 Hello. Welcome back. This is the second session of the Actinflab Symposium on June 21st, 2021. And we're here in the .coms or Communication Organizational Unit. The goal of .coms is to organize the labs internal projects and activities, as well as to carry out all forms of communication with external entities. So it's like our connective tissue and our neuroectoderm in a way. What has been done so far is that we've had about 75 live streams variously on presentations and participatory discussions since July 28th, 2020. And we're taking an active inference approach to communication and learning by doing. Our very first live stream was on the paper narrative as active inference and shortly after a world unto itself human communication as active inference. So that's something that we like thinking about here and that we want to explore. And also some of our lab members framed online communication and team collaboration in terms of active inference in the 2020 paper, Active Inference and Behavior Engineering for Teams. Our aim here is to make active inference accessible, well known and well understood. So let's get right to the questions, Carl. The first question is, how can we show not tell the idea of active inference, for example, through embodied experience, experiments, or what other mechanisms? How can we best communicate in a way that makes it resonate? Well, pursuing that very interesting notion of using the principles of active inference to optimize the role of the .coms team, then at its simplest active inference means that the imperatives for all behavior, and it's likely that most behavior is of an epistemic sort, is to resolve uncertainty. So if you want to engage people and be a service to the people you want to engage, which may be internal members of your own team, then you've got to know what they don't know because that will define the epistemic affordances that will get them engaged with you and you with them that will incur the best kind of belief updating. So practically what that might mean is that it may well be that one has to identify didactic or informative illustrations that are tailored or specialized to the person or people that you're talking about. So I have never thought about using embodied experiences before, but that's a brilliant idea. You're just illustrating to people who want to know how my body and my mind works to illustrate to them the mechanics of it working. Using the language of active inference can be very, very powerful. So as I'm talking, one example of this would be using Socatic suppression as an illustration of the potency of getting your beliefs about the predictability of sensory evidence right. So if you're a physiologist, you would know this is sensory attenuation. If you were in machine learning, you're working with transformers, this would be, I think, attentional selection. Basically deploying the gain or switching on the right channels in order to select those data that are going to resolve the most uncertainty or maximize the information gain subsequent on this sort of covert action, often sold as a sort of mental action in the philosophy literature. So that mental action is really endemic and a vital part of our sensory engagement with the world and beautifully illustrated by Socatic suppression. So this speaks to the notion of attenuating the sensory consequences of your own action so that any evidence that you're not actually acting is precluded from your belief updating. So a clinical example of this would be Parkinson's disease, for example. If I'm sitting still and I wasn't able to ignore all the messages from my muscles that tell me at the moment I am sitting still, then I am never going to be able to realize in a prior intention that I'm going to initiate a movement. Because as soon as I initiate a movement, I have, I put in place a plausible hypothesis that I'm lifting, I'm going to stand up. Immediately all the evidence that happens suggests that I am not standing up. So I'm going to revise my belief. No, I'm not standing up. I'm not in the process of standing up. And it becomes impossible to move. So that would be an example of what would happen if you didn't have this capacity to attend to or to select or apply the principles of optimal Bayesian design in terms of selecting those data for your own belief updating. But a really pragmatic and easy example of that is saccadic suppression. When we do the simplest of movements, epistemic foraging, which is moving our eyes, making saccadic eye movements. Because when we do that, we actually induce masses of visual information on the photoreceptors in the retina, sometimes referred to as retinal slips. So when I look from the left to the right, there's a flood of information that I have caused. And yet it is not useful information because it doesn't tell me anything I didn't already know. So what the brain does, it suppresses that information by transiently suspending the precision or the Kalman gain if you are taking a Kalman filter like perspective on predictive coding as what kind of variational filtering. And that's really easy to demonstrate to an audience. Get them to either fixate on a central stimulus and then pay attention to something that's moving around or the converse, they fixate on the things moving around and then ask them questions about the central stimulus. And with the right timing, it's a very potent illustration of beyond just gathering information but actively selecting and triaging that information in accord with the principles of optimal basing design. So I haven't thought about going beyond that, but I'm sure there are lots of lovely examples of embodied experiences that really do illustrate the active inference in action as it were. I'm just reminded because active inference could be read as if you like a 21st century version of idea motor theories which were very popular in the 19th century and of course that was demonstrated through embodied experiences in a very alluring way through hypnotism and the like. So I can imagine somebody doing a 21st century version of hypnotism and all those wonderful Victorian illusions about the way you use your sense organs or deploy them actively but now in the service of just illustrating some basic phenomena under right active inference. In terms of experiments, the classic ones that immediately come to mind that are really engaging are visual illusions. So on one reading, all visual illusions are just ways of getting at your perceptual priors in the context of Bayesian inference. If you can conjure a particular pattern of sensory