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Aaron Schurger on free will and readiness potential

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What if the most famous experiment against free will was measuring the wrong thing all along? Neuroscientist Aaron Schurger explains why the readiness potential, long interpreted as the brain’s decision signal, may be nothing more than autocorrelated neural noise crossing a threshold, fundamentally undermining decades of conclusions drawn from the Libet experiment. Subscribe for more from the Convergent Science Network podcast series. Aaron Schurger joins Paul Verschure and Tony Prescott to dissect the neuroscience of volition, starting with a careful distinction between free will, conscious will, and agency. The conversation zeroes in on the readiness potential, a slow buildup of brain activity preceding voluntary movement that Benjamin Libet famously used to argue the brain decides before we are aware of deciding. Schurger’s drift-diffusion model offers an alternative: the readiness potential emerges naturally from stochastic neural fluctuations accumulating toward a threshold, not from any preparatory decision process. The evidence spans multiple species and methods. Murakami’s 2014 study found ramping activity in rat premotor cortex consistent with an accumulator model. Schurger’s own experiments show that when subjects are cued to respond at random moments, fast and slow reaction times correspond to different levels of ongoing neural fluctuation , a difference that precedes the unpredictable cue and therefore cannot reflect preparation. The discussion also addresses the Soon and Fried studies that claimed to predict decisions seconds in advance, with Schurger arguing that slightly-better-than-chance classification of brain states is exactly what autocorrelated noise would produce. Key topics include why the Libet paradigm minimizes rather than tests conscious volition, the role of pink noise and temporal autocorrelation in neural circuits, methodological pitfalls of using classifiers on brain data, and what a brain-computer interface approach might reveal about the causal relationship between conscious intention and action. Part of the Convergent Science Network podcast series from the BCBT Summer School.

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Both the triumphs of humanity and its most evil deeds have resulted from collaboration. In a time where humanity is required to aspire to the former and minimize the latter, the question arises of how collaboration arises and why it fails. Surprisingly, this phenomenon, so central to who we are, is not well understood. Hence, a collaborative effort is required to understand collaboration in its full biological, psychological, sociological, cultural, and economic complexity and to translate this understanding into operational impact. This series of podcasts is one step toward achieving these complementary goals. The Collaboration Podcast presents interviews with people who are central orchestrators of collaboration in various domains including business, government, science, art, health, sustainability, and the military. The discussions were conducted by Prof. Dr. Paul F.M.J. Verschure and members of the Program Advisory Committee of the Ernst Strungmann Forum on Collaboration (https://www.esforum.de/forums/ESF32_Collaboration.html) during 2021 and had the goal to sketch a map of opportunities, challenges, and obstacles in human collaboration. The forum took place in May 2022, and now we would like to share this series of interviews with a broader audience. The full report of the Forum will be published in 2023 by MIT Press. The podcast was produced by the Convergent Science Network (https://www.convergentsciencenetwork.org/). Context: The stability of social systems depends critically on realizing sustainable methods of “collaboration,” yet how and by which means collaboration is achieved is not clearly understood; neither are the conditions or processes that lead to its breakdown or failure. Collaboration can be understood as cooperation between agents toward mutually constructed goals. Part of the reason for our lack of understanding is that the phenomenon of collaboration is, by nature, a highly multidisciplinary problem, and effective research into its complexities has been difficult to achieve across the broad range of scientific and technical disciplines involved. The need for a fundamental understanding of collaboration, however, has become increasingly important. Not only does humankind demand answers as it attempts to address critical challenges at multiple scales (e.g., climate change, migration, enhanced automation, social and economic inequality), but ever-increasing technological and economic means of interconnecting people and societies are disrupting long-established, familiar patterns of how we interact. Radical technological changes that are ongoing have the potential to reshape collaboration in ways that are currently hard to predict or influence (e.g., by altering configurations in interaction, information creation, and modes of communication). On one hand, such changes could disrupt hitherto stable forms of collaboration by affecting critical communication channels and traditional roles, as can be observed in the rapidly changing patterns in governance, commerce, and social interaction. Conversely, technology could lead to the emergence of novel, successful forms of collaboration that deviate from traditional “hierarchical” architectures. Evidence of this can be seen in areas as diverse as highly automated manufacturing plants, the open science movement, collaborative software repositories, user-centered services, and the sharing of economy-based modes of organization. Without a fundamental understanding of the mechanisms, processes, and boundary conditions of collaboration, it is not possible to evaluate or predict which of these possible scenarios are sustainable or even plausible. The Forum “How Collaboration Arises and Why it Fails” (May 8–13, 2022, Location: Frankfurt am Main, Germany) Chairs: Andreas Roepstorff and Paul Verschure Program Advisory Committee: Jenna Bednar, Julia R. Lupp, Bhavani R. Rao , Andreas Roepstorff, Ferdinand von Siemens, and Paul Verschure

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  • fast_forward00:00:03 - This is the Convergent Science Network podcast. Leading researchers in the domain
  • fast_forward00:00:10 - of neuroscience, brain theory and technology are interviewed by Paul Vichure and Tony Prescott.
  • fast_forward00:00:21 - So this is Paul Vichure for the Convergent Science Network podcast together with Tony Prescott.
  • fast_forward00:00:30 - Today, we're speaking with Aaron Scherner. Welcome, Aaron, to our podcast.
  • fast_forward00:00:36 - You were speaking today in our BCBT summer school, and you very much delved
  • fast_forward00:00:42 - into the physiology of behavior, of volition, of free will.
  • fast_forward00:00:48 - So do you think we can say anything meaningful about free will at all from a
  • fast_forward00:00:52 - sort of scientific perspective?
  • fast_forward00:00:54 - I think in theory it's possible to do so, but I'm not sure if we've said anything meaningful yet.
  • fast_forward00:01:00 - Okay um but now free will
  • fast_forward00:01:04 - is a constant complex uh concept as
  • fast_forward00:01:08 - she also indicated to your talk um so is
  • fast_forward00:01:11 - that the construct is that is that the natural category of
  • fast_forward00:01:14 - a follow ryle is that what we should be investigating is
  • fast_forward00:01:18 - that the right starting point or would you rephrase it
  • fast_forward00:01:21 - and use a concept like volition or decision
  • fast_forward00:01:24 - making i i i tend to
  • fast_forward00:01:27 - use the the the phrase conscious will uh
  • fast_forward00:01:30 - because yeah i think i think free will
  • fast_forward00:01:33 - is something that if you if you take the
  • fast_forward00:01:37 - the hardcore definition of free
  • fast_forward00:01:39 - will which is uh some uncaused cause uh
  • fast_forward00:01:43 - that's not something you can really grapple with uh scientifically but you can
  • fast_forward00:01:49 - ask questions about the relationship between conscious events and actions and
  • fast_forward00:01:55 - ask if there's any kind of causal relationship between those two.
  • fast_forward00:02:02 - But in some sense, then you first have to also grapple with the so-called heart
  • fast_forward00:02:06 - problem, right? Because now you link it to conscious states.
  • fast_forward00:02:10 - And so how do we access these conscious states in an independent way?
  • fast_forward00:02:15 - Yeah, that's, I mean, that I think is a direction that we're going in.
  • fast_forward00:02:21 - It's not something that anyone has done in the past for that very reason.
  • fast_forward00:02:27 - Uh but we do now have some
  • fast_forward00:02:30 - uh very reliable correlates
  • fast_forward00:02:33 - of conscious perception and so we
  • fast_forward00:02:37 - could potentially start looking at those uh as
  • fast_forward00:02:40 - indicators of the the presence of a conscious
  • fast_forward00:02:43 - intention and ask whether or not it has the right relationship with the subsequent
  • fast_forward00:02:47 - action or at least the neural activity that that brings the action about it's
  • fast_forward00:02:51 - still you're very cautious is right now yeah how you approach this and i understand
  • fast_forward00:02:54 - that it's it's a conceptual minefield so exactly but still you have to leave
  • fast_forward00:02:59 - some of the mystery to have something to explain.
  • fast_forward00:03:03 - So have you just scraped off too much now of
  • fast_forward00:03:06 - of the free will volition phenomenon well we
  • fast_forward00:03:09 - we we scraped away maybe we
  • fast_forward00:03:13 - scraped away the the mystery about the meaning of this
  • fast_forward00:03:16 - uh signature the the readiness potential uh which
  • fast_forward00:03:20 - we thought uh was a sign of a decision uh
  • fast_forward00:03:24 - and uh what what we've shown is that uh
  • fast_forward00:03:27 - this may not be the sign of a decision right it
  • fast_forward00:03:30 - it it uh might be the sign of
  • fast_forward00:03:33 - a random process uh and if we if if
  • fast_forward00:03:36 - we're using this uh this phenomenon
  • fast_forward00:03:39 - this readiness potential as a temporal marker against which to to to compare
  • fast_forward00:03:44 - the time of other events well uh we we We may have been deceiving ourselves
  • fast_forward00:03:51 - by interpreting it in that way, interpreting it as a decision.
  • fast_forward00:03:57 - But now we, so we already raised that quite a bit, right? But just first,
  • fast_forward00:04:01 - I don't want to be too flippant about it.
  • fast_forward00:04:03 - You know, you might still believe it is flippant, but.
  • fast_forward00:04:07 - You said, well, instead of free will, I want to speak of conscious will, right?
  • fast_forward00:04:11 - But you left the notion will in there as a construct, right?
  • fast_forward00:04:16 - Can we do anything with this construct will? Also from a physiological perspective,
  • fast_forward00:04:21 - as a physiologist, is will a useful construct? Can we do anything with that?
  • fast_forward00:04:27 - Well, if it maps onto something like an intention or an urge,
  • fast_forward00:04:32 - something, then yes, I think so.
  • fast_forward00:04:35 - So I use the word will in this context to be almost identical with an intention.
  • fast_forward00:04:47 - Which is, you could say, a mental state or a neural state that represents a
  • fast_forward00:04:53 - commitment to some course of action.
  • fast_forward00:04:56 - An urge is sort of, in the way that I think people would think about it,
  • fast_forward00:05:03 - is almost something which is unconscious.
