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[Music]

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hi i'm julia mosbridge and i'm here to

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present the results

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of a bunch of analyses

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of four online tests

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of psy so

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as you probably already know psy is a

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name for esp

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it includes precognition

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clairvoyance

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psychokinesis

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telepathy and mediumship

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forced choice experiments have been

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traditionally

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extremely tricky to pin down so the

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compared to a trickster

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in fact several authors have said maybe

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there's something going on where we're

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not supposed to exactly figure out

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what's going on with psy especially in

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these forced choice type results which

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seem to skip around

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um it's also been compared to

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the idea that there's this no signaling

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principle because we because of quantum

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constraints on signaling over time

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perhaps we can't use psi to signal

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across space and time in the way that

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you would want to

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given the sufficient evidence for psi

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abilities

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so i previously compared the same sort

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of trickster-like

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phenomenon to a pascal's principle for

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psy where you would push down on a fluid

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in one place and it rises in another

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place like you stress the system

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in one place and then it shows the

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stress elsewhere

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all of these examples are people trying

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researchers aside trying to understand

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especially in forced choice experiments

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how to use the data to really get at

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what's going on in terms of mechanism

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and how to use it potentially for

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applications it seems very tempting the

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data are very clear there's something

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actually going on and then the question

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becomes how do you pin it down

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so what we wanted to present here

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my co-authors dean raiden and mark

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bakuzi and i wanted to present

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a search principle versailles

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given

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and this search principle is especially

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for forged choice experiments because we

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already know that free response

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experiments like remote viewing and

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precognitive remote viewing

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uh show impressive effects but this is

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for forced choice experiments that you

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could do online because that's where we

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can get the most data

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and at the same time have the hardest

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time pinning things down so the search

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principle has

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six elements so first of all it's

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acknowledging that these are small

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effects

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be talking about them all the time the

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same time we talk about the fact that

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when you drop a ball it falls to the

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ground right these are small effects so

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let's admit that which means we're going

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to have to do some work to pull them out

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two

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we are actually early and exploratory

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on the timeline of understanding psy

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and the reason is partly because of

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taboos that have prevented funding in

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this area

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and partly because we only recently in

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the last couple decades had access to

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the internet where we could start

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getting

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experimental subjects on a large scale

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so we're actually very early so let's

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try to remember that

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in terms of accruing data that's the

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next the a in search what we mean what

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we mean by that is that because these

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are small effects and we're early in

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exploratory on the timeline we need to

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accrue lots of data we don't need to

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stop after we have 45 participants we

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need to get more and i'm speaking to

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myself as well as everyone else i'm

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guilty of

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violating all these principles

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um the r would be recognize diversity

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and approach in other words

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because you have a method that works for

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you

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does not mean that someone else might

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develo might not develop a method that

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is

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sufficient and perhaps superior for the

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data that they're looking at and so

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supporting this kind of diversity and

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approach rather than thinking oh well

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you have to do it this way because it's

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been done this way

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that's the direction we need to go we

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believe

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characterize don't impose that's the c

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in search

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what that is about is recognizing that

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if you are if you're trying to track a

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mysterious

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you know you're a naturalist and you're

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trying to track a mysterious butterfly

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that's hard to pin down and hard to cite

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and hard to understand and moves quickly

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the best thing to do is not assume

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that it must move in a particular place

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in a particular way the best thing to do

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is describe it and then once you've

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described it and you understand the way

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it's shaped and the way it moves then

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you hone in on the big results the

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things that seem most obvious and you

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can hone in on those by using a

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pre-registered confirmatory analyses

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that's one way we recommend so

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i'm going to show the results of four

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online psi tests done in collaboration

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with dean rayden and mark bacuzi

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at the institute of genetic sciences and

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at the windbridge institute

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so there's three of them were app-based

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an app called psi

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q initially and then the name was

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changed to psi cubed site three

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um we're on that app we've got

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unconscious precognition we've got a

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test of conscious precognition and we've

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got a test of conscious micro

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psychokinesis

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for that conscious microcycle kinesis

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test we did a pre-registered

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confirmatory analysis with two data

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batches so we saw an effect of the first

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data batch and then we was big enough we

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wanted to see it if it replicated in the

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second data batch

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in the website based test it was of

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precognitive remote viewing

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and again we did uh two data batches

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with a pre-registered confirmatory

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so first let's talk about the app the

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sci-cubed or sci-3 app it was available

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um for ios so for iphones from 2017 to

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2020.

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it is currently unavailable but likely

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will be available again right now it's

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being used

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in some tests in the neurology unit at

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baycrest so we're keeping it offline

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until those are complete

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the random number generator used for all

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three

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of the games offered here which were

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called heart quest future feelings and

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hidden gurus

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the random number generator was a

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pseudo-random number generator xored

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with the accelerometer

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so it ended up being

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a very unpredictable random number

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generator

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so again the three games here

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unconscious precognition that's called

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future feelings

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conscious precognition was called hidden

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gurus conscious micro pk or

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micropsychokinesis is called heart quest

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and we'll go through these in order so

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you could see the results of each

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so future feelings here's the way it

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worked you would see a picture

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a photo and then you had to click the

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face that matched the image mood

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um in this case it would be you had to

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choose between sad and happy it's a

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butterfly so right you're probably gonna

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choose happy but some of these are a

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little difficult to place but all of the

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pictures and all of the um designations

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of what was correct to designate a

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picture were taken from daryl bem's 2011

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retroactive priming experiment

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so he allowed us to have all the images

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and we just reprogrammed them onto the

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iphone

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after you would pick that then you would

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get a word the adjective displayed after

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that would be either matching or

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congruent like this one enjoyable or

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incongruent so if this said ugly that

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would be considered incongruent

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so the score was usually according to

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daryl bend's method is usually the

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response time difference between the

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congruent and incongruent trials which

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we call

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so what we have i'm just going to uh in

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words explain the complex effects and

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then show you a graph that contains

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these effects

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and that'll be sort of the standard of

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what i do throughout the talk so first

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of all the results opposed

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ben's 2011 results significantly in

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other words

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congruent trials in his experiment were

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usually had faster

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responses than incongruent trials we saw

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the exact opposite effect

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but we also saw that there were faster

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responses to positive images that's than

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negative images and this was a major

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bias and so we ended up splitting the

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data into positive and negative images

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so we could look at it more carefully

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this is part of characterizing rather

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than imposing

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um we also saw effects of people who

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rated themselves as female trending

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versus male trending

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we saw differences in people who rated

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themselves as high with having high

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cyber leaf versus low side belief

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and we in a multiple liter regression we

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found that psi belief gender

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extraversion neuroticism

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all predicted interaction the

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interaction term which the most

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informative term that we could derive

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from these data was this interaction

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term the response time difference for

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the positive images minus the response

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time difference for the negative images

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so

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note that's a lot to

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pin down so you can always take a

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screenshot of that and

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let's look at the actual graph

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so

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on the y-axis we have the mean of the

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response time difference so mean of the

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congruent minus the incongruent response

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time

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and um what we have is the

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black bars are for positive

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images and the gray bars are for

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negative images so you can see overall

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that for both positive and negative but

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significantly so for negative

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you have the mean congruent response

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minus

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integrant response time

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is positive there which is the opposite

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of what daryl bem found

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you'll also see an interesting situation

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here that interactions so you could see

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that for women versus men are people who

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are more female trending versus male

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trending

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look at the relationship between

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positive and negative it's flipped

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so women are going negative for positive

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images and positive for negative images

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and men are going in the same direction

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for both although

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less positive

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00:10:18,389 --> 00:10:15,839
for um for negative images

