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From allostatic agents to counterfactual cognisers: active inference, biological regulation, and the origins of cognition

Abstract

What is the function of cognition? On one influential account, cognition evolved to co-ordinate behaviour with environmental change or complexity (Godfrey-Smith in Complexity and the function of mind in nature, Cambridge Studies in Philosophy and Biology, Cambridge University Press, Cambridge, 1996). Liberal interpretations of this view ascribe cognition to an extraordinarily broad set of biological systems—even bacteria, which modulate their activity in response to salient external cues, would seem to qualify as cognitive agents. However, equating cognition with adaptive flexibility per se glosses over important distinctions in the way biological organisms deal with environmental complexity. Drawing on contemporary advances in theoretical biology and computational neuroscience, we cash these distinctions out in terms of different kinds of generative models, and the representational and uncertainty-resolving capacities they afford. This analysis leads us to propose a formal criterion for delineating cognition from other, more pervasive forms of adaptive plasticity. On this view, biological cognition is rooted in a particular kind of functional organisation; namely, that which enables the agent to detach from the present and engage in counterfactual (active) inference.

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Notes

  1. For broader philosophical discussion of these ideas in the context of predictive processing, see Clark (2016), Hohwy (2013) and Wiese and Metzinger (2017). For more technical explications of the free energy principle and its corollaries, see Bogacz (2017), Buckley et al. (2017) and Friston et al. (2017a).

  2. Technically, living systems appear to violate fluctuation theorems that generalise the second law of thermodynamics to nonequilibrium systems (Evans and Searles 1994, 2002; Seifert 2012).

  3. See Linson et al. (2018), for a lucid explication of the deep continuities between thermodynamics and the free energy principle. For a more technical exposition, see Sengupta et al. (2013).

  4. Note that complex organisms may be composed of multiple, hierarchically-nested Markov blankets (for recent discussion, see Allen and Friston 2018; Clark 2017; Kirchhoff et al. 2018; Palacios et al. 2020; Ramstead et al. 2018).

  5. Variational inference techniques are also widely used in machine learning to approximate density functions through optimisation (see Blei et al. 2017).

  6. Of course, just because a system can be described as behaving in a way that minimises variational free energy (maximises Bayesian model evidence, approximates Bayesian inference, etc.) does not guarantee that it actually implements any such computation. The extent to which the free energy principle should be construed as a useful heuristic for describing and predicting adaptive behaviour (a kind of intentional stance; Dennett 1987), versus a more substantive ontological claim, remains an open question. That said, recent progress has been made towards casting the free energy principle as a process theory of considerable explanatory ambition (Friston et al. 2017a).

  7. Note that we interpret the notion of regulation rather broadly here. For philosophical arguments distinguishing regulation from related concepts such as feedback control and homeostasis, see Bich et al. (2016). On this view, regulatory control consists in a special kind of functional organisation characterised in terms of second-order control. This formulation seems broadly in line with our understanding of allostasis (see “Beyond homeostasis: Allostasis and hierarchical generative models”).

  8. Note that the organism’s morphology and internal organisation impose constraints on the way it models and represents environmental dynamics (e.g., Parr and Friston 2018a)—a point we shall elaborate in “Biological regulation in an uncertain world”.

  9. See Parr and Friston (2018b) for a mathematical explanation of the (bound) relationship between variational free energy and model evidence.

  10. While active inference is sometimes narrowly construed as the active or behavioural component of the perception–action loop, the term was originally introduced to characterise the reciprocal interplay between perception and action (e.g., Friston et al. 2009, p. 4). This broader interpretation emphasises the deep continuity of the (Bayesian inferential) processes underwriting perception, learning, planning, and action under the free energy principle (Friston et al. 2017a).

  11. This general understanding of perception need not entail the conscious experience of sensations, just as learning can occur through entirely unconscious—and even artificial—mechanisms. Rather, what is at stake here is the statistical notion of Bayesian belief, where probability distributions encode the conditional probability that sensory observation Y was caused by hidden state X.