  • fast_forward00:05:04 - Unconscious you know i felt an urge you know i don't know where it came from but,
  • fast_forward00:05:08 - something urged me to go to do this um so the
  • fast_forward00:05:12 - origin of the urge the origin of the urge i guess is unconscious but
  • fast_forward00:05:15 - the urge itself is so so the
  • fast_forward00:05:19 - uh so there's an interesting question you
  • fast_forward00:05:21 - could talk about talk about sort of unconscious will potentially as
  • fast_forward00:05:25 - well yeah in fact that's something uh it's very interesting that you bring that
  • fast_forward00:05:29 - up because it's just it's something that i I was just recently debating with
  • fast_forward00:05:34 - colleagues and with some philosophers who really like this idea of the concept
  • fast_forward00:05:41 - of an unconscious intention.
  • fast_forward00:05:45 - But my problem with it is how would you know an unconscious intention if you saw one?
  • fast_forward00:05:51 - I don't know. Behavior? Action? Arousal?
  • fast_forward00:05:57 - Maybe. me well i think it's it speaks to the
  • fast_forward00:05:59 - whole question of agency you know the
  • fast_forward00:06:02 - fact that we have an experience of being agents is
  • fast_forward00:06:06 - why we have this idea of free will in
  • fast_forward00:06:09 - the first place you know if we did things without
  • fast_forward00:06:12 - feeling we were making choices we we
  • fast_forward00:06:15 - wouldn't need to have will but we will explains
  • fast_forward00:06:20 - why we have a uh a feeling of agency so
  • fast_forward00:06:23 - i guess and that feeling of agency is both unconscious and
  • fast_forward00:06:28 - there is a conscious version of it i think it's
  • fast_forward00:06:31 - important though to distinguish agency from volition
  • fast_forward00:06:34 - uh where so age i think we can distinguish between the two i think we we can
  • fast_forward00:06:41 - say that agency is the the uh that doesn't have to be the feeling but the knowledge
  • fast_forward00:06:47 - that my action caused some effect in the world,
  • fast_forward00:06:54 - whereas volition is that my thought caused my action.
  • fast_forward00:07:01 - Uh my my desire or my intention uh was responsible for bringing that action about.
  • fast_forward00:07:09 - You also usually know this distinction between let's say secondary and
  • fast_forward00:07:13 - or primary secondary desires right so you you might
  • fast_forward00:07:15 - have an urge but you must actually desire to execute or follow up on that urge
  • fast_forward00:07:20 - but so it's a multi-level process that you commit yourself to and so so in an
  • fast_forward00:07:27 - And unconscious or subconscious urge or desire would never be identified as such by the agent.
  • fast_forward00:07:34 - It just plays out underneath the radar.
  • fast_forward00:07:36 - Right. Right. So, yeah, you always require by necessity this idea of a secondary
  • fast_forward00:07:41 - or a primary sort of level of urges or desires.
  • fast_forward00:07:47 - Right. And then what you also need is an agent that actually owns these desires.
  • fast_forward00:07:53 - But then there's also what's called the reason responsiveness.
  • fast_forward00:07:55 - Right. so that you can deliberate on that desire.
  • fast_forward00:08:00 - So these are ingredients you would need to then lead up to volition. To volition, right.
  • fast_forward00:08:06 - Reason's responsiveness is an important one that I think in the past has been
  • fast_forward00:08:11 - largely neglected, but that now everyone is talking about, which I think it's quite fair.
  • fast_forward00:08:17 - It's true that if we want to give something this label of volition,
  • fast_forward00:08:21 - it should have this property of responding to reasons this is right and of course
  • fast_forward00:08:28 - this is also your trajectory right because you move to link the study of volition
  • fast_forward00:08:33 - more closely with decision making which of course goes in the direction of.
  • fast_forward00:08:39 - Deliberation and reasoning.
  • fast_forward00:08:42 - But the other ingredient you need is the ability to do otherwise.
  • fast_forward00:08:46 - So if I act following my urge and desire, I must also be able to understand
  • fast_forward00:08:53 - that I have alternatives. I could have done otherwise.
  • fast_forward00:08:56 - Right, I could have done otherwise. Yeah, this is an important one as well.
  • fast_forward00:09:01 - Yeah, and one that's very important from a philosophical point of view,
  • fast_forward00:09:06 - But it's very difficult empirically to show that you could have done otherwise.
  • fast_forward00:09:15 - Absolutely. Yeah. Okay, so this is this conceptual environment of free will,
  • fast_forward00:09:23 - which is complex and minefield, as you said.
  • fast_forward00:09:26 - That, but it's now, now you, you cut through that with, with,
  • fast_forward00:09:29 - with a laser in some sense, by really zooming in on a very specific physiological
  • fast_forward00:09:34 - feature of, of voluntary action, which is this readiness potential that was discovered,
  • fast_forward00:09:41 - in the sixties, um, so why, why do you think the readiness potential as such
  • fast_forward00:09:46 - is, is actually telling us something about, about free will, um,
  • fast_forward00:09:52 - I don't think it is. I think that's the issue, really.
  • fast_forward00:09:56 - I don't think it tells us anything about free will one way or another.
  • fast_forward00:10:01 - I think it tells us something about how the motor system works,
  • fast_forward00:10:05 - some potentially interesting things, but I don't think it's a temporal marker
  • fast_forward00:10:12 - for a decision or a temporal marker for a commitment. it.
  • fast_forward00:10:17 - Because this would go back to these classical experiments on the Bonitov potential,
  • fast_forward00:10:24 - that then fed into the Libet experiments, and you laid this out for us in quite
  • fast_forward00:10:29 - some detail, the Cornelio-Brandicchi 60s experiments.
  • fast_forward00:10:34 - And the big mystery that appeared with Libet was like, well, if I look at.
  • fast_forward00:10:41 - The neural signals or the neural, the correlates, the neural correlates of decision-making,
  • fast_forward00:10:46 - and I compare that to people's ability to declare where they are in the decision-making
  • fast_forward00:10:50 - process in terms of making decisions or committing,
  • fast_forward00:10:53 - to actually make the action, it is, there's, there's a delay, right?
  • fast_forward00:10:57 - So, so the brain only knows what it's going to do before you know it,
  • fast_forward00:11:01 - which is of course already implying a dualism in some sense, right?
  • fast_forward00:11:05 - So, so are you saying it was a surprise? There was no surprise? rise. Come again?
  • fast_forward00:11:12 - When you say it doesn't tell us much about evolution, people are tremendously
  • fast_forward00:11:18 - impressed with this result, right?
  • fast_forward00:11:20 - There have been many attempts to replicate it, and no one could actually have
  • fast_forward00:11:25 - any successful replications of it, and it's very much accepted as a physiological
  • fast_forward00:11:28 - feature of decision-making.
  • fast_forward00:11:31 - Yeah, it replicates extremely well. Yeah, exactly. So in that sense,
  • fast_forward00:11:34 - it's a physiological fact. Yeah.
  • fast_forward00:11:36 - Yeah. But then a huge amount of trees have been killed to produce the papers,
  • fast_forward00:11:43 - the paper on which many words have been written now about what this means with respect to free will.
  • fast_forward00:11:49 - Because well, the brain knows what you're going to do before you know it,
  • fast_forward00:11:52 - and therefore, there's no free will.
  • fast_forward00:11:56 - So where did that interpretation go wrong? Because, okay, you have many methodological
  • fast_forward00:12:00 - concerns, right, we can look at.
  • fast_forward00:12:02 - But in some sense why were people so easily misled about this at the time,
  • fast_forward00:12:09 - well because it it it reliably
  • fast_forward00:12:12 - very reliably precedes self-initiated movement and so the the i think the first
  • fast_forward00:12:19 - and it may be sensible conclusion to come to is that that this is uh this is
  • fast_forward00:12:24 - the brain's way of getting ready to initiate a movement of of of planning and
  • fast_forward00:12:30 - in preparation for movement.
  • fast_forward00:12:33 - But of course, the kind of data that we use to get it out, it's all correlational.
  • fast_forward00:12:39 - So you could say, well, we've made the mistake of, the simple mistake of confusing
  • fast_forward00:12:44 - correlation for causation.
  • fast_forward00:12:47 - It's a bit more than that, but it's that as well. Right.
  • fast_forward00:12:51 - But isn't there also a hidden prior in this whole debate at this stage,
  • fast_forward00:12:57 - right, before we delve into physiology physiology and the methodology.
  • fast_forward00:13:01 - That there is an implicit dualism in the interpretation.
  • fast_forward00:13:06 - There is this whole idea like, oh, every action at any timescale has to be caused
  • fast_forward00:13:12 - by a conscious state, has to be preceded, therefore, by a conscious state,
  • fast_forward00:13:16 - which seems an arbitrary assumption.
  • fast_forward00:13:19 - Yeah, well, no, I think with the readiness potential, what people are saying
  • fast_forward00:13:23 - is that every action, or at least every voluntary self-initiated action,
  • fast_forward00:13:27 - has to be caused by this same preceding neural state,
  • fast_forward00:13:33 - not necessarily conscious.
  • fast_forward00:13:34 - Conscious right um so i think the conscious
  • fast_forward00:13:37 - bit you can you can you can separate that out and in fact and
  • fast_forward00:13:40 - and and we intentionally evaded the
  • fast_forward00:13:44 - topic of consciousness when we first started working with this because we just
  • fast_forward00:13:47 - wanted to ask is this signature this readiness potential the cause of the movement
  • fast_forward00:13:54 - does this represent the brain getting ready to move uh and uh i think that's
  • fast_forward00:14:00 - what we That's what we, if you want to say,
  • fast_forward00:14:03 - debunked in a way.
  • fast_forward00:14:06 - Okay, so here was the redness potential. It's a slow buildup of activity that
  • fast_forward00:14:12 - peaks just before you initiate a movement.
  • fast_forward00:14:16 - It precedes your ability to declare where you are in a position in the decision-making process.
  • fast_forward00:14:23 - And so the first way you debunked that story is to say, well,
  • fast_forward00:14:29 - there's no unitary process underlying this.
  • fast_forward00:14:32 - In some sense, it's an artifact of just averaging many observations, right?
  • fast_forward00:14:39 - Because the idea would be that if you have spontaneous fluctuations in the brain,
  • fast_forward00:14:46 - and these spontaneous fluctuations are sort of slightly biased by some form
  • fast_forward00:14:50 - of evidence, let's say, perceptual evidence about a task or an internally generated cue, then the,
  • fast_forward00:14:57 - Over many trials, this will go to some average state. That will exactly look
  • fast_forward00:15:02 - like what you call a maintenance potential.