information that you know was caused in one way and yet you think your subject or your audience has sufficiently precise prior beliefs that it could only be caused in another way which is not the way you caused it and then you let them experience that and then you reveal how you actually generated those data, then that's a very powerful way of demonstrating the innate priors, the sort of formal priors in terms of the connect term on the sparse coupling on a factor graph. Again it's part of the lived experience. So I think visual illusions would be and there are loads of beautiful illusions out there and all that one would have to do is to harness their beauty and the law and use them as a vehicle to give people insight into their own, usually sub-personal prize about the way the world is constructed. And then in my world what you generally try to do is to actually put this in silicone by just creating little in silicone creatures. Because you've now got active inference with this information geometry, you now have the opportunity not just to simulate what these creatures do but what they perceive because now you've got the qualitative estimates of their posterior beliefs. So you can actually show a subject who's just experienced a visual illusion that this is perfectly base optimal and indeed when you write down this variational message passing scheme and this synthetic subject, this synthetic person also experiences exactly the same illusions and this is base optimal for this kind of work. So you can leverage active inference in that sense and I'm literally referring back to the .edu discussions, what active inference brings to the table because it's got information about stuff out there in the numerics. You can go a lot further than you can if you were doing say deep learning or machine learning because you've got this, you've got this information geometry at hand, the state of your variational autoencoder actually means something in relation to a belief about what generated those data, which you can create lovely little movies, showing what this simulation of you was actually experiencing. So I'm interested, are there any other ways that you've thought about in terms of showing people? Thanks for the answer, it's like look left, look right, now you're an active inference agent and as far as potential avenues for embodiments some of the work with Ryan Smith and others bringing people into the somatosensory dimensions and their own priors and expectations about their body and about motion could be very powerful as well as auditory modalities and indeed active inference is a framework by which we can think about how our perceptions are related to our inference and our action, so in various domains I think they'll be excellent experiments and it brings us to our next question, you actually addressed several areas in your answer, you addressed machine learning as well as neuroscience as well as just everyday lived experience. So how can active inference engage in better dialogue with adjacent areas, for example, machine learning, systems engineering, psychiatry and neuroscience as well as any other fields that you think are relevant to? Then the obvious answer here is either academic or commercial collaboration and what might you what would license that? I think the simplest answer is that the free energy principle and if you like tealogical choral reactive inference is not there and was never intended to replace extant theories, it was there to endorse them and to reveal the interrelationships between them, so anything that's worked and survived in the 21st century has some veracity and a proven utility and therefore it's just a question of reformulating or changing the words so that people can see immediately how their particular formulation relates to somebody else's formulation where both formulations are special cases of the most generic and simplest explanation which for my point of view would be the free energy principle and active inference in the case of sentience, so I think as an integrative framework you're very well positioned to say look can we understand the way that you think about this and can we now articulate this either using simulations or mathematical analysis can we understand what you've been doing in this integrative framework and if we can can we show how it relates to another discipline's formulation of this problem and sometimes you can get synergistic or added value from doing that, so there must be loads of examples that you've written machine learning systems engineering and psychology and neuroscience here, so machine learning for example how would active inference help machine learning, so at the moment there seems to be two answers floating around and we've already sort of discussed a couple of the a couple of these issues in depth, so machine learning commits to usually a normative approach to good behavior that can be quantified by a loss or a value function but we've just said well if we now want these machines to learn to act then we have to go beyond state action value functions and consider the belief based calculus that is active inference which is all about the reduction of uncertainty, so now you are in a position to say well look if you consider your objective functions as a part of a more generic objective function think what you might be able to get from this and of course what you might get from this is a deep learning scheme that actually can now go and solicit the right kind of data to optimize its own learning and people in Bayesian RL might argue well yeah that's what we're doing with a series of bright ideas and heuristics to try and augment classical value functions but you can say well okay you've clearly put a lot of work into that but there is actually a simple objective function already out there that is provably appropriate to describe systems that self-organize and maintain themselves that actually has what you want why don't you try this for example so that would be one example you have to tread carefully because a lot of people have dedicated their lives to solving these problems and they're very reluctant to change their rhetoric or see their contributions as a special case but in many instances certainly from my perspective mathematically they are special cases and sometimes if you catch the entrepreneurs the innovators, the creative academics at the right stage in their career before they have committed to a particular church or ideology or calculus or group or company you can actually point them in the right direction they can be extremely