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Is social decision making for close others consistent across domains and within individuals?
Humans make decisions across a variety of social contexts. Though social decision-making research has blossomed in recent decades, surprisingly little is known about whether social decision-making preferences are consistent across different domains. We conducted an exploratory study in which participants made choices about 2 types of close others: parents and friends. To elicit decision making preferences, we pit the interests in parents and friends against one another. To assess the consistency of preferences for close others, decision making was assessed in three domains-risk taking, probabilistic learning, and self-other similarity judgments. We reasoned that if social decision-making preferences are consistent across domains, participants ought to exhibit the same preference in all three domains (i.e., a parent preference, based on prior work), and individual differences in preference magnitude ought to be conserved across domains within individuals. A combination of computational modeling, random coefficient regression, and traditional statistical tests revealed a robust parent-over-friend preference in the risk taking and probabilistic learning domains but not the self-other similarity domain. Preferences for parent-over-friend in the risk-taking domain were strongly associated with similar preferences in the probabilistic learning domain but not the self-other similarity domain. These results suggest that distinct and dissociable value-based and social-cognitive computations underlie social decision making. (PsycInfo Database Record (c) 2020 APA, all rights reserved)
Structured Knowledge Representation for Image Retrieval
We propose a structured approach to the problem of retrieval of images by
content and present a description logic that has been devised for the semantic
indexing and retrieval of images containing complex objects. As other
approaches do, we start from low-level features extracted with image analysis
to detect and characterize regions in an image. However, in contrast with
feature-based approaches, we provide a syntax to describe segmented regions as
basic objects and complex objects as compositions of basic ones. Then we
introduce a companion extensional semantics for defining reasoning services,
such as retrieval, classification, and subsumption. These services can be used
for both exact and approximate matching, using similarity measures. Using our
logical approach as a formal specification, we implemented a complete
client-server image retrieval system, which allows a user to pose both queries
by sketch and queries by example. A set of experiments has been carried out on
a testbed of images to assess the retrieval capabilities of the system in
comparison with expert users ranking. Results are presented adopting a
well-established measure of quality borrowed from textual information
retrieval
Learning quadrangulated patches for 3D shape parameterization and completion
We propose a novel 3D shape parameterization by surface patches, that are
oriented by 3D mesh quadrangulation of the shape. By encoding 3D surface detail
on local patches, we learn a patch dictionary that identifies principal surface
features of the shape. Unlike previous methods, we are able to encode surface
patches of variable size as determined by the user. We propose novel methods
for dictionary learning and patch reconstruction based on the query of a noisy
input patch with holes. We evaluate the patch dictionary towards various
applications in 3D shape inpainting, denoising and compression. Our method is
able to predict missing vertices and inpaint moderately sized holes. We
demonstrate a complete pipeline for reconstructing the 3D mesh from the patch
encoding. We validate our shape parameterization and reconstruction methods on
both synthetic shapes and real world scans. We show that our patch dictionary
performs successful shape completion of complicated surface textures.Comment: To be presented at International Conference on 3D Vision 2017, 201
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