A model of gestural sequencing in speech is proposed that aspires to producing biologically plausible fluent and efficient movement in generating an utterance. We have previously proposed a modification of the well-known task dynamic implementation of articulatory phonology such that any given articulatory movement can be associated with a quantification of effort (Simko & Cummins, 2010). To this we add a quantitative cost that decreases as speech gestures become more precise, and hence intelligible, and a third cost component that (...) places a premium on the duration of an utterance. Together, these three cost elements allow us to derive algorithmically optimal sequences of gestures and dynamical parameters for generating articulator movement. We show that the optimized movement displays many timing characteristics that are representative of real speech movement, capturing subtle details of relative timing between gestures. Optimal movement sequences also display invariances in timing that suggest syllable-level coordination for CV sequences. We explore the behavior of the model as prosodic context is manipulated in two dimensions: clarity of articulation and speech rate. Smooth, fluid, and efficient movements result. (shrink)

Figner, Mackinlay, Wilkening, and Weber (2009) developed the Columbia Card Task (CCT) to measure risk-taking attitudes. This tool consists of two versions: in the COLD version the decision maker needs to state in advance how many cards (out of 32) they want to turn over (so called static risk taking), in the HOT version they have the possibility of turning over all 32 cards one-by-one until they decide to finish (dynamic risk taking). We argue that the HOT version confounds (...) an individual’s willingness to accept risk with their beliefs in trend continuation vs. trend reversal in a prognostic task. In two experimental studies we show that people believing in trend continuation (momentum subjects) turn over more cards than those believing in trend reversal (contrarians) in the HOT version of the task. However, this is not the case in the COLD version. Thus, we provide evidence that, when considered as a dynamic risk propensity measure, the number of turned over cards in the HOT version of the CCT is a contaminated measure and reflects two phenomena: (1) risk preference and (2) the decision-maker’s belief in trend continuation. We speculate that other dynamic risk taking measures can also be biased by a momentum strategy. (shrink)

Current models of speech motor control rely on either trajectory-based control (DIVA, GEPPETO, ACT) or a dynamical systems approach based on feedback control (Task Dynamics, FACTS). While both approaches have provided insights into the speech motor system, it is difficult to connect these findings across models given the distinct theoretical and computational bases of the two approaches. We propose a new extension of the most widely used dynamical systems approach, Task Dynamics, that incorporates many of the (...) strengths of trajectory-based approaches, providing a way to bridge the theoretical divide between what have been two separate approaches to understanding speech motor control. The Task Dynamics (TD) model posits that speech gestures are governed by point attractor dynamics consistent with a critically damped harmonic oscillator. Kinematic trajectories associated with such gestures should therefore be consistent with a second-order dynamical system, possibly modified by blending with temporally overlapping gestures or altering oscillator parameters. This account of observed kinematics is powerful and theoretically appealing, but may be insufficient to account for deviations from predicted kinematics—i.e., changes produced in response to some external perturbations to the jaw, changes in control during acquisition and development, or effects of word/syllable frequency. Optimization, such as would be needed to minimize articulatory effort, is also incompatible with the current TD model, though the idea that the speech production systems economizes effort has a long history and, importantly, also plays a critical role in current theories of domain-general human motor control. To address these issues, we use Dynamic Movement Primitives (DMPs) to expand a dynamical systems framework for speech motor control to allow modification of kinematic trajectories by incorporating a simple, learnable forcing term into existing point attractor dynamics. We show that integration of DMPs with task-based point-attractor dynamics enhances the potential explanatory power of TD in a number of critical ways, including the ability to account for external forces in planning and optimizing both kinematic and dynamic movement costs. At the same time, this approach preserves the successes of Task Dynamics in handling multi-gesture planning and coordination. (shrink)

As research on mind wandering has accelerated, the construct’s defining features have expanded and researchers have begun to examine different dimensions of mind wandering. Recently, Christoff and colleagues have argued for the importance of investigating a hitherto neglected variety of mind wandering: “unconstrained thought,” or, thought that is relatively unguided by executive-control processes. To date, with only a handful of studies investigating unconstrained thought, little is known about this intriguing type of mind wandering. Across two experiments, we examined, for the (...) first time, whether changes in task demand influence rates of constrained versus unconstrained thoughts. In both experiments, participants completed either an easy (0-back) or hard (2-back) task and responded to intermittently presented thought probes that gauged thought constraint throughout the task. In Experiment 1, we found that participants completing the easy task engaged in unconstrained thoughts more frequently than those completing the difficult task. In Experiment 2, we replicated this result and further demonstrated manipulations of unconstrained thought while also measuring task-relatedness (a common dimension of mind wandering). Finally, exploratory analyses showed associations between constrained thought and age, verbal intelligence, and an assessment of flow (‘deep effortless concentration’), thereby adding further evidence to indicate a dissociation between task-relatedness and constraint. We discuss the methodological and theoretical applications of our findings to the burgeoning field of research on unconstrained thought. (shrink)

Does a concurrent cognitive task affect the dynamics of bimanual rhythmic coordination? In‐phase coordination was performed under manipulations of phase detuning and movement frequency and either singly or in combination with an arithmetic task. Predicted direction‐specific shifts in stable relative phase from 0° due to detuning and movement frequency were amplified by the cognitive task. Nonlinear cross‐recurrence analysis suggested that this cognitive influence on the locations of the stable points or attractors of coordination entailed a magnification (...) of attractor noise without a reduction in attractor strength. An approximation to these findings was achieved through parameter changes in a motion equation in relative phase. Results are discussed in terms of dual‐task performance as limited resources, dynamics rather than chronometrics, and reparameterization rather than degradation. (shrink)

ABSTRACTNew methods of calculating indices from the dot-probe task measure temporal dynamics in attention bias or fluctuations in attention bias towards and away from emotional stimuli over time. H...