287
00:10:20,949 --> 00:10:18,399
the asterisks by the way refer to

288
00:10:23,030 --> 00:10:20,959
significance versus chance

289
00:10:25,750 --> 00:10:23,040
okay look at low psi belief versus high

290
00:10:27,590 --> 00:10:25,760
side belief you see a similar

291
00:10:29,269 --> 00:10:27,600
interaction pattern

292
00:10:31,509 --> 00:10:29,279
that's very intriguing and this is you

293
00:10:33,829 --> 00:10:31,519
can see why we recognized

294
00:10:34,949 --> 00:10:33,839
that this interaction term

295
00:10:37,030 --> 00:10:34,959
between

296
00:10:38,470 --> 00:10:37,040
the response time difference to the

297
00:10:39,990 --> 00:10:38,480
positive and negative was actually the

298
00:10:43,269 --> 00:10:40,000
most informative you could see in the

299
00:10:44,870 --> 00:10:43,279
data that that is in fact the situation

300
00:10:47,269 --> 00:10:44,880
so that was very intriguing to us and

301
00:10:49,670 --> 00:10:47,279
just keep in mind these

302
00:10:52,310 --> 00:10:49,680
i'm going back a slide for a second keep

303
00:10:53,269 --> 00:10:52,320
in mind the last point that psy belief

304
00:10:55,829 --> 00:10:53,279
gender

305
00:10:58,230 --> 00:10:55,839
these personality factors predicted that

306
00:10:59,430 --> 00:10:58,240
interaction term

307
00:11:01,910 --> 00:10:59,440
okay

308
00:11:03,910 --> 00:11:01,920
so now we're going to move on to oh wait

309
00:11:05,509 --> 00:11:03,920
briefly i just want to point out what

310
00:11:07,350 --> 00:11:05,519
what parts of the search principle did

311
00:11:09,829 --> 00:11:07,360
we use for psi here

312
00:11:11,030 --> 00:11:09,839
we looked we assumed small effects we

313
00:11:13,269 --> 00:11:11,040
recognized that it was early and

314
00:11:14,949 --> 00:11:13,279
exploratory we accrued data we

315
00:11:16,710 --> 00:11:14,959
recognized the diversity in the pro the

316
00:11:18,310 --> 00:11:16,720
approach just because it had previously

317
00:11:20,150 --> 00:11:18,320
been analyzed one way we didn't stick

318
00:11:21,990 --> 00:11:20,160
with that we looked at the data so we

319
00:11:23,829 --> 00:11:22,000
could understand what worked and we

320
00:11:26,150 --> 00:11:23,839
characterized without imposing we didn't

321
00:11:28,389 --> 00:11:26,160
hone in on any big results because these

322
00:11:30,230 --> 00:11:28,399
results were not profoundly

323
00:11:34,150 --> 00:11:30,240
big in terms of their statistical

324
00:11:35,430 --> 00:11:34,160
significance so we let that be

325
00:11:37,350 --> 00:11:35,440
so let's move on to conscious

326
00:11:39,829 --> 00:11:37,360
precognition

327
00:11:41,110 --> 00:11:39,839
in this experiment you're giving a well

328
00:11:42,790 --> 00:11:41,120
not experiment it's really supposed to

329
00:11:44,550 --> 00:11:42,800
be a game but it's really it's really

330
00:11:47,590 --> 00:11:44,560
kind of like an experiment right so

331
00:11:48,870 --> 00:11:47,600
you're given this spacescape on the app

332
00:11:51,190 --> 00:11:48,880
and you're supposed to predict where

333
00:11:53,350 --> 00:11:51,200
guru will be if you can see

334
00:11:56,389 --> 00:11:53,360
in the middle here there's a a little

335
00:11:58,710 --> 00:11:56,399
bit of a flying bird that's the guru

336
00:12:01,030 --> 00:11:58,720
it's just showing up on the screen and

337
00:12:02,629 --> 00:12:01,040
just before that the person will have

338
00:12:04,150 --> 00:12:02,639
picked where they think it's going to

339
00:12:06,389 --> 00:12:04,160
show up and the score

340
00:12:07,829 --> 00:12:06,399
is uh from a ranked list of possible

341
00:12:09,110 --> 00:12:07,839
distances between the point that they

342
00:12:10,710 --> 00:12:09,120
picked and the point where the guru

343
00:12:11,509 --> 00:12:10,720
showed up

344
00:12:15,990 --> 00:12:11,519
so

345
00:12:17,590 --> 00:12:16,000
we can avoid the obvious compound of

346
00:12:19,350 --> 00:12:17,600
people clicking in the middle which is

347
00:12:20,949 --> 00:12:19,360
the closest point everywhere on the

348
00:12:24,629 --> 00:12:20,959
screen and getting a high score so it

349
00:12:26,230 --> 00:12:24,639
has to be in a ranked list

350
00:12:28,310 --> 00:12:26,240
there was a little bias that we noticed

351
00:12:29,190 --> 00:12:28,320
in the data so first of all at the top

352
00:12:31,030 --> 00:12:29,200
graph

353
00:12:33,990 --> 00:12:31,040
in terms of where the master or the guru

354
00:12:37,350 --> 00:12:34,000
actually showed up it was equally split

355
00:12:39,430 --> 00:12:37,360
along all four quadrants top bottom left

356
00:12:41,509 --> 00:12:39,440
right but in terms of where people

357
00:12:43,110 --> 00:12:41,519
touched and i showed these data in 2019

358
00:12:43,990 --> 00:12:43,120
but i'm showing them again to make a

359
00:12:45,269 --> 00:12:44,000
point

360
00:12:47,990 --> 00:12:45,279
you can see

361
00:12:50,230 --> 00:12:48,000
those p values are difference from the

362
00:12:51,750 --> 00:12:50,240
above so in other words there's a

363
00:12:54,230 --> 00:12:51,760
significant difference where people

364
00:12:56,470 --> 00:12:54,240
actually touch they prefer the right

365
00:12:58,310 --> 00:12:56,480
top and then next

366
00:13:00,389 --> 00:12:58,320
the bottom right

367
00:13:03,269 --> 00:13:00,399
and then next the left bottom and then

368
00:13:04,150 --> 00:13:03,279
least of all the top left

369
00:13:06,629 --> 00:13:04,160
so

370
00:13:09,670 --> 00:13:06,639
we knew that bias exists existed but in

371
00:13:12,230 --> 00:13:09,680
this case we ignored it by taking

372
00:13:15,190 --> 00:13:12,240
just to the the score this on in this

373
00:13:17,030 --> 00:13:15,200
ranked list as our dependent variable

374
00:13:19,430 --> 00:13:17,040
we saw simple effects so first of all

375
00:13:20,230 --> 00:13:19,440
again we saw that the effects opposed

376
00:13:23,590 --> 00:13:20,240
what

377
00:13:25,670 --> 00:13:23,600
the participant was

378
00:13:27,430 --> 00:13:25,680
uh we saw no gender effects this time no

379
00:13:29,509 --> 00:13:27,440
psy belief effects

380
00:13:31,350 --> 00:13:29,519
we also saw that cyberlife side

381
00:13:32,790 --> 00:13:31,360
confidence gender openness and

382
00:13:34,790 --> 00:13:32,800
neuroticism

383
00:13:36,790 --> 00:13:34,800
predicted the score so these are

384
00:13:38,230 --> 00:13:36,800
relatively simple effects

385
00:13:40,710 --> 00:13:38,240
um and we are

386
00:13:42,629 --> 00:13:40,720
thinking that that may be because we are

387
00:13:45,509 --> 00:13:42,639
absolutely looking at exactly the score

388
00:13:48,150 --> 00:13:45,519
that we showed the user but that is the

389
00:13:49,990 --> 00:13:48,160
least biased score to use so that's why

390
00:13:51,990 --> 00:13:50,000
we did it so you can see here the

391
00:13:52,949 --> 00:13:52,000
significant expectation of opposing

392
00:13:56,230 --> 00:13:52,959
effect

393
00:13:58,310 --> 00:13:56,240
um is negative so in every case whether

394
00:14:00,470 --> 00:13:58,320
you take everyone together women men or

395
00:14:02,870 --> 00:14:00,480
you know female trading male trending

396
00:14:04,710 --> 00:14:02,880
low cyber belief or highest i believe um

397