  12. Technically, actions are physical, real-world states that are not represented within the agent’s generative model (Attias 2003). Rather, the agent infers (fictive) ‘control’ states that explain the (sensory) consequences of its actions (Friston et al. 2012a, d). Action selection (or decision-making) thus amounts to the optimisation of posterior beliefs about the control states that determine hidden state transitions (Friston et al. 2013, 2015b).

  13. Although one might be tempted to subordinate perceptual inference to free energy minimising action, we interpret perception and action as mutually dependent moments within a unified dynamical loop (cf. the perception–action cycle; Fuster 2001, 2004). Ultimately, both modes of active inference are in the service of uncertainty reduction: Percepts without actions are idle; actions without percepts are blind.

  14. Formally speaking, the sensory and active states that compose the Markov blanket render the probability distributions over internal and external states statistically independent of one another (see Pearl 1988). In other words, internal and external states provide no additional information about one another once the Markov blanket’s active and sensory states are known.

  15. Note that value here is not equivalent to expected utility, but rather a composite of utility (extrinsic value) and information gain (epistemic value; see Friston et al. 2015b; Schwartenbeck et al. 2015).

  16. Although we focus here on allostasis, numerous other concepts emphasising the dynamic nature of biological regulation have been proposed in an effort to extend (or transcend) classical notions of homeostatic setpoint control (see for e.g., Bauman 2000; Berntson and Cacioppo 2000 and references therein).

  17. Indeed, evidence of anticipatory physiological regulation antedates Walter B. Cannon’s influential work—Ivan Pavlov’s (1902) Nobel prize-winning research on the digestive system demonstrated that gastric and pancreatic enzymes are secreted before nutrient ingestion (see Smith 2000; Teff 2011).

  18. This formulation is congruent with contemporary efforts to finesse traditional notions of setpoint rigidity with more dynamic accounts of homeostatic control (e.g., Cabanac 2006; Ramsay and Woods 2014; cf. Ashby 1940). It also seems more felicitous to Cannon’s original conception of homeostatic control (see for e.g., Cannon 1939, p. 39).

  19. Note that priors over certain physiological variables (e.g., core temperature, blood pH) are likely to be held with greater precision—and thus restricted to a narrower range of attracting states—than others (e.g., blood pressure, heart rate; see Allen and Tsakiris 2018; Seth and Friston 2016; Yon et al. 2019).

  20. One might protest that all we have done here is pivot from one sort of reactive homeostatic mechanism to another; albeit, one involving responses to an external (rather than internal) threat. Nevertheless, we consider this simple scenario as exemplary of the fundamental principle of allostatic regulation; namely, the modulation of physiological states in anticipation of future conditions, and in the absence of any immediate homeostatic perturbation. This example can easily be extended to capture a rich assortment of allostatic dynamics that play out across increasing levels of abstraction and spatiotemporal scale.

  21. Note that the appeal to expected free energy was also implicit in the predator example of the previous section, insofar as transient increases in homeostatic prediction error were tolerated in order to avoid a much more surprising fate—being eaten!

  22. See Moore (2004) for a thoroughgoing review of such associative learning mechanisms.

  23. More precisely, this capacity depends on the ability to infer the expected free energy of the outcomes associated with various potential state trajectories, as well as the expected likelihood of outcomes under each policy (see Friston et al. 2017a, c; Parr and Friston 2017, 2018b). We have suggested such inferential processes might be facilitated by the co-ordination of exteroceptive sampling and motor activity with periodic regimes of autonomic/interoceptive active inference (Corcoran et al. 2018).

