Concurrent schedules of positive and negative reinforcement: differential-impact and differential-outcomes hypotheses

Abstract

Considerable evidence from outside of operant psychology suggests that aversive events exert greater influence over behavior than equal-sized positive-reinforcement events. Operant theory is largely moot on this point, and most operant research is uninformative because of a scaling problem that prevents aversive events and those based on positive reinforcement from being directly compared. In the present investigation, humans' mouse-click responses were maintained on similarly structured, concurrent schedules of positive (money gain) and negative (avoidance of money loss) reinforcement. Because gains and losses were of equal magnitude, according to the analytical conventions of the generalized matching law, bias (log b (double dagger) 0) would indicate differential impact by one type of consequence; however, no systematic bias was observed. Further research is needed to reconcile this outcome with apparently robust findings in other literatures of superior behavior control by aversive events. In an incidental finding, the linear function relating log behavior ratio and log reinforcement ratio was steeper for concurrent negative and positive reinforcement than for control conditions involving concurrent positive reinforcement. This may represent the first empirical confirmation of a free-operant differential-outcomes effect predicted by contingency-discriminability theories of choice.

Fig 1

Allocation of responses (left column) and time (right column) as a function of relative reinforcement frequency for homogeneous-reinforcement (open data points) and heterogeneous-reinforcement (filled data points) conditions. Behavior and reinforcer ratios were organized as left-side schedule/right-side schedule (see text for details). Lines of best fit (thin, black line for homogeneous reinforcement; thick, grey line for heterogeneous reinforcement) and equations describing them were derived using Equation 1 and least-squares linear regression. Note that axes are scaled differently for different participants.

Fig 2

Allocation of responses (left column) and time (right column) as a function of relative reinforcement frequency, for the heterogeneous reinforcement condition, with ratios organized as negative-reinforcement schedule/positive-reinforcement schedule; see text for details. Lines of best fit and equations describing them were derived using Equation 1 and least-squares linear regression. Note that axes are scaled differently for different participants.