00:14:06,949 --> 00:14:04,720
you'll see this as a negative score and

398
00:14:08,790 --> 00:14:06,959
it's significant overall but not in some

399
00:14:10,949 --> 00:14:08,800
kind of major way

400
00:14:12,629 --> 00:14:10,959
so what do we look at here we used the

401
00:14:15,430 --> 00:14:12,639
idea of small effects early in

402
00:14:17,030 --> 00:14:15,440
exploratory and accruing data

403
00:14:18,949 --> 00:14:17,040
so that's it for that one that was not

404
00:14:21,189 --> 00:14:18,959
that exciting but it was at least it

405
00:14:23,910 --> 00:14:21,199
confirmed this idea that effects often

406
00:14:25,990 --> 00:14:23,920
oppose our expectations

407
00:14:28,150 --> 00:14:26,000
conscious micro pk this is the heart

408
00:14:31,350 --> 00:14:28,160
quest in the last of the three

409
00:14:32,870 --> 00:14:31,360
games or experiments on the sci-3 app

410
00:14:35,350 --> 00:14:32,880
so what you do here is you touch the

411
00:14:37,110 --> 00:14:35,360
screen to make the robot's heart blow if

412
00:14:39,670 --> 00:14:37,120
you have the sound on it'll also make a

413
00:14:42,629 --> 00:14:39,680
tingly sound and it can glow either a

414
00:14:45,030 --> 00:14:42,639
little bit or a lot or not at all

415
00:14:46,710 --> 00:14:45,040
and that is based on the score so the

416
00:14:48,949 --> 00:14:46,720
score is calculated

417
00:14:50,790 --> 00:14:48,959
um based on the relationship between the

418
00:14:53,030 --> 00:14:50,800
trial and reference bits let me explain

419
00:14:54,710 --> 00:14:53,040
what trial and reference bits are so

420
00:14:56,470 --> 00:14:54,720
when you start the game

421
00:14:58,629 --> 00:14:56,480
reference bits get calculated from the

422
00:15:00,310 --> 00:14:58,639
random number generator and there's two

423
00:15:01,750 --> 00:15:00,320
of them so in this case at the bottom of

424
00:15:03,430 --> 00:15:01,760
the screen you could see i've i've

425
00:15:05,430 --> 00:15:03,440
decided that the reference bits were

426
00:15:07,509 --> 00:15:05,440
zero one okay so there's just two of

427
00:15:09,110 --> 00:15:07,519
them it's either zero and a or a one in

428
00:15:11,590 --> 00:15:09,120
each position

429
00:15:13,350 --> 00:15:11,600
um on every trial

430
00:15:15,509 --> 00:15:13,360
new reference bits get calculated in

431
00:15:17,670 --> 00:15:15,519
other words after the person presses the

432
00:15:19,750 --> 00:15:17,680
screen new reference bids are calculated

433
00:15:21,509 --> 00:15:19,760
for the random number generator

434
00:15:23,829 --> 00:15:21,519
so you could see here in the first trial

435
00:15:26,230 --> 00:15:23,839
the ref i mean i'm sorry trial bits

436
00:15:28,710 --> 00:15:26,240
are 0 0 second trial trial bits are zero

437
00:15:30,629 --> 00:15:28,720
one third trial is one one and fourth

438
00:15:32,389 --> 00:15:30,639
trial it's one zero these are all

439
00:15:34,310 --> 00:15:32,399
compared to the reference bits which

440
00:15:36,470 --> 00:15:34,320
remain the same throughout the game

441
00:15:38,550 --> 00:15:36,480
so you could get a score of one if one

442
00:15:40,870 --> 00:15:38,560
of those bits matches you could get a

443
00:15:42,470 --> 00:15:40,880
score of two if both of them match

444
00:15:43,269 --> 00:15:42,480
and a score of zero if neither of them

445
00:15:45,189 --> 00:15:43,279
match

446
00:15:47,749 --> 00:15:45,199
and that's what indicates one two or

447
00:15:49,670 --> 00:15:47,759
zero that's what indicates whether

448
00:15:52,629 --> 00:15:49,680
uh the heart

449
00:15:54,949 --> 00:15:52,639
will glow and whether the app will sing

450
00:15:57,030 --> 00:15:54,959
if it's zero nothing happens if it's one

451
00:15:58,710 --> 00:15:57,040
there's a little bit if it's two it's

452
00:16:01,350 --> 00:15:58,720
like lee

453
00:16:03,749 --> 00:16:01,360
so that's the task

454
00:16:05,430 --> 00:16:03,759
this one was super interesting um so

455
00:16:07,990 --> 00:16:05,440
first of all no effect for the score

456
00:16:10,949 --> 00:16:08,000
that we showed the participants

457
00:16:13,910 --> 00:16:10,959
and nothing going on with trial bits

458
00:16:16,150 --> 00:16:13,920
and yet a huge effect for reference bits

459
00:16:18,629 --> 00:16:16,160
in other words we thought

460
00:16:20,629 --> 00:16:18,639
people would be trying to match

461
00:16:23,590 --> 00:16:20,639
reference and trial bits to increase

462
00:16:26,470 --> 00:16:23,600
their score what happened was

463
00:16:28,150 --> 00:16:26,480
apparently people were changing the

464
00:16:29,110 --> 00:16:28,160
proportion of zeros and the reference

465
00:16:31,350 --> 00:16:29,120
bits

466
00:16:32,710 --> 00:16:31,360
to try to change their score not what we

467
00:16:36,310 --> 00:16:32,720
thought and not what we told them the

468
00:16:37,670 --> 00:16:36,320
task was about but that's what happened

469
00:16:40,949 --> 00:16:37,680
and

470
00:16:43,189 --> 00:16:40,959
um the the app uses the same exact

471
00:16:45,509 --> 00:16:43,199
function to call to create the

472
00:16:47,990 --> 00:16:45,519
reference and the trial bits and so we

473
00:16:50,069 --> 00:16:48,000
cannot blame it on that function

474
00:16:52,710 --> 00:16:50,079
um because they showed very different

475
00:16:54,310 --> 00:16:52,720
effects um there was a big gender effect

476
00:16:57,269 --> 00:16:54,320
for reference bits

477
00:16:58,790 --> 00:16:57,279
and uh we ended up with a dependent

478
00:17:00,389 --> 00:16:58,800
variable that was the first reference

479
00:17:02,550 --> 00:17:00,399
spent light on this minus the second

480
00:17:04,549 --> 00:17:02,560
reference bit and psy belief

481
00:17:06,470 --> 00:17:04,559
age openness conscientiousness

482
00:17:08,069 --> 00:17:06,480
extroversion all predicted that

483
00:17:09,990 --> 00:17:08,079
interaction

484
00:17:11,590 --> 00:17:10,000
so we did a pre-registered confirmatory

485
00:17:13,029 --> 00:17:11,600
analysis and that confirmed the

486
00:17:15,829 --> 00:17:13,039
reference bit effect

487
00:17:17,990 --> 00:17:15,839
and the gender difference although in

488
00:17:20,309 --> 00:17:18,000
one sense the gender difference opposed

489
00:17:22,710 --> 00:17:20,319
the pre-registered analysis and in a

490
00:17:24,390 --> 00:17:22,720
different sense it replicated it and i

491
00:17:25,829 --> 00:17:24,400
will explain

492
00:17:27,510 --> 00:17:25,839
so here's the

493
00:17:29,510 --> 00:17:27,520
data batch one

494
00:17:30,710 --> 00:17:29,520
in which we first saw the reference bit

495
00:17:33,029 --> 00:17:30,720
effect

496
00:17:35,190 --> 00:17:33,039
here we have a proportion of zeros in

497
00:17:37,350 --> 00:17:35,200
each reference bit

498
00:17:40,070 --> 00:17:37,360
on the y-axis and then we have all

499
00:17:42,710 --> 00:17:40,080
trials this is a trial level analysis of

500
00:17:43,750 --> 00:17:42,720
all trials trials for women and trials

501
00:17:46,230 --> 00:17:43,760
from men

502
00:17:48,230 --> 00:17:46,240
so again the software function that

503
00:17:50,310 --> 00:17:48,240
creates the reference bits and the trial

504
00:17:52,150 --> 00:17:50,320
bits is exactly the same it's just

505
00:17:55,029 --> 00:17:52,160
called at different times

