Subcritical escape waves in schooling fish

Abstract

Theoretical physics predicts optimal information processing in living systems near transitions (or pseudo-critical points) in their collective dynamics. However, focusing on potential benefits of proximity to a critical point, such as maximal sensitivity to perturbations and fast dissemination of information, commonly disregards possible costs of criticality in the noisy, dynamic environmental contexts of biological systems. Here, we find that startle cascades in fish schools are subcritical (not maximally responsive to environmental cues) and that distance to criticality decreases when perceived risk increases. Considering individuals’ costs related to two detection error types, associated to both true and false alarms, we argue that being subcritical, and modulating distance to criticality, can be understood as managing a trade-off between sensitivity and robustness according to the riskiness and noisiness of the environment. Our work emphasizes the need for an individual-based and context-dependent perspective on criticality and collective information processing and motivates future questions about the evolutionary forces that brought about a particular trade-off.

Wild animals suppress the spread of socially-transmitted misinformation

“…We therefore focused on a set of models based on features of decision-making previously described in the perceptual decision-making literature on the basis of behavioural or neurophysiological evidence (Table S1). Multi-source perceptual decision-making models can be organized broadly into two classes on the basis of their assumptions about how stimuli from neighbors are integrated during decision-making: pooling models [11, 33, 34], and averaging models [2, 25, 28, 40, 41]. Moreover, we consider variants of common decision-making models for each candidate class, including models in which raw visual stimuli are linearly combined to form visual features [11, 33], as well as models in which driving sensory features are nonlinear combinations of raw inputs [23, 42].…”

“…We designed an individual-based model to simulate the dynamics of populations comprising individuals who follow a response rescaling rule interacting on a featureless 2-dimensional plane. We compared the results of these simulations to expectations from two widely-used models of social contagion: simple and fractional contagion [7, 8, 25, 38, 41]. Model parameters, including those that control agent movement, sensing, and decision-making were set to their corresponding empirical estimates (Table S2).…”

Fish shoals resemble a stochastic excitable system driven by environmental perturbations

“…It has been shown that the density of sulphur molly shoals exhibits substantial variation over the course of a day 22 , which in turn modulates the effective strength of social interactions 31,43 . This suggests local density to be one core variable controlling the self-organization of living systems to criticality 28 and thus a variable to quantify over longer periods of time. However, individual behavioural parameters such as individual speed 44 or attention to conspecifics 45 have been shown to have strong effects on collective behaviour, which may yield alternative mechanisms for self-organization towards criticality through modulation of individual behaviour 46 .…”

“…This spontaneous and stochastic surface-wave activity may be viewed as analogous to the resting-state activity in neuronal systems 26,27 . The system thus provides an optimal set-up for empirically testing the criticality hypothesis and investigating the benefits and potential trade-offs of critical behaviour in the wild 28 . Due to the high predatory bird activity in the system 22,24 , we hypothesized that these fish shoals would probably benefit from being in a constant state of including animal groups, remains sparse 2,4,14 .…”

A cognitive computational approach to social and collective decision-making

“…Animals often observe the flight responses or food choices of conspecifics, pedestrians watch to see whether others dare to dash across the road, and investors track the choices of other investors. In such situations, early choices can cascade through the group via social contagion (Bikhchandani et al, 1998;Mann, 2018;Poel et al, 2022;Tump et al, 2020). The collective outcome of these dynamics is thus strongly influenced by how individuals integrate personal and social information over time, and how they time their decisions.…”