Temporal Dynamics in Human Interaction

Cybernetics and Human Knowing. Vol. 21, nos. 1-2, pp. 80-97

Temporal Dynamics in Human InteractionSune Vork Steffensen1 and Sarah Bro Pedersen2

Elaborating a model first presented in Uryu, Steffensen, and Kramsch (2014), the article discusses two conceptualizations of time and time scales in human interaction. One takes a starting point in a scalar hierarchy of tiered time scales and the other starts from a specification hierarchy of temporal ranges. While the time scales of the former by definition are observer-dependent, those of the latter relate the temporal dynamics of complex dialogical systems (Steffensen, 2012) to a series of causal frames, including physical, biological, social, cognitive and interactional constraints. Being the outcome of an evolutionary trajectory towards growing complexity, these constraints are the enabling conditions of human interaction, and as such they give rise to multistable dialogical systems. While this article focuses on the presentation of an ecological model of temporal ranges and time scales, its methodological implications and interpretive potentials are explored in Pedersen and Steffensen (2014).Keywords: time scales, temporal ranges, temporal dynamics, interactivity, interaction, dialogical system, multistability

Our challenge is to understand how forces with very different time scales mesh together in the current moment

to determine the emergence of linguistic form.—Brian MacWhinney

0. Prologue

During a 2009 intercultural Thanksgiving dinner, arranged by an NGO at an Americanuniversity, in order to allow visiting scholars to socialize and make friends, fourwomen, a German, a Russian and two Japanese, all middle class academics, discussthe election of Joseph Ratzinger as pope in 2005.3 The German woman, Bianka, isenthusiastic about the election of her compatriot as pope, though she reports that“when he became the Pope, there was a problem… because … he was … ah…” Atthis point one of the Japanese woman, Michiko, finishes Bianka’s turn by saying “hewas, uh, Nazi?” Distraught by Michiko’s faux pas, Bianka merely responds “Nazi…”before she vanishes out of the conversation. According to the researcher’s renderingof the situation, “Bianka was offended and the taboo term ‘Nazi’ remaineduncontested, hanging like a cloud over the subsequent conversation” (Uryu,Steffensen, & Kramsch, 2014, p. 53). What happened? What caused Bianka’semotional distress?

1. Corresponding author; University of Southern Denmark, Centre for Human Interactivity, Campusvej 55.Email: [email protected]

2. University of Southern Denmark, Centre for Human Interactivity, Campusvej 55. Email: [email protected]. The example is originally from Uryu (2009); here it is adopted from Uryu, Steffensen & Kramsch (2014).

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Temporal Dynamics in Human Interaction 81

Nothing indicates that they had any intentions of insulting each other, or that theywere part of any opposing social forces. And after all, Michiko did only say what atthe time was a well-known fact, namely that Ratzinger, like many other Germans ofthe period, were mandatorily affiliated with a Nazi organization during Hitler’sregime. Further, that fact that Michiko completes Bianka’s utterance is mundane:

It’s not altogether unheard of for two persons to collaborate to produce a single sentence. Thenormal way that is done … is that, say, one person produces an almost complete sentence and findshimself searching for a last word or a last phrase which he can’t find, and the other offers itcompleting as-yet-incomplete utterances with syntactically coherent parts is a routinely doablething. (Sacks, 1992, p. 145)

So, what happened?

1. Introduction

Most scholars within the contemporary study of human language and interactionwould agree that any given act, action or activity is influenced both by local, situated,interactional dynamics and by non-local, trans-situational, cultural dynamics.However, when it comes to the methodological question of how to interpret real-lifeinteraction, to a very large degree, different approaches confine themselves to specificdomains of explanation. For instance, ethnomethodological conversation analysisadopts “the participants’ perspective, from which the individuals involved in socialaction interpret what is happening moment by moment in their interaction” (Liddicoat,2011, p. 73). Following this perspectival guideline, the approach takes the theoreticalview that, unless participants orient to sociocultural phenomena, these cannot beattributed causal, enabling or conditioning effects and, for this reason, they areirrelevant to the analysis of interaction. The emphasis on participant perspective canbe traced to the proof procedure suggested by Sacks, Schegloff, and Jefferson:

Since it is the parties’ understandings of prior turns’ talk that is relevant to their construction of nextturns, it is THEIR understandings that are wanted for analysis. The display of those understandingsin the talk of subsequent turns affords both a resource for the analysis of prior turns and a proofprocedure for professional analyses of prior turns―resources intrinsic to the data themselves.(Sacks, Schegloff, & Jefferson, 1974, p. 729)

According to the suggested proof procedure, Bianka’s reaction only proves the factthat Michiko’s turn was a faux pas, and not why the use of the term Nazi had such animpact.

