Abstract
An essential function of the human visual system is to locate objects in space and navigate the environment. Due to limited resources, the visual system achieves this by combining imperfect sensory information with a belief state about locations in a scene, resulting in systematic distortions and biases. These biases can be captured by a Bayesian model in which internal beliefs are expressed in a prior probability distribution over locations in a scene. We introduce a paradigm that enables us to measure these priors by iterating a simple memory task where the response of one participant becomes the stimulus for the next. This approach reveals an unprecedented richness and level of detail in these priors, suggesting a different way to think about biases in spatial memory. A prior distribution on locations in a visual scene can reflect the selective allocation of coding resources to different visual regions during encoding ("efficient encoding"). This selective allocation predicts that locations in the scene will be encoded with variable precision, in contrast to previous work that has assumed fixed encoding precision regardless of location. We demonstrate that perceptual biases covary with variations in discrimination accuracy, a finding that is aligned with simulations of our efficient encoding model but not the traditional fixed encoding view. This work demonstrates the promise of using nonparametric data-driven approaches that combine crowdsourcing with the careful curation of information transmission within social networks to reveal the hidden structure of shared visual representations.
Highlights
We found that our KDE results were not highly correlated with explicit measures of image regions obtained using a recent behavioral patch rating procedure known to be predictive of overt attention [36, 37] (SI Appendix, SI Text and Fig. S19 have details). These results suggest that overt attention only explains part of the variability in spatial memory priors, we cannot completely rule out that unique eye movement patterns specific to our task could be mediating precision and bias, despite the fact that our noise masking experiments suggest otherwise
We show that using the data from the last iteration of the serial reproduction experiments can predict the rate of convergence and the dynamics of all previous iterations and in the case of efficient encoding, predicts the positive correlations between discrimination results and priors estimated from the serial reproduction experiment (Fig. 3)
Exploring spatial memory biases using serial reproduction demonstrates that the study of shared perceptual representations can be approached by recasting experimentation as algorithm design and through the lens of information transmission inside carefully curated social networks
Summary
KDEs were computed using the data from the last iteration of the chains. We computed a Gaussian kernel centered at the point with a diagonal covariance matrix. We set the kernel width to a conservative value of 0.025 for shapes and 0.040 for natural images. These values were chosen based on the ranges of the estimates obtained from the unregularized nonparametric kernels. The final KDE was calculated by summing all of the Gaussian kernels and normalizing. Results of this procedure were used for all statistical analys
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
