Academia.edu no longer supports Internet Explorer.
To browse Academia.edu and the wider internet faster and more securely, please take a few seconds to upgrade your browser.
2013
Degeneracy is a ubiquitous property of complex adaptive systems, which refers to the ability of structurally different components to perform the same function in some conditions, and different functions in other conditions [1]. This work is based on the hypothesis supposing a causal link between the level of degeneracy in the system and the strength of long-range correlations in its behavior [2]. In this experiment, we manipulated degeneracy through accuracy constraints: we supposed that accuracy constraints should reduce the number of relevant behavioral solutions, and thus decrease degeneracy in the system involved in the production of performance. Additionally, we hypothesized that accuracy constraints should have a selective effect on variables directly affected by accuracy, but not on other variables. Participants performed a reciprocal aiming task, with 3 levels of difficulty differing only in target size (Figure 1, left). They performed 512 successive reciprocal pointings, an...
2011
We address the complex relationship between the stability, variability, and adaptability of psychological systems by decomposing the global variance of serial performance into two independent parts: the local variance (LV) and the serial correlation structure. For two time series with equal LV, the presence of persistent long-range correlations (or 1/f  noise) increases the global variance. We hypothesized that a coadjustment between these two determinants of variability constitutes a resource for adaptive systems whose appropriate functioning under critical conditions requires the outcome variance to be limited. To test this hypothesis, we looked at the bimanual coordination dynamics at comfortable (stable) and critical (close to phase transition) frequencies. Results showed that a negative correlation appeared gradually as the theoretical stability of coordination modes decreased and reached significance only in the critical condition. We propose that the emergence of a mutual adjustment between LV and serial correlations might be an indicator of effective adaptation to stabilize behavior.
Journal of Experimental …, 2009
Everyday life features uncertain and ever-changing situations. In such environments, optimal adaptive behavior requires higher-order inferential capabilities to grasp the volatility of external contingencies. These capabilities however involve complex and rapidly intractable computations, so that we poorly understand how humans develop efficient adaptive behaviors in such environments. Here we demonstrate this counterintuitive result: simple, low-level inferential processes involving imprecise computations conforming to the psychophysical Weber Law actually lead to near-optimal adaptive behavior, regardless of the environment volatility. Using volatile experimental settings, we further show that such imprecise, low-level inferential processes accounted for observed human adaptive performances, unlike optimal adaptive models involving higher-order inferential capabilities, their biologically more plausible, algorithmic approximations and non-inferential adaptive models like reinforce...
2003
During reaching movements, the brain's internal models map desired limb motion into predicted forces. When the forces in the task change, these models adapt. Adaptation is guided by generalization: errors in one movement influence prediction in other types of movement. If the mapping is accomplished with population coding, combining basis elements that encode different regions of movement space, then generalization can reveal the encoding of the basis elements. We present a theory that relates encoding to generalization using trial-by-trial changes in behavior during adaptation. We consider adaptation during reaching movements in various velocity-dependent force fields and quantify how errors generalize across direction. We find that the measurement of error is critical to the theory. A typical assumption in motor control is that error is the difference between a current trajectory and a desired trajectory (DJ) that does not change during adaptation. Under this assumption, in all force fields that we examined, including one in which force randomly changes from trial to trial, we found a bimodal generalization pattern, perhaps reflecting basis elements that encode direction bimodally. If the DJ was allowed to vary, bimodality was reduced or eliminated, but the generalization function accounted for nearly twice as much variance. We suggest, therefore, that basis elements representing the internal model of dynamics are sensitive to limb velocity with bimodal tuning; however, it is also possible that during adaptation the error metric itself adapts, which affects the implied shape of the basis elements.
Human Movement Science, 2019
Complexity matching is a measure of coordination based on information exchange between complex networks. To date, studies have focused mainly on interpersonal coordination, but complexity matching may generalize to interacting networks within individuals. The present study examined complexity matching in a double, coordinated Fitts' perceptual-motor task with comparable individual and dyadic conditions. Participants alternated touching targets with their left and right hands in the individual condition, or analogously with the left hand of one partner and the right hand of another in the dyadic condition. In Experiment 1, response coupling was manipulated by making targets drift either randomly or contingently based on prior responses. Here, drift refers to the variability in the target movements between response locations. Longrange correlations in time series of inter-response intervals exhibited complexity matching between the left and right hands of dyads and individuals. Response coupling was necessary for complexity matching in dyads but not individuals. When response coupling was absent in the dyadic condition, the degree of complexity matching was significantly reduced. Experiment 2 showed that the effect of coupling was due to interactions between left and right responses. Results also showed a weak, negative relationship between complexity matching and performance as measured by total response time. In conclusion, principles and measures of complexity matching apply similarly within and between individuals, and perceptual-motor performance can be facilitated by loose response coupling.