In contrast, various methods of discourse analysis tend to interpret particular actsand action as expressions of actors’ culture, for example, by claiming that “The self nolonger uses language to express itself; rather the language speaks through the person.The individual self becomes a medium for the culture and its language” (Kvale, 1992,p. 36). In Foucault-inspired discourse analysis, discourses, qua cultural resources,frame the possibility space of the interaction and, paradoxically, the more emphasis an

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82 Sune Vork Steffensen and Sarah Bro Pedersen

analyst places on the cultural frame, the less manoeuvring space is granted to theparticipants. Both idiosyncratic variability and individual agency are thus precluded.Given this focus on single aspects of interaction, the interaction analyst, at least in theidiographic humanities, is caught in a methodological juxtaposition between viewingparticipants as agential atoms in a social void, or as unconscious zombies in the grip ofdiscourses and cultural conventions. At best, pluralist approaches can recur to amethodological mixture of conversational, discursive, and sociocultural methods.4

However, while methodological pluralism may represent an attractive alternativeto purism, there is a risk of ad hoc theorizing in that no extant model sufficiently andsatisfactorily integrates the methods within a consistent and resilient framework.Interestingly, such models have begun to emerge in recent years, and one of the mainexplanatory mechanisms to account for how the same phenomenon cansimultaneously be subsumed under different explanantia, is that of time scales. Thenotion of time scales is predominantly associated with complexity theory ordynamical systems theory. For a general introduction, see Hooker (2011); for alinguistic application, see Larsen-Freeman and Cameron (2008). In dynamicaltheories, time scales are invoked to emphasize that natural, living and social systemsare self-organizing wholes that cannot be captured by linear, unidirectional andreductionist models.

A recent example of an attempt to get a grasp of the various time scales that cometo the fore in human language, is Nick Enfield’s (2014) chapter in the CambridgeHandbook for Linguistic Anthropology. He identifies six time scales: microgenetic,ontogenetic, phylogenetic, enchronic, diachronic, and synchronic; or MOPEDS forshort (Enfield, p. 320) which, he suggests, “correspond roughly with distinct researchperspectives.” For instance, the cognitive processes of speech production andperception unfold on a microgenetic time scale, while the overall evolution of thelanguage capacity in humans unfolds on a phylogenetic time scale. Applied to theexample in the prologue, the conversation takes place on Enfield’s enchronic timescale, while the intercultural dynamics relate to a diachronic time scale. Enfieldequates the six time scales (and hence the corresponding linguistic disciplines) todifferent layers of a multi-layered model that consists of six so-called frames. Theseframes, defined by “different types of underlying processes and causal-conditionalmechanisms” (Enfield, p. 320), “conveniently group similar or tightly interconnectedsets of causal mechanisms under single rubrics, and … together they cover most ofwhat we need for providing answers to our questions in research on language”(Enfield, p. 320).

In this article, we take a starting point that is congenial with Enfield’s approach totime scales as dependent on causal frames. The gain from such an approach is that thetemporal dynamics of a given phenomenon are grounded in ecological systems, ratherthan being mere measures of time.5 However, as compared to Enfield’s MOPEDS

4. This is not to be confused with mixed methods approaches that by definition integrate quantitative and qualitative methods, see Johnson and Onwuegbuzie (2004).

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model, our model differs in two ways: First, it rejects the modeling of time scales asco-extensive with (and thus defined by) causal frames; it does so by suggesting thattime scales are analytical constructs defined by the interplay between such causalframes. Second, our model does not share Enfield’s focus on language or linguisticform; rather, it aims to provide a heuristic frame for investigating human interaction.The proposed model thus takes into consideration various dynamics that shape theactivities, events and processes that constitute this complex phenomenon. Thesedynamics both span the local and situated interaction and its non-local, trans-situational sociocultural framing (Steffensen & Cowley, 2010). These time scales “allmesh in the current moment” (MacWhinney, 2005, p. 191) of human interaction.

The model presented here was first presented in Uryu, Steffensen and Kramsch(2014). In this elaboration, we begin with a tiered conception of time scales(section 2); we then present a model that pivots on the notion of temporal ranges(section 3). In section 4, we explicate how the model focuses on human interaction,and in section 5 we discuss a few of the model’s implications. In continuance of thisarticle, another article in this special issue (Pedersen & Steffensen, 2014 shows howour ecological model serves in the investigation of real-life human interaction at anemergency ward.

2. Time Scales, Tiered Models and Scalar Hierarchies

Many a scientific paper provides a model of time as a horizontal black arrow followedby a t. This model is well tailored for illustrating change, as defined as a differencebetween two states at tn and tn+1. But as a model of time per se the horizontal arrow isinadequate: There is no mechanism that indexes events with a time code determinedby universal metronomic clockwork. Since an observer observes, not time per se, butdifferences between states, it is misleading to present time as a factor sui generis. Sowhat is to be done? It is, after all, even less attractive to posit a model that excludestime from account. While widely used in structuralist theories this strategy precludesdescriptions of evolution, development and social change. The challenge is thus todevelop a model that describes temporal dynamics without appealing to a clock-basedview where time becomes a steady pulsating rhythm that underlies existence.