  • fast_forward00:15:04 - But at heart, it is essentially just an integration of a highly noisy signal.
  • fast_forward00:15:10 - But even on single trials, it's the case. You don't really have to average trials
  • fast_forward00:15:14 - to run into this problem.
  • fast_forward00:15:15 - Problem um when you when you select a single trial based on the time of the movement itself,
  • fast_forward00:15:24 - um you've in a way selected a biased sample right because that that little piece
  • fast_forward00:15:32 - of data that you're looking at ends with a movement uh and if you want to understand
  • fast_forward00:15:39 - how movement works You also want to know what happens when there is no movement.
  • fast_forward00:15:43 - So, of course, in this case, because you have this biased sample,
  • fast_forward00:15:51 - you see everything through the lens of, well, this is what happens before a movement.
  • fast_forward00:15:56 - And you're you will if you
  • fast_forward00:15:59 - if you look at that you'll recover in the average or on
  • fast_forward00:16:02 - individual trials a tendency for there to be a a
  • fast_forward00:16:05 - ramping phenomenon ramp up to in on the assumption that this is a threshold
  • fast_forward00:16:10 - crossing type of phenomenon right and you also test that hypothesis right by
  • fast_forward00:16:15 - asking your subjects to respond as quickly as they could where you would queue
  • fast_forward00:16:21 - you would queue the response, right?
  • fast_forward00:16:24 - And then you would sort of link
  • fast_forward00:16:25 - the queuing to where they would be in that integration process, right?
  • fast_forward00:16:30 - So you would assume there's some decision threshold, and if they're further
  • fast_forward00:16:33 - away from the decision threshold, you would predict if you now force somebody
  • fast_forward00:16:35 - to respond, the reaction time should be longer than they are forced to respond
  • fast_forward00:16:39 - when they're closer in this direction.
  • fast_forward00:16:41 - When they just happen to be closer, right? Of course, yeah. And this then just
  • fast_forward00:16:44 - depends on this sort of highly variable fluctuation.
  • fast_forward00:16:49 - Right, right, right. There's a lot of data fluctuation who sourced you,
  • fast_forward00:16:52 - you don't know about. So-
  • fast_forward00:16:56 - So with that, you came to this idea that you could explain the readiness potential,
  • fast_forward00:17:01 - and also the performance from the perspective of a so-called drift diffusion model.
  • fast_forward00:17:06 - Actually, I integrate this noisy signal.
  • fast_forward00:17:09 - When I hit threshold, I'm going. It's highly variable.
  • fast_forward00:17:13 - Averaging across trials gives me something that looks like this readiness potential.
  • fast_forward00:17:16 - Potential but um then when
  • fast_forward00:17:19 - you tested that on your on your subjects you could
  • fast_forward00:17:24 - show that that your model that implements this kind
  • fast_forward00:17:27 - of integration of a noisy fluctuation when you
  • fast_forward00:17:30 - compared that to the eg signal you got from your from your
  • fast_forward00:17:34 - subjects that it gave
  • fast_forward00:17:36 - you gave you a similar kind of response right so you see that indeed if you
  • fast_forward00:17:41 - compare fast and slow response trials you see that this integration process
  • fast_forward00:17:46 - has reached a different level and has some offset that's the difference between
  • fast_forward00:17:50 - these two traces that you extract yeah and importantly there's a difference,
  • fast_forward00:17:54 - uh not just before the movement but before the queue you make
  • fast_forward00:17:57 - the movement and and so these are these these uh
  • fast_forward00:18:01 - interruptions are random i don't even
  • fast_forward00:18:03 - know when the interruptions were going to happen nobody the computer only knows
  • fast_forward00:18:06 - right so uh what you see as a as a as a difference in electrical potential preceding
  • fast_forward00:18:14 - the cue can't possibly be a preparatory process because you can't prepare for
  • fast_forward00:18:19 - a movement that you don't know you're going to make. Right.
  • fast_forward00:18:23 - That's an important insight from this model that indeed there is no preparation.
  • fast_forward00:18:30 - But if you compare the model to the actual physiology, there is indeed a difference in the offset.
  • fast_forward00:18:38 - But in terms of the details of how the trace evolves, it's not identical.
  • fast_forward00:18:45 - So if you would really lay the traces on top of each other, I would do some
  • fast_forward00:18:48 - sort of correlation measure.
  • fast_forward00:18:50 - I would not get one. Right. It's qualitative.
  • fast_forward00:18:54 - What the model gives you relative to the reality is qualitative.
  • fast_forward00:18:59 - Right. So for instance, the model gives a much stronger decay in the trace than
  • fast_forward00:19:06 - you observe in your subjects.
  • fast_forward00:19:09 - While for the fast trials, it seems very constant in the model,
  • fast_forward00:19:15 - practically constant, the level, while in your subject it is still sort of sloping
  • fast_forward00:19:21 - down. There's some form that you get, right? So the difference is here.
  • fast_forward00:19:24 - Now, this in itself is not a criticism because model doesn't have to be identical
  • fast_forward00:19:28 - to what you measure, but when is a model good enough?
  • fast_forward00:19:31 - Right, so to what extent did you feel this model was good enough to explain that data?
  • fast_forward00:19:36 - Well, I think it was good enough because it made a novel prediction,
  • fast_forward00:19:41 - prediction uh and albeit qualitative
  • fast_forward00:19:45 - but it it it told us we should find a
  • fast_forward00:19:47 - difference between fast and slow responses uh in
  • fast_forward00:19:51 - this particular direction uh and that
  • fast_forward00:19:55 - we found now the fact that it doesn't map on to the reality exactly that leaves
  • fast_forward00:20:00 - uh some more questions for us to grapple with uh in the next iteration of experience
  • fast_forward00:20:07 - but it also leaves the door open and in some sense in the discussion part of
  • fast_forward00:20:11 - your talk, as we came to that,
  • fast_forward00:20:13 - I could also say, well, maybe the model that explains the data could even be simpler.
  • fast_forward00:20:17 - Maybe it doesn't need to be drift-to-fusion. Right. I gave an example, yeah.
  • fast_forward00:20:22 - Another example, maybe I could have just an oscillator that can exist in two modes, right?
  • fast_forward00:20:28 - And it's a high-energy mode and a low-energy mode.
  • fast_forward00:20:31 - And this would then account for the difference between the two traces.
  • fast_forward00:20:34 - And it completely depends on intrinsic property and this flip it between the two.
  • fast_forward00:20:38 - Right. It would be a completely solipsistic model, but in terms of the physiology,
  • fast_forward00:20:44 - I could also account for this difference in offset, right? Right.
  • fast_forward00:20:47 - So, why this drift-to-fusion? So, drift-to-fusion is not the simplest model.
  • fast_forward00:20:51 - Um, well, you can go simpler. I mean, what you need, what I said,
  • fast_forward00:20:56 - uh, after the talk, when we were, when we were discussing this,
  • fast_forward00:21:00 - what you need at a minimum is pink noise, how you get that pink noise.
  • fast_forward00:21:04 - Maybe it, maybe you can just say, well, I don't know how, I don't care how I got it. It's just there.
  • fast_forward00:21:09 - Uh, the drift diffusion model gives you a sort of principled way that's grounded
  • fast_forward00:21:14 - in prior research that gives, that gives you this pink noise. Right.
  • fast_forward00:21:20 - Um, but then. Okay, so now we have an alternative explanation,
  • fast_forward00:21:23 - right, of the retinence potential.
  • fast_forward00:21:29 - So how many studies have really confirmed, you think, in your mind, the model you propose?
  • fast_forward00:21:35 - Five or six different studies have come out that support the theory in one way or another.
  • fast_forward00:21:43 - Another um so an important one
  • fast_forward00:21:47 - was the 2014 study by murakami with rats
  • fast_forward00:21:50 - who found a ramping like activity uh in
  • fast_forward00:21:54 - the premotor cortex of rats when they were
  • fast_forward00:21:57 - doing a task where they could basically spontaneously stop
  • fast_forward00:22:00 - waiting for a big reward and just go immediately get
  • fast_forward00:22:03 - a small reward correct yeah okay
  • fast_forward00:22:06 - yeah you mentioned you mentioned that that result was um but
  • fast_forward00:22:11 - ramping activity as such is that sufficient as a
  • fast_forward00:22:14 - signature well it's okay so uh i
  • fast_forward00:22:17 - spoke very quickly it was not just ramping activity but
  • fast_forward00:22:21 - it was ramping activity that consistently reached
  • fast_forward00:22:24 - the same level just at the moment that the that the rat left the waiting station
  • fast_forward00:22:29 - and went to get the reward uh so it at least it's very consistent with the the
  • fast_forward00:22:36 - idea of a of an accumulator of the this would be the output put of an accumulator, right?
  • fast_forward00:22:41 - We would expect it to look just like that.
  • fast_forward00:22:45 - I mean, you pointed to some interesting evidence from crayfish and rat as well,
  • fast_forward00:22:52 - that I guess suggests that this readiness potential is something that's common to brains. Yeah.
  • fast_forward00:22:59 - And therefore, probably if the crayfish is doing it, what we normally think
  • fast_forward00:23:07 - of as conscious volition isn't going to be a factor.
  • fast_forward00:23:11 - Yeah, I would agree with you. I mean, and you also talked about the idea that
  • fast_forward00:23:18 - this experiment, actually, there is a weak imperative, you said, to me.
  • fast_forward00:23:22 - So it's almost that if people are making a conscious choice,
  • fast_forward00:23:25 - it's when they walk into the experiment and agree to do this task. Exactly.
  • fast_forward00:23:29 - And the task is designed so that specifically you're asked to suspend volition
  • fast_forward00:23:34 - and wait for that urge to come.
  • fast_forward00:23:37 - So it's really a task that's weighted against having any conscious volition
  • fast_forward00:23:42 - that we would normally think of.
  • fast_forward00:23:44 - As conscious volition you know sort of choosing to
  • fast_forward00:23:48 - buy a house or move country or something like these are
  • fast_forward00:23:51 - conscious choices um whereas
  • fast_forward00:23:55 - deciding to lift your finger in this task or whatever is is it's really designed
  • fast_forward00:24:01 - to to remove anything but the most minimal of conscious choice yeah i think
  • fast_forward00:24:06 - so i mean it's to that extent is it really that surprising i mean for you the
  • fast_forward00:24:11 - result isn't uh surprising but i guess you thought though the.
