Keywords:

When people make decisions, they often prefer to receive information that supports rather than conflicts with their decision. To date, this effect has mainly been investigated in the context of decisions about gains, whereas decisions about losses have received less attention. Based on Prospect Theory, we expected information search to be differently affected by whether people previously have decided about gains or losses. Three studies have revealed that selectivity of information search is stronger after gain-framed rather than after loss-framed decision problems. An investigation of the underlying psychological processes revealed that gain decisions are made with increased subjective decision certainty (i.e. they are easier and less effortful to make), which in turn systematically increases confirmatory information search.

Selective exposure to information (confirmatory information search) addresses the phenomenon that people tend to systematically prefer information that is consistent rather than inconsistent with their attitudes, standpoints, and decisions [START_REF] Festinger | A theory of cognitive dissonance[END_REF][START_REF] Frey | Recent research on selective exposure to information[END_REF][START_REF] Jonas | Confirmation bias in sequential information search after preliminary decisions: An expansion of dissonance theoretical research on "selective exposure to information[END_REF]Lundgren & Prislin, 1996). With regard to decision making, this phenomenon has been consistently shown for both individual (Fischer, Jonas, Frey, & Schulz-Hardt, 2005;[START_REF] Frey | Recent research on selective exposure to information[END_REF][START_REF] Jonas | Confirmation bias in sequential information search after preliminary decisions: An expansion of dissonance theoretical research on "selective exposure to information[END_REF][START_REF] Jonas | Information search and presentation in advisor-client interactions[END_REF][START_REF] Jonas | The influence of mood on the search for supporting vs. conflicting information[END_REF], and group decision making (Schulz-Hardt, [START_REF] Schulz-Hardt | Biased information search in group decision making[END_REF][START_REF] Schulz-Hardt | Productive conflict in group decision making: Genuine and contrived dissent as strategies to counteract biased information seeking[END_REF], whereas the most important practical relevance lies in the fact that this tendency can severely jeopardize decision quality [START_REF] Janis | Groupthink[END_REF]Kray & Galinski, 2003). The present research investigates whether this tendency for confirmatory information search is differently affected by gain-vs. loss-framed decision problems.

Research on Confirmatory Information Search

To date, various situational variables that increase confirmatory information search have been identified, for example: strong commitment (Schwarz, Frey, & Kumpf, 1981), high choice [START_REF] Frey | A clarification of selective exposure: The impact of choice[END_REF], limited availability of information (Fischer et al., 2005), negative mood [START_REF] Jonas | The influence of mood on the search for supporting vs. conflicting information[END_REF], or sequential information presentation [START_REF] Jonas | Confirmation bias in sequential information search after preliminary decisions: An expansion of dissonance theoretical research on "selective exposure to information[END_REF].

Selective exposure effects have been explained both by cognitive and motivational accounts:

Dissonance theory -a purely motivational account -suggests that decision makers systematically prefer supporting information in order to reduce the aversive motivational state of dissonance (cf. [START_REF] Festinger | A theory of cognitive dissonance[END_REF][START_REF] Frey | Recent research on selective exposure to information[END_REF][START_REF] Jonas | Confirmation bias in sequential information search after preliminary decisions: An expansion of dissonance theoretical research on "selective exposure to information[END_REF]. From a more cognitive perspective, [START_REF] Schulz-Hardt | Confirmation bias in accuracy-motivated decision-making: A cognitive explanation for biased information seeking[END_REF] suggested that information search is determined by subjective information quality. However, because decision makers can not evaluate information quality independently from their own standpoint [START_REF] Ditto | Motivated skepticism: Use of differential decision criteria for preferred and non-preferred conclusions[END_REF][START_REF] Ditto | Motivated sensitivity to preference-inconsistent information[END_REF], and a priori decision-relevant knowledge [START_REF] Kunda | The case for motivated reasoning[END_REF], consistent

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Decision framing and information search 4 information receives a subjective quality advantage, and thus is systematically preferred in information search (see also Fischer et al., 2005).

Since most previous paradigms employed to investigate information search were designed to test theories, less emphasis was given to the ecological validity of these decision paradigms. Yet, one shortcoming of previous research on information search is that people have predominantly decided about gains or positively framed decision alternatives (e.g. which one of several positively described books or holiday trips they would prefer; e.g., [START_REF] Frey | Reversible and irreversible decisions: Preference for consonant information as a function of attractiveness of decision alternatives[END_REF][START_REF] Jonas | Information search and presentation in advisor-client interactions[END_REF][START_REF] Jonas | The influence of mood on the search for supporting vs. conflicting information[END_REF]. However, in real life decisionmaking, the focus often lies on risks and drawbacks of specific decision alternatives. In other words, people are frequently confronted with loss decisions or loss-framed decision scenarios, respectively. For example, individuals have to decide which of two products not to buy, or investment strategies might be described with regard to their risk of loosing money rather than the potential to make high gains. These possibilities of being confronted with negatively (loss) framed decision scenarios have not yet been addressed by research on biased information search. Derived from Prospect Theory [START_REF] Kahneman | Prospect theory: An analysis of decision under risk[END_REF], which assumes that decision processes can be strongly influenced by whether people make decisions about gains or losses, we propose that gain-vs. loss-framed decision problems also fundamentally change the way in which people search for new decision-relevant information.

Decision Framing and Information Search

Kahneman and [START_REF] Kahneman | Prospect theory: An analysis of decision under risk[END_REF] presented a significant theory on risky choice called Prospect Theory, which addressed the problem of gain and loss decision framing. Among the assumptions of the theory was the proposition that losses appear larger than gains; that is, losses are more averse in intensity than the pleasure derived from equal-sized gains. Simply shifting reference points can determine whether alternatives are viewed as a chance to compensate losses or as a chance to make gains. [START_REF] Tversky | Loss aversion in riskless choice: A referencedependent model[END_REF] reviewed many experiments that demonstrated the effect of loss aversion, for example: loss framing

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Decision framing and information search 5 increases risk taking in decision-making, or negotiators are less likely to reach an agreement when the attributes of bargaining are framed as losses rather than as gains (see also Kahneman, Knetsch, & Thaler, 1990;[START_REF] Kahneman | Prospect theory: An analysis of decision under risk[END_REF][START_REF] Tversky | The framing of decisions and the psychology of choice[END_REF].

How might gain vs. loss decision frames differently affect the search for new decisionconsistent and decision-inconsistent information? We propose that subjective decision certainty is a crucial variable mediating the impact of decision frame on information search. This is supported by [START_REF] Schneider | Framing and conflict: aspiration level contingency, the status quo, and current theories of risky choice[END_REF] who argued that loss decision frames induce more conflict and thus decrease the decision certainty and subjective reliability of the own decision preference. Similarly, [START_REF] Dunegan | Framing, cognitive modes, and image theory: Toward an understanding of a glass half full[END_REF] suggests that negatively phrased decision alternatives may evoke more thorough evaluations in order to avoid failure in decision-making (see also [START_REF] Lopes | Between hope and fear: The psychology of risk[END_REF]. As a consequence of lowered decision certainty and increased fear of failure in loss-framing conditions, decision makers may be more careful and thus exert themselves to a greater extent when selecting additional decision-relevant information, which in turn should result in a more even-handed information search. Empirical support for our hypotheses is provided by [START_REF] Ditto | Motivated sensitivity to preference-inconsistent information[END_REF] showing that decision makers exhibit more effortful cognitive analysis when faced with negative, unfavorable prospects. In a similar vein, [START_REF] Dawson | Motivated reasoning and performance on the Wason Selection Task[END_REF] found that individuals with skeptical mindsets (induced by having participants to consider threatening task rules) are less prone to exhibit a confirmation bias in context of a Wason task. Finally, research by Denes-Raj and Epstein (1994) demonstrated that people display "careless" and irrational decision making with gambles in the domain of gains (i.e. they prefer a gamble of 7 chances out of 100 to win over 1 chance in 10), whereas this error is less pronounced in the domain of losses.

To summarize, we propose that decision makers in a gain (compared to a loss) frame are not too diligent in their decision-making (i.e. exert themselves less) and thus come rather easily to a tentative, snap decision that they are quite confident about (in terms of self-perception the decision was easy and thus appears to be highly valid). However, high decision certainty after preliminary decisions should systematically increase decision makers' reliance on information that fits to their subjectively highly valid position (i.e., consistent information). In contrast, if decision makers are in a loss frame, they are more careful and thus unwilling to make a snap decision (so the decision appears to be more difficult). As a result they experience less decision certainty, which, in turn, should finally lead them to be more careful and thus even-handed in their information search. The following experiments test this line of reasoning.

STUDY 1

The first study tests whether gain framed decision indeed lead to greater selectivity in information search and subjective decision certainty than loss framed decisions. We also tested whether this expected effect is mediated by differences due to subjective decision certainty.

Method

Participants and design. One hundred sixteen participants (59 female and 57 male, 16 to 52 years of age; M = 28.90, SD = 7.74) from Ludwig-Maximilians-University, Munich, and pedestrians in a pedestrian zone located near the university participated in this experiment.

