Valence in Context: Asymmetric Reactions to Realized Gains and Losses. Abigail B. Sussman. University of Chicago. Author Note

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1 This is a preliminary version. Final version published as: Sussman, A. B. (2017). Valence in context: Asymmetric reactions to realized gains and losses. Journal of Experimental Psychology: General, 146(3), 376. Valence in Context: Asymmetric Reactions to Realized Gains and Losses Abigail B. Sussman University of Chicago Word count: 15,800 Abstract word count: 144 Author Note Abigail Sussman, Booth School of Business, University of Chicago. Acknowledgements: The author is grateful for useful feedback from Eldar Shafir, Dan Bartels, Reid Hastie, Chris Hsee, Danny Oppenheimer, Oleg Urminsky, George Wu as well as members of the Wu and Consumer Cognition Labs. Additional thanks to David Mackenzie for programming and Halley Bayer for research assistance. Portions of this paper are based on studies described in the author s doctoral dissertation and have benefitted from presentation feedback at the annual meeting of the Society for Judgment and Decision Making and at the marketing seminar at Wharton. This work was supported by the Beatrice Foods Co. Faculty Research Fund at The University of Chicago Booth School of Business. Correspondence concerning this article should be addressed to Abigail Sussman, University of Chicago Booth School of Business, 5807 S. Woodlawn Ave, Chicago, IL Contact: asussman@chicagobooth.edu

2 VALENCE IN CONTEXT 2 Valence in Context: Asymmetric Reactions to Realized Gains and Losses Abstract The current research documents a novel pattern of preferences across nominally equivalent outcomes. When evaluating the outcome of completed experiences, people are sensitive to the magnitude of component (i.e., gross) gains and losses rather than responding solely to the net outcomes. However, people do not consistently favor outcomes that minimize losses (a pattern consistent with loss aversion), nor those that maximize gains (a pattern consistent with a positivity bias). Instead, preferences are context dependent. Holding net outcomes constant, people prefer positive outcomes that have lower magnitudes of component gains and losses. In contrast, people prefer negative outcomes that have higher magnitudes of component gains and losses. A shift in focus occurs such that people prioritize the contrasting attribute (e.g., negative when the net outcome is positive) in the evaluation process. The paper concludes by discussing implications for a broad range of judgments and decisions. Keywords: judgment, attribute weighting, value, preference

3 VALENCE IN CONTEXT 3 Valence in Context: Asymmetric Reactions to Realized Gains and Losses Decision-makers frequently choose from imperfect options, which have negative attributes in addition to positive ones. A patient may select a treatment with greater efficacy, but more extreme side effects than an alternative; a dieter may choose to burn calories through exercise, but consume more calories than she otherwise would have that day; and a consumer may purchase a laptop that has longer battery life, but also has a heavier weight than its competition. In each of these cases, people select between actions or products that have associated benefits (i.e., positive attributes), but also associated costs (i.e., negative attributes). In this paper, I ask how satisfied people will be with outcomes resulting from option sets in which the costs and benefits of possible alternatives vary in magnitude, but yield equivalent overall value. To understand how people react to outcomes that possess both positive and negative attributes but have equivalent overall value, we must first understand how people weigh each of these values relative to the other. To gain insight into this process, I examine responses to financial outcomes, in which a dollar of gains (positive attributes) and a dollar of losses (negative attributes) have equal and opposite values, and neither should objectively receive priority. From an economic perspective, the additively determined final outcome should be the only relevant factor. Imagine that you come back from a weekend of gambling, and have won $200 more than you started with. Presumably, you would feel good about this outcome. However, you could have reached this outcome in several different ways. For example, imagine that during the weekend, you won $600 and lost $400 in two separate gambles. Consider this the high magnitude scenario in which you encountered higher levels of component (i.e., gross) positive

4 VALENCE IN CONTEXT 4 attributes (gains) but also higher levels of negative attributes (losses). Alternatively, imagine that you won $300 and lost $100 in two separate gambles. Consider this the low magnitude scenario in which you encountered lower levels of negative attributes but also lower levels of positive attributes. Note that these outcomes were completed in the past and have no probabilistic properties such that risk is not a direct concern. Would this distribution of payouts affect how good you feel about your success? Next, imagine a similar situation, but instead of having won $200 during the weekend, you lost $200. Now, you either won $400 and lost $600 in individual gambles (the high magnitude scenario) or won $100 and lost $300 (the low magnitude scenario). Would the distribution of payouts affect how you feel about your failure in this case, and if so, how might it be different from the previous scenario? Following the logic of traditional economics, the makeup of these outcomes should be irrelevant, since the outcomes are certain, completed in the past, and financially equivalent. However, to the extent that people engage in mental accounting (e.g., Thaler, 1985), they may delay integrating these financial outcomes, and respond differently to gains and losses. Assuming that people are loss averse (e.g., Benartzi & Thaler, 1995; Kahneman, Knetsch, & Thaler, 1990; Kahneman & Tversky, 1979; Tversky & Kahneman, 1991), a dollar of losses would receive more weight than an equivalent dollar of gains. This would suggest that people prefer the low magnitude scenarios, which involve smaller overall losses. Alternatively, from the perspective of an overall positivity bias (e.g., Mezulis, Abramson, Hyde, & Hankin, 2004; Taylor & Brown, 1994; Walker, Skowronski, & Thompson, 2003), a dollar of gains would receive more weight than an equivalent dollar of losses. This theory would suggest instead that people prefer the high magnitude scenarios, which involve larger gross positive outcomes.

