Colin Twomey

Department of Biology
University of Pennsylvania

Colin

About Me

From "fright waves" in schooling fish to the emergence of shared vocabularies in human language, I study collective behavior across species and spatial scales, in the lab and in the field.

Currently, I'm a MindCORE Postdoctoral Research Fellow at the University of Pennsylvania, where I work with Joshua Plotkin. Before coming to Penn, I did my doctoral dissertation work with Iain Couzin at Princeton University studying collective motion in fish schools. Prior to Princeton, I studied computer science as a Goldwater Scholar at Colgate University, and worked briefly on algorithms for combinatorial optimization as a Fulbright Fellow at the Université Libre de Bruxelles. In the past I've also worked in industry as a software developer (as an intern at Sun Microsystems and at a small startup outside of Boston), and as a data science consultant for an agriculture technology startup based in Princeton and Berkeley, CA.

Contact

email
crtwomey{at}sas{dot}upenn{dot}edu
mail
Dr. Colin R. Twomey
Department of Biology
University of Pennsylvania
Philadelphia, PA 19103

Research

I try to identify the natural algorithms underlying collective behavior in animal and human groups. How do persistent patterns and properties of a group emerge from the repeated interactions of its constituent members? In schooling fish for example, the collective motion of a school is not determined by any one leader, but emerges from the attraction, repulsion, and alignment of individuals throughout the group. Common to all animals is the use of sensory systems to make informed decisions, and in my research I investigate in particular the role that sensory systems play in shaping and constraining collective behavior.

  • Fish field of view
    Behavioral cascades
    Startle responses in fish schools are critical for avoiding predation. A single individual startling can induce a wave of startle responses across a group. What sensory information, social and non-social, makes a fish more or less likely to startle in response to the startle of a neighbor? Answering this question allows us to uncover the network of sensory information in a group, and to say when and how information may propagate through a school at any given moment. Fright wave
  • Within-group structure
    What are the right levels of organization to look at to understand collective behavior? We may be deceiving ourselves that collective properties at the group level are always best explained by characterizing interactions at the individual level. Instead, it is interesting to ask whether or not there may be simpler mesoscale descriptions that allow strongly interacting components to be considered as a unit, and instead investigate the weak but non-negligible couplings between these intermediate-scale and possibly ephemeral subgroups. Group structure
  • Collective cognition
    Consensus decisions are a common feature of groups in the animal world. In humans, the words we use to communicate with others are themselves a product of a collective consensus process. Despite the apparent flexibility of such a process, there are surprising instances in which vocabularies of independent linguistic origin converge to very similar representations. The most famous of these are words for describing color. While languages vary in the number of words and the ways in which these words partition the space of visible light, they do so in a remarkably constrained way. Why should there so often be simple and intelligible mappings between the color words of completely unrelated languages? In my work, I investigate how this may arise from a collective consensus process that is fundamentally constrained by the shared physiology of our perception of color. This has implications for how other animal groups may arrive at shared, learned representations of stimuli, even in the absence of a shared vocabulary. Color vocabularies

Code


Publications

  1. Devereux, H.L., Twomey, C.R., Turner, M.S., and Thutupalli, S. (2021) Whirligig beetles as corralled active Brownian particles. Journal of the Royal Society Interface 18:20210114.
  2. Williams, J.M., Foster, W., Twomey, C.R.*, Burdge, J., Ahmed, O.M., Pereira, T.D., Wojick, J.A., Corder, G., Plotkin, J.B., and Abdus-Saboor, I. (2020) A machine-vision approach for automated pain measurement at millisecond timescales. eLife 2020;9:e57258.
  3. Twomey, C.R., Hartnett, A.T., Grobis, M.M., and Romanczuk, P. (2020) Searching for structure in collective systems. Theory in Biosciences (special issue on quantifying collectivity) https://doi.org/10.1007/s12064-020-00311-9.
  4. Sosna, M.M.G., Twomey, C.R., Bak-Coleman, J. Poel, W., Daniels, B.C., Romanczuk, P., and Couzin, I.D. (2019) Individual and collective encoding of risk in animal groups. PNAS 116(41):20556-20561.
  5. Hein, A.M., Gil, M.A., Twomey, C.R., Couzin, I.D., and Levin, S.A. (2018) Conserved behavioral circuits govern high-speed decision-making in wild fish shoals. PNAS 115(48):12224-12228.
  6. Rosenthal, S.B., Twomey, C.R.*, Wu, H.S., and Couzin, I.D. (2015) Revealing the hidden networks of interaction in animal groups allows prediction of complex behavioral contagion. PNAS 112(15):4690-4695.
  7. Strandburg-Peshkin, A., Twomey, C.R., Bode, N.W.F., Kao, A.B., Katz, Y., Ioannou, C.C., Rosenthal, S.B., Torney, C.J., Wu, H.S., Levin, S.A., and Couzin, I.D. (2013) Visual sensory networks and effective information transfer in animal groups. Current Biology 23(17):pR709-R711.
  8. Twomey, C.*, Stutzle, T., Dorigo, M., Manfrin, M., and Birattari, M. (2010) An analysis of communication policies for homogenous multi-colony ACO algorithms. Information Sciences 180(12):2390-2404. doi:10.1016/j.ins.2010.02.017.
  9. Frey, F., Delph, D.F., Dinneen, B., and Twomey, C. (2007) Evolution of sexually dimorphic flower production under sexual, fertility, and viability selection. Evolutionary Ecology Research 9:1-19.
* co-first author paper.