3.1. Introducing mechanistic explanations

In recent efforts to build a methodological basis for the brain sciences, the mechanistic approach has become influential (Machamer et al. Reference Machamer, Darden and Craver2000; Craver Reference Craver2007). We can conceive this in two different ways: One is as a positive description of what neuroscientists factually do, the other is to extend the mechanistic approach to a full-fledged normative theory about causal explanations in general (Craver and Tabery Reference Craver, Tabery and Zalta2015). In this paper, I combine these two views: I raise the question whether dual systems can be reconciled with the research strategies that neuroscience normally employs, and I use the generalized conception of mechanistic explanations in developing my alternative approach. I hasten to add that I present one specific interpretation of the mechanistic approach, against the background of the ongoing critical debate (see e.g. Waskan Reference Waskan2011; Rusanen Reference Rusanen2014).

The original motivation of the mechanistic approach was the diagnosis that neuroscientific explanations do not fit into the mould of deductive-nomological explanations, variants of which are mostly regarded as the standard of scientific explanations in general: Scientists generate testable hypotheses from theories that claim universality, and then evaluate empirical tests based on predictions to modify the theories. In the neurosciences (and the life sciences in general, see Bechtel and Abrahamsen Reference Bechtel and Abrahamsen2005), researchers rarely employ theories of this kind. Experimental neuroscientific research is mostly about identifying specific mechanisms that generate observable phenomena. That means, researchers postulate a certain causal structure embodied in neurophysiological structures, and then try to find out whether this hypothesis can be empirically vindicated.

A mechanism is a grouping of parts that are organized in a peculiar way to produce a phenomenon. Accordingly, for a strict methodological approach it is necessary (Craver and Tabery Reference Craver, Tabery and Zalta2015):

1. 1. To give a clear and unequivocal identification of the phenomenon in question, which relates to the systemic level, and not to the parts alone (such as addictive behaviour of the individual as manifest in choosing among different alternative actions);

2. 2. To identify the boundaries of the mechanism, i.e. which parts are included, and which are not (for example, does a causal explanation of addictive behaviour only include parts of the brain, or does it, as in psychosocial approaches, also include external entities such as peer-groups?);

3. 3. To identify the parts of the mechanism, which might require dissecting composite parts into smaller constituent parts (for example, in neuroeconomic approaches to addiction one would move beyond higher-level brain structure to the identification of specific dopaminergic reward circuits);

4. 4. To identify the organization of the parts, such as the different levels on which elementary entities and composite entities interact (for example, as in the previous case, identify the entire architecture of various circuits, their location and interrelation);

5. 5. To identify the process dynamics that generate the phenomenon, especially, initial and termination conditions (for example, do we want to explain a single act of choice, or the development of a disposition to act that results in a series of actions of a specific type?).

An important question about the relationship between mechanistic explanations and deductive-nomological explanations is the underlying notion of causality, as many conceptions of causality require reference to universal laws (Dowe Reference Dowe and Zalta2008). The most prominent approach is to adopt the interventionist and counterfactual notion of causality as seminally elaborated by Woodward (Reference Woodward2003) (there is a debate over this claim, see Woodward Reference Woodward2011): In this view, identifying causal factors works via constructing counterfactuals by which their determining role can be identified by varying forms of interventions into the phenomenon in question. This approach is especially appropriate in the context of the neurosciences, which mainly work via interventions in identifying mechanisms; further, the interventionist account is also congenial to clinical applications of neuroscientific knowledge. This would also match with the use of behavioural economics for designing behavioural policy interventions.

A central task in mechanistic explanations is to identify parts and levels the interaction of which produces effects that are not merely additive. This includes the possibility of emergent properties on higher levels which are not identical to properties of parts. The difference lies in the specific patterns of organizing the elements into the whole of the mechanism (Craver Reference Craver2007 speaks of multi-level mechanisms). This is a controversial issue, on which I take a non-reductionist position: Higher levels manifest emergent properties, but this emergence is causally explained by the interaction of the constituent parts (this is the position seminally developed by Bunge Reference Bunge1977, Reference Bunge1979). In this view, a mechanistic approach does not imply that psychological constructs would be identical to properties that neuronal phenomena have, but that the emergence of the relevant properties can be causally explained on the neuronal level. In other words, a phenomenon such as ‘fastness’ involves neuronal mechanisms causally without necessarily, exclusively and unequivocally assigning this property to a specific type of lower-level neurophysiological process (this issue relates to the extremely large and rich literature on supervenience in the philosophy of mind; for an overview see McLaughlin and Bennett Reference McLaughlin, Bennett and Zalta2018, and in the context of mechanistic explanations see Harbecke Reference Harbecke2014).