5 VALENCE IN CONTEXT 5 The current research leverages existing knowledge of attribute weighting to better understand evaluations in contexts where people have made tradeoffs across positive and negative attributes. In this examination, all known outcomes have been realized, and all paths to final outcomes contain both gains and losses. Rather than consistently preferring to minimize negatives (a pattern that could stem from loss aversion) or to maximize positives (a pattern that could stem from a positivity bias), I find that preferences are context dependent. I propose that people will initially determine whether a series of gains and losses results in a net positive or net negative outcome. Subsequently, the attributes that contrast this net background will receive priority in the evaluation process. Thus, when evaluating two equal net outcomes with more losses than gains, people will prefer higher magnitudes of gains, and tolerate the accompanying increase in losses. In contrast, when evaluating two equal net outcomes with more component gains than losses, people will prefer lower magnitudes of losses, and tolerate the accompanying reduction in gains. Assume there is a base number of dollars gained, G > 0; a base number of dollars lost, L < 0; additional dollars gained, α>0; and additional dollars lost, β<0, where α = β : If G + L > 0: (G + α) + (L + β) If G + L < 0: (G + α) + (L + β) G + L G + L Research from a variety of perspectives within cognitive and social psychology is consistent with the intuition that contrasting attributes would receive priority in judgments and decisions. This research extends from social psychological theories of assimilation and contrast (e.g., Mussweiler, 2003, Schwarz & Bless, 1992) to accounts stemming from extremity bias in information processing (e.g., Helson, 1964; Sherif & Sherif, 1967; Skowronski & Carlston,

6 VALENCE IN CONTEXT , 1989), to psychophysical properties of diminishing marginal sensitivity (e.g., Fechner, 1966). Importantly, although my findings are consistent with existing theories that appeal to how people process contrasting information, existing theories are silent on situations of the type I examine here. I propose that people differentially focus on either positive or negative attributes as component parts of completed outcomes when they are evaluating these final outcomes. Furthermore, the final outcome determines which attribute (positive or negative) takes priority in the evaluation. Recently, research specific to perceptions of wealth (Sussman & Shafir, 2012) uncovered a pattern of preferences that appears consistent with this account. At the same absolute level of net worth, people who have positive net worth are perceived as wealthier when they hold lower amounts of both assets and debt. In contrast, people with (equal) negative net worth are perceived as wealthier when they hold higher amounts of assets and debt. In the context of financial wealth, the findings could potentially be attributable to a range of explanations. For example, evaluating those with assets and debt may lead observers to make inferences about the people holding the wealth that could drive the underlying patterns. Thus, while prior research identified a similar pattern in a specific context, the current research aims to understand whether this pattern may reflect a more general process. Current Research In the current research, I examine how preferences vary as a function of both relative magnitudes of gains and losses and also the valence of the net outcome. This paper does not examine risky choices, but instead focuses on whether and what preferences people have for the process that leads to a given set of outcomes, once they are completed. The studies in this paper examine outcomes in scenarios after completed gambles because this setting allows for clean

7 VALENCE IN CONTEXT 7 numeric comparisons across alternatives. This also minimizes the ability for participants to draw strong inferences about the individual experiencing the outcome, since the outcome is outside of the individual s control. The studies minimize the role of risk as a relevant consideration by examining only outcomes that have occurred with certainty, in the past. A key contribution of this paper is demonstrating that certain patterns that may seem analogous to those previously attributed to risky choice persist in stable environments where there is no uncertainty and all outcomes are known. Since all cases examined involve both gains and losses, it is not clear whether the parallels would be to risk-preferences in the context of a mixed gamble (the most direct comparison), or to preferences for gambles in either the gain or the loss domain. I predict that people will have an overall preference for options (i) with higher magnitudes of positive and negative attributes when the final outcome is net negative, and (ii) with lower magnitudes of positive and negative attributes when the final outcome is net positive, holding the net outcome constant in each case. Furthermore, I posit that a parallel shift in focus occurs such that the contrasting attribute gains priority in the evaluation process. In the studies that follow, I test these hypotheses in the context of financial outcomes (experiments finding similar patterns in several other domains can be found in the Supplementary Materials). Importantly, there is no risk versus reward tradeoff involved in the situations I examine. I limit the current investigation to global evaluations (e.g., satisfaction ratings) where beliefs about outcome variance or inferences about risk are objectively less relevant, but I do not examine forecasting or choice measures in which these factors could potentially be applicable. To ensure that these findings reflect an underlying psychological process that goes beyond implementation of rote normative rules, I limit the investigation to cases in which one unit of each positive and negative attribute is equal and opposite in magnitude, and could