Temporal patterns in human movement, and in speech in particular, occur on multiple timescales. Regularities in such patterns have been observed between speech gestures, which are relatively quick movements of articulators (e.g., tongue fronting and lip protrusion), and also between rhythmic units (e.g., syllables and metrical feet), which occur more slowly. Previous work has shown that patterns in both domains can be usefully modeled with oscillatory dynamical systems. To investigate how rhythmic and gestural domains interact, an experiment was conducted in (...) which speakers performed a phrase repetition task, and gestural kinematics were recorded using electromagnetic articulometry. Variance in relative timing of gestural movements was correlated with variance in rhythmic timing, indicating that gestural and rhythmic systems interact in the process of planning and producing speech. A model of rhythmic and gestural planning oscillators with multifrequency coupling is presented, which can simulate the observed covariability between rhythmic and gestural timing. (shrink)

This study investigates interpersonal processes underlying dialog by comparing two approaches, interactive alignment and interpersonal synergy, and assesses how they predict collective performance in a joint task. While the interactive alignment approach highlights imitative patterns between interlocutors, the synergy approach points to structural organization at the level of the interaction—such as complementary patterns straddling speech turns and interlocutors. We develop a general, quantitative method to assess lexical, prosodic, and speech/pause patterns related to the two approaches and their impact on (...) collective performance in a corpus of task-oriented conversations. The results show statistical presence of patterns relevant for both approaches. However, synergetic aspects of dialog provide the best statistical predictors of collective performance and adding aspects of the alignment approach does not improve the model. This suggests that structural organization at the level of the interaction plays a crucial role in task-oriented conversations, possibly constraining and integrating processes related to alignment. (shrink)

With the advent of computers in the experimental labs, dynamic systems have become a new tool for research on problem solving and decision making. A short review of this research is given and the main features of these systems (connectivity and dynamics) are illustrated. To allow systematic approaches to the influential variables in this area, two formal frameworks (linear structural equations and finite state automata) are presented. Besides the formal background, the article sets out how the task demands (...) of system identification and system control can be realised in these environments, and how psychometrically acceptable dependent variables can be derived. (shrink)

The current study examined whether pigeons could learn to use abstract concepts as the basis for conditionally switching behavior as a function of time. Using a mid-session reversal task, experienced pigeons were trained to switch from matching-to-sample (MTS) to non-matching-to-sample (NMTS) conditional discriminations within a session. One group had prior training with MTS, while the other had prior training with NMTS. Over training, stimulus set size was progressively doubled from 3 to 6 to 12 stimuli to promote abstract concept (...) development. Prior experience had an effect on the initial learning at each of the set sizes but by the end of training there were no group differences, as both groups showed similar within-session linear matching functions. After acquiring the 12-item set, abstract-concept learning was tested by placing novel stimuli at the beginning and end of a test session. Prior matching and non-matching experience affected transfer behavior. The matching experienced group transferred to novel stimuli in both the matching and non-matching portion of the sessions using a matching rule. The non-matching experienced group transferred to novel stimuli in both portions of the session using a non-matching rule. The representations used as the basis for mid-session reversal of the conditional discrimination behaviors and subsequent transfer behavior appears to have different temporal sources. The implications for the flexibility and organization of complex behaviors are considered. (shrink)

A fundamental challenge in the cognitive sciences is discovering the dynamics that govern behavior. Take the example of spoken language, which is characterized by a highly variable and complex set of physical movements that map onto the small set of cognitive units that comprise language. What are the fundamental dynamical principles behind the movements that structure speech production? In this study, we discover models in the form of symbolic equations that govern articulatory gestures during speech. A sparse symbolic regression (...) algorithm is used to discover models from kinematic data on the tongue and lips. We explore these candidate models using analytical techniques and numerical simulations and find that a second-order linear model achieves high levels of accuracy, but a nonlinear force is required to properly model articulatory dynamics in approximately one third of cases. This supports the proposal that an autonomous, nonlinear, second-order differential equation is a viable dynamical law for articulatory gestures in speech. We conclude by identifying future opportunities and obstacles in data-driven model discovery and outline prospects for discovering the dynamical principles that govern language, brain, and behavior. (shrink)