The experiment was based on a 2 (decision framing: gain vs. loss) x 2 (type of decision: probability-based vs. definite outcome) x 2 (type of information: consistent vs. inconsistent) factorial design with repeated measures on the last factor.

Material and procedure. Following previous research on framing effects (cf. [START_REF] Tversky | The framing of decisions and the psychology of choice[END_REF], we used a decision paradigm on risky stock trading in which participants had to decide between a probability-based and a fixed outcome strategy; probabilities and expectancy values for both strategies concerning the decision outcome were modeled on the 'Asian disease' problem of [START_REF] Tversky | The framing of decisions and the psychology of choice[END_REF]. Before participants were asked to make a preliminary decision on the probability-based or fixed outcome investment alternative, they received some general information about stock investments and then read the following passage: "An investor is facing the loss of 30,000 Euro (about $37,500) due to the falling price of his stock investments. To stop the escalation of losses, his bank has suggested two different investment strategies. In your opinion, which of the two strategies should the investor follow?" In the gain-framing condition, participants had to decide between Strategy A, which would result in 10,000 Euro (about $12.500) being saved, and Strategy B, which entailed a 1/3 probability that the entire 30,000 Euro would be saved, but a 2/3 probability that nothing would be saved. In the loss-framing condition, participants had to decide between Strategy A, which would result in the loss of 20,000 Euro (about $25.000), and Strategy B, for which there was a 1/3 probability that nothing would be lost but a 2/3 probability that the entire 30,000 Euro would be lost. After the preliminary decision for the probability-based or fixed outcome alternative (participants were told that they could revise their preliminary decision at the end of the experiment), participants indicated their subjective decision certainty1 as well as subjectively experienced dissonance [START_REF] Elliot | On the motivational nature of cognitive dissonance: Dissonance as psychological discomfort[END_REF].

Subsequently, participants were informed that additional information concerning the decision problem was available, and that this could help them to re-evaluate their preliminary decision and to reach an optimal final decision. The additional pieces of information were said to consist of twelve one-page statements written by former participants, half of them consistent and the other half inconsistent with their previous decision. Each statement was summarized by a main thesis of one sentence, indicating whether the corresponding article supported or contradicted the risky (probability-based) investment strategy (A) or the sure (definite outcome) investment strategy (B) 2 . The participants could choose freely among the available articles and the within-subject dependent variable was the number of consistent and inconsistent pieces of additional information chosen (for some subsequent analyses we also used the confirmation bias, which is represented by the difference between the number of selected consistent and inconsistent pieces of information). After the information search the experiment was over and participants were debriefed.

Results and Discussion

Check for manipulation and interfering effects: First of all, participants' information search did not depend on the type of decision they made. Neither a main effect, F < 1, p > .34, nor an interaction with type of decision frame occurred, F < 1, p > .37. Information search also did not depend on participants' age or gender, all Fs < 1.21, all ps > .28. Furthermore, the classic framing effect on decision making could be replicated. Participants in the gain-framing decision context predominantly chose the fixed (definite) outcome (N = 34) rather than the probabilistic decision alternative (N = 18), whereas participants in the loss-framing decision context predominantly preferred the probabilistic (N = 42) to the definite outcome decision alternative (N = 17), 2 (1, 114) = 16.98, p < .001. With regard to dissonance arousal, a oneway ANOVA revealed no significant main effect for gain vs. loss framing, F(1, 114) = 0.01, p = .98, η 2 = .001, indicating that participants felt a similar arousal of dissonance in the gain (M = 2.12, SD = 1.35) and loss framing condition (M = 2.13, SD = 0.98). Hence, it is unlikely that subsequent results are due to differences in dissonance arousal.

Information search. Cell means and standard deviations for the chosen consistent and chosen inconsistent articles as well as for the confirmation bias are shown in Table 1. A 2 (decision framing) x 2 (type of decision) x 2 (type of information) analysis of variance 2 An example of a main thesis in favor of the sure (definite outcome) investment strategy (A) was: "The implementation of investment Strategy A would be a good decision." An example of a main thesis conflicting with the sure (definite outcome) investment strategy (A) was: "One should be careful with investment Strategy A". Similar expressions were used for information supporting or contradicting the risky (probability-based) investment strategy (B).

(ANOVA) with repeated measures on the last factor revealed a significant main effect for the within-factor 'type of information', F(1, 112) = 14.90, p < .001, η 2 = .12, indicating that participants selected more consistent than inconsistent pieces of information. More important for our hypotheses, this main effect was qualified by a significant two-way interaction between 'type of information' and 'type of framing', F(1, 112) = 6.70, p = .01, η 2 = .06. In the case of gain framing, participants selected more consistent than inconsistent articles, F(1, 55) = 13.55, p = .001, η 2 = .20. In contrast, in the case of loss framing, there was no significant difference between selected consistent and inconsistent articles, F(1, 59) = 1.74, p = .19, η 2 = .03.

Decision certainty. A one-way ANOVA with decision certainty as the dependent variable revealed a significant main effect, F(1, 114) = 13.92, p = .001, η 2 = .11, indicating that participants in the gain-framing condition (M = 7.21, SD = 2.34) felt more certain about the validity of their decision preference than participants in the loss-framing condition (M = 5.63, SD = 2.20).

Mediational analysis. In the following analysis (cf. [START_REF] Baron | The moderator-mediator variable distinction in social psychological research: Conceptual, strategic, and statistical considerations[END_REF], we tested whether the effect of gain vs. loss framing on biased information search (confirmation bias) was mediated by differences due to subjective decision certainty. First of all, a regression analysis revealed a significant regression weight for decision framing predicting the confirmation bias, β = .23, t(114) = 2.49, p = .01. Second, decision framing also significantly predicted the expected mediator 'decision certainty', β = .33, t(114) = 3.73, p = .001. Finally, both 'decision framing' and 'decision certainty' were used as predictors for the confirmation bias. The overall regression was significant, R² = .30, F(2, 113) = 7.37, p = .01. Decision certainty received a significant regression weight, β = .20, t(113) = 2.15, p = .03, whereas the regression weight for decision framing decreased to β = .16 and was no longer significant, t(113) = 1.68, p = .10. A Sobel Test revealed that the regression weight of the independent

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Decision framing and information search 10 variables (decision framing) marginally decreased when the mediator (decision certainty) was statistically controlled for (Z = 1.88, p = .06).

To summarize, Study 1 provided evidence that participants' information search is more biased after gain than after loss-framed decision problems. In addition, participants reported a higher sense of decision certainty in the gain rather than the loss-framing condition.

Furthermore, a mediation analysis revealed that the effect of gain and loss decision framing on biased search was partially mediated by differences due to decision certainty.

STUDY 2

The next study was designed to replicate the findings of the preceding study with another decision paradigm and to make a stronger point about the causal role of decision certainty when accounting for the framing effect. Although mediation analyses suggested that decision certainty mediates the impact of decision framing on information search, these analyses are correlational in nature and thus can not definitely determine cause and effect in this relationship. In order to test whether a specific variable mediates a causal relationship the expected mediator can be directly manipulated. Thus, in Study 2 (besides gain and loss decision framing), we directly manipulated decision certainty and subsequently measured the preference for decision-consistent and decision-inconsistent pieces of information. If the framing effect is indeed based on differences due to subjective decision certainty, then artificially inducing high decision certainty should result in increased confirmatory information search independent from the induced decision frame.

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Decision framing and information search 11 design with repeated measures on the last factor (the last within factor 'type of information' represents the dependent variable).

Material and procedure. The procedure in Study 2 was nearly identical to the previous study with the exceptions that we (a) used a German translation of the classic Asian disease paradigm [START_REF] Tversky | The framing of decisions and the psychology of choice[END_REF] and (b) directly manipulated decision certainty after participants' preliminary decision. After participants individually enrolled in the experimental lab, they were given a questionnaire that asked them to imagine that Germany was preparing for an outbreak of a dangerous Asian disease that was forecast to kill 600 people. Two alternative medical programs to combat the disease had been proposed. Participants were informed that the exact scientific estimates of the consequences of the programs were as follows: Gain Frame: (1) if Program A is adopted, 200 people will be saved; (2) if Program B is adopted, there is a 1/3 probability that 600 people will be saved and a 2/3 probability that no one will be saved; Loss Frame: (1) if Program A is adopted, 400 people will die; (2) if Program B is adopted, there is a 1/3 probability that no one will die and a 2/3 probability that 600 people will die. In addition, participants were informed that this decision problem had one correct solution. After participants were asked to make a preliminary decision, half of the participants were told that previous studies had revealed that normally more than 80 percent of participants make a correct decision and thus select the right medical program. The other half of participants received no such extra information. Subsequently, as a manipulation check of decision certainty was carried out 3 .

