In recent times, interest in the concept of intuition has

Raab and Laborde Research Quarterly for Exercise and Sport ©2011 by the American Alliance for Health, Physical Education, Recreation and Dance Vol. 82...
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Raab and Laborde Research Quarterly for Exercise and Sport ©2011 by the American Alliance for Health, Physical Education, Recreation and Dance Vol. 82, No. 1, pp. 1–

When to Blink and When to Think: Preference for Intuitive Decisions Results in Faster and Better Tactical Choices Markus Raab and Sylvain Laborde

Intuition is often considered an effective manner of decision making in sports. In this study we investigated whether a preference for intuition over deliberation results in faster and better lab-based choices in team handball attack situations with 54 male and female handball players of different expertise levels. We assumed that intuitive choices—due to their affective nature—are faster when multiple options are to be considered. The results show that athletes who had a preference for intuitive decisions made faster and better choices than athletes classified as deliberative decision makers. It is important that experts were more intuitive than near-expert and nonexpert players. The results support a take-the-first heuristic defining how options are searched for, how option generation is stopped, and how an option is chosen. Implications for the training of intuitive decision making are presented.

Key words: affect, decision making, deliberation, handball

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n recent times, interest in the concept of intuition has been renewed and a scientific approach to exploring its foundation has begun (Gigerenzer, 2007; Sadler-Smith, 2008). For instance, in management science, intuition is defined as an involuntary, difficult to articulate, affectladen recognition or judgment that is based on prior learning and experiences and is formed without deliberative or conscious rational choice (see, e.g., Dane & Pratt, 2007; Sadler-Smith, 2008, for a fuller account). In psychology, intuition refers to a judgment that appears in consciousness quickly, relies on no deep knowledge of reasons for that judgment, and is strong enough to act on (Gigerenzer, 2007). In sports, decisions are often affect laden, and novices and experts alike are driven by many situations to react quickly and emotionally. Yet, how effective are intuitive decisions, and do individual differences, such as gender or expertise, drive our

Submitted: January 20, 2009 Accepted: August 21, 2009 Markus Raab is with the Department of [AQ: Which Dept.?] at the German Sport University–Cologne. Sylvain Laborde is with the UFR STAPS at the University of Caen Basse–Normandy.

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decision-making preferences? There is a large research industry in management science and psychology (see Naqvi, Shiv, & Bechara, 2006; Plessner, Betsch, & Betsch, 2008, for overviews), but very little in sports—a domain known for its emotional choices and decisions under time pressure. Although research over the last 30 years clearly shows that intuitive choices produce fast responses, it is unclear whether intuition or deliberation produces the most correct choices. Furthermore the relationship between emotions and response quality has yet to be understood in sports decision making. In the following we will apply the concept of intuitive and deliberative decisions to tactical choices, such as to whom to pass the ball in team handball. These choices are an important component of performance in team sports, and they provide a showcase for applications of intuitive decision making in sports. Individual differences in choice preferences will be examined using different levels of expertise and gender. Because sports teams are almost always composed of one gender and are often limited to one level of expertise, insights about gender- and expertise-specific choices will allow the development of appropriate tactical-training interventions. Intuitive and Deliberative Decision Making The first approach that explains intuitive and deliberative decision making is an automatic information-