As a NP-hard problem that needs to be solved in real time, the dynamic task allocation problem of unmanned aerial vehicle swarm has gradually become a difficulty and hotspot in the current planning field. Aiming at the problems of poor real-time performance and low quality of the solution in the dynamic task allocation of heterogeneous UAV swarm in uncertain environment, this paper establishes a dynamic task allocation model that can meet the actual needs and uses the binary (...) wolf pack algorithm to solve it, so as to propose a dynamic task allocation method of heterogeneous UAV swarm in uncertain environment. In this method, a dynamic mechanism of attacking while searching and priority attacking of important targets is designed. A dynamic task allocation model of multitarget, multitask, heterogeneous multiaircraft platform and multiconstraint is established based on the target cost-effectiveness ratio and task execution time window. In addition, one-dimensional 0–1 coding method is adopted to encode the task allocation scheme. Furthermore, the wolf pack algorithm is introduced in brief. This paper focuses on the BWPA with the good computational robustness and strong global search ability to solve the dynamic allocation model. According to the simulation results, the designed task allocation method not only has good adaptability to the change of target and UAV number, as well as good stability and scalability, but also can effectively solve the dynamic task allocation problem of heterogeneous UAV swarm in unknown environment. Therefore, the established model and solution method can provide a useful reference for task allocation and other related problems. (shrink)

A dynamic context model of interactive behavior was developed to explain results from two experiments that tested the effects of interaction costs on encoding strategies, cognitive representations, and response selection processes in a decision-making and a judgment task. The model assumes that the dynamic context defined by the mixes of internal and external representations and processes are sensitive to the interaction cost imposed by the task environment. The model predicts that changes in the dynamic context may lead to (...) systematic biases in cognitive representations and processes that eventually influence decision-making and judgment outcomes. Consistent with the predictions by the model, results from the experiments showed that as interaction costs increased, encoding strategies and cognitive representations shifted from perception-based to memory-based. Memory-based comparisons of the stimuli enhanced the similarity and dominance effects, and led to stronger systematic biases in response outcomes in a choice task. However, in a judgment task, memory-based representations enhanced only the dominance effects. Results suggested that systematic response biases in the dominance context were caused by biases in the cognitive representations of the stimuli, but response biases in the similarity context were caused by biases in the comparison process induced by the choice task. Results suggest that changes in interaction costs not only change whether information was assessed from the external world or from memory but also introduce systematic biases in the cognitive representation of the information, which act as biased inputs to the subsequent decision-making and judgment processes. Results are consistent with the idea of interactive cognition, which proposes that representations and processes are contingent on the dynamic context defined by the information flow between the external task environment and internal cognition. (shrink)

BackgroundInnovations in technology have contributed to rapid changes in the way that modern biomedical research is carried out. Researchers are increasingly required to endorse adaptive and flexible approaches to accommodate these innovations and comply with ethical, legal and regulatory requirements. This paper explores how Dynamic Consent may provide solutions to address challenges encountered when researchers invite individuals to participate in research and follow them up over time in a continuously changing environment.MethodsAn interdisciplinary workshop jointly organised by the University of Oxford (...) and the COST Action CHIP ME gathered clinicians, researchers, ethicists, lawyers, research participants and patient representatives to discuss experiences of using Dynamic Consent, and how such use may facilitate the conduct of specific research tasks. The data collected during the workshop were analysed using a content analysis approach.ResultsDynamic Consent can provide practical, sustainable and future-proof solutions to challenges related to participant recruitment, the attainment of informed consent, participant retention and consent management, and may bring economic efficiencies.ConclusionsDynamic Consent offers opportunities for ongoing communication between researchers and research participants that can positively impact research. Dynamic Consent supports inter-sector, cross-border approaches and large scale data-sharing. Whilst it is relatively easy to set up and maintain, its implementation will require that researchers re-consider their relationship with research participants and adopt new procedures. (shrink)

