The Kantian Brain: Brain Dynamics from a Neurophenomenological Perspective
Fazelpour S. & Thompson E. (2015) The Kantian Brain: Brain Dynamics from a Neurophenomenological Perspective. Current Opinion in Neurobiology 31: 223–229. Available at http://cepa.info/2333
Table of Contents
The Kantian brain
Bridging brain and mind
Current research on spontaneous, self-generated brain rhythms and dynamic neural network coordination cast new light on Immanuel Kant’s idea of the ‘spontaneity’ of cognition, that is, the mind’s capacity to organize and synthesize sensory stimuli in novel, unprecedented ways. Nevertheless, determining the precise nature of the brain-cognition mapping remains an outstanding challenge. Neurophenomenology, which uses phenomenological information about the variability of subjective experience in order to illuminate the variability of brain dynamics, offers a promising method for addressing this challenge.
What implications do contemporary views of the brain as a complex, self-organizing system with nonlinear dynamics (Buszáki 2006) have for longstanding philosophical considerations about the relationship between the mind and the world? Here we highlight basic theoretical ideas and relevant empirical data regarding brain rhythms and dynamic coordination that indicate that mental phenomena arise from the brain’s self-organized and spontaneous, pattern-generating activity, and not simply from stimulus-driven processing (Singer 2013; Tognoli & Kelso 2014). We call this view ‘the Kantian brain’ (Northoff 2012) because it confirms Immanuel Kant’s (1724–1804) idea of the ‘spontaneity’ of cognition, by which he meant the capacity to organize and automatically synthesize sensory stimuli in unprecedented ways relative to those stimuli, thereby yielding novel, structured cognitions (Kant 1999; Hanna 2014). As a number of theorists have emphasized, spontaneous and self-organized brain activity is a complex and large-scale expression of basic phenomena of biological self-organization (Maturana & Varela 1980; Kelso 1995; Friston 2013). Here, too, we find support for Kantian ideas, for Kant also argued that being self-producing – ‘autopoietic,’ in contemporary terms (Maturana & Varela 1980) – was the distinguishing property of living beings (Kant 2001; Juarrero Roqué 1985; Thompson 2007). Nevertheless, despite these insights from philosophy and modern science, determining the precise nature of the mapping between the neural level of network activity and the psychological level of mental function – the brain and the mind – remains an outstanding challenge, especially given the increasing evidence that this mapping is likely to be many–many rather than one–one (Park & Friston 2013; Pessoa 2014). Furthermore, the challenges multiply if we aim to include consciousness – the subjective and experiential aspects of the mind – as part of the mapping (Cosmelli, Lachaux & Thompson 2007). We propose that, to make headway on these challenges, we need to add the theoretical perspective and methodological approach of ‘neurophenomenology’ (Lutz & Thompson 2003) to that of the Kantian brain, thereby paralleling the philosophical evolution from Kant to phenomenological philosophy of mind (Gallagher & Zahavi 2012), while enriching phenomenology with the tools of neuroscience and experimental psychology. Neurophenomenology stresses the usefulness of obtaining detailed, first-person reports of moment-to-moment subjective experience in order to uncover information about brain rhythms and dynamic coordination relevant to mental functions and consciousness (Lutz, Lachaux, Martinerie & Varela 2002; Cosmelli & Thompson 2007; Petitmengin & Lachaux 2013). After briefly reviewing neurophenomenology in the second part of this paper, we conclude by highlighting its usefulness for relating spontaneous, self-organized brain activity to self-generated, spontaneous cognition (Christoff 2012).
The Kantian brain
Consider that, at each moment, our perceptual engagement with the environment is rich and complex: we are faced with a world filled with sights, sounds, and smells, among many other things, which we experience as objects and events located in space and time. Moreover, what we perceive shows up with varying degrees of significance and relevance, depending on our current states of expectation, attention, motivation, and emotion (Pessoa 2013). Despite this diversity, however, each perceptual moment is experienced as a unified whole (Bayne 2010). Furthermore, as perception unfolds, the successive moments seem to constitute a coherent, integrated stream (Dainton 2006). As Kant famously argued (Kant 1999), such synchronic and diachronic unities characteristic of perception occur as a result of an organizing and synthesizing function belonging to the mind’s self-activity. Kant called this function ‘spontaneity’ and distinguished it from ‘receptivity’, which is the capacity to receive sense impressions. Thus, according to Kant, to explain perception we cannot proceed just by considering stimulus-induced responses to sensory input; rather, we must take account of the ongoing, intrinsic, and spontaneous self-organizing activity of the mind, and the rules or laws governing it.
