Phenomenology and mechanisms of consciousness: Considering the theoretical integration of phenomenology with a mechanistic framework

The axiomatic method of Integrated Information Theory

Integrated Information Theory was first presented by Tononi (2004) and further developed by Tononi and colleagues (Balduzzi & Tononi, 2009; Oizumi, Albantakis, & Tononi, 2014; Tononi, Boly, Massimini, & Koch, 2016). IIT is a novel approach to consciousness which has the ambition to be not only a theory of consciousness (understood as a subjective experience), but also a method of measuring consciousness and a research program for searching for the mechanisms responsible for the phenomenon of consciousness. The main claim of IIT is that “consciousness has to do with the capacity to integrate information” (Tononi, 2004, p. 2). The idea behind it is that consciousness is basically the ability to integrate and generate information, i.e., to differentiate various perceptual states and to integrate them in one coherent percept. According to Tononi, generating information is a common feature of many living and artificial systems. The integration of information is, however, not so common and is the key aspect of consciousness. The greater the amount of integrated information, the higher the degree of consciousness.

Proponents of IIT rightly claim that empirical research alone cannot answer the hard questions about consciousness and “should be complemented by a theoretical approach” (Tononi et al., 2016, p. 450). IIT is an example of a top-down approach to consciousness. In contrast to bottom-up approaches, which begin from an empirical study of actual neural mechanisms, it begins by formulating phenomenological axioms. The notion of axiom is related to mathematics, and it does not seem that the proponents of IIT use it differently or metaphorically. They claim to “follow the classical tradition according to which an ‘axiom’ is a self-evident truth, . . . truths about consciousness—the only truths that, with Descartes, cannot be doubted and do not need proof” (Oizumi et al., 2014, p. 2). Furthermore, IIT’s axioms of consciousness are thought to express “essential properties” (Oizumi et al., 2014, p. 15) of consciousness. Accordingly, there are five phenomenological axioms: (a) existence (consciousness exists); (b) composition (consciousness is structured, i.e., “it consists of multiple aspects”); (c) information (consciousness is informative, i.e., “each experience differs in its particular way from other possible experiences”); (d) integration (consciousness is integrated in a non-reducible way to its components); and (e) exclusion (“each experience excludes all others—at any given time there is only one experience having its full content”; Oizumi et al., 2014, pp. 2–3). Each of these axioms, depending on the version of IIT, is usually supplemented with a short phenomenological description which serves as an illustration.

The next methodological step is deriving postulates from these axioms. The postulates concern individual mechanisms: (i) “Mechanisms in a state exist. A system is a set of mechanisms”; ii) “Elementary mechanisms can be combined into higher order ones”; iii) “A mechanism can contribute to consciousness only if it specifies ‘differences that make a difference’ within a system”; iv) “A mechanism can contribute to consciousness only if it specifies a cause–effect repertoire (information) that is irreducible to independent components”; v) “A mechanism can contribute to consciousness at most one cause–effect repertoire, the one having the maximum value of integration/irreducibility”; vi) “A set of elements can be conscious only if its mechanisms specify a set of ‘differences that make a difference’ to the set – i.e. a conceptual structure”; vii) “A set of elements can be conscious only if its mechanisms specify a conceptual structure that is irreducible to non-interdependent components (strong integration)”; viii) “Of all overlapping sets of elements, only one set can be conscious – the one whose mechanisms specify a conceptual structure that is maximally irreducible (MICS) to independent components” (Oizumi et al., 2014, p. 3). These postulates concern the physical realization of consciousness, i.e., they must deliver constraints on the model of organization and properties of individual neural mechanisms and systems of mechanisms if they are to generate conscious experiences. Finally, IIT delivers a mathematical framework to describe the properties of the mechanisms of consciousness and to measure the degree of consciousness.

This raises the following question: what concept of mechanism is used in IIT? Are the mechanisms referred to in IIT mechanisms in the sense in which the term is used in philosophy of science? If so, then IIT can in fact be regarded as a mechanistic approach, and would therefore qualify as a candidate for an integrated approach, because it also purports to take the phenomenological level into account. In a mechanistic framework, a mechanism is understood as “a structure performing a function in virtue of its component parts, component operations and their organization” (Bechtel, 2008, p. 13). In IIT, the definition is as follows: “Mechanism: Any subsystem of a system, including the system itself, that has a causal role within the system” (Oizumi et al., 2014, p. 4). This characterization seems very broad since it includes nothing about parts, activities, and relations between them. More details about mechanisms are in the postulates of IIT. According to those postulates, mechanisms of consciousness have a hierarchical structure; many mechanisms create a system, they perform operations and functions, and the behavior (experience) of a system is irreducible to its component parts. Tononi’s mechanisms are, generally speaking, hierarchical informational mechanisms whose main function is to generate and integrate information, and in this way generate conscious experience.