This paper explores some of the insights offered by a dynamic systems approach into the nature habits. “Dynamic systems approach” is used here as an umbrella term for studies of cognition, behavior, or development as systems of elements that change over time (e.g. Thelen and Smith 1994, 2006), while “dynamical systems” is reserved for studies that use differential equations to describe time-based systems (e.g., Schöner and Kelso 1988, Tschacher and Dauwalder 2003). The following discussion draws primarily from the coordination (...) class='Hi'>dynamics research of J.A. Scott Kelso (1995, 2012), which stems from Hermann Haken’s theory of synergetics (1977, 2003). However, the view of habits presented here is more of an interpretive application than a literature review, as the work on which it draws does not address habits explicitly. Perhaps this is because conventional notions of habit are too broad and loose to be captured succinctly in dynamic terms. Dynamical studies of human behavior have focused on more specific capacities such as motor coordination (Thelen et al. 1987), perception (Tuller et al. 1994), and learning (Kostrubiec et al. 2012). Yet this variety of applications suggests that the scope of the dynamic approach overlaps significantly with the domain of habits, so that dynamic concepts could be used to challenge and refine our conventional notions of habitual behavior. Accordingly, the goal of this paper is to raise questions about the nature of habits rather than present a comprehensive scientific theory. For a dynamic systems approach, stability is “the central concept” (Schöner and Kelso 1988, 1515). The “essential issues are the stability of the system, as indexed by the behavior of some collective measure of the multiple components, and the changes in stability over time” (Thelen and Smith 2006, 289). Intuitively, it would seem that the characteristic stability or stabilities of a system—its preferred states—are its habits. But the connection between stable states and habitual behavior is not as straightforward as it appears. The preferred states of a dynamic system are not simply “built in”; rather they depend on the interactive dynamics of the system’s components as well as the interactive couplings of the system with its environment. The following discussion explores the implications of four features of dynamic system stability for our understanding of habit: 1) stability is relative to timescale, and system stabilities at different timescales are interdependent; 2) the attractor landscape describing the characteristic stabilities of a system can be altered by various control parameters, including situational parameters; 3) systems can have multiple stabilities, such that the stability they exhibit at any given time may depend on their recent history; 4) learning processes tend to affect a whole cluster of interrelated stabilities and not just one stability in isolation. In light of these features of dynamic stability, it seems that there is no straightforward way to map conventional notions of personal habits onto stabilities of the human person considered as a nested dynamic system of body, brain, and environment (Chiel and Beer 1997). Should we consider as habits only the “intrinsic” stabilities of brain and body, regardless of the variety of behaviors that can arise from these stabilities in different situations? Or should only consider as habits those patterns of behavior that are regularly observed within a certain type of situation, regardless of how differently these patterns might be assembled at the body-brain level? From the dynamic perspective, stability and change are ubiquitous features at every level or spatiotemporal scale of description. Thus it seems arbitrary to apply the term “habit” only to one kind or level of stability, and perhaps this explains why the term is seldom used in dynamic systems literature. Yet it could be argued that this multilevel complexity is an advantage, as it can be used to challenge conventional notions of habit in interesting ways. Let us suppose that for any level of human behavior that can be described as a dynamic system, the stabilities or preferred states of that system—its “attractor landscape”—are at least analogous to habits, and should be considered as such. What is revealed by this broader, dynamic perspective?First, this view calls into question the usual timescale of habits, which is typically restricted to stable features of personality and behavior on the timescale of months or years (Lewis 2000). From a dynamic perspective, these stabilities are in principle no different from stabilities at faster and slower timescales. Moreover, different timescales of stability are interrelated: while the habits of any given timescale are shaped by the “deeper” habits of a slower timescale, they also can lead to changes at this deeper level. In other words, the “force of habit” is not one-way: habits are shaped by the behaviors that they themselves constrain. For example, mood is shaped by habits of personality, while a string of similar moods can lead to changes of personality. And though it may seem strange to think of moods as temporary emotional habits on the timescale of hours or days, their way of shaping and being shaped by emotional states on the timescale of seconds or minutes is analogous to the relationship between personality and mood. Likewise one can treat emotional states as habits that contribute to the attractor landscapes for thoughts and sensorimotor activity at an even faster timescale, while personality itself can be seen as evolving over the very “deep” landscape of developmental habits (Thelen and Smith 2006). Thus a dynamic view opens up a wider range of timescales across which the concept of habit might apply. But more importantly, even if we choose to restrict habit to just one of these levels, the interaction of timescales suggests that our understanding of any one level should draw upon at least two neighboring levels (Kelso 1995), if not the entire nested system. Second, a dynamic view of habit will include regular variations of the attractor landscape that occur in relation to changing parameters. In some cases these variations can be represented by bifurcation diagram that shows how the attractor landscape changes in relation to a single control parameter (Kelso and Engstrom 2006, 124-137). Most human behaviors, however, require that multiple parameters are taken into account. Now, provided that the important variables and parameters for describing characteristic variations of a certain behavior for an individual can be determined (a very difficult task, in most cases), one could, in theory, construct a comprehensive “habit topology” for that behavior: a map of how that behavior’s preferred states change in relation to various parameters. Notice that, depending on the parameters involved, some stabilities of this habit topology will be visited by the system more or less regularly. For instance, in the case of quadruped motion, assuming that the parameter of speed varies regularly over its natural range, a quadruped (e.g. horse) regularly visits the various gaits (walk, trot, gallop) that make up the preferred states of its habit topology (Schöner and Kelso, 1988, 1516; Hoyt and Taylor, 1981). However, for some behaviors, especially those that are sensitive to multiple parameters, certain regions of the habit topology can remain “hidden” because the required values of the relevant control parameters are rarely if ever encountered. Imagine, for example, that a person who never dances might be found, on one occasion, happily dancing the night away. Conventionally speaking, this behavior is not habitual. But from a dynamic view, it is hard to say. Perhaps on that occasion the person encountered just the right combination of circumstances—excellent mood, pleasurable company, Afro-Cuban music, fantastic mojitos, etc.—that made, for that person, dancing a very deep (and enjoyable) stability. If these circumstances will regularly facilitate the same behavior for that person, cannot we say that dancing is habitual for them in those circumstances? The point here is not to insist on this characterization, but to question our conventional understanding of habits, which typically involves assumptions about “normal” circumstances. Are habitual behaviors only rightfully considered as such if we regularly encounter the circumstances that facilitate their expression? If not, how would we determine the unexpressed or latent habits of a person, independently of circumstances? Third, it is important to consider that the attractor landscape of a particular behavior commonly has multiple stabilities even within restricted parameter values (Schöner and Kelso 1988, 1518). This phenomenon of multistability implies that “habitual behavior” may not be always the same even when all relevant variables are the same. In such cases, which stability describes the habit? The stability into which the system enters may depend on its recent history or on a symmetry breaking. For example, as represented by the much-studied HKB model (Chemero 2009), coordinated finger movement—waving the two index fingers in time with a metronome, where tempo or speed is the main control parameter—exhibits bistability at slower speeds and monostability at faster speeds (Kelso 1995). Within the slower, bistable regime, which of the two stable states the system exhibits may depend on its historical trajectory: for instance, if the system has just entered the bistable regime from the monostable regime, it will remain in the preferred state of the latter. Although coordinated finger movement may not seem representative of human behavior, coordination dynamics and its telltale characteristics—such as multistability—have been found in a wide range of behavioral and neural systems (Schöner and Kelso 1988; Kelso 2012), suggesting an important implication: perhaps what a person presently exhibits as habitual behavior within certain circumstances does not uniquely characterize what is habitual for that person even within those circumstances, but must also be understood as a result of a particular historical trajectory. The importance of history is also indicated by the phenomenon of hysteresis (Haken 2003, 8-9), i.e., when the state or behavior of a system is influenced by the residual stability of an antecedent regime. Thus certain behaviors might persist even after the attractor landscape has changed so that they are no longer “habitual” within the current context.Finally, if we define learning in terms of alterations to the attractor landscape for a particular behavior such that some states are newly stabilized while others are destabilized (Kostrubiec et al. 2012), the interrelatedness of habits as implied by the previous three points indicates that habits are rarely altered in a piecemeal fashion: learning affects entire clusters of habits that are composed of shared components. That is, learning affects an entire “habit space,” and not just individual habits in isolation (Kelso 1995, 159-186). This further complicates the picture of personal habits, as well as the kinds of learning strategies we should adopt to change them. For instance, it suggests that certain habitual behaviors that are difficult to target can be altered indirectly by focusing on other, related behaviors. It also suggests that in many cases—especially where a complex task demands a finely articulated set of behaviors—training should focus on shaping the overall “habit space” rather than each individual behavior. Moreover, as implied by the definition of learning just given, the dynamical approach suggests that habits are a necessary condition for learning and not just a product—contrary to the so-called “blank slate” model (Kostrubiec et al. 2012). In conclusion, we see that a fairly simple definition of habit in terms of dynamic stability yields a number of insights that question our conventional notion of personal habits as slow-changing, context-independent, uniquely repetitive, and discrete behaviors. At the same time, a dynamical view can give an account of the relative fixity of behaviors that we take to be habitual in the conventional sense. However, by showing that this fixity is dynamically and relationally constituted, a dynamical view reveals the elaborate context in which all habits are embedded. Moreover, human habits appear to be instances of a very general pattern: all living systems exhibit self-organized stabilities that reduce their degrees of freedom, producing robust but flexible repertoires of behavior. (shrink)