506
00:17:57,750 --> 00:17:55,039
and so the fact that this line at point

507
00:17:59,590 --> 00:17:57,760
five which represents chance

508
00:18:01,669 --> 00:17:59,600
is not exceeded

509
00:18:03,750 --> 00:18:01,679
by the trial bits but is definitely

510
00:18:05,909 --> 00:18:03,760
exceeded significantly by the reference

511
00:18:08,070 --> 00:18:05,919
bits for both the first and second

512
00:18:09,710 --> 00:18:08,080
reference bits overall

513
00:18:12,950 --> 00:18:09,720
suggest that there's some

514
00:18:14,950 --> 00:18:12,960
micropsychokinesis going on in that area

515
00:18:17,190 --> 00:18:14,960
so look at the trials for women in the

516
00:18:19,029 --> 00:18:17,200
trials for men it looks like

517
00:18:21,190 --> 00:18:19,039
trials from women

518
00:18:23,029 --> 00:18:21,200
are having a huge effect in other words

519
00:18:25,430 --> 00:18:23,039
women are really pulling this micro

520
00:18:26,950 --> 00:18:25,440
psychokinesis effect and men are barely

521
00:18:29,110 --> 00:18:26,960
pulling it and it's only in the first

522
00:18:31,190 --> 00:18:29,120
reference bit that they're pulling it

523
00:18:33,430 --> 00:18:31,200
so let's go back to let's go to data

524
00:18:35,270 --> 00:18:33,440
batch two so we registered an analysis

525
00:18:36,870 --> 00:18:35,280
we pre-registered this analysis and then

526
00:18:38,549 --> 00:18:36,880
we looked at these data

527
00:18:41,750 --> 00:18:38,559
and again we have the same effects

528
00:18:44,630 --> 00:18:41,760
reference bits um

529
00:18:46,789 --> 00:18:44,640
uh both reference bit one and two we see

530
00:18:48,789 --> 00:18:46,799
significantly more zeros than chance but

531
00:18:50,630 --> 00:18:48,799
the trial bits nope

532
00:18:52,310 --> 00:18:50,640
um we look at trials for women let's

533
00:18:53,270 --> 00:18:52,320
look at the previous data match for a

534
00:18:56,470 --> 00:18:53,280
second

535
00:18:59,110 --> 00:18:56,480
same situation first reference

536
00:19:01,110 --> 00:18:59,120
has a lower value proportion zeros than

537
00:19:02,950 --> 00:19:01,120
the second reference spin but they're

538
00:19:05,830 --> 00:19:02,960
both above chance

539
00:19:08,070 --> 00:19:05,840
but let's look at data batch one again

540
00:19:09,750 --> 00:19:08,080
for men we just had a measly effect

541
00:19:11,510 --> 00:19:09,760
there and it was only in the first

542
00:19:12,630 --> 00:19:11,520
reference bit

543
00:19:14,310 --> 00:19:12,640
here

544
00:19:15,990 --> 00:19:14,320
we have a big effect

545
00:19:18,950 --> 00:19:16,000
it is still greater in the first

546
00:19:20,630 --> 00:19:18,960
reference bit so the question is

547
00:19:22,310 --> 00:19:20,640
what do you want to call the pattern and

548
00:19:23,270 --> 00:19:22,320
this comes to trying to characterize

549
00:19:25,430 --> 00:19:23,280
things

550
00:19:27,669 --> 00:19:25,440
in the pre-registration we called the

551
00:19:30,070 --> 00:19:27,679
fact that women were significantly seem

552
00:19:32,150 --> 00:19:30,080
to be significantly producing more zeros

553
00:19:33,750 --> 00:19:32,160
in the either reference but then then

554
00:19:35,830 --> 00:19:33,760
and especially the second reference but

555
00:19:38,310 --> 00:19:35,840
we called that the effect and that did

556
00:19:39,669 --> 00:19:38,320
not replicate but what did replicate was

557
00:19:43,830 --> 00:19:39,679
this

558
00:19:48,789 --> 00:19:46,630
so that reminds us of that it's really

559
00:19:49,830 --> 00:19:48,799
important to improve more data

560
00:19:51,750 --> 00:19:49,840
really important to recognize the

561
00:19:53,590 --> 00:19:51,760
diversity and approach to characterize

562
00:19:55,830 --> 00:19:53,600
not impose and then hone in on the big

563
00:19:58,310 --> 00:19:55,840
results what replicates what doesn't and

564
00:19:59,510 --> 00:19:58,320
what actually is the effect

565
00:20:01,190 --> 00:19:59,520
sometimes you're not right about

566
00:20:03,590 --> 00:20:01,200
choosing the effect the first time right

567
00:20:05,430 --> 00:20:03,600
so then you go okay well what about this

568
00:20:07,669 --> 00:20:05,440
keep going

569
00:20:10,070 --> 00:20:07,679
okay so we talked about the first three

570
00:20:12,470 --> 00:20:10,080
online sci tests withdrawal force choice

571
00:20:14,789 --> 00:20:12,480
the last online site test is also forced

572
00:20:17,190 --> 00:20:14,799
choice but it's based on a precognitive

573
00:20:18,630 --> 00:20:17,200
remote viewing task

574
00:20:19,510 --> 00:20:18,640
this is a website

575
00:20:22,070 --> 00:20:19,520
based

576
00:20:24,390 --> 00:20:22,080
task with significantly fewer unique

577
00:20:27,669 --> 00:20:24,400
logins but they were very committed this

578
00:20:29,430 --> 00:20:27,679
is a one trial task and it was based on

579
00:20:31,430 --> 00:20:29,440
i wrote this book with teresa chung

580
00:20:33,270 --> 00:20:31,440
called the premonition code and it was

581
00:20:35,590 --> 00:20:33,280
based on the idea of using the book to

582
00:20:37,830 --> 00:20:35,600
bring people to this website so that

583
00:20:39,990 --> 00:20:37,840
they could do this experiment

584
00:20:41,110 --> 00:20:40,000
and the way it works

585
00:20:43,750 --> 00:20:41,120
is it has

586
00:20:46,070 --> 00:20:43,760
six steps i'm not going to show all six

587
00:20:48,549 --> 00:20:46,080
but it brings people through

588
00:20:50,549 --> 00:20:48,559
a single trial

589
00:20:52,149 --> 00:20:50,559
called controlled precognition which is

590
00:20:53,590 --> 00:20:52,159
really precognitive remote viewing what

591
00:20:55,190 --> 00:20:53,600
you're trying to do

592
00:20:57,110 --> 00:20:55,200
is get information

593
00:20:59,510 --> 00:20:57,120
about a future image

594
00:21:01,990 --> 00:20:59,520
before that image is shown to you

595
00:21:03,430 --> 00:21:02,000
and it takes you through these steps

596
00:21:05,750 --> 00:21:03,440
physical uh physical preparation

597
00:21:07,430 --> 00:21:05,760
practical preparation mental preparation

598
00:21:10,070 --> 00:21:07,440
making sure you write everything down

599
00:21:11,909 --> 00:21:10,080
doing scans it's a long process the

600
00:21:13,430 --> 00:21:11,919
actual user of the website does not have

601
00:21:15,029 --> 00:21:13,440
to do this process they could click

602
00:21:17,270 --> 00:21:15,039
through these buttons

603
00:21:20,149 --> 00:21:17,280
by pressing the word future

604
00:21:21,190 --> 00:21:20,159
multiple times it will take them a few

605
00:21:23,029 --> 00:21:21,200
seconds

606
00:21:25,590 --> 00:21:23,039
to get to the actual

607
00:21:27,830 --> 00:21:25,600
task that records their response that's

608
00:21:30,230 --> 00:21:27,840
this one most people whoever did not

609
00:21:31,669 --> 00:21:30,240
they actually did a lot of what we asked

610
00:21:33,669 --> 00:21:31,679
them to do as far as we could tell in

611
00:21:36,789 --> 00:21:33,679
terms of the time it took for them to

612
00:21:39,430 --> 00:21:36,799
start and then to get to this this page

613
00:21:41,909 --> 00:21:39,440
so this task says your next your next

614
00:21:43,110 --> 00:21:41,919
step is to experience the target so what