We can contrast this idea with the view that the overall function of the mind in perception is to represent the external world by discriminating various features of the sensory input, a function that can be decomposed into a set of more specific subfunctions, each devoted to detecting distinct features with differing degrees of complexity. At the level of neuronal implementation, this view takes each subfunction to map uniquely onto a distinct population of neurons (Barlow 1972) or anatomical region (Kanwisher 2010), with more complex features being processed further away from the sensory detectors. The functional organization of the brain, therefore, is seen as consisting in the serial, bottom-up processing of the sensory input along hierarchically organized pathways or cortical areas (Felleman & Essen 1991).
Neuroscientific studies of structural and functional brain connectivity in the past two decades, however, provide strong support for a view of the mind much closer to that which Kant envisioned. Neural structures sustaining perceptual functions are seen as involving parallel processing within networks of widely distributed regions whose functional connectivity is mediated by the coherent oscillatory activity across the affiliated regions of the networks themselves (Buszáki 2006; Singer 2013; Tognoli & Kelso 2014; Varela, Lachaux, Rodriguez & Martinerie 2001). The high degree of recurrent connections within these networks further indicates the constitutive role of the brain in dictating the significance of the incoming sensory input in light of the organism’s current states of expectation, motivation, and attention (Engel, Fries & Singer 2001; Gilbert & Sigman 2007). According to longstanding ideas from neural network research (Grossberg 1980; Grossberg 2013) and predictive coding theories (Rao & Ballard 1999; Clark 2013; Hohwy 2013), perception comprises competitive interactions between feedback and feedforward pathways at multiple hierarchical levels, with feedback pathways carrying generated expectations or predictions about incoming sensory signals, and feedforward pathways conveying the mismatch between the expectations or predictions and the sensory input. Furthermore, such feedforward and feedback interactions occur within the larger context of the ongoing, metastable activity of the entire brain network (Tognoli & Kelso 2014; Kelso 1995; Friston 1997; Freeman & Holmes 2005; Kelso 2012), which also involves the homeostatic and affective states of the body mediated through the various structural connections between the cortex and subcortical structures, including the hypothalamus and amygdala (Pessoa 2013; Carvalho & Damasio 2013). Thus, far from viewing the brain as a stimulus-driven device, these theories and findings indicate that the brain is an active system engaged in modeling the world in and through its self-generated activity in close coupling with the rest of the body.
Bridging brain and mind
From the foregoing perspective on the Kantian brain, we should not expect there to be the kind of one–one mapping between brain regions and mental functions predicted by the view of the brain as a stimulus-driven device. Instead, given that brain dynamics are context-dependent and task-dependent, the same anatomical region may subserve different mental functions in different contexts, and the same mental function may be realized across a functionally connected network of distributed anatomical regions (Park & Friston 2013; Pessoa 2014). It follows that the explanatory heuristics of ‘decomposition’ and ‘localization of function’ (Bechtel & Richardson 1993) – differentiating a system into separable components and assigning specific functions to those components – are inadequate to characterize brain function when applied at the anatomical level. Nevertheless, with the increasing tendency to map cognitive functions onto functional connectivity networks (Park & Friston 2013), the question arises of whether we should expect a one–one mapping from connectivity networks to cognitive functions. The answer may well be negative. As Pessoa (Pessoa 2014) has recently argued, given the shifting dynamical and context-dependent properties of networks, we should not anticipate a one–one mapping even when the network approach is adopted, for whether a given region belongs to a given network can change according to context, and hence the individuation of networks will not provide us with a list of non-overlapping regions. Thus, the difficulties involved in the brain-cognition mapping are not likely to be alleviated when connectivity networks, as opposed to anatomically distinct regions, are taken as the appropriate structural unit.
How should the study of the brain’s functional organization proceed in the face of this complexity? One approach seeks to make more precise our understanding of the neural side of the brain-cognition mapping. For example, we need to go beyond the notion of ‘functional connectivity,’ taken as statistical correlation among the distributed regions affiliated with a given network, for such statistical dependencies, though revealing of co-activation patterns, do not provide information about the directions of influence between nodes of the network. Accordingly, researchers have proposed new methods for analyzing causal connectivity dynamics in order to elucidate further the functional organization of brain networks (Park & Friston 2013; Bressler & Seth 2011; Bedo, Ribary & Ward 2014).