3.2. Assessing dualist theories from the mechanistic perspective

If we evaluate dualistic theories from the perspective of mechanistic explanations, we can start from the fundamental issue of distinguishing between psychological and neuroscientific explanations. One interpretation of dualities would simply be that these are psychological constructs, and that neuroscientific reduction is not necessary. For example, Alós-Ferrer and Strack (Reference Alós-Ferrer and Strack2014) think that the dualities relate to a ‘continuum’ or a ‘continuous variable’, which leaves open the question whether this refers to varying performances of the same mechanism or a mixture of different mechanisms, and seems to stay in tension with the idea that one system intervenes after the other system has already started to operate (the so-called ‘default interventionist’ view, Evans Reference Evans2010, which is not followed by Strack and Deutsch Reference Strack and Deutsch2004 who think that the systems operate in parallel).

A rigorous interpretation of this position would claim that, for example, the ‘reflective system’ is a psychological construct that has autonomous causal powers on this ontological level, and that it might relate to many neurophysiological aspects of the brain in a very complex way, in the sense of being an emergent property that cannot be and need not be assigned to a specific part and neurophysiological structure. In other words, for explaining observed behaviour, it is not necessary to move to lower analytical levels: This position corresponds to the views of economists who ponder what additional explanatory power can be generated from the neurosciences (e.g. Bernheim Reference Bernheim, Glimcher, Camerer, Fehr and Poldrack2009), and who may even reject any relevance (Gul and Pesendorfer Reference Gul, Pesendorfer, Caplin and Schotter2008).

However, most protagonists of the dual systems view declare that the systems somehow relate to specific parts of the brain. In psychology, one reason is the need to design clinical interventions: For example, manipulating certain psychological phenomena, identified as dysfunctional, by pharmacological means requires the identification of lower-level mechanisms with which the pharmaceutical substance would interfere. Pragmatic interventionism requires deeper-level mechanistic analysis, unless it also remains on the level of the psychological constructs (for example, applying psychotherapy for addiction). In this sense and corresponding to the previous remarks on Woodward’s notion of causality, clinical approaches need to apply a mechanistic perspective at least implicitly. Considering behavioural economics, approaches such as nudging would also work without further dissecting lower-level mechanisms, which explains the somewhat loose connection between behavioural economics and neuroeconomics.

For neuroscientists and neuroeconomists, relating psychological constructs to neurophysiological structures and processes is a central goal of research. Accordingly, dualities must refer to certain brain mechanisms. This is often done, however, in a sloppy way, if we take the methodological standards of mechanistic explanations as an assessment criterion, as sketched in the list of five criteria in the previous section. Referring to behavioural economics applications, and to the five criteria listed above, we diagnose the following.

1. 1. Identification of phenomena. The phenomena in question are not clearly identified, as they are mostly specified as deviations from a scenario of ‘rational’ behaviour, defined in different ways, such as deviations from an explicit rational choice model in games, or as unhealthy choices etc. This is a problem because this means that contextual factors have at least implicit causal powers. For example, loss aversion may not be a universal phenomenon, as it vanishes in certain market contexts: Even Kahneman (Reference Kahneman2011: 297ff) concedes that once individuals are framed as ‘traders’ in games such as the famous coffee mug exchange, the endowment effect would evaporate. Accordingly, there is an intensive discussion in experimental economics about how far experimental results in the lab transfer to the field (Levitt and List Reference Levitt and List2007; Kagel Reference Kagel, Schotter and Schotter2015). In addition, when comparing experiments with real-world situations, the serious problem emerges that apparently similar actions (such as drinking a glass of beer) can be embedded in entirely different action types and goal structures. For example, drinking beer in a pub differs from drinking beer at home, as in the former case goals such as shared emotions in the group may embed or even dominate the narrower goal of drinking beer (Hommel and Wiers Reference Hommel and Wiers2017). In human action, the goal of a specific behavioural pattern is essential for delineating the phenomenon, so that the internal perspective of the agent needs to be made explicit. Whereas in experimental economics this mainly refers to cognitive acts such as interpreting experiments (for example, interpreting a public goods game as a ‘gamble’, Karlan Reference Karlan2005), in behavioural economics we need to take non-cognitive goal structures into consideration that are neurophysiologically embodied.