  24. We emphasise again that the conscious, reflective character of these intuitive examples should not detract from the idea that the possibility of such experiences is underwritten by more basic, unconscious allostatic mechanisms. For example, the growth onset of a horse’s winter coat is not assumed to represent a strategic decision on the part of the horse, but rather a physiological response to seasonal changes in photoperiod. Similarly, a rabbit might schedule her foraging bouts to balance energy gain against predation risk, even though she might not be capable of representing and evaluating these concerns explicitly (this trade-off may, for instance, be implicitly encoded within the animal’s circadian rhythm—see “Model 2: Hierarchical active inference”).

  25. See Morville et al. (2018) for discussion of the nontrivial challenges posed by high-dimensional homeostatic needs in uncertain environments. The ability to reliably navigate such complex demands speaks also to the notion of competence in artificial intelligence research (see Miracchi 2019).

  26. This gloss on the environmental complexity thesis is reminiscent of W. Ross Ashby’s law of requisite variety (1956, 1958; cf. Conant and Ashby 1970), and is clearly in line with recent neuroscientific interest in the brain’s teleonomic function as a sophisticated biological regulator (for discussion, see Williams and Colling 2018). Although Godfrey-Smith (1996, pp. 76–79) briefly remarks upon the connection between cybernetic accounts of homeostatic control and cognitive function, he rejects their strong continuity on the grounds that cognition can sustain biological viability through actions that circumvent homeostatic mechanisms. We concur that non-trivial definitions of homeostasis and cognition invoke concepts that are distinct from one another, and argue below that this distinction can be cashed out in terms of their constitutive inferential architectures.

  27. See Baltieri and Buckley (2017) and McGregor et al. (2015) for alternative formulations of ‘minimal’ active inference.

  28. In fact, real E. coli realise a similar ‘adaptive gradient climbing’ strategy by integrating chemosensory information about the ambient chemical environment over time, and modulating the probability of tumbling as a function of attractant rate of change (Berg and Brown 1972; Falke et al. 1997). More recent work has indicated that such chemotactic activity approximates optimal Kalman filtering (Andrews et al. 2006), where hidden states are estimated on the basis of prior and present observations weighted by their uncertainty (Kalman 1960; Kalman and Bucy 1961; see Grush 2004, for discussion). As Kalman filtering constitutes a special case of Bayesian filtering (one that is equivalent to predictive coding; Bastos et al. 2012; Friston et al. 2010b, 2018), chemotaxis can be cast as a gradient descent on variational free energy. Notice that our model is deliberately simpler than this scheme, since sensory prediction errors are not modulated by an uncertainty (precision) parameter.

  29. The story changes if the organism’s receptors are compatible with molecules it cannot metabolise, or that afford low nutritional value (assuming such molecules are prevalent enough to significantly interfere with chemotaxis). See Sterelny (2003, pp. 20–26) for discussion of the challenges posed by ‘informationally translucent environments’ that confront organisms with ambiguous (or misleading) cues. Environmental translucence calls for greater model complexity; e.g., the capacity to integrate information harvested across multiple sensory channels (cf. robust tracking; Sterelny 2003, pp. 27–29).

  30. Indeed, one might construe the minimal model as a simplified analogue of Ashby’s (1960) ‘Homeostat’.

  31. See Godfrey-Smith (2016b) for a complementary discussion of this topic in relation to microbial proto-cognition and metabolic regulation.

  32. One might call this entity a Spencerian creature; i.e. an organism that responds to environmental change through “the continuous adjustment of internal relations to external relations” (Spencer 1867, p. 82; see discussion in Godfrey-Smith 1996, pp. 70–71). From an active inference perspective, this creature is the embodiment of pure perception; i.e. an organism that reconfigures its internal states (updates its model) in accordance with external conditions, without ever seeking to alter such conditions (cf. Bruineberg et al. 2018; Corcoran 2019).

