A matching law analysis of risk tolerance and gain–loss framing in football play selection.

Abstract

Using an operant model of choice, the generalized matching law, we evaluated widely held assumptions that play selection in American-rules football is risk averse and that it is subject to gain–loss framing effects. Consistent with risk aversion, relatively risky passing plays occurred less often than expected, an effect depicted by the matching law as a bias for selecting rushing plays. Consistent with gain–loss framing, magnitude of the rushing bias varied with game score. These effects were evident in analyses that treated risk as a categorical variable (passing > rushing). When risk was instead quantified for specific game situations, risk aversion remained evident but framing effects disappeared. Overall, the study illustrates how analytical tools derived from operant choice theory can be useful in testing predictions, based on cognitive decision theory, about behavior under the control of probabilistic outcomes.

Historically Low Productivity by the United States Congress: Snapshot of a Reinforcement-Contingency System in Transition

“…9 suggested that the resulting function was biphasic, separate trend lines were determined for the ascending and descending phases via least-squares linear regression in an iterative process in which the "break point" between phases was selected to maximize the combined variance accounted for by the two trend lines. The slopes of the resulting trend lines then were compared, using a technique based on analysis of covariance (Motulsky and Christopoulis 2006;Zar 1999) that has been employed previously in behavioral research (e.g., Critchfield and Stilling 2014;Magoon and Critchfield 2008;Stilling and Critchfield 2010). The slopes were significantly different, F(1, 37) = 13.77, p=.0007.…”

The Fuzzy Concept of Applied Behavior Analysis Research

“…Previously, we mentioned analyses of terrorist acts (Nevin, 2003) and of consumer demand for indoor tanning (Reed et al, 2013). Other investigations have examined whether matching theory can reveal Bdeviance^in youth conversation patterns (McDowell & Caron, 2010a, 2010b; whether reinforcement principles shed light on the legislative behavior of members of the US Congress (Critchfield, Haley, Sabo, Colbert, & Macropoulis, 2003;Critchfield, Reed, & Jarmolowicz, 2015;& Waldrop, 1972); whether public policy changes affected use of child-adapted automobile seating (Seekins, Fawcett, Cohen, Elder, Jason, Schnelle, & Winett, 1988); and whether behavior during elite sport competition is explained by various aspects of behavior theory (e.g., Critchfield & Stilling, 2015;Mace, Lalli, Shea, & Nevin, 1992;Poling, Weeden, Redner, & Foster, 2011;Reed, Critchfield, & Martens, 2006;Seniuk, Williams, Reed, & Wright, 2015;Vollmer & Bourret, 2000).…”

Application of the matching law to Mixed Martial Arts

“…Given that “perfect” matching (i.e., slope is equal to 1) is rarely observed in research, this allows for an analysis of data that is easier to interpret (Reed & Kaplan, ). Researchers have demonstrated applications of the GME in team sports such as baseball (Cox, Sosine, & Dallery, ; Poling, Weeden, Redner, & Foster, ), basketball (Alferink, Critchfield, & Hitt, ; Romanowich, Bourret, & Vollmer, ; Schenk & Reed, ; Vollmer & Bourret, ), football (Critchfield & Stilling, ; Falligant, Boomhower, & Pence, ; Reed, Critchfield, & Martens, ; Stilling & Critchfield, ), and hockey (Seniuk, Williams, Reed, & Wright, ). These studies have demonstrated that the GME describes choice in athletic competition (Critchfield & Reed, ; Reed & Kaplan, ) and have advanced research on the matching law by focusing on using interpretations of sensitivity (i.e., overmatching, undermatching, and bias) to explain the role of specific contextual variables termed explanatory flexibility (Stilling & Critchfield, ), and how matching may predict athletic success (Alferink et al, ; Seniuk et al, ).…”