8 VALENCE IN CONTEXT 8 meaningfully be additively combined. I focus on these situations because they have the benefit of retaining an uncontroversial, rational calculation of worth (dollars won minus dollars lost), which is useful as a benchmark for comparison. These contexts stand in contrast to ones in which there are not clear equivalent units representing positive and negative attributes (e.g., positive and negative traits in a job candidate), or in which a lexicographic or multiplicative decision rule could be the correct one (e.g., number of positive and negative product reviews), and one could expect people to arrive at the preferences described above. While I believe that the findings presented here are likely to have implications for a much broader set of outcomes, an initial focus on financial judgments provides clarity for evaluating the hypotheses. The paper begins by providing several demonstrations of preferences for higher or lower magnitudes of component gains and losses given net positive or negative overall outcomes. Experiment 1 examines people s responses to outcomes of gambles presented to them in a survey setting. Experiment 2 builds on this investigation by testing whether the same reactions apply to outcomes that people experience, measuring responses to varied numbers of tokens won and lost in an experimental game. To provide additional assurance that people are not overgeneralizing preferences for risk based on implicit beliefs about outcome probabilities, Experiment 3 examines the case of a coin toss that explicitly equates the probability of gains and losses, using both a between (3a) and within (3b) subject design. Additionally, the experiment demonstrates that this pattern of preferences does not serve merely as a tie-breaker. Instead, it can be strong enough to lead to preference reversals in which participants prefer outcomes with lower economic value when they have the preferred magnitude of gains and losses. Next, I present evidence in support of a shift in focus towards the contrasting attribute ; i.e., to losses when the net outcome is positive and to gains when the net outcome is negative.

9 VALENCE IN CONTEXT 9 Experiment 4a includes coding of participants first reactions to better understand the role that attention as well as a variety of related and alternative factors (e.g., counterfactual thinking, ratios, inferred risk) may be playing in the observed pattern. This examination provides initial support for a shift in focus from positive to negative attributes as a function of the net outcome. Data suggests that other mechanisms may also be involved, albeit to a lesser degree. Experiment 4b builds on this finding, demonstrating causal mediation occurring through this shift in focus. Consistent with an attention-based account, Experiment 5 examines recall and shows that people are more likely to recall the value of the contrasting attribute. The paper concludes with Experiment 6 examining downstream consequences of differential focus, finding variations in perceived purchasing power of dollars gained versus dollars lost. For each study, I report all manipulations and all measures. All participants were included in data analysis reported throughout the paper, and no exclusions were made for the purpose of analysis. Sample sizes were determined in advance, as described in the methods section for each experiment. In all studies, participants collected through Mechanical Turk were selected to have a location in the United States, and to have a HIT approval rate on Mechanical Turk greater than or equal to 95%. Studies were posted to discourage repeat survey takers, and across studies, very few responses (fewer than 3%) came from people who participated in multiple studies. Results and significance levels do not change materially when excluding these participants. Experiment 1 I begin the exploration of positive and negative attribute weighting by examining how attributes of each type influence evaluations in the context of imagined gambles. Method

10 VALENCE IN CONTEXT 10 Participants. Fifty US residents were recruited online from Amazon.com s Mechanical Turk platform, and were paid $0.35. I targeted a sample size of 50 to be comparable to that in earlier studies from similar work (e.g., Sussman & Shafir, 2012, Experiment 1). Participants ranged in age from 18 to 63 years (M = 32), and 34% were female. Design and procedure. Participants were told to imagine that they had been gambling with friends the previous weekend, and they were asked to state how they would feel if they had experienced each of a series of outcomes. Participants saw four scenarios in a random order, presented sequentially with one on each screen. Two of the outcomes were matched to have equivalent positive net outcomes, but higher or lower magnitudes of component gains and losses. The other two outcomes were matched to have equivalent net negative outcomes, but higher or lower magnitudes of component gains and losses. In other words, these outcomes were designed using a 2 (net outcome: positive vs. negative) by 2 (magnitude: high vs. low) within-subject design. For example, participants read: Over the course of the weekend, you won $800, but you lost $600 (taking home $200 more than you started with). How good or bad would you feel about the outcome at the end of the weekend? 1 In another question, the values switched from $800 and $600 to $300 and $100. They responded on a scale from 1- I couldn t feel worse about the outcome to 10- I couldn t feel better about the outcome. The set of outcomes participants saw were randomly selected from one of two possible sets, with net outcomes of either positive or negative $200 or $500. The numbers provided to participants were counterbalanced, see Appendix A for additional details. Participants completed demographic questions before exiting the survey. 1 A parallel version of this study was run, with no mention of net outcomes (i.e., the amount taken home), which yielded very similar results.

11 VALENCE IN CONTEXT 11 Results and Discussion A two (net outcome: positive, negative) by two (magnitude: high, low) within-subject analysis of variance revealed a main effect of net outcome (F(1, 49) = , p<.001, ηp 2 =.81), such that people felt better about the outcomes when they won more than they lost, as would be expected if participants were paying attention and answering reasonably. There was no main effect of magnitude (F(1,49)<1, ns). However, consistent with my hypothesis, there was a significant interaction between net outcome and magnitude (F(1, 49) = 24.6, p<.001, ηp 2 =.34). In other words, given an equivalent net positive outcome, participants reported feeling better when they had lower magnitudes of wins and losses (M = 8.20, SD = 1.23) then when they had higher magnitudes of wins and losses (M = 7.34, SD = 1.79; t(49) = 3.67, p =.001). However, given an equivalent net negative outcome, participants reported feeling better when they had higher (M = 3.70, SD = 2.28) rather than lower (M = 2.72, SD = 1.74; t(49) = 3.41, p =.001) magnitudes of wins and losses, see Figure 1. Insert Figure 1 about here These findings are consistent with participants prioritizing negative attributes when evaluating a net gain (preferring lower magnitudes of losses even though they were accompanied by an equivalent reduction in gains), but prioritizing positive attributes when evaluating a net loss (preferring higher magnitudes of gains even though they were accompanied by an equivalent increase in losses). Experiment 2 In Experiment 1, participants reported patterns consistent with those proposed, demonstrating that perceptions of outcomes can vary not only as a result of the net outcome, but

