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• W2767688719 abstract "Learning in Multiple-cue Judgment Tasks Bettina von Helversen (Bettina.vonhelversen@unibas.ch) University of Basel, Department of Psychology, Missionsstr, 62a 4057, Basel, Switzerland Jorg Rieskamp (joerg.rieskamp@unibas.ch) University of Basel, Department of Psychology, Missionsstr, 62a 4057, Basel, Switzerland Abstract mapping model described participants’ responses well in tasks that could not be solved by a linear model and where participants had knowledge about the cues’ polarity; that is, the sign of the correlation between a cue and the criterion. The exemplar model performed well, in non-linear environments with no prior knowledge about cue polarity, and a linear additive model performed well if the task structure was linear. In our daily lives we often make quantitative judgments based on multiple pieces of information such as evaluating a student’s paper based on form and content. Psychological research suggests that humans rely on several strategies to make multiple-cue judgments. The strategy that is used depends on the structure of the task. In contrast, recent research on learning in judgment tasks suggests that learning is relatively independent of task structure. In a simulation study we investigated how the performance of several learning models is influenced by the structure of the task and the amount of learning experience. We found that a linear additive neuronal network model performed well regardless of task structure and amount of learning. However, with little learning a heuristic model performed similarly well, and with extensive learning, associative learning models caught up with the linear additive model. Learning in Multiple-cue Judgment Tasks Although many studies in multiple-cue judgment research rely on extensive learning phases, there have been relatively few attempts to understand and model the learning process. However, the learning process is crucial to understand how people come to make judgments and which cognitive processes they rely on. Particularly, if — as suggested — people rely in their judgment on multiple cognitive processes, this should also be reflected in the learning phase. Additionally, the learning phase itself could play an important role in determining how later judgments are made. Recently, Kelley and Busemeyer (2008) compared how well several models could describe the learning process in various multiple-cue judgment tasks. They compared a rule-based neuronal network model with a delta-learning rule (e.g. Gluck & Bower, 1988), which can be seen as a learning version of a linear additive model with an associative connectionist network model (ALM, Busemeyer, Byun, DeLosh, & McDaniel, 1997; Busemeyer, Myung, & McDaniel, 1993). They found that the rule-based neuronal network models described the learning process best in the majority of the tasks, suggesting that learning may be relatively independent of task structure. These results are somewhat contrary to the research by Juslin et al. (2008) and von Helversen and Rieskamp (2009) on multiple-cue judgments, suggesting that humans rely on a variety of strategies, depending on the structure of the task (e.g. Juslin, et al., 2008; Rieskamp & Otto, 2006). This raises the question of whether learning depends on the task structure and what may be the mechanisms that lead to a switch in cognitive processing during learning. In this paper we investigate two reasons that may cause a shift in cognitive processing during learning in a multiple-cue judgment task. One reason to rely on different learning strategies may be that their learning performance differs depending on the structure of the task. Thus, we will Keywords: Learning; multiple-cue judgments; Compu- tational modeling Multiple-cue Judgments When judging objects on a continuous criterion such as the quality of a research paper, people often rely on multiple sources of information. For example, the clarity of the writing, the novelty of the research and the methodological precision may be used as important aspects for evaluating a paper. Several models have been developed to describe how humans solve these judgment problems. Traditionally, linear additive models have been employed to capture how humans weigh and integrate information. Social Judgment Theory (SJT; see Doherty and Kurz, 1996; Cooksey, 1996) relied on multiple-linear regression models to capture decision policies and researchers have used this approach successfully to describe judgments in many areas (see Brehmer, 1988). Similarly, Anderson (1981) suggested that humans combine information in a linear additive fashion. However, recently it has been suggested that humans may have multiple cognitive strategies available to make multiple-cue judgments. Juslin, Karlsson, and Olsson (2008) suggested that depending on the structure of the tasks, humans may switch between a rule-based cue abstraction approach and a similarity–based exemplar approach. Similarly, von Helversen and Rieskamp (2008, 2009) suggested the mapping model, a heuristic model for multiple-cue judgments, and showed that the model that was best in describing participants’ behavior depended on the task structure. More specifically, they showed that the" @default.
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• W2767688719 title "Learning in Multiple-cue Judgment Tasks" @default.
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