I agree with the top-down approach and including the phenomenological level in creating a theoretical framework for mechanisms of consciousness, but the proposed axiomatic method seems problematic. Various critical arguments against IIT’s axiomatic method have already been presented in several papers (e.g., Bayne, 2018; Pokropski, 2018), and I am not going to repeat them. Instead, I would like to propose a new argument which does not concern any specific axiom proposed by IIT, but concerns the nature of axiomatic method in general. I believe that the axiomatic method IIT proposes shares a common myth about the role of axioms in science: it is often thought that mathematical axiomatic theories are derived from a set of self-evident axioms. However, the truth is that axioms are not self-evident and do not precede a theory: they are a method of systematizing scientific knowledge (Kitcher, 1984, pp. 218–221). Consider, for example, Euclidean geometry, whose main theorems were already known before it was formulated by Euclid of Alexandria around 300 BCE. Euclid formulated five axioms which, however, were not considered self-evident for centuries. The fifth axiom, the so-called parallel postulate, was considered especially problematic and far from obvious; the rejection of this axiom resulted in the discovery of non-Euclidean geometries in the 19th century. The contemporary view on Euclidean geometry consists of not 5 but 20 axioms, introduced by David Hilbert in 1899. As Kitcher (1984) argues, axioms should not be understood as self-evident basic principles of a theory, but as a method of systematization of a domain of an already existing theory. Axiomatization systematizes the domain and unifies the theory, showing that theorems are derivable from a certain group of basic principles. These axiomatic principles are not self-evident, but they justify themselves by doing the work of systematization.

IIT does not introduce a phenomenological theory that would be systematized by their proposed axioms. The lack of a phenomenological theory also raises questions concerning their choice of axioms. One could formulate another set of alleged phenomenological truths, and there are plausible candidates, including, for instance, subjectivity (every conscious experience is subjective, i.e., it is an experience of a subject), perspective (every experience is an experience from a certain perspective), unity (consciousness is primarily a unitary stream of experiences), intentionality (consciousness is about something), and temporality (consciousness is a temporally bound process). IIT does not give plausible arguments as to why their set of proposed axioms covers all of the essential properties of consciousness, because it does not propose a phenomenological theory in support of those axioms to begin with.

To conclude, the notion of phenomenology used in IIT is very broad, and it is used as synonymous with experience or consciousness. IIT’s phenomenological axioms are thought to express the “essential properties” of this dimension and to deliver a basis for mechanistic postulates. However, the axiomatic method proposed in IIT seems implausible. It sees the role of axioms in scientific theories incorrectly. Furthermore, the proposed set of phenomenological axioms seems arbitrary and is not self-evident. If one wants to search for the axioms of consciousness, one should first introduce a plausible phenomenological theory of consciousness with empirical consequences, and then, perhaps, introduce a set of axioms which will systematize and unify the theory. It is likely, however, that the axiomatic method is not the best approach to integrating the science of consciousness either way.

Phenomenologically informed research

If not phenomenological axioms, then what approach to phenomenology is a better candidate for integrating the level of first-person experience with searching for mechanisms of consciousness? If we want to avoid the mistake of IIT’s axiomatic method, i.e., introducing essential properties of consciousness without theoretical support, then we need to search for a plausible phenomenological theory suitable for integration with empirical findings.

Front-loaded phenomenology is one way of incorporating phenomenological theory into naturalistic cognitive sciences. It requires neither training subjects nor acknowledging a phenomenological theory; it simply proposes the use of phenomenological concepts and distinctions from phenomenological literature in the early stages of research, namely in experimental design. “Phenomenology comes into the picture by contributing to the experimental design, by providing clear phenomenological distinctions, which also inform part of the analytic framework for interpreting the results” (Gallagher & Brøsted Sørensen, 2006, p. 126). As an example of such an experiment, proponents of this approach use research on the sense of agency (Chaminade & Decety, 2002; Farrer & Frith, 2002) and the sense of ownership (the so-called alien hand experiment first done by Nielsen, 1963).