2. 2. Boundaries of mechanisms. Delineating the boundaries of the mechanisms is mostly done with reference to the rational ideal type, again. This leads to overlooking the possible interactions between different mechanisms that supposedly ‘stand alone’, even including the possibility that the true identification of the mechanism would combine two supposedly different mechanisms into one integrated causal response pattern to perceived environmental situations. This can be even demonstrated by formal economic reasoning: For example, Findley and Caliendo (Reference Findley and Caliendo2014) show that a combination of hyperbolic time preferences with myopia can produce exponential time discounting on the behavioural level, and Steiner and Stewart (Reference Steiner and Stewart2016) show that apparently distorted weighting of probabilities as posited by prospect theory can be a ‘rational’ response to uncertainty in social interactions that cause the ‘winner’s curse’, enabling an agent to avoid it. Thus, ensembles of mechanisms might be the relevant units that generate behaviour. This raises the more fundamental question whether such ensembles might include external entities, along the lines of recent theories about the extended mind and distributed cognition (Ross Reference Ross, Davis and Hands2012; building on Clark and Chalmers Reference Clark and Chalmers1998; Clark Reference Clark2011). One important issue is how far external means of representation interact with internal neuronal processing: For example, different mechanisms of representing information trigger reversals in the loss aversion function, as in the case of frequentist versus probabilistic representations (see Glimcher Reference Glimcher2011: 383ff). Even more radically, it is an open question how and where the border line between brain and body is drawn, which is particularly important in the context of social interactions. Social neuroscience suggests direct embodied coordination across different individuals (Oullier and Basso Reference Oullier and Basso2010; Gallese Reference Gallese2014).

3. 3. Parts of mechanisms. Most dualist theories remain on the higher level of brain architecture, such as assigning the reflective and logical processes to the frontal cortex: On this coarse level of analysis, one cannot distinguish between ‘irrational’ and ‘rational’ forms of intuition: Rational intuition was famously established in the Iowa Gambling task and is discernible in many ways, such as in intuitive preferences for valid logical judgements, even accompanied by affectual phenomena (Handley and Trippas Reference Handley and Trippas2015). In many contexts, a disaggregate approach to brain connectivity shows that lower-level and higher-level structures interact, such as in mentalizing (e.g. Lombardo et al. Reference Lombardo, Chakrabarti, Bullmore, Wheelwright, Sadek, Suckling and Baron-Cohen2009). This creates difficulties in unequivocally assigning certain constituents of dualities to brain structures on a higher level of aggregation: Decomposing the processes results in the identification of parts that belong to different structures.

4. 4. Architecture of mechanism. Accordingly, dualist theories mostly fail to identify a precise picture of the brain architecture that underlies certain behavioural phenomena, which is crucial because often bottom-up and top-down processes interact, with the latter referring to the cognitive categorization of external contextual factors. The classical example is empathy, which is grounded in certain basal neurophysiological mechanisms, but at the same time depends on higher-level cognitive triggers (Singer and Lamm Reference Singer and Lamm2009; Decety Reference Decety2015). The role of cognitive mechanisms in generating impulsive reactions is salient in the strong impact of attention on realizing certain action patterns (e.g. Puglisi et al. Reference Puglisi, Leonetti, Landau, Fornia, Cerri and Borroni2017). As Spunt (Reference Spunt2015) convincingly argues, this comes to the fore when realizing that the different properties that supposedly make up the dualities often are orthogonal to each other, such that, for example, ‘automaticity’ does not unequivocally and exclusively go along with ‘unconscious’ or ‘uncontrollable’.

5. 5. Process boundaries. Finally, there is no clear distinction between single acts of choice and series of similar choices through time, which is crucial as it has been shown, for instance, that bundling single choices has direct consequences for the validity of assumptions about time preferences, which would entail very different identification points for initial and terminal conditions of the respective cognitive and affective mechanisms. Bundling was proposed by Ainslie (Reference Ainslie1992, Reference Ainslie, Ross, Spurrett, Kincaid and Stephens2007) as overcoming behavioural dysfunctions resulting from hyperbolic time preferences, which have also been approached in a dual systems perspective, as mentioned previously. This relates to the previous point, as process types are defined in more complex architectures, such as involving the construction of the self and personal identity (Damasio Reference Damasio2010).