12 VALENCE IN CONTEXT 12 also as a function of the magnitude of gains and losses that contribute to it. While Experiment 1 examined evaluations of hypothetical situations, the current experiment moves to evaluations of consequential outcomes that participants experience. This extension examines the possibility that participants were unable to imagine the scenarios described in Experiment 1 accurately. Participants in Experiment 1 see all information about final outcomes as well as component gains and losses simultaneously, before making their evaluation. This leaves open the possibility that the order people experience these outcomes would exert strong influence on their subsequent judgments (cf., Baucells, Weber, & Welfens, 2011; Heyman, Mellers, Tishcenko & Schwartz, 2004). Here, participants accumulate points as they play the game, with the order of intermediate outcomes randomized. Within the same conditions, some participants see losses before wins, and other see wins before losses; some participants see improving trends and others see worsening trends, as determined by chance. Thus, the current experiment controls for the possibility that participants would be responding in a particular way as a result of the order that these items are presented. An additional characteristic of the current design is that net outcomes are never explicitly presented to participants, only the level of component gains and losses. When summary data is presented, participants see one value indicating the amount of gains and one value indicating the amount of losses, irrespective of whether their net outcome is a gain or a loss. Thus, the number of explicit numeric references to positive and negative values is held constant. Method Participants. One-hundred sixty-eight participants were recruited online through Mechanical Turk. I targeted a sample size of approximately 160 by starting with the initial sample size of 50 per cell used in Experiment 1, and adjusting upwards to 80 per cell based on

13 VALENCE IN CONTEXT 13 anticipation of a somewhat smaller effect size given the more intricate and less straightforward design in the current experiment. Participants completed the study for a base pay of $0.35 with a bonus of $1.00 plus or minus any money won or lost in the experiment. Participants ranged in age from 18 to 69 (M = 28.8), and 40% were female. Design and procedure. Participants read the following instructions: On the following pages, you will be playing two different games of chance. You will be playing for real money in each of the games. Along the way, you will receive tokens to help you keep track of how much you are winning and losing. You will get one green token for every 10 cents that you win (this money is yours to keep at the end of the game) and one red token for every 10 cents that you lose (this is money you will owe at the end of the game). Participants then played two superficially distinct versions of a roulette game, the Variable game and the Even-Odd game 2, one after the other, presented in a random order, see Appendix B in the online supplement for additional details. Participants were randomly assigned to the high magnitude condition for one game and the low magnitude condition for the other in a 2 (withinsubject magnitude: high vs. low) by 2 (between-subject net outcome: positive vs. negative) mixed design. In addition to their base pay, participants were given a $1.00 bonus at the start of the experiment. Any money that they won or lost in the game was added to or subtracted from this initial bonus amount. Participants were informed that they could not lose more than the value of their bonus. Note that this game was described as a game of chance since the outcome of individual trials varied randomly (although subject to constraints as described below), and there were multiple possible outcomes a participant could obtain at the conclusion of 2 These names are used for explanatory purposes here only; participants did not see these labels in the experiment.

14 VALENCE IN CONTEXT 14 the game. The mechanism for determining the trial-by-trial and final outcomes was intentionally vague to avoid providing false information. In the Variable Game, participants saw a wheel, similar to a roulette wheel, with sections stating values ranging from to -0.50, see Figure 2a. Participants moved a lever to choose a speed for the wheel to spin. They received the amount stated on the wheel when it stopped (in cents) if the number was positive, and they would owe the amount stated on the wheel when it stopped if the number was negative. For each $0.10 that they won, a green token would appear on the side of the screen and for each $0.10 that they lost, a red token would appear. These remained visible on the screen throughout the game. Participants played 10 rounds of this game. Half of participants were assigned to the net positive outcome condition, and half to the net negative outcome condition 3. Payouts for each trial were randomized throughout the 10 rounds of the game, subject to constraints. A participant could win or lose any amount that appeared on the wheel (ranging from a gain of $0.50 to a loss of $0.50) on a given trial. One participant in a given condition could win money in the first round while another participant in the same condition could lose money in the first round. However, in aggregate across all rounds of the game, the final outcome was randomized across four possible final outcomes, determined by the participant s condition. At the end of the game, the total amount won or lost would be the same for participants assigned to the same condition, but the trial-by-trial amount varied for each person. In the high magnitude condition with positive outcomes, the sum of participant wins would equal 12 green tokens ($1.20) and the sum of participant losses would equal eight red tokens ($0.80), for a net four token ($0.40) gain. These numbers were reversed in the case of the high magnitude condition with negative outcomes (win eight tokens for $0.80, lose 12 tokens for 3 Data were first collected with the positive net outcome conditions followed by the negative net outcome conditions, with equal numbers (n =84) in each condition.