Complexity theory is a promising approach for modelling dynamical, that is,temporally variable, phenomena. It has developed through mathematical (differentialand non-linear) modelling of a series of measurements of any given phenomenon. Forinstance, Van Orden, Holden and Turvey (2003) report on the reaction times of tensubjects over 1,100 trials where they responded to a visual signal by uttering /ta/ into amicrophone. Participant reaction times were aperiodic, natural fractals: The patternsof variation were similar within ten, a hundred and a thousand trials. This result isnontrivial because the fractal “signature of variation in the laboratory performance isthe dynamical signature of intentional behavior” (Van Orden et al., 2003, p. 338). The

5. The grounding in ecological systems makes the time scale model presented in this paper compatible with the ecological naturalism proposed by Steffensen & Fill (2014).

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reason is that living systems uphold a behavioral flexibility by self-organizing to staynear a critical state between “one or the other kind of behavior” (Van Orden et al.,p. 332). At any time this self-organized criticality can collapse into one state or theother, which appears as if the system opts for one behavior or the other—it makes achoice, or engages in purposive behavior. Purposive behavior thus relies on the livingsystem’s self-organized criticality, and in measurement this appears as pink noise or“statistically self-similar, positively correlated, background noise” (Van Orden et al.,p. 335). Self-similarity implies a repetition of “similar features across nested scales ofspace or time” (Van Orden et al., p. 333). Borrowing an example from Van Orden,Kloos, and Wallot (2011, p. 642), the branching of a tree exhibits statistical self-similarity as, “from the bottom to the top of a tree, branches become thinner indiameter as they become more numerous.” In this example, the same pattern plays outon many temporal scales, which leads to the emergence of a spatial fractal. Butbasically, the fractal pattern is caused by only one thing: the tree’s tendency to grow.The same goes for the /ta/-experiment, although here the scales on which the self-similar pattern emerges are series of trials, that is, ten, hundred or thousand trials. Themain insight from such work is that living systems can only be understood if we takeall of the relevant self-similar scales into consideration: “timescales of embodimentcompose a hierarchy ranging from glacial scales of evolution to online kinematics”(Van Orden et al., 2003, p. 347). In other words, we can only really comprehend livingsystems as multiscalar phenomena: living systems—and phenomena depending onliving systems—simply cannot be located on a single time scale.

This link between complex models of dynamical, living systems and a multiscalarmodel of language and interaction is what motivates MacWhinney, Enfield and otherscholars (e.g., Rączaszek-Leonardi, 2010; Rączaszek-Leonardi & Kelso, 2008) to callfor dynamical models in the language sciences. However, at the same time this linkgives rise to a challenge: on the one hand, the time scales evoked by complexitytheory are mathematical constructs that capture patterns in a data set with a givennumber of data points, even if these are collected over months rather than minutes. Forinstance, Delignières, Fortes, and Ninot (2004) studied self-esteem by collecting1,024 data points, that is, approximately the same number as Van Orden et al. (2003),but their data points had an extension of 512 days (i.e., two measurements a day), incontrast to Van Orden et al. (2003) who collected their data within minutes or hours.One important implication of this is that time scales are intrinsically observer-dependent: the observer sets a given pace of measurement, and one cannot describebetween-measurement variation (Van Orden et al., 2003, p. 338). Thus, in the self-esteem study, how participants’ self-esteem fluctuates during a day is left out ofaccount.

On the other hand, living, ecological systems do not result from observation.Thus, although we might subscribe to some variant of (ontic) structural realism (seeLadyman & Ross, 2007), we cannot a priori assume that the measurement and thedynamical model reflect the phenomenon under study. The strength of dynamicalmodelling is at the same time its weakness: It is capable of modelling very different

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ontological realms, even realms that are fictitious: “Given a set of assumptions andidealizations, even ontologically unreal phenomena lend themselves to complexmodelling” (Steffensen & Fill, 2014, p. 15). In other words, while mathematicalmodels yield temporal patterns that indicate the existence of multiple time scales inthe data set, they do not in themselves provide extra-model cues to provide anexplanatory model of the patterns.

This challenge parallels Harnad’s symbol grounding problem: “How can thesemantic interpretation of a formal symbol system be made intrinsic to the system,rather than just parasitic on the meanings in our heads?” (Harnad, 1990, p. 335). Asreviewed by Cowley (2007), Taddeo and Floridi (2005) conclude that the problemremains unsolved because “symbols seem value-laden. However, this impressionrelies on human interpretation, because the manipulation of the symbols is hard-coded” (Cowley, 2007, p. 84). In parallel, to make sense of time scales, that is, toestablish a frame of reference that allows us to describe and explain temporal patternsin human activity, the mathematical model is necessarily parasitic on sense-makingabilities.