Because emotion regulation processes operate over time, they potentially change the context in which subsequent ER processes occur. To test this proposal, fifty-two healthy participants completed the ER choice task. Thirty standardized low- and high-intensity negative images were used to generate different emotional contexts in which participants selected between distraction or reappraisal strategies to decrease the intensity of their negative emotion. Participants then implemented their selected strategy and rated their negative emotion. Using a dynamic perspective, we examined as predictors (...) of ER strategy choice, in addition to current stimulus intensity, several contextual factors from the immediately preceding trial: preceding stimulus intensity and strategy choice, and the intensity of negative affect following the previous strategy implementation and thus preceding the current trial. Results replicated earlier findings that participants are more likely to choose distraction for high-intensity images. Extending earlier findings, selecting reappraisal in the preceding trial and greater negative affect preceding the current trial were associated with lower odds of choosing distraction. The lack of significant interactions among the current and preceding trial factors suggests that these effects on ER choice were direct and not through moderating the effect of current stimulus intensity. These findings support dynamic theories of ER. (shrink)

In dynamic system control, cognitive mechanisms and abilities underlying performance may vary depending on the nature of the task. We therefore investigated the effects of system structure and its interaction with cognitive abilities on system control performance. A sample of 127 university students completed a series of different system control tasks that were manipulated in terms of system size and recurrent feedback, either with or without a cognitive load manipulation. Cognitive abilities assessed included reasoning ability, working memory capacity, and (...) cognitive reflection. System size and recurrent feedback affected overall performance as expected. Overall, the results support that cognitive ability is a good predictor of performance in dynamic system control tasks but predictiveness is reduced when the system structure contains recurrent feedback. We discuss this finding from a cognitive processing perspective as well as its implications for individual differences research in dynamic systems. (shrink)

