Protecting the Ego: Motivated Information Selection and Updating∗

We investigate whether individuals self-select feedback that allows them to maintain their motivated beliefs. In our lab experiment, subjects can choose the information structure that gives them feedback regarding their rank in the IQ distribution (ego-relevant treatment) or regarding a random number (control). Although beliefs are incentivized, individuals are less likely to select the most informative feedback in the ego-relevant treatment. Instead, many individuals select information structures in which negative feedback is less salient. When receiving negative feedback with lower salience subjects update their beliefs less, but only in the ego-relevant treatment and not in the control. Hence, our results suggest that individuals sort themselves into information structures that allow them to misinterpret negative feedback in a self-serving way. Consequently, subjects in the IQ treatment remain on average overconfident despite receiving feedback. JEL-codes: D83, D91, C91


Introduction
In many instances, individuals can choose the source from which they receive feedback about their ability. For example, politicians and executives can surround themselves with subordinates who give their honest opinion or loyalists who habitually praise the capabilities of their superior. In educational contexts, individuals select supervisors and mentors that differ in their feedback style. Similarly, college students select majors with more or less compressed grading distributions and thus differing informativeness of grades (Ahn et al., 2019;Sabot and Wakeman-Linn, 1991).
Standard models predict that individuals have a strict preference for choosing the most informative feedback source because this leads to more accurate beliefs and, in turn, betterinformed decisions. However, if individuals have motivated beliefs, for example the desire to hold the belief that they have high ability (Bénabou and Tirole, 2002;Köszegi, 2006), they may prefer feedback structures that allow them to preserve this positive self-view. 1 In this paper, we study individuals' preferences over feedback concerning their ability.
Our experiment resembles the situation where a student can choose between two supervisors: supervisor A and supervisor B. The student knows that both supervisors aim to give clear, positive feedback when the student's performance is good. However, the student also knows that supervisor A usually tells the student clearly when she considers the work poor, while supervisor B abstains from giving any comment when she is critical about the student's work. Hence, not receiving feedback from supervisor B is in fact negative feedback, but it is framed in a way that is not explicit. If the student anticipates that this framing will allow to disregard potential negative feedback, the student may prefer supervisor B, which can lead to the formation and maintenance of overconfident beliefs.
We conduct the first lab experiment in which individuals can select the information structure that provides noisy feedback about their ability. We compare information selection when feedback is related to relative IQ test performance to a control condition, in which feedback relates to the draw of a random number. Feedback is instrumental since individuals are incentivized for the accuracy of their beliefs. Nonetheless, we find that many subjects in the IQ treatment do not choose the most informative feedback structure. Instead, our results suggest that they choose feedback that allows them to maintain their belief that they have high ability.
We provide evidence for a mechanism of ego protection so far unrecognized in the literature: When feedback is ego-relevant, individuals choose information structures in which 1 We are agnostic about whether individuals derive utility from holding themselves in high regard (Köszegi, 2006), whether they do so to gain a strategic advantage in persuading others (Von Hippel and Trivers, 2011), or to motivate themselves (Bénabou and Tirole, 2002). However, we note that holding inaccurate beliefs about ability is costly in many domains, for example, it leads to suboptimal management decisions (Malmendier and Tate, 2005) or over-entry into competition (Camerer and Lovallo, 1999). negative feedback is less salient and, thus, potentially easier to misinterpret. We consider a signal to be less salient when it is framed in a neutral way (i.e., not linked to a positive or negative cue), although it carries either positive or negative information when its context is taken into account. In the experiment, a negative signal with high salience is presented as a red sign with the description "You are in the bottom half", while a negative signal with low salience is a grey sign with the description "...". Whereas the lower salience of feedback should not matter for a rational Bayesian updater, our results show that it leads individuals to update less in response to unpleasant news. When information is not ego-relevant, we do not find evidence for such asymmetric updating. Thus, the results are consistent with the explanation that individuals select feedback that allows them to distort their beliefs in a self-serving way.
The key feature of our design is that subjects can choose the information structure they receive feedback from. Information structures are presented in the form of two urns with varying compositions of positive and negative signals. Depending on whether the subject is in the top or bottom half of the distribution, signals are drawn from one urn or the other.
This design allows us to vary the properties of the information structures and cleanly identify preferences for ego-relevant information. In particular, by varying the composition of signals in the urns, we vary the informativeness, salience, and skewness of feedback. Informativeness describes the noisiness of the signals. Salience refers to the way in which positive and negative signals are framed, holding informativeness constant. We vary the salience of feedback by framing it as either green/red signals with an explicit description (high salience) or grey signals without description (low salience). An information structure is skewed if positive signals are more or less informative than negative signals (Masatlioglu et al., 2017;Nielsen, 2020). For example, an information structure is positively skewed if a potential positive signal is more precise than a negative signal.
The incentivization of beliefs gives us a clear prediction for subjects in the control condition: we expect that subjects select the most informative feedback structure in order to maximize their expected payoff. In contrast, subjects who derive utility from the belief that they rank high in the IQ distribution may prefer an information structure that allows to maintain this belief-for example, by selecting feedback that is less informative, positively skewed, or makes negative feedback less salient (which potentially facilitates future belief distortion). We find that individuals seek different information when the rank is ego-relevant versus when it is not. When the ego is at stake, subjects are more likely to choose information structures that are less informative (treatment difference of 13.5 percentage points) and that make negative feedback less salient (treatment difference of 26.4 percentage points). However, we find no evidence that subjects in the ego-relevant treatment are more likely to choose information structures that are positively skewed. Our findings are based on aggregate treatment differences in information structure choices, and reinforced when looking at within-individual choice patterns (in the control, we classify 89.3 percent of subjects as information maximizers, and in IQ only 65.5 percent). Furthermore, we find that subjects who are classified as avoiding information according to the Information Preference Scale by Ho et al. (2021) are more likely to choose an information structure that is less informative and features less salient negative feedback. We do not find evidence for heterogenous information preferences by gender, cognitive ability, or prior beliefs.
Moreover, we find that the subsequent belief updating process is influenced by the information structure. Subjects in the IQ treatment react less to negative feedback than to positive feedback, but only when negative feedback is less salient. We find the first indication of this when subjects receive signals from the information structure that they self-select (endogenous treatment). The results are corroborated by a treatment in which subjects are exogenously placed into information structures to eliminate potential selection issues (exogenous treatment). Furthermore, in the control condition, subjects react similarly to positive and negative feedback irrespective of its salience. Therefore, asymmetric updating in the ego-relevant treatment is unlikely to be explained by difficulty in understanding less salient signals.
