Abstract
One big challenge faced by cognitive science is the development of a unified theory that integrates disparate scales of analysis of cognitive phenomena. In this paper, I offer a unified framework that provides a way to integrate neural and behavioral scales of analysis of cognitive phenomena—typically addressed by neuroscience and experimental psychology, respectively. The framework is based on the concept of resonance originated in ecological psychology and aims to be the foundation for a unified theory for radical embodiment; that is, a unified theory for that dissident part of cognitive science that shares a methodological commitment to dynamic systems theory and remains skeptical about the adequacy of mechanism and representationalism as the guiding ideas in the field. In the course of my presentation, I analyze different issues regarding the requirements and constraints unification poses to radical embodiment.
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(from Warren 2006, p. 374, figure 7)
(from Tognoli and Kelso 2014, p. 37; figure 1). (Color figure online)
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Notes
This fact is salient regarding some contemporary theories that aim to explain sets of very different phenomena. For example, self-organized criticality (Bak 1990) aims to account for the behavior of complex systems in terms of their self-organization around stable states (critical states) and the transitions between these states (catastrophes). The theory has been applied to disparate phenomena such as earthquakes or the intentionality of cognitive systems (Juarrero 1999). However, there is no commitment to the idea that the phenomena under the scope of the theory are essentially the same or of the same kind beyond the fact that there are some common theories and methods to capture them. Earthquakes and intentions, for example, are characterized in terms of self-organized criticality, but they still are qualitatively different phenomena in many regards.
For example, the activity within the nervous system, measured in milliseconds or fractions of milliseconds, belongs to the neural scale and is studied by neuroscience while the displacement of the hips in a walking task, measured in seconds or fractions of seconds (the usual cut-off for that measurement is 5 Hz), belongs to the behavioral scales and is studied by experimental psychology. Of course, the distinction between these two spatiotemporal scales may be blurry in some specific situations—as it is sometimes between biology, physiology, or psychology, for example—but they usually are easily distinguished.
Allen Newell’s (1990) own proposal is a clear example of it, but it is not the only one. Other examples are the ACT-R cognitive architecture (Anderson 1983, 2007), the Semantic Pointer Architecture developed by Chris Eliasmith (2013), or the Adaptive Resonance Theory (Grossberg 2013); as well as more recent proposals based on Bayesian models of cognitive systems (Clark 2015; Friston 2010).
Reasons for such rejection are diverse and there is no space in this paper to go through all of them. Some paradigmatic examples have to do with intrinsic problems of computation, like the frame problem (McCarthy and Hayes 1968); others have to do with the intrinsic knowledge a system based on computation needs for perceiving and acting, like the problem of the loans of intelligence (Dennett 1978; Kugler and Turvey 1987); and others with the coordination of all the effectors of a system to generate the desired behavior, as the issue labeled as “the Charles V problem” in the literature on motor control (Meijer 2001).
All the aspects of this description must be met. Notice, for example, that assuming a strategy based on dynamical explanations or using the tools of dynamical systems theory as a methodology do not per se make an approach an instance of radical embodiment. It is possible to use dynamical systems theory to describe a cognitive system while holding a computational or representational understanding of the system—e.g., some cognitive architectures that offer dynamical descriptions of neural systems and still hold computational assumptions, as in the case of Izhikevich (2007), or representational assumptions, an in the case of dynamic field theory (Schöner et al. 2016). In both cases, we would not be talking about radical embodiment.
The concept of resonance has been used in different contexts in cognitive science, but always in relation to coupling—e.g., single-neuron activity (Kasevich and LaBerge 2011), motor resonance and mirror neurons (Leonetti et al. 2015), adaptive resonance theory (Grossberg 2013), resonant processes for sequential effects in psychology (Gökaydin et al. 2016).
Notice that Gibson explicitly rejects the idea that perceptual information can be characterized as a kind of Shannon information (Gibson 1979, pp. 62–63, 242–244) and the idea that perceptual systems are channels of information (Gibson 1966, pp. 1–6, 47–58). In this sense, even the most basic notions of information-processing (e.g., the transformation of some signal in a channel) might not apply to the ecological notion of resonance. For a different example of a system able to detect information without internal processing, see the pole planimeter (Runeson 1977).
See Raja (2018) for a schematic description of the framework and a justification of its plausibility.