Thus, in summarizing this section, we can conclude that dualities in behavioural economics and psychology fail to meet the methodological standards of mechanistic explanations. They navigate between the Scylla of being by far too general to formulate meaningful psychological causal explanations that properly account for the system boundaries in which action is determined and the Charybdis of failing to properly identify causal mechanisms on the neurophysiological level. But I do not want to stop at this negative conclusion: The next task is to show how the mechanistic approach can help to identify alternative and promising heuristics of research.

4.1. Dual function as alternative conceptualization of duality

I think that there is a straightforward solution to the methodological troubles with dualistic theories which builds on the general concept of ‘function’. This perspective relates to Marr’s (Reference Marr1982) celebrated methodology of cognitive sciences, which distinguishes between three analytical levels, the computational, the algorithmic and implementation: On the computational level (1), we would ask for tasks that a certain mechanism is expected to realize, and define a computational solution for that; next, we look at alternative algorithmic methods (2) which then can be implemented and materialized in different embodiments (3) (such as a human brain or AI). Marr’s approach has been invoked by Glimcher’s (Reference Glimcher2003: 133ff) seminal methodological grounding of the emerging discipline of neuroeconomics (though not followed up in Glimcher Reference Glimcher2011). However, Glimcher’s argument reveals a tension that I can use productively in developing my alternative functionalist approach to dualities.

Neuroeconomics clearly adopts the mechanistic methodology in focusing on empirical research on the neuronal mechanisms that underlie human (and other animal) decision making (Glimcher and Rusticchini Reference Glimcher and Rusticchini2004; Fehr and Rangel Reference Fehr and Rangel2011). However, in defining neuroeconomics, Glimcher exploits Marr’s approach to introduce a major methodological shift: He argues that on the computational level, economic theory is the relevant reference. This stands in direct tension with the mechanistic methodology, as economics in terms of its mathematical models now serves as a source of generalizations in the neurosciences along the lines of the deductive-nomological model. This view has been challenged by Ross (Reference Ross2008, Reference Ross, Davis and Hands2012) in distinguishing between a ‘molecular’ and a ‘molar’ approach to neuroeconomics, which raises the intricate question on which level economic theory applies. According to Ross, economic theory may apply for specific kinds of mechanisms within the individual but does not apply for behaviour as the outcome on the individual level, as far as this is approached in de-contextualized and generic terms, that is, without behaviour being scaffolded by institutions that constrain economic choices. In other words, in generic terms we could not conceive of individuals as being economic actors, but only parts of them. Obviously, this opens the possibility that Glimcher’s use of economics in neuroscience could be reconciled with psychological theories that deal with higher-level outcomes of complex interactions between lower-level mechanisms, even if contradicting the economic model on the level of the individual: The condition is giving up the claim that economic theory can directly explain individual behaviour, unless certain specific contextual conditions are fulfilled.

This possibility is indicated by Glimcher’s own extension of Marr’s framework. Glimcher introduces evolutionary theory to explain the functions on the computational level. This is not directly evident from Marr’s own writings but appears reasonable when considering Marr’s distinction between ‘what’ and ‘why’ questions: On the computational level, we need not only to identify what the process computes, but also why, and put this in a mathematical expression. This leads us immediately to the question how far Marr’s methodology harmonizes with the mechanistic approach. The ‘what’ question is also central in mechanistic explanations, and mechanistic philosophers have indeed argued that Marr’s computational and algorithmic level, taken together, are ‘sketches of mechanisms’ (Piccinini and Craver Reference Piccinini and Craver2011). However, other commentators think that mechanistic explanations only involve bottom-up reductionist explanations whereas Marr’s ‘why’ question implies a top-down explanation (Rusanen Reference Rusanen2014).

In fact, there are two different ways to interpret Marr’s ‘why’. One has been suggested by Shagrir (Reference Shagrir2010) and is referred to as a mechanistic framework by Shagrir and Bechtel (Reference Shagrir and Bechtel2014). Responses to the ‘why’ question would relate to identifying properties in the environment that define physical constraints on the range of possible computational solutions, so that we can explain why the specific form of the computation (the ‘what’) is a proper solution to the task. Shagrir and Bechtel think that this is essential for delineating the phenomenon that can be approached as a mechanism.