Overall, and admittedly simplistically, we can distinguish between two ways ofconstruing time scales: One is to conceptualize time as a stream of moments, but onmultiple scales; different processes are thus depicted in tiers, ordered by duration, forexample, from evolution to neural firing. The other focuses on how the interplay ofecological systems gives rise to increasingly complex behavioral patterns. The latterwill be discussed in section 3; first, we will focus on an example of the former, namelyLarsen-Freeman and Cameron’s (2008, p. 169) list of “timescales relevant to face-to-face conversation.” In this list they include the following scales:

• the mental processing timescale of milliseconds• the microgenetic timescale of online talk• the discourse event timescale …, on the scale of hours• the series of connected discourse events …, on the scale of weeks, months,

and years• the ontogenetic scale of an individual’s life, and• the phylogenetic timescale (Larsen-Freeman & Cameron, 2008, p. 169)

A similar logic, though with focus on education, can be identified in Jay Lemke’stable of “Representational Timescales for Education and Related Processes” (Lemke,2000, p. 277). The sense-making device of these models is a nested hierarchy ofduration: the word Nazi is part of Michiko’s utterance which is part of a conversationwhich is part of connected discourse events (their encounters) which is part of theirautobiographies, and so forth. Such models imply a (spatially organized) metonymicrelation between time scales where a focal scale is part of higher scales and composedof lower scales. Salthe (1991, p. 252f) refer to this model as a scalar hierarchy whichdefines a focal scale (or focal level) of behavior that is “derived from ‘initiatingconditions’ arising from the dynamics of systems nested at a lower level” (Salthe,

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p. 252) and simultaneously “regulated by boundary conditions operating at the next(and still higher) scalar level(s) above the focal level” (Salthe, p. 252). What suchmodels promise is that if one takes the neighboring scales into due consideration, itwill be possible to describe the phenomenon under scrutiny on its (focal) time scale,e.g. conversations on a conversational time scale, enabled by vocal activity andconstrained by the discourse event. The scalar model of time surely has its merits. Itreminds us that human interaction exploits fast, bodily dynamics in meaning-making,and that it is constrained by larger social circumstances. But in doing so it adopts aspatial, metonymic model of time, not unlike classical structural models of languagein terms of ranks: clause, group/phrase, word, morpheme (see Halliday &Matthiessen, 2004, p. 20). Thus, any delineated sequence of time can be described aspart of events on various (and in principle infinitely many) time scales, just likeMichiko’s uttering the stressed syllable /na/ can simultaneously be described as partof: a morpheme, a word, a noun phrase, and a sentence.6

Metonymic models are apt for describing static spatial structures because theyallow one to zoom in on a specific zone within a larger structure (e.g., a city on a mapor a word in a written sentence). But they are not fit for describing dynamicalstructures because the larger whole (say, the discourse event or the conversation) isnot an ontologically autonomous structure independent of the part (e.g., the flow ofutterance activities). The latter is not caused, enabled or constrained by the former: itemerges as human beings, on basis of individual and collective intentions, aspirationsand expectations, interpret past events and anticipate future events. Larsen-Freemanand Cameron’s discourse event is a reified abstraction of a series of past activities, andas such it lacks any causal power. In conclusion, the scalar time model exploits a folktheoretical idea of nested durations as its meaning-making device, and in doing so itreifies a number of theoretical constructs that have a dubious ontological status. Incontrast, what is needed is a naturalized approach that takes a starting point in realecological systems.

3. Temporal Ranges, Time Scales and Specification Hierarchies

The previous discussion of a time scale model that uses scalar hierarchies to modeltime has given rise to two important insights. First, we are bound to conclude that timescales are observer-dependent and, for this reason, they do not express the temporalityof any single natural phenomenon; second, natural phenomena always exist onmultiple time scales. Thus, strictly speaking, we cannot claim the existence of a“microgenetic timescale of online talk” (Larsen-Freeman & Cameron, 2008, p. 169):If we opt for a single time scale, then we can only get a grasp of a single,nonexhaustive dimension of talk. If we in contrast want to embrace a phenomenon,such as talk, in its ecological totality, then we need to investigate a multiplicity of time

6. In fact, one might suspect that this temporal model is indeed a result of what Per Linell (2005) calls written language bias. Such a bias did not only shape linguistics, but also philosophy and metaphysics (see Steffensen, 2004).

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scales. There simply is no single time scale of an ecological phenomenon.Accordingly, we can conclude that scalar models of time scales are too simplistic.Acknowledging that such ecological phenomena as brain activity (Anderson, inpress), cognitive performance, human conversations, and social practices all and eachunfold on multiple time scales, we need a model that does not reduce time scales to atiered hierarchy of duration. In what follows we will present such a model with astarting point in the self-similar nature of human behavior.

In an ideal or Platonic universe, self-similar fractals across time scales would beall-encompassing, from the tiniest attoseconds to the time scale of the universe’sexpansion from the big bang to the apocalypse. However, such perfect fractals aremathematical constructs. In contrast, natural fractals “display a rougher, moreirregular form of self-similarity, statistical self-similarity, across a limited range ofscales” (Van Orden et al., 2003, p. 334). Uryu et al. (2014) use the notion of a limitedrange of scales to introduce the concept of a temporal range, that is, a limited range oftime scales within which self-similarity appears. Thus, in the example of the fractalbranching of a tree, within the temporal range of time scales spanning from the tree’smetabolism, over its seasonal growth to its full ontogeny, it exhibits self-similarity inits branching behavior. If, metaphorically, we conceive of time as a movement (see theNewtonian t-arrow), then we can depict temporal ranges as a series of vertical rangesorthogonal to the progression of time (see figure 1).