  • fast_forward00:24:16 - Why why had the scientific community become so obsessed with this yourself yeah
  • fast_forward00:24:21 - well i think it i mean it is it is just a matter of lifting your finger it is
  • fast_forward00:24:27 - a very yeah it is a very uh simple,
  • fast_forward00:24:32 - act um.
  • fast_forward00:24:37 - Yeah, I'm sorry, I lost my train of thought. Well, I mean, people have pointed
  • fast_forward00:24:41 - to Labette as evidence against conscious control.
  • fast_forward00:24:44 - But if you're a defender of conscious control, you can just say this experiment
  • fast_forward00:24:48 - isn't representative of what we mean by conscious will.
  • fast_forward00:24:52 - You know, it's the minimal amount of conscious will.
  • fast_forward00:24:56 - So even if Labette was right and you were wrong, this doesn't really tell us
  • fast_forward00:25:01 - anything about conscious volitional actions.
  • fast_forward00:25:04 - Yeah, I would agree. I mean, you need a signature of a decision,
  • fast_forward00:25:10 - and then you need to know the relationship between the time of that decision
  • fast_forward00:25:16 - and the time of your conscious decision.
  • fast_forward00:25:20 - And if that signature is not reliable or doesn't mean what you think it means,
  • fast_forward00:25:24 - then you can't make those inferences.
  • fast_forward00:25:26 - So given that that's the case, that the Lebet experiment isn't a useful experiment
  • fast_forward00:25:31 - for doing this, and the readiness potential isn't a useful signal,
  • fast_forward00:25:36 - what would be another way of getting at conscious volition that we could imagine
  • fast_forward00:25:41 - experimentally that would be more powerful?
  • fast_forward00:25:44 - I think a more powerful way that I alluded to at the end of the talk is to use
  • fast_forward00:25:49 - a closed loop feedback system, or if you want to call it a brain-computer interface,
  • fast_forward00:25:56 - to drive some external signal directly from cortical activity.
  • fast_forward00:26:04 - And then you can ask questions about what happens when the feedback from,
  • fast_forward00:26:10 - you might say an intention,
  • fast_forward00:26:14 - if you will, comes far earlier than you expected it to, far earlier than your brain expected it to.
  • fast_forward00:26:22 - And that can tell you some things about conscious, at least the conscious feeling of volition.
  • fast_forward00:26:30 - But still, it would then depend on reportability, you know.
  • fast_forward00:26:35 - You'd have to give a report, yeah. Yeah. Yeah, so it doesn't,
  • fast_forward00:26:39 - in the context of saying no report paradigm, this doesn't work. Right.
  • fast_forward00:26:46 - But the other thing is that, you like this more common result, which is interesting.
  • fast_forward00:26:52 - Okay, we see some integration to threshold. We see how nicely lined up before
  • fast_forward00:26:57 - this sort of self-initiated action to move from one port in the task as a mouse
  • fast_forward00:27:03 - to the feeding port or the reward port, okay?
  • fast_forward00:27:07 - But look at premotor core type, right? Well, usually also in this whole debate
  • fast_forward00:27:13 - on demolition, people point more to, let's say, medial frontal structures, SMA.
  • fast_forward00:27:20 - So more advanced in this hierarchy, right?
  • fast_forward00:27:24 - So is it not an issue that we're getting a little bit, let's
  • fast_forward00:27:28 - say unclear but also localization of these phenomena no I think the area of
  • fast_forward00:27:33 - cortex that they were looking at in the rat m2 I think is the rodent is the
  • fast_forward00:27:41 - rat homologue of the SMA if I'm not mistaken all right,
  • fast_forward00:27:46 - so we have a reinterpretation of the Leavitt experiment and sometimes you're
  • fast_forward00:27:53 - saying it's less magical than it looks like but still there is some source of
  • fast_forward00:27:58 - let's say spontaneous activity that comes from somewhere,
  • fast_forward00:28:01 - so is the spontaneous activity an echo of something that you might want to call,
  • fast_forward00:28:07 - volition a sound agent that is trying to manipulate this or are you really thinking
  • fast_forward00:28:14 - about but just noise that is floating around in neural circuits.
  • fast_forward00:28:18 - I think it's the latter. I think it's just noise.
  • fast_forward00:28:23 - I think you need more than that to explain and to account for volition.
  • fast_forward00:28:30 - So what are the properties of neural noise in these carnival circuits?
  • fast_forward00:28:36 - Well, one of the most important properties is that it's temporally autocorrelated.
  • fast_forward00:28:39 - Uh and that uh that is
  • fast_forward00:28:42 - key in order to give you the the kind
  • fast_forward00:28:45 - of result that we it's temporary auto-correlated temporally auto-correlated
  • fast_forward00:28:49 - in another way of saying it's pink it's pink
  • fast_forward00:28:51 - noise it's not white noise uh so how much physiological evidence is there that
  • fast_forward00:28:56 - there's pink noise in in these parts of the brain there's there's noise in the
  • fast_forward00:29:02 - brain and in behavior tends to be pink uh in fact i would turn that around and
  • fast_forward00:29:06 - challenge you to find white noise Anywhere in the brain.
  • fast_forward00:29:10 - Sure. I would go more to the other direction. I'm not a big believer in noise.
  • fast_forward00:29:14 - Noise just means there's a source of variability that you haven't identified yet.
  • fast_forward00:29:18 - Ah, yes. So this is more where I was going with that.
  • fast_forward00:29:22 - So to say pink noise, I think just means, well, we lump a lot of things together
  • fast_forward00:29:26 - and it looks like pink noise.
  • fast_forward00:29:29 - Right? But it either means there's some dynamical state that evolves with the memory.
  • fast_forward00:29:35 - So as long as you have a dynamical system with some kind of memory,
  • fast_forward00:29:39 - then you have your pink noise.
  • fast_forward00:29:42 - So in the way you decompose the render's potential into, let's say,
  • fast_forward00:29:47 - multiple highly variable traces, maybe they themselves can again be decomposed
  • fast_forward00:29:52 - in something more mechanistic that we can understand that's more deterministic than pink noise itself.
  • fast_forward00:29:58 - Is that reasonable? Or you think there's really a a pink noise source in the brain somewhere.
  • fast_forward00:30:03 - Oh, I see. No, I wouldn't say that there's a pink noise source in the brain
  • fast_forward00:30:06 - somewhere, but it isn't.
  • fast_forward00:30:08 - I think one of the interesting things about the model is that this kind of simple
  • fast_forward00:30:13 - accumulator, its output has a 1 over F power spectrum. It's pink.
  • fast_forward00:30:22 - And that, as I mentioned before, that helps to sort of ground it in some neurophysiology
  • fast_forward00:30:28 - that has been well characterized in perception research, in decision making research in general.
  • fast_forward00:30:35 - So we have an instance where a very different kind of decision,
  • fast_forward00:30:39 - a decision that is not made on the basis of a stimulus, at least not one that
  • fast_forward00:30:44 - you have right there at hand,
  • fast_forward00:30:45 - is governed maybe by the same kind of mechanism that all decisions are.
  • fast_forward00:30:51 - The brain didn't have to reinvent the wheel for this kind of decision.
  • fast_forward00:30:57 - So now we have a reinterpretation of the readiness potential.
  • fast_forward00:31:01 - In some sense, you've deconstructed a notion of volition.
  • fast_forward00:31:06 - And you said, well, we can just think about it in the same way we think about decision-making.
  • fast_forward00:31:10 - And it doesn't really matter whether you want to call it volition or not.
  • fast_forward00:31:13 - It's a decision-making process that we're looking at. Yeah, yeah.
  • fast_forward00:31:16 - And then there are more recent results. And you point, for instance,
  • fast_forward00:31:20 - to this work by Fried, but also Soon and others.
  • fast_forward00:31:26 - Who then started to add more of the black box science to the whole story.
  • fast_forward00:31:33 - So now we're going to say, okay, can I, as opposed to do a post-op analysis,
  • fast_forward00:31:39 - I was going to say, can I predict decisions given some classifier extracting
  • fast_forward00:31:45 - features from my physiology?
  • fast_forward00:31:48 - And I couldn't do this in a free case on single cells. Right.
  • fast_forward00:31:51 - And in the case of Soon, you can do that looking at f and y.
  • fast_forward00:31:57 - Right. Right. So again, it was a bit of a game changer, right?
  • fast_forward00:32:00 - It's this whole black box approach.
  • fast_forward00:32:02 - And then surprisingly, these approaches seem to work.
  • fast_forward00:32:07 - They could predict what the decision was going to be by the subject with some
  • fast_forward00:32:15 - improved performance as compared to random.
  • fast_forward00:32:18 - For Freed, it was 75% or something like this, right?
  • fast_forward00:32:22 - And they could predict 500 milliseconds before the action was initiated of what the subject would do.
  • fast_forward00:32:28 - But for Soon, it was even more extreme, that's with Dylan Hines.
  • fast_forward00:32:32 - More extreme, it could be up to six or six to eight seconds or something like
  • fast_forward00:32:36 - that, some extreme time window. With fMRI, yeah.
  • fast_forward00:32:39 - And then the point was, okay, it almost seems to violate the basic principles
  • fast_forward00:32:44 - of the universe, right? Because now I can.
  • fast_forward00:32:48 - Look into the future. Yeah, I mean, what I think would have violated the basic
  • fast_forward00:32:52 - principles of the universe would be if you couldn't classify what someone was
  • fast_forward00:32:58 - going to do based on prior brain activity.
  • fast_forward00:33:01 - That would be strange, right? As if that decision just emerged out of ether.
  • fast_forward00:33:09 - But say, seconds might be a bit long. They can predict it before you even get the cue.
  • fast_forward00:33:14 - Yeah, well, I mean, let's say you're talking about a right or a left-hand movement.
  • fast_forward00:33:18 - I mean, it's, it's, it doesn't seem that unusual to me to think that several
  • fast_forward00:33:22 - seconds beforehand, your brain might be in a state that, uh,
  • fast_forward00:33:26 - will tend to bias it slightly towards one or the other.
  • fast_forward00:33:31 - Um, so I, I, I don't find that to be too hard to believe.
