Neural bases of individual variation in decision time

Abstract

People make decisions by evaluating existing evidence against a threshold or level of confidence. Individuals vary widely in response times even when they perform a simple task in the laboratory. We examine the neural bases of this individual variation by combining computational modeling and brain imaging of 64 healthy adults performing a stop signal task. Behavioral performance was modeled by an accumulator model that describes the process of information growth to reach a threshold to respond. In this model, go trial reaction time (goRT) is jointly determined by the information growth rate, threshold, and movement time (MT). In a linear regression of activations in successful go and all stop (Go+Stop) trials against goRT across participants, the insula, supplementary motor area (SMA), pre-SMA, thalamus including the subthalamic nucleus (STN), and caudate head respond to increasing goRT. Among these areas, the insula, SMA, and thalamus including the STN respond to a slower growth rate, the caudate head responds to an elevated threshold, and the pre-SMA responds to a longer MT. In the regression of Go+Stop trials against the stop signal reaction time (SSRT), the pre-SMA shows a negative correlation with SSRT. These results characterize the component processes of decision making and elucidate the neural bases of a critical aspect of inter-subject variation in human behavior. These findings also suggest that the pre-SMA may play a broader role in response selection and cognitive control rather than simply response inhibition in the stop signal task.

Anticipating conflict: Neural correlates of a Bayesian belief and its motor consequence

“…Insula activation has been associated with a prolonged RT in auditory syllable identification (Binder et al, 2004) and fear–disgust two-choice discrimination (Thielscher and Pessoa, 2007). We recently simulated stop signal task performance with a leaky accumulator model and showed that both posterior pre-SMA/SMA (x = −6, y = −1, z = 55) and insula increased activation to prolonged RT, manifesting as a slower growth rate in information accumulation (Hu et al, 2014). In a recent study of motion perception, insula activation is associated with a lower percentage of coherence in moving direction of random dots and prolonged response time, supporting an underlying mechanism of slower information accumulation (Ballanger, 2009).…”

A dual but asymmetric role of the dorsal anterior cingulate cortex in response inhibition and switching from a non-salient to salient action

“…1A) as in our previous work (Farr et al, 2012; Hu et al, 2014; Winkler et al, 2013). There were two trial types: go and stop, randomly intermixed.…”

Response Inhibition

“…These results suggest an interaction between the rIFC and the pre-SMA, with attention to stop signal facilitating motor response inhibition via a medial prefrontal-basal ganglia circuit (Duann et al, 2009;Sharp et al, 2010). On the other hand, the pre-SMA also responds to prolonged movement time in a diffusion model of the SST performance (Hu et al, 2013), sequential movement or movement planning (Colebatch, 2007;Kansaku et al, 2005;Shibasaki, 2012;Weidner et al, 2009), and both promotion and suppression of M1 activity (Cieslik et al, 2011), again suggesting that it does not mediate solely the "stop" process. It is plausible that the pre-SMA plays a role in action planning of which response inhibition is a critical component.…”

“…In studies of the SST, while the stop process has been a focus of investigation, examining the go process, particularly with respect to the likelihood of stop signal, would elucidate the correlates of proactive control. In this vein, model-based analysis can also help distinguish between the component processes of decision making during stop signal inhibition (Hu et al, 2013;White et al, 2014). Purple lines indicate the cortico-pontine-cerebellar-thalamic-cortical circuits for learning in movement control.…”