We assess explanations other than motivated reasoning for the treatment effects. The treatment variation in the ego-relevance of the state allows us to distinguish cognitive biases (i.e., general systematic errors regarding how people search and process new information, such as confirmation bias) from motivated biases (i.e., biases that are driven by a desire to hold positive views of oneself). We provide evidence that the treatment differences cannot be explained by cognitive biases such as confirmation-seeking or contradiction-seeking behavior, differences in cognitive ability, or confusion about the experimental design. Subjects' freetext explanations for their choices indicate that they make a "gut-level" decision to prefer positive-looking signals over explicit negative signals when feedback is ego-relevant.
Our results shed light on the conditions under which individuals with a desire to protect their ego can distort their beliefs. Bénabou and Tirole (2002) show in a two-selves model that it can be optimal for individuals to avoid or distort feedback when the belief in high ability has consumption or motivation value. However, since belief distortion in the model by Bénabou and Tirole (2002) is costly, manipulation of beliefs is only beneficial within the realms of the "reality constraints," implying that individuals cannot simply choose the beliefs they like. Our results suggest that selecting an information structures in which unpleasant feedback is easier to misinterpret can be one way to relax these reality constraints.
The idea that an individual selects an information structure that allows negative feedback to be interpreted in a self-serving way can be seen as a form of self-deception. It is a longstanding philosophical puzzle whether self-deception is possible at all, given that the same mind has to be the deceiver and the deceived (e.g., Mele, 2001). In the moral domain, Saccardo and Serra-Garcia (2020) find direct evidence for anticipated belief distortion. In their experiment, many advisors postpone acquiring the information whether the product they ought to recommend is beneficial for the advisee or not, thereby reducing the salience of the information. These subjects are also more likely to underrespond to the information when it is in conflict with their incentives and to behave ultimately in a self-serving way, showing that individuals are somewhat sophisticated about their future belief distortion. We find suggestive evidence that anticipated belief distortion also contributes to the maintenance of overconfident beliefs about intelligence. Our results show that subjects who select feedback that is less informative and in which negative feedback is less salient remain overconfident about their IQ rank over the course of the experiment. In contrast, subjects who receive explicit negative feedback are, on average, no longer overconfident at the end of the experiment.
Our findings contribute to the literature on how individuals process positive and negative feedback regarding their ego-relevant characteristics. Bénabou and Tirole (2016) argue that when ego-relevant beliefs are involved, people tend to process information differently depending on whether the information is more or less desirable. 2 For instance, people might tend to ignore or discount negative news, while more readily incorporating good news into their (posterior) beliefs. 3 However, the resulting experimental evidence on this mechanismasymmetric updating-is mixed. On the one hand, Charness and Dave (2017), Eil andRao (2011), andMöbius et al. (2014) find positive asymmetry in updating. On the other hand, a number of studies either find no asymmetry (Barron, 2021;Buser et al., 2018;Gotthard-Real, 2017;Grossman and Owens, 2012;Schwardmann and Van der Weele, 2019) or even the opposite asymmetry (Coutts, 2019;Ertac, 2011;Kuhnen, 2015). Moreover, in a paper related to how errors in updating can be driven by motivated beliefs, Exley and Kessler (2019) find that people update their beliefs based on completely uninformative signals, but only when the signals carry positive information and the updating state is ego-relevant. Our paper helps explain the existing results by studying belief updating in different information structures.
We show that asymmetric updating is only observed when the framing of the information structure allows subjects to interpret the signals in a self-serving way. In contrast, we do not observe asymmetric updating when positive and negative feedback is highly salient.
We complement the literature on information avoidance by giving subjects more control over the amount and type of feedback they receive. 4 Eil and Rao (2011) and Möbius et al. 2 Related literature on motivated reasoning studies the demand for and consumption of political news. This literature shows evidence that consumers prefer like-minded news (Garz et al., 2020;Gentzkow and Shapiro, 2010). Moreover, Chopra et al. (forthcoming) show in a series of online experiments that people's demand for political news goes beyond the desire for acquiring more informative news.
3 Selective recall of ego-relevant feedback is documented in the experiments of Chew et al. (2020) and Zimmermann (2020). Both papers find that negative feedback on IQ test performance is more likely to be forgotten, compared to positive feedback.
4 For a review of the information avoidance literature, see the survey of Golman et al. (2017). In the health domain, Ganguly and Tasoff (2016) and Oster et al. (2013) provide empirical evidence that people avoid medical testing. In a financial context, Karlsson et al. (2009) andSicherman et al. (2015) show that investors check their portfolios less often when the market is falling.
(2014) present experimental evidence that a considerable proportion of subjects who have received prior noisy information regarding their relative rank in ego-relevant domains (intelligence and attractiveness) have a negative willingness to pay to have their rank fully revealed.
In contrast, subjects in our study choose the noisy signals that they would like to receive before any feedback is given. On the one hand, we find that subjects indeed choose less informative feedback when it is ego-relevant. On the other hand, they disregard unpleasant information using a mechanism that is so far unexplored in the literature: subjects in the ego-relevant treatment choose information structures that makes unpleasant feedback less salient.
Our results add novel insights about motivated cognition to the literature on how signal structures affect belief updating. While Epstein and Halevy (2019) and Fryer et al. (2019) find that ambiguous signals increase deviations from Bayes rule, Enke (2020) and Jin et al. (2021) illustrate that individuals find it difficult to interpret the lack of a signal. Enke et al. (2020) provide evidence that individuals update more in response to signals, which are framed with valenced cues that activate associative memory. In contrast to these experiments, we vary the ego-relevance of the state and show that the framing of signals affects whether subjects update asymmetrically.
Finally, our research adds a motivated beliefs perspective to the literature on information structure selection. Within this literature, several papers study preferences about the timing and skewness of information disclosure in settings where information structures have-in contrast to our experiment-no instrumental value (Falk and Zimmermann, 2016;Nielsen, 2020;Zimmermann, 2014). For example, Masatlioglu et al. (2017) find that individuals have a preference for positively skewed information sources; that is, information structures that resolve more uncertainty regarding the desired outcome than the undesired one. Some experimental papers study preferences over information structures in settings where information has instrumental value (but is not ego-relevant). Charness et al. (2021) and Montanari and Nunnari (2019) study how people seek information from biased information structures and show that many individuals do not maximize the informativeness. Hoffman (2016) finds that businesspeople are on average too confident in their expert knowledge and underpay for instrumental information, possibly to avoid negative feedback. We show that the tendency to select less informative feedback is more pronounced in ego-relevant domains and can be one driver for the prevalence of overconfident beliefs.
The remainder of this paper is organized as follows. In Section 2, we describe our experimental design, which comprises two treatment variations: ego-relevance of the rank and endogenous/exogenous information structure allocation. In Section 3, we present our experimental results. First, we study how participants select their preferred information structures depending on the ego-relevance of the rank. Second, we study subsequent belief updating. In Section 4, we discuss our findings, and in Section 5 we conclude.