15 VALENCE IN CONTEXT 15 $1.20). In the low magnitude condition with positive payouts, the sum of participant wins would instead equal only $0.50 and the sum of participant losses would equal $0.10 for a total of a $0.40 gain. Again, these numbers were reversed in the case of the low magnitude condition with negative payouts (win one token for $0.10, lose five tokens for $0.50). Thus, the study was designed so that participants would always gain or lose $0.40 at the end of the game, but this outcome would be composed of a higher or lower magnitude of wins and losses. Insert Figure 2 about here After playing this game, a screen would appear that stated their final payouts, both verbally, including the amount earned and lost as well as visually, indicating the total number of green and red tokens earned, see Figures 2b and 2c. The net total was never calculated for participants. Participants were then asked to rate their satisfaction with their experience in the game as well as their satisfaction with the final outcomes, each on a scale from one to six, with one indicating not at all satisfied and six indicating completely satisfied. In the Even-Odd game, participants saw a different wheel similar to a roulette wheel, this time with odd and even numbers on alternating sections. Participants selected whether they thought that the wheel would land on an even or an odd section of the wheel when it stopped, and then set the wheel into motion. If they guessed correctly, a green token would appear on the screen representing a $0.10 win, and would stay on the screen through the remainder of the game. If they guessed incorrectly, a red token would appear on the screen representing a $0.10 loss. Again, participants were randomly assigned across four possible sets of final payouts consistent with the 2 (within-subject magnitude: high vs. low) by 2 (between-subject net

16 VALENCE IN CONTEXT 16 outcome: positive vs. negative) mixed design. In the high magnitude conditions, participants played 20 rounds of the game. Outcomes of the game with positive payouts were designed to register 12 wins and eight losses in a random order, equivalent to $1.20 of positive payouts and $0.80 of negative payouts, for a net positive payout of $0.40 at the games completion. These numbers were reversed in the case of the game with negative payouts, where participants instead lost 12 rounds ($1.20) and won eight rounds ($0.80), for a net negative payout of $0.40. On any given trial, the outcome could have been positive or negative, subject to the constraints of the final outcomes of the game. Participants in the low magnitude conditions played only six rounds of the game, which was designed to register five wins and one loss in a random order, equivalent to $0.50 of positive payouts and $0.10 of negative payouts, again totaling $0.40 for cases with the positive payout. In the case of the game with negative payouts, this pattern was reversed and participants instead lost five rounds ($0.50) and won one ($0.10), for the same net negative payout again of $0.40. Again, participants always gained or lost $0.40 at the end of the game, but this outcome was composed of a higher or lower number of wins and losses. After playing this game, participants were again asked to rate their satisfaction with their experience as well as with the outcomes of the game. After completing both games, participants indicated which of the two games they thought they did better in. To summarize, participants played two different games, the Variable game and the Even- Odd game, presented in a random order. One game (randomly assigned for each participant to be either the Variable or the Even-Odd game) had high magnitude payouts (winning $1.20 and losing $0.80 or vice versa) and the other had low magnitude payouts (winning $0.50 and losing $0.10 or vice versa). Within participants, both games had equal net outcomes, either net positive

17 VALENCE IN CONTEXT 17 (+0.40) or net negative (-0.40). After playing each game, participants rated their satisfaction with various aspects of the game. They were asked: How satisfied are you with your experience in the game? and How satisfied are you with your final outcomes?, and responded on a scale from 1- not at all satisfied to 6- completely satisfied. After completing both games, they were asked: Of the two games you just played, which game do you think you did better in? They completed demographics before being debriefed on the study design and purpose, exiting, and receiving their bonus. Results and Discussion Within-subject comparisons revealed that participants in the net positive condition were more likely to consider their performance in the low magnitude game to be better than in the high magnitude game (76% vs. 24%; one-sample binomial difference from 50%, Z = 4.69, p <.001). But, participants in the net negative condition were more likely to consider their performance in the high magnitude game to be better than in the low magnitude game (81% vs. 19%; one-sample binomial test difference from 50% Z = 5.56, p <.001). Additionally, participants in the net positive condition reported being significantly more satisfied with both the experience and the outcome when they received lower magnitude payouts (Mexperience = 5.00, Moutcome = 5.17) than when they received higher magnitude payouts (Mexperience = 4.27, Moutcome = 4.02; paired-sample ts = 6.44 and 9.35 respectively, ps <.001), see Figure 3. This pattern reversed for participants in the net negative condition. These participants instead reported being significantly more satisfied with both the experience and the outcome when they received higher magnitude payouts (Mexperience = 1.96, Moutcome = 1.68) than when they received lower magnitude payouts (Mexperience = 1.40, Moutcome = 1.24; paired-sample ts = 4.28 and 3.65 respectively, ps <.001), see Figure 3.