Next, participants had the chance to read and select eight pieces of additional information (two supporting and two conflicting with medical Program A; two supporting and two conflicting with medical Program B). Thus, four additional pieces of information 3 Participants indicated on a scale from 1 (not at all) to 10 (definitely) to what extent they were sure that their decision was correct, to what extent their decision was final, and to what extent their decision was still in progress. Because all three items were highly correlated (r values ranging from .47 to .56, all p values < .001), they were collapsed to a scale of decision certainty (α = .76). The latter two variables (finality of decision, decision in progress) were assumed to be even more strongly related to decision certainty than the additional variables (identification and satisfaction with the decision) used in Study 1.

supported the participants' preliminary decision and four pieces of information conflicted with it4 . After the information search participants made a final decision, were informed about the aim of the experiment, thanked for their participation, and dismissed.

Results and Discussion

Check for manipulation and interfering effects. Of the 70 participants, three had to be excluded from further analyses because of missing values in the dependent variable information search. Therefore, 67 participants remained for the following analyses. Selectivity of information search was not significantly associated with participants' age or gender (all Fs < 1). Type of preliminary decision was significantly associated with the confirmation bias (i.e. participants who chose the sure alternative exhibited a stronger confirmation bias, M = 0.87, than those who chose the risky alternative, M = -0.25, F [1, 59] = 4.60, p < .04), but did not interact with type of decision frame, F < 1.25, p > .26, nor with the certainty induction, F < 1.11, p > .30. There also was no three-way interaction, F < 1, p > .63. Thus, type of decision is not supposed to be confounded with the experimental design. In addition, we replicated the classic effect of framing on decision-making (cf. [START_REF] Tversky | The framing of decisions and the psychology of choice[END_REF] only for the control (gain frame/sure alternative: N = 11; gain frame risky alternative: N = 6; loss frame/sure alternative: N = 4; loss frame/risky alternative: N = 13; 2 [1, 34] = 5.85, p < .02],

but not for the high certainty conditions. With regard to subjective decision certainty, participants in the gain frame condition reported higher subjective decision certainty (M = 6.28, SD = 1.90) than participants in the loss frame decision (M = 5.30, SD = 1.98), F(1, 63) = 4.66, p = .04, η 2 = .07. Furthermore, participants with certainty induction reported higher subjective decision certainty (M = 6.29, SD = 1.78) than those with no certainty induction (M = 5.23, SD = 2.07), F(1, 63) = 5.31, p = .03, η 2 = .08. To summarize, the manipulation of decision certainty was successful.

Information search. Means and standard deviations are depicted in Table 2. Firstly, a significant two-way interaction between type of information and decision frame, F(1, 63) = 11.39, p < .01, η 2 = .15. Follow-up analyses revealed that overall, participants in the gainframing conditions significantly preferred consistent (M = 2.63, SD = 1.13) to inconsistent pieces of decision-relevant information (M = 1.81, SD = 1.12), F(1, 31) = 10.42, p < .01, η 2 = .25. In contrast, a more balanced preference for consistent (M = 2.03, SD = 1.25) over inconsistent pieces of information (M = 2.26, SD = 1.12) was found in the loss-framing conditions, F < 1, p > .35. Secondly, a significant two-way interaction between 'decision certainty' and 'type of information', F(1, 63) = 8.49, p < .01, η 2 = .12. Follow-up analyses revealed that participants with high certainty induction exhibited a significant preference for consistent (M = 2.68, SD = 1.07) over inconsistent pieces of information (M = 1.94, SD = 1.13), F(1, 33) = 11.53, p < .01, η 2 = .26. In contrast, no significant difference between selected consistent (M = 1.94, SD = 1.27) and selected inconsistent pieces of information (M = 2.15, SD = 1.15) occurred without an additional certainty induction, F < 1, p > .46.

Most important for our hypotheses about the underlying psychological mechanism, we found a significant three-way interaction between decision frame, decision certainty, and type of information, F(1, 63) = 8.49, p < .01, η 2 = .12. Given no extra certainty induction, we observed a significant two-way interaction between decision frame and type of information, F(1, 31) = 19.11, p < .001, η 2 = .38, indicating that participants in the gain frame condition marginally preferred consistent over inconsistent articles, F(1, 15) = 4.35, p = .055, η 2 = .23; in contrast, participants in the loss-framing condition significantly preferred inconsistent over consistent articles, F(1, 16) = 22.86, p < .001, η 2 = .59. However, differences in selectivity between gain-and loss-framed decision contexts vanished when high decision certainty was induced. Accordingly, for the conditions with high decision certainty induction, no interaction occurred between decision frame and type of information, F(1, 32) = 0.11, p > .74, η 2 = .01.

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Decision framing and information search 14

To summarize, Study 2 replicated the framing effect of Study 1; that is, decision makers exhibited a stronger confirmation bias in the context of gain-rather than loss-framed decision problems. Moreover, Study 2 further supported the assumption that subjective decision certainty plays an important role in explaining this effect: when participants received feedback that it was very probable that their decision was correct (cue for high decision certainty), a confirmation bias occurred for both the gain-and the loss-framed decision problem.

A shortcoming of the previous two studies is that the two factors 'decision framing' and 'type of decision' are not independent because gain framing compels most people to choose a risky strategy, whereas loss framing motivates people more strongly to choose the safe strategy. This confounding could represent a problem for the interpretation of our data because participants somehow self-selected their level on the factor 'type of decision'. To rule out this possible confounding, in the next study we employed another type of decision case and framing manipulation, which did not force the participants to choose between a decision alternative with a probabilistic (risky) vs. definite (sure) outcome.

STUDY 3

The last study aims (a) to replicate the framing effect of the previous studies with an alternative, less confounded, more realistic and involving decision scenario, (b) to shed further light on the psychological processes underlying the effect of decision framing on information search (i.e., whether decision makers exert themselves more in the context of loss rather than gain frames), and (c) to extend the framing effect to the assessment of information quality (which is a variable that is closely tied to information search; see Fischer et al., 2005;[START_REF] Schulz-Hardt | Confirmation bias in accuracy-motivated decision-making: A cognitive explanation for biased information seeking[END_REF].

Method

Participants and design. One hundred five participants (60 female and 45 male, 19 to Decision framing and information search 15 participated in this experiment. Participants in a social psychology class were asked if they would be willing to participate in a study on economic decision-making and market research.

The experiment was based on a 2 (decision framing: gain vs. loss) x 2 (type of information: consistent vs. inconsistent) factorial design with repeated measures on the last factor (the last within factor 'type of information' represents the dependent variable).

Material and procedure. When participants arrived at the experimental lab, they were informed that they were to participate in a market research study investigating the product image of chocolate bars and cereal bars. Accordingly, in the gain condition they were asked to make a preliminary decision on whether they wanted to test and eat a chocolate or a cereal bar. In contrast, in the loss condition, participants had to decide which of the two-chocolate or cereal bar-they did not want to test and eat. Please note that both products were physically present. Participants were told that they could revise their preliminary decision at the end of the experiment. After the preliminary decision concerning gain or loss of a chocolate or cereal bar, the experimenter either gave them the selected bar (gain condition) or took away one of the two bars (loss condition).

Next, participants were given the chance to gather additional information concerning their decision. These additional pieces of information were statements by experts on consumer goods (such as chocolate and cereals bars) and were presented in the form of main arguments (as in the previous two studies). In both conditions participants were faced with eight pieces of information, half of which supported and half of which conflicted with their preliminary gain or loss decision. An example of an argument supporting the consumption of the cereal bar was: "One should eat cereal bars because they are very healthy for the bowels." In contrast, an example of a piece of information conflicting with the decision to eat the cereal bar was: "Cereal bars do not taste nice. Thus, one shouldn't eat them." Similar arguments were employed for chocolate bars.

First of all, participants evaluated information quality with regard to its credibility and importance for the final decision (for both items on the following scale: 0 = not at all; 10 = definitely) 5 , and subsequently decided whether they wanted to read the information in more detail or not. Participants were allowed to select freely among the available pieces of information (0-8). After the information search, participants made their final decision and answered to what extent they had exerted themselves in the study (0 = not at all; 10 = very much). Subsequent to the exertion measure, as in Study 1 participants answered questions concerning dissonance arousal in order to control whether dissonance processes are relevant for the expected effect [START_REF] Elliot | On the motivational nature of cognitive dissonance: Dissonance as psychological discomfort[END_REF]. Finally, participants were informed about the hypotheses of the experiment, thanked for their participation, and dismissed.

Results and Discussion

Check for interfering effects: The confirmation bias was not significantly associated with participants' age, gender or preliminary decision, all F values < 1. In addition, no interactions occurred between the participants' preliminary decision and the experimental factor, F < 1. With regard to dissonance processes, a one-way ANOVA revealed no significant main effect for the factor decision framing, F(1, 115) = 1.22, p = .27, η 2 = .01, indicating that no differences in dissonance arousal levels occurred as a function of whether participants made a gain (M = 2.64, SD = 0.89) vs. a loss decision (M = 2.84, SD = 1.08).