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processing approach. It argues that intuitive choices are fast and unconscious associations between a perceived situation and a course of action (Klein, 2003; Plessner et al., 2008, and chapters therein). A second stream of work provides a more emotional account of this continuum between intuitive and deliberative choices, concentrating on the notion of the affect-laden, or gut-feeling component of the aforementioned definitions. Affect is used here as a concept of experience including moods, emotions, attitudes, evaluations, and preferences, but in the following we will concentrate on the preference dimension (which will be measured with a preference scale; see Method section). The preference dimension is assumed to be a part of affect, because it encompasses choices made from an affective perspective, in contrast to those made from a cognitive perspective (Chaudhry, Parkinson, Hinton, Owen, & Roberts, 2009). When we make a decision, the emotional content of the environmental stimuli may play a role, consciously or not. Intuitive decision making from this perspective describes such choices as impulsive (Deutsch & Strack, 2008) or as “feeling is for doing” (Zeelenberg, Nelissen, & Pieters, 2008). The main argument is that emotions can implicitly activate associated goals that manifest themselves behaviorally (Lieberman, 2000). In sports the automatic information-processing approach has been used to research anticipation and tactical choices in ball sports (see Raab & Johnson, 2008; Williams & Ward, 2007, for overviews), but the emotional components have been less rigorously researched. For instance, research showed that receiving instructions on how to discover important cues about the direction of a tennis return resulted in more deliberation than having no instructions (Masters, 2000, Raab, 2003; Smeeton, Williams, Hodges, & Ward, 2005). Until recently, the affect-laden approach has received little attention (Bird & Horn, 1990; Bonnet, Fernandez, Piolat, & Pedinielli, 2008), although authors have acknowledged the need for a combined cognitive and affective approach to fully understand the link between emotions and decision making in sports (Hatfield & Kerick, 2007; Tenenbaum et al., 2009). Two other approaches in the current research in cognitive sport psychology (Moran, 2009) addressed the link between affect and cognitive processing: the processing efficiency theory (Murray & Janelle, 2007) and the attentional control theory (Eysenck, Derakshan, Santos, & Calvo, 2007). Both gave interesting insight about the influence of anxiety on cognition, but studies with these theories have focused so far only on anxiety, whereas the affect-laden approach appears to tease out a more subtle and broader range of emotions, which is necessary to understand athletes’ behavior (Hanin, 2007). Regardless of their preferred style of decision making, people may simply use their affective reaction to an object or behavior as input for their decisions. This mechanism

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is known as the “how-do-I-feel-about-it” heuristic (Schwarz & Clore, 1988), and it accounts for the feedback function of emotion. Emotional past experiences also play a role in current decisions, as explained by the somatic marker hypothesis (Damasio, 1996). This hypothesis states that when confronted with a choice or a situation, we experience emotional somatic markers based on the feelings we had in similar past situations and use them to guide our current choice (e.g., fear protects us from threat). More generally, this hypothesis could explain individual preference for intuitive or deliberative decision making, depending on the degree to which people tend to rely on these somatic markers. In sport decisions, both speed and emotions are important. Thus, the automatic information-processing approach and the affect-laden approach could be used in a complementary rather than exclusive fashion. Therefore we tested intuitive decision making in tactical decisions in ball games by adding an affect-laden perspective of decision making to sports. Given the lack of experimental research on individual components of intuitive and deliberative decision making, we will describe more general previous research on individual differences to derive hypotheses that fit the specific case of intuitive and deliberative choices in sports. Individual Differences in Intuitive Versus Deliberative Decision Making We limit our review of individual differences in intuitive versus deliberative decision making to an emphasis on two important and well researched factors that may influence decision-style preference: gender and level of expertise. In ball sports these factors are important because teams are usually separated along these lines. Nonsports-specific findings have supported the notion that gender and expertise do, in fact, influence preference, so it is natural to ask whether such influence transfers to tactical decisions or whether choice preferences in sports are gender and/or expertise neutral. Gender. Women’s intuitive decisions are often reported to be superior to those of men, although evidence is still quite limited. For instance, Graham and Ickes (1997) showed that women have higher “empathic abilities” of vicarious emotional responding and nonverbal decoding abilities, but no differences occur in empathic accuracy between genders. More directly connected to our investigation, there were no gender differences found between boys and girls on the Rational Experiential Inventory (REI; Epstein, Pacini, Denes-Raj, & Heier, 1996), which differentiates intuitive-experimental and analytical-rational thinking styles. In contrast, there are small but consistent higher preferences for intuitive decision making found with other scales, such as the Preference for Intuition and Deliberation (PID; Betsch, 2004). There seems to be