Experimental Design
To investigate whether individuals choose information structures that protect their ego, we design an experiment that contains (1) exogenous variation in the ego-relevance of beliefs; (2) choices between different information structures; and (3) elicitation of updating behavior within different information structures.
In a 2x2 between-subject design, we vary, on the one hand, whether subjects receive feedback about their relative rank in IQ test performance (IQ treatment) or about a random number (random treatment). This treatment variation allows us to study the impact of motivated beliefs on preferences over feedback. On the other hand, we vary whether subjects receive signals from the information structure they selected into (endogenous treatment) or from an information structure they are exogenously assigned to (exogenous treatment). The latter treatment variation allows us to study how subjects update their beliefs with and without self-selection into feedback.  In the remainder of the experiment, subjects are asked to express their beliefs about the state of the world, which is either related to their rank in the IQ test (IQ treatment) or to the draw of a random number (random treatment). First, we explain the matching probabilities method to the subjects (Karni, 2009), so that they know that they maximize their chance of winning a £6.00 prize by stating their true beliefs (see Appendix B). Next, subjects are informed whether the belief questions refer to their IQ score or their random number and are asked for their prior beliefs. Afterwards, subjects in the endogenous treatment choose the feedback mode they would like to receive signals from, while subjects in the exogenous treatment are shown the feedback mode assigned to them. Feedback modes are presented in the form of urns with a varying composition of signals, as explained below. Finally, three rounds of feedback are drawn from the respective mode. We elicit subjects' posterior beliefs, allowing us to study how subjects update their beliefs in response to receiving signals.

Overview
At the end of the experiment, either the IQ task or the belief elicitation is randomly selected for payout. If the belief elicitation is chosen, one out of the four beliefs (one prior and three posteriors) is randomly selected for payout. Thereby, we aim to exclude any hedging motives that could lead to a reported belief about IQ performance that differs from the true belief (cf. Blanco et al., 2010;Azrieli et al., 2018). Screenshots of the experimental instructions are provided in Appendix H.
Subjects are incentivized to give their true beliefs, so a payoff-maximizing subject would always choose the most informative feedback mode and update according to Bayes rule.
However, in the IQ treatment, subjects' motives to maximize payout can conflict with the desire to protect their beliefs about their (relative) ability. For instance, subjects may be willing to accept a smaller expected payoff in order to not impair their belief that they are in the top of the intelligence distribution. If that is the case, we expect a treatment difference in information structure selection and/or updating behavior depending on whether the beliefs are ego-relevant.

IQ and Random Treatment
We vary the ego-relevance of beliefs by randomizing subjects into an IQ treatment and a random treatment at the session level. Consistent with previous research, we argue that rank in the IQ treatment is ego-relevant (e.g., Eil and Rao, 2011), and that subjects care more for their IQ rank than their random number. We assume that further deviations from the rational benchmark are constant between the IQ and random treatments (this assumption is discussed in Section 4).

IQ Treatment
In the IQ treatment, we inform subjects that the second part of the experiment is related to their relative performance in the IQ test they completed in the beginning.
We tell them that the computer divides participants in their session into two groups: one group of subjects whose score is in the top half of the score distribution and the other with scores in the bottom half. The task is to assess whether their IQ performance is in the top or bottom half of the distribution, compared to all other participants in their session. To increase the ego-relevance of the IQ treatment (Drobner and Goerg, 2021), we explicitly tell subjects that the APM test is commonly used to measure fluid intelligence and that high scores in this test are regarded as a good predictor for academic and professional success, occupation, income, health, and longevity (Sternberg et al., 2001;Gottfredson and Deary, 2004).

Random Treatment
In the random treatment, subjects are shown a randomly drawn number between 1 and 100. We tell subjects that three other numbers between 1 and 100 (with replacement) were drawn. These numbers are not shown to them. Their task is to assess whether the number they are given is in the top or bottom half of the distribution among these four numbers. The four numbers are randomized at the individual level and ties are broken randomly. The task is deliberately designed to generate variation in prior beliefs. 5 If subjects were not given the drawn number or if we compared their number against many numbers, we would have expected a degenerate prior distribution instead.
In Figure A.1, we plot the prior distribution by treatment. As expected, the prior distribution in the random treatment is centered around 50 percent, while priors in the IQ treatment are left-skewed with more mass in the top half of the distribution. Hence, in the analysis, we include priors as a covariate and use a matching strategy to ensure that the differences in priors do not drive differences in feedback choice.

Endogenous and Exogenous Treatment
Moreover, we vary whether subjects endogenously select or are exogenously assigned a feedback mode. The rationale for the exogenous treatment is that it allows us to study updating behavior absent self-selection. In contrast, in the endogenous treatment, subjects in different feedback modes have, on average, different preferences over information structures, which could affect updating behavior.
Endogenous Treatment In the endogenous treatment, subjects make five pairwise choices between feedback modes. Figures H.16 to H.18 illustrate the choice situations. In each of the five choice situations, we vary the properties of the feedback modes, as explained in detail in the next section. Afterwards, one out of the five feedback mode choices is randomly selected 5 In the endogenous treatment, the standard deviation of prior beliefs in the random treatment turns out to be 25.065, compared to 19.993 in the IQ treatment. Hence, the random treatment generates similar variance compared to the IQ treatment.  Table 1 shows the information structures in the experiment. Information structures consist of two urns with 10 balls each. A ball drawn from an urn in the selected information structure constitutes a signal. If an individual's IQ score or random number is in the top (bottom) half of the distribution, balls are drawn from the upper (lower) urn with replacement. This design allows us to cleanly vary the properties of the information structure in a way that is transparent to the subjects.

Feedback Modes
Depending on the information structure, subjects can receive up to three different types of (noisy) signals. Subjects in the IQ (random) treatment can either receive a green signal with a plus (+) sign and the description "You are in the top half" ("Your number is in the top half"), a red signal with a minus (-) sign and the description "You are in the bottom half" ("Your number is in the bottom half"), or a grey signal with the description "...". Figure H.8 shows how the signals are introduced in the instructions.
On the same page, we explain that the informational content of the respective signal depends on the feedback mode and the state. For example, subjects are told that in feedback mode A, they are more likely to get the green (+) signal if they are in the top half of the distribution and that they are more likely to get the red (-) signal if they are in the bottom half of the distribution. 6 Subjects have to correctly answer comprehension questions about these features before they can proceed. In all feedback modes, the green (+) signal increases the posterior that one is in the top half and the red (-) signal increases the posterior that one is in the bottom half. Depending on the feedback mode, the grey signal can be positive, negative, or non-informative feedback.
Information structures differ in their informativeness, skewness, and framing. Informativeness describes how noisy a feedback mode is. Note that a perfectly informative feedback structure could feature only green (+) signals in the top urn and only red (-) signal in the bottom urn. We introduce noise by, for example, adding red (-) signals to the top urn or uninformative grey signals to either urn. The informativeness of signals in our experiment can be described first by their likelihood ratio (LR) and second by the probability of receiving a non-informative signal. Both of these properties are given in the bottom panel of Table 1.
The further away the likelihood ratio is from unity, the more informative the signal and the more it shifts the posterior belief of a Bayesian updater (e.g., the negative signal in Mode A is more informative than the negative signal in Mode B). 7 The probability of receiving a noninformative signal only applies to Modes A and D, in which grey signals are not informative (hence, Mode A is more informative than D).
We call an information structure positively skewed if the positive signals are more informative in terms of their likelihood ratio than the negative signals (as in Modes B and E) and negatively skewed if the negative signals are more informative (as in Mode C).
Finally, information structures differ in their feedback salience. Since the color grey is typically not associated with positive or negative states and the description is not explicit, we say that positive or negative feedback in the form of grey signals is less salient. For example, in Mode B negative feedback is less salient, while in Modes C and E positive feedback is less salient.