Information search. Cell means and standard deviations are shown in Table 3a. A 2 (decision framing) x 2 (type of information) ANOVA with repeated measures on the last factor revealed a significant main effect for type of information, F(1, 103) = 4.49, p = .04, η 2 = .04, indicating that in general participants selected more consistent than inconsistent articles. Moreover, this interaction was qualified by a significant two-way interaction between decision framing and type of information, F(1, 103) = 4.49, p = .04, η 2 = .04. In the case of 5 Since the credibility bias (credibility of supporting minus credibility of conflicting pieces of information) and importance bias (see credibility bias) were highly correlated (r = .41, p < .01), both quality dimensions were collapsed into a scale of information quality ( = .59).

gain decisions, participants significantly preferred consistent to inconsistent articles, F(1, 49) = 8.01, p = .01, η 2 = .14. In contrast, in the case of loss decisions, there was no significant difference between selected consistent and inconsistent articles, F < 0.1.

Information evaluation. Cell means and standard deviations are shown in Table 3b. A 2 (decision framing) x 2 (type of information) ANOVA with repeated measures on the last factor revealed a significant interaction, F(1, 103) = 6.16, p = .02, η 2 = .06. Participants deciding about gains expected consistent articles to be of higher quality than inconsistent articles, F(1, 49) = 14.89, p = .001, η 2 = .23. In contrast, after loss decisions, participants did not indicate a different perceived quality for consistent and inconsistent articles, F(1, 54) = 0.78, p = .38, η 2 = .01.

Exertion. A one-way ANOVA revealed a significant main effect for decision framing, F(1, 115) = 6.17, p = .01, η 2 = .05. Participants in the loss-framing condition (M = 6.23, SD = 2.24) reported that they had exerted themselves more in the study than participants within the gain-framing condition (M = 5.16, SD = 2.44).

GENERAL DISCUSSION

The present research investigated the effects of gain-and loss-framed decision problems on the search for new decision-relevant information. Our results suggested that lossframed decision problems are accompanied by a systematically lower confirmation bias in information search (Studies 1-3) and information evaluation (Study 3) than gain-framed decision problems. In addition, we were able to elucidate some underlying psychological processes: participants exposed to a gain decision frame exerted themselves less (Study 3) and thus experienced higher subjective decision certainty (Studies 1-2) than participants exposed to a loss decision frame. This increased decision certainty, in turn, led to increased confirmatory tendencies in information evaluation and search.

Implications, Limitations, and Future Research

Decision framing and information search 18 From a theoretical perspective, our study revealed that it was a fruitful endeavor to combine research on Prospect Theory with research on biased information search. In the context of Prospect Theory, many studies have investigated the effects of gain and loss framings, for example, on risk taking [START_REF] Tversky | The framing of decisions and the psychology of choice[END_REF], willingness to buy [START_REF] Zhang | Moderating effects of need for cognition on responses to positively versus negatively framed advertising messages[END_REF], or attractiveness of consumer goods (van Dijk & van Knippenberg, 1998). However, none of this research investigated possible differential effects of gain and loss framings on biased information search, which is an important process that precedes decision-making and determines the quality of decision outcomes (Kray & Galinski, 2003;[START_REF] Nemeth | Dissent and the search for information[END_REF].

On a practical level the most important implication of our results concerns the question of how to formulate a decision problem in order to obtain a balanced or self-critical information search, which has been shown to lead to the best decision outcomes (Kray & Galinski, 2003). Since gain decisions are associated with a stronger selectivity in information search than loss decisions, we must state that decision quality could be negatively affected when decision makers focus too intently on gains rather than associated losses. As a consequence, in real life contexts, (such as decision-making in business or politics) one should be careful not to formulate decision problems exclusively as gain decisions: decisionrelated losses should not be suppressed.

A limitation of our research is that two out of the three decision scenarios employed are statistical abstractions without any real consequences. Hence, the question arises whether similar choices and information search tendencies would occur in real life decision-making (e.g. when medical staff has to decide in reality how to save the population from disease).

Future research should address this point and employ more realistic decision scenarios with real consequences for the decision makers. a Four statements were available in each category.

b The 'confirmation bias' corresponded to the search differences between the number of chosen consistent and the number of chosen inconsistent additional information.

* p < .05 (t-test against zero)

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Decision framing and information search 28 Note.

a The 'evaluation bias' corresponded to the evaluation differences between consistent and inconsistent pieces of additional information.

* p < .05 (t-test against zero)

Method

  Participants and design. Seventy participants (34 female and 36 male, 16 to 56 years of age; M = 24.34, SD = 7.80) from Ludwig-Maximilian-University, Munich, participated in this experiment. The experiment was based on a 2 (decision framing: gain vs. loss) x 2 (certainty induction: yes vs. no) x 2 (type of information: consistent vs. inconsistent) factorial

Finally

  , in two out of three studies the effect of decision framing on information search is mainly due to a change in the selection of inconsistent pieces of information. Only in Study 1 is the effect mainly due to consistent pieces of information. Although this inconsistency does not change the main message of the present article (i.e. gain frames increase confirmatory tendencies in information search processes), future research should address the question under what conditions does framing lead to a neglect of inconsistent information or an overrepresentation of consistent information in information search.

Table 1 :

 1 Means and standard deviations for information search as a function of experimental conditions in Study 1. The "confirmation bias" corresponded to the difference between the number of chosen consistent and the number of chosen inconsistent statements.

			Type of information
		Consistent a	Inconsistent a	Confirmation
							bias b
	Experimental condition	M	SD	M	SD	M	SD
	Gain framing	2.71	1.99	1.57	1.73	1.14*	2.32
	( N = 56)
	Loss framing	2.00	1.08	1.75	1.72	0.25	1.47
	( N = 60)
	Note.
	* p < .05 (t-test against zero)

a Six statements were available in each category. b

Table 2 :

 2 Means and standard deviations for information search as a function of experimental conditions in Study 2. The "confirmation bias" corresponded to the difference between the number of chosen consistent and the number of chosen inconsistent statements.

			Type of information
		Consistent a	Inconsistent a	Confirmation
						bias b
	Experimental condition	M	SD	M	SD	M	SD
	Gain framing/no induction	2.25	1.18	1.44	1.03	0.81+	1.56
	(N = 16)
	Loss framing/no induction	1.65	1.32	2.82	0.81	-1.17*	1.01
	(N = 17)
	Gain framing/certainty induction	3.00	0.97	2.19	1.11	0.81*	1,33
	(N = 16)
	Loss framing/certainty induction	2.39	1.09	1.72	1.13	0.67*	1.24
	(N = 18)
	Note.
	+ p < .10 (t-test against zero)
	* p < .05 (t-test against zero)

a Four statements were available in each category. b

Table 3a :

 3a Means and standard deviations for information search as a function of experimental conditions in Study 3.

			Type of information
		Consistent a	Inconsistent a	Confirmation
						bias b
	Experimental condition	M	SD	M	SD	M	SD
	Gain decision	1.68	1.36	1.20	1.34	0.48*	1.20
	( N = 56)
	Loss decision	1.62	1.21	1.61	1.19	0.01	1.12
	( N = 60)
	Note.

Table 3b :

 3b Means and standard deviations for information evaluation as a function of experimental conditions in Study 3.

			Type of information
		Consistent	Inconsistent	Evaluation
						bias a
	Experimental condition	M	SD	M	SD	M	SD
	Gain decision	5.28	1.86	4.16	1.30	1.12*	2.04
	( N = 56)
	Loss decision	4.76	1.36	4.56	1.36	0.20	1.71
	( N = 60)

Participants were asked to what extent (a) they were sure that they had made the right choice, (b) they were satisfied with their decision, and to what extent (c) they identified with their decision. All variables were measured on a scale from 0 (not at all) to 10 (definitely). Since these three items were highly correlated (all r values between .44 and .73, all p values < .001), they were collapsed into a scale of decision certainty ( = .79).

An example of a piece of information supporting medical Program A is: "One should select medical Program A because it is supposed to have the fewer harmful side effects." An example contradicting medical Program B was: "One shouldn't start medical program B because it is less effective with very young and very old people"

Authors' Note

Peter Fischer, Andreas Kastenmüller & Dieter Frey, Department of Psychology, Social Psychology, Ludwig-Maximilians-University, Munich, Germany. Eva Jonas, University of Salzburg, Austria. The research reported in this article was made possible by grants from the German Science Foundation (Deutsche Forschungsgemeinschaft [DFG]), project no. Jo. 388/1-1. Correspondence concerning this manuscript should be addressed to: Peter Fischer, Department of Psychology, Social Psychology, Ludwig-Maximilians-University, Leopoldstrasse 13, 80802 Munich, Germany. Email: pfischer@psy.uni-muenchen.de.


Antonio Pierro
email: antonio.pierro@uniroma1.it

Susanne Leder

Lucia Mannetti

E Tory Higgins

Arie W Kruglanski

Antonio Aiello

Regulatory Mode Effects On Counterfactual Thinking and Regret

de niveau recherche, publiés ou non, émanant des établissements d'enseignement et de recherche français ou étrangers, des laboratoires publics ou privés.

