Bayesian priors in estimates of object location in virtual reality

Color category and inter-item interaction influence color working memory codependently

Our brains do not always encode visual information in a veridical way. Visual working memory (WM) for features such as color can be biased. WM bias comes from several sources. Category priors can lead to WM bias. For example, color WM is biased toward or away from category prototypes. In addition to category knowledge, contextual factors can induce and modulate WM bias; however, these biases of different sources have usually been investigated independently with different tasks. The present study sought to explore how color WM is influenced by both color category and concurrent distractor. Specifically, we asked participants to retain two color items in WM to investigate how the WM representation of the target color is biased by learned category knowledge and contextual inter-item interactions. Our study found that the WM representation of the target color is biased toward or away from the category prototypes and away from the distractor color that is simultaneously held in WM, indicating that both color category and concurrent distractor bias color WM. More importantly, the weight of these two biases depends on the specific color category, suggesting that category priors and inter-item interaction biases are not simply additive but flexible. Furthermore, we revealed that both types of biases arise from perceptual processes.

Relative cue precision and prior knowledge contribute to the preference of proximal and distal landmarks in human orientation

A prevailing argument posits that distal landmarks dominate over proximal landmarks as orientation cues. However, no studies have tested this argument or examined the underlying mechanisms. This project aimed to close this gap by examining the roles of relative cue precision and prior knowledge in cue preference. Participants learned object locations with proximal and distal landmarks in an immersive virtual environment. After walking a path without seeing objects or landmarks, participants disoriented themselves by spinning in place and pointed to the objects with the reappearance of a proximal landmark being rotated -50°, a distal landmark being rotated 50°, or both (Conflict). Heading errors were examined. Experiment 1 manipulated the relative cue precision. Results showed that in Conflict condition, the observed weight on the distal cue (exceeding 0.5) changed with but remained higher than the weight predicted by the relative cue precision. This indicates that besides the relative cue precision, prior knowledge of distal cue dominance also influences orientation cue usage. In Experiments 2 and 3, participants walked a path stopping at one object location. Participants were informed of it explicitly in Experiment 2 but not in Experiment 3. Results showed that distal cue dominance still occurred in Experiment 3. However, in Experiment 2, proximal cue dominance appeared, and it was not predicted by the relative cue precision. These results suggest that prior knowledge of proximal cue dominance might have been invoked by the instruction of locations. Consistent with the Bayesian inference model, human cue usage in orientation is determined by relative cue precision and prior knowledge. The choice of prior knowledge can be influenced by instructions.

Bayesian decision theory and navigation

Spatial navigation is a complex cognitive activity that depends on perception, action, memory, reasoning, and problem-solving. Effective navigation depends on the ability to combine information from multiple spatial cues to estimate one's position and the locations of goals. Spatial cues include landmarks, and other visible features of the environment, and body-based cues generated by self-motion (vestibular, proprioceptive, and efferent information). A number of projects have investigated the extent to which visual cues and body-based cues are combined optimally according to statistical principles. Possible limitations of these investigations are that they have not accounted for navigators' prior experiences with or assumptions about the task environment and have not tested complete decision models. We examine cue combination in spatial navigation from a Bayesian perspective and present the fundamental principles of Bayesian decision theory. We show that a complete Bayesian decision model with an explicit loss function can explain a discrepancy between optimal cue weights and empirical cues weights observed by (Chen et al. Cognitive Psychology, 95, 105-144, 2017) and that the use of informative priors to represent cue bias can explain the incongruity between heading variability and heading direction observed by (Zhao and Warren 2015b, Psychological Science, 26[6], 915-924). We also discuss (Petzschner and Glasauer's , Journal of Neuroscience, 31(47), 17220-17229, 2011) use of priors to explain biases in estimates of linear displacements during visual path integration. We conclude that Bayesian decision theory offers a productive theoretical framework for investigating human spatial navigation and believe that it will lead to a deeper understanding of navigational behaviors.