Experience-based Probability Learning And Ranking Task - Info and Reading Options

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• In the current study, we aim to answer one main research question and two secondary research questions. • Main research question: In a previous project, Liu et al. (in preparation) used a novel event ranking task to investigate people's probability judgments. On each trial of the event ranking task, participants are asked to provide a ranking for an event set consisting of two pairs of complementary events, A, not-A, B, and not-B, in terms of their perceived likelihoods. There is a finite number of possible rankings over four events, meaning the response space is fully enumerable. There are, in total, 75 potential rankings for each unique event set. However, only some of these rankings follow the complement rule, thus deemed logically possible. Specifically, 17 out of 75 rankings are logically possible, in line with the complement rule. The rest are not logically possible, violating the complement rule. Liu et al. (in preparation) further classified participants' responses into four categories: logical rankings, stacked illogical rankings, interlaced illogical rankings, and other illogical rankings. In the previous project, Liu et al. (in preparation) showed that the (conditional) probability of participants providing various types of rankings is dependent on the probabilities of events in the presented event set. Remarkably, this qualitative pattern could be predicted in advance by a mental sampling model proposed by Liu et al. (in preparation). In the previous project, Liu et al. (in preparation) constructed event sets using events regarding the occurrence and non-occurrence of events regularly observed in everyday life in Germany. Participants were presented with descriptions of these events when making their probability judgments. In the current study, we aim to explore whether the qualitative pattern identified in the previous project can be replicated in an experience-based decision-making paradigm. Participants will first learn the probabilities of events through information sampling and then generate rankings of events based on the probabilities they have learned. • Secondary research question a): The rankings will be elicited from participants in two ways. In the first method, participants will be explicitly asked to rank events based on their perceived likelihoods of events. In the second method, participants will conduct pairwise comparisons of events. The outcomes of these comparisons will then be used to determine the rankings. We aim to investigate (1) whether participants' rankings obtained from pairwise comparisons agree with their explicitly provided rankings, and (2) whether error patterns are observed in participants’ rankings, both when elicited through the pairwise comparison method and when explicitly provided. • Secondary research question b): We will also investigate the impact of the frequency at which information is sampled on participants’ responses. To this end, we will manipulate the number of trials through which participants learn the probabilities of different events during the probability learning phases. We assume that with an increased number of learning trials, participants’ estimations of the true probabilities will be more precise.

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