The flexible use of inductive and geometric spatial categories

Bayesian priors in estimates of object location in virtual reality

We investigated the category bias in spatial memory, which reveals the influence of a region (i.e., a spatial category) on memory for specific locations within the region's bounds. The standard approach to investigating the category bias employs a static dot-in-circle task, in which observers indicate the location of a single dot from memory after a brief interval. The agreement in the literature is that these location estimates result from Bayesian principles; however, the priors in the dot-in-circle task are geometric prototypes (the central angular value of each quadrant and two-thirds of the radius from the center of the circle to its circumference). These geometric prototypes are not "true" priors in that they are not pre-existing statistical likelihoods of a target's location before other evidence is considered. In this paper, we tested the category bias with items for which informative priors exist (e.g., a vase, which is expected to be in the center of a table) and found that people favor them over geometric prototypes for estimating angular but not radial target positions. Our work contributes to the literature by showing that localizing common everyday objects in a circular space is not restricted to the use of cues intrinsic to the space. This is important because the majority of the empirical data on the category bias derives from locating targets that have little to no semantic information.

Bayesian average or truncation at boundaries? The mechanisms underlying categorical bias in spatial memory

Spatial memory is often biased by various factors, such as the region a target belongs to, which can be defined based on physical, perceptual, or implicit boundaries. In the typical dot-localization task first introduced by Huttenlocher, Hedges, and Duncan (Psychological Review 98: 352-376, 1991), individuals normally divide the task space into four quadrants delineated at the Cartesian axes (forming "default categories") and show systematic bias in target localization toward the center of the category. At least two mechanisms have been proposed to account for these categorical biases, namely (a) weighted-average of a metric representation and the category prototype representation and (b) truncation of an un-biased metric representation at the category boundary. Both models can account for these findings and cannot be differentiated by existing research methods. Using a new distribution analysis, the current study sought to differentiate between these two models. Participants viewed a dot inside a circle and recalled its location after a delay either with the same blank circle (i.e., the standard dot-in-circle paradigm) or when an alternative V-shaped category boundary was visually presented at retrieval. The data from three experiments showed symmetrical distribution of the errors that shifted toward the category center when people primarily used the default category, supporting the weighted-average model. In contrast, when people primarily used the alternative category, the errors showed a highly skewed distribution, more consistent with the truncation model. Overall, these results provided the first experimental evidence for both mechanisms separately.

Spatial working memory capacity predicts bias in estimates of location

Spatial memory research has attributed systematic bias in location estimates to a combination of a noisy memory trace with a prior structure that people impose on the space. Little is known about intraindividual stability and interindividual variation in these patterns of bias. In the current work, we align recent empirical and theoretical work on working memory capacity limits and spatial memory bias to generate the prediction that those with lower working memory capacity will show greater bias in memory of the location of a single item. Reanalyzing data from a large study of cognitive aging, we find support for this prediction. Fitting separate models to individuals' data revealed a surprising variety of strategies. Some were consistent with Bayesian models of spatial category use, however roughly half of participants biased estimates outward in a way not predicted by current models and others seemed to combine these strategies. These analyses highlight the importance of studying individuals when developing general models of cognition. (PsycINFO Database Record