There is increasing recognition of the importance of counterfactual thinking and the experience of regret in economic decisions (e.g. [START_REF] Inman | Regret in repeat purchase versus switching decisions: The attenuating role of decision justifiability[END_REF][START_REF] Landman | It could have been you": How states exploit counterfactual thought to market lotteries[END_REF][START_REF] Tsiros | Regret: A model of its antecedents and consequences in consumer decision making[END_REF][START_REF] Zeelenberg | Consequences of regret aversion in real life: The case of the Dutch postcode lottery[END_REF]. The process of thinking about "what might have been" is known as counterfactual thinking. Because of its relation to counterfactual thinking, regret has been categorized as a "counterfactual emotion", along with disappointment and relief [START_REF] Kahneman | The simulation heuristic[END_REF][START_REF] Roese | Counterfactual thinking: A critical overview[END_REF]; see also [START_REF] Zeelenberg | Anticipated regret, expected feedback and behavioral decision making[END_REF]. There are some indications in the literature that there may be individual differences in counterfactual thinking and the experience of regret. Landman, for example, hypothesizes that an individual's worldview "could shape the nature and intensity of counterfactuals and emotion" (1995, p.254; see also [START_REF] Landman | Regret: The persistence of the possible[END_REF].

Generally speaking, it is individual differences in the type of counterfactual thinking that have received the most attention, such as whether individuals use upward or downward comparisons (e.g., [START_REF] Sanna | Defensive pessimism, optimism, and simulating alternatives: Some ups and downs of prefactual and counterfactual thinking[END_REF][START_REF] Sanna | Mental simulation, affect, and personality: A conceptual framework[END_REF], or engage in additive or substractive counterfactual thinking (e.g., [START_REF] Roese | Counterfactual thinking and regulatory focus: Implications for action versus inaction and sufficiency versus necessity[END_REF]. In contrast, there is little evidence that individuals differ in their general propensity to engage in counterfactual thinking; i.e., individuals differences in the amount of counterfactual thinking. With respect to regret, most attention has been paid to the conditions under which people show more regret, such as whether the negative outcome of a decision results from an action or an inaction (e.g., [START_REF] Kahneman | The simulation heuristic[END_REF][START_REF] Landman | Regret and Elation following Action and Inaction[END_REF]Zeelenberg, van den Bos, van Djik, & Pieters, 2002), whether it implies switching from or staying with the status quo (e.g., [START_REF] Inman | Regret in repeat purchase versus switching decisions: The attenuating role of decision justifiability[END_REF][START_REF] Tsiros | Regret: A model of its antecedents and consequences in consumer decision making[END_REF], and whether there is decision justifiability (e.g. [START_REF] Connolly | Regret in decision making[END_REF][START_REF] Gilovich | The experience of regret: What, when, and why[END_REF]Zeelenberg, Van Dijk & Manstead, 2000). Less attention has been paid to the potential role in regret of a decision maker's self-regulatory orientation (cf. [START_REF] Camacho | Moral value transfer from regulatory fit: "What feels right is right" and "what feels wrong is wrong[END_REF]. The major purpose of our research was to examine the role of people's regulatory mode orientationtheir chronic or situationally-induced locomotion or assessment concerns-in their general propensity to engage in counterfactual thinking and experience regret.

Assessment Concerns and Locomotion Concerns

Most deliberate human behaviors comprise activities in two essential regulatory modes: a mode of assessment and a mode of locomotion. Assessment "constitutes the comparative aspect of self-regulation concerned with critically evaluating entities or states, such as goals or means in relation to alternatives in order to judge relative quality" (Kruglanski, Thompson, Higgins, Atash, Pierro, Shah, & Spiegel, 2000, p. 794). "What are my options?" "Are there any other possibilities worth considering?" "Which alternative is best?" "What should I do in the future?" "How did I do in the past?" Individuals strong in assessment mode are preoccupied with these kinds of critical evaluations (see [START_REF] Higgins | Regulatory mode: Locomotion and assessment as distinct orientations[END_REF]. By contrast, the locomotion mode "is the self-regulatory aspect concerned with movement from state to state and with committing the psychological resources that will initiate and maintain goal-directed progress in a straightforward manner, without undue distractions or delays" (Kruglanski et al., 2000, p. 794). In the locomotion mode, individuals emphasize "doing", "getting on with it", "making something happen" (see [START_REF] Higgins | Regulatory mode: Locomotion and assessment as distinct orientations[END_REF] rather than critical evaluation. Indeed, individuals strong in locomotion mode might refrain from critical evaluation if such "stopping to reflect" halted steady movement from state to state.

Whereas classic control theory (cf. [START_REF] Carver | Origins and functions of positive and negative affect: A control process view[END_REF][START_REF] Gollwitzer | Action phases and mind-sets[END_REF][START_REF] Higgins | Knowledge accessibility and activation: Subjectivity and suffering from unconscious sources[END_REF][START_REF] Kuhl | Volitional mediation of cognition-behavior consistency: Self-regulatory processes and action versus state orientation[END_REF][START_REF] Mischel | Processes in delay of gratification[END_REF][START_REF] Mischel | Metacognition and the rules of delay[END_REF] conceives of assessment and locomotion as inseparable and interdependent components of any action, Higgins, Kruglanski and their colleagues [START_REF] Higgins | Regulatory mode: Locomotion and assessment as distinct orientations[END_REF][START_REF] Kruglanski | To "do the right thing" or to "just do it": Locomotion and assessment as distinct self-regulatory imperatives[END_REF] have proposed that these functions are independent and that each can be differentially emphasized by individuals, either chronically as a personality disposition or momentarily as situationally induced. To measure chronic individual differences in assessment and locomotion, [START_REF] Kruglanski | To "do the right thing" or to "just do it": Locomotion and assessment as distinct self-regulatory imperatives[END_REF] developed two separate scales. In a comprehensive series of studies, these authors demonstrated the unidimensionality, internal consistency, and temporal stability of each scale. They found that locomotion and assessment tendencies are essentially uncorrelated with each other, that each contribute to self-regulatory success, and that each relates to a distinct task orientation and motivational emphasis. In generating means to goal attainment, assessment relates to generating a greater number of means to be compared, whereas locomotion relates to generating means quickly. In decision making, locomotion relates to a willingness to choose any activity to work on rather than waiting to begin, whereas assessment relates to a willingness to wait in order to investigate and compare the alternative choices. Individuals with a strong assessment orientation want to critically evaluate different options and relate past and future actions to critical standards. Individuals with a strong locomotion orientation want to quickly initiate action and then maintain it without disruption (see [START_REF] Higgins | Regulatory mode: Locomotion and assessment as distinct orientations[END_REF][START_REF] Kruglanski | To "do the right thing" or to "just do it": Locomotion and assessment as distinct self-regulatory imperatives[END_REF].

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Regulatory Mode Effects On Regret 6

Situations can also induce momentary individual differences in assessment and locomotion orientation states. [START_REF] Avnet | Locomotion, assessment, and regulatory fit: Value transfer from "how" to "what[END_REF], for example, had participants give examples from their personal lives of when they behaved in a manner exemplifying either three high locomotion items (taken from the Kruglanski et al. questionnaire, 2000), thereby inducing a locomotion orientation, or three high assessment items, thereby inducing an assessment orientation. Participants were then given a decision-making task in which they chose among different brands of reading lights. They were assigned either a "full evaluation" strategy where they compared all of the alternatives on all of the attribute values, or a "progressive elimination" strategy where they progressively eliminated whichever brand was worst on the first attribute, and then the second attribute, and so on. [START_REF] Avnet | Locomotion, assessment, and regulatory fit: Value transfer from "how" to "what[END_REF] reasoned that the "progressive elimination" strategy would provide participants a stronger sense of "movement" than the "full evaluation" strategy and fit participants in the locomotion condition, whereas the latter would provide more opportunity to make comparisons and fit those in the assessment condition. As predicted, fit effects on increasing the value of the chosen light were found.

Regulatory Mode and Amount of Counterfactual Thinking and Regret Individuals are assumed to engage spontaneously in counterfactual thinking after negative events or outcomes [START_REF] Gleicher | The role of counterfactual thinking in judgments of affect[END_REF][START_REF] Landman | Regret and Elation following Action and Inaction[END_REF]. The experience of regret is considered to be one of the possible emotional consequences of counterfactual thinking. A relation between counterfactual thinking and regret is postulated in various models (e.g. [START_REF] Kahneman | Norm theory: Comparing reality to its alternatives[END_REF][START_REF] Roese | Counterfactual thinking[END_REF] and there is some evidence to support it (Zeelenberg, van Djik, Van der Pligt, Manstead, regret from other negative emotions, such as dissatisfaction or disappointment (e.g. van Dijk & Zeelenberg, 2002;[START_REF] Zeelenberg | On bad decisions and disconfirmed expectancies: The psychology of regret and disappointment[END_REF]. We return to this distinction in the General Discussion. Here we simply begin with the assumption that a greater amount of counterfactual thinking is associated with a greater amount of regret. We propose that chronic and situationally-induced individual differences in assessment versus locomotion concerns relate to individuals' propensity to engage in counterfactual thinking, which in turn relates to the likelihood of experiencing regret.