615
00:21:45,350 --> 00:21:43,120
happens is

616
00:21:47,190 --> 00:21:45,360
two images are being chosen from a

617
00:21:49,510 --> 00:21:47,200
database of images

618
00:21:51,430 --> 00:21:49,520
and they are designed to be pairs that

619
00:21:53,029 --> 00:21:51,440
are mutually exclusive in terms of what

620
00:21:54,710 --> 00:21:53,039
they contain

621
00:21:56,149 --> 00:21:54,720
so these are the graphs representing

622
00:21:58,149 --> 00:21:56,159
what they contain what we're trying not

623
00:22:00,310 --> 00:21:58,159
to do here is show people the images

624
00:22:02,390 --> 00:22:00,320
because what can happen

625
00:22:04,070 --> 00:22:02,400
is that when you show people the images

626
00:22:06,149 --> 00:22:04,080
and you say which was your session which

627
00:22:07,029 --> 00:22:06,159
was your remote viewing session most

628
00:22:08,950 --> 00:22:07,039
like

629
00:22:10,310 --> 00:22:08,960
people can get aspects of both images

630
00:22:11,909 --> 00:22:10,320
because they're looking at the images

631
00:22:14,070 --> 00:22:11,919
even though they're not really the

632
00:22:16,390 --> 00:22:14,080
target yet so we're trying to get around

633
00:22:19,590 --> 00:22:16,400
that by showing these graphs and saying

634
00:22:21,350 --> 00:22:19,600
which graph represents the contents of

635
00:22:23,190 --> 00:22:21,360
what you think is in the target based on

636
00:22:25,430 --> 00:22:23,200
your remote viewing session

637
00:22:27,830 --> 00:22:25,440
so you can see that on the left we have

638
00:22:30,789 --> 00:22:27,840
vegetation and or food

639
00:22:33,350 --> 00:22:30,799
hills mountains and or rocks water

640
00:22:35,190 --> 00:22:33,360
fluids animals humans and our likeness

641
00:22:36,870 --> 00:22:35,200
and on the right we have

642
00:22:38,950 --> 00:22:36,880
words symbols

643
00:22:40,390 --> 00:22:38,960
and or ideas so that's those are these

644
00:22:42,070 --> 00:22:40,400
these are the choices it's either going

645
00:22:44,310 --> 00:22:42,080
to be just word symbols or ideas or it's

646
00:22:45,990 --> 00:22:44,320
going to be some kind of nature scene

647
00:22:47,430 --> 00:22:46,000
and we don't know which yet and the

648
00:22:49,830 --> 00:22:47,440
random number generator also doesn't

649
00:22:51,510 --> 00:22:49,840
know which yet the person has to choose

650
00:22:52,950 --> 00:22:51,520
after the person chooses there's no way

651
00:22:54,390 --> 00:22:52,960
to go back

652
00:22:56,149 --> 00:22:54,400
triggers the random number generator

653
00:22:57,909 --> 00:22:56,159
which in this case is based on network

654
00:22:59,669 --> 00:22:57,919
traffic

655
00:23:01,830 --> 00:22:59,679
to choose one of those two targets it

656
00:23:03,510 --> 00:23:01,840
turns out it was this little one with i

657
00:23:05,110 --> 00:23:03,520
don't know if you can see this little uh

658
00:23:06,710 --> 00:23:05,120
jellyfish there

659
00:23:09,110 --> 00:23:06,720
but that's the living being and then the

660
00:23:11,430 --> 00:23:09,120
water with the rocks

661
00:23:13,510 --> 00:23:11,440
so you're either correct or incorrect

662
00:23:15,430 --> 00:23:13,520
and at the base of the screen it says

663
00:23:17,270 --> 00:23:15,440
remember to send loving feelings back to

664
00:23:19,350 --> 00:23:17,280
yourself while you were working

665
00:23:21,430 --> 00:23:19,360
i actually added that in there because

666
00:23:22,950 --> 00:23:21,440
it turns out from a different set of

667
00:23:25,029 --> 00:23:22,960
experiments i think i spoke about these

668
00:23:27,510 --> 00:23:25,039
last you know in 2019

669
00:23:30,149 --> 00:23:27,520
that feeling unconditional love could

670
00:23:31,830 --> 00:23:30,159
actually seems to be able to support

671
00:23:33,590 --> 00:23:31,840
your ability to do this precognitive

672
00:23:35,430 --> 00:23:33,600
remote viewing task at least in

673
00:23:36,789 --> 00:23:35,440
preliminary experiments so working on

674
00:23:38,070 --> 00:23:36,799
that now

675
00:23:39,990 --> 00:23:38,080
thanks to a grant from the yellow

676
00:23:41,029 --> 00:23:40,000
foundation

677
00:23:42,470 --> 00:23:41,039
so

678
00:23:44,950 --> 00:23:42,480
what do we have

679
00:23:47,190 --> 00:23:44,960
so first of all we have complex effects

680
00:23:48,950 --> 00:23:47,200
so overall effects are opposing the

681
00:23:51,750 --> 00:23:48,960
presumed conscious intent which is to be

682
00:23:53,430 --> 00:23:51,760
correct we also have time course effects

683
00:23:55,750 --> 00:23:53,440
sex at birth effects and a

684
00:23:57,510 --> 00:23:55,760
pre-registered confirmatory analysis

685
00:23:59,750 --> 00:23:57,520
that actually confirms that the

686
00:24:02,310 --> 00:23:59,760
interestingness of the target is related

687
00:24:04,390 --> 00:24:02,320
to accuracy the more interesting targets

688
00:24:08,230 --> 00:24:04,400
people are more accurate at doing that

689
00:24:11,830 --> 00:24:10,070
let's look at the first five trials so

690
00:24:14,310 --> 00:24:11,840
these are data from everyone who did at

691
00:24:16,630 --> 00:24:14,320
least five trials

692
00:24:18,549 --> 00:24:16,640
looking at two different data batches we

693
00:24:21,269 --> 00:24:18,559
see the blue line

694
00:24:23,990 --> 00:24:21,279
is the first batch and the dotted or the

695
00:24:25,990 --> 00:24:24,000
dashed green line is in the second box

696
00:24:28,549 --> 00:24:26,000
batch and you could see the mean

697
00:24:31,909 --> 00:24:28,559
proportion correct on the y-axis the 0.5

698
00:24:33,110 --> 00:24:31,919
red line is its chance so your 50 chance

699
00:24:35,350 --> 00:24:33,120
of getting it right

700
00:24:37,510 --> 00:24:35,360
the interesting thing here to me is that

701
00:24:39,590 --> 00:24:37,520
on trial three for both

702
00:24:41,830 --> 00:24:39,600
the first and the second batch

703
00:24:43,590 --> 00:24:41,840
is the time when people were more likely

704
00:24:45,510 --> 00:24:43,600
to get this correct

705
00:24:46,789 --> 00:24:45,520
now this doesn't control for how long it

706
00:24:48,470 --> 00:24:46,799
was between

707
00:24:49,430 --> 00:24:48,480
their trials it could be all on the same

708
00:24:50,630 --> 00:24:49,440
day

709
00:24:53,750 --> 00:24:50,640
it could be

710
00:24:55,350 --> 00:24:53,760
you know 10 10 weeks apart right

711
00:24:57,909 --> 00:24:55,360
i mean we really don't know right this

712
00:24:59,909 --> 00:24:57,919
is everyone's third trial

713
00:25:01,350 --> 00:24:59,919
but it reminds me that on the first

714
00:25:03,510 --> 00:25:01,360
trial you're either getting it correct

715
00:25:05,750 --> 00:25:03,520
or incorrect the second trial you're

716
00:25:08,070 --> 00:25:05,760
having an emotional response to that

717
00:25:09,510 --> 00:25:08,080
whatever it was and then the third trial

718
00:25:11,190 --> 00:25:09,520
you're i think you're more likely to be

719
00:25:14,149 --> 00:25:11,200
kind of letting go

720
00:25:17,110 --> 00:25:14,159
so that's my interpretation who knows

721
00:25:19,190 --> 00:25:17,120
interesting to see if that replicates

722
00:25:21,750 --> 00:25:19,200
session duration effects so we were very

723
00:25:23,590 --> 00:25:21,760