Abstract In this article, we investigate the merits of an enactive view of cognition for the contemporary debate about social cognition. If enactivism is to be a genuine alternative to classic cognitivism, it should be able to bridge the “cognitive gap”, i.e. provide us with a convincing account of those higher forms of cognition that have traditionally been the focus of its cognitivist opponents. We show that, when it comes to social cognition, current articulations of enactivism are—despite their celebrated successes (...) in explaining some cases of social interaction—not yet up to the task. This is because they (1) do not pay sufficient attention to the role of offline processing or “decoupling”, and (2) obscure the cognitive gap by overemphasizing the role of phenomenology. We argue that the main challenge for the enactive view will be to acknowledge the importance of both coupled (online) and decoupled (offline) processes for basic and advanced forms of (social) cognition. To meet this challenge, we articulate a dynamic embodied view of cognition. We illustrate the fruitfulness of this approach by recourse to recent findings on false belief understanding. Content Type Journal Article Pages 1-23 DOI 10.1007/s11097-011-9223-1 Authors Leon C. de Bruin, Department of Philosophy II, Ruhr-University Bochum, Universitätsstr. 150, 44801 Bochum, Germany Lena Kästner, Department of Philosophy II, Ruhr-University Bochum, Universitätsstr. 150, 44801 Bochum, Germany Journal Phenomenology and the Cognitive Sciences Online ISSN 1572-8676 Print ISSN 1568-7759. (shrink)

This article examines task-setting sequences in university tutorial sessions. Classes from three higher education institutions were audio- and video-recorded. The resulting data, which included both tutor-led and peer group discussions, were transcribed and analysed using conversation analysis. A number of themes emerged from our analysis. First, we found that the tutor's opening turns routinely followed a three-part sequence, the interpersonal and metadiscursive functions of which, we argue, are crucial components in the educative process. Second, we found that students displayed (...) discursively their reluctance to engage in discussion activities and a resistance towards academic or intellectual identities. In contrast to findings from previous studies of tutor—student interaction, we found that interactional power was negotiated in complex and contradictory ways. This, in turn, may embody a range of complex social functions including attention to the `face' concerns of the group, category membership and orientation to broader cultural trends. (shrink)

Perceptual judgments result from a dynamic process, but little is known about the dynamics of number‐line estimation. A recent study proposed a computational model that combined a model of trial‐to‐trial changes with a model for the internal scaling of discrete numbers. Here, we tested a surprising prediction of the model—a situation in which children's estimates of numerosity would be better than those of adults. Consistent with the model simulations, task contexts led to a clear developmental reversal: children made (...) more adult‐like, linear estimates when to‐be‐estimated numbers were descending over trials (i.e., backward condition), whereas adults became more like children with logarithmic estimates when numbers were ascending (i.e., forward condition). In addition, adults’ estimates were subject to inter‐trial differences regardless of stimulus order. In contrast, children were not able to use the trial‐to‐trial dynamics unless stimuli varied systematically, indicating the limited cognitive capacity for dynamic updates. Together, the model adequately predicts both developmental and trial‐to‐trial changes in number‐line tasks. (shrink)

Joint Analysis of EEG and fMRI datasets can bring new insight into brain mechanisms. In this paper, we employed the recently introduced Correlated Coupled Tensor Matrix Factorization (CCMTF) method for analysis of the emotion regulation paradigm based on EEG frontal asymmetry neurofeedback in the alpha frequency band with simultaneous fMRI. CCMTF method assumes that the co-variations of the common dimension (temporal dimension) between EEG and fMRI are correlated and not necessarily identical. The results of the CCMTF method suggested that EEG (...) and fMRI had similar covariations during the transition of brain activities from resting states to task (view and upregulation) states and these covariations followed an increasing trend. The fMRI shared spatial component showed activations in the limbic system, DLPFC, OFC, and VLPC regions, which were consistent with the previous studies and were linked to EEG frequency patterns in the range of 1–15 Hz with a correlation value close to 0.75. The estimated regions from the CCMTF method were then used as the candidate nodes for dynamic functional connectivity (dFC) analysis, in which the changes in connectivity from view to upregulation states were examined. The results of the dFC analysis were compared with a Normalized Mutual information (NMI) based approach in two different frequency ranges (1–15 and 15–40 Hz) as the NMI method was applied to the vectors of dFC nodes of EEG and fMRI data. The results of the two methods illustrated that the relation between EEG and fMRI datasets was mostly in the frequency range of 1–15 Hz. These relations were both in the brain activations and the dFCs between the two modalities. This paper suggests that the CCMTF method is a capable approach for extracting the shared information between EEG and fMRI data and can reveal new information about brain functions and their connectivity without solving the EEG inverse problem or analyzing different frequency bands. (shrink)

Where is logic heading today? There is a general feeling that the discipline is broadening its scope and agenda beyond classical foundational issues, and maybe even a concern that, like Stephen Leacock’s famous horseman, it is ‘riding off madly in all directions’. So, what is the resultant vector? There seem to be two broad answers in circulation today. One is logical pluralism, locating the new scope of logic in charting a wide variety of reasoning styles, often marked by non-classical structural (...) rules of inference. This is the new program that I subscribed to in my work on sub-structural logics around 1990, and it is a powerful movement today. But gradually, I have changed my mind about the crux of what logic should become. I would now say that the main issue is not variety of reasoning styles and notions of consequence, but the variety of informational tasks performed by intelligent interacting agents, of which inference is only one among many, involving observation, memory, questions and answers, dialogue, or general communication. And logical systems should deal with a wide variety of these, making information-carrying events first-class citizens in their set-up. The purpose of this brief paper is to contrast and compare the two approaches, drawing freely on some insights from earlier published papers. In particular, I will argue that logical dynamics sets itself the more ambitious diagnostic goal of explaining why substructural phenomena occur, by ‘deconstructing’ them into classical logic plus an explicit account of the relevant informational events. (shrink)

