The Drivers of Heuristic Optimization in Insect Object Manufacture and Use

Abstract

Insects have small brains and heuristics or ‘rules of thumb’ are proposed here to be a good model for how insects optimize the objects they make and use. Generally, heuristics are thought to increase the speed of decision making by reducing the computational resources needed for making decisions. By corollary, heuristic decisions are also deemed to impose a compromise in decision accuracy. Using examples from object optimization behavior in insects, we will argue that heuristics do not inevitably imply a lower computational burden or lower decision accuracy. We also show that heuristic optimization may be driven by certain features of the optimization problem itself: the properties of the object being optimized, the biology of the insect, and the properties of the function being optimized. We also delineate the structural conditions under which heuristic optimization may achieve accuracy equivalent to or better than more fine-grained and onerous optimization methods.

Ants resort to heuristics when facing relational-learning tasks they cannot solve

“…Cognitive mechanisms have not evolved to accurately reflect the real world, but to provide decisions which maximize fitness gains [28]. Sometimes, quick heuristics (rules of thumb) can surpass more sophisticated strategies by rapidly finding acceptable solutions to a problem at the cost of accuracy [28][29][30]. In other words, if false-positives (actions that lead to an error) only induce minor costs or if the foraging context is highly variable, animals might resort to heuristics instead of learning the precise solution [28,31].…”

Meat ants cut more trail shortcuts when facing long detours

“…Heuristics often provide decision rules which can solve a given task quickly and with reasonable error and can range from simple rules such as 'go left' to sophisticated sets of rules orchestrating behaviours with highly complex outcomes, such as honeycomb construction by bees (Nazzi 2016). Facing a complex challenge, animals might change heuristics or even modify them by learning (Mhatre and Robert 2018). And indeed, ants in our study showed striking individual differences, with different ants settling on different heuristics such as "go left", "go to the more salient cue", or "choose randomly".…”

“…This is in accordance with the finding that animal species deemed to be unable to perform conceptual learning succeeded after the number of training pairs was increased to a point were associative learning became inefficient(Wright and Katz 2006), which indicates that animals employ concept learning only after other strategies fail.Cognitive mechanisms have not evolved to reveal the truth, but to provide decisions which maximise fitness gains(Haselton et al 2015). Sometimes, quick heuristics (rules of thumb) can surpass more sophisticated strategies by cheaply and rapidly finding acceptable solutions to a problem at the cost of accuracy or robustness(Gigerenzer and Gaissmaier 2015;Haselton et al 2015;Mhatre and Robert 2018). In other words, if false-positives (actions which lead to an error) only induce minor costs or if the foraging context is highly variable, animals might resort to heuristics instead of learning(Arkes 1991;Haselton et al 2015).…”

Moths are strongly attracted to ultraviolet and blue radiation

“…This difference in emission is a very modest intensity difference for an eye to detect. Daily terrestrial light levels vary over eight orders of magnitude, thus stimulus intensity is logarithmically rather than linearly coded (Weber‐Fechner law: Mhatre & Robert, 2018). Second, we hypothesize that a stronger lamp in the field could attract more insects because the radiation reaches further into the surrounding space and can thus attract insects from longer distances.…”