Front-loaded phenomenology favors experimental practice over methodological considerations. If phenomenological concepts and distinctions taken from the works of Edmund Husserl, Maurice Merleau-Ponty, and other phenomenologists seem to contribute to our understanding of experience, then let us use them in an experimental design and see what the results are. However, it is not clear what role phenomenological insights play in the whole scientific process. One possibility is that phenomenological terminology, next to scientific or folk psychological concepts, contributes to the body of knowledge which underlies an experimental design. This is certainly plausible, but it does not give phenomenology a strong position. First, this possibility does not move us any further forward in the project of naturalizing phenomenology because background knowledge is something to control rather than to naturalize. Also, it seems unclear why we should choose the phenomenological tradition represented by Gallagher instead of any other approach to first-person phenomena or any other method of description and analysis of subjective experience, including folk psychology.

Another possibility is that phenomenological insights could play the role of heuristics or testable hypotheses. The former, again, does not give priority to phenomenology because formulating heuristics is a process that is more informal than strict and methodologically driven. Heuristics can be formulated on the grounds of any scientific theory, and it is not clear why the phenomenological approach should be preferable. Testable hypotheses are more promising, especially because Gallagher claims that experiments can “test and verify the phenomenological description” (Gallagher & Brøsted Sørensen, 2006, p. 131) and that

it is quite possible that experimenting with phenomenology will lead to a productive mutual enlightenment, where progress in the cognitive sciences will motivate a more finely detailed phenomenological description developed under the regime of phenomenological reduction, and a more detailed phenomenology will contribute to defining an empirical research program. (pp. 131–132)

This sounds promising, but the empirical testability of phenomenological hypotheses is controversial as Husserlian phenomenology was thought to be an a priori transcendental science. Some contemporary Husserlian scholars argue that phenomenological transcendentalism is a sufficient argument to reject the possibility of the naturalization of phenomenology (e.g., Moran, 2013). In response, one can argue for a softer version of phenomenology which would distance itself from Husserlian transcendentalism. However, even if such a “weak” version of phenomenology is possible, arguing for the testability of phenomenological insights through experiments requires recognition of the relation between phenomenological claims and experimental results.

There is, however, yet another problem with Gallagher’s proposal of front-loaded phenomenology. In the examples of the phenomenologically informed experimental designs which he considers, phenomenology seems to provide a description of the explanandum phenomenon rather than testable hypotheses. In both the sense of agency experiment and the alien hand experiment, phenomenological concepts serve to identify and describe the target phenomena, whose explanations are searched for on the neurobiological level. If that is the case, then phenomenological insights are not testable hypotheses and play a different role in the research process, i.e., they contribute to the description of the target phenomenon.

In the context of integration with a mechanistic framework, front-loaded phenomenology seems too simple and methodologically undeveloped to provide constraints on the space of possible mechanisms. To change this, phenomenological insights would have to be supplemented by thinking in terms of cognitive systems, and ultimately translated into a framework that is more mechanistic. This can be achieved by reading phenomenological analyses into functional analyses. The very idea is not so alien to phenomenology. According to McIntyre (1986), Husserlian phenomenology shares similarities with computational functionalism such as mental representations (noemata in Husserlian terminology) and ontological neutrality: “like the functionalists and the computationalists, then, Husserl seeks abstract accounts that would capture what is common to various mental capacities, no matter how different in their natural make-up the entities having these capacities may be” (p. 104). Furthermore, as I mentioned above, in the first book of Ideas, Husserl considered the functions of consciousness, as the main theme of phenomenological research, to be much more important than the qualitative content of experiences. As he writes:

the greatest problems of all are functional problems, or those of the “constitution of consciousness-objectivities.” . . . The point of view of function is the central one for phenomenology. . . . In place of analysis of comparison description and classification restricted to a single particular mental processes, consideration arises of single particularities from the “teleological” point of view of their function, of making possible a “synthetical unity.” (1982, pp. 207–208, para. 86)

The task of phenomenology, as Husserl sees it, is not the description and analysis of particular experiential phenomena but, on the contrary, a sort of functional analysis, which captures the general structure of experience including activities involved in the production of the object of experience.

A historical example of something I would call a phenomenological decomposition, analogous to functional decomposition, is the Husserlian analysis of the perception of temporal objects, for example the perception of a melody (Husserl, 1991). Briefly speaking, Husserl distinguishes several subfunctions which constitute the experience of a temporal object. These subfunctions are retention (retaining in consciousness the parts of the object which are no longer present), protention (anticipating the parts of the object which are not yet present), and primal-impression (the sensual core of the experience in the present moment). Furthermore, he proposed dividing the retentional function into two subfunctions: so-called longitudinal retention and transverse retention. The former is directed towards the “how” of the stream of consciousness, namely, into succeeding phases synthesized into a non-objectified continuity. The latter is directed towards what appears in the flow, i.e., a temporal object. This and other examples of phenomenological analyses seem to show that phenomenology delivers a decomposition of the cognitive phenomenon into subfunctions which constitute the experience.

