Archive for ESP

random generators… unfit for ESP testing?!

Posted in Books, Statistics with tags , , , , , , , on September 10, 2014 by xi'an

“The term psi denotes anomalous processes of information or energy transfer that are currently unexplained in terms of known physical or biological mechanisms.”

When re-reading [in the taxi to Birmingham airport] Bem’s piece on “significant” ESP tests, I came upon the following hilarious part that I could not let pass:

“For most psychological experiments, a random number table or the random function built into most programming languages provides an adequate tool for randomly assigning participants to conditions or sequencing stimulus presentations. For both methodological and conceptual reasons, however, psi researchers have paid much closer attention to issues of randomization.

At the methodological level, the problem is that the random functions included in most computer languages are not very good in that they fail one or more of the mathematical tests used to assess the randomness of a sequence of numbers (L’Ecuyer, 2001), such as Marsaglia’s rigorous Diehard Battery of Tests of Randomness (1995). Such random functions are sometimes called pseudo random number generators (PRNGs) because they [are] not random in the sense of being indeterminate because once the initial starting number (the seed) is set, all future numbers in the sequence are fully determined.”

Well, pseudo-random generators included in all modern computer languages that I know have passed tests like diehard. It would be immensely useful to learn of counterexamples as those using the corresponding language should be warned!!!

“In contrast, a hardware-based or “true” RNG is based on a physical process, such as radioactive decay or diode noise, and the sequence of numbers is indeterminate in the quantum mechanical sense. This does not in itself guarantee that the resulting sequence of numbers can pass all the mathematical tests of randomness (…) Both Marsaglia’s own PRNG algorithm and the “true” hardware-based Araneus Alea I RNG used in our experiments pass all his diehard tests (…) At the conceptual level, the choice of a PRNG or a hardware-based RNG bears on the interpretation of positive findings. In the present context, it bears on my claim that the experiments reported in this article provide evidence for precognition or retroactive influence.”

There is no [probabilistic] validity in the claim that hardware random generators are more random than pseudo-random ones. Hardware generators may be unpredictable even by the hardware conceptor, but the only way to check they produce generations from a uniform distribution follows exactly the same pattern as for PRNG. And the lack of reproducibility of the outcome makes it impossible to check the reproducibility of the study. But here comes the best part of the story!

“If an algorithm-based PRNG is used to determine the successive left-right positions of the target pictures, then the computer already “knows” the upcoming random number before the participant makes his or her response; in fact, once the initial seed number is generated, the computer implicitly knows the entire sequence of left/right positions. As a result, this information is potentially available to the participant through real-time clairvoyance, permitting us to reject the more extraordinary claim that the direction of the causal arrow has actually been reversed.”

Extraordinary indeed… But not more extraordinary than conceiving that a [psychic] participant in the experiment may “see” the whole sequence of random numbers!

“In contrast, if a true hardware-based RNG is used to determine the left/right positions, the next number in the sequence is indeterminate until it is actually generated by the quantum physical process embedded in the RNG, thereby ruling out the clairvoyance alternative. This argues for using a true RNG to demonstrate precognition or retroactive influence. But alas, the use of a true RNG opens the door to the psychokinesis interpretation: The participant might be influencing the placement of the upcoming target rather than perceiving it, a possibility supported by a body of empirical evidence testing psychokinesis with true RNGs (Radin, 2006, pp.154–160).”

Good! I was just about to make the very same objection! If someone can predict the whole sequence of [extremely long integer] values of a PRNG, it gets hardly more irrational to imagine that he or she can mentally impact a quantum mechanics event. (And hopefully save Schröninger’s cat in the process.) Obviously, it begets the question as to how a subject could forecast a location of the picture that depends on the random generation but not forecast the result of the random generation.

“Like the clairvoyance interpretation, the psychokinesis interpretation also permits us to reject the claim that the direction of the causal arrow has been reversed. Ironically, the psychokinesis alternative can be ruled out by using a PRNG, which is immune to psychokinesis because the sequence of numbers is fully determined and can even be checked after the fact to confirm that its algorithm has not been perturbed. Over the course of our research program—and within the experiment just reported—we have obtained positive results using both PRNGs and a true RNG, arguably leaving precognition/reversed causality the only nonartifactual interpretation that can account for all the positive results.”