The second form of response is Glimcher’s evolutionary approach. I think that the two views are not mutually exclusive since they simply interpret the ‘why’ question in different, though complementary ways. This is salient once we introduce the notion of ‘function’. The former view asks for explanations why a specific form of computation has the capacity to fulfil a certain function for the organism in relation to its environment (such as enabling workable vision). The second view explains why this function emerged in the real world: This is ‘why?’ as ‘what for?’. Accordingly, the notion of function seems helpful in reconciling Marr’s approach with the mechanistic methodology: In analysing mechanisms, it is always important to ask for the function that a specific mechanism fulfils. For example, when analysing the composition of a mechanism, we always need to ask what the function of lower-level mechanisms is in enabling the higher-level mechanism to operate properly. Eventually, we also ask what the function of the higher-level mechanism is. This relates to the phenomenon in question, and therefore relates mechanism with environment, as envisaged by Shagrir. In other words, Shagrir’s interpretation of the ‘why’ question relates to the structure of selective constraints, and how these map into mechanisms, and Glimcher’s relates to the process of how this mapping has been realized.

Functionalism is one of the most influential strands of thought in the philosophy of mind, with a rich variety of various approaches that were embroiled in a lively debate for decades (overview in Van Gulik Reference Van Gulik, McLaughlin, Beckermann and Walter2009). I cannot deal with these debates here and remain satisfied with the simplest possible understanding that might prepare a common ground for my move from dual systems to dual functions. In this view, explaining a phenomenon would start out from asking the ‘why’ question first, i.e. identify its function, and then proceed to explain how this function is realized. This is different from some uses of the term in psychology and philosophy of mind which concentrate on the internal relationships between functions within the brain/mind (this is also emphasized in Piccinini and Craver Reference Piccinini and Craver2011). Although I include this meaning, I establish the close connection between function and evolutionary analysis as in concepts of aetiological function developed by theorists of teleosemantics and biosemantics (Millikan Reference Millikan, McLaughlin, Beckermann and Walter2009; MacDonald and Papineau Reference MacDonald and Papineau2006; which differs from the previous internal view, with representatives such as Cummins Reference Cummins1975). This is relevant in the current context, because behavioural economics mainly concentrates on failures of representation and the resulting supposed malfunctioning. Teleosemantics uses a notion of ‘proper functioning’ in order to distinguish between these cases and regular realizations of functions on the level of components of the system that generates the behaviour (for example, what makes a frog respond properly to an environmental cue indicating a fly, and how to account for cognitive failures; see Neander Reference Neander, Macdonald and Papineau2006). In this sense, applying the notion of proper function on the aggregate level necessarily leads to a mechanistic reconstruction of causes of failure, which in turn requires the modeller to apply the notion also on lower levels. Notice that this does not imply the functionalist fallacy in simply assuming that lower-level mechanisms comply with the higher-level function: The possibility of failure is crucial for defining mechanistic research strategies. On first sight, this corresponds to the behavioural economics approach.

However, the functional view is radically de-constructionist about dualistic theories because it would guide research to identify specific mechanisms that realize certain functions, relative to certain contexts of actions. That means, we would no longer try to characterize entire domains of the brain-mind in terms of a certain system with a list of given properties (for example, relating the prefrontal cortex to reflective processes such as planning that have properties such as being ‘slow’) but aim at identifying mechanisms that are specific to certain functions. This is close to the approach of evolutionary psychology which confronts a modularized view of the brain with all kinds of theories that approach the brain as an integrated information processing system (Tooby and Cosmides Reference Tooby, Cosmides and Buss2005). This is where I depart from Glimcher’s (Reference Glimcher2003) use of Marr in a principled way: Glimcher argues that the economic model of choice can be justified by referring to evolution as a process that resolves trade-offs in maximizing fitness. Evolutionary psychology rejects this reference to the notion of ‘general purpose rationality’ and suggests a modular alternative. This corresponds to a mechanistic methodology, since modules in evolutionary psychology are approached as multi-level mechanisms, such as when approaching emotions as higher-level coordinators of various lower-level mechanisms generating a certain behavioural pattern.

In sum, a simplest analytical move would be to re-interpret dual systems as dual functions: All properties that are used for characterizing the systems would be conceived as functional properties.