Phenomenological functional analyses seem to share some similarity with the most known version of functional analysis, designed by Cummins (1975). The explanandum in both cases is a cognitive capacity (in the example above, the capacity is the perception of a temporal object, say, a melody). The target function or capacity is decomposed into a set of interrelated abstract subfunctions or sub-capacities. Then, the decomposition can be repeated for the subfunctions. As a result of this explanatory strategy, the hypothetical functional organization of a cognitive system can be represented in both approaches. Also, in both cases, the functional description abstracts from implementation and physical details. Finally, both approaches stress the explanatory autonomy of such analyses and the irreducibility of psychological states to biology.

That said, I agree that there are also differences. Husserlian considerations are thought to be transcendental ones (concerning the conditions of experience of every subject), whereas Cummins’ analyses are related to the psychological level. Cummins uses dispositional and causal language to define target capacities and sub-capacities, which, furthermore, requires characterizing normal conditions for a specific disposition to occur. Husserl, on the contrary, describes relations between mental phenomena in weaker terms of motivation instead of causation. For example, some perceptual experiences motivate some beliefs. Despite these differences and looking at these approaches as explanatory strategies (in both cases a method of decomposing mental capacities) it seems that both phenomenological and functional decomposition can deliver at least heuristics if not stronger constraints on possible mechanisms.

In fact, Piccinini and Craver (2011) have argued that functional analyses in general, e.g., Cummins style, which deliver decomposition of cognitive tasks into subtasks, can be read as incomplete elliptical mechanistic explanations. Accordingly, functional analyses can deliver a sketch of the possible mechanisms that are responsible for the target function. This sketch, for example, would consist of a system’s functional modules that realize subtasks and are related in such a way that a realization of subtasks amounts to a realization of the target function. At the same time, the sketch would ignore the structural details of the physical mechanism and its parts, but the expectation is that there is a physical structure that is responsible for each. Once the omitted details were added, the explanation and the scheme of the mechanism would be complete. Piccinini and Craver go even further and claim that because of the lack of physical realization details, the psychological/functional explanations are not complete, therefore, they are not a distinct and autonomous kind of explanation and need to be supplemented by a mechanistic explanation. However, according to Roth and Cummins (2017, p. 40), one does not have to accept the strong claim about the nature of the psychological/functional explanation and can accept its autonomy (functional explanations are autonomous because they do not have to refer to implementation details in order to describe the target system’s design and predict its behavior) while still arguing that functional analyses can deliver constraints, e.g., constraints on hypotheses about mechanisms.

Setting aside the question of autonomy, my claim is that phenomenological analyses can be considered analogous to functional analyses and can similarly contribute to the model of the hypothetical mechanism responsible for the target phenomenon. Both the phenomenological and the functional approach share an interest in identifying the cognitive functions involved in the production of mental phenomena, e.g., the experience of an object of perception. The outcome of such analysis, namely the hypothetical functional design, is just a “how-possibly” model and should be supplemented by implementation details. However, it is likely that different implementations of the same functional design are possible.

To sum up, although front-loaded phenomenology is an interesting idea for applying phenomenology in experimental design, it does not explain what role phenomenological insights play in research and the explanatory process and how they are related to experimental results. Furthermore, front-loaded phenomenology seems methodologically too weak to constrain the space of possible mechanisms unless it delivers analyses analogous to functional analyses. My proposal is thus to reconsider phenomenological analyses as a strategy of decomposition of cognitive phenomena into a set of interrelated cognitive functions in an analogous manner to functional decomposition. Such a modification makes clear how phenomenology can contribute to both the sketch of a mechanism and to experimental designs.

Phenomenologically trained subjects and dynamic models

Another proposal for the naturalization of phenomenology is neurophenomenology (Lutz & Thompson, 2003; Varela, 1996), which aims to correlate a disciplined first-person description of experience with neuroimaging data (EEG). In the paper “Neurophenomenology: A Methodological Remedy for the Hard Problem,” Varela (1996) introduces the working hypothesis of neurophenomenology, which states that “phenomenological accounts of the structure of experience and their counterparts in cognitive science relate to each other through reciprocal constraints” (p. 343). Furthermore, as he argues, “disciplined first-person accounts should be an integral element of the validation of a neurobiological proposal, and not merely coincidental or heuristic information” (p. 344). Neurophenomenology is, therefore, a non-reductionist approach to the naturalization of phenomenology that sees phenomenology as having an important role to play as part of empirical cognitive science—namely, delivering reliable first-person descriptions correlated with neural dynamics and validation of empirical theories of consciousness.