A complementary approach is to refine our understanding of the cognitive function side of the mapping. Take, for example, attention. ‘Attention’ is standardly defined as referring to any process by which an organism selects some subset of the available information for enhanced processing (Ward 2008). Investigations of attention accordingly aim to identify the processes constitutive of such selection, such as orienting, filtering, searching, inhibition of distraction, and so forth, and to identify the neural systems that instantiate these processes (Ward 2008; Corbetta & Shulman 2002; Posner & Rothbart 2007). In this view, ‘attention’ refers to a heterogeneous collection of distinct psychological and neural processes. Recently, however, a novel, unifying framework for understanding attention has been presented by philosopher Christopher Mole (Mole 2011). He proposes that attention is not a distinct cognitive process or set of such processes, but rather a mode or manner in which multiple cognitive processes unfold dynamically in coordination with each other. His theory is stated at the level of the agent as a whole engaged in executing some task: a cognitive agent performs a task attentively if and only if that agent’s performance displays ‘cognitive unison.’ This means that none of the cognitive processes that can serve to execute the task is occupied with something that does not serve the task, and in this way the processes jointly operate in the service of the task. An instructive analogy is that of unison in an orchestra. Just as there is no place in the orchestra where unison resides, so there is no place in the brain where attention is located. Rather, attention consists in the way that neural processes are dynamically coordinated with each other so as to enable and sustain the unison of the agent’s cognitive processes in task performance. Although Mole does not relate his theory to neurodynamics, given that unison is an agent-level mode of processing rather than a particular neural process, it seems reasonable to suppose that cognitive unison is facilitated by the large-scale integration and dynamic coordination of oscillatory neural activity through mechanisms such as phase synchrony (Varela, Lachaux, Rodriguez & Martinerie 2001) and cross-frequency coupling (Canolty & Knight 2010; Szczepanski, Crone, Kuperman, August, Parvizi & Knight 2014), which themselves are modes of neural activity and cannot be identified simply with the firings of particular populations of neurons. Thus the model of attention as cognitive unison can serve to refine our understanding of the agent-level and cognitive side of the brain-cognition mapping, and thereby cast new light on the ongoing effort to understand the dynamics of large-scale brain networks (Bressler & Menon 2010).
Another complimentary approach, called ‘neurophenomenology’ (Lutz & Thompson 2003), is to obtain detailed, first-person reports about moment-to-moment subjective experience in order to uncover information about dynamic brain activities relevant to understanding conscious mental processes (Lutz, Lachaux, Martinerie & Varela 2002; Cosmelli & Thompson 2007; Petitmengin & Lachaux 2013). The working assumption of neurophenomenology is that first-person reports – especially from individuals either trained in the kind of metacognitive awareness cultivated in contemplative ‘mindfulness’ practices (Lutz, Slagter, Dunne & Davidson 2008) [Note 1] or probed with refined methods for eliciting tacit experience (Petitmengin & Lachaux 2013; Petitmengin, Remillieux, Cahour & Carter-Thomas 2013) (or both) – can stand in a mutually constraining and illuminating relationship to cognitive-neuroscience evidence about the physiological processes sustaining moment-to-moment experience (Lutz & Thompson 2003). As discussed above, one of the main difficulties facing the brain-cognition mapping is the context-sensitivity of the relation between the two domains. Accordingly, neurophenomenology can be employed to produce more fine-grained first-person reports of the variability of moment-to-moment experience (such as fluctuations of attention, metacognition, emotion, bodily sensation, and memory) in a given individual from trial to trial in an experiment, and such reports can be used to reduce the noise of the neural signals, thereby providing a tighter coupling between the variability of the network dynamics and that of experience. Note that this proposal does not entail that seemingly random fluctuations must be considered useless or detrimental to the cognitive process under investigation. On the contrary, not all apparent random variability can be attributed to noise, and not all noise is useless or detrimental to the brain’s performance, for noise may facilitate certain types of neuronal computations, depending on the context (McDonnell & Ward 2011). Rather, the neurophenomenological proposal is that collecting detailed phenomenological data about experiential variability can provide a crucial additional source of information and a methodological constraint for brain-cognition mappings. Note also that this proposal is compatible with a variety of neuroimaging modes in different experimental contexts. Thus, the neurophenomenological approach has been used, first, to differentiate novel EEG gamma-band phase synchrony patterns predictive of novel and distinct attentional states modulating visual perception (Lutz, Lachaux, Martinerie & Varela 2002); second, to uncover distinct preictal experiences and predictive neurodynamical patterns in epileptic patients with implanted electrodes (Petitmengin, Baulac & Navarro 2006; Petitmengin, Navarro & Le Quyen 2007; Amor, Baillet, Navarro, Adam, Martinerie & Le Quyen 2009); third, to characterize the default-mode network and executive system contributions to mind-wandering using fMRI (Christoff, Gordon, Smallwood, Smith & Schooler 2009; Hasenkamp, Wilson-Mendenhall, Duncan & Barsalou 2012); and fourth, to link objective measures of brain activity with distinct strategies of metacognitive awareness using real-time fMRI neurofeedback (McCaig, Dixon, Kermatian, Liu & Christoff 2012), including relating such measures to reports of ongoing experience (Garrison, Scheinost, Worhunsky, Elwafi, Thornhill TA, Thompson, Safon, Desbordes, Kober, Hampson, Gray, Constable, Papademetris & Brewer 2013; Garrison, Santoyo, Davis, Thornhill TA, Kerr & Brewer 2013). In addition, neurophenomenology is currently being developed (fifth) to relate fine-grained information about the microdynamics of experiential cognitive processes to the trial-by-trial variability in neural processing as measured with intracerebral EEG (Petitmengin & Lachaux 2013; Bagdasaryan & Le Quyen 2013), and sixth, to investigate the neural sources and temporal dynamics of spontaneous thought using fMRI [Note 2] . Here we highlight the usefulness of neurophenomenology for investigating self-generated, spontaneous cognition (Christoff 2012).
Since 2006, there has been a dramatic increase in use of the term ‘mind wandering’ in psychology and cognitive neuroscience, suggesting to some researchers that we may have entered the ‘era of the wandering mind’ in the cognitive and brain sciences (Callard, Smallwood, Golchert & Margulies 2013). As Callard and colleagues (Callard, Smallwood & Margulies 2012) note, the initial stages of the empirical investigation of mind wandering, operationally defined as ‘task unrelated thought,’ took place in a research context still under the influence of the legacy of behaviorism; researchers mistrusted first-person reports and accordingly mainly focused on those cognitive functions that could be investigated using externally oriented tasks. It was within this context that neuroimaging researchers first noticed a set of distributed brain regions, now known as the ‘default mode network’ (DMN) (Buckner 2012), that showed a pattern of co-activation during the resting state and a pattern of deactivation during externally oriented tasks. The subsequent view, however, that the DMN is the primary neural system activated at rest and that its activation mainly subserves mind wandering, has now come to be recognized as inadequate (Christoff 2012; Callard, Smallwood, Golchert & Margulies 2013; Callard, Smallwood & Margulies 2012). First, the spontaneous activity of the brain at rest cannot be reduced to DMN activity, for the whole brain exhibits highly organized, spontaneous activity when not occupied with an externally directed task (Buszáki 2006; Leopold & Maier 2012). Second, mind wandering recruits brain regions beyond those belonging to the DMN, notably regions belonging to executive networks (Christoff 2012; Christoff, Gordon, Smallwood, Smith & Schooler 2009). Third, the purely negative, operational characterization of spontaneous, self-generated thought as ‘task-unrelated’ fails to distinguish among the variety of such forms of thought, many of which are goal-directed (such as autobiographical planning) (Christoff 2013). For these reasons, determining which elements of spontaneous neural activity relate to which elements of spontaneous, self-generated thought remains an outstanding challenge (Callard, Smallwood & Margulies 2012). Accordingly, recent work in this area has emphasized the need for both more phenomenologically nuanced conceptualizations of the various forms that spontaneous cognition can take, and for methods that can provide tighter couplings between the contents and temporal dynamics of spontaneous cognition and the activations of given brain regions and networks (Christoff 2012; Callard, Smallwood, Golchert & Margulies 2013; Callard, Smallwood & Margulies 2012; Christoff 2013).