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agreement that gender differences in decision-making style are largely domain specific, and both evolutionary and environmental explanations have been put forth (Blais & Weber, 2001; Hogarth, 2008). Expertise. A number of findings show that experts use less information (Shanteau, 1992; Yates & Tschirhart, 2006), have a lower number of fixations (Williams & Ward, 2007), and generate a lower number of options with higher quality (Johnson & Raab, 2003) than novices. It has also been found that experts in team handball, the sport we studied, responded faster than less-experienced participants in a video-based option-generation paradigm, in which they first intuitively specified a response before generating more appropriate options and finally selecting the best one (Raab & Johnson, 2007). The experts also used a more intuitive strategy than near-expert or nonexpert players, such as relying more on the first option they generated. Previous research on expertise in decision making (see Williams & Ward, 2007, for an overview) relied mainly on cross-sectional studies, but recently longitudinal support has also been provided (Raab & Johnson, 2007). In summary, there is a fair amount of evidence that experts do decide faster and better than nonexperts, but it is unclear whether a preference for intuitive decision making plays a role in these findings. In addition to recognizing these individual differences regarding gender and expertise, we should take note of the following intraindividual modulation: the existence of strategy preferences in decision making (intuition vs. deliberation) does not imply that the preferred strategy can be used in every decision. Domain, features of the situation (e.g., number and display of cues), time constraints, and other factors can determine the selection of intuitive or deliberative decision strategies (Hammond, Hamm, Grassia, & Pearson, 1987), which may or may not be the preferred strategy. This concept is identified as decisional fit or misfit and is implicated in the subjective perception of decisional success (Betsch & Kunz, 2008). Assumptions About Individual Choice-Style Preferences We predicted that tactical decisions would be faster and result in more correct choices if athletes have a preference for intuitive decision making. Evidence from previous research showed that most expert athletes use an intuitive decision-making style in tactical situations in handball (Raab & Johnson, 2007). If intuitive choice is based on automatic activation of affect-laden information, then this would result in faster decisions compared to those athletes who prefer deliberative decision making (Betsch, 2008; Zajonc, 1980). In addition, we predicted that players with a preference for intuitive decision making would make more correct choices in an option-generation

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task (see Plessner & Czenna, 2008, for a list of studies in various domains in which either intuitive or deliberative choices are more effective). The rationale behind this prediction lies in a characteristic of fast ball games—they require speedy decisions. In such fast-paced games, athletes learn to order the options they generate by their appropriateness in a given situation. Also, athletes generating fewer options are likely to pick a good one quickly if their option set reflects a diversity of option quality (Raab & Johnson, 2007). We predicted that female players would have a higher preference for intuitive decision making than male players. We assumed this effect would be in line with previous findings using the PID scale, which focuses more on the affective nature of decision-making compared to the REI (Epstein et al., 1996). However, we assumed small effect sizes as found in previous research (e.g., η2 = .10; Betsch, 2004). Whereas our previous research indicated that experts, compared to novices, made faster and better decisions, it has not yet been demonstrated that a preference for intuitive decision making is more prevalent in experts compared to nonexperts and near-experts. The rationale for assuming we would find a higher number of players with a preference for intuitive decision making was based on previous findings following selection and training arguments (Raab & Johnson, 2007).

Method Participants Fifty-four young male and female handball players of different expertise levels participated. Classification of expertise was based on current league level. We collected data from 27 male and 27 female handball players (M age = 15.27 years, SD = 1.65). We used the same number of boys and girls in the three groups. Experts (n = 16, M years of training = 8.9) were defined as playing at the highest league level of their age group, near-experts (n = 20, M years of training = 8.3) as playing at the second highest league level of their age group, and nonexperts (n = 18, M years of training 8.1) as playing at the third league level of their age group. We did not consider pure novices in our design, because the concept of intuition we introduced requires some experience. Age, years of training, and tactical knowledge served as control variables. As in our previous research on a larger sample of the same subject pool, expertise was a clear indicator of decision-making performance, which validated our a priori classification of expertise groups. Participants received a small gift for their participation. All participants provided informed consent before participating in the study, which was carried out according to the ethical guidelines and with the approval of the University of Flensburg.

