Is the Free Energy Principle for Real? The literalist fallacy and realism about the FEP
Forthcoming in The British Journal for the Philosophy of Science Short Reads .
Ian Robertson (Centre for Philosophy and Artificial Intelligence Research, university of ErlangenNuremberg)
Julian Kiverstein (Department of Philosophy, University of Amsterdam)
Michael D. Kirchhoff (School of Liberal Arts, University of Wollongong)
‘Intelligence without ambition’, said Salvador Dali, ‘is like a bird without wings’. Certainly, there
exists an ever-increasing number of researchers who take the free energy principle (FEP) and its
mathematical formalism to provide a vital new apparatus for fulfilling even their grandest intellectual
ambitions. And how grand these ambitions are! The FEP was initially invoked as a theoretical
framework in attempts to provide a unified theory of brain function (Friston [2010]). More recently,
its proponents have sought to use it to explain how all biological organisms self-organize so as to
resist the natural tendency towards disorder (for discussion, see Constant [2021]). Not only does the
FEP aim ‘to explain everything about the mind’ (Hohwy [2016], p. 729), but it also does so from the
vantage point of a framework that takes itself to have ‘identified nothing less than the organizing
principle of all life’ (Raviv [2018]).
It has been noticed in popular venues that the FEP, possessing as it does enough ambition to ma ke
Don Quixote blush, is ‘an idea every bit as expansive as Darwin’s theory of natural selection’ (Raviv
[2018]). It is of small wonder, then, that many researchers have interpreted its purportedly prodigious
explanatory scope as a clear theoretical red flag (Froese and Ikegami [2013]; Klein [2018]; for
discussion, see also Hohwy [2015]). After all, as its proponents readily admit, it will initially strike
many as ‘preposterous’ that all cognitive activity somehow adheres to one simple mathematical
principle (Hohwy [2015], p. 1).
To broach the question of whether the FEP can fulfil its dizzying explanatory ambitions, it is first
crucial to understand how exactly the FEP’s mathematical models are supposed to describe the kinds
of phenomena to which they are meant to apply. How, for example, do FEP models of neural activity
provide mathematical descriptions of said neural activity? How does such mathematical modelling
map onto the target phenomena? And here a philosophical controversy has recently reared its head, in
the form of the venerable and ongoing debate between scientific realists and instrumentalists.
FEP researchers are presently divided as to whether we should construe the mathematical models
yielded by the FEP framework as offering something like accurate or approximately accurate
descriptions of their target phenomena (for recent discussion, see Andrews [2020]). Some say yes,
some say no. The naysayers endorse an instrumentalist construal of the FEP, according to which its
models amount merely to heuristic tools for generating, with increasing reliability, o bservational
predictions. FEP models, on this analysis, are not in the business of making statements or providing
accurate descriptions of their target phenomena. FEP models are really just useful fictions (and,
indeed, controversy persists over whether FEP models offer all that much in terms of explanatory
traction as compared to those yielded by other frameworks; see, for example, Colombo and Wright
[2021]).
There has been a surge of papers defending precisely such an instrumentalist construal of the FEP. In
our recent article, we provide a response on behalf of the realist. We argue that realism about FEP
models remains a live and tenable option. Although we do not claim that all FEP models should be
construed as statements or representations of their target phenomena, we hold that the idea that many
of its models do exactly this should be taken seriously. In attempting to secure our realist
conclusions—to advance our case that some FEP models really are in the business of accurately
describing the phenomena they model—we identify what we take to be a pervasive mistake in the
contemporary FEP literature, which we refer to as the ‘literalist fallacy’. We take it that many
instrumentalists make this literalist fallacy in their dismissal of realist interpretations of FEP models.
Before explaining the literalist fallacy, a short description of the FEP is required (for a more technical
description, see our article). Simply put, the FEP offers an information -theoretic solution of an
inference problem (key assumption) that every living organism, from cells to humans, must
overcome: inferring the probability of the causes of one’s sensory observations conditioned on prior
beliefs about such causes. This much looks crucial for survival. The game of life (under the FEP) is
thus to reduce the inherent uncertainty about (unobservable) causes, given their (observable) effects
by continuously updating Bayesian ‘beliefs’ about the causes, given the effects; i.e., P(causes|effects).
