Being a Beast Machine: The Somatic Basis of Selfhood

Seth, Anil K.; Tsakiris, Manos

doi:10.1016/j.tics.2018.08.008

Cited by 249 publications

(254 citation statements)

References 94 publications

(166 reference statements)

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“…In turn, the selected policy is one "I am likely to pursue" and will therefore specify empirical "self" priors (for action) that contextualize self-modelling -which again emphasizes the hierarchical nature of self-modelling discussed above (cf. Butz, 2008;Apps & Tsakiris, 2014;Limanowski & Blankenburg, 2013;Seth & Tsakiris, 2018). As has been pointed out by a number of authors, this leads to the notion of "self-evidencing" inherent in active inference (Hohwy, 2016;cf.…”

Section: "Self-flattening": the Relationship Between Deep Active Infementioning

confidence: 92%

“…First, however, we will briefly introduce some key ideas about internal predictive (self) models in the active inference framework (see Friston, 2010;Hohwy & Michael, 2017;Limanowski & Blankenburg, 2013;Seth & Tsakiris, 2018;Wiese & Metzinger, 2017, for a more exhaustive introduction). Active inference sits within a larger "free-energy principle", according to which any living system -that can be demarcated from its surroundings -will actively try to remain in a set of unsurprising states by maximizing the (marginal) likelihood of sensory samples (Friston, 2010).…”

Section: Self-modelling Based On Predictive Processingmentioning

confidence: 99%

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Attenuating oneself

Limanowski

Friston

2020

PhiMiSci

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In this paper, we address reports of “selfless” experiences from the perspective of active inference and predictive processing. Our argument builds upon grounding self-modelling in active inference as action planning and precision control within deep generative models – thus establishing a link between computational mechanisms and phenomenal selfhood. We propose that “selfless” experiences can be interpreted as (rare) cases in which normally congruent processes of computational and phenomenal self-modelling diverge in an otherwise conscious system. We discuss two potential mechanisms – within the Bayesian mechanics of active inference – that could lead to such a divergence by attenuating the experience of selfhood: “self-flattening” via reduction in the depth of active inference and “self-attenuation” via reduction of the expected precision of self-evidence.

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Section: "Self-flattening": the Relationship Between Deep Active Infementioning

confidence: 92%

Section: Self-modelling Based On Predictive Processingmentioning

confidence: 99%

Attenuating oneself

Limanowski

Friston

2020

PhiMiSci

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“…In mandating that existence necessitates maintaining oneself within a limited repertoire of states via control-oriented predictive regulation (instrumental active inference) (Seth & Tsakiris, 2018), the FEP aligns itself with precursors of this view, cybernetic theories that build on control, feedback and predictive modelling (e.g., the "good regulator theorem") (Conant & Ashby, 1970). Note that while a purely Helmholtzian view of the brain might cast it in terms of inferring hidden causes in the world, casting the predictive machinery in terms of being for ensuring continued existence means that the generative model is not constrained to veridicality.…”

Section: Control-oriented Inferencementioning

confidence: 99%

“…A key reason for this is that interoceptive inference is apt to put greater emphasis on control over discovery (Seth & Friston, 2016), due to "a priori hyper-precision of visceral channels" (Allen & Friston, 2018, p. 7), in which interoceptive signals are assigned very high precision in virtue of communicating information about key physiological variables (Seth, 2015). Grounding the self-model in control-oriented active inference (Seth & Tsakiris, 2018) inflects perception of the affordance landscape in terms of bodily states, an idea which is nicely expressed by Montague and King-Casas:…”