Feedback Mode Choices
Subjects make five pairwise choices between information structures. By carefully varying the information structures they can choose from, we are able to elicit whether subjects have preferences for informativeness, salience, or skewness of feedback depending on its ego-relevance. 6 In the endogenous treatment, we explain these characteristics by always using one feedback mode choice as an example. We use three different examples, as illustrated in Figures H.9 to H.11, and check if the presented example matters for choices in Appendix D. In the exogenous treatment, we explain the signals using the participant's assigned feedback mode: see the screenshot in Figure H.12.
7 In fact, a likelihood ratio of one implies that the signal is fully uninformative about the underlying state.
Every subject makes all five choices. To control for order effects, we vary the order of information structure choices (cf. Appendix D).

Updating Behavior
Besides information structure selection, we also analyze the updating behavior of subjects and how it interacts with the feedback mode. During the updating stage, subjects receive three consecutive signals from one of the feedback modes. After each signal, subjects are asked to report their posterior beliefs. Further, each time they receive a signal and are asked about their beliefs, subjects can view a picture of the feedback mode urns from which they receive information by clicking a button (see Figure H.19 for a screenshot of the choice situation).
Our main interest is to understand differences in updating across feedback modes and according to the ego-relevance of the task. For this reason, we specifically focus on Modes A and B and their interaction with the ego-relevance of the task. On the one hand, a comparison in updating behavior across Modes A and B in the random treatment will allow us to understand whether differences in the information structure drive cognitive biases (i.e., general deviations from Bayes rule). On the other hand, a comparison of updating across IQ and random treatment will allow us to study the extent of motivated biases in updating (i.e., deviations from Bayes rule specific to ego-relevant feedback).

Debriefing
In the last part of the experiment, we ask subjects a battery of questions. First, we ask subjects to complete the Information Preferences Scale by Ho et al. (2021), which is a 13item questionnaire that measures an individual's desire to obtain or avoid information that has an instrumental value but is also unpleasant. The scale measures information preferences in three domains: consumer finance, personal characteristics, and health. Second, we ask subjects to complete the Gneezy and Potters (1997) risk elicitation task. Specifically, each subject receives £1.00 and has to decide how much of this endowment to invest in a risky project with a known probability of success. The risky project returns 2.5 times the amount invested with a probability of one-half and nothing with the same probability. We also ask them a non-incentivized general willingness to take risks question (Dohmen et al., 2011).
Third, we ask subjects to answer two questions in free-form text and they receive £0.50 for their answers. We ask them to advise a hypothetical subject who would be performing the feedback mode choices and updating task. In the endogenous treatment, we additionally ask them to explain their motives for choosing the feedback modes across the five choice situations. Finally, we ask subjects a series of demographic questions including age, gender, and nationality.

Experimental Procedure
The experimental sessions were conducted from June to October 2019 in the Economics Laboratory of Warwick University, United Kingdom. Overall, we recruited 445 subjects through the Sona recruitment system to take part in the experiment. We conducted 14 sessions (216 subjects) for the endogenous treatment and 15 sessions (229 subjects) for the exogenous treatment. Sessions lasted an average of 60 minutes. Participants earned an average payment of £11.00, including the show-up fee of £5.00. We conducted the experiment using oTree (Chen et al., 2016). Descriptive statistics of the sample are provided in Table A.1.
In each session, subjects were randomly assigned a cubicle and general instructions were read aloud. The remaining instructions were provided onscreen. In both the endogenous and exogenous sessions, it was randomly determined whether the cubicle belonged to the IQ or random treatment. Moreover, in the exogenous treatment, it was randomly determined if the cubicle was allocated to Modes A or B.

Results
Our analysis proceeds in two steps: First, we investigate treatment differences between IQ and random in feedback mode choices. Second, we analyze how subjects update in response to signals from the feedback modes.

Information Selection
We first focus on the baseline choice between Modes A and B that vary in informativeness, In order to disentangle preferences for informativeness, salience, and skewness, we elicit subjects' preferences over information structures in four additional choice situations. Figure 3 plots the results. In the informativeness choice, subjects can choose between Mode A and Mode D, where both give balanced feedback but where Mode D is less informative than Mode A. The top left panel of Figure 3 shows that a higher proportion of subjects in IQ choose the less informative Mode D over Mode A. The difference of 13.5 percentage points is statistically significant (t-test, p = 0.002; Wilcoxon rank-sum test, p = 0.002). Hence, the results suggest In contrast to Mode A, Mode B is less informative, positively skewed, and makes negative feedback less explicit. In the main analysis, we test treatment differences in five information structure choices.
We account for multiple hypothesis testing in Table A.4 in the appendix. To control for the family-wise error rate, we calculate p-values based on the procedure by List et al. (2019) and report p-values using Holm (1979) and Bonferroni adjustment. None of the adjustments change our assessment of the effects' statistical significance.

