Preference Reversal Between Impulsive and Self‐control Choice

Abstract

In a concurrent-chains procedure, pigeons chose between reinforcers varying in delay and amount. Reinforcer amount was determined by duration of access to grain, and delay was determined by fixed-interval schedules in the terminal links. Preference was measured by the ratio of responses in initial links. Dependent scheduling of variable-interval schedules in initial links ensured that delay and amount were not confounded with frequency of outcomes, which remained equal for the two choices. In Experiment 1, in components signaled by red keys in the initial links, small and large reinforcers were delivered after delays of 1 s and 10 s respectively. In components signaled by green, additional time was added to both delays. Smaller-sooner reinforcers were preferred in red components. In green components, smaller-sooner reinforcers were preferred at short delays, and choices for the larger-later reinforcer generally increased with increasing duration of the added delay. At longer delays, up to 15 s, the larger-later reinforcer was preferred. That is, the pigeons showed within-session preference reversal, with impulsive choice at short delays in red components and self-control choice at long delays in green components. In Experiment 2, added delay to both reinforcement and reinforcer amount were varied. Sensitivity of initial-link response ratios to ratios of amount increased with increasing duration of the added delay. This interaction between delay and amount was predicted if the temporal discounting functions assumed the magnitude effect in which discounting rate was inversely proportional to amount. It was also predicted by the contextual choice model of performance in concurrent-chains procedures.

The effects of physical activity on impulsive choice: Influence of sensitivity to reinforcement amount and delay

“…Finally, although several outcomes from the current study demonstrate the effects of amount and delay in isolation (e.g., standardizing by the 0 s delay), a potential limitation is that we cannot unequivocally rule out the potential for an interaction between amount and delay (whether simple multiplicative or more complex). Several investigators have explored such interactions between amount and delay, and their conclusions have varied from study to study (e.g., Beeby and White, 2013; Green and Snyderman, 1980). …”

Examining interrelationships between the Cigarette Purchase Task and delay discounting among pregnant women

“…The CPT is an efficient alternative to time-and labor-intensive laboratory drug selfadministration studies (Bickel & Madden, 1999a, 1999b, and is a valid method of studying cigarette demand (Farris, Aston, Abrantes, & Zvolensky, 2017;Madden & Kalman, 2010) that produces demand curves corresponding closely to those involving actual drug consumption (Amlung, Acker, Stojek, Murphy, & MacKillop, 2012;Wilson, Franck, Koffarnus, & Bickel, 2016). Furthermore, because the CPT permits experimental examination of changes in demand without asking study participants to smoke, the task can be useful for research with especially vulnerable populations such as pregnant women or adolescents (Higgins et al, 2017). CPT demand is typically characterized by five indices: Demand Intensity (i.e., number of cigarettes participants estimated smoking per day if cigarettes were free of cost); O max (i.e., peak expenditure, or the total amount of money participants would spend daily on smoking); P max (i.e., the financial price associated with O max ); Breakpoint (BP, i.e., the price at which participants indicated they would quit smoking rather than incur the cost); and α (i.e., overall sensitivity to changes in price, which was calculated as rate of change in elasticity across the demand curve).…”

“…CPT demand is typically characterized by five indices: Demand Intensity (i.e., number of cigarettes participants estimated smoking per day if cigarettes were free of cost); O max (i.e., peak expenditure, or the total amount of money participants would spend daily on smoking); P max (i.e., the financial price associated with O max ); Breakpoint (BP, i.e., the price at which participants indicated they would quit smoking rather than incur the cost); and α (i.e., overall sensitivity to changes in price, which was calculated as rate of change in elasticity across the demand curve). Higgins et al (2017) conducted an initial study validating the CPT with pregnant cigarette smokers. Overall, Hursh and Silberberg's (2008) exponential demand equation provided a good fit for both group and individual-participant cigarette demand among pregnant women, and demand varied in correspondence to two well-validated predictors of individual differences in smoking cessation in this population (i.e., cigarettes smoked per day and antepartum quit attempts).…”

Contingency management works, clients like it, and it is cost-effective

“…Beeby & White, 2013; Kyonka, 2008). Basic research generally upholds the concatenated generalized matching law (Davison & McCarthy, 1988) which assumes the effects of schedule, delay and other reinforcer parameters are additive and independent with no complicated interaction effects (Kyonka, 2008).…”

“…Basic research generally upholds the concatenated generalized matching law (Davison & McCarthy, 1988) which assumes the effects of schedule, delay and other reinforcer parameters are additive and independent with no complicated interaction effects (Kyonka, 2008). However, some experts in the field of behavioral economics argue that reinforcer parameters do, in fact, interact and combine to affect behavior (e.g., Beeby & White, 2013). Clearly, more discussion and inquiry into this potential interplay between reinforcement parameters and behavior is warranted in the human and non-human animal research fields.…”