Response effort discounts the subjective value of rewards

Nishiyama, Ryoji

doi:10.1016/j.beproc.2014.08.002

Cited by 19 publications

(11 citation statements)

References 9 publications

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“…Moreover, although increased cost was associated with a significant decrease in earnings, the decrease was negligible and predictable because more time was needed to complete the larger FR20 avoidance. It is plausible to suggest that increased avoidance cost may have resulted in a devaluation of the negative reinforcer, which also occurs with positive reinforcers (Friman & Poling, ; Hartmann, Hager, Tobler & Kaiser, ; Nishiyama, ). With regard to threat magnitude, results were inconsistent with our prediction as findings showed increased money loss did not increase aversive control and push AA transitions to a lower threat level.…”

Section: Methodsmentioning

confidence: 99%

“Watch out!”: Effects of instructed threat and avoidance on human free‐operant approach–avoidance behavior

Schlund

Treacher

Preston

et al. 2017

J Exper Analysis Behavior

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Approach-avoidance paradigms create a competition between appetitive and aversive contingencies and are widely used in nonhuman research on anxiety. Here, we examined how instructions about threat and avoidance impact control by competing contingencies over human approach-avoidance behavior. Additionally, Experiment 1 examined the effects of threat magnitude (money loss amount) and avoidance cost (fixed ratio requirements), whereas Experiment 2 examined the effects of threat information (available, unavailable and inaccurate) on approach-avoidance. During the task, approach responding was modeled by reinforcing responding with money on a FR schedule. By performing an observing response, participants produced an escalating "threat meter". Instructions stated that the threat meter levels displayed the current probability of losing money, when in fact loss only occurred when the level reached the maximum. Instructions also stated pressing an avoidance button lowered the threat level. Overall, instructions produced cycles of approach and avoidance responding with transitions from approach to avoidance when threat was high and transitions back to approach after avoidance reduced threat. Experiment 1 revealed increasing avoidance cost, but not threat magnitude, shifted approach-avoidance transitions to higher threat levels and increased anxiety ratings, but did not influence the frequency of approach-avoidance cycles. Experiment 2 revealed when threat level information was available or absent earnings were high, but earnings decreased when inaccurate threat information was incompatible with contingencies. Our findings build on prior nonhuman and human approach-avoidance research by highlighting how instructed threat and avoidance can impact human AA behavior and self-reported anxiety.

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Section: Methodsmentioning

confidence: 99%

“Watch out!”: Effects of instructed threat and avoidance on human free‐operant approach–avoidance behavior

Schlund

Treacher

Preston

et al. 2017

J Exper Analysis Behavior

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“…Present study was designed to separate the cognitive and physical effort to better reflect the effort exertion effect on the valuation of a rewarding outcome in effort-based decision making. We aim to further studies such as that of Nishiyama [ 19 ]—where effort is treated as one dimension—to disambiguate results in relation to model fit comparisons. Including the two types of effort can more comprehensibly determine the functional form of effort discounting, as opposed to treating effort as one dimension, which can result in similar portion of variance explained by competing models, such as the hyperbolic and exponential [ 19 ].…”

Section: Introductionmentioning

confidence: 99%

“…We aim to further studies such as that of Nishiyama [ 19 ]—where effort is treated as one dimension—to disambiguate results in relation to model fit comparisons. Including the two types of effort can more comprehensibly determine the functional form of effort discounting, as opposed to treating effort as one dimension, which can result in similar portion of variance explained by competing models, such as the hyperbolic and exponential [ 19 ]. We present a systematic test of a total of seven models to describe effort discounting and to determine if they can account for the functional form of discounting in both physical and cognitive effort conditions.…”

Section: Introductionmentioning

confidence: 99%

Physical and cognitive effort discounting across different reward magnitudes: Tests of discounting models

2017

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The effort required to obtain a rewarding outcome is an important factor in decision-making. Describing the reward devaluation by increasing effort intensity is substantial to understanding human preferences, because every action and choice that we make is in itself effortful. To investigate how reward valuation is affected by physical and cognitive effort, we compared mathematical discounting functions derived from research on discounting. Seven discounting models were tested across three different reward magnitudes. To test the models, data were collected from a total of 114 participants recruited from the general population. For one-parameter models (hyperbolic, exponential, and parabolic), the data were explained best by the exponential model as given by a percentage of explained variance. However, after introducing an additional parameter, data obtained in the cognitive and physical effort conditions were best described by the power function model. Further analysis, using the second order Akaike and Bayesian Information Criteria, which account for model complexity, allowed us to identify the best model among all tested. We found that the power function best described the data, which corresponds to conventional analyses based on the R2 measure. This supports the conclusion that the function best describing reward devaluation by physical and cognitive effort is a concave one and is different from those that describe delay or probability discounting. In addition, consistent magnitude effects were observed that correspond to those in delay discounting research.

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“…Future research could investigate whether emphasizing the aversiveness of working on the assignment in the ADT increases discounting rates. Similarly, the ADT could be adapted to evaluate the effect of how effortful completing a given assignment is, drawing on research into “effort discounting”—or the reduction in reinforcer value as the effort required to access it increases (e.g., Nishiyama, ). Finally, the ADT could be adapted to incorporate the fact that it is often not guaranteed that a particular study activity will produce a reinforcer such as a good grade (e.g., the studied material might not be on an exam).…”

Section: Discussionmentioning

confidence: 99%

A Measure of Delay Discounting Within the Academic Domain

Olsen

Macaskill

Hunt

2018

Behavioral Decision Making

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It is important to better understand the decision-making processes involved in student procrastination, in order to develop interventions that reduce this common problem. Students may procrastinate because studying produces delayed reinforcers; however, no task measuring delay discounting of academic outcomes currently exists. In Experiment 1, we developed and piloted a measure of academic discounting modeled on titrating-amount tasks successfully used in the discounting literature. Participants made hypothetical choices between working for money (the smaller, sooner reinforcer) and working on an assignment that was due at various times (the larger, later reinforcer). Participants showed systematic decreases in the subjective value of the assignment as a function of delay, and the hyperbolic and hyperboloid models described the shape of this decrease in value well. In general, larger delayed rewards are discounted less steeply than smaller delayed rewards (the magnitude effect). In Experiment 2, we observed the magnitude effect in academic discounting: Participants discounted a "not important" assignment more steeply than an "important" assignment. In the hyperboloid model, this change was captured by an increase in the s parameter. Results provide support for the validity of the academic discounting task.

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Response effort discounts the subjective value of rewards

Cited by 19 publications

References 9 publications

“Watch out!”: Effects of instructed threat and avoidance on human free‐operant approach–avoidance behavior

“Watch out!”: Effects of instructed threat and avoidance on human free‐operant approach–avoidance behavior

Physical and cognitive effort discounting across different reward magnitudes: Tests of discounting models

A Measure of Delay Discounting Within the Academic Domain

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