The central issue, then, is how might regulatory mode affect the amount of counterfactual thinking? Our first hypothesis is that individuals with a stronger assessment orientation will have a stronger propensity to engage in counterfactual thinking because of their concern with critical evaluation and their tendency to make comparisons. [START_REF] Kruglanski | To "do the right thing" or to "just do it": Locomotion and assessment as distinct self-regulatory imperatives[END_REF] describe assessors' main aim as "to do the right thing". Since a negative outcome implies having failed "to do the right thing", individuals with a strong assessment orientation should be more prone to engage in the generation of counterfactuals and, in consequence, experience more regret about their choice. Our second hypothesis is that individuals with a stronger locomotion orientation will have a weaker propensity to engage in counterfactual thinking because of their concern with moving smoothly from state to state, without hesitation or interruption. Individuals with a strong locomotion orientation are less likely to pay attention to a decision outcome. They want to "move on" to the next goal pursuit, to just "get on with it" and not dwell on the past. measured chronic individual differences in regulatory mode. Study 3 experimentally manipulated regulatory mode. Studies 1 and 3 used a purchase scenario with a negative outcome and participants were asked with an open question to write down freely the thoughts that had come to their mind. In Study 2, participants were asked to recall a previous personal purchase with a negative outcome and to indicate the amount of counterfactual thoughts that had come to their mind. Regret was also measured in each study. Because Study 2 is a conceptual replication of Study 1, involving an actual purchase made by the participants rather than an imagined purchase scenario, the basic Method and Results for each study will first be presented separately, followed by a discussion of the results for both studies.

Procedure and materials

Participants filled out the locomotion and assessment scales. Then they read a scenario about a computer purchase with a negative outcome. To assess counterfactuals after reading the scenario, participants were asked with an open question to write down the thoughts that had come to their mind. Finally, participants filled out a rating scale measuring regret.

Assessing locomotion and assessment. The Italian version of the Locomotion and

Assessment Scales [START_REF] Kruglanski | To "do the right thing" or to "just do it": Locomotion and assessment as distinct self-regulatory imperatives[END_REF] constitutes two separate 12-item self-report measures designed to tap individual differences in these tendencies. Specifically, respondents rate the extent to which they agree with self-descriptive statements reflecting locomotion (e.g., "By the time I accomplish a task, I already have the next one in mind") or assessment (e.g., I spend a great deal of time taking inventory of my positive and negative characteristics"). Ratings are made on a 6-point Likert type scale with the response alternatives anchored at the ends with 1 (strongly disagree) to 6 (strongly agree).

We computed two composite scores (one for Locomotion and one for Assessment) by summing across responses to each item. Previous studies including Italian samples [START_REF] Kruglanski | To "do the right thing" or to "just do it": Locomotion and assessment as distinct self-regulatory imperatives[END_REF] have demonstrated that the Locomotion and Assessment scales have satisfactory reliability (Cronbach =.82 for the Locomotion Scale and .78 for the Assessment scale). In this sample, the for the locomotion scale was .72 and the for the assessment scale also was .72. Mean locomotion score was 4.41 (S.D. = .63) and mean assessment score was 3.58 (S.D. = .75). In this sample, the two scales were only weakly correlated (r = .21, p < .05), consistent with previous studies that have generally found a pattern of weak correlations between these scales [START_REF] Kruglanski | To "do the right thing" or to "just do it": Locomotion and assessment as distinct self-regulatory imperatives[END_REF].

Scenario. The scenario presented after the locomotion/assessment scales was adopted from those used by Tsiros and Mittal (2000, Study 1) and described the following negative outcome purchase experience:

Paolo is shopping for a laptop computer he needs for work. For some weeks he has informed himself about the different options that are available on the market and has selected two different brands:

Siemens and Compaq. The price and the specifications of the computers are very similar; for example

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Regulatory Mode Effects On Regret 10 both of them offer technical assistance 24-hours a day, seven days a week. After thinking about the two options, Paolo has decided to go with Siemens. Giorgio, a friend of his, who was also looking for a laptop computer, has decided to go with Compaq.

About three months later Paolo has had some problems with his laptop computer. In fact, whenever he called the technical assistance, he had to wait several minutes before he could talk to a technician. On several occasions his computer locked up, and he had to shut it down, losing all of his unsaved work. In addition, on several occasions he could not open any applications, and he had to shut down the system and try again. Finally, after having waited for two weeks, a technician was able to solve these problems.

His friend Giorgio, on the contrary, has had a great experience with his laptop. He never had a problem with his laptop, and on the only occasion he had a problem, he called the technical assistance, and the technician was very helpful and answered his questions immediately.

Counterfactual thinking. After having read the scenario, participants were asked to imagine that they were Paolo, and to write down all the thoughts that came to their mind in this situation-following the so-called "free-thought instruction" used in previous studies to examine how many counterfactuals participants spontaneously generate [START_REF] Tsiros | Regret: A model of its antecedents and consequences in consumer decision making[END_REF]. The responses to this open-ended question were content analyzed, using the same coding scheme as [START_REF] Tsiros | Regret: A model of its antecedents and consequences in consumer decision making[END_REF]. Each response was coded into one of two Assessing regret. To measure the amount of experienced regret in this situation, a rating scale adopted from Tsiros & Mittal (2000, Study 1) was used. The regret scale consists of the three following items, rated on a 7-point scale (items 1 and 2: 1 = not at all, 7 = very much; item 3: 1 = not at all, 7 = definitely): "If you were Paolo, how much would you regret choosing a Siemens rather than Compaq notebook?", "How much would you feel sorry for choosing the Siemens notebook?", and "If you could turn back time, would you decide differently?". The for the regret scale was .85. Mean regret score was 5.16 (S.D. = 1.69).

Results

Control for different versions of scenario. We used two versions of the scenario, alternating which of the two computer brands was chosen by Paolo and by Giorgio. Two oneway ANOVAs revealed that which of the two computer brands was chosen (i.e., the different versions of the scenario) did not have any effect on either the number of counterfactuals produced (F < 1, n.s.) nor the ratings of regret (F < 1, n.s.).

Amount of counterfactual thinking and regret as a function of regulatory mode. Two separate simultaneous multiple regressions were performed: one for counterfactuals regressed on participants' locomotion and assessment scores and another for regret regressed on participants' locomotion and assessment scores. [Note. The zero-order correlation between counterfactual thinking and regret was r = .33, p < .003.] In these analyses, we also entered gender and the interaction of locomotion and assessment as control variables. [No gender effect was found (regret, = .12, ns.; counterfactuals, = .03, ns.)].

As predicted, assessment was significantly positively related to counterfactual thinking ( = .377, p < .001; zero order correlation, r = .28, p < .01), whereas locomotion correlation, r = -.21, p < .05 ). [Notably, these effects remained significant when the analyses were repeated controlling for the number of non-counterfactuals produced.] The mean number of counterfactuals produced by participants overall was .74; the range was from 0-2 and the median was 1. Among predominant assessors (their assessment score higher than their locomotion score), 40% had 2 counterfactual thoughts and another 50% had 1, with only 10% having no counterfactual thoughts. In contrast, among predominant locomotors (their locomotion score higher than their assessment score), only 17% had 2 counterfactual thoughts, 31% had 1, and over half, 52%, had no counterfactual thoughts. Also as predicted, assessment was significantly positively related to regret ( = .284, p < .01; zero order correlation, r = .22, p <.05), whereas locomotion was significantly negatively related to regret ( = -.323, p < .01; zero order correlation, r = -.21, p < .05).

Importantly, there was no interaction effect between locomotion and assessment (regret, = .09, ns.; counterfactuals, = -.14, ns.), reflecting the fact that individual differences in locomotion and individual differences in assessment each had their own independent effects on counterfactual thinking and regret. Procedure and materials. Participants filled out the same locomotion and assessment scales used in Study 1. In this sample, the for the locomotion scale was .73 and the for the assessment scale was .68. Mean locomotion score was 4.21 (S.D. = .64) and mean assessment score was 3.57 (S.D. = .66). In this sample, the two scales were not correlated (r = -.17, n.s.).

Then participants then read the following open question asking them to think of a purchase they had made that had a negative outcome: "We now ask you to think about the last time you bought a product or a service after choosing from different available alternatives, with this experience turning out to be negative (the product/service was not good etc.). Please describe the event briefly in the lines below." The great majority of participants mentioned a relatively expensive product or service, such as a cell phone, car, DVD player, microwave oven, vacation or registration to a private school.

