Psience Quest

(2019-01-26, 12:25 PM)Chris Wrote: [ -> ]Here's a short but quite interesting presentation by Roger Nelson from the Society for Scientific Exploration Conference in 2016 - ten minutes' talk followed by questions. It includes a figure that I don't remember seeing in the published papers, showing an analysis by Peter Bancel concluding that about two thirds of the events showed a positive effect, one sixth no effect, and the other one sixth a "true negative" effect. It would be interesting to see more details of that.

Roger Nelson kindly identified the source of this figure. It's actually from quite an early analysis by Bancel and Nelson, published in the Journal of Scientific Exploration in 2008:
http://global-mind.org/papers/pdf/GCP.JSE.B&N.2008.pdf

The authors modelled the frequency distribution of z scores for events as the sum of three normal distributions. The parameters that gave the best fit were:
(1) Positive: 67%, mean 0.56
(2) Null: 16%
(3) Negative: 17%, mean -0.49.
These combined to give the measured overall effect size of 0.30.

Roger Nelson also kindly sent me a link to a short paper by him entitled "Evoked Potentials and GCP Event Data", which draws a parallel between the transient variations of electrical potential in the body following stimuli and the time-varying correlations during events in the GCP data. The idea is that this may suggest an alternative interpretation of the  results found by Peter Bancel, which he interpreted as evidence of a psi-mediated experimenter effect related to the selection of the start and end points of the events:
http://global-mind.org/papers/pdf/event....ntials.pdf

I've been thinking about this a bit more, and it seems to me that the graphs produced by Peter Bancel - showing the averaged time-variation of the data before, during and after events - aren't really the clinching evidence for a psi-mediated selection mechanism that they might appear.

Here are the graphs:
[Image: SelectionGraphs.jpg]

Both graphs show the time-varying cumulative correlation data. The one on the left is for events where there was flexibility in the choice of start and end times (the events have all been stretched to a duration of 24 hours before averaging), while the one on the right is for 24-hour events starting and ending at midnight, where there was no such flexibility. The key point is that the graph on the left shows that the increase in the cumulative signal during the event is almost exactly cancelled out by decreases before and after the event. But the graph on the right shows no systematic change before or after the event.

The interpretation is that for the events with flexibility, psi has allowed the experimenter to choose start and end points so as to produce a positive effect within each event, at the cost of matching negative contributions before and after. The signal itself remains an ideal random walk, but a favourable period of time has been selected. For the 24-hour events this cannot happen. Instead, presumably, psi has allowed the experimenter to choose favourable days for 24-hour events, and to avoid unfavourable days.

One question that might be asked is why there shouldn't also be an element of choosing whether to define events on favourable days in the left-hand graph, as well as just choosing favourable start and end points. If there were such an element, the negative contributions before and after the event would only partially cancel out the positive contribution from the event itself. But as the graph shows, this didn't happen.

Another question is whether we can actually produce a graph that looks like the left-hand one by defining suitable rules for the selection of start and end points. I doubt we can. Perhaps the simplest rule would be to specify a start period and an end period for each event, and to select start and end points according to the location of the minimum and maximum respectively of the cumulative curve during these periods. I reckon that would produce something of the form below, which doesn't look much like the experimental data:
[Image: SelectionGraphTheoretical.jpg]
There's also a more quantitative objection to the psi-mediated selection mechanism. In the simple model just suggested, the selection within the start and end periods produces a contribution to the cumulative statistic that is proportional to the square roots of the durations of those start and end periods. That's because of the fundamental fact that the deviation of a random walk scales like the square root of time. So the same square-root scaling will also apply to more sophisticated models of selection based on the behaviour of the signal within fixed start and end periods.

That means we can estimate how the size of the effect, expressed as a Z value per event, should vary with the duration of the event (call it N). That depends on whether the start and end periods have fixed lengths, or whether they grow in proportion to the duration of the event. If they have fixed lengths, Z is inversely proportional to the square root of N. If they grow in proportion to the duration, Z is independent of N. Those cases are in contrast to a field-type effect, for which Z would be proportional to the square root of N.