  • fast_forward00:33:35 - So also eight seconds, you would find not too hard to believe in,
  • fast_forward00:33:38 - in the case of the SUD experiments.
  • fast_forward00:33:40 - Yeah. I mean, no, I don't find it too hard to believe. Okay.
  • fast_forward00:33:45 - Okay. Okay, but that's interesting, right? Because with that,
  • fast_forward00:33:47 - you're saying that these fluctuations that would sort of bias you in one way
  • fast_forward00:33:52 - or the other have really a rather long history in the dynamics of the brain.
  • fast_forward00:33:57 - Yeah, I guess that's what that points to, is that if it is related to the phenomenon
  • fast_forward00:34:03 - of autocorrelation, so it has a relatively sluggish time constant.
  • fast_forward00:34:10 - That would also reduce the states that the system can then occupy.
  • fast_forward00:34:16 - Yeah, although bear in mind that you're classifying slightly better than chance.
  • fast_forward00:34:21 - I forget exactly how we're at 58% or 60% correct, where 50% is just a random guess. Right.
  • fast_forward00:34:29 - It would be a very different story, I guess, if it was 80% or 90%. Right.
  • fast_forward00:34:34 - So when you came across this free-to-result with the single cells using the
  • fast_forward00:34:38 - support vector machine,
  • fast_forward00:34:40 - or or soon with this fmi classifier at
  • fast_forward00:34:45 - the time were you were you shocked by that or surprised or this
  • fast_forward00:34:48 - was also already done for you within the realm
  • fast_forward00:34:50 - of the expected um when i
  • fast_forward00:34:54 - when i saw freed result uh i i wasn't too surprised uh i was already at that
  • fast_forward00:35:01 - time i was already working on i was already doing this work uh and had already
  • fast_forward00:35:06 - come to a lot of these conclusions when i When I saw the work of Soon,
  • fast_forward00:35:12 - that was much earlier, that was in 2008, I think.
  • fast_forward00:35:18 - Yeah, I was initially pretty blown away by that.
  • fast_forward00:35:22 - And later...
  • fast_forward00:35:25 - Well sort of revised uh revised my
  • fast_forward00:35:29 - my way of interpreting uh the results
  • fast_forward00:35:32 - yeah right because in the second in the last part of your talk
  • fast_forward00:35:34 - you focus very much on also the methodological challenges that you face with
  • fast_forward00:35:39 - this kind of analysis right and uh and you pointed out the number of caveats
  • fast_forward00:35:44 - like for instance uh if you use a sliding time window to make your your estimates
  • fast_forward00:35:49 - is you better be very clear where you put the reference to estimate.
  • fast_forward00:35:54 - Yeah, you want to align your time axis to the leading edge of the window,
  • fast_forward00:35:58 - not the middle or, God forbid, the tail end of the window, right?
  • fast_forward00:36:03 - Because you don't want your classifier to be able to peek into the very future
  • fast_forward00:36:06 - that it's trying to predict.
  • fast_forward00:36:07 - Right, exactly. That would be cheating. Yeah, absolutely.
  • fast_forward00:36:12 - And then you'll also look at setting up the right reference conditions,
  • fast_forward00:36:17 - like look at conditions without movement, the width movement, right?
  • fast_forward00:36:21 - Yeah, yeah. Those are all the old correlation in the signals that we're analyzing.
  • fast_forward00:36:25 - So when all these caveats point to the fact that they're overestimating these time windows,
  • fast_forward00:36:34 - if there's an error, the error is an overestimation of a time window in which
  • fast_forward00:36:40 - you can reliably predict.
  • fast_forward00:36:42 - You mean, if I understand you correctly, you're
  • fast_forward00:36:45 - saying we're overestimating how far back yeah yeah
  • fast_forward00:36:48 - i'm not sure it means that um because
  • fast_forward00:36:51 - i think uh for example in the soon study they weren't really
  • fast_forward00:36:54 - using a sliding window they were just classifying at each at each tr at each
  • fast_forward00:36:58 - point in time before the movement um no but still they would have another correlation
  • fast_forward00:37:03 - to then take care of yes although i believe they were they they did one reanalysis of some of those data,
  • fast_forward00:37:12 - trying to rule out the possibility that this was just driven by autocorrelation.
  • fast_forward00:37:18 - And at least their conclusion was that it couldn't be entirely accounted for by autocorrelation.
  • fast_forward00:37:25 - But that, of course, leaves open the possibility that it could partly be. Right.
  • fast_forward00:37:31 - But in your own analysis that you presented to also show the relevance of these
  • fast_forward00:37:38 - adjustments to the analysis method,
  • fast_forward00:37:40 - you showed that the whole shape of the, that you already called the scourge
  • fast_forward00:37:44 - of the banana, the whole shape of this randomness potential starts to disappear, right?
  • fast_forward00:37:48 - And at this transition point, at which you can start to predict,
  • fast_forward00:37:51 - it looks much more discrete or very, very punctuated in time. Yeah.
  • fast_forward00:37:57 - It's just less gradual. When you have properly controlled conditions where, as I mentioned before,
  • fast_forward00:38:02 - if you want to talk about predicting the onset of movement, you want to have
  • fast_forward00:38:08 - data that include movements and you want to also have data that don't include
  • fast_forward00:38:12 - movements as a control, right?
  • fast_forward00:38:15 - Right. And ideally, you'd want to have these two and compare them.
  • fast_forward00:38:18 - So in a more recent experiment that we're now just working on getting published, we did just that.
  • fast_forward00:38:25 - So we used an experimental paradigm where you end up with data epochs that either
  • fast_forward00:38:29 - terminate with a movement or terminate without a movement, but are well matched in other respects.
  • fast_forward00:38:34 - And when you apply a sliding window analysis using pattern classifier to that kind of data.
  • fast_forward00:38:41 - You're using a very powerful
  • fast_forward00:38:45 - classifier uh we couldn't tell apart movement from
  • fast_forward00:38:49 - non-movement epochs until the very last moment
  • fast_forward00:38:51 - until until basically the moment at which the movement was was
  • fast_forward00:38:54 - was beginning uh and it certainly was was
  • fast_forward00:38:58 - no fault of the classifier itself because at and after the time of movement
  • fast_forward00:39:03 - the classifier was at at nearly perfect performance uh so you couldn't say oh
  • fast_forward00:39:08 - well it's that's just because your classifier isn't good enough well appears
  • fast_forward00:39:12 - to be good enough in some sense isn't the consequence of that.
  • fast_forward00:39:17 - The shape that we gave to the randomness potential is more an artifact of,
  • fast_forward00:39:23 - let's say, insufficiently tuned methods than that's really in the signal of the brain itself.
  • fast_forward00:39:31 - Like also your examples, if you have a corrected signal,
  • fast_forward00:39:37 - if you take out the autocorrelation effect, that suddenly the meaningful part
  • fast_forward00:39:46 - of your signal starts to look very different.
  • fast_forward00:39:51 - It's much more than, let's say, an S-shaped kind of response,
  • fast_forward00:39:55 - a more compressed transition point than the traditional randomness potential.
  • fast_forward00:40:00 - So having you with that sort of deconstructed the randomness potential as an
  • fast_forward00:40:06 - artifact of insufficient methods. I think you can say that, yeah.
  • fast_forward00:40:09 - I think you can call it an artifact. If you want an artifact of time-locking,
  • fast_forward00:40:17 - at least a tendency to time-lock to crests in an autocorrelated time series. Right, yeah.
  • fast_forward00:40:23 - And if you time-lock to crests in an autocorrelated time series,
  • fast_forward00:40:26 - you recover the autocorrelation function, which looks like a slow curve. Right, exactly.
  • fast_forward00:40:34 - So you've talked about having the right kinds of controls,
  • fast_forward00:40:39 - Patrick Haggart who was here last year did a big experiment I'm sure you know
  • fast_forward00:40:46 - his work on this has developed some quite beautifully controlled paradigms for comparing,
  • fast_forward00:40:53 - volitional with stimulus driven,
  • fast_forward00:40:56 - responses I think he was using sort of random noise movement and you had to
  • fast_forward00:41:01 - say which correctional dots were going in.
  • fast_forward00:41:03 - And his study, I think, also made it a bit more sort of mattered whether or
  • fast_forward00:41:11 - not you, what response you gave, because there was actually a monetary reward.
  • fast_forward00:41:15 - So there's a bit more of an incentive to do the right thing or to do the most rational thing.
  • fast_forward00:41:20 - And his data suggested that in the case of volitional actions,
  • fast_forward00:41:25 - you see a reduction in the amount of noise.
  • fast_forward00:41:28 - That's right. You see a slow reduction in variability over time as you approach the movement.
  • fast_forward00:41:35 - So what do you think of that result, and how would you interpret it?
  • fast_forward00:41:39 - Well, I would really interpret it in the same way. That is to say that that
  • fast_forward00:41:43 - signal, that variability,
  • fast_forward00:41:49 - can vary in much the same way that the time series can vary in an autocorrelated fashion.
  • fast_forward00:41:57 - So the same logic that gets you the readiness potential in the experiments I did could get you this.
  • fast_forward00:42:06 - Let's say decrease in variability but why would it be different between the
  • fast_forward00:42:11 - volitional and the instructed uh trials so well because the the uh in in the volitional case,
  • fast_forward00:42:20 - uh the time the precise time of the movement right uh tends to be uh biased
  • fast_forward00:42:28 - slightly by so it depends on this underlying process right whereas the cued
  • fast_forward00:42:33 - in in the case of a instructed,
  • fast_forward00:42:37 - uh, movement.
  • fast_forward00:42:38 - That's not the case. So that's one of the key aspects of this explanation is that, uh, when the, uh,
  • fast_forward00:42:48 - the way I say it is that when the imperative to move is weak or absent,
  • fast_forward00:42:52 - meaning when this precise moment at which you move is not dictated by some stimulus,
  • fast_forward00:42:57 - uh, then you have this question, okay, uh, I'm going to move approximately now, right?
  • fast_forward00:43:04 - Uh, and so here goes, as I move, and you're left with this question of,
  • fast_forward00:43:08 - well, why did I move just right then when I moved and not 200 milliseconds earlier or later, right?
  • fast_forward00:43:15 - There is some freedom there to play with.