This is getting rather confusing. Avoid using a PRNG for fear the subject infers about the sequence and avoid using a RNG for fear of the subject tempering with the physical generator. An omniscient psychic would be able to hand both types of generators, wouldn’t he or she!?!

“This still leaves open the artifactual alternative that the output from the RNG is producing inadequately randomized sequences containing patterns that fortuitously match participants’ response biases.”

This objection shows how little confidence the author has in the randomness tests he previously mentioned: a proper random generator is not inadequately randomized. And if chance only rather than psychic powers is involved, there is no explanation for the match with the participants’ response. Unless those participants are so clever as to detect the flaws in the generator…

“In the present experiment, this possibility is ruled out by the twin findings that erotic targets were detected significantly more frequently than randomly interspersed nonerotic targets and that the nonerotic targets themselves were not detected significantly more frequently than chance. Nevertheless, for some of the other experiments reported in this article, it would be useful to have more general assurance that there are not patterns in the left/right placements of the targets that might correlate with response biases of participants. For this purpose, Lise Wallach, Professor of Psychology at Duke University, suggested that I run a virtual control experiment using random inputs in place of human participants.”

Absolutely brilliant! This test replacing the participants with random generators has shown that the subjects’ answers do not correspond to an iid sequence from a uniform distribution. It would indeed require great psychic powers to reproduce a perfectly iid U(0,1) sequence! And the participants were warned about the experiment so naturally expected to see patterns in the sequence of placements.


independent component analysis and p-values

Posted in pictures, Running, Statistics, Travel, University life with tags , , , , , , , , , , , , , on September 8, 2014 by xi'an

WariseLast morning at the neuroscience workshop Jean-François Cardoso presented independent component analysis though a highly pedagogical and enjoyable tutorial that stressed the geometric meaning of the approach, summarised by the notion that the (ICA) decomposition


of the data X seeks both independence between the columns of S and non-Gaussianity. That is, getting as away from Gaussianity as possible. The geometric bits came from looking at the Kullback-Leibler decomposition of the log likelihood

-\mathbb{E}[\log L(\theta|X)] = KL(P,Q_\theta) + \mathfrak{E}(P)

where the expectation is computed under the true distribution P of the data X. And Qθ is the hypothesised distribution. A fine property of this decomposition is a statistical version of Pythagoreas’ theorem, namely that when the family of Qθ‘s is an exponential family, the Kullback-Leibler distance decomposes into

KL(P,Q_\theta) = KL(P,Q_{\theta^0}) + KL(Q_{\theta^0},Q_\theta)

where θ⁰ is the expected maximum likelihood estimator of θ. (We also noticed this possibility of a decomposition in our Kullback-projection variable-selection paper with Jérôme Dupuis.) The talk by Aapo Hyvärinen this morning was related to Jean-François’ in that it used ICA all the way to a three-level representation if oriented towards natural vision modelling in connection with his book and the paper on unormalised models recently discussed on the ‘Og.

On the afternoon, Eric-Jan Wagenmaker [who persistently and rationally fight the (ab)use of p-values and who frequently figures on Andrew’s blog] gave a warning tutorial talk about the dangers of trusting p-values and going fishing for significance in existing studies, much in the spirit of Andrew’s blog (except for the defence of Bayes factors). Arguing in favour of preregistration. The talk was full of illustrations from psychology. And included the line that ESP testing is the jester of academia, meaning that testing for whatever form of ESP should be encouraged as a way to check testing procedures. If a procedure finds a significant departure from the null in this setting, there is something wrong with it! I was then reminded that Eric-Jan was one of the authors having analysed Bem’s controversial (!) paper on the “anomalous processes of information or energy transfer that are currently unexplained in terms of known physical or biological mechanisms”… (And of the shocking talk by Jessica Utts on the same topic I attended in Australia two years ago.)