18 VALENCE IN CONTEXT 18 Insert Figure 3 about here Similar patterns emerge when examining each game individually, comparing responses from different participants playing the same game with different outcomes in a between-subjects analysis, see Table 1 for details. A two-way analysis of variance with experience satisfaction in the Variable game as the dependent variable reveals a preference for net positive versus negative outcomes (F(1, 162) = , p <.001, ηp 2 =.65), as expected, and also reveals an overall preference for low versus high magnitude payouts (F(1, 162) = 5.12, p =.025, ηp 2 =.03). Importantly, there is a significant interaction between net outcome valence and preference for high versus low magnitude games (F(1, 162) = 20.29, p <.001, ηp 2 =.11). A two-way analysis of variance with outcome satisfaction in the Variable game shows the same preference for net positive outcomes (F(1, 162) = , p <.001, ηp 2 =.75), an overall preference for low magnitude payouts (F(1, 162) = 18.02, p <.001, ηp 2 =.03), and a significant interaction between net outcome valence and payout magnitude (F(1, 162) = 31.91, p <.001, ηp 2 =.17). Examining the Even-Odd game yields similar results. A two-way analysis of variance with experience satisfaction in the Even-Odd game as the dependent variable reveals a preference for net positive versus negative outcomes (F(1, 163) = , p <.001, ηp 2 =.69) but no overall preference for low versus high magnitude payouts (F(1, 163) = 1.86, ns). Importantly, the significant interaction between outcome valence and preference for high magnitude payouts persists (F(1, 163) = 12.61, p =.001, ηp 2 =.07). Examination of outcome satisfaction in the Even-Odd game shows the same preference for positive versus negative net outcomes (F(1, 163) = , p <.001, ηp 2 =.72), no preference for low magnitude payouts (F(1, 163) =.60, ns),

19 VALENCE IN CONTEXT 19 and a significant interaction between net outcome and preference for high magnitude payouts (F(1, 163) = 28.22, p <.001, ηp 2 =.15). Insert Table 1 about here Consistent with my hypothesis, patterns of preferences for outcomes in games that participants had the opportunity to play paralleled those found for reported gambles. Experiment 3 In experiments 1 and 2, materials were constructed so that participants were evaluating net outcomes of equivalent absolute value. This design established a basic pattern and ensured that participants were considering the component outcomes in isolation, rather than their preferences for risk or expectations about future outcomes. For example, someone might believe that a game with a higher magnitude of total component outcomes (both positive and negative) is generally higher variance than one with a lower magnitude of component outcomes and may consequently be interpreted as riskier. Thus, a preference for higher magnitudes in the context of a net negative outcome but lower magnitudes in the context of a net positive outcome could potentially be reflecting underlying risk preferences. Preferences for risk-seeking in the domain of losses, but risk-aversion in the domain of gains, could be consistent with a prospect theoretic account (Kahneman & Tversky, 1979). This account cannot explain the existing data in a straightforward manner, in part because evaluations are backward looking and there is no role of risk in an outcome that has materialized (see the introduction to Experiment 4 for additional discussion of this point). However, the possibility remains that participants are generalizing preferences for risk to a situation where this set of preferences should not be relevant. To test for this possibility, the current experiment

20 VALENCE IN CONTEXT 20 equalizes risk explicitly. Specifically, it moves from an unfamiliar gambling task to a fair coin flip, with equal probabilities of winning or losing on each turn. If participants were previously overgeneralizing their risk preferences for the future to evaluate outcomes from the past, I would expect that this pattern would disappear when risk was equated. Additionally, the current design varies the absolute value of the outcomes participants observe, having them judge outcomes with absolute values of four, five, and six dollars (a variation of 50 percent across questions). Including this range provides better insight into the strength of participant preferences for a particular magnitude of outcomes, and the relative importance of net outcomes versus the magnitudes of contributing gains and losses. I test whether a preference for magnitudes can be so strong that it leads people to express a preference for objectively worse outcomes. Experiment 3a tests this pattern in a fully between subjects design to examine whether this pattern is sufficiently robust to influence evaluations across participants. Experiment 3b examines the same patterns in a within-subject design to determine whether this pattern emerges only as an artifact of the between-subjects design (e.g., Birnbaum, 1999), or is robust to within-subject evaluation. Experiment 3a Method Participants. Two-hundred nine participants were recruited online through Mechanical Turk, and they completed the study in exchange for $0.50. I targeted a sample size of 50 participants per cell (200 total) to match that in Experiment 1, based on an expectation of a similar effect size given the return to a scenario design. Participants ranged in age from 19 to 74 (M = 34), and 48% were female.

21 VALENCE IN CONTEXT 21 Design and Procedure. Participants were randomly assigned to one of four conditions in a two (net outcome: positive vs. negative) by two (magnitude: high vs. low) between-subjects design. All participants were told to imagine that they were flipping a fair coin, and betting that the coin would land on heads. They would be paid one dollar for each time the coin landed on heads, and they would lose one dollar each time the coin landed on tails. Participants then saw three sets of completed outcomes, presented one per page in a random order. For a given participant, all three final outcomes were either positive or negative and the number of coin flips was either high or low, in accordance with the participants randomly determined condition. The number of flips ranged from 9-12 in the low condition and in the high condition. The absolute value of the net outcomes was the same across conditions ($4, $5, and $6), see Appendix C for specific numbers used. Participants were told the number of heads and tails, along with the corresponding dollar outcome. For example, they were told that the outcome was: 4 heads (win $4); 8 tails (lose $8). The net result was not presented to participants. On the same page, participants were asked to rate their satisfaction with the outcome on a scale from 1- extremely dissatisfied to 7 extremely satisfied. After completing these three questions, participants responded to a question asking them their perceptions of the chances of getting heads in a coin flip like the ones they viewed. This question was included to ensure that participants believed that the coin flip was actually fair 4. Finally, participants responded to demographics before exiting the survey. Results and Discussion I first averaged participant ratings across all three trials, but see Figure 4a for results broken down by question. Next, I conducted a two (net outcome: positive vs. negative) by two 4 A post-test confirmed that participants believed it was equally risky to bet on fair coin flips in situations like the ones described in the survey, despite the fact that the number of flips differed.