  • fast_forward00:43:19 - And if the answer to that question is, well, there are spontaneous fluctuations
  • fast_forward00:43:24 - in neural activity and they bias the precise moment at which you move,
  • fast_forward00:43:28 - then if that's the case, then you're guaranteed to recover a slow gradual buildup.
  • fast_forward00:43:35 - But I don't really see how this answers Tony's challenge because Tony's referring
  • fast_forward00:43:40 - to the Peter Haggard claiming... Patrick. Patrick, sorry.
  • fast_forward00:43:46 - Voluntary control, lower variability. Cube control, higher variability, right?
  • fast_forward00:43:52 - Or no reduction in variability. Okay.
  • fast_forward00:43:55 - But then cube control, then we can go back to the literal decision-making.
  • fast_forward00:44:00 - When you start to drive, you sort of drive the integrators, right?
  • fast_forward00:44:04 - And as soon as you start to add a drive to these integrators,
  • fast_forward00:44:08 - you start to overcome the more spontaneous fluctuation.
  • fast_forward00:44:11 - So you would expect to see lower variability there.
  • fast_forward00:44:15 - So I don't really see how the explanation... Well, that's what you get.
  • fast_forward00:44:19 - You get a decrease in variability.
  • fast_forward00:44:21 - No, it was a voluntary case of this lower variability. Right, right. Yeah.
  • fast_forward00:44:25 - Well, I was making the example, if you go to the decision-making literature
  • fast_forward00:44:28 - where everything is cubed, right?
  • fast_forward00:44:30 - Right. Then also for your model, if I start to drive your model with an external
  • fast_forward00:44:35 - input coming from an external queue, I would start to reduce variability.
  • fast_forward00:44:39 - Yeah, and you would abolish this early tail readiness potential.
  • fast_forward00:44:48 - So this is, okay, my claim, and this one goes used now.
  • fast_forward00:44:51 - If you take the Haggard challenge, you say, okay, volition, higher variability.
  • fast_forward00:44:56 - No, volition, lower variability, queued, higher variability.
  • fast_forward00:44:58 - But if you take your standard drift diffusion model with some,
  • fast_forward00:45:02 - let's say, noise if I now start to get an external Q as an additional input,
  • fast_forward00:45:06 - I start to reduce the noise effect right?
  • fast_forward00:45:09 - So isn't that the encountering to it from this drift diffusion perspective?
  • fast_forward00:45:14 - It's true that it does pose a bit of a challenge but I think it's not.
  • fast_forward00:45:21 - I think the case is not at all closed by that evidence because again Again,
  • fast_forward00:45:26 - that variability could itself be fluctuating in a random way.
  • fast_forward00:45:34 - Okay, we're leaving a bit of a cop-out now. Well, essentially,
  • fast_forward00:45:39 - I mean, that's one, I would say, to be fair, that's one of the weaknesses of
  • fast_forward00:45:46 - the model or the explanation is that you can always keep saying,
  • fast_forward00:45:49 - well, that could also vary, right?
  • fast_forward00:45:53 - That could also fluctuate spontaneously. Simultaneously. So if somebody comes
  • fast_forward00:45:56 - up with a new phenomenon.
  • fast_forward00:45:58 - Oh, we just have another free parameter, right? And now we have the fluctuation we need.
  • fast_forward00:46:02 - You said, well, that could fluctuate as well.
  • fast_forward00:46:04 - But maybe another response could be, those to the Tony's challenge,
  • fast_forward00:46:10 - in some of the people always ignore that, that,
  • fast_forward00:46:14 - the agent itself is represented, is sending a signal to a decision-making stage.
  • fast_forward00:46:20 - Mm-hmm. Right? So, you get a vision that the parietal areas,
  • fast_forward00:46:23 - right, around the temporal parietal junction that are implicated in states of
  • fast_forward00:46:29 - self and agency are generating internal cues, right?
  • fast_forward00:46:34 - Right, right, right. So, in some sense, so the argument would then be internal
  • fast_forward00:46:37 - cues have a higher gain, let's say, than externally generated cues.
  • fast_forward00:46:41 - And then, that way, I might account for it. But that's what you said.
  • fast_forward00:46:45 - But I think Patrick's result doesn't imply necessarily that that's due to conscious will, as you said.
  • fast_forward00:46:52 - It could be due to some other internal attentional process.
  • fast_forward00:46:56 - He's agnostic. Patrick would be agnostic about that.
  • fast_forward00:47:00 - So it doesn't necessarily help the people who want to claim a role for conscious
  • fast_forward00:47:06 - volition, which I think Patrick is one of those.
  • fast_forward00:47:10 - So it could be some other unconscious that's happening, maybe it's linked to attention in some way.
  • fast_forward00:47:19 - Is attention relevant there?
  • fast_forward00:47:22 - I don't know, why do you bring up attention? It's very confusing.
  • fast_forward00:47:26 - Because you're in a task where you're having to make a decision.
  • fast_forward00:47:31 - So you're focusing on, am I going to, in his task, interrupt this trial,
  • fast_forward00:47:36 - go to the next trial? Well, that's something you're fixating on.
  • fast_forward00:47:39 - And also you're instructed. And you're instructed to be. Yeah,
  • fast_forward00:47:42 - well, there's money involved. So you make more money if you make good choices.
  • fast_forward00:47:46 - So that would be a reason for being more focused, which might reduce your neural noise.
  • fast_forward00:47:54 - Okay, that's fair enough. But then...
  • fast_forward00:47:59 - So now we're having a problem right because we have many problems but let's start with
  • fast_forward00:48:06 - aaron's problem first and so
  • fast_forward00:48:11 - so okay great we do fantastically well here's libid
  • fast_forward00:48:14 - look very confusing conscious will is an illusion uh
  • fast_forward00:48:18 - what's the causal relation between conscious states and
  • fast_forward00:48:21 - action and so on right you resolve that conundrum
  • fast_forward00:48:24 - by saying well you just missed you have misinterpreted your
  • fast_forward00:48:27 - signal right essentially right yeah well what's uh what's the
  • fast_forward00:48:30 - relation between the readiness potential and action yeah sure
  • fast_forward00:48:33 - yeah so you have said look you have over
  • fast_forward00:48:36 - interpreted an artifact yeah right
  • fast_forward00:48:39 - and you can you can reduce the whole thing into very single model where we just
  • fast_forward00:48:44 - have spontaneous fluctuations that that have a little memory so they can add
  • fast_forward00:48:48 - up to to some decisions i showed right and it brings in the realm of the standard
  • fast_forward00:48:53 - a decision yeah I mean it accounts for the data in a parsimonious way right but then.
  • fast_forward00:48:59 - Then you start to get worried about all the methodologies involved and also
  • fast_forward00:49:04 - these crazy long-range predictions people can make about decisions up to eight
  • fast_forward00:49:09 - seconds before they happen which
  • fast_forward00:49:11 - led to all sort of further speculation certainly in the popular press,
  • fast_forward00:49:16 - and then you start to apply all sorts of methodological caveats to say well
  • fast_forward00:49:21 - Well, actually, the real signal might look very different from the randomness potential.
  • fast_forward00:49:26 - The real signal looks much more like a rather constant baseline state that shows
  • fast_forward00:49:31 - a very rapid transition to a sort of an upstate shortly before the decision. Yeah. Correct. Yeah.
  • fast_forward00:49:37 - But with that, you invalidate your earlier drift-diffusion model. That's now irrelevant.
  • fast_forward00:49:44 - Doesn't invalidate it right in fact it it it the
  • fast_forward00:49:47 - idea is that it confirms it uh so the
  • fast_forward00:49:50 - the uh the you say two
  • fast_forward00:49:54 - competing camps or two competing hypotheses are for an early decision which
  • fast_forward00:49:59 - is you would might say i mean he's not here for us to ask him but i would say
  • fast_forward00:50:02 - as libit's view the decision is early the the the neural what i call the neural
  • fast_forward00:50:08 - decision right uh to it to contrast it with the conscious decision.
  • fast_forward00:50:14 - This is an early decision, whereas our model says, well, no,
  • fast_forward00:50:18 - the decision only happens, and Murakami used the same model,
  • fast_forward00:50:24 - or the same kind of model, essentially.
  • fast_forward00:50:26 - The decision doesn't happen until the threshold is crossed, and that happens
  • fast_forward00:50:31 - very late in the game, very close to the time of the movement.
  • fast_forward00:50:34 - And so that if you were, the upshot of that, one prediction that it makes is
  • fast_forward00:50:39 - that if you were if you compare movement to no movement uh they should look very similar until,
  • fast_forward00:50:45 - the very last moment and until uh that that threshold if you will has been crossed right so.
  • fast_forward00:50:56 - We started with a very complex picture on free will and now we end up with something
  • fast_forward00:51:00 - relatively pragmatic physiological controllable some sense right we're in the
  • fast_forward00:51:07 - decision-making decision-making domain, yeah?
  • fast_forward00:51:09 - Is that where you want to be? Or do you want to, again, jump out of that and
  • fast_forward00:51:15 - go back to this fundamental question around conscious will, as you call it?
  • fast_forward00:51:20 - Because sometimes, now it's out, it's out in the picture, right? Doesn't matter, do you?
  • fast_forward00:51:25 - Now we're looking at decision-making about self-generated actions.
  • fast_forward00:51:30 - Yeah, I think we should stay firmly grounded in the world of decision-making.
  • fast_forward00:51:35 - And self-generated actions.
  • fast_forward00:51:38 - And I think there's more that we can do with this model and models like it.
  • fast_forward00:51:44 - I think even if ultimately the model is all wrong.
  • fast_forward00:51:48 - I think just the idea of taking this kind of phenomenon and bringing it down
  • fast_forward00:51:54 - to earth and grounding it in a computational model was an important step.
  • fast_forward00:52:00 - And we've already gone beyond with this same model.
  • fast_forward00:52:05 - We've done something very simple, which is just to add a second threshold,
  • fast_forward00:52:10 - slightly lower than the threshold for activating movement, let's say.