4.2. A workhorse: ‘fast’ versus ‘slow’ functions

Let us move to the constructive stage of my argument. I will approach the pivotal duality between ‘fast’ and ‘slow’ systems in terms of functions. ‘Fastness’ is a function that is necessary for certain aspects of survival, such as being able to respond to the threat of a predator. ‘Slowness’ is a function that may be necessary if larger quantities of information need to be processed, with no immediate threat in place. If we consider speed as a function, we do no longer assign ‘speed’ as a property to specific types of brain processes, but we look at how the brain evolved specific mechanisms that can realize this function in relation to a task: This is Marr’s computational level. Impulsive processes may realize this function, but so do certain kinds of reflective processes. There is no absolute notion of speed, but only speed relative to the fulfilment of specific functions.

I run through the list of requirements for mechanistic explanations again and ask how we can make use of the distinction between fast and slow functions to arrange the empirical material of neuroscientific research and draw the methodological conclusion.

1. 1. Identification of phenomenon. Speed is always relative to the action that is necessary to fulfil a certain function properly. In the standard neuropsychological model of action, reflection is not necessarily slower than the action taken, even if triggered by an impulsive response in the first place. Action requires the formation of an action potential, and then realization. Reflective processes can set on and even reach conclusion while the action potential is still building, thus enabling continuous real-time reflective control of behaviour, which might appear to be as ‘impulsive’ to the external observer as the ‘original’ impulsive urge. This even applies for higher-level brain structures, such as the dualism between the cortical and limbic regions: It is perfectly possible that reflective feedback loops across brain areas operate faster than the action potential translates into action (for strong empirical support of this important point, see Cunningham and Zelazo Reference Cunningham and Zelazo2007). This is also important when considering interactions between impulsive and automatic processes, such as when learning to suppress pain, even to the extent that feelings of pain are completely blocked.

   Methodological conclusion: In delineating phenomena, speed is a functional requirement that allows for multiple realizability of functions. Absolute speed as a property of neuronal mechanisms should not matter for identifying adequate mechanistic explanations of observed behaviour.

2. 2. Boundaries of mechanisms. One of the most challenging issues in dual systems views is the role of memory in guiding behaviour. In Kahneman’s approach, the distinction between ‘experienced utility’ and ‘decision utility’ (Kahneman et al. Reference Kahneman, Wakker and Sarin1997) does not harmonize well with his own dual systems concept, because decision utility is seen as a source of dysfunctions, yet it relates to the reflective and even conscious type of process. Experienced utility relates with the impulsive system in the sense that immediate sensory inputs are involved, such as in the famous experiments when test persons put their hands into cold water, later incorrectly remembering this experience according to the ‘peak-end rule’ which would underlie decision utility. One could argue, as Kahneman apparently does, that the retrieval of information from memory is a ‘fast’ process of its own kind, but then we would need to assume a large variety of different kinds of interacting systems (compare Berridge’s Reference Berridge2009 addition of a ‘learning system’). This is certainly true for memory (overview in Michaelian and Sutton Reference Michaelian, Sutton and Zalta2017). Two aspects loom large in functional analysis. The first is that memory can be distributed over external media. This is salient in the emergent discipline of cultural neuroscience that focuses on the interaction between external cultural media and human neuroplasticity, implying that similar situations, as seen from the perspective of the external observer, can go along with very different neurophysiological structures across cultures (for a survey, see Han et al. Reference Han, Northoff, Vogeley, Wexler, Kitayama and Varnum2013). Hence, functional analysis would suggest that the boundaries of mechanisms need to be extended to include those external media (language, symbolic artefacts etc.). The second aspect is that in most experimental research informed by the dual systems approach, working memory is in focus, as cognitive load matters. But memory is a very complex multi-systems phenomenon, and, for example, retrieval from information stored in episodic memory can interplay with external media in creating fast responses to environmental settings which are even conscious, though not planned along the lines of the economic model of decision making. For example, Shohamy and Daw (Reference Shohamy and Daw2015) suggest a ‘retrospective integration model’ as opposed to the standard neuroeconomic ‘prospective integration model’. This works via the continuous real-time ‘replay’ of decision scenarios triggered by similarities across past and present contexts, thus creating a preparedness for action which allows for fast responses.

   Methodological conclusion: In realizing functions, there is no unequivocal mapping between properties of a behaviour identified by the external observer and the boundaries of mechanisms generating it.