Next, participants filled out a regret item similar to the first item of Study 1 ("How much did you regret your purchase?"; mean regret score was 3.91, S.D. = 1.77) and the following item measuring counterfactual thinking: "When rethinking about negative experiences like the one you described, people often develop thoughts such as 'If only … it would have gone better' (e.g. 'If only I had gone to bed early the night before the exam, instead of going to the cinema, I would have gotten a better grade'; 'If only I had informed myself better, I would not have bought that product/service', etc.). Please indicate how many thoughts like that came to your mind when you originally had the experience you described above." (1 = no thought like that, 7 = many thoughts like that; mean counterfactual score was 3.73, S.D. = 1.85).

Results

Amount of counterfactual thinking and regret as a function of regulatory mode. As in

Study 1, we performed separate simultaneous multiple regressions for the regret measure and for the counterfactual thinking measure. [Note. The zero-order correlation between counterfactual thinking and regret was r = .55, p <.001] In these analyses, we also entered gender and the interaction of locomotion and assessment as control variables. [No gender effect was found (regret, = -.02, ns.; counterfactuals, = -.02, ns.)]. As predicted, assessment was significantly positively related to counterfactual thinking ( = .301, p < .005; zero order correlation, r = .34, p <.004), whereas locomotion was significantly negatively related to counterfactual thinking ( = -.231, p < .05; zero order correlation, r = -.27, p < .01). Also as predicted, assessment was significantly positively related to regret ( = .203, p < .05; zero order correlation, r = .29, p <.009), whereas locomotion was significantly negatively related to regret ( = -.348, p < .002; zero order correlation, r = -.43, p <.001). Importantly, there was no interaction effect between locomotion and assessment (regret, = .12, ns.; counterfactuals, = -.04, ns.), once again reflecting the fact that individual differences in locomotion and individual differences in assessment each had their own independent effects on counterfactual thinking and regret.

Discussion of Studies 1 & 2

The results of Studies 1 and 2 indicate that the regulatory modes of locomotion and assessment, as predicted, relate significantly to a propensity for counterfactual thinking and experiencing regret. Both studies found that individuals higher in assessment generated more counterfactuals after a negative outcome and experienced more regret, whereas people higher in locomotion generated less counterfactuals and experienced less regret. These two effects were independent of one another and there was no interaction effect between locomotion and assessment. These results answer the call of [START_REF] Kasimatis | Individual differences in counterfactual thinking[END_REF] for identifying chronic individual differences that predict the amount of counterfactual thinking rather than the type of counterfactual thinking.

In the General Discussion, after reporting the results of Study 3, we will discuss the implications of our findings for regulatory mode, counterfactual thinking, and the experience of regret. Here, we wish to raise the issue of what is the relation between counterfactual thinking and regret. Because the regulatory mode questionnaire, counterfactual measure, and regret measure were taken in the same session in Studies 1 and 2, there is the issue of potential measure contamination. Although the assessment scale includes some general items related to critical evaluation, there are no items concerning counterfactual thinking or experiencing regret in the regulatory mode questionnaire. Most important, the same predicted relations between counterfactual thinking and assessment versus locomotion, and between regret and assessment versus locomotion, were found in both Studies 1 and 2 despite the fact that the participants in Study 1 received the counterfactual measure before the regret measure, whereas the participants in Study 2 received the regret measure before the counterfactual measure. That is, the predicted effects of assessment and locomotion were obtained for counterfactual thinking when it appeared first in Study 1, and the predicted effects of assessment and locomotion were obtained for regret when it appeared first in Study 2.

Beyond the measurement issue, there is the psychological issue of how best to characterize the relations among regulatory mode, counterfactual thinking and regret. It makes sense to begin the path with the locomotion and assessment orientations (as the predictors) given that, in both studies, locomotion and assessment were measured first in general terms and the counterfactual thinking and regret experience concerned a particular event (with the event being new in Study 1). [This is also reasonable given that regulatory mode is manipulated in Study 3 and the basic results were replicated.] The issue, then, is whether counterfactual thinking or regret is the mediator. As discussed earlier, the literature suggests that it is counterfactual thinking that induces regret rather than the reverse. Thus, one model is that higher assessment increases counterfactual thinking, higher locomotion decreases counterfactual thinking, and then amount of counterfactual thinking determines amount of regret (Model 1). An alternative model would reverse this direction, with regret being the mediator and determining counterfactual thinking (Model 2). Notably, when counterfactual thinking was controlled as the mediator, Study 1 found that the (significant) influence of assessment on regret was reduced to non-significance ( = .10, p < .11), but the (significant) effect of locomotion remained significant [although reduced] (to = -.251, p < .04, from = -.323), and Study 2 also found that the (significant) influence of assessment on regret was reduced to non-significance ( = .068, p > .48), but the (significant) effect of locomotion remained significant [although reduced] (to = -.244, p < .02, from = -.348).

Given these findings, a third model must be considered that adds to Model 1 a direct path from locomotion to regret (Model 3).

To test which model fit the data better in each study separately, we compared, via LISREL, the above three alternative path analysis models. In Study 1, we found that Model 1 fit the data (χ 2 (2) = 5.18, p = 0.07; CFI = .87) better than Model 2 (χ 2 (2) = 8.73, p = 0.01; 1 or Model 2. In Study 2, we found that Model 2 fit the data (χ 2 (2) = 4.32, p = 0.12; CFI = .96) better than Model 1 (χ 2 (2) = 11.47, p = 0.03; CFI = .84), and Model 3 fit the data better (χ 2 (1) = .88, p = 0.35; CFI = 1.00) than Model 1 or Model 2. [Notably, in both studies Model 3 fits the data better than an alternative Model 4 where regret is the mediator and, in addition to Model 2, there is included a direct path from higher locomotion to decreased counterfactual thinking: Study 1, χ 2 (1)=6.80, p = 0.01; CFI =.76; Study 2, χ 2 (1)=4.16, p = 0.04; CFI =.98.] What these results suggest is that both Model 1 and Model 2 receive some support, but the best model overall is Model 3, where not only is counterfactual thinking the mediator between assessment increasing and locomotion decreasing regret, but there is also a direct path from higher locomotion to decreased regret. A third study was conducted to obtain further support for our basic results. Whereas Study 1 and 2 investigated the influence of regulatory mode orientation on counterfactual thinking and regret by measuring chronic individual differences in locomotion and assessment, the regulatory modes of locomotion and assessment were situationally induced in Study 3.

Study 3

Method

Participants. 94 students of the University of Rome (62 women and 32 men) participated in the study on a voluntary basis. The students attended different psychology majors. Their mean age was 25.14 years (S.D. = 2.19). There were not significant effects of gender.

Procedure and material. Participants were randomly assigned to the locomotion (N = 47) and assessment (N = 47) conditions. Locomotion and assessment were manipulated by asking participants to think of three different situations in which they personally exemplified either high locomotion or high assessment behaviors and to write them down. For locomotion, they were asked to: "Think of a day when you made many different things."; "Think of a time when you finished one project and did not wait long before you started a new one."; "Think of a time when you decided to do something and you could not wait to get started." For assessment, they were asked to: "Think of some occasion in which you compared yourself with other people."; Think of some occasion in which you thought about your positive and negative characteristics."; "Think of some occasion in which you critiqued work done by others or yourself."). The situations were taken from items in the locomotion and assessment scales of the regulatory mode questionnaire used in Studies 1 and 2. This experimental manipulation of locomotion and assessment states has been shown to be effective by [START_REF] Avnet | Locomotion, assessment, and regulatory fit: Value transfer from "how" to "what[END_REF].

Participants were told that we were conducting two different studies-a study on "personal memories" and a "consumer" study. After completing the "personal memories" questionnaire, which experimentally induced locomotion and assessment, they filled in the "consumer study" questionnaire containing the same purchase scenario and regret scale ( = .74) used in Study 1. Counterfactuals were measured by a rating scale as in Study 2. Specifically, we asked participants: "When rethinking about experiences like that of Paolo, people often develop thoughts such as 'If only … it would have gone better' (e.g. 'If only I had gone to bed early the night before the exam, instead of going to the cinema, I would have gotten a better grade'; 'If only I had informed myself better, I would not have bought that product/service', etc.). Please indicate how many thoughts like that would have came to your mind if you were Paolo." (1 = no thought like that, 7 = many thoughts like that). [Note. The zero-order correlation between counterfactual thinking and regret was r = .59, p <.001].

Results

As predicted, two one-way ANOVAs were found: a significant main effect for counterfactual thinking, F(1,92) = 10.07, p < .005, and a significant main effect for regret F(1,92) = 4.91, p < .05, with participants in the assessment condition producing significantly more counterfactuals (M = 5.74) and more regret (M = 5.97) than participants in the locomotion condition (counterfactuals M = 4.78; regret M = 5.50) (cfr. Fig 1).