Psychological Review, 1983
This article develops a general behavior-regulation model of learned performance related to the equilibrium approach of Timberlake (1980) and Timberlake and Allison (1974). The model is based on four assumptions: (a) Both the instrumental and contingent responses are regulated with respect to their own set point~; (b) these set points can be measured in a free baseline when both responses are relatively unconstrained and simultaneously available; (c) a reinforcement schedule can be seen as a constraint function that cross-couples the environmental effects of regulatory systems underlying the instrumental and contingent responses, thereby challenging their set points; and (d) molar behavior change under a schedule represents a compromise between the deviations from set points forced by the constraint function. These assumptions are translated into a set of coupled differential equations describing two regulatory systems related by a schedule. After providing an exact solution for this model, we derive as special cases two current alternative models of learned performance (Allison, 1976; Staddon, 1979). Finally, we demonstrate that the model is consistent in form with data from a variety of simple schedules. This article resulted from a year-long collaboration between the authors. We thank each other for our patience, enthusiasm, and good humor. Completion of the article was supported in part by National Science Foundation Grants BS79 15117 and BNS 82 10139 and Bell Laboratories.
Self organized criticality (SOC) has been studied as a universal property of complex adaptive systems. In a series of experiments we could show that principles of SOC also apply to a complex class of motor learning tasks. These tasks (e.g., "Roller ball") are characterized by the fact that they do not only exhibit a simple improvement of score values ("Learning Curves") but also involve a sharp transition from a failure state (not being able to solve the task) to success (being able to perform the task). The system is controlled by two critical parameters, "skill level" and "task difficulty" that can induce the transition from failure to success. The skill parameter thereby plays the role of the sand dropped slowly on the sand pile in the classic demonstration model of SOC. The connection between the two processes is given by the generally accepted assumption/axiom that skill level increases with practice time. In the class of experiments that we try to model the learner has control over a continuous parameter that quantifies the difficulty of the task. Experimental results could confirm a conceptual prediction from the psychology of the flow phenomenon, namely that learners tend to a condition where skill level matches task difficulty. Here it is quantitatively interpreted as difficulty levels for which the expected success rate is close to 50%. In our discrete, stochastic, piece-wise linear map model we further explore conditions for parameters that make this connection between motor learning and models of SOC more explicit and quantitative.
Psychonomic Bulletin & Review, 1999
—Our main goal was to test a hypothesis that transient changes in performance of a steady-state task would result in motor equivalence. We also estimated effects of visual feedback on the amount of reorganization of motor elements. Healthy subjects performed two variations of a four-finger pressing task requiring accurate production of total pressing force (F TOT) and total moment of force (M TOT). In the Jumping-Target task, a sequence of target jumps required transient changes in either F TOT or M TOT. In the Step-Perturbation task, the index finger was lifted by 1 cm for 0.5 s leading to a change in both F TOT and M TOT. Visual feedback could have been frozen for one of these two variables in both tasks. Deviations in the space of finger modes (hypothetical commands to individual fingers) were quantified in directions of unchanged F TOT and M TOT (motor equivalent – ME) and in directions that changed F TOT and M TOT (non-motor equivalence – nME). Both the ME and nME components increased when the performance changed. After transient target jumps leading to the same combination of F TOT and M TOT , the changes in finger modes had a large residual ME component with only a very small nME component. Without visual feedback, an increase in the nME component was observed without consistent changes in the ME component. Results from the Step-Perturbation task were qualitatively similar. These findings suggest that both external perturbations and purposeful changes in performance trigger a reorganization of elements of an abundant system, leading to large ME change. These results are consistent with the principle of motor abundance corroborating the idea that a family of solutions is facilitated to stabilize values of important performance variables. Published by Elsevier Ltd. on behalf of IBRO.