Tetris provides a difficult, dynamic task environment within which some people are novices and others, after years of work and practice, become extreme experts. Here we study two core skills; namely, choosing the goal or objective function that will maximize performance and a feature-based analysis of the current game board to determine where to place the currently falling zoid so as to maximize the goal. In Study 1, we build cross-entropy reinforcement learning models to determine whether different goals result (...) in different feature weights. Two of these optimization strategies quickly rise to performance plateaus, whereas two others continue toward higher but more jagged heights. In Study 2, we compare the zoid placement decisions made by our best CERL models with those made by 67 human players. Across 370,131 human game episodes, two CERL models picked the same zoid placements as our lowest scoring human for 43% of the placements and as our three best scoring experts for 65% of the placements. Our findings suggest that people focus on maximizing points, not number of lines cleared or number of levels reached. They also show that goal choice influences the choice of zoid placements for CERLs and suggest that the same is true of humans. Tetris has a repetitive task structure that makes Tetris more tractable and more like a traditional experimental psychology paradigm than many more complex games or tasks. Hence, although complex, Tetris is not overwhelmingly complex and presents a right-sized challenge to cognitive theories, especially those of integrated cognitive systems. (shrink)

In conversation, individuals work together to achieve communicative goals, complementing and aligning language and body with each other. An important emerging question is whether interlocutors entrain with one another equally across linguistic levels (e.g., lexical, syntactic, and semantic) and modalities (i.e., speech and gesture), or whether there are complementary patterns of behaviors, with some levels or modalities diverging and others converging in coordinated fashions. This study assesses how kinematic and linguistic entrainment interact with one another across levels of measurement, and (...) according to communicative context. We analyzed data from two matched corpora of dyadic interaction between—respectively—Danish and Norwegian native speakers engaged in affiliative conversations and task‐oriented conversations. We assessed linguistic entrainment at the lexical, syntactic, and semantic level, and kinetic alignment of the head and hands using video‐based motion tracking and dynamic time warping. We tested whether—across the two languages—linguistic alignment correlates with kinetic alignment, and whether these kinetic‐linguistic associations are modulated either by the type of conversation or by the language spoken. We found that kinetic entrainment was positively associated with low‐level linguistic (i.e., lexical) entrainment, while negatively associated with high‐level linguistic (i.e., semantic) entrainment, in a cross‐linguistically robust way. Our findings suggest that conversation makes use of a dynamic coordination of similarity and complementarity both between individuals as well as between different communicative modalities, and provides evidence for a multimodal, interpersonal synergy account of interaction. (shrink)

Several tasks in artificial intelligence require the ability to find models about knowledge dynamics. They include belief revision, fusion and belief merging, and abduction. In this paper, we exploit the algebraic framework of mathematical morphology in the context of propositional logic and define operations such as dilation or erosion of a set of formulas. We derive concrete operators, based on a semantic approach, that have an intuitive interpretation and that are formally well behaved, to perform revision, fusion and abduction. (...) Computation and tractability are addressed, and simple examples illustrate the main results. (shrink)

Emerging parallel processing and increased flexibility during the acquisition of cognitive skills form a combination that is hard to reconcile with rule‐based models that often produce brittle behavior. Rule‐based models can exhibit these properties by adhering to 2 principles: that the model gradually learns task‐specific rules from instructions and experience, and that bottom‐up processing is used whenever possible. In a model of learning perfect time‐sharing in dual tasks (Schumacher et al., 2001), speedup learning and bottom‐up activation of instructions can (...) explain parallel behavior. In a model of a complex dynamic task (Carnegie Mellon University Aegis Simulation Program [CMU‐ASP], Anderson et al., 2004), parallel behavior is explained by the transition from serially organized instructions to rules that are activated by both top‐down (goal‐driven) and bottom‐up (perceptually driven) factors. Parallelism lets the model opportunistically reorder instructions, leading to the gradual emergence of new task strategies. (shrink)

Dynamic resilience is a novel concept that aims to quantify how individuals are coping while operating in dynamic and complex task environments. A recently developed dynamic resilience measure, derived through autoregressive modeling, offers an avenue toward dynamic resilience classification that may yield valuable information about working personnel for industries such as defense and elite sport. However, this measure classifies dynamic resilience based upon in-task performance rather than self-regulating cognitive structures; thereby, lacking any supported self-regulating cognitive links to the (...) dynamic resilience framework. Vagally mediated heart rate variability (vmHRV) parameters are potential physiological measures that may offer an opportunity to link self-regulating cognitive structures to dynamic resilience given their supported connection to the self-regulation of stress. This study examines if dynamic resilience classifications reveal significant differences in vagal reactivity between higher, moderate and lower dynamic resilience groups, as participants engage in a dynamic, decision-making task. An amended Three Rs paradigm was implemented that examined vagal reactivity across six concurrent vmHRV reactivity segments consisting of lower and higher task load. Overall, the results supported significant differences between higher and moderate dynamic resilience groups' vagal reactivity but rejected significant differences between the lower dynamic resilience group. Additionally, differences in vagal reactivity across vmHRV reactivity segments within an amended Three Rs paradigm were partially supported. Together, these findings offer support toward linking dynamic resilience to temporal self-regulating cognitive structures that play a role in mediating physiological adaptations during task engagement. (shrink)