Information Selection-Within Individual
So far we have looked at aggregate treatment differences in separate choices and analyzed what we can learn from them. We now examine the within-subject choice patterns. To perform this within-analysis, we suppose that each subject has a fixed preference over information structures (conditional on the treatment) and chooses information structures according to this preference. In line with our experimental design, we focus on three preferences for information structures: maximize the informativeness, seek positive skewness, and reduce salience of negative feedback/increase salience of positive feedback. We estimate the fraction of subjects who consistently choose information structures that conform to these preferences. 9 First, we calculate the fraction of subjects who make choices according to each of these preferences and the fraction of subjects who make choices that do not conform to one of these preferences. Second, we allow subjects to make mistakes and estimate which of the preferences can best explain subjects' choice patterns using a finite mixture model. Hence, we estimate the share of preference types and the amount of implementation noise (γ) necessary to classify subjects. The estimation strategy is described in detail in Appendix C.
In Table 2, we compare the relative prevalence of the implied preferences by treatment.
In the first two columns, we present the empirically observed fraction of subjects who adhere to a given preference when they are not allowed to make mistakes. In the third and fourth columns, we show the estimated fractions using the finite mixture model. Note that 26.4 percent of subjects in the IQ and 36.8 percent in the random treatment are not classified if we do not allow for mistakes. In contrast, in the finite mixture model we use maximum likelihood to assign to every subject the preference that describes her choice pattern best.
First, consider the strategy to maximize the informativeness of information structures.
There are fewer subjects in the IQ treatment who consistently maximize the informativeness than in the random treatment. When using the finite mixture model, the share increases from 40.9 to 65.5 percent in IQ and from 53.8 to 89.3 percent in the random treatment, suggesting that many subjects aim to maximize the informativeness of signals but make mistakes. 10 The treatment difference of 23.8 percent is statistically significant.
Second, there are significantly more subjects in the IQ than in the random treatment who exhibit a preference for reducing the salience of negative feedback but not of positive feedback.
While more than 30 percent of subjects follow such a preference in IQ, there are few to none who are categorized as such in the random treatment. For the salience of feedback, we find the largest treatment difference, at a statistically significant 34.5 percentage points.
Finally, there are only a few subjects who consistently choose feedback modes that are positively skewed. In particular, there are no subjects in the IQ and 3.8 percent of subjects in the random treatment. In the finite mixture model, the share in the random treatment increases to 10.7 percent. However, note that it only requires three consistent choices to be attributed to this preference (in contrast to four for the other preferences). Overall, the results do not suggest that subjects choose positively skewed feedback to protect their ego.
9 "Maximum information" predicts that subjects make choices according to A B, C, D, E, "Positive skewness" predicts B A; E A; A C, and "Salience of feedback" predicts B A; A C, E; B E. Note that subjects can follow more than one preference but we assume that they have one dominant preference when these preferences conflict.
10 In Appendix D, we exploit the order in which feedback modes are presented and find suggestive evidence that the difference is, to a large degree, driven by subjects who do not understand that Mode E reveals less information than Mode A. To sum up, the within-individual choices support our findings from the individual information structure choices. When looking at internally consistent choice patterns, subjects in IQ prefer less informative feedback modes as well as modes in which positive feedback is explicit but negative feedback less salient. Moreover, we find few subjects who have a preference for positive skewness but, if anything, the share is higher in the random than in the IQ treatment.

Heterogeneity in Information Structure Selection
We investigate heterogeneity in information structure selection based on self-reported information preferences, gender, prior beliefs, and performance in the IQ quiz. We focus on the baseline choice as it combines all three channels of ego protection: informativeness, skewness, and salience.   (Ho et al., 2021). Low prior indicates if a subject reports a prior below 50 and Low IQ indicates if a subject has not scored more than the median in the IQ quiz. Robust standard errors in parentheses. * p < 0.10, * * p < 0.05, * * * p < 0.01

Information Preference Scale
The respondent has to indicate her preference on a 4-point scale from "Definitely don't want to know" to "Definitely want to know." In the first column of Prior Beliefs In the third Column of Table 3, we investigate whether the effect of the egorelevant treatment is different depending on the reported prior beliefs. "Low Prior" indicates that an individual reports a prior that is lower than 50%, while the reference group reports a prior above or equal to 50%. We do not observe that subjects with priors below 50% are affected differently by the treatment compared to individuals with priors above 50%. 12 IQ Score Finally, in the last column of Table 3, we analyze whether there is a differential treatment effect for subjects who performed better or worse in the IQ quiz. "Low IQ" indicates that a subject has correctly solved the same number or fewer of the Raven matrices than the median (12). The statistically insignificant and small interaction term suggests that there is no differential treatment effect depending on the performance in the IQ task (i.e., their measured cognitive ability).

Information Selection and Overconfidence
After subjects made the five information structure choices, one of these choices was randomly selected and the corresponding decision of the subject was implemented. Before analyzing belief updating in detail, we check descriptively how the selected information structure relates to the development of overconfident beliefs about the IQ rank.
In Figure 4,  Although not statistically significant, the main coefficient of the prior is negative, which is consistent with confirmation-seeking behavior. In Section 4, we discuss confirmation-seeking behavior in more detail.
13 Benoît et al. (2015) show that true overconfidence is observed when the average stated probability to be in the top 50% of the distribution is significantly larger than 50%.
14 In fact, after three rounds of feedback, subjects in Mode A have an average belief that is statistically indistinguishable from 50% (t-test, p = 0.806), while subjects in Mode B have an average belief that is still These results suggest that selecting an information structure that is less informative and makes negative feedback less salient indeed leads to maintaining overconfident beliefs in the IQ treatment. Moreover, in Appendix G, we show that this pattern is not observed in the random treatment. These descriptive patterns motivate us to investigate belief updating by information structure in more detail in the next section.

Belief Updating
We aim to investigate how subjects process the signals they receive from different feedback modes. First, we introduce the estimation framework for analyzing potential deviations from Bayesian updating. Then, we analyze updating in both the endogenous and exogenous treatment. While subjects in the endogenous treatment receive signals from the self-selected feedback mode, subjects in the exogenous treatment are allocated to a feedback mode.

Estimation Framework
We follow the approach developed by Grether (1980) and Möbius et al. (2014) to estimate updating behavior. The framework allows individuals to put different weights on the prior and the positive or negative signals they may receive, nesting the Bayesian benchmark as a special case. In the case of binary signals, Bayes rule can be written in the following form: logit(µ t ) = logit(µ t−1 ) + 1(s t = pos)ln(LR pos ) + 1(s t = neg)ln(LR neg ) where µ t is the belief at time t and LR k is the likelihood ratio of the signal s t = k ∈ {pos, neg}.
To estimate the model, we add an error term and attach coefficients to the prior and to the positive and negative signals an individual receives: (2) logit(µ it ) = δ prior logit(µ i,t−1 ) + β pos 1(s it = pos)ln(LR pos ) + β neg 1(s it = neg)ln(LR neg ) + it where δ prior captures the weight of the prior while β pos and β neg measure the responsiveness to positive and negative signals, respectively. it captures non-systematic errors in updating.
significantly larger than 50% (t-test, p = 0.002). The difference between Mode A and B in final posterior minus prior is statistically significant (Welch's unequal variances t-test, p = 0.022; Wilcoxon exact rank-sum test, p = 0.007).
The estimated β coefficients in the random treatment (i.e., where the state is not egorelevant) inform us how updating behavior deviates from the Bayesian benchmark. Following the literature on belief updating, we interpret these deviations as being driven by "cognitive" biases, while the differential updating across ego-relevant and non-ego relevant states allows us to identify "motivated" biases in processing information. In particular, we test whether there is asymmetric updating (β pos = β neg ). For example, subjects in the ego-relevant treatment might have a desire to put more weight on positive rather than negative signals when forming their posteriors.
Moreover, we investigate whether there are cognitive or motivated biases across information structures. Hence, we investigate whether the properties of an information structure have implications for updating behavior.

