Behavioural Processes 90 (2012) 424–427

Contents lists available at SciVerse ScienceDirect

Behavioural Processes journal homepage: www.elsevier.com/locate/behavproc

Short report

Discounting the freedom to choose: Implications for the paradox of choice
Derek D. Reed ∗ , Brent A. Kaplan, Adam T. Brewer
University of Kansas, United States

a r t i c l e

i n f o

a b s t r a c t
Organisms prefer to make their own choices. However, emerging research from behavioral decision making sciences has demonstrated that there are boundaries to the preference for choice. Specifically, many decision makers find an extensive array of choice options to be aversive, often leading to negative emotional states and poor behavioral outcomes. This study examined the degree to which human participants discounted hypothetical rewards that were (a) delayed, (b) probabilistic, and (c) chosen from a large array of options. The present results suggest that the “paradox of choice” effect may be explained within a discounting model for individual patterns of decision making. © 2012 Elsevier B.V. All rights reserved.

Article history: Received 5 December 2011 Received in revised form 14 February 2012 Accepted 27 March 2012 Keywords: Choice Choice overload Discounting Paradox of choice Search costs

As described by Catania, “Whatever else is involved in the concept of freedom, it at least involves the availability of alternatives” (p. 89; 1975). Thus, within a behavioral framework, freedom may be loosely conceptualized as the ability to choose. In Catania’s seminal article – as well as two further replications (e.g., Catania, 1980; Catania and Sagvolden, 1980) – he supports this notion through experimental demonstrations that pigeons’ responses in the initial links of concurrent-chain schedules indicated a preference for choice (as compared to a forced choice option), even when rate of reinforcement and the variety of schedule-correlated stimuli were controlled. Similar findings have been replicated with rats (e.g., Voss and Homzie, 1970) and monkeys (e.g., Suzuki, 1999), as well as humans in both laboratory (e.g., Brigham and Sherman, 1973) and applied (e.g., Tiger et al., 2006) settings. The argument in favor of choice is intuitively appealing; the ability to choose is preferred, likely due to both ontogenic and phylogenic origins (Catania, 1975; Catania and Sagvolden, 1980; Iyengar, 2010). Despite such lore regarding the value of choosing, there appears to be paradoxical boundary conditions for the preference for choice. For example, in a follow up study to his 1975 article, Catania (1980) varied the number of terminal link free choice keys, but failed to show any increase in preference – as measured by initial link rates of responding – beyond that obtained when only two terminal link free choice keys were available. In another curious demonstration of the preference for choice, Hayes et al. (1981)

∗ Corresponding author at: Department of Applied Behavioral Science, University of Kansas, 4048 Dole Human Development Center, 1000 Sunnyside Avenue, Lawrence, KS 66045-7555, United States. E-mail address: dreed@ku.edu (D.D. Reed). 0376-6357/$ – see front matter © 2012 Elsevier B.V. All rights reserved. http://dx.doi.org/10.1016/j.beproc.2012.03.017

documented pigeons’ consistent initial link preference for constraint (i.e., no ability to choose) over free choice, despite amount of food obtainable being held constant across conditions. While not extensively studied in behavior analysis, the differential effects of choice architecture on the preference for choosing have gained notoriety in social psychology and marketing research. Such research has identified negative effects of too much choice on effective decision making; this paradoxical phenomenon has subsequently been termed the “paradox of choice” (Schwartz, 2004). In particular, decision making researchers have found that increased numbers of choice options may result in poor behavioral outcomes, such as decrements in self-control, stamina, and executive functioning (Vohs et al., 2008), perhaps leading some humans to prefer fewer choice alternatives. The paradox of choice phenomenon has traditionally been measured by comparing two groups; one who chose among small (few) and the other from large (extensive) numbers of options. In the seminal study on the paradox of choice by Iyengar and Lepper (2000), researchers presented either six or 24 jam varieties to customers shopping at a sampling booth in an upscale foods store. While more customers approached the booth displaying many samples, more purchases were made when fewer were presented. This finding has been replicated across numerous commodities, such as chocolates (Chernev, 2003; Iyengar and Lepper, 2000) and retirement plans (Iyengar et al., 2004). Wieczorkowska and Burnstein (1999) propose that search costs may provide an explanation for this paradox of choice. The search cost hypothesis has origins in the animal foraging literature (e.g., Dallery and Baum, 1991; Krebs and Davies, 1997) and is operationalized as the time, risk, and effort associated with making a decision. For example, Dar-Nimrod et al. (2009) found