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Material and Measurement The instructions asked athletes to name, as quickly and as accurately as possible (stressing that speed and accuracy were equally important), the first option for the player in ball possession that came to mind after the frozen frame of video clips from a video test. The video was frozen such that the player with the ball did not reveal cues that would lead or mislead participants in their decision making—for example, participants could not simply imitate the player’s choice. Then they were told to generate further appropriate options for that player and, after considering these alternative courses of action, to choose the option they considered to be the best. Each clip was over 10 s long to cover the development of the attack situation and ended in a frozen frame of 6 s. We recorded the verbal responses, resulting in dependent variables of decision time, option generation time, quality of first option and final option, and number of options. We used the same video test as in our previous investigations (Johnson & Raab, 2003; Raab & Johnson, 2007). The test was ecologically valid in that the correlation between decisions in the lab and decisions rated by coaches of the same athletes in real competitions was significant and high (Johnson & Raab, 2003). Furthermore the verbal response was used rather than a more ecologically valid behavioral response to allow faster option-generation processes; however, previous research with the same paradigm but more realistic behavioral responses did not reveal a different result pattern (Raab, 2003). The video clips contain attack situations in team handball filmed from a position 2 m above the ground from the line in the middle of the field. This camera position covered all players from the angle of a midfield playmaker deciding between different pass or shoot options. There were 15 clips of specific sessions of male and female near-expert teams. The 15 clips were identical for all groups, and we used attack and defense situations that are defined for all age groups by the German Handball Association. Furthermore the best options were distributed almost equally to the positions of the field. We used four validation procedures to develop the final test, which are described in more detail in Johnson and Raab (2003) and Raab and Johnson (2007). We asked expert coaches (1 man, 1 woman, both routinely involved in judging videos of games) who had received the highest license in Germany to rate similarity to realistic situations and match to specific defense and attack patterns. Decision quality was rated by another group of experts (1 man, 1 woman, both junior national coaches who are involved in talent scouting of male and female players of the respective age groups). They rated those options in these clips that were appropriate and marked the best options that were used. Only the best options (agreed on by the coaches) were used to calculate the decision quality of our participants. Item analysis from a previous sample

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of athletes was used to calculate a selection index and ensured mean difficulty for the array of expertise tested. In addition, we used pretested female and male clips due to the nature of our gender-specific assumption. Even if there is a tendency for female and male players to decide more appropriately on their same-gender clips, such differences are not significant; furthermore, postexperiment interviews revealed that the gender of the presented clips induced no significant loss of concentration or use of a different set of strategic choices. Decision time for the first option and generation time for all options was measured in milliseconds with a 1,000-Hz sample rate by integrating the digital video clips and the digital audio signals of the responses from the participants by using rectangular signals for synchronization at the start of the frozen frame and at any recording of verbal response. We used the PID scale (German version) that measures affect- and cognition-based decision making. The inventory has 18 questions distributed equally on the subscales Preference for Intuition (PID-I) and Preference for Deliberation (PID-D). Answers are given on a 5-point scale from 1 = I don’t agree to 5 = I totally agree. Items from the PID-D are, for example, “Before making decisions I first think them through” or “I think before I act.” Items from the PID-I subscale are, for example, “With most decisions it makes sense to completely rely on your feelings” or “My feelings play an important role in my decisions.” In studies of samples totaling more than 2,500 participants, Betsch (2004) showed that reliability varies between .78 and .84 (PID-D) and .78 and .81 (PID-I) and that habitual preferences are stable over time. Participants scoring high on the PID-I scale show fast decision making and score high on emotion-associated personality dimensions such as extraversion, agreeableness, and openness to experience. Those scoring high on PID-D show conscientious perfectionism with a high need for structure (Schunk & Betsch, 2006). After the experiment we assessed participants’ age, years of training, and tactical knowledge by giving them multiple-choice questions that had been validated previously (Johnson & Raab, 2003; r = .86, p > .01, df = 19). We used one item to check how well they understood the instructions and one to measure general motivation (4-point Likert-type scale, in which 4 = excellent or complete). Data Analysis All dependent variables were normally and unimodally distributed after outlier reduction (2.1% outliers were present; 1.4% in previous samples, Raab & Johnson, 2007). Neither expertise level nor gender systematically produced a large number of outliers that could influence the results. The recorded verbal responses to the video test were coded as options by two raters, 1 man and 1 woman to avoid gender bias in the ratings (r = .86, p < .01; df = 53). All