The FEP offers the insight that what is called ‘variational free energy’ can be used as a mathematical
proxy that functions as a bound on the ‘surprise’ inherent in sensory observations — such that
minimizing free energy is equivalent to reducing uncertainty about causes, thereby precluding
surprising sensory effects that would be uncharacteristic of the kind of life in question (for a
simplified, philosophically friendly introduction to the FEP, see Mann et al. [2022]).
What, then, do we mean by the literalist fallacy? In the context of the FEP, we claim that the literalist
fallacy occurs when the truth of instrumentalism about FEP models is inferred on the basis of an
overly literal and demanding notion of what a realist must claim about how the mathematics of its
models map onto their target phenomena. We point to examples of this, namely, where realists about
the FEP are taken by their instrumentalist opponents as being committed to organisms having to
somehow literally embody or operate in direct accordance with the mathematical structure of the FEP
models invoked to model them. The literalist fallacy, then, stems from a mistaken and impoverished
idea of what scientific realism really amounts to. Indeed, although our article concerns only the FEP
framework, we take something akin to the literalist fallacy to pervade much of the debate between
scientific realists and instrumentalists.
Ultimately, we suspect that many instances of the literalist fallacy stem from a misunderstanding of
how scientific realists seek to account for abstraction and idealization in contemporary scientific
modelling enterprises. One such instance of this, we submit, is in (Ramstead et al. [2020]). While
investigating whether FEP models amount to more than predictive instruments, they claim that
‘instrumentalist accounts in the philosophy of science suggest that scientific models are useful
fictions: they are not literally true, but “true enough”, or good enough to make useful predictions
about, and act upon, the world’ ([2020], p. 4). But all scientific realists will here insist that models can
be literally partially true of their target phenomena. In fact, that a model contains sufficient truth to
have predictive prowess seems to be exactly in keeping with paradigm statements of scientific realism
(for philosophical discussion of how the distinction between scientific realism and instrumentalism
tends to be carved up by philosophers, see Haack [2004]; see also Williamson [2017]; Massimi
[2021]).
In order to expose the faulty assumptions leading to the literalist fallacy, and to demonstrate that
realism about the FEP remains a tenable option, our BJPS article considers a host of ways to interpret
FEP models under realist construals. We note that models are now being used productively to gain
insight into a plethora of different phenomena: decision-making under uncertainty (Friston et al.
[2012]), optimal control (Catal et al. [2019]), psychopathology (Schwartenbeck et al. [2015]), active
scene construction (Mirza et al. [2016]), electrophysiological responses (Friston et al. [2017]), and so
on (for a detailed overview of different applications of FEP models, see Da Costa et al. [2021]).
Adopting active inference models of eye-tracking as a concrete case study, we additionally claim that
the effectiveness of such models constitutes a body of prima facie evidence for realism.
We refrain here from discounting or downplaying the deeply vexed philosophical questions t hat
emerge when we pose Eugene Wigner’s ([1960]) famous questions as to why mathematics seems to
have an ‘unreasonable effectiveness’ in guiding scientific endeavour. That said, avoiding the literalist
fallacy is crucial when properly evaluating realist answers about the explanatory capacity of the FEP’s
mathematical framework. In doing so, we should be clear that scientific realists rarely strive to
provide ‘complete, non-distorted, perfectly accurate representations’, as Weisberg ([2007], p. 657)
correctly notes, and that, given this, short-term realist practice usually ‘involves the wilful
introduction of distortion’ in exchange for long-term gain and increasing representational accuracy. In
assessing the various ways that we might adopt a realist interpretation of FEP models, our article
considers at length how such models incorporate complex idealizations and abstractions. We also
consider whether FEP models are best construed as making Galilean abstractions.
As the FEP’s research potential continues to be realized (with the active inference scheme being
invoked as a process theory), questions about the applicability of its formalism become increasingly
pressing (Colombo and Wright [2021]; Van Es and Hipólito [2022]; Ramstead et al. [unpublished]).
Avoiding the literalist fallacy will help to ensure that we do not rule out one potentially promising set
of answers.
Acknowledgements We would like to thank Elena Walsh, Karl Friston, and Mads Dengsø for their
feedback on a previous version of this short piece.