3. 3. Parts of mechanisms. Many dualistic theories posit a clear distinction between the types of mechanism that define the two systems, which is not vindicated empirically. For example, recent work on relative speeds of belief-based and logical structure-based behaviour in solving cognitive tasks has shown that logical processing often operates on an intuitive level, even unconscious, and finds expression in affective valuations (Handley and Trippas Reference Handley and Trippas2015). The duality is most salient in Strack and Deutsch’s (Reference Strack and Deutsch2004) canonical model that distinguishes between ‘associative’ information processing in the impulsive system and a propositional mode of information processing in the reflective system. Yet, they also recognize a bidirectional linkage between conceptual content and behavioural schemata, without pursuing possible implications for the overall validity of the duality. If we consider the semantics that is operative in propositional structures, the boundaries between different types of information processing become blurred or even obsolete. Meanings are practices and actions which go along with valuations that are affective in turn (Pinker Reference Pinker2007). Hence, ‘reflectivity’ may be related with associative processes as well, as in those theories of thought and meaning that build on metaphor as the fundamental notion (Fauconnier and Turner Reference Fauconnier and Turner2002; Lakoff Reference Lakoff and Gibbs2008). In this perspective, a supposedly linguistically mediated reflective system would build on the same elements of information processing as the impulsive system, so that the conceptual difference between the systems would collapse. One example for this is Lades’ (Reference Lades2014) attempt at broadening the scope of the concept of ‘impulsivity’ to include reflective reference to self-images and identity-related needs, which are always symbolically mediated, and can signal states of deprivation of similar strength as states of physiological deprivation.

   Methodological conclusion: Functional analysis should avoid any essentialist pre-assignment of types of parts to types of mechanisms.

4. 4. Architecture of mechanism. Concepts such as selfhood point towards the question of architectures of function fulfilment. One empirical problem with dual systems theories is that they either assume a time lag between the two responses of the system (impulsiveness first, control via reflection later) or do not detail the mechanisms that govern the parallelism between the two. The latter implies we would need to introduce a higher-level mechanism that chooses among the two reaction patterns. This idea has been emerging in dual systems theories recently (Stanovich Reference Stanovich2011); one systematic model is Foxall’s (Reference Foxall2016) who suggests a tripartite model where the notion of reflection refers to a meta-cognitive level which decides which of the two types of processes, impulsive or algorithmic, will operate. Evidently, this implies that the higher-level function operates with at least the same speed as the impulsive system. In the psychological literature, two phenomena are especially difficult to align with the dualism between fast and slow: attention and motivation (Spunt Reference Spunt2015; Wiers and Gladwin Reference Wiers, Gladwin, Deutsch, Gawronski and Hofmann2017). Attention is a complex mechanism of its own standing that plays a central role in triggering other mechanisms, including even reflexes, so that one needs to speak of ‘prepared reflexes’ (Hommel and Wiers Reference Hommel and Wiers2017). Motivation is similarly, if not more complex than attention, and is crucial to understand differential performances across individuals in manifesting dysfunctional behaviour. These observations lead us to consider the more general question of how to account for parallelism systematically. A functional approach allows for the possibility that behaviour is generated by competing mechanisms of which one is selected according to a ‘winner takes all’ principle. This can explain behavioural variety within and across individuals.

   Methodological conclusion: Functional analysis implies that speed and complexity are not unequivocally related; complex architectures can operate with high speed, especially if parallelism is considered.

5. 5. Process boundaries. Equating intuition with fast responses exclusively cannot be vindicated empirically. For example, in interpersonal relations, many intuitions build up slowly, even undergirded by reflection. We might feel spontaneous sympathy with a person, yet this emotion also unfolds through time, though still being driven by intuitions. In comparison, reflective or propositional classifications of persons might operate with much higher speed. For example, if I know that a person is a policewoman, I might automatically reach certain conclusions about predicting her behaviour, whereas building expectations on personal trust as an intuition would need much longer time. This may be generalized in the sense that often a misplaced assumption is made that ‘emotions’ are states, whereas reflection is a process, which immediately seems to suggest that the former are ‘fast’, once they are triggered. But in fact, emotions are also processes, and depending on the nature of the emotions these can work at different speeds (Goldie Reference Goldie2014).

   Methodological conclusion: Identification of functions implies complex time structures of function fulfilment that map into a large variety of possible mechanisms enabling proper fulfilment.