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free responses could be coded as valid options based on the list of options we used in previous research (Johnson & Raab, 2003). Options were coded as correct when they matched the best options indicated by the expert coaches, and the mean number of correct choices was calculated per participant. Because gender or expertise may alter what a best option is, the coaches were young national coaches who select for these specific expertise groups the best players for national teams. Furthermore, the attack and defense presented in the video clips represented only those tactics played in all gender and age groups and prescribed by the German Handball Association. The clubs and coaches provided us with descriptions of their training during a previous longitudinal study, so we were sure these tactics had been used by each group we tested (Raab & Johnson, 2008). Number of options represents the number of options generated including the first intuitive one, averaged over number of trials. We did not count the best option in the number of generated options because it was one of the previously generated options and therefore not newly generated. Decision time was measured using the verbal response compared to the video signal from the start of the frozen frame (1-ms precision) to the first option generated. Generation time was the duration from the start of the clip to the end of option generation when participants named the best option. Generation time was measured from the beginning of the situation, as participants may generate and potentially verbalize during the development of the situation options. We calculated both mean decision and generation time for each clip for each participant. Spearman-Brown coefficients of reliability for the total sample were .68 for choice quality, .79 for number of options, .90 for decision time, and .84 for generation time. Spearman-Brown coefficients for reliability for groups by expertise or gender were not significantly different from the ones reported. According to the procedure described by Betsch (2004), participants who scored above the group median on the intuitive scale (PID-I) and below the group median on the deliberative scale (PID-D) were categorized as intuitive; those who scored below the group median on the PID-I and above the group median on the PID-D were categorized as deliberative; and those who scored above or below the group medians of both scales were categorized as situation dependent, that is, sometimes intuitive and sometimes deliberative. We found only eight participants who were situation dependent. We eliminated these eight participants who were almost equally distributed to all three expertise groups (3, 2, 3) and gender (5, 3) indicating no specific bias to these factors. We analyzed before the presented results whether there would be any differences if we included the situation-dependent participants, and the results showed there were none. Therefore we only contrast intuitive and deliberative decision makers to dissociate the preferences as much as possible.

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Significance criterion was p < .05. Effect size will be presented if t or F values are higher than 1 to avoid interpretation of potentially unreliable effects. We used Cohen’s f to provide the same metric throughout the paper regardless of whether only two groups (gender) or more groups (expertise) were compared. We ran a 2 × 3 × 2 (Gender × Expertise × Preference) multivariate analysis of variance (MANOVA) including as dependent variables quality of initial choice, quality of final choice, number of generated options, decision time, and generation time. Gender, expertise, and preference were between-participants factors. We ran an a priori sample size estimation for the MANOVAs with global factors and specific analyses for three groups and five dependent response measures, setting power, alpha-level, and expected effect sizes from our previous research (Raab & Johnson, 2008) using GPOWER 3.0 software [AQ: Mfg’s, city, state?]. Results of the analyses reveal that we have an appropriate power with a total sample size (after removing the 8 situationdependent participants) of 46 for the planned effects.

Results The participants rated their motivation excellent at 3.11 (SD = .62) and their understanding of the video instructions complete at 3.72 (SD = .45). The instruction check did not reveal that the instructions were used differently in the different strategy types. Using a repeated measures analysis of variance (ANOVA), we found no differences between expertise groups or genders on tactical knowledge: expertise, F(1, 44) = .27, p > .05; gender, F(1, 44) = 0.16, p > .05. This is relevant, because tactical knowledge may influence an athlete’s preference to rely on more or less knowledge. First, we ran a MANOVA (expertise, gender, preference) using first and final choice quality, generation and decision time, and number of generated options as dependent variables (see Table 1 for means and standard deviations for all dependent variables). There were main effects of expertise and preference, a Gender × Expertise interaction, and a Gender × Preference interaction, but no significant main effect of gender, two-way interaction of expertise and preference, or higher order interaction of expertise, gender, and preference. For efficiency we will refer only to the predicted effects in our hypotheses. Expertise and Gender by Expertise Effects We found a significant main effect of expertise, [AQ: This symbol didn’t translate]  = .48; F(2, 44) = 3.19, p < .05, f = .48, indicating better and faster choices by increasing expertise as shown in our previous research (Raab & Johnson, 2007). A follow-up least significant difference post hoc test (Fisher’s LSD) showed experts to be significantly

