Mnemonic convergence in social networks: The emergent properties of cognition at a collective level

Abstract

The development of shared memories, beliefs, and norms is a fundamental characteristic of human communities. These emergent outcomes are thought to occur owing to a dynamic system of information sharing and memory updating, which fundamentally depends on communication. Here we report results on the formation of collective memories in laboratory-created communities. We manipulated conversational network structure in a series of real-time, computer-mediated interactions in fourteen 10-member communities. The results show that mnemonic convergence, measured as the degree of overlap among community members' memories, is influenced by both individual-level information-processing phenomena and by the conversational social network structure created during conversational recall. By studying laboratory-created social networks, we show how large-scale social phenomena (i.e., collective memory) can emerge out of microlevel local dynamics (i.e., mnemonic reinforcement and suppression effects). The social-interactionist approach proposed herein points to optimal strategies for spreading information in social networks and provides a framework for measuring and forging collective memories in communities of individuals.mnemonic reinforcement effect | socially-shared retrieval-induced forgetting | social networks | collective memories | emergent phenomena

SAMPL: The Spreading Activation and Memory PLasticity Model

“…Methods Memory enhancement and forgetting effects including retrieval-induced forgetting and the context repetition effect have been observed in studies of dyadic as well as multi-agent conversation 10,12,13 . We assessed whether SAMPL can simulate the synchronization of remembered and forgotten items in conversational networks 10,12 . Before testing the model in a network of multi-agent simulations, we conducted a dyadic agent-based modeling study.…”

“…However, this model has not been shown to explain the enhancement of related but unpracticed items, is computationally expensive, and has so far only been tested on small memory networks. Another proposed explanation of RIF is the non-monotonic plasticity hypothesis (NMPH) 10 . This approach applies a "rich get richer, poor get poorer" update rule that shapes activation dynamics, enhancing associations between strongly activated items and weakening associations between moderately activated items, whether or not they were practiced.…”

“…We validate SAMPL by comparing its output to human behavior in simulations of human memory experiments 2,9,10 . We simulate three representative memory phenomena: retrieval-induced forgetting, the context repetition effect, and memory alignment in multi-person conversational networks.…”

How Facebook, fake news and friends are warping your memory

“…Although people in the single cluster agreed on the same set of information, says Coman, those in the two sub-clusters generally converged on different 'facts' about the fictional volunteers 5 . This effect is evident in real-world situations.…”

Editors’ Introduction: Remembering With Others: Conversational Dynamics and Mnemonic Outcomes

“…For example, communities comprising 10 individuals arrived at a collective memory with fewer conversations than communities of 50 or 100 individuals. However, if larger communities have more connections, this may facilitate the emergence of a shared rendering of the past (see Coman, Momennejad, Drach, & Geana, for similar results) as well.…”