D.D. Reed et al. / Behavioural Processes 90 (2012) 424–427

425

that some humans were more likely to expend resources (e.g., spending more time completing paperwork or driving further distances) in order to choose from a larger number of options, yet these individuals subsequently reported less satisfaction with their decision. To date, no specific behavioral processes have been presented as possible explanations for this paradox of choice effect. In a similar study, Reed et al. (2011) asked human participants to make repeated choices across progressively increasing numbers of options. This repeated decision task preparation was used to determine within-subject “breakpoints”; that is, the number of options at which participants would switch preference from more options to fewer. Results indicated that a majority of participants began to switch to limited-choice (i.e., participants preferred fewer options) when the manychoice option featured only six alternatives. Analyses of the aggregated data indicate that the percentage of participants choosing the many-choice option decreased in a hyperboliclike discounting function as the size of the many-choice option increased. Collectively, these finding suggest that the processes underlying the paradox of choice may have relations to discounting (i.e., decreases in the subjective value for rewards as the delay of, odds against, or effort required for their receipt increase). One interpretation of these findings is that preference for more choice may be discounted as a function of search costs (i.e., number of options), similar to effort discounting. From a translational perspective, this interpretation of the paradox of choice seems warranted given previous suggestions that the kinds of search costs associated with this phenomenon are time (cf. delay discounting), risk (cf. probability discounting), and effort (cf. effort discounting). What remains unanswered, however, is whether (a) such a discounting effect related to the paradox of choice is documented within-subject and (b) discounting of many options is related to either delay or probability discounting.

5, and 10 years. For the probability discounting assessment, the SR was certain, while the LR was associated with a percent chance of receipt. The probabilities of receiving the larger reward (i.e., probability conditions) consisted of .95, .90, .70, .40, .10, and .05. These probabilities of receipt (p) were converted to “odds against” receipt ( ) by subtracting p from 1.00 and dividing the difference by p. Thus, values consisted of .05, .11, .43, 1.50, 9, and 19. Finally, the options discounting assessment pitted an SR available from two options against an LR from 3, 18, 36, 108, 180, and 360 options. Participants were informed that they could (hypothetically) choose only one reward from these options, but with only 15 s to make the decision. Participants were told the options were subjectively different (i.e., varied in quality) but that they each were worth the given amount (e.g., 2 options worth $500 each or 36 options worth $1000 each). Without such a time limit, it would be unclear whether participants may demonstrate a preference for larger rewards from extensive options simply because they could conceivably take days to evaluate all the options. With no control on deliberation times, any preference for smaller rewards from fewer options could be attributed to a delay discounting effect wherein the deliberation time for the larger array may be so overwhelming (due to the sheer number of items to choose from) that an impulsive choice (the smaller reward from fewer options) is more preferable. In sum, this assessment measured the amount of money participants were willing to forgo in order to choose from a smaller array of options. Following the instructions, participants completed six practice trials to familiarize themselves with the questions. The sequence of values within each discounting task was randomized accordingly to a computer algorithm. However, the sequence of discounting task presentation was constant for all participants; (a) delay, (b) options, and (c) probability. 2. Results and discussion The top panel of Fig. 1 depicts subjective value (median score within group) as a function of reward delay, odds against ( ) reward, or number of extensive choice options. Subjective values followed a decreasing trend for all three discounting dimensions. To better understand the differences in the participants’ discounting across modalities (delay, choice, and probability), we employed a quantitative analysis using Green and Myerson’s hyperboloid model (2004; see also, Myerson and Green, 1995; McKerchar et al., 2010): V= A (1 + kX) s 1. Method Seventy-six undergraduates (60 females) enrolled in an introductory psychology course (ages ranged from 18.33 to 44.83 yr [M = 20.82 yr, SD = 3.16]) were recruited and received extra credit for participation. Groups of three to ten participants were tested in a quiet computer lab. Participants sat at individual computers and monitors during the 30 min session. A computer administered adjusting amount discounting task was used to calculate participants’ indifference points (i.e., the point at which both alternatives’ subjective values are equal). The task used was equivalent to that employed by Du et al. (2002). In this task, participants were asked to make a series of choices between two hypothetical monetary options; one large option (always $1000; LR for “large reward”) and the other small (an adjusting amount, always less than $1000; SR for “smaller reward”). The adjusting procedure consisted of six trials for each condition (i.e., value) of each discounting assessment. Within this procedure, choice of the SSR led to a 50% decrease in the amount SSR on the next trial. Contrarily, choice of the LLR led to a 50% increase in the amount of the SSR on the subsequent trial. The “subjective value” of the reward for each condition was calculated as the next SR value at the end of the sixth trial that would be predicted by the aforementioned titration procedure. For the delay discounting assessment, the SR was always available immediately, while the LR was available after a delay. Delay values (i.e., delay conditions) consisted of .08 (1 month), .50, 1, 3,