  33. One might play with the idea of entities that could exist like this quite happily once the ideal, invariant niche is discovered—perhaps deep within rocky crevices or underwater (one is reminded of the sea squirt that consumes its own brain after settling upon a permanent home, but the anecdote turns out to be an exaggeration; see Mackie and Burighel 2005). However, entities of this sort would surely fail to qualify as adaptive biological systems—at least insofar as the notion of adaptability implies some capacity to maintain one’s viability in the face of time-varying environmental dynamics (cf. ‘mere’ vs. ‘adaptive’ active inference; Kirchhoff et al. 2018). Moreover, such entities would also fail to qualify as agents in any biologically relevant sense (see for e.g., Moreno and Etxeberria 2005).

    Interestingly, this scenario is reminiscent of a common criticism levelled against the free energy principle: the so-called dark-room problem (Friston et al. 2012e). The thrust of this argument is that free energy minimisation should compel agents to seek out the least-surprising environments possible (e.g., a room devoid of stimulation) and stay there until perishing. Various rejoinders to this charge have been made (see for e.g., Clark 2018; Hohwy 2013; Schwartenbeck et al. 2013), including the observation that this strategy will inevitably lead to increasing free energy on account of accumulating interoceptive prediction error (Corcoran 2019; Pezzulo et al. 2015). More technically, “itinerant dynamics in the environment preclude simple solutions to avoiding surprise” (Friston et al. 2009, p. 2), where the environment referred to here includes the biophysical conditions that obtain within the organism, as well as without. This is to say that the attractors around which adaptive biological systems self-organise are inherently unstable—both autopoietic (‘self-creating’) and autovitiating (‘self-destroying’)—thus inducing itinerant trajectories (heteroclinic cycles) through state-space (Friston 2011, 2012b; Friston and Ao 2012; Friston et al. 2012c).

    In other words, dark rooms may very well appeal to creatures like us (e.g., as homeostatic sleep pressure peaks towards the end of the day), but the value such environments afford will inevitably decay as alternative possibilities (e.g., leaving the room to find breakfast after a good night’s sleep) become more salient and attractive (cf. alliesthesia, the modulation of affective and motivational states according to (time-evolving) physiological conditions; Berridge 2004; Cabanac 1971).

  34. Note that the allostatic treatment of circadian regulation may in principle be extended to periodic phenomena spanning shorter or longer timescales; e.g., ultradian and circannual rhythms.

  35. This scenario is not meant to imply that circadian rhythms are actually acquired in this fashion (although they are clearly susceptible to modulation through external cues). Rather, the idea we are trying to illustrate here is the way hierarchical architectures ground adaptive regulation over longer timescales by dint of their capacity to capture recurrent, slowly evolving patterns of environmental variation.

  36. Notice that the agent forms a representation of a hidden cause corresponding to diurnal patterns of temperature variation despite its lack of exteroceptive sensitivity to such variables as temperature, viscosity, light, etc. Rather, it detects regular changes in its dynamics that cannot be ascribed to its own actions (which average out across the 24 h period), and infers some hidden external process as being responsible for these changes. It might not be right to say the agent represents ambient temperature per se, nor indeed the higher-order causes of the latter’s oscillation (sun exposure, planetary rotation, etc.). Our agent lacks sufficient hierarchical depth to arrive at such conclusions, collapsing these fine-grained distinctions into a fairly ‘flat’, undifferentiated representation of diurnal variation.

  37. For discussion on the representational status of circadian rhythms, see Bechtel (2011) and Morgan (2018a, b).

  38. The remarkable robustness of circadian oscillations is thrown into relief whenever one traverses several time-zones—a good example of how strongly-held (i.e. high-precision or ‘stubborn’; see Yon et al. 2019) allostatic expectations persist in the face of contradictory sensory evidence (i.e. the phase-shifted photoperiod and feeding schedule, to which the system eventually recalibrates; Asher and Sassone-Corsi 2015; Menaker et al. 2013).

  39. For further discussion of counterfactual representation under predictive processing, see Clark (2016, Ch. 3), Friston et al. (2012b), Friston (2018), Palmer et al. (2015), Pezzulo et al. (2015) and Seth (2014, 2015).

