Individual Zone of Optimal Functioning (IZOF): A Probabilistic Estimation

Abstract

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).