“The Literalist Fallacy and the Free Energy Principle: Model-Building, Scientific Realism, and
Instrumentalism” is now out in The British Journal for the Philosophy of Science: The Literalist
Fallacy and the Free Energy Principle: Model-Building, Scientific Realism, and Instrumentalism | The
British Journal for the Philosophy of Science: Vol 0, No ja (uchicago.edu)
References
Andrews, M. [2021]: ‘The Math Is Not the Territory: Navigating the Free Energy Principle ’,
Biology and Philosophy, 36, pp. 1–19.
Colombo, M. and Wright, C. [2021]: ‘First Principles in the Life Sciences: The Free -Energy
Principle, Organicism, and Mechanism’, Synthese, 198, pp. 3463–88.
Constant, A. [2021]: ‘The Free Energy Principle: It’s Not about What It Takes, It’s about What
Took You There’, Biology and Philosophy, 36, pp. 1–17
Da Costa, L., Parr, T., Sajid, N., Veselic, S., Neacsu, V. and Friston, K. [2020]: ‘Active
Inference on Discrete State Spaces: A Synthesis’, Journal of Mathematical Psychology, 99,
available at <https://doi.org/10.1016/j.jmp.2020.102447>.
Di Paolo, E., Thompson, E. and Beer, R. [2022]: ‘Laying Down a Forking Path: Tensions
between Enaction and the Free Energy Principle’, Philosophy and the Mind Sciences, 3,
available at <https://doi.org/10.33735/phimisci.2022.9187>.
Friston, K. [2010]: ‘The Free-Energy Principle: A Unified Brain Theory?’, Nature Reviews
Neuroscience, 11, pp. 127–38.
Froese, T. and Ikegami, T. [2013]: ‘The Brain Is Not an Isolated “Black Box”, Nor Is Its Goal
to Become One’, Behavioral and Brain Sciences, 36, pp. 213–14.
Friston, K. J., FitzGerald, T., Rigoli, F., Schwartenbeck, P. and Pezzulo, G. [2017]: ‘Active
Inference: A Process Theory’, Neural Computation, 29, pp. 1–49.
Haack, S. [2004]: ‘Realism’, in I. Niiniluoto, M. Sintonen and J. Woleński (eds), Handbook of
Epistemology, Dordrecht: Springer, pp. 415–36.
Hohwy, J. [2016]: ‘The Neural Organ Explains the Mind’, in T. Metzinger and J. M. Windt
(eds), Open Mind: Philosophy and the Mind Sciences in the 21st Century , Cambridge, MA:
MIT Press, pp. 729–50.
Klein, C. [2018]: ‘What Do Predictive Coders Want?’, Synthese, 195, pp. 2541–57.
Mann, S. F., Pain, R. and Kirchhoff, M. D. [2022]: ‘Free Energy: A User’s Guide’, Biology
and Philosophy, 37, pp. 1–35.
Massimi, M. [2021]: Perspectival Realism, Oxford: Oxford University Press.
Ramstead, M. J., Friston, K. J. and Hipólito, I. [2020]: ‘Is the Free-Energy Principle a Formal
Theory of Semantics? From Variational Density Dynamics to Neural and Phenotypic
Representations’, Entropy, 22, p. 889.
Ramstead, M. J. D., Sakthivadivel, D. A. R. and Friston, K. J. [unpublished]: ‘On the Map –
Territory Fallacy Fallacy’, available at
<https://doi.org/10.48550/arXiv.2208.06924>.
Raviv, S. [2018]: ‘The Genius Neuroscientist Who Might Hold the Key to True AI’, Wired
Magazine, available at <https://www.wired.com/story/karl-friston-free-energy-principleartificialintelligence/>.
Schwartenbeck, P., FitzGerald, T., Mathys, C., Dolan, R., Wurst, F., Kronbichler, M. and
Friston, K. [2015]: ‘Optimal Inference with Suboptimal Models: Addiction and Active
Bayesian Inference’, Medical Hypotheses, 84, pp. 109–17
Weisberg, M. [2007]: ‘Three Kinds of Idealization’, The Journal of Philosophy, 104, pp. 639–
59.
Williamson, T. [2017]: ‘Model-Building in Philosophy’, in R. Blackford and D. Broderick
(eds), Philosophy's Future: The Problem of Philosophical Progress, New York: John Wiley,
pp. 159–71.
Wigner, E. [1960]: ‘The Unreasonable Effectiveness of Mathematics in the Natural Sciences’,
Communications in Pure and Applied Mathematics, 13, pp. 1–14.