ABSTRACT Dance requires optimal range-of-motion and cognitive abilities. Mental imagery is a recommended, yet under-researched, training method for enhancing both of these. This study investigated the effect of Dynamic Neuro-Cognitive Imagery (DNI™) training on developpé performance (measured by gesturing ankle height and self-reported observations) and kinematics (measured by hip and pelvic range-of-motion), as well as on dance imagery abilities. Thirty-four university-level dance students (M age = 19.70 + 1.57) were measured performing three developpé tasks (i.e., 4 repetitions, 8 consecutive seconds (...) hold, and single repetition) at three time-points (2 x pre-, 1 x post-intervention). Data were collected using three-dimensional motion capture, mental imagery questionnaires, and subjective reports. Following the DNI™ intervention, significant increases (p <.01) were detected in gesturing ankle height, as well as in hip flexion and abduction range-of-motion, without significant changes in pelvic alignment. These gains were accompanied by self-reported decrease (p <.05) in level of difficulty experienced and significant improvements in kinesthetic (p <.05) and dance (p <.01) imagery abilities. This study provides evidence for the motor and non-motor benefits of DNI™ training in university-level dance students. (shrink)

Individuals make decisions under uncertainty every day based on incomplete information concerning the potential outcome of the choice or chance levels. The choices individuals make often deviate from the rational or mathematically objective solution. Accordingly, the dynamics of human decision-making are difficult to capture using conventional, linear mathematical models. Here, we present data from a two-choice task with variable risk between sure loss and risky loss to illustrate how a simple nonlinear dynamical system can be employed to capture (...) the dynamics of human decision-making under uncertainty (i.e., multi-stability, bifurcations). We test the feasibility of this model quantitatively and demonstrate how the model can account for up to 86% of the observed choice behavior. The implications of using dynamical models for explaining the nonlinear complexities of human decision-making are discussed, as well as the degree to which nonlinear dynamical systems theory might offer an alternative framework for understanding human decision-making processes. (shrink)

There has been considerable debate in the literature about the relative merits of information processing versus dynamical approaches to understanding cognitive processes. In this article, we explore the relationship between these two styles of explanation using a model agent evolved to solve a relational categorization task. Specifically, we separately analyze the operation of this agent using the mathematical tools of information theory and dynamical systems theory. Information-theoretic analysis reveals how task-relevant information flows through the system to be combined (...) into a categorization decision. Dynamical analysis reveals the key geometrical and temporal interrelationships underlying the categorization decision. Finally, we propose a framework for directly relating these two different styles of explanation and discuss the possible implications of our analysis for some of the ongoing debates in cognitive science. (shrink)

This paper presents a learning theory pertinent to dynamic decision making (DDM) called instancebased learning theory (IBLT). IBLT proposes five learning mechanisms in the context of a decision‐making process: instance‐based knowledge, recognition‐based retrieval, adaptive strategies, necessity‐based choice, and feedback updates. IBLT suggests in DDM people learn with the accumulation and refinement of instances, containing the decision‐making situation, action, and utility of decisions. As decision makers interact with a dynamic task, they recognize a situation according to its similarity to past (...) instances, adapt their judgment strategies from heuristic‐based to instance‐based, and refine the accumulated knowledge according to feedback on the result of their actions. The IBLT's learning mechanisms have been implemented in an ACT‐R cognitive model. Through a series of experiments, this paper shows how the IBLT's learning mechanisms closely approximate the relative trend magnitude and performance of human data. Although the cognitive model is bounded within the context of a dynamic task, the IBLT is a general theory of decision making applicable to other dynamic environments. (shrink)

For the dynamical hypothesis to be defended as a viable alternative to a computational perspective on natural cognition, the role of biological constraints needs to be considered. This task requires a detailed understanding of the structural organization and function of the dynamic nervous system, as well as a theoretical approach that grounds cognitive activity within the constraints of organism and ecological context.

Accurately predicting other people's actions may involve two processes: internal real-time simulation (dynamic updating) and matching recently perceived action images (static matching). Using a priming of body parts, this study aimed to differentiate the two processes. Specifically, participants played a motion-controlled video game with either their arms or legs. They then observed arm movements of a point-light actor, which were briefly occluded from view, followed by a static test pose. Participants judged whether this test pose depicted a coherent continuation of (...) the previously seen action (i.e., “action prediction task”). Evidence of dynamic updating was obtained after compatible effector priming (i.e., arms), whereas incompatible effector priming (i.e., legs) indicated static matching. Together, the results support action prediction as engaging two distinct processes, dynamic simulation and static matching, and indicate that their relative contributions depend on contextual factors like compatibility of body parts involved in performed and observed action. (shrink)