Alternative model systems for cognitive variation: eusocial-insect colonies

The origin and maintenance of cognitive variation between individuals is an open problem, central to understanding how cognition emerges and evolves. Mapping neural dynamics to variation in macroscale cognitive patterns remains elusive given the large size of neural populations. Simpler alternative model systems, like eusocial insects, exhibit collective cognitive properties like decision-making, attention, and memory. Individual insects can be observed and manipulated in real time and colonies can be assembled from specifically selected individuals. There is an established framework to study consistent behavioral variation between colonies, which drives evolutionary change. Models from cognitive psychology have produced insights about collective colony function. Eusocial insects as alternative models of ‘collective brains’ may provide a new lens to study cognitive variation. Understanding the origins and maintenance of cognitive variation in animal populations is central to the study of the evolution of cognition. However, the brain is itself a complex, hierarchical network of heterogeneous components, from diverse cell types to diverse neuropils, each of which may be of limited use to study in isolation or prohibitively challenging to manipulate in situ. Consequently, highly tractable alternative model systems may be valuable tools. Eusocial-insect colonies display emergent cognitive-like properties from relatively simple social interactions between diverse subunits that can be observed and manipulated while operating collectively. Here, we review the individual-scale mechanisms that cause group-level variation in how colonies solve problems analogous to cognitive challenges faced by brains, like decision-making, attention, and search. Understanding the origins and maintenance of cognitive variation in animal populations is central to the study of the evolution of cognition. However, the brain is itself a complex, hierarchical network of heterogeneous components, from diverse cell types to diverse neuropils, each of which may be of limited use to study in isolation or prohibitively challenging to manipulate in situ. Consequently, highly tractable alternative model systems may be valuable tools. Eusocial-insect colonies display emergent cognitive-like properties from relatively simple social interactions between diverse subunits that can be observed and manipulated while operating collectively. Here, we review the individual-scale mechanisms that cause group-level variation in how colonies solve problems analogous to cognitive challenges faced by brains, like decision-making, attention, and search. a mathematical model describing the behavior of two variables of interest. While the Hodgkin–Huxley model is 4D (uses four equations to describe changes in four different variables), the Morris–Lecar model is 2D, since it describes only two variables: the membrane voltage V and a recovery variable w. 2D models are highly tractable mathematically since they can be graphically represented using a plane where each axis shows the value of one of the variables. a model in which a system is simulated as a collection of individual units with agency (‘agents’), which is characterized by rules governing how those units behave over time and in response to interactions with other agents. a simplified description of ensembles of units and fine-scale processes focusing on relations between larger-scale components and thus reducing the dimensionality of the model (e.g., using temperature and density to represent important properties of a gas that might otherwise be modeled as a collection of molecules in motion). a network of individuals that can collectively acquire, store, process, and respond to environmental information (learning, memory, decision-making) and become familiar with, value, and interact productively with environmental features (exploring, exploiting) to meet existential needs (survival, growth). the ability of animal groups to collectively solve problems analogous to cognitive challenges faced by individual brains, like decision-making, attention regulation, and exploration–exploitation balance search. coarse-grained representations of decision-making processes, where a single variable represents noisy evidence accumulation about a stimulus, from a starting point to a decision boundary (one for each alternative). The average rate of evidence accumulation is called the ‘drift rate’. A decision is made when the accumulation of evidence reaches one of the boundaries. a type of coarse graining that represents the dynamics of a collection of units by the dynamics of a single, average unit. in their simplest form, these models comprise a set of nodes (or vertices) linked in pairs by edges. In neural network models, nodes usually represent neurons with a certain level of activity (or firing rate) and edges represent synaptic connections with an assigned synaptic weight.