2 results
22 - On optimal decision making in brains and social insect colonies
- from Part III - Action selection in social contexts
- Edited by Anil K. Seth, University of Sussex, Tony J. Prescott, University of Sheffield, Joanna J. Bryson, University of Bath
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- Book:
- Modelling Natural Action Selection
- Published online:
- 05 November 2011
- Print publication:
- 10 November 2011, pp 500-522
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Summary
Summary
The problem of how to compromise between speed and accuracy in decision making faces organisms at many levels of biological complexity. Striking parallels are evident between decision making in primate brains and collective decision making in social insect colonies: in both systems separate populations accumulate evidence for alternative choices, when one population reaches a threshold a decision is made for the corresponding alternative, and this threshold may be varied to compromise between the speed and accuracy of decision making. In primate decision making simple models of these processes have been shown, under certain parameterisations, to implement the statistically optimal procedure that minimises decision time for any given error rate. In this chapter, we adapt these same analysis techniques and apply them to new models of collective decision making in social insect colonies. We show that social insect colonies may also be able to achieve statistically optimal collective decision making in a very similar way to primate brains, via direct competition between evidence-accumulating populations. This optimality result makes testable predictions for how collective decision making in social insects should be organised. Our approach also represents the first attempt to identify a common theoretical framework for the study of decision making in diverse biological systems.
Introduction
Animals constantly make decisions. Habitat selection, mate selection, and foraging require investigation of, and choice between, alternatives that may determine an animal's reproductive success. For example, many animals invest considerable time and energy in finding a safe home (Franks et al., 2002; Hansell, 1984; Hazlett, 1981; Seeley, 1982). Similarly an animal may frequently have to deal with ambiguous sensory information in deciding whether a predator is present or not (Trimmer et al., 2008).
6 - Extending a biologically inspired model of choice: multi-alternatives, nonlinearity, and value-based multidimensional choice
- from Part I - Rational and optimal decision making
- Edited by Anil K. Seth, University of Sussex, Tony J. Prescott, University of Sheffield, Joanna J. Bryson, University of Bath
-
- Book:
- Modelling Natural Action Selection
- Published online:
- 05 November 2011
- Print publication:
- 10 November 2011, pp 91-119
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- Chapter
- Export citation
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Summary
Summary
The Leaky Competing Accumulator (LCA) is a biologically inspired model of choice. It describes the processes of leaky accumulation and competition observed in neuronal populations during choice tasks and it accounts for reaction time distributions observed in psychophysical experiments. This chapter discusses recent analyses and extensions of the LCA model. First, it reviews the dynamics and it examines the conditions that make the model achieve optimal performance. Second, it shows that nonlinearities of the type present in biological neurons improve performance when the number of choice-alternatives increases. Third, the model is extended to value-based choice, where it is shown that nonlinearities in the value function, explain risk-aversion in risky-choice and preference reversals in choice between alternatives characterised across multiple dimensions.
Introduction
Making choices on the basis of visual perceptions is an ubiquitous and central element of human and animal life, which has been studied extensively in experimental psychology. Within the last half century, mathematical models of choice reaction times have been proposed which assume that, during the choice process, noisy evidence supporting the alternatives is accumulated (Laming, 1968; Ratcliff, 1978; Stone, 1960; Vickers, 1970). Within the last decade, data from neurobiological experiments have shed further light on the neural bases of such choice. For example, it has been reported that while a monkey decides which of two stimuli is presented, certain neuronal populations gradually increase their firing rate, thereby accumulating evidence supporting the alternatives (Gold and Shadlen, 2002; Schall, 2001; Shadlen and Newsome, 2001). Recently, a series of neurocomputational models have offered an explanation of the neural mechanism underlying both, psychological measures like reaction times and neurophysiological data of choice. One such model, is the Leaky Competing Accumulator (LCA; Usher and McClelland, 2001), which is sufficiently simple to allow a detailed mathematical analysis. Furthermore, as we will discuss, this model can, for certain values of its parameters, approximate the same computations carried out by a series of mathematical models of choice (Busemeyer and Townsend, 1993; Ratcliff, 1978; Shadlen and Newsome, 2001; Vickers, 1970; Wang, 2002).