‘Sherrington … argued that the nervous system responsible for this simplest class of determinate decision making [harm avoidance] could be … composed of three critical elements: a selective sensory element, … a detector for … events in the outside world…; a selective motor element … that led to the activation of muscles; and a point of contact between these two systems (… the integrative element).’[1] A sensor fires when it detect something, however, since they are imperfect (both natural or man-made) an additional element is required to collect evidence over time (integrate) to remove errors and noise from the signal – as in a sequential-sampling model.[2]
These models serve as the basis for more complex discriminating behavioural diffusion models, such as directional motion tracking. Consider a juvenile primate watching its mother walk through long grass: as the adult moves rightward it displaces grass, but the juvenile will also see leftward, upward, and downward movement. To tack its mother, the juvenile must discern which of these multiple movements is the primary movement.
Diffusion models add an integration step to collect the relative difference between two options and trigger an action only when the difference exceeds a threshold. Diffusion models have accurately accounted for the observed reaction times and accuracy in experiments compared to accumulator and race models.[2] Diffusion models can also account for the fast erroneous actions which have been experimental observed.[2] Diffusion models using the relative difference implicitly treat evidence for one alternative as evidence against another alternative preventing the errors of multiple conclusions and race-conditions possible in accumulator and counter models which do not have mutual inhibition.[2]
More importantly, supporting additional integration steps and changed criteria allows the diffusion model to be primed with preconditions to become adaptive to historical observations of probability and reward. Experiments have shown that a primate trained to expect a greater reward for a particular option (e.g. looking right) will become adept at reacting earlier to the more rewarding option.[1] Experiments have also shown that primates learn patterns and use historical probability to anticipate the more likely option.[1]
The diffusion model is the most accurate at modelling experimental observations and is capable of describing how a primate adapts-to and takes-advantage-of- the historical probability and relative reward from available options. Therefore, the diffusion model can be used in future research as it is the most capable of describing the range of complex observed behaviours.
References
1. Glimcher, P.W. 2003, ‘The neurobiology of visual-saccadic decision making’, Annual Review of Neuroscience, vol. 26, no. 1, pp. 133-79. LINK
‘Over the past two decades significant progress has been made toward understanding the neural basis of primate decision making, the biological process that combines sensory data with stored information to select and execute behavioral responses. The most striking progress in this area has been made in studies of visual-saccadic decision making, a system that is becoming a model for understanding decision making in general. In this system, theoretical models of efficient decision making developed in the social sciences are beginning to be used to describe the computations the brain must perform when it connects sensation and action. Guided in part by these economic models, neurophysiologists have been able to describe neuronal activity recorded from the brains of awake-behaving primates during actual decision making. These recent studies have examined the neural basis of decisions, ranging from those made in predictable sensorimotor tasks to those unpredictable decisions made when animals are engaged in strategic conflict. All of these experiments seem to describe a surprisingly well-integrated set of physiological mechanisms that can account for a broad range of behavioral phenomena. This review presents many of these recent studies within the emerging neuroeconomic framework for understanding primate decision making.’
2. Smith, P.L. & Ratcliff, R. 2004, ‘Psychology and neurobiology of simple decisions’, Trends in Neurosciences, vol. 27, no. 3, pp. 161-8. LINK
‘Patterns of neural firing linked to eye movement decisions show that behavioral decisions are predicted by the differential firing rates of cells coding selected and nonselected stimulus alternatives. These results can be interpreted using models developed in mathematical psychology to model behavioral decisions. Current models assume that decisions are made by accumulating noisy stimulus information until sufficient information for a response is obtained. Here, the models, and the techniques used to test them against response-time distribution and accuracy data, are described. Such models provide a quantitative link between the time-course of behavioral decisions and the growth of stimulus information in neural firing data.’