The importance of decision onset

Abstract

The neural mechanisms of decision making are thought to require the integration of evidence over time until a response threshold is reached. Much work suggests that response threshold can be adjusted via top-down control as a function of speed or accuracy requirements. In contrast, the time of integration onset has received less attention and is believed to be determined mostly by afferent or preprocessing delays. However, a number of influential studies over the past decade challenge this assumption and begin to paint a multifaceted view of the phenomenology of decision onset. This review highlights the challenges involved in initiating the integration of evidence at the optimal time and the potential benefits of adjusting integration onset to task demands. The review outlines behavioral and electrophysiolgical studies suggesting that the onset of the integration process may depend on properties of the stimulus, the task, attention, and response strategy. Most importantly, the aggregate findings in the literature suggest that integration onset may be amenable to top-down regulation, and may be adjusted much like response threshold to exert cognitive control and strategically optimize the decision process to fit immediate behavioral requirements.

Keywords: diffusion model; integration onset; non-decision time; sequential sampling model.

Fig. 1.

Drift-diffusion model. The drift-diffusion model views decision making as the integration of differential evidence to 1 of 2 response bounds. Thin gray lines represent sample traces of the decision variable in different simulated trials. A decision process ends as soon as the decision variable reaches 1 of the 2 bounds. Thin black line highlights 1 example trace that reaches the correct (upper) boundary ∼220 ms after integration onset. Green and red curves above and below the upper and lower bound represent the distribution of decision times for the correct and wrong choices. The total reaction time is defined as the sum of decision time and non-decision time. Solid blue vertical bar at time 0 represents the distribution of the decision variable at the time of integration onset. Straight black line emerging from the origin represents mean path of the decision variable in the absence of absorbing boundaries.

Fig. 2.

The challenge of decision onset. Figure outlines the challenges of decision onset within the context of a serial (A) and a hybrid serial/parallel (B) model of decision making. Transitions between stages are indicated by rectangular blocks and black lines if the stages occur in series or by slanted unbounded polygons if the stages occur in parallel. A: the serial model is largely consistent with the diffusion model elaborated in Decision onset and encoding of relevant information. Following afferent delays, relevant information is extracted and encoded in visual short-term memory (VSTM). Integration begins only after the VSTM memory trace has been established. Consequently, drift rate is constant over the entire integration period. B: the hybrid serial/parallel model of decision making is divided into 2 layers (encoding and integration) that are typically believed to occur in different cortical areas or distinct neural populations within a layer. Middle: cartoon of momentary sensory information that is provided to the integration stage at each point in time. Prior to stimulus onset the momentary evidence fluctuates around a mean μ of 0. Once sensory-evoked activity reaches the relevant cortical areas both the mean and the variance σ of the momentary evidence increase toward a steady state until the preprocessing has isolated the relevant stream of information. We conceptualize preprocessing as the process of setting up a pipeline to stream task-relevant information to the integrator (see Cascade vs. stage models of processing). The parallel nature of the preprocessing reflects the fact that a sensory area will encode certain aspects of a stimulus even before the task-relevant stream of information has been isolated. The key challenge of decision onset is to balance the need to use all task-related information with the need to prevent the integration of noise or misleading information.