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Published online: 15 April 2016
© Psychonomic Society, Inc. 2016
Abstract Previous work demonstrates that memory for simple stimuli can be biased by information about the distribution of which the stimulus is a member. Specifically, people underestimate values greater than the distribution's average and overestimate values smaller than the average. This is referred to as the central tendency bias. This bias has been explained as an optimal use of both noisy sensory information and category information. In largely separate literature, cognitive load (CL) experiments attempt to manipulate the available working memory of participants in order to observe the effect on choice or judgments. In two experiments, we demonstrate that participants under high cognitive load exhibit a stronger central tendency bias than when under a low cognitive load. Although not anticipated at the outset, we also find that judgments exhibit an anchoring bias not described previously.
Keywords Judgment · Memory · Anchoring · Working memory · Cognitive constraints · Cognitive load
Memory is an essential function, yet a large body of research suggests that memory exhibits systematic biases. One wellknown bias is central tendency, when individuals remember stimuli as being more typical of the category of which they are members (Goldstone, 1994). Once considered a perceptual or mnemonic distortion (e.g., Poulton, 1989), this bias has been described as resulting from an adaptive, Bayesian process that combines inexact memories of individual stimuli with prior knowledge about the distribution of the category. Combining information in this manner improves the average accuracy ofjudgments, even though it introduces bias into individual estimates.
Huttenlocher and colleagues (Crawford, Huttenlocher, & Engebretson, 2000; Crawford, Huttenlocher, & Hedges, 2006; Duffy, Huttenlocher, & Crawford, 2006; Duffy, Huttenlocher, Hedges, & Crawford, 2010; Huttenlocher, Hedges, & Vevea, 2000) proposed the category adjustment model (CAM). In it, categories are summarized as distributions of values along some stimulus dimension, such as size or shape, and a stimulus as a particular value along this dimension. For most categories, the average value of the distribution is the prototypical value (Duffy & Crawford, 2008).
The CAM is similar to other Bayesian models that have been used to explain biases in memory for size estimation (Ashourian & Loewenstein, 2011), time perception (Jazayeri & Shadlen, 2010), and hue bias (Olkkonen & Allred, 2014;...