Figure 1: Temporal Ranges Comprising Increasingly Fewer Time Scales

Since the multiple time scales within a temporal range per definition exhibits self-similarity, its unity can be traced to a single causal frame.7 For instance, in Van Orden

7. Uryu et al.’s (2014) model makes use of the term organizing principle (in the singular). Mainly to avoid the misunderstanding that we subscribe to a theory of monocausality, we adopt Enfield’s term causal frame. We explicate that this frame is a unitas multiplex: it is a unity (a frame, an organizing principle), but the frame can be constituted by a multitude of causal forces. For instance, the emergence of life counts as a causal frame, but life is best conceived as an extremely complex interplay of forces and processes, rather than a single principle.

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et al.’s example (2003, p. 331), the causal frame of the type of living systems thatpsychology describes, is purposive behavior. However, as is well-known fromcomplexity theory, dynamical systems can have, and usually do have, multiple (setsof) causal frames, that is, they may exist within a nested hierarchy of temporal ranges.If one, for instance, compares the dynamical system of a river with the dynamicalsystem of a human conversation (see Steffensen, 2012), the former is a purely physicalphenomenon, governed by few causal frames (gravity, etc.). The latter, in contrast, isnot just governed by physical causes, but also by causal frames related to biology,meaning-making, purposive behavior, sociality, and awareness. For that reason, therepresentation in figure 1 is inapt in that it suggests that temporal ranges are separableand independent. That, of course is not at all the case: Dynamical systems are rarely(perhaps even never) governed by a single causal frame. The temporal ranges in figure1 are simple temporal ranges, governed by a single causal frame. Simple temporalranges can thus only describe a single dimension of a complex system, the choice ofwhich is of course observer-dependent. To amend this shortcoming, we need tointroduce complex temporal ranges, that is, temporal ranges governed by anincreasing number of causal frames. This is illustrated in figure 2:

Figure 2: Complex Temporal Ranges Comprising Increasingly Fewer Time Scales and Constrained by Increasingly More Causal Frames

What figure 2 shows is complexification occurring as more complex phenomenaemerge over time. In complexity science, emergence is understood as the result ofinteracting structures which due to this interaction self-organizes into a structure thatis unpredictable from the interactions of its components. Thus, figure 2 claims the

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existence of a series of such self-organization events, that is, the existence ofembedded emergence. Somewhat heuristically, we may say that, on the left of themodel we have the full temporal range of the entire physical universe. Physicalstructures span all known time scales, stretching from the longest galactic processes tothe quickest subatomic events. Within the temporal range pertaining to physicalreality, metabolic processes emerged some 3.5 billion years today, which thus gaverise to a temporal range pertaining to life, which much later gave rise to a speciesknown as Homo sapiens sapiens. In other words, life is a causal frame for allsubsequent emergences within temporal range.

Unsurprisingly, life unfolds on a narrower temporal range than physics. Intechnical terms, this is because of the so-called adiabatic principle: “the fastersomething happens, the less energy is transferred. Conversely, very slowly varyingprocesses appear as a stable background on the timescale of faster ones” (Lemke,2000, p. 279). What this means is that metabolic processes on planet Earth are largelyunaffected by subatomic and galactic events. According to the adiabatic principle thetemporal range of life emerges symmetrically within the temporal range of physics:metabolic processes on planet Earth neither alter nuclear physics nor long-rangeinterstellar gravity. Thus, just as a river carves out a canyon, so does life carve outtime scales that are both slower than life and faster than life. This is what gives rise tothe ladder-like shape in figures 1 and 2.

In this way we have moved away from a scalar model of time. Rather thanassuming a scalar hierarchy of time scales discussed in section 2, an ecological modelof time pivots on a specification hierarchy of temporal ranges (Salthe, 1991, p. 254).Specification hierarchies are well suited to the modelling of emergent structuresbecause they establish a recurrent asymmetry between a more general range (to theleft in figures 1 and 2) and a more specific range (to the right). For instance, “all livingsystems, are, at their least specified, ‘merely’ physical systems, but not all physicalsystems are living” (Salthe, p. 254). Even the simplest living systems are thus morecomplex than non-living systems. In the words of the proposed model, they aregoverned by additional causal frames (e.g., metabolism).

