Automated monitoring of behavior reveals bursty interaction patterns and rapid spreading dynamics in honeybee social networks.

Social networks mediate the spread of information and disease. The dynamics of spreading depends, among other factors, on the distribution of times between successive contacts in the network. Heavy-tailed (bursty) time distributions are characteristic of human communication networks, including face-to-face contacts and electronic communication via mobile phone calls, email, and internet communities. Burstiness has been cited as a possible cause for slow spreading in these networks relative to a randomized reference network. However, it is not known whether burstiness is an epiphenomenon of human-specific patterns of communication. Moreover, theory predicts that fast, bursty communication networks should also exist. Here, we present a high-throughput technology for automated monitoring of social interactions of individual honeybees and the analysis of a rich and detailed dataset consisting of more than 1.2 million interactions in five honeybee colonies. We find that bees, like humans, also interact in bursts but that spreading is significantly faster than in a randomized reference network and remains so even after an experimental demographic perturbation. Thus, while burstiness may be an intrinsic property of social interactions, it does not always inhibit spreading in real-world communication networks. We anticipate that these results will inform future models of large-scale social organization and information and disease transmission, and may impact health management of threatened honeybee populations.

The importance of formative assessment in science and engineering ethics education: some evidence and practical advice.

Recent research in ethics education shows a potentially problematic variation in content, curricular materials, and instruction. While ethics instruction is now widespread, studies have identified significant variation in both the goals and methods of ethics education, leaving researchers to conclude that many approaches may be inappropriately paired with goals that are unachievable. This paper speaks to these concerns by demonstrating the importance of aligning classroom-based assessments to clear ethical learning objectives in order to help students and instructors track their progress toward meeting those objectives. Two studies at two different universities demonstrate the usefulness of classroom-based, formative assessments for improving the quality of students' case responses in computational modeling and research ethics.

Escape from fraught states in a coordination game.

Through a behavioural coordination game played by groups of humans and simulated with agent-based models, we investigated a social network dilemma that we call fraughtness. Seven players, connected to one another in various topologies via a computer network, each had to move a slider to the left or right along a horizontal bar on their screen. The goal was for all the players to move their slider to the same side. Players received feedback indicating the degree to which they and their neighbours agreed about the choice of side. When the topology had a hierarchical branching structure, the groups often got stuck in fraughtness: players on one branch favoured one side, while players on the other branch favoured the other; because all were receiving supportive local feedback, nobody wanted to change. Nevertheless, after being stuck in fraughtness for some time, most groups managed to escape it. Fraughtness is arguably an analog of generally negatively viewed social phenomena like polarization and echo chambers. Our analyses suggest that while fraughtness is problematic, it is closely linked to successful structure formation-it thus may be most effective to focus not on how to banish it, but on how to resolve it.

Observations on the responsible development and use of computational models and simulations.

Most previous works on responsible conduct of research have focused on good practices in laboratory experiments. Because computation now rivals experimentation as a mode of scientific research, we sought to identify the responsibilities of researchers who develop or use computational modeling and simulation. We interviewed nineteen experts to collect examples of ethical issues from their experiences in conducting research with computational models. We gathered their recommendations for guidelines for computational research. Informed by these interviews, we describe the respective professional responsibilities of developers and users of computational models in research. In particular, we examine whether developers should disclose the full computational codes, and we explain how developers and users should minimize harms from improper uses of models.

Lifetime-preserving reference models for characterizing spreading dynamics on temporal networks.

To study how a certain network feature affects processes occurring on a temporal network, one often compares properties of the original network against those of a randomized reference model that lacks the feature in question. The randomly permuted times (PT) reference model is widely used to probe how temporal features affect spreading dynamics on temporal networks. However, PT implicitly assumes that edges and nodes are continuously active during the network sampling period - an assumption that does not always hold in real networks. We systematically analyze a recently-proposed restriction of PT that preserves node lifetimes (PTN), and a similar restriction (PTE) that also preserves edge lifetimes. We use PT, PTN, and PTE to characterize spreading dynamics on (i) synthetic networks with heterogeneous edge lifespans and tunable burstiness, and (ii) four real-world networks, including two in which nodes enter and leave the network dynamically. We find that predictions of spreading speed can change considerably with the choice of reference model. Moreover, the degree of disparity in the predictions reflects the extent of node/edge turnover, highlighting the importance of using lifetime-preserving reference models when nodes or edges are not continuously present in the network.

