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| title | chunk | source | category | tags | date_saved | instance |
|---|---|---|---|---|---|---|
| Cognitive social structures | 2/3 | https://en.wikipedia.org/wiki/Cognitive_social_structures | reference | science, encyclopedia | 2026-05-05T13:46:59.963804+00:00 | kb-cron |
==== Locally Aggregated Structures (LAS) ==== One approach to studying a cognitive social structure is to measure each member's direct connections (ego networks). In other words, ask each individual to report who they are related to (e.g. friends with) in the network. That is, consider the individual's own relationships. Then connect all of these local networks to form the whole network. Building a network model in this way can be achieved by asking individuals who they relate to (e.g. “Who do you go to for advice?”), known as a Row Dominated Locally Aggregated Structure, or who is related to them (e.g. “Who goes to you for advice?”), known as a Column Dominated Locally Aggregated Structure. There are two ways to combine local structures into the full locally aggregated structure. Taking the intersection of the local networks results in a network in which relations exist if both members perceive it. In a friendship network, this means that a friendship only exists if both people consider the other a friend. The union of the local networks is a structure consisting of relations that at least one involved member perceives. In a friendship network, this is equivalent to saying a friendship exists between two people if at least one of them considers the other a friend.
==== Consensus Structures ==== The consensus structure of a cognitive social structure is the network that more than a certain number of people perceive. This is accomplished by fixing a threshold such that a relation is said to exist if and only if the proportion of members who perceive that relation is greater than the threshold. For example, setting the threshold to 0.5 means that the relation between two people exists if the majority of people believe it exists. Similarly, a relation between two people does not exist if a majority of people do not perceive it.
== Empirical Findings == Many species are able to represent social structures, yet humans are able to represent disproportionately large social structures (based on cortical thickness in the brain). Research suggests that this is, at least in part, due to the use of schemas. Schemas are a pre-established method of organizing and perceiving the world. Similar to a template, schemas provide a basic scaffolding that allow humans to make assumptions about a social structure without remembering every detail individually. This preserves neural resources, allowing for representation of larger structures. Some research suggests that a basic schema people utilize is based on small-world network properties. Namely, one tends to believe that their social network contains groups of people who are highly intraconnected, and that these groups, or clusters, are connected via short paths. Other work suggests that this is particularly true of one's own group, but not for others. For example, Alice likely believes that all of her friends are friends with each other, but other groups are not as connected. In addition to making network representation efficient, schemas, as well as other biases, lead to systematic errors in network perception. These errors in individual and group perceptions has been the focus of much of the research related to cognitive social structures. In research, a typical method of measuring cognitive social structures involves
(a) listing one’s direct ties, either in general or in terms of whom they’ve interacted with recently, or (b) completing a table that lists all members in the rows and columns by checking off whom everyone is connected to.
=== Learning Networks === Specific factors have been shown to influence how easily and how well people are able to learn new networks. As discussed above, people use schemas to represent networks. It makes sense, therefore, that structures that are consistent with these schemas are easier to learn. Specifically, behavioral research suggests that individuals are better at learning networks that group members by positive relations (e.g. "liking") and divide groups by negative relations (e.g. "disliking"), individuals are better able to learn people who are at the extremes of a hierarchy, rather than in the middle, and larger networks are easier to remember if they are balanced (if one person is friends with two others, than those two are also friends). People are also better at remembering large networks if they include kin labels (e.g. "Mother", "Uncle", "Cousin", etc.) than if they do not.