Attention and choice across domains.

Smith, Stephanie M.; Krajbich, Ian

doi:10.1037/xge0000482

Cited by 90 publications

(117 citation statements)

References 60 publications

(96 reference statements)

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“…Data Set 2. Participants (N = 44) in this study (Smith & Krajbich, 2018) rated 147 food items on a scale from −10 to 10 before making 200 choices among positively rated foods (rating > 0). The maximum absolute-value difference for any trial was 5.…”

Section: Datamentioning

confidence: 99%

Gaze Amplifies Value in Decision Making

2018

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Section: Datamentioning

confidence: 99%

Gaze Amplifies Value in Decision Making

2018

Self Cite

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“…Indeed, comparing both data sets, the application of simplified versions of the strategy seems to be an adequate explanation for the quantitatively different effects when considering all dependent measures. In contrast, we have no evidence to believe that the varied presentation format might have introduced systematically different gaze behavior and hence caused the different effects (Smith & Krajbich, ).…”

Section: Discussionmentioning

confidence: 78%

Forward inference in risky choice: Mapping gaze and decision processes

Schoemann

Schulte‐Mecklenbeck

Renkewitz

et al. 2019

Behavioral Decision Making

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The study of cognitive processes is built on a close mapping between three components: overt gaze behavior, overt choice, and covert processes. To validate this overt–covert mapping in the domain of decision‐making, we collected eye‐movement data during decisions between risky gamble problems. Applying a forward inference paradigm, participants were instructed to use specific decision strategies to solve those gamble problems (maximizing expected values or applying different choice heuristics) during which gaze behavior was recorded. We revealed differences between overt behavior, as indicated by eye movements, and covert decision processes, instructed by the experimenter. However, our results show that the overt–covert mapping is for some eye‐movement measures not as close as expected by current decision theory, and hence question reverse inference as being prone to fallacies due to a violation of its prerequisite, that is, a close overt–covert mapping. We propose a framework to rehabilitate reverse inference.

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“…Therefore, there is a larger change in the utility difference between the in-and outgroup setting for the prosocial decision maker, which should lead to more pronounced differences in processing effort between the in-and outgroup setting than among individualistic decision makers in decision time (H2a), number of fixations (H2b), and amount of inspected information (H2c). Finally, because information that is weighted more highly in the decision process receives more attention while the choice is made 27,28 , Equation 2, in parallel to the expectations for choices and cognitive effort, implies that stronger prosocial preferences are associated with more visual attention directed at others' outcomes 13 . Stronger prosocial preferences are expected to lead to ingroup bias in attention to others' outcomes (H2d) compared to more individualistic preferences.…”

mentioning

confidence: 99%

Prosocial Preferences Condition Decision Effort and Ingroup Biased Generosity in Intergroup Decision-Making

Rahal

Fiedler

Dreu

2020

Sci Rep

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Ingroup favoritism and discrimination against outgroups are pervasive in social interactions. To uncover the cognitive processes underlying generosity towards in-and outgroup members, we employ eyetracking in two pre registered studies. We replicate the well-established ingroup favoritism effect and uncover that ingroup compared to outgroup decision settings are characterized by systematic differences in information search effort (i.e., increased response times and number of fixations, more inspected information) and attention distribution. Surprisingly, these results showed a stronger dependency on the in-vs. out-group setting for more individualistic compared to prosocial participants: Whereas individualistic decision makers invested relatively less effort into information search when decisions involved out-group members, prosocial decision makers' effort differed less between inand outgroup decisions. Therein, choice and processing findings showed differences, indicating that inferences about the decision process from choices alone can be misleading. Implications for intergroup research and the regulation of intergroup conflict are discussed. Decisions often affect not only the decision makers themselves, but also other people. Sometimes, these other people belong to the same group or community as the decision makers, and sometimes they belong to more or less rivaling outgroups. Research with experimental as well as natural groups has revealed that, in general, people are more generous when their decisions affect the outcomes of other individuals seen as part of one's ingroup, rather than some rivaling outgroup 1. At the same time, there is growing evidence that some people are more likely to display such ingroup bias than others: In intergroup contests, individuals with stronger prosocial preferences, who prefer fairness and care about own as well as other people's welfare, are more likely to make costly contributions favoring their ingroup than individuals with weaker prosocial preferences 2-5 (but see 6). Here we address the still poorly understood attentional mechanisms behind such moderated ingroup bias in cooperation and generosity. We do so using eye-tracking technology as a tool to capture the cognitive processes underlying human judgment and decision-making. Eye-tracking allows tracing overt attention allocation through recording gaze behavior during visual information search 7,8 , showing what is processed at a specific point in time, how much attention is allocated to specific pieces of information, and how much information is surveyed overall. Prior evidence shows that information about ingroup members is processed more thoroughly compared to information about outgroup members 9-12. Going further, eye-tracking also harbours the potential to track fine-grained details of decision processes, such as the extent of information search (i.e., number of fixations) while decisions are formed, and the cognitive processing effort invested 11,12. Moreover, the proportion of attention allocated towards or nona...

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Attention and choice across domains.

Cited by 90 publications

References 60 publications

Gaze Amplifies Value in Decision Making

Gaze Amplifies Value in Decision Making

Forward inference in risky choice: Mapping gaze and decision processes

Prosocial Preferences Condition Decision Effort and Ingroup Biased Generosity in Intergroup Decision-Making

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