creative I'm not so sure about systems engineering but certainly I always celebrate the expected free energy with just taking away various bits and pieces, various sources of uncertainty as reducing to KL control and then what I say is what KL control is what grown-up engineers use in the control theoretic setting so that would be another example so you could also I don't know this because it's not my field but certainly in terms of introducing a fault tolerance in control theoretic approaches in engineering where the fault tolerance required uncertainty about the operation of some external part you could again motivate a more complete objective function that takes you beyond KL control and introduces the information gain into the mix because to get from a complete objective function or to KL control you have to ignore uncertainty about the latent states that are in the mapping from latent states of the plant you're controlling to the sensors or the observables so you're moving from a partial observed Markov decision process for example to an observable one and then accept to be energy becomes KL control risk sensitive control in economics so you could say why don't you just augment the KL control and then put this extra term in and now what you've got is a kind of anticipatory fault tolerance in the sense that if there's uncertainty about latent causes that's automatically resolved in the way that you go and switch on various sensors or switch off various sensors there's a principle way of doing that I think to have any influence you need to be able to show or provide proof of principle that this more integrative more universal approach to problems can offer speed ups or increased efficiency or do what the people actually in that field wanted to do so for example you've got to be able to show that active inference can outperform sort of vanilla deep learning by an order of magnitude which isn't easy to do because of course most benchmarks in machine learning are actually inference problems so if you just recast it it's actually quite a trivial thing to do just by saying well actually what you've been dealing with is an inference problem which looks a lot like a well shot learning from the point of view of somebody in machine learning but I think there will be some pressure to get people's attention to make yourself attractive in terms of they will now have some first of all find you interesting and also have the potential that they can place an epistemic trust in you you've got to sort of give them a clue and a cue as to why they should engage with you and very often there's a two way or two road exchange so one simple example of that which I see emerging in the field is the use of deep learning to amortize certain mappings when they can be amortized in active inference themes to evaluate the expected for example or doing very deep tree searches so that's the kind of innovation you're seeing coming out of 20 year olds at the moment who haven't yet decided whether we're going to do deep learning or active inference because they want to do both and do it very very effectively so that's a nice example from my perspective of that integrative role that could be played or you could play thanks I really heard this yes and maximum from communication and improvisation it's like yes there's been a disciplinary way of approaching it and we're going to be working together to come back to first principles or to make it more efficient so that's really powerful how does active inference help us rethink the nature of online communication where so much of our communication nowadays does occur that's a big question isn't it and certainly in the context of social media politics fake news and the like you could take that question in lots of different directions which I won't do because that's not my field of expertise but just off the cuff in terms of first principles what is communication it's the ability for me to infer what you meant it's the hermeneutics problem if it's the hermeneutics problem that's most efficiently resolved in terms of dyadic or multi-system interactions when we come back to first principles which is the generative model when we share the same narrative or same generative model so in terms of helping how does active inference help us rethink the nature of online communication and I think just from a first principles point of view it would be the importance of establishing who is talking to who and if you want to optimise the efficiency of that exchange literally from the point of view of the principle of least action the speed with which you can resolve uncertainty and minimise your uncertainty or surprise then it's ensuring that like-minded communicators are actually communicating because it's only them that will understand each other so everybody has to speak the same language and share narrative and share generative model and then just by things like rate distortion theorem or rewriting that in terms of active inference the joint free energy minimisation between two interlocutors that's the most efficient shared path of least action how does that help engineer or intervene on things I'm not so sure so just in reference to communications with people like Maxwell and other colleagues there is this interesting notion that if communication if the real problem of communication is not really the messages that you send but the inferring whether to send the messages to this person or not that itself now becomes conditional upon inferring whether it's my in-group or that's a creature or a person like me then the question is how does self-organisation say in terms of social media exchange how is that underwritten by an inference about the kind of people who I am listening to or I am talking to and what are the basic principles of that with minimisation of complexity in our generative models what it may be a useful hypothesis to say there's an inevitable course-graining of the way that we conceive of the people that we generate information for say on social media and reciprocally the kinds of people that I will be able to solicit by listening to this Twitter feed or that Wikipedia page or this news channel so understanding how people carve up whether they are like to the degree of similarity to them may be very useful in just getting an idea of the dynamics of message passing amongst communities that will be defined by on average how each member of that ensemble, individual course-grains and has a generative model of the kinds of people in the communication grain. Just to finish this which is something