______________________________

Insert Figure 1 here ______________________________ To examine whether the different amount of regret in the two regulatory mode orientations was mediated by counterfactual thinking, we performed an analysis of covariance (ANCOVA) with counterfactuals as a covariate. This analysis showed that the main effect of regulatory mode on regret was not significant when counterfactuals were 48.05, p <. 001. These results suggest that the influence of locomotion and assessment on regret was mediated by counterfactual thinking. The results of Study 3 demonstrate that the hypothesized relations between regulatory modes-locomotion and assessment-and both counterfactuals and regret hold true not only for chronic regulatory mode predispositions but also for situationally-induced regulatory modes.

General Discussion and Conclusions

The results of our studies suggest that assessment and locomotion concerns, both as chronic individual predispositions and as situationally-induced states, influence the amount of people's counterfactual thinking and their experienced regret. These findings contribute to our understanding of regulatory mode, counterfactual thinking, and regret. Let us begin with regulatory mode. In previous studies of regulatory mode, relatively little attention has been paid to the postactional evaluative phase of self-regulation (see [START_REF] Gollwitzer | Action phases and mind-sets[END_REF]. The present findings indicate that assessment concerns and locomotion concerns are clearly distinct in this phase, with individuals higher in assessment delving more into possible alternatives to past actions (i.e., comparing what was done to what might have been done) and individuals higher in locomotion engaging less in such reflective thought (i.e., moving on and not dwelling on the past). In addition, the direct effect of higher locomotion decreasing regret is worth noting. What this suggests is that, separate from decreasing the amount of counterfactual thinking per se, individuals with locomotion concerns want to move on, to get on with it. They don't want to get bogged down in concerns about the past. Regret is about the past and not the future. Thus, individuals with locomotion concerns are less likely to experience regret.

With respect to counterfactual thinking, the present findings suggest that regulatory mode is a factor determining the amount of counterfactual thinking. This has several implications. To begin with, it identifies a personality variable that influences the propensity to engage in counterfactual thinking (see [START_REF] Kasimatis | Individual differences in counterfactual thinking[END_REF]. But it does more than this. Study 3, after all, shows the same basic effect when assessment and locomotion are situationally induced. Together, these findings could help to explain why the literature has found both stability and instability in individuals' propensity to engage in counterfactual thinking [START_REF] Kasimatis | Individual differences in counterfactual thinking[END_REF]. The chronic individual differences in assessment and locomotion could provide the stability. The situationally-induced assessment and locomotion could create the instability.

In addition to this, it should be noted that regulatory mode as a factor in the amount of counterfactual thinking is different from what has been emphasized previously in the literature. Previous factors have mostly predicted the type of counterfactual thinking rather than the amount of counterfactual thinking. As one example, promotion-focused individuals prefer additive counterfactuals whereas prevention-focused individuals prefer subtractive counterfactuals [START_REF] Roese | Counterfactual thinking and regulatory focus: Implications for action versus inaction and sufficiency versus necessity[END_REF])-a difference in type of counterfactual thinking and not in amount of counterfactual thinking. As another example, individuals in a deliberative versus an implementational mindset have more pessimistic expectations [START_REF] Armor | The effects of mindset on behavior: Self-regulation in deliberative and implemental frames of mind[END_REF], which in turn is related to the use of more upward comparisons but not to a general propensity for more counterfactual thinking (see [START_REF] Sanna | Defensive pessimism, optimism, and simulating alternatives: Some ups and downs of prefactual and counterfactual thinking[END_REF][START_REF] Sanna | Mental simulation, affect, and personality: A conceptual framework[END_REF].

in the literature-self-enhancement, self-protection, self-improvement [START_REF] Sanna | Counterfactual thinking and self-motives[END_REF]-are outcome motives. In contrast, the difference in motivation for counterfactual thinking between individuals with assessment concerns versus locomotion concerns relates to a process or strategic difference. Those with assessment concerns are strategically motivated to engage in counterfactual thinking because it permits making comparisons. Those with locomotion concerns are strategically motivated not to engage in counterfactual thinking because it interferes with smooth and uniform movement to the next state. The assessment motive for counterfactual thinking is of particular interest. In some ways, it relates to self-improvement. But it is not about how to improve future outcomes per se. It is about how to make the decision-making process itself better. It involves critical reflection on both what was good and what was bad about the process-the essence of evaluative criticism. This particular "critical reflection" motive needs more direct study in future research on counterfactual thinking.

The present findings also shed some light on the experience of regret. As mentioned earlier, the experience of regret has been distinguished from the experience of disappointment (e.g. van Dijk & Zeelenberg, 2002;[START_REF] Zeelenberg | On bad decisions and disconfirmed expectancies: The psychology of regret and disappointment[END_REF]. Van Dijk & Zeelenburgh (2002) report that the experience of regret is different from that of disappointment in that disappointment is about an undesirable outcome, an outcome below expectations, whereas regret involves thinking about one's personal responsibility for what happened, the possibility that one made a mistake, and feeling a tendency to kick oneself and want to correct the mistaken decision. Critical reflection on the process of one's faulty decision making would produce such thinking, and such postactional critical reflection is more likely to be engaged in by individuals with assessment concerns and less likely by individuals with locomotion concerns. Regret, then, is not simply about negative outcomes per se but about one's role in the decision process itself, and critical self-reflection concerns the decision process itself-what was good and what was bad about the manner in which the decision was made.

The results of Study 3 also specifically increase our understanding of the experience of regret by suggesting that regulatory mode could contribute as a situational variable to how much people engage in counterfactual thinking and experience regret. Some situations or activities might naturally induce a stronger locomotion orientation, such as exercising, whereas others situations might naturally induce a stronger assessment orientation, such as reading a newspaper [START_REF] Higgins | Regulatory mode: Locomotion and assessment as distinct orientations[END_REF]. While people are involved in these different situated activities, if they contemplate a decision they made that turned out poorly, their counterfactual thinking and experience of regret might be greater in the assessment situation than in the locomotion situation. More generally, the influence of locomotion and assessment on counterfactual thinking and regret should be tested for different decision situations that may call for either locomotion or assessment. An example could be different kinds of economic decisions involving more or less time pressure and/or more or less comparisons that need to be made.

Regulatory mode could also moderate some regret effects that have been described in the literature. For example, because individuals with a high locomotion orientation are concerned with moving from one state to another, taking action can be seen as a necessary aspect of their mode orientation. For this reason, the classic action effect of feeling more regret for taking actions than refraining from action should be weaker for individuals with a high (vs. a low) locomotion orientation (whether chronic or momentary). Similarly, the status quo effect of feeling more regret when switching to something new than when sticking to the status quo (e.g., Tsiros & Mittal, 2000, Study 3) should be weaker for individuals with a high (vs. a low) locomotion orientation because switching from the status quo involves moving from an old state to a new state and such movement suits locomotion concerns.

The present studies examined the relation between regulatory mode orientations and experienced post-decisional regret. There is some empirical evidence that regret is experienced not only after a bad decision outcome, but also when anticipating making a decision that could turn out poorly (e.g. [START_REF] Miller | Counterfactual thought, regret, and superstition: How to avoid kicking yourself[END_REF][START_REF] Zeelenberg | Anticipated regret, expected feedback and behavioral decision making[END_REF]. In such anticipatory situations, individuals may engage in prefactual thoughts (e.g. [START_REF] Mcconnell | What if I find it cheaper someplace else?: Role of prefactual thinking and anticipated regret in consumer behavior[END_REF]. Regulatory mode should also influence the amount of prefactual thinking and anticipated regret. Analogous to the results of our present studies, individuals with a high (vs. low) assessment orientation (chronic or momentary) should produce more prefactual thinking and anticipate greater regret, whereas individuals with a high (vs. low) locomotion orientation should produce less prefactual thinking and anticipate lesser regret. In particular, compared to individuals with a high locomotion orientation, those with a high assessment orientation should be more sensitive to the effects that anticipated regret can have, including making riskier choices (e.g. [START_REF] Zeelenberg | Consequences of regret aversion: Effects of expected feedback on risky decision making[END_REF][START_REF] Zeelenberg | Consequences of regret aversion 2: Additional evidence for effects of feedback on decision making[END_REF], inaction inertia (e.g. Tykocinski & Pittman, 1995), and postponing their

  students of the University of Rome (37 women and 46 men) participated in the study on a voluntary basis. The students attended different psychology majors. Their mean age was 23.89 years (S.D. = 3.74).

  categories: a "what-if"/counterfactual thought or not a counterfactual thought. The first category included responses that: "alter reality, create hypothetical scenarios, or express an opinion as to what might have been had a different decision been made" (Tsiros and Mittal, 2000, p. 411). The following are examples of Study 1 participants' counterfactual thoughts: "I should have asked for advice instead of deciding by myself", "I should have chosen the other brand", and "I should have made the same choice as Giorgio."

  students of the University of Rome (67 women and 14 men) participated in the study on a voluntary basis. The students were all psychology majors. Their mean age was 24.91 years (S.D. = 4.81).

FIG. 1 .

 1 FIG. 1. Mean Regret and Counterfactual Thinking (CFT) Ratings for Locomotion and