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(p < .05) better than nonexperts in generating first option and best option, to have a lower number of generated options, and to have a faster generation time of first option. Mean generation time did not differ. Differences between near-experts and nonexperts are mainly marginal. The data replicate nicely that expertise enhances the use of the take-the-first heuristic in that participants relied mainly on the first option they generated. In addition the mean correct choices of participants decreased from the first to the later-generated second and third options. That is, experts showed better performance compared to near-experts and nonexperts. By generating fewer options and picking the first option, they were more often correct and made faster choices. Significant correlations exist for the total sample between first option choice time and option generation time (r = .33, p < .05), but none between choice quality and decision times for any indication of speed–accuracy trade-offs. A significant interaction of expertise and gender was found, F(2, 44) = 3.08, p < .05, f = .46. Follow-up analyses revealed that changes in variables over expertise groups are more prominent for girls concerning choice time for the first option (p < .05) and more prominent for boys concerning first (p < .05) and best choice (p < .05) quality. No effects are found for number of options generated or generation time. Gender and Preference by Gender Effects The gender main effect is not significant, F < 1. We predicted that female handball players would have a higher preference for intuitive decision making than boys. Indeed girls were more intuitive than boys in this sample, F(1, 44) = 2.82, p < .05, f = .43. On average, the girls (PID-I: M = 3.12; Mdn = 3.01, SD = .41) had higher intuition scores than the boys (PID-I: M = 3.01; Mdn = 3.00, SD = .71), but there were no differences in the PID-D scores (PID-D, boys: M = 3.02; Mdn = 3.18, SD = .71; PID-D, girls: M = 3.28;

Mdn = 3.05, SD = .49). These findings replicate previous data sets in direction and approximately in quantitative scores (e.g., Betsch, 2004: boys, PID-I: M = 3.1; PID-D: M = 3.6; girls, PID-I: M = 3.5; PID-D: M = 3.6). Preference Effects The main effect of preference did not reach significance in an ANOVA, F(1, 44) = 1.61, p = .09, f = .24. Due to an F value higher than 1 and the effect size, we ran follow-up analyses. The results revealed that athletes classified with a preference for intuitive decisions made their first choice faster, F(1, 44) = 1.39, p < .05, f = .21, and had a better first option, F(1, 44) = 3.81, p < .05, f = .57, and better best options, F(1,44) = 1.14, p < .05, f = .17, than athletes classified as deliberative decision makers (see Figure 1). There were no differences for generation time or number of generated options.

Discussion The goal of this study was to analyze the effects of intuitive and deliberative decision making in sports. We argued that a preference for intuitive decision making results in faster and more correct choices than a preference for deliberative decisions. In addition we predicted higher preference for intuitive decision making in experts compared to near-experts and nonexperts. We also predicted that girls would have a stronger preference for intuitive decision making than boys. We found support for the general and the differential account of benefits of intuitive decision making in tactical choices in team handball. In general the interaction of expertise and gender supports the take-the-first heuristic developed in cross-sectional research (Johnson & Raab, 2003), extended to longitudinal research (Raab & Johnson, 2007), and now differentiated by preferences for intuition and deliberation as well as

Table 1. Means and standard deviations by expertise and gender for all dependent variables First option Decision time Number of options Generation time Best option

Experts Near experts Nonexperts Boys Girls Boys Girls Boys Girls M SD M SD M SD M SD M SD M SD 5.93 4.02

1.94 1.11

4.20 1.06 3.26 1.83

4.01 4.31

1.91 .90

3.77 3.88

3.22

1.14

2.75

.24

3.13

1.0

3.24 1.01

22.18 5.31

7.53 1.29

21.75 4.10

6.91 1.33

27.31 3.47

6.70 1.27

22.99 3.45

2.31 0.97

3.55 1.54

3.41 6.44

0.61 2.41

3.07 1.87 3.98 2.13

3.51

.98

3.92 1.65

25.63 3.37

6.40 1.50

23.91 2.95 2.61 1.55

Note. N = 46; first and best option refer to number of correct choices based on expert rating; Number of options refers to options other than the best option; decision time is time in seconds between freezing of the scene and first option generated; generation time is time in seconds betweens start of the scene and final choice.