Evidently, the obvious next question is: Which causal frames do we need to takeinto account? In principle, there are a limited number of ranges because, unlike ascalar hierarchy, a specification hierarchy is truncated. In the most general (left) end, itis truncated by the axiomatic claim that nothing exists beyond physics, and thus therecan be no extra-physical temporal range. In the most specific (right) end, thetruncation “reflects the nature of the observer” (Salthe, 1991, p. 254). Thus, dependingon the research interests of the observer—human interaction, mammalian physiology,bacterial metabolism, ecosystems, geological changes, particle physics,etc.—different specification hierarchies of temporal ranges can be taken intoconsideration. Establishing each and every temporal range that is relevant to humaninteraction goes beyond this article, but in figure 3 we offer a heuristic overview ofsome relevant temporal ranges.

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Figure 3: Complex Temporal Ranges in Human Interaction (a heuristic approximation)

Figure 3 implies that human interaction is governed by multiple temporal ranges/causal frames: The individual awareness (orientation) of the participants, theirbehavioral specificities, the trajectory of the dialogical system, their autobiographicaland ontogenic (conscious and embodied) memories, a (more or less) sharedsociocultural environment (including societal and organizational formations), theirbiological capacity for intersubjective and emotional sense-making (includingindividual abduction), and, finally, the most basic principles of the physical universe.8

Like MacWhinney, Enfield, Rączaszek-Leonardi and other language scholars, weargue for an emergentist model that gives rise to increasingly complex temporalranges. The main characteristic of our model in figure 3 is that it does not relatespecific time scales to specific systems or domains. For instance, there is no singletime scale of evolution, because evolution comprises all time scales within thetemporal range pertaining to organic life forms, ranging from fast metabolicprocesses, over the dynamics of selection pressures, to the trans-generationalstabilization of DNA molecules. Likewise, the model implies that many temporalranges co-exist in the same data set. Thus, on the single time scale of theconversational trajectory (i.e., the middle zone in figure 3), numerous interactional,

8. Physical principles are rarely taken into account in interaction studies, but they can be shown to operate as a taken-for-granted background for such studies. For instance, in gesturing the gesture stroke is typically performed as an upward motion, demanding energy due to gravity. Gesture retraction typically follows a downward path. Likewise, voice dynamics depend on breathing (a biological causal frame) which depends on air pressure (a physical causal frame).

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social and biological ranges are at work, simply because the interaction is constrainedby multiple ecological systems.9 In the light of this argumentation, we can reformulateBrian MacWhinney’s (2005) challenge at the beginning of this article, by asking thequestion: How does one identify and discern the multiple causal frames thatsimultaneously determine the conversational trajectory?

In order to answer this question, two important, complementary strategies arecurrently being developed. One takes a quantitative approach and exploits that humaninteraction flows as a dynamical system, that is, it is “a temporarily coordinatedfunctional whole, consisting of two [or more] sub-systems” (Rączaszek-Leonardi,Rakowski, Kurczych, Radkowska, & Plewczyński, 2012).

Such systems can be modelled through non-linear statistics, and this approach hasgenerated profound and interesting insights in human interaction. However, asdiscussed in section 2, the mathematical insights need to be grounded in aninterpretive understanding of the phenomenon under scrutiny. Hence, the otherstrategy, while accepting the premise that interaction is a dynamical, non-linearsystem, depends on a qualitative approach to human interaction, that is, it depends onobservational investigation of dialogical systems (Steffensen, 2012), mainly based onvideo-recordings.

Like other dynamical systems, a dialogical system can never fully satisfy all itscausal frames, and will thus not “settle into law-governed ‘comfort zones’”(Steffensen & Hodges, 2010), but alternate between stable states, in order to over timesatisfy all the constraints and thus to uphold a conversational balance. Conversing isthus like juggling in that it is, as (Bakhtin, 1986, p. 287) said, “all and always on theboundary.” Importantly, such alternations in the trajectory of the dialogical systemindicate that the constraining causal frames constitute a heterarchy, that is, they areco-operant, but without relying on a fixed hierarchy. The frames are in tension, and theweight with which they influence activities varies over time. We hypothesise that thetrajectory of the dialogical system undergo phase transitions, that is, points in timewhere a causal frame within this overall heterarchy becomes more prominent, or lessso, in how it shapes the dialogical system. It is in these changes, we hypothesise, thatwe get a glimpse of the specific causal frames. In the Thanksgiving dinner presentedin the prologue, we witness such a phase transition where the sociocultural frame,triggered by the word Nazi, abruptly becomes the dominant causal frame. In a sense,Nazi becomes a strong attractor for the dialogical system which cannot escape theemotional basin constituted by both their post-war memories and the supra-individualhistories of Germans, Japanese and Americans.10

In Kelso’s terms (1995), the dialogical system exhibits multistability in that italternates between two or more conflicting states: There is tension between a state of

9. Surely, the observation of some systems depends on the reformatting and mediation of data; for instance, (Konvalinka et al., 2011) show how heart rates may synchronize between related individuals, and this biological dimension of social relatedness evidently depend on a measurement apparatus that reformats biological data in a way that allows them to be correlated with social activities and relations.