Neurophenomenology recognizes the problem of introspective reports as an unreliable source of information about one’s inner life. Our beliefs about what we experience are often shaped by background knowledge and folk psychology. The reliability of introspective reports was also ruled out by cognitive research on phenomena using illusions, self-deceptions, confabulations, etc. (e.g., Dennett, 1991). It is true that first-person access to consciousness is different from third-person access, but that does not mean that it is infallible, thus introspective reports need to be elaborated and validated. One option is to elaborate more sophisticated methods of phenomenological interviews or to train subjects to become more aware of and cautious about their experiences and language of description (Froese, Gould, & Seth, 2011).

In neurophenomenology, descriptions are produced by experimental participants trained in phenomenological method, which is a simplified version of Husserlian phenomenological reduction. The vocabulary used to describe studied experiences is elaborated together with the experimenter, who guides the participant. In short, participants are trained (a) to suspend their commonsensical and theoretical beliefs about their experiences and mental states in general and (b) to imaginatively vary their own experience in order to apprehend and describe invariants, which are used to identify the structural properties of experiences. The objective of the phenomenological method used in neurophenomenology is precisely to describe this invariant structure of experiences. According to Varela, this kind of phenomenological structural description can provide constraints on empirical observations (1996, p. 343). It is not entirely clear what kind of constraints Varela has in mind. It seems clear that phenomenological description can inform empirical research e.g., searching for neural correlates of experience often requires a first-person report. As I will argue below, neurophenomenology can deliver a more informative input, which is a dynamical model of studied experience.

It is not easy to see how neurophenomenology works in practice since only a few experiments have been done in accordance with this paradigm. An example is the study by Lutz on the degrees of perceptual readiness, i.e., a subjective feeling of preparation to perceive the emerging stimulus. In this study, participants used categories such as steady readiness, fragmented readiness, and unreadiness (Lutz, 2002). Descriptions of the participant’s lived experience are then correlated with their EEG recordings of neural activity. Proponents of neurophenomenology argue that their approach differs from the study of neural correlates of consciousness (NCC). What they are interested in is not activations of the specific brain regions that are allegedly responsible for a specific conscious experience, but a synchronization of the distributed neuronal assemblies hypothetically responsible for the whole studied experiential process. What is correlated, then, is first-person descriptions of experience with dynamics of distributed brain processes. The correlation task in neurophenomenology is, however, not an easy one. Not only are correlations of experiential categories with EEG recordings ex-post problematic, but the too-low spatial resolution of EEG makes it impossible to correlate experience with functionally different neural processes.

An improved version of neurophenomenology was recently proposed by Petitmengin (Petitmengin & Lachaux, 2013). What she proposes is to study the micro-dynamics of experience both at the first-personal and neural level. According to Petitmengin, we can learn to investigate our cognitive micro-processes involved in experience, which are otherwise usually not experienced in everyday activities. To do so one has to focus on particular occurrences of a cognitive process and shift attention from “what” is experienced to “how” it is experienced. In this way, the very process of the emergence of experience can be grasped and analyzed. This process can also be supported by the interviewer in the so-called “elicitation interview,” which was elaborated by Petitmengin (2006) and used with success in treatments of epileptic patients. Then, the first-person descriptions are correlated with recordings from intracranial EEG (iEEG), the spatio-temporal resolution of which is very high and sufficient to picture the neural dynamics of singular experiences.

It seems that the best input that neurophenomenology can give to a mechanistic framework is a description of the dynamics of neural activity correlated with dynamics of experience. According to Lutz, the best way to describe these dynamics is the dynamical systems theory (DST), whose “central idea is that the dynamical trajectories of global moments are shaping the ‘dynamical landscape’ of the system into a specific geometry (named phase space)” (2002, p. 155). The result of neurophenomenological study can be a formal dynamic model of a studied phenomenon capturing its stability phases as well as trajectory of change. In this sense, neurophenomenology can be understood as a type of dynamic explanation, which sees explanation as a formal (mathematical) dynamic model of a target phenomenon (for a general discussion of dynamic explanations see, e.g., Chemero, 2000; van Gelder, 1995; for a discussion of DST and phenomenology see, e.g., Yoshimi, 2017).