I have heard and I have found a really interesting notion that again would be great if people could simulate this and understand the maths behind it is that the only evolutionary stable from the point of view of the free energy principle the only one that will be selected by a process of Bayesian model selection the only partitioning of groups is a 50-50 in-group out-group in the sense that anything that departs from that sort of dynamically unstable the evolutionary stable partition means that the smaller group, the out-group the old man out will necessarily ultimately be absorbed into the larger group so the only stable partitioning is 50-50 which makes a lot of sense when you look at Trump versus Biden when you look at Brexit versus not Brexit that wherever you look all the important allegiances in terms of our political ideological and possibly even theological communication seeps this bit right down the middle and perhaps it can be no other way but trueism that inherits from all of these marginal likelihood or free energy minimising processes implemented at multiple levels of hierarchical which is, you know, communication is just message passing and message passing is just the way you articulate belief updating and belief updating just is the process of inference just is the path at least action according to the FEP The 50-50 politics it's maximally confusing something we all experience and a few key points there about the nature of online communication is that at the core it is dyadic even when you're broadcasting to many it's actually about that connection and the hermeneutic relationship of unpacking meaning and then also you brought up the importance of context and identity and who's talking to who and our inference is about that which is essential to rhetoric and something that often gets left off when people take big data approaches to online discourse The next question is how does active inference help us think about science communication and participation specifically as we move into broader citizen science initiatives and as scientists are in the loop something you've been recently involved in as well with society and with decision making so as science and the nature of science is changing who is doing it and how they communicate it how does active inference help us navigate that? I'm sure you've thought about this much more deeply than I have but just drawing upon my experience in terms of science communication during the coronavirus epidemic I think you're absolutely right as with the previous questions I think you can take the principles of active inference and just think about what does that mean for optimal communication and belief updating and shared belief updating and shared narratives or not and use that as a point of reference for the way that you articulate your own science and you've asked all the challenging and exactly right questions about how you communicate how you engage other scientists or other partners within or beyond academia and I think the same principles apply exactly to the public and just to reinforce your beautiful observation that all communication is dialect from the point of view of the person communicating so this kind of person as a unitary object I am talking to or this population or this mentality or this discipline so it is fundamentally diadic from the point of view of the person generating the messages that may or may not incur belief updating and the recipients and these kinds of principles I'm sure would be useful in terms of science communication but at that level I don't know that there's much that I would have to what you already know and possibly already are implementing there is another level though which is using active inference not as a model for the way that we work and communicate and participate but as a a statistical observation model of data so in a sense you can use the principles of active inference really to make the most of data pertinent to a particular domain so again I'm thinking here of the dynamic causal modeling of the epidemiological and behavioral data that has been generated by the coronavirus epidemic you can certainly use the perceptual inference side of it if you like the Bayesian filtering side of it but also in principle the data mining or the optimal Bayesian design to select which data are useful or not in a very practical way when assimilating big data in the service of understanding the system at hand how does a how does a a spike propagate from one neuron to another neuron a neural network or how does a virus propagate from one person to another person in a neural population or a network, a population network and then you can certainly use the data to, you can apply active inference to build generative models of how you think that occurs and when immediately comes, you know, comprehensive you've got to put in all of the things that generate those data so you can't miss out any factors that are important, be they psychological, be they behavioral be they viral, be they transport related, all of these things have to go to your generative model to best explain the data when we do this in a practical way, we both use the instance rates from PCR testing and Google mobility data and Department of Transportation data, anything that speaks to and reduces uncertainty about all the factors necessary that are entailed by your generative model in this instance is not a discrete space, it's written down this one is actually a discrete space model and the active inference is not explicitly part of it in the sense that we're not trying to predict people's behavior, but it does serve an indirect guide through the principles of Bayesian optimum design and all that basically means is do I invest computational resources and thereby incur computational and statistical complexity by including or attending to this kind of data or not and then you can actually evaluate the information by including that data or that data so for example do you need Google retail estimates or workplace activity or just one, if you include both that means the complexity increases and you have to wait another half hour before you get the results for your dashboard or do you not have a more parsimonious model in the sense that you have now in the same sense and that's saccadic suppression of retinal slip you've actually said no I don't need that, I've got my kind of data just by focusing on these data and then once you've got that in mind you can now go foraging for different kinds of data, different collaborators from different disciplines who've got different