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moderated by gender. Effect sizes of expertise effects replicate the robustness of this finding with high scores. In general, effects are present for the dependent choice variables such as first and best options and decision time, as well as the number of options generated. We think that the expertise effects found in this study are quite robust given that this is now our third study replicating the usefulness of the take-the-first heuristic over a wide range of expertise, in cross-sectional and longitudinal studies, and using a large array of independent and dependent variables and specific variations of the tactical decision-making paradigm (Johnson & Raab, 2003; Raab, 2003; Raab & Johnson, 2007). The basic finding is that experts use very little information and rely on an intuitively generated option that is often better than subsequently generated options. We are aware that the experts in our paradigm could come up with a generated

first option from other previously generated options during the video clips. Whereas our experiment cannot rule out this possibility, our participants did not report any option generation before the freeze-frame. In addition, due to the dynamics of team handball play, early option generation in video clips—even if possible—would be of limited value, as the defense and attack situation changes before the first choice is requested. Furthermore previous research showed that early fixations are indeed toward the first option they verbally generated. This finding supports the notion that the first option generated is named before other options are deliberated (Raab & Johnson, 2007). Future research could set up a strict threshold for when a decision is defined as intuitive. A further limit is the low number of correct option selections. We used a quite conservative criterion that an option was labeled correct only if this option was named by the expert panel as the

AQ: Please provide new art with bolder type and heavier line weights in the X & Y axes, as these will not reproduce properly in the final version.

Figure 1. High and low preference for intuitive decision making for experts, near experts, and nonexperts according to (a) decision time (in seconds) and (b) choice quality (number of correct responses). Error bars represent standard errors.

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best option. Previous research on option generation with about the same number of different options generated showed that if one lowers that criterion, higher performance scores can be demonstrated (Johnson & Raab, 2003). Further research may need to combine different ways to judge the appropriateness of options. Evidence that experts use little information is quite old (Shanteau, 1992), and whether such a strategy is adaptive has been discussed for many applications (Gigerenzer, Todd, & the ABC Research Group, 1999). In addition to showing effects for choice variables, we have replicated these findings for gaze behavior in the same paradigm (Raab & Johnson, 2007) and for other choice environments such as decisions under risk or time pressure. For instance, we have shown in basketball that playermakers under time pressure decide for the option with the highest initial preference (Raab & Johnson, 2004). Concerning gender differences, we replicated previous research in other domains showing that women have a slightly higher preference for intuition than men. As in previous research the effect is quite small (Betsch, 2004), but it is higher compared to other domains such as lotteries (Schunk & Betsch, 2006). We speculate that the domain and the assessment of the preference seem to play a major role if expertise or gender differences are found. For instance, studies using the REI scale found no gender differences (Blais & Weber, 2001; Epstein et al., 1996), whereas studies using the PID did find them (Betsch, 2004; this study). A further way to validate these finding would be to test both scales in the same domain and to predict behavior based on the extent to which affect-laden responses are required. The environmental explanations of girls’ potential preference for intuitive decision making provided in psychological and economic tasks (Blais & Weber, 2001; Hogarth, 2008; Langan-Fox & Shirley, 2003) should be tested in sports in future research. This study has some limitations. We believe it is the first of its kind in sports; therefore, we are not prepared to speculate whether these findings generalize beyond team handball. Even within the same sport there may be other situations in which athletes with a preference for intuitive decision making are not as effective as their deliberative counterparts. In addition, this paradigm may have caused some of the result pattern, whereas an option-selection paradigm in which athletes choose from a predetermined set of options may be less likely to do so. Given that we used different levels of expertise, individuals’ movement capacity can limit the options they generate; therefore, this variable may need to be analyzed in future research. However, previous research on the learning of decision making in different expert groups in team handball, volleyball, and basketball leads us to speculate that these findings are not paradigm specific (see Raab, 2003). Another limitation is that choices in this study were made in the lab, without the emotional pressure of com-