22 VALENCE IN CONTEXT 22 (magnitude: high vs. low) between-subjects analysis of variance to examine reported satisfaction ratings. This analysis revealed a main effect of outcome valence such that participants reported greater satisfaction with positive (M = 5.38, SD = 1.28) than with negative (M = 2.00, SD = 1.39) outcomes overall (F(1, 205) = , p <.001, ηp 2 =.64), as would be expected from participants responding sensibly to the scenarios. There was also a modest directional preference for low magnitude outcomes (MLOW = 3.85, SDLOW = 2.38; MHIGH = 3.50, SDHIGH = 1.81; F(1, 205) = 3.18, p =.076, ηp 2 =.02). Consistent with earlier findings, there was a significant interaction between the net outcome and magnitude (F(1, 205) = 44.73, p <.001, η 2 =.18). Participants reported greater satisfaction in the low magnitude than the high magnitude conditions when the net outcomes were positive (MLOW = 5.98, SDLOW =.78; MHIGH = 4.55, SDHIGH = 1.61; F(1, 205) = 35.91, p <.001, ηp 2 =.15), but this pattern reversed when the net outcomes were negative (MLOW = 1.64, SDLOW = 1.09; MHIGH = 2.46, SDHIGH = 1.37; F(1, 205) = 12.02, p =.001, ηp 2 =.06). The data was also analyzed including only participants (80% of the sample) who reported that they believed the likelihood of getting heads in a coin flip like the ones they viewed was 50 percent. This analysis yielded equivalent patterns. Participants in this subset reported greater satisfaction in the low magnitude than the high magnitude conditions when the net outcomes were positive (MLOW = 5.87, SDLOW =.79; MHIGH = 4.57, SDHIGH = 1.54; F(1, 205) = 23.99, p <.001, ηp 2 =.13), and this pattern again reversed when the net outcomes were negative (MLOW = 1.56, SDLOW = 1.03; MHIGH = 2.56, SDHIGH = 1.38; F(1, 205) = 14.52, p <.001, ηp 2 =.08; interaction F(1,163) = 37.94, p <.001, ηp 2 =.19). Insert Figure 4 about here

23 VALENCE IN CONTEXT 23 Next, I move to examine the question of whether these preferences are sufficiently strong as to lead participants to rate their satisfaction with an outcome higher when it has lower economic value, but the preferred distribution of positive and negative outcomes. To test this possibility most directly, I conduct the same analysis as above, but examine responses to the most extreme economic outcomes for each condition, rather than average responses for each participant. Specifically, since I expected the low magnitude outcome to be rated most satisfying for net positive outcomes, I examined whether participant ratings in response to receiving the net economic outcome with the lowest value (four dollars) when the magnitude was low would be different from those receiving the net economic outcome with the highest value (six dollars) when the magnitude was high. In contrast, I examined the reverse for cases with negative net outcomes, comparing responses of participants to questions with the lowest net economic outcome (negative six dollars) when the magnitude was high to those with the highest net economic outcome (negative four dollars) when the magnitude was low. As with the average responses, the main effect of net outcome persisted 5. Participants reported greater satisfaction with net positive (M = 5.18, SD = 1.44) than with net negative (M = 2.07, SD = 1.36) outcomes (F(1, 205) = , p <.001, ηp 2 =.55). There were no meaningful differences across high and low magnitude outcomes overall. Importantly, the significant interaction between net outcome and magnitude persisted (F(1, 205) = 16.19, p <.001, ηp 2 =.07). Participants continued to report greater satisfaction in the low magnitude than the high magnitude conditions when the net outcomes were positive (MLOW,+$4 = 5.56, SDLOW,+$4 = 1.12; MHIGH,+$6 = 4.66, SDHIGH,+$6 = 1.67; F(1, 205) = 11.26, p =.001, ηp 2 =.05), but this pattern 5 This analysis is conducted on the full sample of participants. However, results are very similar when limited to those who reported believing that the likelihood of the coin toss coming up tails was 50%.

24 VALENCE IN CONTEXT 24 reversed when the net outcomes were negative (MLOW,-$4 = 1.80, SDLOW,-$4 = 1.27; MHIGH,-$6 = 2.42, SDHIGH,-$6 = 1.41; F(1, 205) = 5.46, p =.020, ηp 2 =.03). Experiment 3b Method Participants. Two-hundred two participants were recruited online through Mechanical Turk, and they completed the study in exchange for $0.50. I targeted the same sample size (200 total) as in Experiment 3a. Participants ranged in age from 19 to 73 (M = 33), and 39% were female. Design and Procedure. The design was nearly identical to Experiment 3a, with one modification. Each participant saw all 12 coin-flip outcomes, presented one per page, in a random order. Results and Discussion As in Experiment 3a, participant ratings were first averaged across the three trials for each condition (i.e., low magnitude net negative outcome, high magnitude net negative outcome, low magnitude net positive outcome, high magnitude net positive outcome), but see Figure 4b for results broken down by question. Next, I conducted a two (net outcome: positive vs. negative) by two (magnitude: high vs. low) within-subjects analysis of variance to examine reported satisfaction ratings. This analysis revealed a main effect of net outcome such that participants reported greater satisfaction with positive (M = 5.17, SD = 1.00) than with negative (M = 2.26, SD = 1.22) outcomes overall (F(1, 201) = , p <.001, ηp 2 =.78), as would be expected from participants responding sensibly to the scenarios. Consistent with earlier findings, there was also a significant interaction between the net outcome and magnitude (F(1, 201) = , p <.001, η 2 =.50). Participants reported greater satisfaction in the low magnitude than