Section: The Self In Active Inferencementioning

confidence: 99%

Dissolving the self

Deane

2020

PhiMiSci

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Psychedelic drugs such as psilocybin, LSD and DMT are known to induce powerful alterations in phenomenology. Perhaps of most philosophical and scientific interest is their capacity to disrupt and even “dissolve” one of the most primary features of normal experience: that of being a self. Such “peak” or “mystical” experiences are of increasing interest for their potentially transformative therapeutic value. While empirical research is underway, a theoretical conception of the mechanisms underpinning these experiences remains elusive. In the following paper, psychedelic-induced ego-dissolution is accounted for within an active inference framework, as a collapse in the “temporal thickness” of an agent’s deep temporal model, as a result of lowered precision on high-level priors. The argument here is composed of three moves: first, a view of the self-model is proposed as arising within a temporally deep generative model of an embodied organism navigating an affordance landscape in the service of allostasis. Next, a view of the action of psychedelics as lowering the precision of high-level priors within the generative model is unpacked in terms of a high Bayesian learning rate. Finally, the relaxation of high-level priors is argued to cause a “collapse” in the temporal thickness of the generative model, resulting in a collapse in the self-model and a loss of the ordinary sense of being a self. This account has implications for our understanding of ordinary self-consciousness and disruptions in self-consciousness present in psychosis, autism, depression, and dissociative disorders. The philosophical, theoretical and therapeutic implications of this account are touched upon.

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“…In the language of Expected Utility Theory (which explains behaviour by appealing to the principle of maximizing expected rewards), the logarithm of such prior beliefs is equivalent to the utility function (Zeki, Goodenough, & Zak, 2004). This component of G has variously been referred to as utility, extrinsic value, or instrumental value (Seth, 2015; Friston, FitzGerald, et al, 2017; Seth & Tsakiris, 2018; Biehl, Guckelsberger, Salge, Smith, & Polani, 2018); hereafter we will use the term instrumental value. A related but subtly different perspective is provided by the right-hand side of the second line of Equation (10): in this formulation, prior preferences enter the free energy through an ‘expected risk’ term.…”

Section: Free Energy Minimization and Active Inferencementioning

confidence: 99%

Deep Active Inference and Scene Construction

Heins

Mirza

Parr

et al. 2020

Preprint

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14Adaptive agents must act in intrinsically uncertain environments with complex latent structure. Here, 15 we elaborate a model of visual foraging -in a hierarchical context -wherein agents infer a higher-order 16 visual pattern (a 'scene') by sequentially sampling ambiguous cues. Inspired by previous models of scene 17 construction -that cast perception and action as consequences of approximate Bayesian inference -we 18 use active inference to simulate decisions of agents categorizing a scene in a hierarchically-structured set-19 ting. Under active inference, agents develop probabilistic beliefs about their environment, while actively 20 sampling it to maximise the evidence for their internal generative model. This approximate evidence 21 maximization (i.e. self-evidencing) comprises drives to both maximise rewards and resolve uncertainty 22 about hidden states. This is realised via minimization of a free energy functional of posterior beliefs 23 about both the world as well as the actions used to sample or perturb it, corresponding to perception 24and action, respectively. We show that active inference, in the context of hierarchical scene construction, 25 gives rise to many empirical evidence accumulation phenomena, such as noise-sensitive reaction times 26 and epistemic saccades. We explain these behaviours in terms of the principled drives that constitute the 27 expected free energy, the key quantity for evaluating policies under active inference. In addition, we report 28 novel behaviours exhibited by these active inference agents that furnish new predictions for research on 29 evidence accumulation and perceptual decision-making. We discuss the implications of this hierarchical 30 active inference scheme for tasks that require planned sequences of information-gathering actions to infer 31 compositional latent structure (such as visual scene construction and sentence comprehension). Finally, 32 we propose experiments to contextualise active inference in relation to other formulations of evidence 33 accumulation (e.g. drift-diffusion models) in tasks that require planning in uncertain environments with 34 higher-order structure. 35

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Being a Beast Machine: The Somatic Basis of Selfhood

Cited by 249 publications

References 94 publications

Attenuating oneself

Attenuating oneself

Dissolving the self

Deep Active Inference and Scene Construction

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