  • fast_forward00:52:15 - That we we say well this this lower
  • fast_forward00:52:19 - threshold represents sort of self-monitoring process
  • fast_forward00:52:22 - so that when when we
  • fast_forward00:52:25 - cross that lower threshold some information
  • fast_forward00:52:28 - is generated uh to the
  • fast_forward00:52:31 - effect that well a movement is very likely to
  • fast_forward00:52:34 - happen very soon because i'm very close to the
  • fast_forward00:52:37 - threshold um and using that
  • fast_forward00:52:40 - uh simple variant uh of the
  • fast_forward00:52:43 - model we were able to confirm makes and confirm some predictions about uh the
  • fast_forward00:52:49 - conscious urge to move what but a bit called w time uh for the will um so on
  • fast_forward00:52:57 - uh on trials where the subject waited a longer time to produce a movement meant,
  • fast_forward00:53:02 - the assumption is that the ramping signal was ramping not as steeply as in trials
  • fast_forward00:53:12 - when the subject responded early.
  • fast_forward00:53:14 - And so the delay between the crossing of those two thresholds would be longer.
  • fast_forward00:53:19 - And that would tell us something about the relationship between subjects W time,
  • fast_forward00:53:24 - when they felt they had the urge to move, and the movement itself, and that prediction.
  • fast_forward00:53:29 - But that's something quite disturbing about.
  • fast_forward00:53:32 - Our notion of free will, which what we have in Chobha Chitral now as my moment
  • fast_forward00:53:39 - of choice, I decided to make a movement,
  • fast_forward00:53:42 - turns out to be just some internal monitoring system reporting that a bit of
  • fast_forward00:53:47 - my brain that I'm not aware of has passed the threshold and I'm going to make
  • fast_forward00:53:52 - a movement whether I urge it or not.
  • fast_forward00:53:55 - That's what it may be. So it's really quite a frightening conclusion for people
  • fast_forward00:54:01 - who would like to be even free will because it shows that,
  • fast_forward00:54:04 - these feelings that we have about will could just
  • fast_forward00:54:07 - be completely wrong because that because you would you would have a feeling
  • fast_forward00:54:12 - that i've willed them but it turns out you haven't something in your brain has
  • fast_forward00:54:17 - happened and this has popped into consciousness and you've interpreted that
  • fast_forward00:54:20 - as well if it's true for that event it could be true for all sorts of other
  • fast_forward00:54:24 - decisions you make in your your life,
  • fast_forward00:54:25 - that some bit of your brain made something happen. And then it was reported up to consciousness.
  • fast_forward00:54:31 - Although I wouldn't say that it's, uh, you, you, you said that maybe that feeling was wrong.
  • fast_forward00:54:37 - Uh, but I wouldn't say interpretation of it that we put on it is that we made a choice.
  • fast_forward00:54:42 - Ah, I see. So mens rea doesn't hold that.
  • fast_forward00:54:46 - In that model no not very well yeah so so that's an important point because
  • fast_forward00:54:51 - in the trust of the feeling i was having like you you invited us to some big
  • fast_forward00:54:55 - party um with lots of uh booze and,
  • fast_forward00:54:59 - god knows what then entertainment but then you open a door and then it's an
  • fast_forward00:55:04 - empty room and there's no no no drinks no snacks no nothing yeah decision making
  • fast_forward00:55:09 - yeah so haven't you haven't you
  • fast_forward00:55:11 - now actually throwing out the dancers with the bath water, um,
  • fast_forward00:55:16 - because of the limitations of your methods?
  • fast_forward00:55:19 - I mean, the, the goal here was not to prove that free will exists, right.
  • fast_forward00:55:24 - But was, was to shed some light on how this all works. No, you shed some light
  • fast_forward00:55:28 - on it by turning the light off.
  • fast_forward00:55:30 - Well, then you turned the light
  • fast_forward00:55:32 - on and the room was empty. The room was empty. I think, I don't know.
  • fast_forward00:55:35 - When the risk is, he actually turned on the lights in another room.
  • fast_forward00:55:41 - Right. We have shifted. We have shifted now somewhere else.
  • fast_forward00:55:46 - No, I think this is where we thought the party was. You put the lights on.
  • fast_forward00:55:49 - There is no party. There's no will.
  • fast_forward00:55:51 - There's just these things causing stuff to happen, and you heard about it.
  • fast_forward00:55:56 - Yeah. So, I mean, what do we do with that? But it was deeply dissatisfactory.
  • fast_forward00:56:02 - Well, only if you wanted to believe in conscious will.
  • fast_forward00:56:07 - Because you believed that there was a party to go to. That's right.
  • fast_forward00:56:11 - I like parties, but the thing is there are things, there are things like,
  • fast_forward00:56:15 - like mens rea and there are, there are costs of failure, right?
  • fast_forward00:56:19 - So if we really believe with Wagner and his friends that, that conscious will
  • fast_forward00:56:27 - is an illusion and, or not operational,
  • fast_forward00:56:30 - there's a huge cost associated with that because we're dehumanizing ourselves
  • fast_forward00:56:35 - and we're giving up the sense of responsibility for action. The cost of that is huge.
  • fast_forward00:56:41 - And if you do that to protect yourself.
  • fast_forward00:56:45 - Relatively primitive scientific models and methods, then I think that's a very naive move.
  • fast_forward00:56:51 - Because I do believe, given the cost of failure, we ought to critically question the models we use.
  • fast_forward00:56:58 - And we already know drift diffusion is great.
  • fast_forward00:57:00 - We do really boneheaded tasks as a macaque monkey for months on end.
  • fast_forward00:57:06 - Great. We get drift diffusion.
  • fast_forward00:57:09 - But as soon as As I make the decision test, it's a little bit more complicated.
  • fast_forward00:57:13 - Drift and fusion is not predictive of anything.
  • fast_forward00:57:17 - If you give a complex brain a stupid test, you get a stupid code.
  • fast_forward00:57:20 - And the stupid code can be deciphered by boneheaded neuroscientists.
  • fast_forward00:57:24 - But the thing is, if you give a complex brain a complex task, it uses complex codes.
  • fast_forward00:57:29 - And that's where the beauty is. And that's where the scientific challenges are.
  • fast_forward00:57:32 - So what I feel now, we're a bit on the slippery slope. Oh, my methods are not really helping me.
  • fast_forward00:57:38 - Let's just rephrase the problem. let's scrape away all the complexity so at
  • fast_forward00:57:43 - least I can hold on to the method that has given me tenure.
  • fast_forward00:57:47 - And it's not for you, okay, but the socialness of the field, right?
  • fast_forward00:57:50 - Drift diffusion drives me mad indeed because I think it is distorting our understanding
  • fast_forward00:57:55 - of decision making and certainly of voluntary decision making.
  • fast_forward00:57:58 - I don't think you can just point to drift diffusion for this.
  • fast_forward00:58:01 - So all you need is for that noise to be auto-associated, yeah?
  • fast_forward00:58:08 - So that's all you need for this result to happen and the problem for your version is that you want to.
  • fast_forward00:58:16 - Have some role for that experience of making the choice and this result shows
  • fast_forward00:58:23 - that you can have an experience of making the choice without having made any
  • fast_forward00:58:28 - choice in consciousness that matters so so that i mean there may be other situations
  • fast_forward00:58:34 - in which you make a choice that matters,
  • fast_forward00:58:35 - but in this situation,
  • fast_forward00:58:38 - you have the qualia, you have the experience of making a choice,
  • fast_forward00:58:42 - But no, we've shown that... I appreciate your intent to mediate here,
  • fast_forward00:58:46 - whether to take offense to your summary.
  • fast_forward00:58:50 - Because the point I was making earlier, Aaron is showing to us that pink noise
  • fast_forward00:58:56 - doesn't do the job, unless it's some really special case of it,
  • fast_forward00:59:00 - because he shows a much more S-shaped transition, right?
  • fast_forward00:59:04 - Where you don't have sort of gradual accumulation of anything.
  • fast_forward00:59:07 - There's accumulation of nothing and a very rapid switch. Well,
  • fast_forward00:59:10 - that's when we squeeze the pink noise out of the picture.
  • fast_forward00:59:12 - Exactly. Yeah. But that would be the real ground truth of the decision-making signal.
  • fast_forward00:59:20 - Abrupt. Yes. Late and abrupt. So it looks very different than this sort of autocorrelation.
  • fast_forward00:59:26 - That autocorrelation signals some background thing that happens has nothing
  • fast_forward00:59:29 - to do with the decision-making process.
  • fast_forward00:59:31 - Right. This is the consequence.
  • fast_forward00:59:35 - It's, I mean, it is, it's part and parcel of the decision-making process,
  • fast_forward00:59:39 - but it's just that But when we look at it through the lens of this event-locked
  • fast_forward00:59:43 - averaging, we see it in that misleading way.
  • fast_forward00:59:46 - But it's like a non-specific contributor.
  • fast_forward00:59:49 - Yeah. It's not a decisive contributor decision, right? Right.
  • fast_forward00:59:52 - So, but I think it would be, as scientists, we must also be willing to say, well, we don't know.
  • fast_forward01:00:00 - We don't know, but it's more than decision-making, because for the free will case,
  • fast_forward01:00:07 - there is a layer of the ability to reflect upon the decision,
  • fast_forward01:00:12 - to own the urges to act, to not only act,
  • fast_forward01:00:18 - but to also, and I want to act, I own this urge to act, and I experience that,
  • fast_forward01:00:24 - and I can declare it, and I could have done something else.
  • fast_forward01:00:28 - But there are many requirements of which the decision-making process is one
  • fast_forward01:00:32 - of the requirements, but it's not the whole story. It's only one of the necessary conditions.
  • fast_forward01:00:36 - And there I think we should be careful. So yes, studying decision-making can
  • fast_forward01:00:40 - be fantastically useful.
  • fast_forward01:00:41 - Drift diffusion models can be a great tool to do that, but they're not the whole story.
  • fast_forward01:00:46 - So this is why I got a little bit excited, because I feel like,
  • fast_forward01:00:49 - oh, we have to be careful not to collapse the complexity of voluntary action
  • fast_forward01:00:53 - into the more reduced view of decision-making,
  • fast_forward01:00:56 - which is only a necessary condition i mean i think what what we
  • fast_forward01:00:59 - could agree is that in this task it's a very minimal task for
  • fast_forward01:01:03 - any kind of volitional control and uh i think what aaron's done is produced
  • fast_forward01:01:09 - the sort of uh the minimal model of that and occam's razor said well why should
  • fast_forward01:01:13 - there be anything more if this random process that's also correlated is enough
  • fast_forward01:01:18 - to explain the result then.
