Abstract
According to active inference (which subsumes the framework of predictive processing), action is enabled by a top-down modulation of sensory signals. Computational models of this mechanism complement ideomotor theories of action representation. Such theories postulate common neural representations for action and perception, without specifying how action is enabled by such representations. In active inference, motor commands are replaced by proprioceptive predictions. In order to initiate action through such predictions, sensory prediction errors have to be attenuated. This paper argues that such top-down modulation involves systematic (but paradoxically beneficial) misrepresentations. More specifically, the paper first argues for the following conditional claim. If active inference provides an accurate computational description of how action is enabled in the brain, then action is enabled by systematic misrepresentations. Furthermore, it is argued that an inference to the best explanation provides reason for believing the antecedent is true: Firstly, active inference provides a crucial extension to ideomotor theories. Secondly, active inference explains otherwise puzzling phenomena related to sensory attenuation, e.g. in force-matching or self-tickling paradigms. Taken together, these reasons support the claim that action is indeed enabled by systematic misrepresentations. The claim casts doubt on the assumption that representations are systematically beneficial to the extent that they are true: if the argument in this paper is sound, systematically beneficial misrepresentations may lie at the heart of our neural architecture.
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Notes
Positive illusions are false beliefs that depict states of affairs as overly positive (in some cases, this may systematically promote well-being and mental health, cf. Taylor 1989). A particular example is the optimism bias (also called unrealistic optimism, cf. Weinstein 1980), which refers to overly positive expectations about future events. As such, an optimism bias need not be beneficial for the agent, but at least a moderate optimism bias seems to be (cf. Sharot 2011, p. R944).
The earliest pre-cursor of ideomotor theories seems to be the view put forward in Herbart (1825, pp. 464 f.).
Technically, both active inference and predictive processing are corollaries of Karl Friston’s free-energy principle (cf. Friston 2010). The free-energy principle provides a general formulation of self-organization where, in the context of the brain, prediction errors can be regarded as a proxy for free energy.
Perceptual and active inference are sometimes described as distinct, but complementary processes. However, “active inference” is also used as a generic term for the computational processes underpinning both action and perception (cf. Friston et al. 2012b, p. 539). This is the primary sense in which the term is used in this paper.
Note here a distinction between ascending and descending prediction errors. The ascending prediction errors from the spinal cord are those that revise estimates in the central nervous system, while descending prediction errors are sent to the periphery to activate classical motor reflexes. Attenuation of ascending prediction errors enables movement, while attenuation of both ascending and descending prediction errors is necessary to prohibit echopraxia, mimicry, or mirror movements during action observation. Thanks to an anonymous referee for pointing this out.
From the point of view of Karl Friston’s free-energy principle, the relevance of attenuating sensory precision is even more obvious. Active inference is here regarded as a means to minimize free energy by changing sensory samples. Under quite general assumptions, minimizing free energy entails minimizing prediction error. The reduction of prediction errors in active inference necessarily depends upon resampling the world and, by implication, the attenuation of sensory precision. This means, in the long-term, the misrepresentation (attenuation) of sensory precision reduces prediction errors and is quintessentially beneficial for any sentient agent, because it enables active inference and thereby enables the agent to stay within viable regions of its state space. This is the premise of the free-energy principle, in which prediction errors can be regarded as free energy—and both are proxies for negative (Bayesian) model evidence. I am grateful to an anonymous referee for emphasizing this point.
As an anonymous reviewer remarked, if one identifies attention with predictions of precisions (not with the process of optimizing these predictions), and if the precision predicted does not correspond to the actual signal-to-noise ratio of sensory inputs (as in sensory attenuation or spatial attention) then, by definition, attention can be called a beneficial misrepresentation.
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Acknowledgements
I am highly grateful to Jakob Hohwy for providing very detailed and helpful feedback on an earlier version of this paper. Some of the ideas present in this paper were presented in a talk I gave at a workshop in the context of the Carnap lectures in March 2014 in Bochum. I am grateful to the audience of that workshop, special thanks to the organizers Albert Newen and Tobias Schlicht, and to Peter Brössel and Daniel Dennett. A previous version of this paper was presented at the Journal Club of the theoretical philosophy group at Mainz University, organized by Thomas Metzinger; thanks to all participants. Another version was presented at Spindel 2014 in Memphis. Thanks to the organizer Shaun Gallagher, as well as to the audience of that conference. Thanks to two anonymous referees for providing a number of very helpful comments. Part of the work on this paper was supported by a scholarship of the Barbara Wengeler foundation.
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Wiese, W. Action Is Enabled by Systematic Misrepresentations. Erkenn 82, 1233–1252 (2017). https://doi.org/10.1007/s10670-016-9867-x
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DOI: https://doi.org/10.1007/s10670-016-9867-x