Constructing preference from sequential samples: The impact of evaluation format on risk attitudes.

Abstract

The cognitive mechanism underlying decisions based on sequential samples has been found to be affected by whether multiple alternatives are evaluated together or whether each alternative is evaluated individually. In this experiment, we examined whether evaluation format can also lead to different preference orders among risky alternatives. We hypothesized that because of differences in computational demands posed by the 2 evaluation formats, there would be differences in the type of the cognitive mechanism deployed: a risk-return mechanism, that trades off the mean reward and risk of an alternative, or a selective-accumulator mechanism, that sums the rewards of each alternative, with a higher weight to more extreme payoffs. Each participant rated the same set of alternatives (sequences of payoffs from slot machines) in both a one-by-one and a grouped evaluation format. The mean and the variance of the payoff distributions of each alternative were varied orthogonally. As predicted, in the grouped (but not in the one-by-one) condition, the impact of the variance on participants’ ratings interacted with the mean payoff. Specifically, participants were risk averse for alternatives with a low mean payoff and risk seeking for alternatives with a high mean payoff. Computational modeling showed that the majority of participants were best described by a risk–return model in the one-by-one condition but by a selective-accumulator model in the grouped condition. Our results underline the importance of studying the cognitive foundations of risk attitudes in order to understand how they are shaped by the structure of a given decision task.

Selective Integration: An Attentional Theory of Choice Biases and Adaptive Choice

“…More recently, such a framing reversal of risk preference was reported by Tsetsos, Chater, and Usher (2012) in a selection between rapid sequences of payoffs (see also Erev, Ert, Plonsky, Cohen, & Cohen, 2017). A similar effect was recently reported by Vanunu, Pachur, and Usher (2019), who showed a modulation of risk biases by the evaluation format: one by one versus in groups.…”

The averaging of numerosities: A psychometric investigation of the mental line

“…In three experiments we examined the ability of human participants in a task of averaging of numerosity stimuli (sets of dots). This extends the range of operations on which numerosity representations were used from comparisons, addition or subtractions on pairs of stimuli, to the averaging of multiple stimulian operation that is of key importance to decision-making (Brusovansky et al, 2017;Vanunu et al, 2018;Weber, 2010). This task also extends the domain of stimuli on which the extraction of summary statistics was established, from domains such as size, orientation, emotional expression or object category (Ariely, 2001;Chong & Treisman, 2005;Dakin, 2001;Habrman et al, 2009;Haberman & Whitney, 2011;Khayat & Hochstein, 2018Parkes et al, 2001;Robitaille & Harris, 2011) to the domain of nonsymbolic numerosities across temporal sequences.…”

“…This is important for several reasons. First the estimation of the average is critical for common life activities, like decision-making, in which one has to estimate the utility of alternatives that vary across time or attributes (Betsch, Kaufmann, Lindow, Plessner, & Hoffmann, 2006;Brusovansky, Glickman, & Usher, 2018;Brusovansky, Vanunu, & Usher, 2017;Pleskac, Yu, Hopwood, & Liu, 2019;Roe, Busemeyer, & Townsend, 2001;Spitzer, Waschke, & Summerfield, 2017;Tsetsos, Chater, & Usher, 2012;Usher & McClelland, 2004;Vanunu, Pachur, & Usher, 2018;Zeigenfuse, Pleskac, & Liu, 2014). Second, recent research has indicated an impressive ability of human subjects in estimating summary statistics (in particular the average) of perceptual properties of sets of elements, such as size, orientation, and even emotional expression (Ariely, 2001;Chong & Treisman, 2005;Dakin, 2001;Haberman, Harp & Whitney, 2009;Haberman & Whitney, 2011;Khayat & Hochstein, 2018;Parkes, Lund, Angelucci, Solomon, & Morgan, 2001;Robitaille & Harris, 2011).…”

“…(ii) Does the precision of the estimate increase (decrease) with the length of the sequence? An increased precision with sequence length would indicate that the ANS system can operate not only for single (or pairs of) stimuli but also for multiple ones, and that it can contribute to the mechanism for the formation of preferences over sequences of numerical values or payoffs (Brusovansky et al, 2018 ; Vanunu et al, 2018 ; Zeigenfuse et al, 2014 ).…”

Goal-dependent flexibility in preferences formation from rapid payoff sequences

“…Assuming that people can deploy both summation and averaging (as a non-symbolic process), which of them would be deployed in a specific case may be determined by characteristics of both individuals and the task. For example, a population-coding mechanism of averaging may be more difficult to deploy when the alternatives are presented and evaluated together, as this requires the maintenance and the separation of multiple reward distributions simultaneously (Brusovansky et al, 2017; Vanunu, Pachur, & Usher, 2018).…”