curious the time that it took between

724
00:25:26,789 --> 00:25:23,600
pressing the start button and going

725
00:25:28,390 --> 00:25:26,799
through all those six steps to

726
00:25:30,470 --> 00:25:28,400
choosing one of those graphs

727
00:25:33,350 --> 00:25:30,480
we were wondering if that had any impact

728
00:25:35,430 --> 00:25:33,360
on how people's performance

729
00:25:37,430 --> 00:25:35,440
moved or changed over time so

730
00:25:39,029 --> 00:25:37,440
let's look here at about

731
00:25:40,070 --> 00:25:39,039
in the in the first batch we have the

732
00:25:43,269 --> 00:25:40,080
blue

733
00:25:46,630 --> 00:25:43,279
terms of proportion correct on the

734
00:25:48,950 --> 00:25:46,640
y-axis the second batch is the is uh

735
00:25:52,230 --> 00:25:48,960
this dash green line again

736
00:25:54,390 --> 00:25:52,240
50 correct is chance so this doesn't

737
00:25:56,950 --> 00:25:54,400
replicate so in the first batch they're

738
00:25:58,789 --> 00:25:56,960
doing better than chance right between

739
00:25:59,909 --> 00:25:58,799
five and eight

740
00:26:01,830 --> 00:25:59,919
minutes

741
00:26:03,190 --> 00:26:01,840
and on the second batch they're doing

742
00:26:06,149 --> 00:26:03,200
significantly worse than chance so

743
00:26:07,669 --> 00:26:06,159
that's kind of probably spurious the

744
00:26:10,390 --> 00:26:07,679
thing that's very interesting is for

745
00:26:11,669 --> 00:26:10,400
both batches right between 20 and 25

746
00:26:13,350 --> 00:26:11,679
minutes

747
00:26:14,870 --> 00:26:13,360
session duration

748
00:26:16,870 --> 00:26:14,880
they were worse

749
00:26:18,390 --> 00:26:16,880
than any other time now that's just

750
00:26:19,909 --> 00:26:18,400
quantitative in the first match and

751
00:26:21,350 --> 00:26:19,919
that's significantly worse than chance

752
00:26:22,950 --> 00:26:21,360
in the second batch

753
00:26:24,230 --> 00:26:22,960
so

754
00:26:25,669 --> 00:26:24,240
and you in fact you can see here that

755
00:26:28,549 --> 00:26:25,679
performance in the second batch was

756
00:26:31,110 --> 00:26:28,559
generally worse than chance um

757
00:26:33,190 --> 00:26:31,120
what's interesting about that to me is

758
00:26:34,950 --> 00:26:33,200
that there's an intentional

759
00:26:37,269 --> 00:26:34,960
shift around

760
00:26:38,950 --> 00:26:37,279
15 20 25 minutes

761
00:26:42,390 --> 00:26:38,960
that people talk about

762
00:26:44,070 --> 00:26:42,400
um in in learning in medical students in

763
00:26:45,669 --> 00:26:44,080
in all sorts of contexts where you're

764
00:26:47,350 --> 00:26:45,679
trying to get people to pay attention in

765
00:26:49,190 --> 00:26:47,360
naturalistic settings

766
00:26:53,350 --> 00:26:49,200
there's an intentional shift around that

767
00:26:55,909 --> 00:26:53,360
time and it just makes me wonder if um

768
00:26:57,510 --> 00:26:55,919
when you stop a session right at the

769
00:26:59,669 --> 00:26:57,520
time your attention is shifting and then

770
00:27:02,390 --> 00:26:59,679
you choose which of these two graphs

771
00:27:04,630 --> 00:27:02,400
that those data match with those that

772
00:27:06,149 --> 00:27:04,640
the session data match with it might be

773
00:27:07,990 --> 00:27:06,159
an attentional flip that actually

774
00:27:09,430 --> 00:27:08,000
doesn't serve you so

775
00:27:12,470 --> 00:27:09,440
again that

776
00:27:14,710 --> 00:27:12,480
needs more understanding

777
00:27:17,350 --> 00:27:14,720
the sex at birth effects were super

778
00:27:19,630 --> 00:27:17,360
interesting and it suggests that

779
00:27:22,149 --> 00:27:19,640
um women are in fact using

780
00:27:23,669 --> 00:27:22,159
micropsychokinesis to make themselves

781
00:27:24,789 --> 00:27:23,679
worse

782
00:27:26,710 --> 00:27:24,799
okay

783
00:27:29,190 --> 00:27:26,720
great so we have

784
00:27:31,590 --> 00:27:29,200
um females and i'm calling

785
00:27:33,909 --> 00:27:31,600
i'm calling them females because they

786
00:27:35,590 --> 00:27:33,919
said that their sex at birth was female

787
00:27:37,990 --> 00:27:35,600
and the ones who are calling males they

788
00:27:39,510 --> 00:27:38,000
said their sex at birth was male so

789
00:27:41,269 --> 00:27:39,520
that's how that's going

790
00:27:43,269 --> 00:27:41,279
um we have three columns here the

791
00:27:45,190 --> 00:27:43,279
proportion the grand mean proportion

792
00:27:47,510 --> 00:27:45,200
correct the brand means for the

793
00:27:49,269 --> 00:27:47,520
targets that were selected and the grand

794
00:27:51,350 --> 00:27:49,279
mean for the targets that were presented

795
00:27:52,389 --> 00:27:51,360
let me explain the difference

796
00:27:54,950 --> 00:27:52,399
but first

797
00:27:56,630 --> 00:27:54,960
show that first effect women

798
00:27:58,630 --> 00:27:56,640
across the board regardless of what was

799
00:28:00,470 --> 00:27:58,640
selected and was presented

800
00:28:01,430 --> 00:28:00,480
are performing significantly worse than

801
00:28:03,510 --> 00:28:01,440
men

802
00:28:05,350 --> 00:28:03,520
and that and they're performing

803
00:28:06,230 --> 00:28:05,360
significantly worse than chance

804
00:28:08,310 --> 00:28:06,240
okay

805
00:28:10,389 --> 00:28:08,320
so let's go to selected versus presented

806
00:28:12,950 --> 00:28:10,399
so you can understand this difference

807
00:28:14,830 --> 00:28:12,960
selected means the software is actually

808
00:28:17,909 --> 00:28:14,840
selecting

809
00:28:21,110 --> 00:28:17,919
um one of these uh one of these two

810
00:28:23,269 --> 00:28:21,120
positions for the graph that for the

811
00:28:25,430 --> 00:28:23,279
image that will be presented so remember

812
00:28:29,430 --> 00:28:25,440
we have

813
00:28:31,590 --> 00:28:29,440
right

814
00:28:33,830 --> 00:28:31,600
and the software after people are

815
00:28:35,430 --> 00:28:33,840
selecting a or b the software is

816
00:28:38,230 --> 00:28:35,440
selecting an image that is represented

817
00:28:39,669 --> 00:28:38,240
by this or this right

818
00:28:43,269 --> 00:28:39,679
so

819
00:28:45,190 --> 00:28:43,279
people generally want to select

820
00:28:47,110 --> 00:28:45,200
the first position whether it's on the

821
00:28:49,510 --> 00:28:47,120
top of their screen or on the left they

822
00:28:51,510 --> 00:28:49,520
want to select the first position

823
00:28:53,110 --> 00:28:51,520
so this is the proportion of people who

824
00:28:54,950 --> 00:28:53,120
actually selected

825
00:28:56,710 --> 00:28:54,960
the second position

826
00:28:57,990 --> 00:28:56,720
in this graph because i had to choose

827
00:29:00,630 --> 00:28:58,000
one of those positions because of the

828
00:29:02,789 --> 00:29:00,640
bias so look at the second position

829
00:29:04,710 --> 00:29:02,799
women are significantly

830
00:29:07,350 --> 00:29:04,720
less likely than chance

831
00:29:09,990 --> 00:29:07,360
to select the second position men are

832
00:29:11,669 --> 00:29:10,000
also but but less impressively

833
00:29:13,029 --> 00:29:11,679
also less likely than a chance to choose

834
00:29:14,710 --> 00:29:13,039
the second position what does the

835
00:29:16,710 --> 00:29:14,720
software do

836
00:29:18,710 --> 00:29:16,720
for women

837
00:29:20,789 --> 00:29:18,720
