Speed-accuracy tradeoff by a control signal with balanced excitation and inhibition

Abstract

Lo CC, Wang CT, Wang XJ. Speed-accuracy tradeoff by a control signal with balanced excitation and inhibition. J Neurophysiol 114: 650-661, 2015. First published May 20, 2015 doi:10.1152/jn.00845.2013.-A hallmark of flexible behavior is the brain's ability to dynamically adjust speed and accuracy in decision-making. Recent studies suggested that such adjustments modulate not only the decision threshold, but also the rate of evidence accumulation. However, the underlying neuronallevel mechanism of the rate change remains unclear. In this work, using a spiking neural network model of perceptual decision, we demonstrate that speed and accuracy of a decision process can be effectively adjusted by manipulating a top-down control signal with balanced excitation and inhibition [balanced synaptic input (BSI)]. Our model predicts that emphasizing accuracy over speed leads to reduced rate of ramping activity and reduced baseline activity of decision neurons, which have been observed recently at the level of single neurons recorded from behaving monkeys in speed-accuracy tradeoff tasks. Moreover, we found that an increased inhibitory component of BSI skews the decision time distribution and produces a pronounced exponential tail, which is commonly observed in human studies. Our findings suggest that BSI can serve as a top-down control mechanism to rapidly and parametrically trade between speed and accuracy, and such a cognitive control signal presents both when the subjects emphasize accuracy or speed in perceptual decisions. decision making; speed-accuracy tradeoff; top-down control; balanced input THE ABILITY TO DYNAMICALLY adjust speed vs. accuracy is a salient feature of decision-making (Bogacz et al. 2010; Gold and Shadlen 2002;Heitz 2014;Wang 2008;Wickelgren 1977): if "to get it right" is the priority, we slow down to gather more information and gain a better performance. On the other hand, if time is at a premium (e.g., when detecting a predator), we make a quick judgment at the potential cost of accuracy. Speed-accuracy tradeoff (SAT) can result from a trial-by-trial learning process, which is believed to depend on synaptic plasticity and reward information in the cortex and basal ganglia (Balci et al. 2010; Gold and Shadlen 2002;Lo and Wang 2006;Wang et al. 2013). However, adjustment can often be made quickly, "on the fly," based on either task instruction, a changing environment, or a subject's own will (Edwards 1965;Forstmann et al. 2008;Luce 1986;Palmer et al. 2005;Wickelgren 1977).SAT is commonly considered in terms of adjusting a decision threshold of an integrator (Bogacz et al. 2006;Edwards 1965), such as that described by the drift diffusion model (DDM) (Luce 1986;Ratcliff 1978). In this framework, under speed emphasis the decision threshold is decreased, so it can be reached faster to trigger a response, whereas accuracy emphasis is instantiated by an increase of the decision threshold to integrate more information before a decision is made. The idea of changing decision threshold is intuitively appealin...

Neural Mechanisms of Post-error Adjustments of Decision Policy in Parietal Cortex

“…Similar changes in LIP and prefrontal neurons have been shown to implement changes of decision bound for speed-accuracy tradeoff (Hanks et al, 2014; Heitz and Schall, 2012). Thus, our results contribute to mounting evidence that changes in decision bound are not implemented through elevated end-point as one might expect, but instead through mechanisms that directly accelerate or decelerate accumulation of evidence toward a fixed threshold (Hanks et al., 2014; Lo et al, 2015). …”

Relating accumulator model parameters and neural dynamics

“…But these parameters have very different implications for the underlying neural mechanisms. For example, adjustments in neural activity at the time of RT are associated with changes in the strength of cortico-striatal connections (Lo & Wang, 2006), whereas adjustments of the baseline may be implemented through background excitation and inhibition (Lo, Wang, & Wang, 2015). Although the measured onset time and growth rate of neural dynamics appear to be less diagnostic about specific underlying parameters, variation in measured neural baseline and neural activity at RT can be associated more directly with variation in the starting point and threshold parameters of accumulator models.…”

Neural Mechanisms for Executive Control of Speed-Accuracy Tradeoff

“…Instead, SEF, like preSMA, monitors performance and exerts indirect influence on motor processes to adjust RT (Scangos et al, 2013;Stuphorn et al, 2010). Such indirect influence can be described as urgency, as suggested by others (e.g., Thura and Cisek, 2016), and implemented in abstract spiking network models (Lo et al, 2015). Earlier research showed that intracortical electrical microstimulation of sites in SEF can speed or slow RT according to task context (Stuphorn and Schall, 2006).…”