10. For further details, see Uryu et al. (2014).

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interpersonal affiliation and caring between Michiko and Bianka, and a state ofintercultural resistance and conflict between their respective cultural groupings. Suchtensions and dynamics of the conversational trajectory are due to the fact thatintercultural dynamics are incarnated in interpersonal dynamics: The slowness ofsocioculture is intertwined with the fastness of embodied co-existence.

4. Temporal Ranges and Time Scales in Human Interaction

In the previous section we have presented our specification hierarchical model oftime. The model entails a strong anti-reductionist stance. Like all emergence-basedmodels it refuses that any given phenomenon, say conversation, is reducible toindividual brain activity, biological functions, not to mention biochemistry andphysics. But it also rejects equally reductionist models thriving in those approaches tolanguage that are based in the humanities and the social science. They often make anepistemological cut that divides meaning from behavior, social interaction frombiological coordination, mental content from life, or symbolic structures from theirmetabolic genesis. These approaches have replaced a bio-reductionism with a socio-reductionism, and the result is that we lose sight of the full spectrum of complexitiesin human interaction.

The presentation has also made it clear that from an ecological point of view, timeis a phenomenon that relies on emergent dynamics of living, ecological systems. Wecan thus contrast the lived time of ecological systems, and the clock time of theobserver-dependent event of measuring such systems. While the former pertains to thetemporal ranges, the latter comes to the fore with the time scales. However, appeal totime scales—understood as a hierarchy of duration—has proven to be a powerfulheuristic in describing ecological systems, and having established an interpretivedevice that allows us to make sense of each time scale, we can now reconceptualizethe ways in which time scales relate to lived time. The key to fitting observer-dependent time scales into the model of temporal ranges is, again, the adiabaticprinciple, that is, the fact that two adjacent temporal ranges are not fully co-extensive.If they were, there would be no emergence because the emergent phenomenon at themore complex target range would be fully explainable by the dynamics of the simplersource range. In other words, each emergent temporal range recalibrates the dynamicsof a portion, and only a portion, of the more basic temporal range. Thus, for eachadditional increasingly complex temporal range that emerges, there is “a zone on thetime scale continuum that is comprised by the temporal range N, but not comprised bythe [emergent] temporal range N+1” (Uryu et al., 2014, p. 46). Accordingly, one canhypothesize that those zones correspond with the time scales an observer sets out toinvestigate. In Figure 4 these time scales are indicated in parenthesis, and very muchwith a mere heuristic status in the model.

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Figure 4. A Heuristic Model of Time Scales as Zones Differentiating Temporal Ranges

To exemplify, if we study human interaction with a focus on the conversational timescale, figure 4 shows how utterance activities and conversational awareness emergesout of the dialogical system which can be described on an event time scale and on aninter-bodily time scale. Furthermore, since the temporal range corresponding to thedialogical system is self-similar, the overall event achieved by the dialogical systemand the minute synchronicities between the participants are identical: The event isontologically indistinguishable from the interactivity of the participants (seeSteffensen, 2013)—it takes an observer perspective to constitute this distinction.Similarly, the dialogical system emerges from human ontogeny, that is, ourontogenetic development as well as our autobiographical memories of it. In turn, theindividual ontogenies that mesh in the dialogical system are born out of (more or lessshared) sociocultures, “minimally … defined as the existence of intra-species groupdifferences in behavioral patterns and repertoires, which are not directly determinedby ecological circumstances … and which are learned and transmitted acrossgenerations” (Sinha, Forth.). Given this temporal range, we find a natural relationbetween a sociocultural time scale and the behavioral time scale: It is a relation ofself-similarity, and we thus need not establish a relation of co-constitution betweenthese two time scales.

With these remarks in the back of our minds, let us again turn our attention toBianka and Michiko and the emotionally tense Thanksgiving dinner. Why did theintroduction, or perhaps even the intrusion, of the Nazi symbol so dramatically changethe dialogical trajectory? The key to understand this event, we suggest, lies in the self-

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similarity of the temporal ranges. In this case, the point of interest is the self-similarcharacteristics of the Nazi symbol within the temporal range of human socioculture.As Uryu et al. (2014, p. 53) conclude, in the United States the Nazi symbol “hasceased to be the name of a political party in the 30’s and 40’s in Germany—it hasbecome the very incarnation of evil.” Both the sociocultural time scale of Americanculture and the interactional time scale of the Thanksgiving emerge from thesociocultural temporal range. Consequently, both social systems (i.e., small-scale orlarge-scale coordinated wholes, such as families, academics, Americans, etc.) anddialogical system (i.e., face-to-face coordinating wholes, such as Bianka, Michiko,and the other women) are constrained by the causal frame pertaining to the symbolicdynamics of Nazi within the complex sociocultural frame. Hence, the trajectory of thedialogical system is intrinsically linked to cultural changes that are accumulated inhere-and-now semiotic symbols: the Nazi symbol entails that the sociocultural frametakes the lead in the heterarchy of forces that form the present moment, and itconstrains dynamics on the conversational time scale, the event time scale and theautobiographical time scale.11 Or in the words of Uryu et al.:

Six decades of postwar trauma and cultural accusation and guilt has accumulated into a high-energetic symbol, and on the latter [i.e., the conversational time scale] the full energy of this symbolis released in a short moment, exploding in strong emotional and cultural cascades in the entiredialogical system. (Uryu et al., 201, p. 53)

In conclusion, the interaction between Michiko and Bianka ceases to be merely adialogical system of coordinative dynamics between interlocutors. Socioculturalresources with their own temporal fingerprints mesh with the interaction in a non-linear way. In this case, the mechanism behind this remarkable dialogical trajectorycan neither be boiled down to individual inclinations nor cultural templates. Rather,this particular outcome is caused by the non-linear, unpredictable interaction between(a) the conversational norms of completing unfinished turns (on the conversationaltime scale), (b) the sociocultural dynamics of symbolic configurations, and (c) theasymmetry in the autobiographical and emotional sensitivity of the interlocutors.

5. Conclusions

Human interaction flows in dialogical systems that remain inexplicable until one turnsto temporal dynamics. By elaborating the model presented in Uryu et al. (2014), wehave laid the ground for overcoming the simplistic models of time found both inreducing temporality to a single temporal principle (e.g., sequentiality in conversationanalysis) and in proposing a tiered model of time. Our model thus represents a steptowards understanding the temporal dynamics of dialogical systems. The key to thisunderstanding is the realization that human interaction is a profoundly ecological

11. And logically also the developmental and the inter-bodily time scale, but our data neither comprises longitudinal observational studies of the participants, nor detailed video records of inter-bodily coordination, etc.

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phenomenon irreducible to models of sense-making or microsociology. We will endthe article by pointing to two implications of an ecological model of temporality inhuman interaction.

First, it is noteworthy that the human ecology is extended by symbolic structures,that is, the coordinative dynamics of human interaction is sense-saturated (seeSteffensen, 2011). Participants in dialogical systems recruit sociocultural resources innon-linear and unpredictable ways. As illustrated by the example of Michiko andBianka, dialogical systems are far more complex than expected, because they do notdepend on merely the here-and-now interactions between participants, but also on avast reservoir of sociocultural and autobiographical resources. Human interactiondepends on discontinuous transitions between temporal ranges, or more technically: Itdepends on recalibrations of the heterarchy of causal frames governing interaction. Inthe lived time of human interaction, participants move from one temporal range toanother—human beings are temporal rangers wandering around in a temporallandscape of multiple pasts, presents and futures. A key question in the investigationof interaction therefore is what determines the dialogical trajectory through thistemporal landscape. Undoubtedly, what governs these transitions between temporalranges is as complex a mechanism as time itself. Evidently, it is irreducible toindividual intentions, as well as to supra-individual structures.

Second, the ecological model of time emphasizes an ecosystemic view on howorganisms relate to their environment. An ecosystemic approach to human cognition,interaction and sense-making stresses the importance of understanding organism andenvironment, not as two separate, interacting systems, but as a whole. As Rosch(1999, p. 74) points out, “When mind and world are considered separate, causal orexplanatory efficacy is attributed either to the mind or the world. … At best the mindand world will be said to interact.” The way to avoid such dualist models of the mind-world relation is to work with a monistic mind-world model. One example of such amodel is Timo Järvilehto’s organism-environment system: “All concepts referring tomental activity—like perception, emotion, memory, etc.—describe only differentaspects of the organization and dynamics of the whole organism-environment system”(Järvilehto, 1998, p. 330).

What the ecological model of time adds to this systemic view is that it is thetemporal dynamics of natural systems that enable them to achieve relative autonomywithin the environment. Thus, according to the logic of the specification hierarchy, allsystems emerge from extant systems, and in time they dissolve back into these largersystems. As all systems exist within temporal ranges that are truncated in both itsfastest and its slowest end, the temporal range of the environment comprises timescales that are both faster and slower than the system under investigation. This meansthat all ecological systems are constrained by causal frames that are not shared withtheir environment. Thus, to understand the asymmetric function and organization ofsuch systems, we must understand their temporality, that is, how they are born out oflarger ecosystemic movements. Another key question thus is how to identify theecosystemic constraints that govern how causal frames emerge and dissolve.

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Due to space limitations, this article has not engaged with methodologicalconsiderations of how one pursues the investigation of time scales, temporal ranges,causal frames and sociocultural recruitment in dialogical systems. Therefore, thisarticle has an argumentative sibling that picks up on these questions, namely Pedersenand Steffensen (2014).

Acknowledgements

We would like to thank our two editors, as well as two anonymous reviewers, for theircomments on the first draft of this article. In particular, we thank Stephen Cowley forpointing out that range is also a verb in English, and that human beings are thusrangers in time. In writing this article, one person has shaped it more than any other:Joanna Rączaszek-Leonardi offered us the most meticulous and detailed critique ofthe first version of the article, and to the degree that the present version is of anyvalue, it is to a large extent thanks to her effort.

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