Updating in the Endogenous Treatment
First, we briefly consider belief updating in the endogenous treatment. Here, subjects receive signals from the feedback mode they chose in the randomly selected choice situation. We restrict the analysis to Modes A and B to allow comparisons with the exogenous treatment.
A word of caution may be necessary here as the analysis of belief updating in the endogenous treatment can only be seen as suggestive for two main reasons: First, subjects self-select into feedback modes, that is, subjects in Mode A and B may not be comparable.  Table A.5 in the appendix presents the estimation results of Equation (2) separately by treatment and feedback mode. In Mode A, we do not find evidence for asymmetric updating in either treatment since the coefficients β P os and β N eg are of similar magnitude and we cannot reject the null hypothesis that they are equal (Wald tests, p = 0.620 in IQ and p = 0.928 in random). In Mode B, in contrast, we observe that β P os is larger than β N eg , both in the IQ and the random treatment. However, as noted above, due to the small number of subjects in Mode B, we lack the statistical power to reject the null hypothesis that the coefficients are equal. The minimum detectable difference in coefficients is 0.598 for the IQ treatment and 0.622 in the random treatment (for p < 0.05 at 80% power), which is well above the differences that we find (0.349 in IQ and 0.204 in random). 16 15 Part of the reason for this imbalance is the fact that Mode A features in four choice situations and Mode B only in two, such that the probability is twice as high that a choice is drawn, in which Mode A could have been selected. 16 To calculate the minimum detectable difference (MDD), we recode the independent variables such that one coefficient indicates the difference between the positive and negative updating coefficients. The p-value on this variable is equivalent to the Wald test p-value reported in the bottom of Table A.5. The standard  (2)  To have more statistical power and to exclude self-selection into feedback modes, we now turn our focus to belief updating in the exogenous treatment.

Updating in the Exogenous Treatment
In the exogenous treatment, subjects are randomly assigned to Modes A or B. In the IQ treatment, 55 (55) subjects update according to Mode A (B), and in the random treatment 57 (62) subjects update according to Mode A (B).
In Table 4, we display the estimation results for the exogenous treatment. As before, we do not find evidence for asymmetric updating in Mode A. In both IQ and random treatment the coefficients β P os and β N eg are of similar magnitude and are not significantly different from each other (Wald tests, p = 0.715 in IQ and p = 0.685 in random).
However, we find asymmetric updating in Mode B in the IQ treatment: when information is ego-relevant and negative signals are less salient (i.e., framed as grey signals), subjects error of this coefficient times 2.8 gives us the minimum detectable difference that would be necessary to find a statistically significant coefficient at the 0.05 level with 80% power. The idea behind the factor 2.8 is the following: For the t-test to be significant, the coefficient has to be 1.96 standard errors away from zero. To have a 80% probability of drawing a t-value greater than 1.96, the "true" t-statistic has to be 1.96+0.84=2.8, with 0.84 being the inverse normal at the 80th percentile (see, e.g., Ioannidis et al., 2017).
update less in response to negative than in response to positive signals. The updating coefficient for positive signals is about twice as large as for negative signals and the coefficients differ significantly (Wald test, p = 0.028). However, this is not the case when feedback is not ego-relevant: in the random treatment, subjects update, if anything, more in response to negative, less salient feedback, but the difference in coefficients is not statistically significant (Wald test, p = 0.325).
In the exogenous treatment, we have more statistical power compared to the endogenous treatment. However, to detect more subtle differences in updating, we would need a larger sample size. The minimum detectable difference in coefficients at 80% power is 0.422 (IQ) and 0.494 (random) in Mode A, while it is 0.295 (IQ) and 0.506 (random) in Mode B. It is notable that we are relatively well powered for detecting a difference in the IQ treatment in Mode B, but that we have less statistical power to detect a difference in the random treatment.
However, since our subjects in the random treatment update, if anything, more in response to negative feedback, we still interpret it as suggestive evidence in favor of a motivated bias in updating (instead of a cognitive bias).
This interpretation is also supported by results from Chow tests in the bottom of Table 4.
Subjects update significantly less in response to negative feedback in Mode B when signals are ego-relevant. However, when information is not ego-relevant, the difference is not statistically significant.
The previous analysis studies updating in a Bayesian framework that takes prior beliefs and signal informativeness into account. However, since it is derived from Bayes' rule it imposes a lot of structure. To control for differences in prior beliefs between treatments in a more flexible way, we follow a nearest-neighbor matching strategy. We compute the treatment difference in final posterior beliefs after matching subjects in IQ and control based on their prior beliefs. First, we trim the prior belief distribution to ensure common support between treatments. 17 Next, individuals are matched to at least 1, 2, or 5 neighbors in the other treatment by minimizing the distance in priors. The average treatment effect is calculated by comparing the average final posterior in one treatment to the posteriors of the neighbors in the other treatment.  show that this does not always seem to be possible. In fact, our results suggest that asymmetric updating arises only in the information structure that features negative signals that are less salient and, thus, easier to misperceive. In the next section, we further discuss and interpret our experimental results.

Discussion
Our experimental findings show systematic differences across treatments in the way subjects choose between different feedback modes. We interpret these results as evidence for preferences over information structures driven by motivated reasoning (individuals' desire to have high opinions of themselves and self-enhancement motives).
We now discuss whether treatment differences could alternatively be explained by cognitive biases. In doing so, we follow the key features that distinguish motivated thinking from cognitive failures according to Bénabou and Tirole (2016).

Endogenous Directionality
A distinct feature of motivated reasoning is that it is directed toward some end (for example, the belief that one is highly intelligent). In contrast, general failures in cognitive reasoning that depend on one's prior beliefs, like confirmation-seeking and contradiction-seeking behavior, can go in either direction.
Confirmation-seeking behavior, or confirmation bias, is the tendency to search for, interpret, favor, and recall information in a way that confirms preexisting beliefs. 18 On the contrary, contradiction-seeking behavior is the tendency to favor information that goes against one's prior beliefs. In a non-ego-relevant setting, Charness et al. (2021) find that many individuals choose information structures that confirm their prior beliefs, but relatively few subjects can be classified as contradiction-seeking. 19 In our experiment, confirmation-seeking could potentially explain treatment differences in information structure selection, as individuals in the IQ treatment have slightly higher priors on average. For instance, confirmation-seeking subjects with high priors could prefer Mode B since it is less informative about the negative state. However, even when controlling for prior beliefs, we see that subjects in the ego-relevant treatment are more likely to choose Mode B compared to those in the random treatment. In fact, all treatment differences hold when controlling for prior beliefs in regressions (Table A.2) and when employing a matching strategy on priors (Table A.3).
Finally, it is relevant to note that, in the informativeness choice, the treatment difference cannot be explained by confirmation-or contradiction-seeking behavior. Taken together, these results show that confirmation bias falls short of explaining the treatment differences in the information selection stage of our experiment.