it's choosing

838
00:29:22,470 --> 00:29:20,799
um the actual

839
00:29:25,269 --> 00:29:22,480
what it should be doing it's producing

840
00:29:27,269 --> 00:29:25,279
this nice split between first and second

841
00:29:29,909 --> 00:29:27,279
position right that's the actual

842
00:29:31,669 --> 00:29:29,919
software uh present presentation of the

843
00:29:33,590 --> 00:29:31,679
targets

844
00:29:35,430 --> 00:29:33,600
what is it doing for men

845
00:29:38,149 --> 00:29:35,440
it's actually choosing

846
00:29:39,590 --> 00:29:38,159
the second position less in other words

847
00:29:41,430 --> 00:29:39,600
okay so you can interpret this either

848
00:29:43,830 --> 00:29:41,440
way so women are either making things

849
00:29:46,070 --> 00:29:43,840
worse by making the software behave in a

850
00:29:47,909 --> 00:29:46,080
non-biased way or men are making things

851
00:29:50,870 --> 00:29:47,919
better for themselves by having the

852
00:29:53,510 --> 00:29:50,880
software behave according to their bias

853
00:29:55,269 --> 00:29:53,520
either way interesting interactions

854
00:29:56,950 --> 00:29:55,279
with sex at birth

855
00:29:59,350 --> 00:29:56,960
and their performance here especially

856
00:30:02,950 --> 00:29:59,360
related to what they're choosing versus

857
00:30:07,110 --> 00:30:05,190
just another task reminder for this next

858
00:30:08,630 --> 00:30:07,120
section on interestingness it's really

859
00:30:10,950 --> 00:30:08,640
important to get it that this is a

860
00:30:12,549 --> 00:30:10,960
binary choice

861
00:30:14,710 --> 00:30:12,559
and at the same time you do see this

862
00:30:16,230 --> 00:30:14,720
information on the screen you just don't

863
00:30:17,350 --> 00:30:16,240
know which of these is going to be the

864
00:30:18,470 --> 00:30:17,360
target

865
00:30:20,710 --> 00:30:18,480
okay

866
00:30:23,190 --> 00:30:20,720
so we did an interestingness analysis

867
00:30:26,389 --> 00:30:23,200
because people have said

868
00:30:27,909 --> 00:30:26,399
that in remote viewing the luminosity or

869
00:30:29,430 --> 00:30:27,919
the interestingness

870
00:30:31,110 --> 00:30:29,440
which aren't necessarily the same thing

871
00:30:32,549 --> 00:30:31,120
but in this in this particular

872
00:30:36,549 --> 00:30:32,559
conversation i'm just going to conflate

873
00:30:38,230 --> 00:30:36,559
them um actually support accuracy so

874
00:30:40,870 --> 00:30:38,240
more interesting targets are actually

875
00:30:42,389 --> 00:30:40,880
easier to describe for a remote viewer

876
00:30:44,470 --> 00:30:42,399
so i wanted to test this out or we

877
00:30:46,870 --> 00:30:44,480
wanted to test this out and if you look

878
00:30:48,310 --> 00:30:46,880
at the y-axis what we have here is the

879
00:30:51,029 --> 00:30:48,320
proportion

880
00:30:53,510 --> 00:30:51,039
of the targets presented to a bunch of

881
00:30:56,470 --> 00:30:53,520
amazon mechanical turk workers that were

882
00:30:58,950 --> 00:30:56,480
rated interesting

883
00:31:00,310 --> 00:30:58,960
for these two situations

884
00:31:04,070 --> 00:31:00,320
dark blue

885
00:31:05,669 --> 00:31:04,080
is the trials that were correct

886
00:31:08,389 --> 00:31:05,679
and

887
00:31:09,990 --> 00:31:08,399
blue outline or white bars the trials

888
00:31:11,509 --> 00:31:10,000
that were incorrect these aren't all

889
00:31:12,710 --> 00:31:11,519
trials by the way the i actually took

890
00:31:15,110 --> 00:31:12,720
the top

891
00:31:17,190 --> 00:31:15,120
eight um targets that were most likely

892
00:31:19,509 --> 00:31:17,200
to be correct and the top eight targets

893
00:31:21,830 --> 00:31:19,519
that were most likely to be incorrect

894
00:31:23,029 --> 00:31:21,840
so that we could look at consistent data

895
00:31:24,950 --> 00:31:23,039
so there are certain targets that people

896
00:31:26,470 --> 00:31:24,960
kept getting right certain targets that

897
00:31:28,070 --> 00:31:26,480
people kept getting wrong

898
00:31:30,389 --> 00:31:28,080
these were all presented in that second

899
00:31:32,070 --> 00:31:30,399
position again the unbiased place to

900
00:31:33,430 --> 00:31:32,080
look for

901
00:31:34,950 --> 00:31:33,440
these results

902
00:31:37,350 --> 00:31:34,960
so what you can see is when you take all

903
00:31:38,870 --> 00:31:37,360
data together the proportion of the ones

904
00:31:40,230 --> 00:31:38,880
that were correct more likely to be

905
00:31:42,710 --> 00:31:40,240
correct or rated

906
00:31:44,070 --> 00:31:42,720
significantly more interesting by these

907
00:31:45,110 --> 00:31:44,080
judges who had no idea about the

908
00:31:46,710 --> 00:31:45,120
experiment they were just looking at

909
00:31:49,509 --> 00:31:46,720
pictures and rating them

910
00:31:51,269 --> 00:31:49,519
um then the ones that were

911
00:31:53,750 --> 00:31:51,279
more likely to be incorrect

912
00:31:55,909 --> 00:31:53,760
let's take out the words because if you

913
00:31:56,789 --> 00:31:55,919
look at this

914
00:31:58,950 --> 00:31:56,799
people

915
00:32:00,549 --> 00:31:58,960
this just looks like i don't want to

916
00:32:01,830 --> 00:32:00,559
pick this one i want to pick this one

917
00:32:03,590 --> 00:32:01,840
there's more stuff going on and it's

918
00:32:05,590 --> 00:32:03,600
more likely to match my session so

919
00:32:07,669 --> 00:32:05,600
people will have a bias there it's not

920
00:32:09,669 --> 00:32:07,679
actually going to match the bias of

921
00:32:11,110 --> 00:32:09,679
the software and it doesn't but i just

922
00:32:12,630 --> 00:32:11,120
checked out what if we just took out the

923
00:32:14,549 --> 00:32:12,640
words you see the same effect but it's

924
00:32:15,590 --> 00:32:14,559
not significant

925
00:32:17,350 --> 00:32:15,600
take out

926
00:32:19,110 --> 00:32:17,360
look at only

927
00:32:20,470 --> 00:32:19,120
targets without subjects people think

928
00:32:22,230 --> 00:32:20,480
subjects or

929
00:32:24,310 --> 00:32:22,240
animals humans are more interesting than

930
00:32:25,750 --> 00:32:24,320
non-animals humans you still see the

931
00:32:27,750 --> 00:32:25,760
effect

932
00:32:29,509 --> 00:32:27,760
you narrow it down to one element so

933
00:32:31,990 --> 00:32:29,519
it's a one element graph compared to

934
00:32:33,750 --> 00:32:32,000
another one element graph and you still

935
00:32:36,310 --> 00:32:33,760
see the effect so

936
00:32:39,110 --> 00:32:36,320
we pre-registered these analyses

937
00:32:41,029 --> 00:32:39,120
with the kessler registration unit

938
00:32:42,630 --> 00:32:41,039
and we did it again on the second batch

939
00:32:44,389 --> 00:32:42,640
of data and here

940
00:32:45,909 --> 00:32:44,399
um because there were fewer people in

941
00:32:48,950 --> 00:32:45,919
the second batch i had to go with the

942
00:32:50,789 --> 00:32:48,960
top five correct and top five incorrect

943
00:32:52,870 --> 00:32:50,799
but again you see the same effect here

944
00:32:54,070 --> 00:32:52,880
it's significant with words no subjects

945
00:32:54,950 --> 00:32:54,080
one element

946
00:32:57,669 --> 00:32:54,960
so

947
00:32:59,990 --> 00:32:57,679
that's an example

948
00:33:01,830 --> 00:33:00,000
of using all of these

949
00:33:04,630 --> 00:33:01,840
