Action, observation and mental imagery: Can one implicitly learn in all cases?

info:eu-repo/semantics/publishe

Implicit motor learning in discrete and continuous tasks: Toward a possible account of discrepant results

Can one learn implicitly, that is, without conscious awareness of what it is that one learns? Daily life is replete with situations where our behavior is seemingly influenced by knowledge to which we have little access. Riding a bicycle, playing tennis or driving a car, all involve mastering complex sets of motor skills, yet we are at a loss when it comes to explaining exactly how we perform such physical feats. Thus, while it is commonly accepted and hence unsurprising that we have little access to the cognitive processes involved in mental operations, it also appears that knowledge itself can remain inaccessible to report yet influence behavior. Reber, who coined the expression “implicit learning” in 1967, defined it as “the process whereby people learn without intent and without being able to clearly articulate what they learn” (Cleeremans, Destrebecqz, & Boyer, 1998). The research described in this chapter is positioned at the confluence of two different domains: Implicit Learning on the one hand, and Skill Acquisition on the other. The two domains have remained largely independent from each other, but their intersection nevertheless constitutes a field of primary import: the implicit motor learning field. The hallmark of implicit motor learning is the capacity to acquire skill through physical practice without conscious recollection of what elements of performance have improved. Unfortunately, studies dealing with implicit motor learning are not very abundant (Pew, 1974; Magill & Hall, 1989; Wulf & Schmidt, 1997; Shea, Wulf, Whitacre, & Park, 2001). These studies provide an apparently straightforward demonstration of the possibility of unconsciously learning the structure of a complex continuous task in a more efficient way than explicit learning allows. Nevertheless, other evidence seems to challenge this view. Indeed, recent studies (Chambaron, Ginhac, Ferrel-Chapus & Perruchet, 2006; Ooteghem, Allard, Buchanan, Oates & Horak, 2008) suggest that taking advantage from the repetition of continuous events may not be as easy as previous research leads us to believe. Indeed, these studies have suggested that sequence learning in continuous tracking tasks might be artefatctually driven by peculiarities of the experimental material rather than by implicit sequence learning per se. Consequently, a central goal of this chapter will be to reconcile these discrepant results so as to better characterize the conditions in which implicit motor learning occurs. Moreover, understanding what facilitates or prevents learning of regularities in motor tasks will be useful both in sport and in motor rehabilitation fields.SCOPUS: ch.binfo:eu-repo/semantics/publishe

Action, observation, et imagerie mentale d’une action: Peut-on apprendre implicitement dans tous les cas?

info:eu-repo/semantics/publishe

Measuring high spatiotemporal variability in saltation intensity using a low-cost Saltation Detection System : Wind tunnel and field experiments

The commonly observed over prediction of aeolian saltation transport on sandy beaches is, at least in part, caused by saltation intermittency. To study small-scale saltation processes, high frequency saltation sensors are required on a high spatial resolution. Therefore, we developed a low-cost Saltation Detection System (SalDecS) with the aim to measure saltation intensity at a frequency of 10 Hz and with a spatial resolution of 0.10 m in wind-normal direction. Linearity and equal sensitivity of the saltation sensors were investigated during wind tunnel and field experiments. Wind tunnel experiments with a set of 7 SalDec sensors revealed that the variability of sensor sensitivity is at maximum 9% during relatively low saltation intensities. During more intense saltation the variability of sensor sensitivity decreases. A sigmoidal fit describes the relation between mass flux and sensor output measured during 5 different wind conditions. This indicates an increasing importance of sensor saturation with increasing mass flux. We developed a theoretical model to simulate and describe the effect of grain size, grain velocity and saltation intensity on sensor saturation. Time-averaged field measurements revealed sensitivity equality for 85 out of a set of 89 horizontally deployed SalDec sensors. On these larger timescales (hours) saltation variability imposed by morphological features, such as sand strips, can be recognized. We conclude that the SalDecS can be used to measure small-scale spatiotemporal variabilities of saltation intensity to investigate saltation characteristics related to wind turbulence