Cognitive Ability
Cognitive errors in processing and interpreting information depend on individuals' cognitive ability and analytical sophistication. That is, more able and more analytically sophisticated agents are less prone to cognitive biases. On the other hand, motivated reasoning does not necessarily imply a negative correlation.
18 See Nickerson (1998) for a review of the psychological literature on confirmation bias. 19 In the random treatment, we find some support for confirmation-seeking as a cognitive bias. In the baseline choice we find that subjects with a high prior are slightly more likely to choose Mode B (although the difference is not statistically significant) and in the baseline reversed choice they are less likely to choose Mode C (t-test, p = 0.068). However, in the skewness over framing choice, subjects with high priors are slightly less likely to choose Mode E (t-test, p = 0.238), speaking against confirmation-seeking behavior. In general, our experiment is not designed to provide conclusive evidence regarding confirmation-seeking behavior since the information structures vary both in framing and informativeness. It is an interesting question for future research how confirmation-seeking individuals trade off informativeness and salience of feedback.
By taking into account participants' abstract reasoning ability, measured by their IQ scores, our results do not seem to be driven by cognitive ability. Two pieces of evidence support this conclusion. First, individuals across treatment groups do not vary in their cognitive ability. 20 Second, our treatment differences in feedback mode selection are robust to controlling for individuals' cognitive ability (see Table A.2 in the appendix).

Confusion
Our experimental design features some rather complex elements and thus might have affected participants' understanding. To tackle this we paid close attention to the way we presented the experimental instructions. We also ensured understanding by letting participants answer comprehension questions (see screenshots in Figures H.2,H.4,and H.15). Moreover, participant confusion is unlikely to affect our conclusions as it is held constant across treatments.
Furthermore, if we look at the informativeness choice, in which the information structures only differ in the likelihood of receiving an uninformative signal and where we held constant their skewness and framing, we see that less than five percent of subjects in the random treatment make the suboptimal choice. This finding is reassuring as it implies that subjects understood our experimental instructions and were sufficiently incentivized to make well thought-out decisions. Bénabou and Tirole (2016) argue that motivated beliefs evoke and trigger emotional reactions, whereas cognitively driven biases do not. While we do not measure participants' emotions in the experiment, we find some suggestive evidence for emotions arising in the IQ treatment in the free-text questions.

Emotional Involvement: Heat vs Light
In the post-experimental questionnaire, we asked subjects to explain how they chose between feedback modes. In the IQ treatment, we find that many subjects report a "gut-level" response to select the more positive-looking feedback and avoid explicit negative (red) signals. 21 In Appendix F, we conduct a quantitative content analysis of the free-text responses.
We enlisted three research assistants, who had no information about the treatment variation, to code the free-text responses according to a pre-defined codebook (shown in Appendix F.1).
A coding is counted if at least two out of the three research assistants coded a response in the same way. 20 Mean IQ score in the IQ treatment is 11.34, while it is 11.41 in the random treatment. We cannot reject the H0 that IQ scores are significantly different between IQ and random (t-test, p = 0.882; Wilcoxon rank-sum test, p = 0.780).
21 Subjects who chose Mode B in the IQ treatment stated, for example, "I always chose the feedback that looked the more positive. For example, with the most green dots/the less red dots", "My mind told me to avoid red and go for green", "I took a preference for those featuring green, motivational I guess?", etc.
In Table F.1, we find that subjects in the IQ treatment are more likely to state that they preferred the feedback mode that featured more green signals (Fisher's exact test, p = 0.001) and less red signals (Fisher's exact test, p = 0.060) compared to the random treatment.
Subjects in the random treatment are more likely to state that they made their selection according to the feedback's informativeness (Fisher's exact test, p = 0.013). We do not find a statistically significant treatment difference for any of the other explanations (e.g., ease of understanding feedback, confirmation of prior belief). In Table F.2, we find that the stated preference for more green/less red signals and for more information is indeed correlated with observed behavior.

Conclusion
This paper documents an experiment to study individuals' preferences toward information structures and subsequent belief updating if information is ego-relevant or not. Our results from the information selection stage show that individuals in the ego-relevant treatment are more likely to choose feedback modes that are less informative and that make negative feedback less salient, compared to the control. The results from the belief updating stage indicate that individuals' belief formation is asymmetric (i.e., individuals respond more to positive news than to negative news), but only in the ego-relevant condition and when the negative feedback is less salient, and therefore easier to misperceive. These findings are consistent with individuals selectively choosing information structures that allow them to protect their ego.
Our results suggest that while individuals might have a motivated tendency to process information differently depending on its valence, their ability to do so depends on the "reality constraints" in the environment. We provide evidence that the framing and salience of feedback is one dimension of "reality constraints" that limits individuals' ability to nurture motivated beliefs. Zimmermann (2020) shows that raising the incentives to recall negative feedback can constitute another "reality constraint." Similarly, Drobner (forthcoming) shows that subjects, who are informed that uncertainty about the ego-relevant state will be resolved at the end of the experiment are less likely to update in a motivated way. This raises a question for future research regarding other dimensions in the environment that may constrain individuals from maintaining motivated beliefs and how consciously people engage in motivated thinking.
We find that subjects update symmetrically when feedback is made explicit, but that they update asymmetrically when negative feedback is less salient. In light of the mixed evidence on asymmetric updating in the literature, it would be interesting to study in more detail how the propensity to update asymmetrically depends on the framing and style of feedback. Moreover, future research could investigate how subjects choose between information structures, in which no asymmetric updating was found.
Taken together, our findings suggest that motivated information selection might play an important role in producing overconfident beliefs. Indeed, it is often the case that in our everyday life, we can choose our information sources and exert some control over the type of signals that we receive. This can help us protect ourselves from having to adjust our ego-relevant beliefs downwards. Further research could investigate in real-world settings how preferences over information structures impact how individuals sort into feedback environments, for example, in educational contexts.
While overconfidence is costly in some settings, it may be beneficial in others, for example, for motivational purposes or when trying to persuade others. Therefore, it depends on the context whether institutions should restrict or enhance individuals' ability to select their feedback sources. Future studies could investigate how much discretion over feedback sources is optimal in educational or professional environments.
Online Appendix for "Protecting the Ego: Motivated Information Selection and Updating"    whether English is the native language. The risk measure is by Gneezy and Potters (1997). Robust standard errors in parentheses. * p < 0.10, * * p < 0.05, * * * p < 0.01   (2)

B Belief Elicitation Mechanism
After the IQ task, we tell subjects that for the following part of the experiment we will ask them their beliefs regarding some events. In particular, they are told that they will be asked four belief questions and that one question will be chosen at random to count for payment.
Then, we explain our belief elicitation procedure. We use the belief elicitation mechanism proposed by Karni (2009) Given the complexity of this belief elicitation mechanism, we decided to make instructions intuitive for subjects by walking them through an example and explaining how their answer would affect their chances betting on the event or the lottery, and their chances of winning the prize. We also emphasize that truthful reporting is the answer that maximizes the chances of earning the prize. The exact wording of the instructions is provided as a screenshot in Figure H.3. To ensure that subjects understand the main features of this elicitation procedure, we asked subjects to answer comprehension questions (see Figure H.4).