  40. Note that our use of counterfactual semantics here is not intended to imply that cognition bears any necessary resemblance to linguistic processing; it is simply adopted as a convenient way of characterising the logic of model selection under active inference.

  41. Interestingly, recent psychological evidence suggests that counterfactual scenarios deemed more similar to previously experienced events are perceived as more plausible and easier to envisage (i.e. simulate) than more distant alternatives (Stanley et al. 2017). This observation lends weight to the idea that humans evaluate competing counterfactual predictions in accordance with their proximity to actual states of affairs, where proximity or similarity might be cashed out in terms of (Bayesian) model evidence (see Fitzgerald et al. 2014).

  42. Risk and ambiguity are also known as irreducible uncertainty and (parameter) estimation uncertainty, respectively (de Berker et al. 2016; Payzan-LeNestour and Bossaerts 2011). Note that uncertainty can be decomposed in various other ways, depending on the domain of interest (see for e.g., Bland and Schaefer 2012; Bradley and Drechsler 2014; Kozyreva and Hertwig 2019).

  43. This characterisation of risk and ambiguity is broadly consistent with descriptions in economics (e.g., Camerer and Weber 1992; Ellsberg 1961; Kahneman and Tversky 1979; Knight 1921) and neuroscience (e.g., Daw et al. 2005; Hsu et al. 2005; Huettel et al. 2006; Levy et al. 2010; Payzan-LeNestour and Bossaerts 2011; Preuschoff et al. 2008; for a review, see Bach and Dolan 2012). Importantly, these two sorts of uncertainty rest upon the precision (inverse variability) of the likelihood mapping between outcomes and hidden states—and transitions amongst hidden states that may or may not be under the creature’s control. Technically, the first sort of precision relates to observation noise, while the second relates to system or state noise, i.e. volatility. Formally, volatility can be construed as the (inverse) precision over transition probabilities (i.e. confidence about the way hidden states evolve over time; Parr and Friston 2017; Parr et al. 2019; Sales et al. 2019; Vincent et al. 2019). This formulation suggests that volatile environments will tend to generate more surprising outcomes than stable environments, insofar as their states are apt to change in ways that are difficult to anticipate. Note that the term volatility is used differently in various contexts (see for e.g., Behrens et al. 2007; Bland and Schaefer 2012; Mathys et al. 2014).

  44. One caveat to this claim is that the (neuro)physiological mechanisms and cognitive operations required to enrich and exploit counterfactual predictive models may themselves engender additional costs (e.g., planning a new course of action requires time, energy, and effort; see Zénon et al. 2018). We assume that the costs incurred by such processes ‘pay for themselves’ over the long-run (or at least tend to on average), insofar as they enable the agent to exploit prior experience in ways that are conducive to adaptive behaviour (see Buzsáki et al. 2014; Pezzulo 2014; Pezzulo et al. 2017; Suddendorf et al. 2018). It is also worth pointing out that some of the costs engendered by counterfactual inference-supporting architectures may be mitigated by a variety of adaptive strategies (e.g., model updating during sleep, habitisation of behaviour under stable and predictable conditions; see Fitzgerald et al. 2014; Friston et al. 2017b; Hobson and Friston 2012; Pezzulo et al. 2016).

  45. For the purposes of this brief discussion, we limit the scope of epistemic action to instances where the organism actively intervenes on its environment in order to resolve uncertainty. It is worth noting, however, that the concept can also refer to mental actions or cognitive operations that reduce uncertainty (see for e.g., Metzinger 2017; Pezzulo et al. 2016; Pezzulo 2017). On this broader understanding, one might construe the different varieties of counterfactual processing described above as covert modes of epistemic action.

  46. Such activity is sometimes referred to as epistemic foraging, where the agent seeks out information about the way state transitions are likely to unfold (Friston et al. 2017d; Mirza et al. 2016; Parr and Friston 2017). For a nice example of epistemic foraging in wild dolphins, see Arranz et al. (2018).

