Competitive retrieval strategy causes multimodal response distributions in multiple-cue judgments.

Abstract

Research on quantitative judgments from multiple cues suggests that judgments are simultaneously influenced by previously abstracted knowledge about cue-criterion relations and memories of past instances (or exemplars). Yet extant judgment theories leave 2 questions unanswered: (a) How are past exemplars and abstracted cue knowledge combined to form a judgment? (b) Are all past exemplars retrieved from memory to form the judgment (integrative retrieval) or is the judgment based on one exemplar (competitive retrieval)? To address these questions we propose and test a new model, CX-COM (combining Cue abstraction with eXemplar memory assuming COMpetitive memory retrieval). In a first step, CX-COM recalls only a single exemplar from memory. In a second step, the initially retrieved judgment is adjusted based on abstracted cue knowledge. Qualitatively, we show that CX-COM naturally captures judgment patterns that have been previously attributed to multiple strategies. Next, we tested CX-COM quantitatively in 2 experiments and found that it accounts well for people's judgment behavior. In the second experiment we additionally tested 2 qualitative predictions of CX-COM: The existence of multimodal response distributions within participants and systematic variability in judgments depending on the distance between similar exemplars in memory. The empirical results confirm CX-COM's assumptions. In sum, the evidence suggests that CX-COM is a viable new model for quantitative judgments and shows the importance of considering judgment variability in addition to average responses in judgment research.

Exemplar-based judgment or direct recall: On a problematic procedure for estimating parameters in exemplar models of quantitative judgment

“…4 It should be noted that a recall probability of 1 is what most studies aim for when applying an extensive training phase. Also, as most exemplar models are based on the assumption that all exemplars and their corresponding criterion values are remembered correctly and are all used in the subsequent judgment process (cf., Nosofsky & Palmeri, 1997;Albrecht et al, 2019), participants should learn all exemplars correctly. Note also that we added no additional error to the generated judgment data, so in principle one would expect perfect parameter recovery.…”

“…Equation 3is the extension of the context model (Medin & Schaffer, 1978) from binary to a continuous criterion as suggest by Juslin et al (2003;see also Elliot & Anderson, 1995;Juslin & Persson, 2002). It involves many simplifying assumptions, such as not directly modeling the exemplar retrieval process (cf., the EBRW model of Nosofsky & Palmeri, 1997), assuming that all exemplars are used when making a judgment (cf., Nosofsky & Palmeri, 1997;Albrecht, Hoffmann, Pleskac, Rieskamp, & von Helversen, 2019), and that all exemplars, their cues, and their criterion values are remembered and recalled without error. However, a detailed modeling of the recall and retrieval process is not intended with this model as it is used in the multiplecue judgment literature, since it is mainly used as a tool to classify rule-and exemplar-based processes of judgments.…”

“…In the alternative case if there is no strong representation of a perfectly matching exemplar (the exemplar has not been overlearned, yet, or the probe is new), the similarity-weighted reconstruction as described in the original exemplar model takes place. Hence, all exemplars are recalled (cf., Nosofsky & Palmeri, 1997;Albrecht et al, 2019), and the judgment is built buy the similarity-weighted averaging process, for ease of differentiation, we will henceforth simply call this second process "judgment". Of course, this judgment is also based on the recall of the exemplars.…”

When the eyes have it and when not: How multiple sources of activation combine to guide eye movements during multiattribute decision making.

“…Memory processes have been investigated in a variety of decision and judgments tasks (e.g., Albrecht et al, 2020; Bröder et al, 2010; Erickson & Kruschke, 1998; Hoffmann et al, 2013, 2014, 2016; Juslin, Jones, et al, 2003; Juslin et al, 2008; Juslin, Olsson, et al, 2003; Medin & Schaffer, 1978; Nosofsky, 1988; Nosofsky & Palmeri, 1997; Olsson et al, 2006; Persson & Rieskamp, 2009; Rieskamp & Otto, 2006; Rouder & Ratcliff, 2004; von Helversen & Rieskamp, 2008, 2009). The influence of memory in these tasks is frequently described by exemplar-based accounts (Hahn & Chater, 1998; Pothos, 2005).…”

The similarity-updating model of probability judgment and belief revision.

“…This belief-updating mechanism can be seen as an instance of an anchoring-and-adjustment process (Chapman & Johnson, 2002;Epley & Gilovich, 2001, 2006Hogarth & Einhorn, 1992;Tversky & Kahneman, 1974). Anchoring-and-adjustment models have recently been discussed as a resource-efficient way to combine the result of different cognitive functions (Albrecht et al, 2020;Lieder et al, 2018;Millroth et al, 2019). In our model, people average the probabilities of two sequentially presented pieces of evidence (Hogarth & Einhorn, 1992) but then adjust the probability as a result of a similarity bias.…”