- Title Information
- Title
- Cognitive and Computational Accounts of Adaptive Inference in Dynamic Social Networks
- Type of Resource (primo)
- dissertations
- Name:
Personal
- Name Part
- Xia, Alice
- Role
- Role Term:
Text
- creator
- Name:
Personal
- Name Part
- FeldmanHall, Oriel
- Role
- Role Term:
Text
- Advisor
- Name:
Personal
- Name Part
- Frank, Michael
- Role
- Role Term:
Text
- Reader
- Name:
Personal
- Name Part
- Levari, David
- Role
- Role Term:
Text
- Reader
- Name:
Corporate
- Name Part
- Brown University. Department of Cognitive, Linguistic, and Psychological Sciences
- Role
- Role Term:
Text
- sponsor
- Origin Information
- Copyright Date
- 2026
- Physical Description
- Extent
- xiii, 193 p.
- digitalOrigin
- born digital
- Note:
thesis
- Thesis (Ph. D.)--Brown University, 2026
- Genre (aat)
- theses
- Abstract
- Social success depends not only on who we interact with in the moment, but also on the many connections those individuals have within the community. When deciding whether to share sensitive information, for example, we often anticipate how it might travel through others—gauging the risks of diffusion across the broader network. How do people represent social networks, and how do those representations guide social inference and behavior?
Across three chapters, in both artificial and real-world environments, I show that a representational format capturing patterns of indirect connectivity underlies adaptive inference. Specifically, a representation that encodes global topology via accumulated indirect paths (Katz communicability) best explains strategic communication behavior and accurate predictions about which relationships form and dissolve up to six months into the future, providing converging evidence that knowledge of global network topology meets the cognitive demands of different inferential problems with important social consequences.
At the same time, indirect connectivity does not capture everything people know about relational structure. Relational asymmetry—cases in which two individuals hold divergent views of their connection—contains information that global connectivity alone cannot explain. I show that asymmetric ties are prevalent yet volatile, resolving toward symmetry over time, and that relational asymmetry shapes how the broader community reasons about the network. Together, these findings reframe social network cognition as active and adaptive—and reveal that the relationship between mental representations and network structure is not one-way: how people model their social world shapes how that world changes.
- Subject (fast)
(authorityURI="http://id.worldcat.org/fast", valueURI="http://id.worldcat.org/fast/00866477")
- Topic
- Cognition--Social aspects
- Subject
- Topic
- Social Judgment
- Subject (fast)
(authorityURI="http://id.worldcat.org/fast", valueURI="http://id.worldcat.org/fast/01122709")
- Topic
- Social perception
- Subject (fast)
(authorityURI="http://id.worldcat.org/fast", valueURI="http://id.worldcat.org/fast/01122816")
- Topic
- Social psychology
- Subject (fast)
(authorityURI="http://id.worldcat.org/fast", valueURI="http://id.worldcat.org/fast/01122678")
- Topic
- Social networks
- Subject (fast)
(authorityURI="http://id.worldcat.org/fast", valueURI="http://id.worldcat.org/fast/00889067")
- Topic
- Decision making--Social aspects
- Language
- Language Term (ISO639-2B)
- English
- Record Information
- Record Content Source (marcorg)
- RPB
- Record Creation Date
(encoding="iso8601")
- 20260516