It so happens that in 2015 (in "Evidence for Psi", edited by Broderick and Goertzel) Bancel tested the dependence of Z on N for events whose duration was 12 hours or less (thus, fortuitously, excluding the 24-hour events for which he now believes the mechanism of psi-mediated selection is different). He called this a signal-to-noise test. The result was that he rejected what he then described as the "data selection hypothesis", in which Z was independent of N. The result of the statistical test would translate to a (one-tailed) p value of .0037. That rejected hypothesis would be equivalent to the start and end periods growing in proportion to the duration of the event. The alternative, where the start and end periods had fixed lengths, would have been even more strongly rejected. (The 2015 paper was based on 426 events, and therefore represented about 85% of the complete series.)

Peter Bancel's conclusion at that time was that both a simple selection hypothesis and a straightforward global consciousness field hypothesis had to be rejected:
"The analysis of data structure rejects the simple selection hypothesis at a reasonably high level of confidence. The signal-to-noise analysis provides the most clearcut support for this conclusion. ...Tests for a loophole to circumvent the XOR no-go suggest that a straightforward conception of proto-psi global consciousness is also not tenable. ...
The analyses, then, provide good arguments for rejecting both simple models and we are forced to look elsewhere for an explanation."
https://books.google.com/books?id=KVyQBQ...&lpg=PA274

(2019-02-08, 07:29 PM)Chris Wrote: [ -> ]The interpretation is that for the events with flexibility, psi has allowed the experimenter to choose start and end points so as to produce a positive effect within each event, at the cost of matching negative contributions before and after. The signal itself remains an ideal random walk, but a favourable period of time has been selected.

Thanks for this analysis, Chris. Some of the maths is a bit beyond me (i.e. the ratios of effect size to duration) but I can at least ask a question re the above quote:

Do you have any opinion on whether it is reasonable to expect that the matching negative contributions occur immediately before and after the events? That is, why would we expect them to occur there as opposed to, say, anywhere else in the data? More generally, why would we expect them to occur at all, just as we (supposedly) don't expect them to occur for the full-day events?

Also:

(2019-02-08, 07:29 PM)Chris Wrote: [ -> ]It so happens that in 2015 (in "Evidence for Psi", edited by Broderick and Goertzel) Bancel tested the dependence of Z on N for events whose duration was 12 hours or less

The chapter in this book in which Peter Bancel describes this test can be read in full on researchgate.net without Google Books blanking out various pages. (It's been referenced in this thread already in case of confusion).

(2019-02-08, 07:29 PM)Chris Wrote: [ -> ]I've been thinking about this a bit more, and it seems to me that the graphs produced by Peter Bancel - showing the averaged time-variation of the data before, during and after events - aren't really the clinching evidence for a psi-mediated selection mechanism that they might appear.

Here are the graphs:
[Image: SelectionGraphs.jpg]

Both graphs show the time-varying cumulative correlation data. The one on the left is for events where there was flexibility in the choice of start and end times (the events have all been stretched to a duration of 24 hours before averaging), while the one on the right is for 24-hour events starting and ending at midnight, where there was no such flexibility. The key point is that the graph on the left shows that the increase in the cumulative signal during the event is almost exactly cancelled out by decreases before and after the event. But the graph on the right shows no systematic change before or after the event.

The interpretation is that for the events with flexibility, psi has allowed the experimenter to choose start and end points so as to produce a positive effect within each event, at the cost of matching negative contributions before and after. The signal itself remains an ideal random walk, but a favourable period of time has been selected. For the 24-hour events this cannot happen. Instead, presumably, psi has allowed the experimenter to choose favourable days for 24-hour events, and to avoid unfavourable days.

One question that might be asked is why there shouldn't also be an element of choosing whether to define events on favourable days in the left-hand graph, as well as just choosing favourable start and end points. If there were such an element, the negative contributions before and after the event would only partially cancel out the positive contribution from the event itself. But as the graph shows, this didn't happen.

Another question is whether we can actually produce a graph that looks like the left-hand one by defining suitable rules for the selection of start and end points. I doubt we can. Perhaps the simplest rule would be to specify a start period and an end period for each event, and to select start and end points according to the location of the minimum and maximum respectively of the cumulative curve during these periods. I reckon that would produce something of the form below, which doesn't look much like the experimental data:
[Image: SelectionGraphTheoretical.jpg]
There's also a more quantitative objection to the psi-mediated selection mechanism. In the simple model just suggested, the selection within the start and end periods produces a contribution to the cumulative statistic that is proportional to the square roots of the durations of those start and end periods. That's because of the fundamental fact that the deviation of a random walk scales like the square root of time. So the same square-root scaling will also apply to more sophisticated models of selection based on the behaviour of the signal within fixed start and end periods.