(1)

In this hyperboloid model, A represents the amount of the reward, k represents degree/rate of discounting, X represents the values of the independent variable, and s represents participants’ individual differences with respect to the nonlinear scaling of amount and the independent variable (IV; e.g., delay, choice options, and odds against reward receipt in probability discounting). Because of differing modalities (and thus, IV scaling), we focused our analysis on the s parameter to evaluate potential differences across modalities. We first established the contribution of the s parameter to an understanding of participants’ discounting by evaluating whether s significantly differed than 1.0 (thus, the parameter adds potential information beyond that obtained using a simplified hyperbolic model; e.g., Mazur, 1987) using the Wilcoxon Signed Rank test. For delay, choice, and probability, s significantly differed from 1.0 (W = −1472 [p = .0005], −1405 [p < .0001], −904 [p = .02], respectively). Using the Kruskal–Wallis test, we found that s varied significantly across modalities (H[2] = 10.93, p = .004), with Dunn’s Multiple Comparison test identifying a

426

D.D. Reed et al. / Behavioural Processes 90 (2012) 424–427

1000 800 600 400 200 0 0 20 40 60 80 100 120

Subjective Value of $1000

Delay (in Months)
1000 800 600 400 200 0 0 1000 800 600 400 200 0 0 100 4 8 12 16 20 60 120 180 240 300 360

participants employ an elimination procedure to pare down large choices to more manageable/preferred numbers? Research by Tversky (1972) provides some direction for future studies on this topic. Whether choosing food from restaurants/foraging patches or a life/reproductive partner, organisms are faced with innumerable decisions each day. Understanding why there are boundaries to preference for choice will aid behavioral scientists in constructing effective choice architecture (i.e., ways we present options) to maximize the enrichment of environmental conditions. We believe that a link between discounting and the paradox of choice provides an impetus for interdisciplinary translation and collaboration, both with human and non-human species. Acknowledgements The authors thank Leonard Green for assistance with data analysis and computer programming, as well as Allen Neuringer, Benjamin Lovett, and Peter Killeen for their suggestions on earlier drafts of this manuscript. References
Brigham, T.A., Sherman, J.A., 1973. Effects of choice and immediacy of reinforcement on single response and switching behavior of children. Journal of the Experimental Analysis of Behavior 19, 425–435. Catania, A.C., 1975. Freedom and knowledge: an experimental analysis of preference in pigeons. Journal of the Experimental Analysis of Behavior 24, 89–106. Catania, A.C., 1980. Freedom of choice: a behavioral analysis. In: Bower, G.H. (Ed.), The Psychology of Learning and Motivation, vol. 14. Academic Press, New York. Catania, A.C., Sagvolden, T., 1980. Preference for free choice over forced choice in pigeons. Journal of the Experimental Analysis of Behavior 34, 77–86. Chernev, A., 2003. When more is less and less is more: the role of ideal point availability and assortment in consumer choice. Journal of Consumer Research 30, 170–183. Dallery, J., Baum, W.M., 1991. The functional equivalence of operant behavior and foraging. Animal Learning & Behavior 19, 146–152. Dar-Nimrod, I., Rawn, C.D., Lehman, D.R., Schwartz, B., 2009. The maximization paradox: the costs of seeking alternatives. Personality and Individual Differences 46, 631–635. Du, W., Green, L., Myerson, J., 2002. Cross-cultural comparisons of discounting delayed and probabilistic rewards. The Psychological Record 42, 479–492. Floresco, S.B., Tse, M.T.L., Ghods-Sharifi, S., 2008. Dopaminergic and glutamatergic regulation of effort- and delay-based decision making. Neuropsychopharmacology 33, 1966–1979. Green, L., Myerson, J., 2004. A discounting framework for choice with delayed and probabilistic rewards. Psychological Bulletin 130, 769–792. Hayes, S.C., Kapust, J., Leonard, S.R., Rosenfarb, I., 1981. Escape from freedom: choosing not to choose in pigeons. Journal of the Experimental Analysis of Behavior 36, 1–7. Iyengar, S., 2010. The Art of Choosing. Twelve, New York, NY. Iyengar, S.S., Huberman, G., Jiang, W., 2004. How much choice is too much: determinants of individual contributions in 401K retirement plans. In: Mitchell, O.S., Utkus, S. (Eds.), Pension Design and Structure: New Lessons from Behavioral Finance. Oxford University Press, Oxford, pp. 83–95. Iyengar, S.S., Lepper, M.R., 2000. When choice is demotivating: can one desire too much of a good thing? Journal of Personality and Social Psychology 79, 995–1006. Krebs, J.R., Davies, N.B. (Eds.), 1997. Behavioural Ecology: An Evolutionary Approach. , 4th ed. Blackwell, Oxford. Mazur, J.E., 1987. An adjusting procedure for studying delayed reinforcement. In: Commons, M.L., Mazur, J.E., Nevin, J.A., Rachlin, H. (Eds.), Quantitative Analyses of Behavior: The Effect OF Delay and of Intervening Events on Reinforcement Value, vol. 5. Erlbaum, Hillsdale, NJ, pp. 55–73. McKerchar, T.L., Green, L., Myerson, J., 2010. On the scaling interpretation of exponents in hyperboloid models of delay and probability discounting. Behavioural Processes 84, 440–444. Myerson, J., Green, L., 1995. Discounting of delayed rewards: models of individual choice. Journal of the Experimental Analysis of Behavior 64, 263–276. Prévost, C., Pessiglione, M., Météreau, E., Cléry-Melin, M.L., Dreher, J.C., 2010. Separate valuation subsystems for delay and effort decision costs. The Journal of Neuroscience 30, 14080–14090. Reed, D.D., DiGennaro Reed, F.D., Chok, J., Brozyna, G.A., 2011. The “tyranny” of choice: choice overload as a possible instance of effort discounting. The Psychological Record 61, 547–560. Schwartz, B., 2004. The Paradox of Choice. HarperCollins, NY. Suzuki, S., 1999. Selection of forced- and free-choice by monkeys (Macaca fascicularis). Perceptual and Motor Skills 88, 242–250.

