An official website of the United States government
A ubiquitous type of collective behavior and decision-making is the coordinated motion of bird flocks, fish schools, and human crowds. Collective decisions to move in the same direction, turn right or left, or split into subgroups arise in a self-organized fashion from local interactions between individuals without central plans or designated leaders. Strikingly similar phenomena of consensus (collective motion), clustering (subgroup formation), and bipolarization (splitting into extreme groups) are also observed in opinion formation. As we developed models of crowd dynamics and analyzed crowd networks, we found ourselves going down the same path as models of opinion dynamics in social networks. In this article, we draw out the parallels between human crowds and social networks. We show that models of crowd dynamics and opinion dynamics have a similar mathematical form and generate analogous phenomena in multiagent simulations. We suggest that they can be unified by a common collective dynamics, which may be extended to other psychological collectives. Models of collective dynamics thus offer a means to account for collective behavior and collective decisions without appealing to a priori mental structures.
more »
« less
From physics to social interactions: Scientific unification via dynamics.
The principle of dynamical similitude—the belief that the same behavior may be exhibited by very different systems—allows us to use mathematical models from physics to understand psychological phenomena. Sometimes, model choice is straightforward. For example, the two-frequency resonance map can be used to make predictions about the performance of multifrequency ratios in phys- ical, chemical, physiological and social behavior. Sometimes, we have to dig deeper into our dynamical toolbox to select an appro- priate technique. An overview is provided of other methods, including mass-spring modeling and multifractal analysis, that have been applied successfully to various psychological phenomena. A final demonstration of dynamical similitude comes from the use of the same multifractal method that was used to extract team-level experience from the neurophysiological data of individual team members to the analysis of a large scale economic phenomenon, the stock market index. Continual development of analytical methods that are informed by and can be applied to other sciences allows us to treat psychological phenomena as continuous with the rest of the natural world.
more »
« less
- Award ID(s):
- 1255922
- PAR ID:
- 10085377
- Date Published:
- Journal Name:
- Cognitive systems research
- Volume:
- 52
- ISSN:
- 1389-0417
- Page Range / eLocation ID:
- 640-657.
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
More Like this
-
-
Psychological safety and turn-taking have both been listed as key factors needed for collaboration in teams to emerge. Specifically, prior work has shown that increased communication in teams can lead to high psychological safety. Prior work on turn-taking as a measure of communication has mostly focused on its inclusivity in a team rather than its frequency. While the gender composition of the team can impact both participation as well as team psychological safety, there is a lack of research at the individual level. As such, this study provides the first attempt at connecting turn-taking, gender, and psychological safety through the analysis of members of fifteen engineering design student teams during the concept generation stage of their project. Specifically, we gathered video data to study how the number of turns and self- turns in a team impact psychological safety at both the individual and the team levels. We also examined how gender impacts participation and individual perceptions of psychological safety. The results found that turns and self-turns have a significant positive impact on an individual’s perception of the team’s psychological safety. However, no such relationship was found at the team level, indicating that there may be additional underlying factors in team level psychological safety. While we found that gender did not impact individual turn-taking, it did affect an individual’s perception of their psychological safety. These results provide quantitative evidence of the role of team communication on psychological safety.more » « less
-
Gait complexity is widely used to understand risk factors for injury, rehabilitation, the performance of assistive devices, and other matters of clinical interest. We analyze the complexity of out-of-the-lab locomotion activities via measures that have previously been used in gait analysis literature, as well as measures from other domains of data analysis. We categorize these broadly as quantifying either the intrinsic dimensionality, the variability, or the regularity, periodicity, or self-similarity of the data from a nonlinear dynamical systems perspective. We perform this analysis on a novel full-body motion capture dataset collected in out-of-the-lab conditions for a variety of indoor environments. This is a unique dataset with a large amount (over 24 h total) of data from participants behaving without low-level instructions in out-of-the-lab indoor environments. We show that reasonable complexity measures can yield surprising, and even profoundly contradictory, results. We suggest that future complexity analysis can use these guidelines to be more specific and intentional about what aspect of complexity a quantitative measure expresses. This will become more important as wearable motion capture technology increasingly allows for comparison of ecologically relevant behavior with lab-based measurements.more » « less
-
Abstract Recent years have seen a surge of exciting developments in the computational tools available to social neuroscientists. This paper highlights and synthesizes recent advances that have been enabled by the application of such tools, as well as methodological innovations likely to be of interest and utility to social neuroscientists, but that have been concentrated in other sub-fields. Papers in this special issue are emphasized—many of which contain instructive materials (e.g. tutorials and code) for researchers new to the highlighted methods. These include approaches for modeling social decisions, characterizing multivariate neural response patterns at varying spatial scales, using decoded neurofeedback to draw causal links between specific neural response patterns and psychological and behavioral phenomena, examining time-varying patterns of connectivity between brain regions, and characterizing the social networks in which social thought and behavior unfold in everyday life. By combining computational methods for characterizing participants’ rich social environments—at the levels of stimuli, paradigms and the webs of social relationships that surround people—with those for capturing the psychological processes that undergird social behavior and the wealth of information contained in neuroimaging datasets, social neuroscientists can gain new insights into how people create, understand and navigate their complex social worlds.more » « less
-
Neural dynamics can reflect intrinsic dynamics or dynamic inputs, such as sensory inputs or inputs from other brain regions. To avoid misinterpreting temporally structured inputs as intrinsic dynamics, dynamical models of neural activity should account for measured inputs. However, incorporating measured inputs remains elusive in joint dynamical modeling of neural-behavioral data, which is important for studying neural computations of behavior. We first show how training dynamical models of neural activity while considering behavior but not input or input but not behavior may lead to misinterpretations. We then develop an analytical learning method for linear dynamical models that simultaneously accounts for neural activity, behavior, and measured inputs. The method provides the capability to prioritize the learning of intrinsic behaviorally relevant neural dynamics and dissociate them from both other intrinsic dynamics and measured input dynamics. In data from a simulated brain with fixed intrinsic dynamics that performs different tasks, the method correctly finds the same intrinsic dynamics regardless of the task while other methods can be influenced by the task. In neural datasets from three subjects performing two different motor tasks with task instruction sensory inputs, the method reveals low-dimensional intrinsic neural dynamics that are missed by other methods and are more predictive of behavior and/or neural activity. The method also uniquely finds that the intrinsic behaviorally relevant neural dynamics are largely similar across the different subjects and tasks, whereas the overall neural dynamics are not. These input-driven dynamical models of neural-behavioral data can uncover intrinsic dynamics that may otherwise be missed.more » « less
- Free Publicly Accessible Full Text
-
Accepted Manuscript1.0
- Journal Article:
- https://doi.org/https://doi.org/10.1016/j.cogsys.2018.07.033