That means we can estimate how the size of the effect, expressed as a Z value per event, should vary with the duration of the event (call it N). That depends on whether the start and end periods have fixed lengths, or whether they grow in proportion to the duration of the event. If they have fixed lengths, Z is inversely proportional to the square root of N. If they grow in proportion to the duration, Z is independent of N. Those cases are in contrast to a field-type effect, for which Z would be proportional to the square root of N.

It so happens that in 2015 (in "Evidence for Psi", edited by Broderick and Goertzel) Bancel tested the dependence of Z on N for events whose duration was 12 hours or less (thus, fortuitously, excluding the 24-hour events for which he now believes the mechanism of psi-mediated selection is different). He called this a signal-to-noise test. The result was that he rejected what he then described as the "data selection hypothesis", in which Z was independent of N. The result of the statistical test would translate to a (one-tailed) p value of .0037. That rejected hypothesis would be equivalent to the start and end periods growing in proportion to the duration of the event. The alternative, where the start and end periods had fixed lengths, would have been even more strongly rejected. (The 2015 paper was based on 426 events, and therefore represented about 85% of the complete series.)

Peter Bancel's conclusion at that time was that both a simple selection hypothesis and a straightforward global consciousness field hypothesis had to be rejected:
"The analysis of data structure rejects the simple selection hypothesis at a reasonably high level of confidence. The signal-to-noise analysis provides the most clearcut support for this conclusion. ...Tests for a loophole to circumvent the XOR no-go suggest that a straightforward conception of proto-psi global consciousness is also not tenable. ...
The analyses, then, provide good arguments for rejecting both simple models and we are forced to look elsewhere for an explanation."
https://books.google.com/books?id=KVyQBQ...&lpg=PA274

I know this sort of maths floats your boat and I kinda love you for it, but I find it dreadfully dry. What are you saying here? Is love affecting electronic devices or not?

(2019-02-08, 11:59 PM)Laird Wrote: [ -> ]Thanks for this analysis, Chris. Some of the maths is a bit beyond me (i.e. the ratios of effect size to duration) but I can at least ask a question re the above quote:

Do you have any opinion on whether it is reasonable to expect that the matching negative contributions occur immediately before and after the events? That is, why would we expect them to occur there as opposed to, say, anywhere else in the data? More generally, why would we expect them to occur at all, just as we (supposedly) don't expect them to occur for the full-day events?

I realised while I was writing that post that it wasn't really feasible to explain the mathematical details, so it ended up more as a summary of what I'd concluded.

But I'll try to have a go some time at explaining the arguments about the dependence of Z on event duration, as those are essentially straightforward.

I'm sure it is reasonable to expect the matching negative contributions to occur immediately before and after the events. The reason for that is simply that if you adjust the end point (say) so as to increase the average size of the correlation statistic during the event, then by that choice you are necessarily going to decrease the average size of the correlation statistic in the period immediately after the event. It's essentially no different from taking a set of unbiased statistics and selecting the largest values. The ones that aren't selected will tend to be smaller than expectation. The only difference here is that you're constrained to selecting a set of values generated during a continuous period of time. But exactly the same principle applies - if you select a set of larger-than-average values, the ones left will be smaller than average.

If I've got it right, this graph is showing the theoretical expectation - for the average time-dependent cumulative correlation statistic - when the start and end points are chosen by looking for its minimum within a fixed start period, and its maximum within a fixed end period. The negative contributions are reflected by the negative slope of the graph before the first cusp and after the second:

My other suggestion would be that if people find statistics too dry, then the Global Consciousness Project is probably not for them.

There are plenty of statistics-free discussion threads here to choose from.

(2019-02-09, 09:20 AM)Chris Wrote: [ -> ]My other suggestion would be that if people find statistics too dry, then the Global Consciousness Project is probably not for them.

There are plenty of statistics-free discussion threads here to choose from.

Thanks. I’ll pop back here every so often and until someone explains what’s going on in this ‘experiment’, I won’t worry my pretty little head with it.

Thanks, Chris, I understand what you're saying.

I wonder though whether your graph needs to have such abrupt changes at its maxima and minima - could those not be smooth? Don't have access to graphing software right now so can't illustrate what I mean. Anyhow, I don't see that it makes any difference to your point and argument.