However, at first glance the neurophenomenological approach, understood as a type of dynamic explanation, seems contrary to the mechanistic approach (e.g., Thompson, 2007). Such opinions are based on a misunderstanding of the mechanistic model of explanation. As I mentioned above, the main explanatory strategies of the mechanistic approach are a decomposition of a system’s behavior into subfunctions and a localization of these subfunctions in the parts of the system (Bechtel & Richardson, 2010, pp. 23–24). The dynamic approach, on the contrary, abstracts from the composition of systems and focuses on their evolution over time. Proponents of the dynamic approach criticize the strategy of localization as inadequate in the case of human cognitive systems, which, as they argue, are not decomposable, and therefore we cannot localize functions in specific regions of the brain (e.g., Lamb & Chemero, 2014). The notion of localization they use is a simple or direct localization. However, as Bechtel and Richardson (2010) argue, direct localization is not an aim of explanation but a simple heuristic in the preliminary stage of research. In the course of research, direct localization may have to be abandoned and replaced by the notion of distributed or complex localization. Distributed localization is, however, still localization.

Decomposition is more problematic. Introducing decomposition as an explanatory strategy, mechanists (e.g., Bechtel & Richardson, 2010) usually follow Simon’s (1962) and Wimsatt’s (1986) division into several levels of decomposability: a system can be decomposable, nearly decomposable, minimally decomposable, and non-decomposable. Decomposable systems are simple aggregative systems in which the sum of a part’s activities equals the activity of the whole. Decomposable systems have a clear hierarchical organization, and the activity of the parts can be explained independently of the whole. Also, the organization of the parts is irrelevant in principle. Nearly decomposable systems are more complex and difficult to decompose since the components of such systems interact with each other, and the higher level of the organization determines lower levels to some degree. Thus, in nearly decomposable systems we have not only bottom-up but also top-down relations. Minimally decomposable systems are systems in which components are not only organized and interact with each other, but in which top-down relations are more relevant for the whole than bottom-up relations. Finally, there are non-decomposable systems in which top-down determinations make decomposition into parts impossible. Such systems have no hierarchy whatsoever.

Now the question is whether or not cognitive systems can be studied as decomposable systems. While mechanists hypothesize that cognitive systems are nearly decomposable systems, dynamists argue that living cognitive systems are either minimally decomposable or non-decomposable. For example, Thompson argues that the brain is a dynamic network of processes with strong top-down determinations and is thus minimally decomposable if not non-decomposable (2007, pp. 417–447). However, he also admits that sometimes it is useful for explanatory purposes to characterize the brain as nearly decomposable (pp. 422–423). Interestingly, Bechtel and Richardson (2010) also argue that decomposability is a heuristic strategy which may fail. It is clear, therefore, that decomposability should be understood as an epistemic category, not an ontological one. Both decomposition and localization are fallible heuristic strategies, but it does not follow that the mechanistic model of explanation is incorrect; instead, it needs to be further elaborated and integrated with other models of explanation.

Despite these differences, some of which seem to be based on misunderstandings, it was argued recently that dynamic and mechanistic explanations are not exclusive but complementary (Kaplan & Bechtel, 2011; Zednik, 2011). An example of a successful explanatory process that began from dynamic modeling and was then successively supplemented with mechanistic details is the Hodgkin–Huxley model of action potential. The original dynamic model consists of a set of equations describing the dynamics of the behavior of neurons, which informed further discovery of mechanistic components such as ion channels (Kaplan & Bechtel, 2011). Furthermore, Bechtel and Richardson (2010) argue that the mechanistic model of explanation is not limited to phenomena produced by decomposable or nearly decomposable systems, but that it can also give an account of non-linear dynamics in minimally decomposable systems. This makes it possible to explain mechanistically emergent phenomena (pp. xliv–xlvii).

To sum up, neurophenomenology can be considered as a candidate for integration with a mechanistic framework if it is a type of dynamic explanation and delivers a dynamic model of the target phenomenon. A dynamic model can be informative for the mechanistic approach and deliver constraints on the dynamic characteristics of possible mechanisms. Furthermore, dynamic modeling is an efficient way to describe not only complex systems which are distributed across the brain–body environment, but also to describe evidence for continuous inter-level relations in minimally decomposable systems. If neurophenomenology develops in this direction and delivers such dynamic models, then it is plausible it will provide constraints on the space of possible mechanisms and guide the search for the mechanisms responsible for conscious experience.