  • fast_forward01:01:21 - Quite possibly all there is the disturbing thing for you
  • fast_forward01:01:24 - is that the quality are associated with it
  • fast_forward01:01:27 - are like the quality are associated with other decisions so i think the pressure
  • fast_forward01:01:32 - is now on people who want to defend you know sort of some strong role for consciousness
  • fast_forward01:01:36 - and decision making to find another paradigm where where you can demonstrate
  • fast_forward01:01:42 - that because you mean where volition then comes
  • fast_forward01:01:46 - in as a separate factor, or what?
  • fast_forward01:01:48 - Yes. Yeah, well, I mean, or in this paradigm, to show that it's more than just
  • fast_forward01:01:53 - this minimal model, this, I mean, that there might be something else that's
  • fast_forward01:01:58 - happening in those last few hundred milliseconds.
  • fast_forward01:02:01 - Right. Would you agree with the summary there? Yeah, and I think,
  • fast_forward01:02:03 - I mean, I think one of the, again, one of the things that, that is to be recommended
  • fast_forward01:02:09 - about the, about this model is its simplicity. It's a very simple model,
  • fast_forward01:02:13 - and it accounts for the data.
  • fast_forward01:02:15 - And that puts, I think that reverses the burden of proof, in a sense, now to the other side.
  • fast_forward01:02:23 - Say well, why would millions of years of evolution result in a human who lives
  • fast_forward01:02:32 - one full second behind their own self decisions?
  • fast_forward01:02:37 - Oh, I have an answer to that, but that's not an interview. But the thing is,
  • fast_forward01:02:39 - we already know, as you know, Libet, he understood the consequence of his observation
  • fast_forward01:02:46 - or the implications of his interpretation.
  • fast_forward01:02:48 - So he said, well, maybe the free will acts in holding back the action,
  • fast_forward01:02:53 - in interrupting the action, stopping the action. Right, right.
  • fast_forward01:02:57 - And actually, it turns out, if you start to do tasks where you have stop signals,
  • fast_forward01:03:01 - so like countermanding, then drift-of-fusion models stop working.
  • fast_forward01:03:06 - They're not predictive of anything. Okay.
  • fast_forward01:03:09 - So you really have your counter-example.
  • fast_forward01:03:12 - As soon as you start to voluntarily withhold action because you get the stop
  • fast_forward01:03:16 - signal, then just integration to threshold is not explanatory in any way of
  • fast_forward01:03:22 - the performance of a macaque monkey.
  • fast_forward01:03:25 - But the other way to recover your notion of human and of choice is to embrace
  • fast_forward01:03:34 - the unconscious as part of the self and a source of your decision-making. Oh, absolutely.
  • fast_forward01:03:40 - And if you do that, then none of this is a worry because you just say,
  • fast_forward01:03:45 - well, I am not just my conscious process.
  • fast_forward01:03:48 - I'm the whole of what my brain is doing in the context of the body.
  • fast_forward01:03:52 - And that collective system is making sensible decisions. And some of them are
  • fast_forward01:03:57 - being reported to consciousness.
  • fast_forward01:03:58 - It shouldn't bother me if most of them aren't as long as they're good decisions.
  • fast_forward01:04:02 - As long as your conscious urge is a meaningful best guess on the part of your
  • fast_forward01:04:10 - brain as to the decision that you made and the time that you made it.
  • fast_forward01:04:13 - And if consciousness is able to monitor that and say, well, hang on a minute,
  • fast_forward01:04:17 - we made a poor choice there.
  • fast_forward01:04:18 - Maybe we can, maybe there's a role for consciousness If you guys could finally read my papers.
  • fast_forward01:04:24 - I know. I'm telling you what was in your papers. Ah, thank you.
  • fast_forward01:04:27 - You know that I see consciousness and also volition act towards the future.
  • fast_forward01:04:33 - And all real-time control, as the ones you manipulate as experiments, is all subconscious.
  • fast_forward01:04:39 - And consciousness is catching up because consciousness tries to reconfigure
  • fast_forward01:04:42 - you for the future. So you will yourself into the future.
  • fast_forward01:04:45 - And that's the big mistake people make. They think real-time control,
  • fast_forward01:04:48 - because they never program robots to control anything.
  • fast_forward01:04:50 - They think real-time control is conscious.
  • fast_forward01:04:53 - It cannot be. It has to be automated quick, because that's your survival system.
  • fast_forward01:04:58 - It has to act in real-time, and anything can happen to you.
  • fast_forward01:05:01 - But what you then do, you reassess, and that's why the monitoring also comes in.
  • fast_forward01:05:05 - You have to reassess, you revalue, you extract norms from your environment,
  • fast_forward01:05:09 - and you reconfigure your real-time control, so in the future,
  • fast_forward01:05:12 - you can will yourself to be better.
  • fast_forward01:05:14 - So in that sense, indeed, this is I'm not scared by that we're converging on
  • fast_forward01:05:19 - a common view that would be really scary so Aaron, so now that we all agree
  • fast_forward01:05:24 - and are humming along and you promised to read all my papers.
  • fast_forward01:05:30 - So you have been wading through this really difficult territory this minefield
  • fast_forward01:05:35 - of volition and you're still standing which is amazing, that's a real accomplishment
  • fast_forward01:05:40 - so you're a great scientist,
  • fast_forward01:05:43 - So given that experience and these accomplishments, what is Aaron's law that
  • fast_forward01:05:47 - we should follow to understand the brain?
  • fast_forward01:05:50 - Well, I think what we should start looking at and what hasn't been the case
  • fast_forward01:05:55 - in the past is look at instances where conscious states might actually play
  • fast_forward01:06:05 - a role in an upcoming decision.
  • fast_forward01:06:07 - Decision so there's i i i think
  • fast_forward01:06:11 - that in literature there's been a maybe because it's
  • fast_forward01:06:14 - easier to do maybe because it's sexier but there's been a bias
  • fast_forward01:06:17 - toward finding examples of things that you can do without consciousness um and
  • fast_forward01:06:24 - that that leaves open the possibility that there are some things that you do
  • fast_forward01:06:29 - need consciousness for uh but we haven't been spending our time uh investigating
  • fast_forward01:06:34 - So how does that allude to the law?
  • fast_forward01:06:37 - Ah, I don't know about a law. Look, that's a little mug or something.
  • fast_forward01:06:43 - It's like 10 words. So it's Aaron's law. Aaron's law is some actions,
  • fast_forward01:06:49 - some volitional actions might actually be conscious,
  • fast_forward01:06:53 - might actually have a conscious antecedent, let's say.
  • fast_forward01:07:01 - Um so we've we've we've
  • fast_forward01:07:04 - looked at a lot of examples of of tasks where uh
  • fast_forward01:07:08 - you you consciousness seems
  • fast_forward01:07:12 - not to be involved so aaron's law is some consciousness
  • fast_forward01:07:15 - might play a role sometimes yeah sometimes maybe rarely but maybe sometimes
  • fast_forward01:07:20 - right all right uh the other thing is so we were in in paris recently for a
  • fast_forward01:07:26 - new machines conference which was fantastic and Tony likes Paris now,
  • fast_forward01:07:31 - so he wants to go back to Paris,
  • fast_forward01:07:33 - and he will come visit you four years from now.
  • fast_forward01:07:35 - Okay. And he will come and check, he will come to the notebook and check whether
  • fast_forward01:07:39 - you have actually falsified or confirmed the key hypothesis for your scientific program.
  • fast_forward01:07:47 - So what's the key hypothesis you would like to see tested in that time frame?
  • fast_forward01:07:54 - Four years. Four years. Prediction, yeah.
  • fast_forward01:08:02 - Or maybe by then the channel tower will be filled up with concrete.
  • fast_forward01:08:05 - Yeah. Two years. Maybe. Two years, yeah. Okay. Earlier.
  • fast_forward01:08:12 - I think what I'd like to see done is a test of whether or not,
  • fast_forward01:08:18 - to the extent that this can be done, whether or not one can draw a causal arrow
  • fast_forward01:08:25 - between conscious states and actions.
  • fast_forward01:08:28 - And if we could get a handle on how to come up with a paradigm to test that,
  • fast_forward01:08:35 - I think that would add sort of counterweight to the kind of research that's
  • fast_forward01:08:39 - been done over the past decades.
  • fast_forward01:08:41 - What's the specific prediction then from that?
  • fast_forward01:08:44 - Ah, I guess it's true. I didn't give you a prediction. That's all right.
  • fast_forward01:08:48 - Yeah. So I've paid enough attention.
  • fast_forward01:08:50 - Okay, so, yeah. So, okay, a big prediction is this.
  • fast_forward01:08:58 - You can, what the model, one of the things that the model says is that the,
  • fast_forward01:09:04 - let's say the character of the noise will in part determine the shape of the readiness potential.
  • fast_forward01:09:11 - So if you, let's start with describing the power spectrum of pink noise has
  • fast_forward01:09:21 - this one over F slope to it.
  • fast_forward01:09:24 - So that the slope of the power spectrum gives you what we what you could call
  • fast_forward01:09:30 - the character of the noise or the degree of autocorrelation in the noise.
  • fast_forward01:09:36 - So one prediction that follows from that highly counter counterintuitive prediction,
  • fast_forward01:09:43 - or at least a novel prediction,
  • fast_forward01:09:46 - is that the character of the noise, the 1 over f exponent of the noise,
  • fast_forward01:09:52 - should predict at an individual subject level the shape of the readiness potential.
  • fast_forward01:09:58 - Isn't that the retrodiction? Don't
  • fast_forward01:10:00 - you really know that, given the results you have? No. No, not at all.
  • fast_forward01:10:06 - So, no, that's very much a prediction and a strong one.
  • fast_forward01:10:11 - It's difficult to test, but it is testable. All right. Tony, you wrote it down?
  • fast_forward01:10:18 - It's on record on tape. Yeah. Aaron
  • fast_forward01:10:21 - Sugar, thank you very much for this conversation. Thank you for having me.
  • fast_forward01:10:27 - The CSN podcast was produced by the Convergent Science Network of Biometrics
  • fast_forward01:10:32 - and Biohybrid Systems, a project funded by the European 7th Research Framework Program.
  • fast_forward01:10:40 - For more interviews, recorded lectures, or upcoming conferences in the field
  • fast_forward01:10:46 - of biometrics and biohybrid systems, go to csnnetwork.eu.
  • fast_forward01:10:52 - Music.

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