Exchange schedules in token economies: A preliminary investigation of second‐order schedule effects

Abstract

Basic research shows that token-production and exchangeproduction schedules in token economies affect each other as second-order schedules (i.e., the exchange-production schedule's requirements affect responding toward the token-production schedule). This relationship has not been investigated with children in academic settings despite the widespread use of token economies in this context. This study compared the effects of fixed-ratio (FR) and variable-ratio (VR) exchange-production schedules of equal ratios (2, 5, and 10) on responding toward an FR 1 tokenproduction schedule with a child diagnosed with autism. A concurrent chains assessment was also conducted to assess the participant's relative preference for FR and VR exchange-production schedule arrangements within her typical discrete trial training. Results showed no difference in response rate between the two schedule types. However, the concurrent chains assessment revealed an exclusive preference for the VR arrangement.

Effects of and preference for fixed-ratio and time-based exchange-production schedules in a token system.

“…One potential reason for the lack of differential responding across FR and YT conditions was that the token-production (FR 1) and token-exchange schedules (FR 1) were held constant and dense across the conditions. This constitutes a demonstration of second-order schedule effects, which is consistent with basic and applied research examining the exchange-production schedule (Argueta et al, 2019; Falligant & Kornman, 2019; Falligant et al, 2020; Foster et al, 2001; Leon et al, 2016; Waddell et al, 1972). This finding provides support for arranging exchange-production schedules in applied settings based on convenience because responding during token production appears to be relatively insensitive to differences in the exchange-production schedule alone.…”

“…Token reinforcement systems are commonly implemented as part of behavioral programming, but there are many remaining scientific and practical questions related to specific components of token systems and token reinforcement schedules that, when answered, could improve their implementation in applied settings (Hackenberg, 2018). In particular, with a few exceptions (e.g., Argueta et al, 2019; De Luca & Holborn, 1990; Falligant et al, 2020; Falligant & Kornman, 2019; Repp & Deitz, 1975) the effects of schedules that comprise token reinforcement systems (i.e., token production, exchange production, token exchange) have not been examined as thoroughly in the applied and translational literatures as they have in the basic literature. Exchange-production schedule variations and their effects on responding during token production are important to understand in applied settings because the frequency of exchange opportunities affects the pace of instruction, which impacts the rate of acquisition of target behavior and the rate of challenging behavior exhibited during instruction for both typically developing children and those with developmental disabilities (Cariveau et al, 2016; Carnine, 1976; Francisco & Hanley, 2012).…”

“…A few recent translational studies with humans have examined features of exchange-production schedules in token systems (Argueta et al, 2019; Falligant et al, 2020; Falligant & Kornman, 2019). Argueta et al (2019) directly compared the effects of FR and VR exchange-production schedules on responding during token production with a child with an Autism Spectrum Disorder, a systematic replication of the Foster et al (2001) investigation with pigeons. As in Foster et al, following a brief baseline phase, Argueta et al, alternated three FR (2, 5, 10) and VR (2, 5, 10) exchange-production schedules, followed by a return to baseline.…”

“…FR exchange-production schedules similar to the ones evaluated by Argueta et al (2019), Falligant and Kornman (2019), and Falligant et al (2020) are commonly implemented as a component of behavioral interventions in the applied literature (e.g., Cividini-Motta & Ahearn, 2013; Kahng et al, 2003) and in practice (e.g., exchange occurs when the individual fills their token board). Time-based schedules are also common in the applied literature (e.g., Ayllon & Azrin, 1965; Gilley & Ringdahl, 2014; Phillips et al, 1971) and in practice (e.g., exchange occurs at set times throughout the day).…”

Pavlovian learning and conditioned reinforcement

“…Instead, it is common to establish a new CS/conditioned‐reinforcer function using a NS➔ ( p = 1 US) contingency, use the CS as a conditioned reinforcer that is always exchanged for the backup reinforcer, and then to switch to a gradually more intermittent exchange‐production contingency (e.g., Argueta et al, 2019; Leon et al, 2016). The latter contingencies were previously discussed in the context of Principle 3, and those points will not be repeated here.…”