Birth of an abstraction: a dynamical systems account of the discovery of an elsewhere principle in a category learning task.

Human participants and recurrent ("connectionist") neural networks were both trained on a categorization system abstractly similar to natural language systems involving irregular ("strong") classes and a default class. Both the humans and the networks exhibited staged learning and a generalization pattern reminiscent of the Elsewhere Condition (Kiparsky, 1973). Previous connectionist accounts of related phenomena have often been vague about the nature of the networks' encoding systems. We analyzed our network using dynamical systems theory, revealing topological and geometric properties that can be directly compared with the mechanisms of non-connectionist, rule-based accounts. The results reveal that the networks "contain" structures related to mechanisms posited by rule-based models, partly vindicating the insights of these models. On the other hand, they support the one mechanism (OM), as opposed to the more than one mechanism (MOM), view of symbolic abstraction by showing how the appearance of MOM behavior can arise emergently from one underlying set of principles. The key new contribution of this study is to show that dynamical systems theory can allow us to explicitly characterize the relationship between the two perspectives in implemented models.

Characterization of spatiotemporally complex gait patterns using cross-correlation signatures.

We hypothesize that spatiotemporal joint coupling patterns during gait are closely associated with musculoskeletal injury mechanics. Previous studies examining joint coupling, have primarily focused on coupling between single pairs of neighboring body segments or joints; thus falling short of characterizing the full spatiotemporal complexity across the entire gait apparatus. This study proposes the reliance on properties of the temporal cross-correlation of distinct joint variables as a means to characterize and detect differences in multiple segmental coupling pairs and to quantify how these couplings change between different gait conditions or test groups. In particular, for each subject, a characteristic diagram array is obtained whose entries include the maximum values of the cross-correlation between all pairs of joint variables as well as the associated phase shifts at which these maxima are recorded. Paired t-tests are then used to highlight significant differences in the corresponding entries between two gait conditions. In the present study, this technique was applied to angular displacement and velocity histories across 12 lower extremity joint variables, for healthy subjects with and without a brace on the right knee. As expected, the statistical analysis indicated that the temporal cross-correlations associated with the right knee-angle variables differed the most between the two gait conditions. In addition, significant differences (p<0.01) were found in the coupling between other pairs of joint variables, establishing a characteristic spatiotemporal signature for the changes from normative gait that result from reduced mobility at the knee.

A rigorous dynamical-systems-based analysis of the self-stabilizing influence of muscles.

In this paper, dynamical systems analysis and optimization tools are used to investigate the local dynamic stability of periodic task-related motions of simple models of the lower-body musculoskeletal apparatus and to seek parameter values guaranteeing their stability. Several muscle models incorporating various active and passive elements are included and the notion of self-stabilization of the rigid-body dynamics through the imposition of musclelike actuation is investigated. It is found that self-stabilization depends both on muscle architecture and configuration as well as the properties of the reference motion. Additionally, antagonistic muscles (flexor-extensor muscle couples) are shown to enable stable motions over larger ranges in parameter space and that even the simplest neuronal feedback mechanism can stabilize the repetitive motions. The work provides a review of the necessary concepts of stability and a commentary on existing incorrect results that have appeared in literature on muscle self-stabilization.

Nonlinear dynamics as an essential tool for non-destructive characterization of soft nanostructures using tapping-mode atomic force microscopy.

Tapping-mode atomic force microscopy provides a means for successful and non-intrusive characterization of soft physical and biological structures at the nanoscale. Its full potential can only be realized, provided that the response of the oscillating probe tip to the strongly nonlinear, near-field force interactions with the structure and the intermittency of contact can be accurately modelled, analysed, controlled and interpreted. To this end, this paper reviews some experimental observations of fundamentally nonlinear behaviour of the tip dynamics. It discusses the nonlinear phenomenology that explains their presence in the tapping-mode operation of the atomic force microscope. Particular emphasis is placed on the coexistence of different steady-state responses and their origin in transitions across regions of rapidly varying force characteristics. The heuristics of a recently developed method for treating such transitions are presented and insights into its implications are drawn from related micro- and nanoscale applications.