We introduce a two-perception probabilistic concept of adaptation (TPPCA), which accounts for fast and slow adaptation processes. The outcome of both processes depends on the perceptual difference (termed herein a quantum) of how an individual perceives his or her abilities, skills, and capacities (βv) to interact, cope, and perform a given task (δi). Thus, the adaptation process is determined by (βv – δi). Fast adaptation processes target aspects that require immediate responses while slow adaptation processes involve ongoing adaptation to long-term demands. We introduce the TPPCA in several domains of inquiry, which rely on fast adaptation processes (perceptual–cognitive–action coupling, performance routines, psychological crisis, reversal states), slow adaptation processes (i.e., career aspirations, burnout), and processes that can be either fast or slow (i.e., flow, affect and mood changes, emotion regulation).
Gershon Tenenbaum, Andrew Lane, Selen Razon, Ronnie Lidor, and Robert Schinke
Akihito Kamata, Gershon Tenenbaum, and Yuri L. Hanin
The Individual Zone of Optimal Functioning (IZOF) model postulates the functional relationship between emotions and optimal performance, and aims to predict the quality of upcoming performance with respect to the pre-performance emotional state of the performer. Several limitations associated with the traditional method of determining the IZOF are outlined and a new probabilistic approach is introduced instead. To reliably determine the boundaries of the IZOF and their associated probabilistic curve thresholds, performance outcomes that vary in quality, as well as the emotional intensity associated with them, are taken into account. Several probabilistic models of varying complexity are presented, along with hypothetical and real data to illustrate the concept. The traditional and the new methods are contrasted in one actual set and two hypothetical sets of data. In all cases the proposed probabilistic method was found to show greater sensitivity and to more accurately represent the data than the traditional method. The development of the method is a first stage toward developing models that take into account the interactive nature and multidimensionality of the emotional construct, as well as the fluctuations in emotional intensity and performance throughout the competition phases (i.e., momentum).
Alfred Nimmerichter, Bernhard Prinz, Matthias Gumpenberger, Sebastian Heider, and Klaus Wirth
Purpose: To evaluate the predictive validity of critical power (CP) and the work above CP (W′) on cycling performance (mean power during a 20-min time trial; TT20). Methods: On 3 separate days, 10 male cyclists completed a TT20 and 3 CP and W′ prediction trials of 1, 4, and 10 min and 2, 7, and 12 min in field conditions. CP and W′ were modeled across combinations of these prediction trials with the hyperbolic, linear work/time, and linear power inverse-time (INV) models. The agreement and the uncertainty between the predicted and actual TT20 were assessed with 95% limits of agreement and a probabilistic approach, respectively. Results: Differences between the predicted and actual TT20 were “trivial” for most of the models if the 1-min trial was not included. Including the 1-min trial in the INV and linear work/time models “possibly” to “very likely” overestimated TT20. The INV model provided the smallest total error (ie, best individual fit; 6%) for all cyclists (305  W; 19.6 [3.6] kJ). TT20 predicted from the best individual fit-derived CP, and W′ was strongly correlated with actual TT20 (317  W; r = .975; P < .001). The bias and 95% limits of agreement were 4 (7) W (−11 to 19 W). Conclusions: Field-derived CP and W′ accurately predicted cycling performance in the field. The INV model was most accurate to predict TT20 (1.3% [2.4%]). Adding a 1-min-prediction trial resulted in large total errors, so it should not be included in the models.
.g., activation; see Johnson, Edmonds, Kamata, & Tenenbaum, 2009 ; Kamata, Tenenbaum, & Hanin, 2002 ). Essentially, this probabilistic approach reflects a postpositivist research paradigm, in which the linkage between a predictor variable and an outcome variable do not represent a deterministic function (one
Brigid M. Lynch, Suzanne C. Dixon-Suen, Andrea Ramirez Varela, Yi Yang, Dallas R. English, Ding Ding, Paul A. Gardiner, and Terry Boyle
applied these methods. The potential outcomes approach stems from counterfactual reasoning, an epistemological approach to understanding causality. Pearl 20 (whose profound contribution to epidemiology has been described as the “marriage of the counterfactual and probabilistic approaches to causation
Katherine L. Hsieh, Yaejin Moon, Vignesh Ramkrishnan, Rama Ratnam, and Jacob J. Sosnoff
: a probabilistic approach . Gait Posture . 2017 ; 60 : 235 – 240 . PubMed ID: 29288962 doi:10.1016/j.gaitpost.2017.12.015 10.1016/j.gaitpost.2017.12.015 37. Hof AL , Gazendam MG , Sinke WE . The condition for dynamic stability . J Biomech . 2005 ; 38 ( 1 ): 1 – 8 . PubMed ID: 15519333
Sarah A. Roelker, Elena J. Caruthers, Rachel K. Hall, Nicholas C. Pelz, Ajit M.W. Chaudhari, and Robert A. Siston
, Davidson BS . A probabilistic approach to quantify the impact of uncertainty propagation in musculoskeletal simulations . Ann Biomed Eng . 2015 ; 43 ( 5 ): 1098 – 1111 . PubMed ID: 25404535 doi:10.1007/s10439-014-1181-7 10.1007/s10439-014-1181-7 25404535 13. Wagner DW , Stepanyan V , Shippen