so we started with these small effects

950
00:33:06,310 --> 00:33:04,640
these time course effects and awareness

951
00:33:07,750 --> 00:33:06,320
that it was early in exploratory we

952
00:33:09,590 --> 00:33:07,760
accrued data

953
00:33:11,990 --> 00:33:09,600
we recognized that

954
00:33:14,470 --> 00:33:12,000
the way we were looking at the data

955
00:33:15,909 --> 00:33:14,480
might not be the way um that's the most

956
00:33:17,430 --> 00:33:15,919
useful way to look at the data when we

957
00:33:18,549 --> 00:33:17,440
started thinking about interestingness

958
00:33:21,430 --> 00:33:18,559
then we characterized the

959
00:33:23,669 --> 00:33:21,440
interestingness without imposing right

960
00:33:25,190 --> 00:33:23,679
we didn't decide that there's one way to

961
00:33:27,830 --> 00:33:25,200
look at interestingness we just asked a

962
00:33:29,750 --> 00:33:27,840
bunch of m turkers to describe that to

963
00:33:31,909 --> 00:33:29,760
to rank uh the targets by

964
00:33:33,669 --> 00:33:31,919
interestingness and we honed in on the

965
00:33:35,830 --> 00:33:33,679
big result

966
00:33:36,950 --> 00:33:35,840
so what do we conclude with all this so

967
00:33:39,029 --> 00:33:36,960
first of all

968
00:33:40,630 --> 00:33:39,039
nothing new but expectation opposing

969
00:33:42,789 --> 00:33:40,640
effects which some people call sign

970
00:33:44,710 --> 00:33:42,799
missing but in this case there's plenty

971
00:33:47,029 --> 00:33:44,720
of side going on it's just opposing the

972
00:33:49,509 --> 00:33:47,039
expectations of people and including the

973
00:33:51,110 --> 00:33:49,519
experimenters expectation opposing

974
00:33:52,950 --> 00:33:51,120
effects are rampant

975
00:33:54,310 --> 00:33:52,960
big effects seem generally to be

976
00:33:56,549 --> 00:33:54,320
consistent

977
00:33:59,190 --> 00:33:56,559
um at least in this case

978
00:34:00,710 --> 00:33:59,200
when you ask people to do a task that

979
00:34:02,630 --> 00:34:00,720
does not predict the mechanism that

980
00:34:06,230 --> 00:34:02,640
they're using to do the task so for

981
00:34:09,109 --> 00:34:06,240
example when people were doing the um

982
00:34:10,550 --> 00:34:09,119
precognition task turns out either women

983
00:34:13,430 --> 00:34:10,560
or men or both depending on how you

984
00:34:15,190 --> 00:34:13,440
think about it we're using micro pk

985
00:34:17,750 --> 00:34:15,200
right or at least there's evidence for

986
00:34:20,149 --> 00:34:17,760
at least some micro pk so what you ask

987
00:34:21,109 --> 00:34:20,159
them to do and what they do it's very

988
00:34:22,950 --> 00:34:21,119
different

989
00:34:25,109 --> 00:34:22,960
and basically we just want to reinforce

990
00:34:27,829 --> 00:34:25,119
that these search principles actually

991
00:34:33,190 --> 00:34:30,710
and i wanted to show this picture of

992
00:34:34,790 --> 00:34:33,200
the folks in the movie arrival these are

993
00:34:36,710 --> 00:34:34,800
two scientists

994
00:34:39,190 --> 00:34:36,720
who are trying to understand what those

995
00:34:40,629 --> 00:34:39,200
weird hand-like creatures are the

996
00:34:44,230 --> 00:34:40,639
heptapods

997
00:34:45,669 --> 00:34:44,240
um floating in a vast milky sea of goo

998
00:34:47,990 --> 00:34:45,679
they're trying to understand how they

999
00:34:50,149 --> 00:34:48,000
communicate and what they recognize is

1000
00:34:53,349 --> 00:34:50,159
that their communication

1001
00:34:54,470 --> 00:34:53,359
is extremely contextual

1002
00:34:56,310 --> 00:34:54,480
in that

1003
00:34:58,790 --> 00:34:56,320
each of these blobs that you see on the

1004
00:34:59,589 --> 00:34:58,800
circle are related to the other blobs

1005
00:35:02,069 --> 00:34:59,599
and

1006
00:35:03,030 --> 00:35:02,079
contextual in time so the areas of the

1007
00:35:04,630 --> 00:35:03,040
circle

1008
00:35:06,950 --> 00:35:04,640
are all being

1009
00:35:07,910 --> 00:35:06,960
uh presented at once even though it's an

1010
00:35:09,349 --> 00:35:07,920
ordered

1011
00:35:11,430 --> 00:35:09,359
concept like you would have a human

1012
00:35:13,190 --> 00:35:11,440
sentence in time everything's being

1013
00:35:15,349 --> 00:35:13,200
presented at once and yet everything

1014
00:35:17,589 --> 00:35:15,359
depends on everything else

1015
00:35:21,430 --> 00:35:17,599
this is kind of the way i'm i'm seeing

1016
00:35:22,550 --> 00:35:21,440
psy now is we really are starting

1017
00:35:24,710 --> 00:35:22,560
to

1018
00:35:26,950 --> 00:35:24,720
hone in on the

1019
00:35:29,990 --> 00:35:26,960
impressive contextuality

1020
00:35:32,069 --> 00:35:30,000
of psy and maybe we need to not be so

1021
00:35:34,390 --> 00:35:32,079
surprised or maybe

1022
00:35:35,270 --> 00:35:34,400
i need not to be so surprised

1023
00:35:37,990 --> 00:35:35,280
um

1024
00:35:39,670 --> 00:35:38,000
that it's not so easy to track down it

1025
00:35:41,190 --> 00:35:39,680
it's like a completely different

1026
00:35:43,589 --> 00:35:41,200
language

1027
00:35:45,990 --> 00:35:43,599
and we are just starting to learn it

1028
00:35:48,470 --> 00:35:46,000
especially at the level of forced choice

1029
00:35:51,430 --> 00:35:48,480
versions of these kinds of

1030
00:35:54,950 --> 00:35:53,030
so i just want to thank all the

1031
00:35:57,589 --> 00:35:54,960
participants in the experiments all the

1032
00:35:58,390 --> 00:35:57,599
folks who volunteered for this iq psi

1033
00:36:00,310 --> 00:35:58,400
cube

1034
00:36:02,150 --> 00:36:00,320
packations

1035
00:36:03,589 --> 00:36:02,160
teresa chung my co-author on the

1036
00:36:05,990 --> 00:36:03,599
premonition code

1037
00:36:09,510 --> 00:36:06,000
the ions research team

1038
00:36:12,230 --> 00:36:09,520
the designers and coders of the psy3 app

1039
00:36:15,270 --> 00:36:12,240
greg travis david mix arna delorme

1040
00:36:17,910 --> 00:36:15,280
cameron bamer and mikey siegel hibsco

1041
00:36:19,990 --> 00:36:17,920
limited who supports the website as well

1042
00:36:21,829 --> 00:36:20,000
as woodbridge institute but of course

1043
00:36:24,630 --> 00:36:21,839
mark is actually a co-author so i don't

1044
00:36:26,790 --> 00:36:24,640
know how to put that in here

1045
00:36:29,190 --> 00:36:26,800
the all foundation funders

1046
00:36:32,230 --> 00:36:29,200
uh for dean and myself jeff and leslie

1047
00:36:34,230 --> 00:36:32,240
olsner charlie and murray maureen pelton

1048
00:36:35,510 --> 00:36:34,240
the hummingbird foundation

1049
00:36:37,270 --> 00:36:35,520
and um

1050
00:36:39,829 --> 00:36:37,280
of course my co-authors

1051
00:36:41,990 --> 00:36:39,839
and their institutions so mark makuuzi

1052
00:36:43,750 --> 00:36:42,000
from the windbridge institute dean

1053
00:36:44,950 --> 00:36:43,760
rayden from the institute of atlantic

1054
00:36:47,190 --> 00:36:44,960
sciences

1055
00:36:48,069 --> 00:36:47,200
and all of all of my institutions as

1056
00:36:50,230 --> 00:36:48,079
well

1057
00:36:51,990 --> 00:36:50,240
university of san diego the institute

1058
00:36:53,990 --> 00:36:52,000
for love and time

1059
00:36:56,230 --> 00:36:54,000
and the institute of knowledge sciences

1060
00:36:58,630 --> 00:36:56,240
so thank you everybody i so appreciate