A current challenge in neuroscience and systems biology is to better understand properties that allow organisms to exhibit and sustain appropriate behaviours despite the effects of perturbations (behavioural robustness). There are still significant theoretical difficulties in this endeavour, mainly due to the context-dependent nature of the problem. Biological robustness, in general, is considered in the literature as a property that emerges from the internal structure of organisms, rather than being a dynamical phenomenon involving agent-internal controls, the organism body, and the environment. Our hypothesis is that the capacity for behavioural robustness is rooted in dynamical processes that are distributed between agent ‘brain’, body, and environment, rather than warranted exclusively by organisms’ internal mechanisms. Distribution is operationally defined here based on perturbation analyses. Evolutionary Robotics (ER) techniques are used here to construct four computational models to study behavioural robustness from a systemic perspective. Dynamical systems theory provides the conceptual framework for these investigations. The first model evolves situated agents in a goal-seeking scenario in the presence of neural noise perturbations. Results suggest that evolution implicitly selects neural systems that are noise-resistant during coupling behaviour by concentrating search in regions of the fitness landscape that retain functionality for goal approaching.The second model evolves situated, dynamically limited agents exhibiting minimal cognitive behaviour (categorization task). Results indicate a small but significant tendency toward better performance under most types of perturbations by agents showing further cognitive behavioural dependency on their environments. The third model evolves experience-dependent robust behaviour in embodied, one-legged walking agents. Evidence suggests that robustness is rooted in both internal and external dynamics, but robust motion emerges always from the system-in-coupling. The fourth model implements a historically dependent, mobile-object tracking task under sensorimotor perturbations. Results indicate two different modes of distribution, one in which inner controls necessarily depend on a set of specific environmental factors to exhibit behaviour, then these controls will be more vulnerable to perturbations on that set, and another for which these factors are equally sufficient for behaviours. Vulnerability to perturbations depends on the particular distribution. In contrast to most existing approaches to the study of robustness, this thesis argues that behavioural robustness is better understood in the context of agent-environment dynamical couplings, not in terms of internal mechanisms. Such couplings, however, are not always the full determinants of robustness. Challenges and limitations of our approach are also identified for future studies.
Journal of Neurophysiology, 2010
2012
Using an approach that combines experimental studies of bimanual movements to visual stimuli and theoretical modeling, the present paper develops a dynamical account of sensorimotor learning, that is, how new skills are acquired and old ones modified. A significant aspect of our approach is the focus on the individual learner as the basic unit of analysis, in particular the quantification of predispositions and capabilities that the individual learner brings to the learning environment. Such predispositions constitute the learner's behavioral repertoire, captured here theoretically as a dynamical landscape ("intrinsic dynamics"). The learning process is demonstrated to not only lead to a relatively permanent improvement of performance in the required task-the usual outcome-but also to alter the individual's entire repertoire. Changes in the dynamical landscape due to learning are shown to result from two basic mechanisms or "routes": bifurcation and shift. Which mechanism is selected depends the initial individual repertoire before new learning begins. Both bifurcation and shift mechanisms are accommodated by a dynamical model, a relatively straightforward development of the well-established HKB model of movement coordination. Model simulations show that although environmental or task demands may be met equally well using either mechanism, the bifurcation route results in greater stabilization of the to-be-learned behavior. Thus, stability not (or not only) error is demonstrated to be the basis of selection, both of a new pattern of behavior and the path (smooth shift versus abrupt qualitative change) that learning takes. In line with these results, recent neurophysiological evidence indicates that stability is a relevant feature around which brain activity is organized while an individual performs a coordination task. Finally, we explore the consequences of the dynamical approach to learning for theories of biological change.
Translational Sports Medicine
Acta Psychologica, 2008
The relation between reaction time and the number of elements in a response has been shown to depend on whether simple or choice RT paradigms are employed. The purpose of the present study was to investigate whether advance information about the number of elements is the critical factor mediating the influence between reaction time and response elements. Participants performed aiming movements that varied in terms of the number of elements and movement amplitude. Prior to the stimulus, advance information was given about the number of elements and movement amplitude, movement amplitude only, number of elements only, or no information about the response. Reaction time and movement time to the first target increased as a function of number of elements only when the full response or the number of elements was specified in advance of the stimulus. The implication of these results for current models of motor programming and sequential control of aiming movements are discussed.