25 VALENCE IN CONTEXT 25 the high magnitude conditions when the net outcomes were positive (MLOW = 5.51, SDLOW = 1.01; MHIGH = 4.82, SDHIGH = 1.28; F(1, 201) = 73.64, p <.001, η 2 =.27), but this pattern reversed when the net outcomes were negative (MLOW = 1.97, SDLOW = 1.14; MHIGH = 2.54, SDHIGH = 1.46; F(1, 201) = 73.03, p <.001, η 2 =.27). 40% of participants expressed a flip in preferences such that they strictly preferred high to low magnitudes when the net outcomes were negative, but strictly preferred low to high magnitudes when the net outcomes were positive. This number increases to 66% when including people who had a strict preference in one comparison and were indifferent to the other. The data was also analyzed including only participants (90% of the sample) who reported that they believed the likelihood of getting heads in a coin flip like the ones they viewed was 50 percent. This analysis yielded roughly equivalent patterns. Next, I move to revisit the question of whether these preferences are sufficiently strong as to lead participants to rate their satisfaction with an outcome higher when it has lower economic value, but the preferred distribution of positive and negative outcomes. I conduct the same analysis as above, but examine responses to the most extreme economic outcomes for each condition only (see methods of Experiment 3a for additional detail). As with the average responses, participants reported greater satisfaction with positive (M = 5.11, SD = 1.12) than with negative (M = 2.27, SD = 1.26) outcomes overall (F(1, 201) = , p <.001, ηp 2 =.75). The significant interaction between the net outcome and magnitude persisted (F(1, 201) = 32.69, p <.001, ηp 2 =.14). Participants continued to report greater satisfaction in the low magnitude than the high magnitude conditions when the net outcomes were positive (MLOW,+$4 = 5.29, SDLOW,+$4 = 1.17; MHIGH,+$6 = 4.94, SDHIGH,+$6 = 1.39; F(1, 201) = 16.73, p <.001, ηp 2 =.08), but this pattern reversed when the net outcomes were negative

26 VALENCE IN CONTEXT 26 (MLOW,-$4 = 2.13, SDLOW,-$4 = 1.27; MHIGH,-$6 = 2.42, SDHIGH,-$6 = 1.50; F(1, 201) = 11.81, p =.001, ηp 2 =.06). 13% of participants expressed a flip in preferences such that they strictly preferred winning $4 composed of low magnitudes to winning $6 composed of high magnitudes and also strictly preferred losing $6 composed of high magnitudes to losing $4 composed of low magnitudes. This number increases to 44% when including people who had a strict preference between one pair and were indifferent to the other. Discussion Results in Experiments 3a and 3b were consistent with the patterns of preferences observed in Experiments 1 and 2. They demonstrated that the patterns are sufficiently strong that they can lead to reversals in which people are more satisfied with a worse financial outcome when it is accompanied by the preferred magnitudes of component gains and losses. This preference held for both a between and within subject design. The experiments tested outcomes that varied by up to 50% of the net amount and found that patterns of preference were strong enough to generate preference reversals in those cases. Finally, by examining a coin toss with equal probability of heads or tails, this study that differences in risk preferences (or an overgeneralization of these risk preferences) across gains and losses are not primarily responsible for these patterns. In the experiments that follow, I take a closer look at additional factors, including a shift in the focus on component gains versus losses, which may play a role in the observed effect. Experiment 4 Thus far, each experiment has provided an additional demonstration of a shift in preference such that holding net outcomes constant people favor options with lower magnitudes of gains and losses when the net outcome is positive but favor options with higher

27 VALENCE IN CONTEXT 27 magnitudes of gains and losses when the net outcome is negative. For example, people prefer winning $200 and losing $100 to winning $500 and losing $400, but they prefer losing $500 and winning $400 to losing $200 and winning $100. I propose that this shift occurs because people are focusing more on the attribute that contrasts with the overall outcome valence when evaluating the final outcome. Specifically, they prioritize losses relative to gains in the context of net gains (i.e., focus relatively more on losing either $100 or $400 than on winning $200 or $500 in the example above). This leads people to prefer lower magnitudes of each so that they minimize the more highly attended to negative component outcomes. But, people prioritize gains relative to losses in the context of net losses (i.e., focus relatively more on winning either $200 or $500 than on losing $100 or $400 in the example above). This leads people to prefer higher magnitudes of each so that they maximize the more highly attended to positive component outcomes. This shift is consistent with prior research on contrast effects in other areas. For example, the inclusion/exclusion model posited by Schwarz and Bless (1992), suggests a pattern of overweighting contrasting features. In this model, information that is used in forming a representation of the target as part of a category leads to assimilation, while contrast effects are expected to emerge whenever the information that comes to mind is excluded from the target category. Similar parallels of overweighting contrasting attributes can be drawn to accounts stemming from social psychological theories of reactions to perceived norms (e.g., norm theory; Kahneman & Miller, 1986), from extremity bias in information processing (e.g., Helson, 1964; Sherif & Sherif, 1967; Skowronski & Carlston, 1987, 1989), and to basic psychophysical properties such as diminishing sensitivity (Fechner, 1966).