For the rest of the paper, I will use the formula ‘relevant information’ to refer to this information. In general, I take ecological information (Gibson 1966, 1979) to be the best characterization of information for radical embodiment, but I do not want to preclude the use of other available notions of information (e.g., O’Regan and Noë 2001; Oyama 2000). Ecological information is revealed at the scale of behavior and is not described in terms of semantic content (Gibson 1979, p. 55 and ff.; Turvey et al. 1981; Turvey and Kugler 1984; Segundo-Ortín et al. 2019) as it is not related to the state of affairs of the environment in terms of truth–values. For example, we generate optic flow by moving through the environment and that optic flow is informative of our movement (e.g., centrifugal flow specifies forward locomotion). But crucially, specific patterns of optic flow are neither true nor false of our movements, but lawfully related to them—in the ecological jargon, it is said that patterns of the optic flow are specific of movements. Due to the lack of a truth–value relationship with the environment, ecological information is said to be non-semantic, but specificational. These are the features to expect from any form of ‘relevant information’ used in the proposed model, in particular, and in radical embodiment, in general.
This feature is common to the explanation of all complex systems. For example, if one wants to explain the performance of a soccer team in a given game, she will minimally need to account for the technical skills of the individual players and for the tactical scheme of the whole team. These are two different scales of analysis (individual and collective), but they are strongly interrelated: the success of the tactical scheme depends on the interpretation of it by the individual players that concurrently depends on their technical skills; and the success of technical skills depends on the position of the player within the tactical scheme. Both scales are relevant to the explanation and irreducible to each other.
Gibson entertained an idea akin to informational coupling: “If the invariants of this [environmental] structure [i.e., ecological information] can be registered by a perceptual system, the constants of neural input will correspond to the constants of stimulus energy, although the one will not copy the other. But then meaningful information can be said to exist inside the nervous system as well as outside” (1966, p. 267).
For other examples of the plausibility of the proposed model of ecological resonance and the notion of informational coupling, see Raja (2018).
I remain agnostic regarding whether a cognitivist/representationalist account of the phenomenon of informational coupling and, more generally, of the phenomenon of ecological resonance is possible. Some scholars in the ecological tradition have argued that ecological information plays the functional role of representations in the theory and that the wording used might not be crucial in this case (Golonka and Wilson 2019). Otherwise, maybe the contemporary notion of structural representation (Rescorla 2009; Shea 2014; Ramsey 2016; Gładziejewski and Miłkowski 2017) could accommodate the notion of resonance as informational coupling. However, I think it is fair to claim that a representational account of resonance is not necessary and further argumentation should be given in order to justify such understanding.
The same applies βo.
For example, in the case of Fajen and Warren’s model (2003), although the organism’s integration of information occurs at the neural scale, the cognitive phenomenon itself (control of locomotion) remains distributed through the organism–environment system; that is, the cognitive phenomenon remains constituted (at least partially) by the behavioral scale: “[C]ontrol is distributed over the agent–environment system. I interpret this statement to imply that biology capitalizes on the regularities of the entire system as a means of ordering behavior” (Warren 2006, p. 358).
The HKB model was first proposed by Haken et al. (1985) as a model for phase transitions in human hand movements, but it was rapidly generalized to capture phase transitions in many other kinds of systems (e.g., Jirsa et al. 1998; Mechsner et al. 2001; Pellecchia et al. 2005; Temprado et al. 2002). In general, the HKB model is able to predict the change of the relative phase (ϕ) between two oscillators (e.g., fingers, legs, metronomes) over time; namely, how the behavior of the two oscillators is stable or not over time regarding different regimes (e.g., in-phase regime, anti-phase regime, and so on).
Remember that the tau-coupling function τND = KτO − ED by K to reflect the strength of the coupling itself. However, the informational variable, tau (τ), is a component of both dynamics (behavioral and neural).
The framework is probably not unified enough for Nagel’s standards as it entails no reduction between different explanatory scales, but it counts for sure as a unified framework in the sense promoted by Newell (1990).
Importantly, the phenomenon of resonance allows for a more complex scenario in which the driven systems also affects the dynamics of the driving system when they are resonating to each other. In this sense, the mechanism of resonance should also account for the other direction: the dynamics at the neural scale being the driving component and the dynamics at the behavioral scale being the driven one.
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Acknowledgements
I am grateful to Michael Anderson, Jonathan Bowen, Tony Chemero, Valerie Hardcastle, Tom Polger, and the audience of the meeting ‘Reconceiving Cognition’ organized by the Centre for Philosophical Psychology at the University of Antwerpen (Belgium) for their helpful comments and suggestions at different stages of the development of this work.
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Raja, V. Resonance and radical embodiment. Synthese 199 (Suppl 1), 113–141 (2021). https://doi.org/10.1007/s11229-020-02610-6
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DOI: https://doi.org/10.1007/s11229-020-02610-6