Abstract
This paper presents a blueprint for an ecological cognitive architecture. Ecological psychology, I contend, must be complemented with a story about the role of the CNS in perception, action, and cognition. To arrive at such a story while staying true to the tenets of ecological psychology, it will be necessary to flesh out the central metaphor according to which the animal perceives its environment by ‘resonating’ to information in energy patterns: what is needed is a theory of resonance. I offer here the two main elements of such a theory: a framework (Anderson’s neural reuse) and a methodology (multi-scale fractal DST).
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It is important to note that I use the wording “computational approach” or “computationalism” in the broadest possible sense. This means that by these terms I refer both to classic computational approaches (Fodor 1975) and to new computational-like ones (e.g., Clark 2015). If it is more comfortable for the reader, “computational approach” may be understood as “information-processing approach”.
See Chemero (2009) for a contemporary account of ecological psychology.
The explanation in ecological terms is, of course, more technical and complicated and involves the ecological variable tau, for example. I left these details out of the text to avoid technicalities that do not add too much to its purpose. See Todd (1981) for a deeper study of those technicalities.
Gibson (1966) famously introduced the concept of perceptual system to capture the role of many parts of organisms and their very action as constitutive elements of perceptual processes. The visual system, for example, is constituted by the CNS, but also by the peripheral nervous system, and the eyes placed in a movable head, which at the same time is placed in a movable body, etc. All these elements are relevant to explain visual perception, and a theory of resonance based just on the CNS-environment interactions will be inadequate for grasping its complexity.
Both Gibson’s main books (1966, 1979) and classic texts on ecological psychology (Turvey et al. 1981; Michaels and Carello 1981; Lombardo 1987; Reed 1997) offer arguments against computation. Such arguments may be also found in the contemporary ecological psychology literature (e.g., Richardson et al. 2008; Chemero 2009; Michaels and Palatinus 2014).
In his influential textbook Cognitive Psychology (1967), Neisser claims: “These patterns of light at the retina are… one-sided in their perspective, shifting radically several times each second, unique and novel every moment… bear little resemblance to either the real object that gave rise to them or to the object of experience that the perceiver will construct… Visual cognition, then, deals with the process by which a perceived, remembered, and thought-about world is brought into being from as unpromising a beginning as the retinal patterns.” (pp. 7–8).
Marr (1982), for instance, famously offered a mechanism for the construction of a 3D image from a 2D one, going through an intermediate step known as 2D1/2.
Chemero’s example can be further reinforced. In the classical interpretation of the visual event, perception happens at the end of the inner computational chain: we perceive the representation of the of outer object, and such a representation is at the last link of the chain. According to most ecological psychologists, perception is the whole chain. Perception is a state of the whole animal-environment system as a physical system. The inner end of the chain is not special in any regard. This is another way in which the idea of computation is incompatible with the main tenets of ecological psychology.
I will not deny the possibility of finding a way to interpret Anderson’s proposals in After Phrenology as computational. However, I think there are unambiguous claims explicitly pointing to anti-computationalism, such as: “[I]t is worth an initial if brief reflection on an important disanalogy between the brain and a computer: whereas a computer is typically understood as a device that carries out a specific instruction set on (and in response to) inputs, brain responses to stimuli are characterized instead by specific deviations from intrinsic dynamics.” (Anderson 2014, p. xx). Or: “My current approach to this problem… is to quantify the functional properties of neural assemblies in a multidimensional manner, in terms of their tendency to respond across a range of circumstances—that is, in terms of their dispositional tendencies—rather than trying to characterize their activities in terms of computational or information-processing operations.” (Anderson 2014, p. xxii–xxii).
Interestingly enough, it is possible that Gibson also had in mind an idea similar to neural reuse for the functional organization of the brain. In his Gibson biography, Reed (1988) refers and quotes some unpublished works in the Gibson archive at Cornell University. He writes: “Considering the capacity of the nervous system to adjust to stimulation in many different ways, Gibson hypothesized that ‘a given set of neurons is equipotential for various different functions in perception and behavior. The same neuron may be excited for different uses at different times. [Therefore] neurons, nerves, and parts of the brain have a vicarious function. A nerve cell is not the same unit in a different combination of nerve cells.’ (Ibid.)” (Reed 1988, p. 224; emphasis is mine). The similarity with neural reuse is, again, astonishing and reinforces the parallelism between both theories I am defending here.
This aspect is crucial regarding NSF’s grand challenges for the sciences of the mind. More on this in the last section.
This is the collective variable of the HKB model, one of the most famous instantiations of the explanation of a behavior by the appeal to an ecological variable (Haken et al. 1985).
Any kind of comprehensive account of such details is completely out of the scope of this work. However, the NSF’s Tutorial in Contemporary Non-Linear Methods for the Behavioral Sciences (edited by Guy van Orden and Michael Riley) is a good introduction to the field. See: https://www.nsf.gov/sbe/bcs/pac/nmbs/nmbs.jsp.
A full account of active search is out of the scope of this paper, but the idea is that once the CNS system is described in terms of metastability and self-organized criticality, active search becomes a phenomenon easy to explain.
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Raja, V. A Theory of Resonance: Towards an Ecological Cognitive Architecture. Minds & Machines 28, 29–51 (2018). https://doi.org/10.1007/s11023-017-9431-8
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DOI: https://doi.org/10.1007/s11023-017-9431-8