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petition. It could be of interest to study how athletes with a preference for intuitive or deliberative decision making differ in their tendency to choke under pressure, and how option generation and selection processes are impaired in this case (Kalis, Mojzisch, Schweizer, & Kaiser, 2008). Research programs directly examining the influence of emotion on preference style are still to be conducted. There are many natural continuations from this first line of research. As indicated, it seems valid to replicate these findings using different tasks in the same sport, generalize it to other sports, and extend it to different levels of expertise. Methodologically, further tests of the preference scale (e.g., PID vs. REI) and the decision task (option selection vs. option generation) seem a fruitful way to test the robustness of these findings. The relationship between intuition and other individual differences, such as personality (Salter, Evans, & Forney, 1997) and emotional intelligence (Downey, Papageorgiou, & Stough, 2006), has yet to be explored, a concept that suggests individuals differ in the extent they attend to, process, and use affect-laden information (Petrides & Furnham, 2003). As we demonstrated, experts showed a preference for intuitive decisions, but whether intuition can be developed with appropriate training is a question of great interest to coaches (Rogers, 2005). The answer lies in furthering our understanding of intuition. For instance, the influence of emotion on preference style has not yet been explored, and more research is required on the electrophysiological processes involved in intuitive and deliberative decisions (McCraty, Atkinson, & Bradley, 2004). If an athlete’s positive emotional reactions could be associated with correct actions, intuition might be enhanced. The coach could reward appropriately when the athlete performs well (e.g., give positive feedback), rather than focus on reducing mistakes. Coaches may also find it interesting to understand how explicit or implicit knowledge influences preference for intuitive or deliberative decisions (van Zuijen, Simoens, Paavilainen, Naatanen, & Tervaniemi, 2006). More important is the generalization to affect in sports. As we discussed, we limited ourselves to preferences only. Within the concept of preferences, we can easily see many other important dimensions that should be tested, such as risk prone versus risk averse (Blais & Weber, 2001) and maximizing versus satisficing (Parker, Bruine de Bruin, & Fischhoff, 2007). Furthermore, affect is not purely an issue of preferences; moods, emotions, attitudes, and evaluations are still not well understood in sports decisions. The topic of combining emotions with decision making has had its impact in fields such as psychology (Mellers, 2000) and economics (Cohen, 2008), but in sports its study is just beginning (see the integration of emotional processes in decision field theory, Busemeyer, Dimperio, & Jessup, 2007; and decision field theory applied to sports, Johnson, 2006). Future research should focus on the influence of emotions on sport decisions, and

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more specifically on how the different components of the decision-making process (Tenenbaum et al., 2009) are affected. An influence of emotions has been demonstrated at different levels involved in athletes’ decisions: cognitive (e.g., visual attention, Nieuwenhuys, Pijpers, Oudejans, & Bakker, 2008), behavioral (e.g., movement pattern, Pijpers, Oudejans, & Bakker, 2005), and physiological (e.g., vagal and cardiac reactivity, Spalding, Jeffers, Porges, & Hatfield, 2000). The need to go beyond positive and negative emotions is also evident; for example, in a soccerrelated cognitive task, there were no changes with happiness compared to a neutral state, whereas hope yielded a faster reaction time (Woodman et al., 2009). Analyzing general and individual effects of emotions on decision making would allow us to provide more detailed recommendations to those who love sports and may explain the magic when superstars have their moments of intuition.

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Authors’ Notes This study was supported by a German Federal Grant for Sport Science (BISp VF 07/08/66) and TransCoop (Raab/Johnson16601). The study is part of a larger research program on longitudinal changes in decision making in sports. The authors thank Anita Todd and the Performance Psychology Research Group at the Institute of Psychology, German Sport University Cologne for helpful comments on an earlier version or this paper. Please address correspondence concerning this article to Markus Raab, German Sport University Cologne, Am Sportpark Müngersdorf 6, Cologne, Germany 50933. E-mail: [email protected]

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