A role for ecologically-tuned chunking in the evolution of advanced cognition demonstrated by modelling the cleaner fish market problem

Abstract

What makes cognition advanced is an open and not precisely defined question. One perspective involves increasing the complexity of associative learning, from conditioning to learning sequences of events ( chaining) to representing various cue combinations as chunks. Here we develop a weighted-graph model to study the conditions for the evolution of chunking ability, based on the ecology of the cleaner fish Labroides dimidiatus. Cleaners must learn to serve visitor clients before resident clients, because a visitor leaves if not attended while a resident waits for service. This challenge has been captured in various versions of the ephemeral-reward task, which has been proven difficult for a range of cognitively capable species. We show that chaining is the minimal requirement for solving the laboratory task, that involves repeated simultaneous exposure to an ephemeral and permanent food source. Adding ephemeral-ephemeral and permanent-permanent combinations, as cleaners face in the wild, requires individuals to have chunking abilities to solve the task. Importantly, chunking parameters need to be calibrated to ecological conditions in order to produce adaptive decisions. Thus, it is the fine tuning of this ability which may be the major target of selection during the evolution of advanced associative learning.