perspectives but also crucially different data that will inform and shrink your uncertainty about the model parameters and also very importantly about the structural form of the model, do I need this note underneath, is this interaction important or not, is this degree of non-nearity justified by the data, so all of these questions effectively hypotheses about how this system is responding or would respond if you were to be on it, all of those questions now become amenable to an evidence-based analysis because you've got a charity model underneath the hood, so that would be an indication of the principles that underwrite active inference even though your computer program is not actually doing active inference but it's certainly been deployed using the principles of active inference awesome and we heard that integrative approach, yes we're going to include multiple data sets potentially of unconventional type and we're going to have a principled way of deciding to include that data and also as you brought up at the end who to include in the conversation and there was one piece you said in there about the dyadic nature of communication where a speaker is always I think you said something like speaking to a person or to a group or to a community and it relates to our next question which is how can we appropriately interact with shared and nested generative models potentially across scales be it person, team, or community do we think about these levels of analysis as active inference agents in their own right or how do we for example speak to a community or speak to a level of analysis that's broader than the personal? I think that's a great and challenging question clearly there has been some provisional work in academia looking at sort of Markov blankets of Markov blankets which is effectively from a stats point of view, from physics point of view at least what we're talking about here as a physicist you'd be tackling this with things like the apparatus of the renormalization group which tells you immediately something interesting that the existence of this nested structure if underwritten by or if it is a renormalization group means that there are certain functional forms that are conserved so what that means is from your practical point of view that there will be certain kinds of behavior that are actually conserved at different scales so what works in terms of talking to your children should also work as a president talking to your community or a governor talking to your state or a team leader talking to your assembled team simply because in order for there to be a hierarchical nesting that supports that hierarchical structure that has to be this conservation usually mathematically written down as a functional form of a Lagrangian it could be the sort of marginalized group or the surprise that we're talking about that underwrites these sort of most likely paths of least action so that actually paradoxically slightly makes the problem slightly simpler because what you're saying is what works at one level will work at all levels so what you do is find the course grading operator that takes you from one level to the next so what that would look like I think would be very very application domain specific so I think that there is a great challenge ahead which is taking the single particle FEP approach now into a world where it matters where the world is actually and we've already discussed the importance of thinking about worlds where all the particles are identical whereas half the particles vote for Trump and the other half vote for Biden and this is interesting to reflect upon pre 21st century physics that was so powerful in articulating this kind of dynamics because it just dealt with the simplifying assumption that my idealized gas was an ensemble of identical particles and then you can spin off from that equilibrium physics and everything that led from the kind of cycles and engines through to current technology so it's a powerful assumption if you just make some simplifying assumptions but just because we've already said well perhaps that's not the best kind of assumption to make when you're dealing with political mechanics at least you're like one bipartisan so that would require revisiting of that kind of physics from our point of view or your point of view basically simulating active inference agents ensembles of active inference agents particles but where now there's a heterogeneity in play and then asking the questions what are at the next scale the free energy minimizing or potential minimizing solutions at the next scale up so we come back to why is it the case that people are all split 50-50 which has an enormous impact on the interactions at the scale below I think or I'm just hand waving here because I don't think there are any formal answers and I think those formal answers will probably have to come out of agent based and possibly stochastic agent based modelling initiatives but with the twist you're making each agent itself an active inference agent so while each individual member of the ensemble is trying to minimize that free energy also the ensemble through cooperation and a shared narrative is minimizing the joint free energy and what that means when you move from one scale to the next scale you know this is if you're in physics I imagine that this is the problem of beyond non-equilibrium steady states because you're actually now dealing with the multi-scale aspect of non-equilibrium at best we have good models of turbulent flow which is a laser physics that take us beyond equilibrium physics where all the particles are the same into non-equilibrium physics but I don't know that there's an equivalent maths or metaphor in physics that would really speak to the hierarchical method so I think this is a really open and important research area that I can only recommend is dealt with via numerical analysis basically predicated on underlying principles Thanks for the answer there and it made me wonder if agree to disagree is a narrative that can be shared even when there is a 50-50 split and I think it brings us nicely to the final question of dot coms can you move people and teams into a co-transformative space or as some of your recent work discussed an interactionist space I'm sure you know the answers to all this I suspect that I'm now realising you already know the answers because of your knowing smiles when I say something that you recognise answering that on the basis of what you just said so I think that's another really useful insight that agreeing to disagree is a surprise