C Maximum Likelihood Estimation of Within-Subject Choice Patterns
We use a finite mixture model to estimate the share of subjects who exhibit consistent choice patterns that pertain to one of three preferences ("Maximum information," "Positive skewness," or "Salience of feedback"). We allow for a deviation between the observed choice and the choice prescribed by a subject's preference: y ic = I {s ic (s p ) + γ ic ≥ 0}, where y ic is the choice by subject i in choice situation c (0 for the first alternative and 1 for the second alternative). s ic is the choice that is prescribed by the preference s p (coded by -1 for the first alternative and 1 for the second alternative). I {.} is 1 if the term in brackets is positive and 0 otherwise. ic is an iid error term that is type 1 extreme value distributed. γ scales the variance of the error term and can be interpreted as the amount of implementation noise to be estimated. Thus, the more an individual's choices align with those prescribed by the respective preference, the smaller the estimated implementation noise will be.
The likelihood of subject i to follow preference s p over all choices c is The resulting log likelihood is I ln( P π(s p )π i (s p )), which is summed over all I subjects by treatment and where P represents the set of preferences we consider. π(s p ) is the estimated fraction of the sample with preference p. For identification, one preference is taken as reference category and its share is implied by the fact that shares have to sum to one. For the estimation, we adapt the code by Dal Bó and Fréchette (2011).

D Investigation of Order Effects
The subjects in our experiment make five consecutive choices between information structures.
Since only one of these five choices is randomly selected to be implemented, each individual choice can be treated as an independent choice. However, one may be concerned about potential order effects if subjects' subsequent choices are affected by their previous choices, for example, due to a preference for consistency. Moreover, in the experimental instructions, we always use the first feedback mode choice as an example to explain the choice situation (see Figures H.9 to H.11). Hence, it is possible that subjects have a better understanding of the feedback mode choice that is presented first. However, note that potential order effects do not affect our results if they are constant between IQ and random treatments. Nonetheless, to check if there are differential order effects between treatments, we vary the order in which subjects make pairwise choices. The different orders with the respective number of subjects are presented in Table D.1. In Table D.2, we interact the treatment dummy with a dummy indicating if a subject makes feedback mode choices according to the first, second, or third order. Most importantly, as indicated by the statistically insignificant interaction effects, we do not find support for differential order effects between treatments. This suggests that order effects are of no concern for our conclusions.
Interestingly, in Column (4), we observe that in both treatments significantly fewer subjects select Mode E in the skewness over framing choice when this choice represents the first feedback mode choice (i.e., in Order 3). This could be explained by the fact that in Order 3 this choice is presented first and is used as an example in the instructions (cf. Figure H.11).
Hence, subjects may better understand that Mode E is, in fact, less informative than Mode A. This is supported by the observation that the fraction of subjects who follow a strict preference to maximize the informativeness in Table 2 increases substantially from 0.409 to 0.500 in the IQ treatment, and from 0.538 to 0.689 in the random treatment when taking out the skewness over framing choice. Robust standard errors in parentheses. * p < 0.10, * * p < 0.05, * * * p < 0.01 E Information Preference Scale (Ho et al., 2021) • As part of a semi-annual medical checkup, your doctor asks you a series of questions.

F Content analysis of free-text responses
In the post-experimental questionnaire, we asked subjects to explain how they decided between feedback modes. In particular, we asked: "Please explain, in general, how you decided between feedback modes across the five scenarios. For example, why did you choose one feedback mode over another? What specific characteristics of the feedback modes were you looking at?" To analyze the free-text responses, we enlisted three research assistants to code the responses according to a pre-defined codebook. The research assistants were not familiar with the experiment, except for the instructions shown in Appendix F.1. The research assistants were only given the randomly sorted list of free-text responses, which did not contain additional variables or information about the treatment variation. A coding is counted if at least two out of three research assistants agreed on the coding. In the IQ treatment, subjects are more likely to state that they preferred the feedback mode with more green signals (p = 0.001) and less red signals (p = 0.060) compared to the random treatment. In the random treatment, they more often gave the explanation that they chose the feedback mode according to its informativeness (p = 0.013). The frequency of all the other explanations does not differ significantly between treatments.
In Table F.2, we explore whether the stated explanations correlate with information structure choices. The desire to receive more green signals and less red signals is indeed highly correlated with the propensity to select the respective feedback mode (e.g., Mode B in the baseline choice). Similarly, individuals who stated that they had maximized the informativeness or minimized the number of grey signals chose the more informative feedback mode in the baseline and informativeness choice. 23

Text analysis of survey responses
We conducted a lab experiment, in which subjects could select the type of feedback/signals that they would like to receive. The feedback informed them whether they were in the top or bottom half of a distribution. There are three types of possible signals (green, red, or grey balls): Subjects had to make decisions between two "feedback modes" each, which were presented in the form of urns with a varying composition of balls/signals. Signals would be drawn from the top urn if the subject was in the top half, or from the bottom urn if the subject was in the bottom half of the distribution. In total, there were five choice scenarios. Below you see an example for the choice scenario between Mode A and Mode B: After the experiment, we asked the subjects: "Please explain, in general, how you decided between feedback modes across the five scenarios. For example, why did you choose one feedback mode over another? What specific characteristics of the feedback modes were you looking at?"

Your task
We give you the responses given by the 216 subjects to the question above. Your task is to code their responses according to the following codebook. If a response matches a category, type a 1 in the respective column. A response can match with more than one category. If a response does not match any of the categories or is difficult to understand, type a 1 in the column "none". In case of doubt, please classify it as "none" instead of guessing what is meant.
The possible categories are given on the next page.

Categories
• more_greens: chose the feedback mode with more green balls o Example: "I choose the feedback mode which had more green balls" • more_reds: chose the feedback mode with more red balls o Example: "I was looking for the more negative approach (i.e. red balls) over the positive balls" • more_greys: chose the feedback mode with more grey balls o Example: "maybe more Grey than red (-) balls" • less_greens: chose the feedback mode with less green balls o Example: "I choose the feedback mode which had less green balls" • less_reds: chose the feedback mode with less red balls o Example: "I wanted the feedback mode that had less red" • less_greys: chose the feedback mode with less grey balls (or more green and red balls) o Example 1: "If there were less '...' signs I was more likely to choose the feedback mode" o Example 2: "I chose one feedback mode over another if the feedback mode showed more red or green counters." • more_information: chose the feedback more that is more informative/precise or has the largest difference in signals between top and bottom urn o Example 1: "The mode I chose looks like the most informative one " o Example 2: "I was more concerned about having more coloured balls than grey balls as that would give a clearer picture of whether I was in the top half or bottom half." • easier_to_understand: chose the feedback more that is easier to understand o Example: "I decided to choose the more simpler feedback modes (mostly B and E) as they were easier to read and interpret" • confirmation: chose the feedback mode that is more likely to confirm the initial belief o Example: "Assuming I was in the bottom I wanted the feedback modes that would be most likely to confirm that." • random: chose the feedback mode randomly o Example: "i really did not understand the task very well, so I just chose randomly" • none: statement cannot be classified by one of the above statements o Example: "Known values of balls preferred."                        to state his/her posterior belief about his/her relative rank following a red (-) signal.