Response time (RT) is an oft-reported behavioral measure in psychological and neurocognitive experiments, but the high level of observed trial-to-trial variability in this measure has often limited its usefulness. Here, we combine computational modeling and psychophysics to examine the hypothesis that fluctuations in this noisy measure reflect dynamic computations in human statistical learning and corresponding cognitive adjustments. We present data from the stop-signal task (SST), in which subjects respond to a go stimulus on each trial, unless instructed not to by a subsequent, infrequently presented stop signal. We model across-trial learning of stop signal frequency, P(stop), and stop-signal onset time, SSD (stop-signal delay), with a Bayesian hidden Markov model, and within-trial decision-making with an optimal stochastic control model. The combined model predicts that RT should increase with both expected P(stop) and SSD. The human behavioral data (n = 20) bear out this prediction, showing P(stop) and SSD both to be significant, independent predictors of RT, with P(stop) being a more prominent predictor in 75% of the subjects, and SSD being more prominent in the remaining 25%. The results demonstrate that humans indeed readily internalize environmental statistics and adjust their cognitive/behavioral strategy accordingly, and that subtle patterns in RT variability can serve as a valuable tool for validating models of statistical learning and decision-making. More broadly, the modeling tools presented in this work can be generalized to a large body of behavioral paradigms, in order to extract insights about cognitive and neural processing from apparently quite noisy behavioral measures. We also discuss how this behaviorally validated model can then be used to conduct model-based analysis of neural data, in order to help identify specific brain areas for representing and encoding key computational quantities in learning and decision-making.
Humans completing successive trials of a skilled task vary their body states (e.g. joint angles and velocities) from one trial to the next. These fluctuations in repeated performance contain information about the underlying control system. The reduction of goal-level error has been hypothesized as the driver of this control system. However, previous studies have shown that the empirical trial-to-trial behavior cannot be explained by error-correction alone.
Motor Control, 2010
When trying to understand human motor control probably the most fundamental aspect to consider is that the neuromechanical system is redundant. A task as trivial as inserting a key into a keyhole can be successfully achieved with the key approaching from a range of different orientations. Biomechanical analysis of the multisegmented arm and hand pointing the key immediately reveals that even for a single key orientation, an infi nite set of joint confi gurations exists. Redundancy also exists at the level of muscles, such that many different muscle contractions will achieve the same joint orientation. In fact, redundancy resides across all levels of the neuromechanical system ranging from molecular, neuronal, and muscular, to behavioral processes (see also Herzog, this volume). With this multiplicity of options to achieve a given task, it is no surprise that variability at the behavioral level is ubiquitous.
Necessity vs. choice 4.2 Constraints 4.3 Specifications for an artificial system 4.4 Specifications for a natural system 4.5 Examples of specifications and constraints B. Embeddedness and failure .
Lecture Notes in Physics, 2003
PLOS ONE
The aim of this experiment was to assess if the previously supported relationship between the structure of motor variability and performance changes when the task or organismic constraints encourage individuals to adjust their movement to achieve a goal. Forty-two healthy volunteers (aged 26.05 ± 5.02 years) performed three sets of cyclic pointing movements, 600 cycles each. Every set was performed under different conditions: 1) without a target; 2) with a target; 3) with a target and a financial reward. The amount of performance variability was analysed using the standard deviation of the medial-lateral (ML) and anterior-posterior (AP) axes and the bivariate variable error. The structure of the variability was assessed by Detrended Fluctuation Analysis (DFA) of the following time series: the coordinate values of the endpoint in ML, AP axes and resultant distance (RD), the hand orientation and the movement time. The performance of the task constrained with a target, or a target and reward, required higher implication to adjust an individual's movements to achieve the task goal, showing a decrease in dispersions and lower autocorrelation. Under the condition without a target, variability dispersion was positively related to autocorrelation of the movement values from ML axis and RD time series, and negatively related to the values from the hand orientation time series. There was a loss of the relationship between variability structure and performance when the task was constrained by the target and the reward. That could indicate different strategies of the participants to achieve the objective. Considering the results and previous studies, the relationship between variability structure and performance could depend on task constraints such as feedback, difficulty or the skill level of participants and it is mediated by individual constraints such as implication or intentionality.
Loading Preview
Sorry, preview is currently unavailable. You can download the paper by clicking the button above.