | 26 | 0.29 | 0.19 – 0.38 | 8×10-6 | 82.5 | 0.049 |
Peer Review | 12 | 0.38 | 0.27 – 0.48 | 1×10-5 | 43.5 | 0.012 |
No Peer Review | 14 | 0.22 | 0.05 – 0.39 | 0.014 | 85.2 | 0.048 |
n | Effect size | 95% CI | Rhat | |
---|---|---|---|---|
Overall | 26 | 0.29 | 0.18 – 0.39 | 1 |
Peer Review | 12 | 0.36 | 0.23 – 0.48 | 1 |
No Peer Review | 14 | 0.23 | 0.04 – 0.42 | 1 |
Sensitivity analysis of the overall effect size, didn’t reveal any change from Rho 0 to Rho 1, suggesting that the degree of correlations among the dependent effect sizes don’t affect its magnitude.
Another “sensitivity analysis” was carried out excluding the Mossbridge and the Tressoldi studies in order to control whether different authors could obtain similar results. The main results of this analysis by using the same frequentist multilevel random model, is reported in Table 3.
Both the frequentist and the Bayesian analyses support the evidence of an overall main effect of approximately .29, and a small difference between the peer and non-peer reviewed studies. These findings will be commented further in the discussion of the comparison with Mossbridge et al.
The search method used and the small number of people interested in this research field, guarantee that from an empirical point of view, any publication bias is almost absent.
Unfortunately, there is no consensus about what tests are statistically more valid (Carter et al., 2017).
All the traditional tests, like the Fail-Safe, the Trim-and-Fill, the Funnel Plot have been criticized for their limitations (Jin et al., 2015; Rothstein, 2008). We hence applied the Copas selection model which is recommended by Jin et al. (2015).
The Copas selection model was implemented using the metasens package (Schwarzer et al., 2016). The results are presented in the Table 4. With this statistic, it emerges that there is no apparent statistical publication bias.
This update of the Mossbridge et al. (2012) meta-analysis related to the so called predictive anticipatory activity (PAA) responses to future random stimuli, covers the years 2008- October 2017. Overall, we found 18 new studies describing a total of 34 effect sizes. Differently from the statistical approach of Mossbridge et al., in this meta-analysis we used a frequentist and a Bayesian multilevel model which allows an analysis of all effect sizes reported within a single study instead of averaging them.
Both the frequentist and the Bayesian analyses converged on similar results, making our findings quite robust. The overall effect size 0.29, 95% CI = 0.18 - 0.39, overlaps to that reported in the original paper: 0.21, 95% CI = 0.13–0.29, even if the heterogeneity is substantially higher: I2= 80.5 vs 27.4.
The high level of heterogeneity is expected considering the varieties of experimental protocols and the diversity of dependent variables, from heart rate to pupil dilation.
Furthermore, we did not find substantial differences between peer and not-peer reviewed papers as in the original paper.
We found very interesting evidence of presentiment distilled from the conventional post-stimulus psychological research of Jolij and Bierman, who have performed a long series of experiments using a face detection paradigm. Additionally, the work of Kittenis found prestimulus effects from a conventional research program and pre-registered single-trial work of Mossbridge represent an important conceptual replication in countering both the use of questionable research practices and expectancy effects arguments.
A promising development of this line of research is the development of paradigms that use software in real-time to predict meaningful future outcomes before they occur, e.g. (Franklin et al., 2014)
This update confirms the main results reported in Mossbridge et al. (2012) original meta-analysis and gives further support to the hypothesis of predictive physiological anticipation of future random events.
The limitations of the present meta-analysis are similar to most meta-analyses which include non pre-registered studies that cannot be controlled for the degree of freedoms in the methodology and data analysis in the course of their implementations, making them prone, for example, to the so-called “questionable research practices” (John et al., 2012).
The solution is that of prospective meta-analyses (Watt & Kennedy, 2017), based on preregistered studies where the methods and data analyses have been declared and made public beforehand.
Underlying data for this meta-analysis is available from FigShare: https://doi.org/10.6084/m9.figshare.5661070.v1 (Tressoldi, 2017) under a CC BY 4.0 licence
Supplementary File 1 – Completed PRISMA checklist.
Click here to access the data.
Supplementary File 2 – List of references used in this analysis.
Click here to access the data.
Supplementary File 3 – contains Table S1: Summary of the selected studies along with their corresponding effect sizes, variance and standard error. Table S2: results obtained with the multilevel approach suggested by Assink & Wibbelink, 2016.
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Are the rationale for, and objectives of, the Systematic Review clearly stated?
Yes
Are sufficient details of the methods and analysis provided to allow replication by others?
Yes
Is the statistical analysis and its interpretation appropriate?
Partly
Are the conclusions drawn adequately supported by the results presented in the review?
Partly
References
1. Galak J, Leboeuf RA, Nelson LD, Simmons JP: Correcting the past: failures to replicate ψ.J Pers Soc Psychol. 2012; 103 (6): 933-48 PubMed Abstract | Publisher Full TextCompeting Interests: No competing interests were disclosed.
Are the rationale for, and objectives of, the Systematic Review clearly stated?
Yes
Are sufficient details of the methods and analysis provided to allow replication by others?
No
Is the statistical analysis and its interpretation appropriate?
Partly
Are the conclusions drawn adequately supported by the results presented in the review?
Yes
Competing Interests: No competing interests were disclosed.
Alongside their report, reviewers assign a status to the article:
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Version 1 28 Mar 18 |
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