Recent studies indicate that neurophenomenology can help to meet these needs. Using real-time functional magnetic resonance imaging, Garrison, Brewer, and colleagues provided experienced meditators versus non-meditators with online feedback from their brain activity during an ongoing focused attention task (Garrison, Scheinost, Worhunsky, Elwafi, Thornhill TA, Thompson, Safon, Desbordes, Kober, Hampson, Gray, Constable, Papademetris & Brewer 2013). Not only were the subjective reports used to provide a guiding constraint on the specific region of the DMN – the posterior cingulate cortex (PCC) – that showed the strongest correlation with the momentary experience of mind wandering, but also the subjects were able volitionally to influence the activity of those regions after they had gained familiarity with these correlations. When instructed to volitionally decrease the feedback graph, meditators, but not non-meditators, showed significant deactivation of the PCC. This study indicates that experienced meditators can volitionally modulate their own brain activity, and thus suggests new ways to investigate the degree to which various cognitive strategies activate or deactivate hypothesized brain regions and networks in single subjects.
Similarly, in another study (Allen, Smallwood, Christensen, Gramm, Rasmussen, Jensen, Roepstorff & Lutz 2013), Allen and colleagues used the Error Awareness Task (EAT) combined with thought-probe elicited first-person reports of the subjective intensity, ruminative quality, and variability of mind-wandering experience in order to examine in detail the relation between metacognitive monitoring, on the one hand, and task performance and activity in specific DMN regions, on the other hand. By analyzing BOLD signal responses to the thought probes and the task, the contributions of the DMN and the executive and salience networks to task performance were dissociated, and novel evidence that variability in mind-wandering experience is related to greater metacognitive ability was provided.
As these studies illustrate, neurophenomenology provides both a method for examining the robustness of the correlation between the activation of a given region or network and a cognitive process, and a potential source of evidence for investigations concerned to move beyond patterns of co-activation to patterns of influence among brain regions, while also taking into account how such patterns may be modulated by volitional cognitive strategies and metacognitive awareness on the part of the subject (Hasenkamp, Wilson-Mendenhall, Duncan & Barsalou 2012; McCaig, Dixon, Kermatian, Liu & Christoff 2012; Garrison, Scheinost, Worhunsky, Elwafi, Thornhill TA, Thompson, Safon, Desbordes, Kober, Hampson, Gray, Constable, Papademetris & Brewer 2013; Garrison, Santoyo, Davis, Thornhill TA, Kerr & Brewer 2013; Bagdasaryan & Le Quyen 2013) [Note 3] .
Although the two studies just mentioned (Garrison, Scheinost, Worhunsky, Elwafi, Thornhill TA, Thompson, Safon, Desbordes, Kober, Hampson, Gray, Constable, Papademetris & Brewer 2013; Allen, Smallwood, Christensen, Gramm, Rasmussen, Jensen, Roepstorff & Lutz 2013) used fMRI, a recent EEG study of mind wandering with experience sampling also dissociated the DMN from task-directed networks (Kirschner, Kam Y, Handy & Ward 2012). This study found that, during periods of mind wandering, there was significantly more neural phase synchrony between brain regions associated with the DMN, whereas during periods of visual task performance, there was significantly more neural phase synchrony within a task-specific brain network that shared some of the same brain regions. Although this study did not collect detailed phenomenological information about the experience of internally directed attention during mind wandering versus the experience of external, task-directed attention, collecting such information could prove beneficial for relating variabilities in the neural phase synchrony patterns to variabilities in subjective experience.
The contemporary view of the brain as a complex, self-organizing system with a rich variety of spontaneous, self-generated modes of activity confirms Kant’s idea of the spontaneity of cognition. At the same time, this view brings to the fore the need for new concepts and methods to relate spontaneous brain activity to spontaneous cognition, including its conscious manifestations. Neurophenomenology is based on the recognition of this challenge and accordingly proposes a method for studying the variability and spontaneity of cognition and subjective experience in relation to the variability and spontaneity of neurodynamics. The hallmark of this method is to use fine-grained phenomenological information acquired from the first-person perspective to help illuminate the neural network dynamics that enable and sustain agent-level cognition. Although neurophenomenology is still in its infancy, recent results suggest a promising future.
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Lutz A, Jha AP, Dunne JD, Saron CD: Investigating the phenomenological and neurocognitive matrix of mindfulness-related practices, submitted for publication
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Ellamil M, Fox KCR, Dixon ML, Pritchard S, Todd RM, Thomspon E, Christoof K: The origins of thought: neural sources and temporal dynamics of spontaneous thinking, in preparation
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