Number of Options

Odds Against

log s

10 1 0.1 0.01 0.001

Delay

Choice

Probability

Fig. 1. Subjective value of $1000 as a function of delay (top), number of options (middle), and probability (bottom) from group aggregate (median) data (see text for details). Distribution of s parameters from individual group members is depicted in the bottom panel (error bars depict medians with interquartile ranges; note the logarithmic scale).

significant difference in s between choice and probability discounting. That is, s did not vary between delay and choice discounting, or between delay and probability discounting. A visual depiction of s distributions within each modality is presented in the bottom panel of Fig. 1. These findings replicate other comparisons suggesting that effort and delay discounting are functionally similar, despite differing in neurological processes in humans (Prévost et al., 2010) and rodents (Floresco et al., 2008). These results suggest that perhaps the paradox of choice occurs because of the (a) delayed consequences associated with the effort of deliberating on the decision or (b) effort associated with the search costs. This finding is promising for decision making scientists because linking discounting and “too much choice” decision making processes opens the possibility for many new experimental and analytical approaches to understanding this phenomenon. These data also underscore the need for further nonhuman research on the paradox of choice to rule out species-specific effects, as well as a neuroscience-based approach to researching choice in the presence many alternatives. Two limitations are worth noting, however. First, we did not evaluate the dimensions of large choice sets that may result in the paradox of choice phenomenon with respect to discounting. This may consist of varying qualitative differences. Second, the procedures we employed cannot speak to the processes associated with evaluating large data sets. For example, do

D.D. Reed et al. / Behavioural Processes 90 (2012) 424–427 Tiger, J.H., Hanley, G.P., Hernandez, E., 2006. An evaluation of the value of choice with preschool children. Journal of Applied Behavior Analysis 39, 1–16. Tversky, A., 1972. Elimination by aspects: a theory of choice. Psychological Review 79, 281–299. Vohs, K.D., Baumeister, R.F., Schmeichel, B.J., Twenge, J.M., Nelson, N.M., Tice, D.M., 2008. Making choice impairs subsequent self-control: a limited-resource

427

account of decision-making, self-regulation, and active initiative. Journal of Personality and Social Psychology 94, 883–898. Voss, S.C, Homzie, M.J., 1970. Choice as a value. Psychological Reports 26, 912–914. Wieczorkowska, G., Burnstein, E., 1999. Adapting to the transition from socialism to capitalism in Poland: the role of screening strategies in social change. Psychological Science 10, 98–105.