  47. It is interesting to remark how epistmic action contributes to the practical utility of cognition as understood under the environmental complexity thesis. Following Dewey (1929), Godfrey-Smith (1996, pp. 116–120) notes that cognition is most likely to be useful in environments that comprise a mixture of regularity and unpredictability. Specifically, distal states should vary in ways that are a priori unpredictable (but worth knowing about), while maintaining a stable relationship with proximal states (see also Dunlap and Stephens 2016). The capacity to engage in epistemic action enhances the potential utility of cognition precisely insofar as it helps the agent to reduce uncertainty over this mapping, thus affording more precise knowledge (or novel insight; Friston et al. 2017b) about the state of the world and its possible alternatives.

  48. Godfrey-Smith thus rejects strong continuity, the view that “[l]ife and mind have a common abstract pattern or set of basic organizational properties. […] Mind is literally life-like” (1995, p. 320, emphasis in original). Evan Thompson (2007) has defended a position similar to this (‘deep continuity’), albeit with the addition of an existential-phenomenological supplement (for discussion, see Wheeler 2011). This view inherits from Maturana’s canonical account of autopoiesis, where one finds the strongest expression of life–mind continuity: “Living systems are cognitive systems, and living as a process is a process of cognition” (Maturana and Varela 1980, p. 13, emphasis added; see also Heschl 1990).

  49. It is perhaps worth noting that other scholars have used the criterion of “detachment” (or “decouplability”) to distinguish representational versus non-representational agents, rather than cognitive versus non-cognitive agents (cf. Clark and Grush 1999; Grush 2004). Without digressing into a discussion of the relationship between representational and cognitive systems, we remark that our view conceives of cognition as a computational architecture that engages in a particular subset of representational operations—i.e. the generation, manipulation, and evaluation of counterfactual model predictions. These operations are situated within a broader class of uncertainty-resolving processes, including the homeostatic and allostatic representational schemes outlined in “Biological regulation in an uncertain world”.

  50. ‘Minimal cognition’ is perhaps more closely associated with a rather different set of philosophical views than those espoused by Godfrey-Smith (e.g., anti-representationalism, situated and embodied cognition; Barandiaran and Moreno 2006; Beer 2003; van Duijn et al. 2006). We take the main thrust of our argument to be equally applicable to these positions.

  51. When pressed, Godfrey-Smith seems to hold this view: “I do not claim that bacteria exhibit cognition; this is at most a case of proto-cognition” (2002, p. 223, emphasis added).

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Acknowledgements

AWC is supported by an Australian Government Research Training Program (RTP) scholarship. JH is supported by the Australian Research Council (DP160102770, DP190101805). This research has received funding from the European Union’s Horizon 2020 Framework Programme for Research and Innovation under the Specific Grant Agreement No. 785907 (Human Brain Project SGA2 to GP). We would like to thank participants at the Science of the Self research forum and the 22nd Annual Meeting of the Association for the Scientific Study of Consciousness for feedback on earlier presentations of this work. We also wish to thank Louise Kyriaki, Dan Williams, members of the Cognition & Philosophy Lab—especially Stephen Gadsby, Andy Mckilliam, Kelsey Perrykkad, and Iwan Williams—and two anonymous reviewers for insightful comments on earlier versions of this manuscript.

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Corcoran, A.W., Pezzulo, G. & Hohwy, J. From allostatic agents to counterfactual cognisers: active inference, biological regulation, and the origins of cognition. Biol Philos 35, 32 (2020). https://doi.org/10.1007/s10539-020-09746-2

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Keywords

  • Complexity
  • Uncertainty
  • Cognition
  • Allostasis
  • Homeostasis
  • Free energy principle
  • Active inference
  • Environmental complexity thesis
  • Adaptation
  • Representation
  • Interoception
  • Biorhythms
  • Life-mind continuity