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Bianca Miarka, Katarzyna Sterkowicz-Przybycien, and David H. Fukuda

, & Sterkowicz, 2015 ), a technical-tactical (T-T) model ( Sterkowicz, Sacripanti, & Sterkowicz-Przybycien, 2013 ), with accurate biomechanical and statistical analyses, is needed to evaluate contextual information during judo competition. A probabilistic neural network (PNN) is a statistical method that may be

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Gershon Tenenbaum, Andrew Lane, Selen Razon, Ronnie Lidor, and Robert Schinke

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).

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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).

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Michael B. Johnson, William A. Edmonds, Akihito Kamata, and Gershon Tenenbaum

The purpose of this article is to present the procedural steps used to derive a person’s Individual Affect-Related Performance Zones (IAPZs). An IAPZ is that range of affect (i.e., arousal and pleasure) within which an individual has a probability of performing at a particular level (e.g., optimal, moderate, or poor). This methodology has been used in a number of research studies but has yet to be operationalized in the literature. The purpose of this procedure is to facilitate training programs designed to improve human performance in any number of domains via idiosyncratic control over affect. The methodology described consists of eight steps: (a) collecting data, (b) categorizing affect and performance level, (c) converting the data, (d) performing logistical ordinal regressions, (e) creating IAPZ curves, (f) creating IAPZ profile charts, (g) plotting within competition states onto IAPZ profile charts, and (h) utilizing IAPZs to select, implement, and evaluate performance enhancement strategies.

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Shikha Prashad, Yue Du, and Jane E. Clark

learning ( Bennett, Howard, & Howard, 2007 ; Curran, 1997 ; Dennis, Howard, & Howard, 2006 ; Howard et al., 2004 ; Jiménez, Méndez, & Cleeremans, 1996 ; Reed & Johnson, 1994 ) and have used different types of probabilistic sequences ( Cleeremans & McClelland, 1991 ; Peigneux et al., 2000

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Edson Filho

( Filho, Moraes, & Tenenbaum, 2008 ). In practice, the IZOF account has been operationalized through different approaches, including the individual affective probabilistic zones method, which estimates a probability of poor, moderate, and optimal performance based on an independent variable of interest (e

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Todd C. Pataky

measured and requires just minimal and relatively well-accepted processing; if the value of the proposed probabilistic approach is clear for this relatively simple and accurate VGRF case, then it should also be clear for less accurately measured/estimated quantities, like joint angles and moments. Methods

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Mark De Ste Croix, Abigail Priestley, Rhodri Lloyd, and Jon Oliver

change in score standardized to 0.2 of the between-subject SD from the pretest condition. Probabilistic inference of each observed change being greater than the smallest worthwhile effect using the thresholds 25%–75% as possibly, 75%–95% as likely, 95%–99.5% as very likely, and >99.5% as most likely

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Fabio R. Serpiello and Will G. Hopkins

standardized mean effects. 14 Uncertainty in the estimates of effects is presented as 90% compatibility limits. Probabilistic decisions about true (infinite sample) magnitudes accounting for the uncertainty were based on 1-sided hypothesis tests of substantial magnitudes. 15 The P value for rejecting a

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William A. Edmonds, Derek T.Y. Mann, Gershon Tenenbaum, and Chris M. Janelle

An exploratory investigation is reported to test the utility of Kamata, Tenenbaum, and Hanin’s (2002) probabilistic model in determining individual affect-related performance zones (IAPZs) in a simulated car-racing task. Three males completed five separate time-trials of a simulated racing task by which self-reported affective states (i.e., arousal and pleasure) and physiological measures of arousal (i.e., heart rate and skin conductance) were integrated with performance and measured throughout each trial. Results revealed each performer maintained unique IAPZs for each of the perceived and physiological measures in terms of the probability and range of achieving each zone. The practical applications of this approach are discussed.