minimising base optimal explanation for the exchange with others but it does rest upon committing to the hypothesis that you are not like me, you are not like minded and that's okay so I've now classified you as somebody who's not like minded and I've resolved the ambiguity among the hypotheses that you are either like minded or you're not like minded, normally we would resolve those first impressions within a few seconds based upon all these epistemic cues we offer each other to define the kind of person that we are so we make that job as easy as possible or as a signalling to make this so we know our place and I use that phrase because of course there was a paper called knowing your place that exploited a shared generative model that allowed you to be in a particular position in some space even if you and I shared the same understanding of political ideology but I know my place because I'm right-wing and you're your place because you're left-wing or vice versa and then we can quite happily exchange and agree to disagree so I think that that's a wonderful perspective to have and to endorse it and that is a base optimal perspective from both sides of the disagreement that's resolving uncertainty in a bounded rational way so applying that notion to co-transform to space it reminds me of the problem where certain patients in psychiatry have committed to a particular influence about whether they belong out there in that kind of environment or not and they have decided that they do not belong out there and that those people are not like them and they start to avoid so in a very simple by the way and if they're in psychiatry and this example but I think it's illustrative and useful in that respect so take depression or agrophobia which is a completely base optimal response if I have committed to the hypothesis that out there is full of people who are not like me and potentially will upset, confuse and render me uncertain and possibly injure me in some way so withdraw into that corner of into your house or into that silo if you're working in teams is a perfectly base optimal response that says that you've got a precise belief that this is where you belong, these are the people that you speak to and not those people so and that's usually perfectly functional in psychiatry that will be a neurotic defense but it can become pathological when you become housebound or you become say if you've got a pathological hypothesis like your body got dysmorphobic and you nearly die because of a failure to eat properly so when you say co-transformative I imagine what you mean is you want to transform two teams into one team or at least enable them to work together if that's right and you're not impartial you're facing the same challenge that a psychiatrist faces in terms of enabling people to revise the precision of their precise beliefs about who they can interact with and who they should interact with that's not an easy thing but it's certainly doable and it basically usually reduces to presenting evidence to a group or a person that it can be another way so that they start to revise their prior beliefs or at least the precision of their prior beliefs in a safe space where it's okay to explore other hypotheses and to think about other ways of interacting so this would normally be the objective of psychotherapy to by illustration very much in the same way you were talking about illustrating or educating by embodied experience very much psychotherapy is thought to work like this you provide a psychologically embodied experience where you can try out different styles and different hypotheses and in so doing you paradoxically introduce the right kind of uncertainty about different styles of engagement and who you are and who you are talking to and by relaxing that precision you give the patient or the naughty team that's become too siloed the latitude to explore other ways of behaving so I would imagine that most of most of the tried and trusted procedures procedures to get teams into a co-transformative space use one or more of those mechanisms what will active infants bring to the table it would just bring the narrative that everybody everybody trying to get to talk to each other can come to share so they can see through the process of becoming more collaborative or exchanging ideas more fluently or working with the same lexicon or mechanics or code it will enable them to having the same narrative will actually shape their prior space and understand the mechanics of actually enlarging the hypothesis space in terms of interaction styles so that was an incredibly hand waving answer but it was in part informed by almost my understanding of the question from the point of view of a psychiatrist who wants to transform the way that a patient relates to her world and drugs can help I mean that literally in those drugs that are responsible neurobiologically for setting the precision if you can temporarily suspend the precision in order to reveal other latent apriori hypotheses in terms of the way that I am or the way that I interact and the way that I behave or the way that I perceive that can actually have long lasting effects on bringing those other hypotheses to the table in the moment in subsequent interactions so perhaps the most compelling example of this is in terms of how I engage with my loved ones as a dying person who is near death and the ultimate loss those are not necessarily the best or most functional hypotheses or ways of being there are other ways of dying there are other ways of dying there are other ways of dying there are other ways of dying hypotheses or ways of being there are other ways of dying gracefully but to get at them sometimes having a managed challenge to your 5-HT2A acceptance via things like psilocybin other related drugs just allows you to suspend for a moment your very precise beliefs about the kind of thing I am and allows you to have other ways of being and perceiving which can be very useful when it comes to trying out other hypotheses in your this is your cancer journey but one can also imagine similar scenarios when you get locked in to a particular way of interacting either within a team or between teams in a larger organization so that would mean you have to go on a retreat and take lots of magic mushrooms I don't know how I would hear from you Professor Friston but there we have it, drugs for teams thank you for this excellent interval with .com's unit we're going to take another 5 minute break and we'll return for the final session for .tools thanks again everyone and we'll see you in 5 minutes