Sequential sampling models are a class of models that are widely supported by empirical and modelling studies in perceptual decision-making. These models propose that noisy sensory information for each choice alternative is accumulated over time until a particular decision threshold is reached, which in turn leads to a response associated with that threshold (see Forstmann et al., (2016) for a nice review). Standard sequential sampling models like the drift diffusion model (DDM) assume that this decision process is context-dependent but time-invariant, meaning that both the rate at which the evidence is accumulated and the decision threshold can vary across different contexts but remain fixed over the course of a single decision. One drawback of this assumption is that the time required to make a choice increases with ambiguity in sensory evidence. This can lead to suboptimal behaviour in contexts that require subjects to strike a balance between response speed and accuracy (the speed-accuracy tradeoff), especially when the potential cost of continued deliberation increases with time. Now, however, a paper from Murphy and colleagues (2016) has provided convergent behavioural, electrophysiological and model-based evidence for the presence of a dynamic ‘urgency signal’ during perceptual decision-making which strongly refutes the assumption of a time-invariant decision policy and suggests that human decision-makers may be considerably more flexible than previously thought.
Perceptual decision-making tasks that solely prioritise accuracy rather than the speed of choices do not in principle invoke time-dependency. Even in tasks which should promote a dynamic speed-accuracy tradeoff, human decision-makers have been found to display an accuracy bias whereby choices are slower and more cautious than required, which leads to lower task payoffs on average. Under such conditions, standard sequential sampling models provide a good fit for the data without the need to incorporate a time-dependent component in the decision policy. In a new twist on common experimental designs in the field of perceptual decision-making, Murphy and colleagues (2016) applied an incentive scheme during performance of a standard two-alternative motion discrimination task that laid an especially heavy monetary penalty (10x that of an incorrect decision) on failure to make a decision within a stipulated time (1.4 seconds). In contrast, the magnitude of reward and penalty was the same for correct and incorrect trials, respectively. Thus, failure to make a decision within the temporal deadline cost participants on this task ten correct trials whereas an incorrect choice cost just one correct trial. Such an incentive scheme reduces the accuracy bias and should lead to strong time-dependency, if human decision-makers are capable of it.
Murphy et al. first examined the empirical conditional accuracy functions relating accuracy to reaction time (RT), which provide a window onto variation in the amount of accumulated evidence that subjects required for decision commitment. These functions suggested two phenomena when subjects performed with versus without the deadline on choices: a ‘static’, time-invariant lowering of the required evidence coupled with a gradual decrease in required evidence as time progressed within a single trial. Moreover, approximately zero evidence was required to make a decision around the time of the deadline, which resulted in chance performance when decisions took that long to be made but in very few missed deadlines. The latter findings in particular are hallmarks of time-dependency in the decision process. Mechanistically, these empirical observations may arise from two distinct sources in the framework of a sequential sampling model: a decision threshold might collapse over time within a trial; or, the threshold could remain fixed and some form of additional input (an urgency signal) might instead be added to the evidence accumulation process itself as the trial progresses. To distinguish between these possibilities, Murphy et al. examined brain activity (in the form of EEG) recorded during task performance. They found that electrophysiological signals in the µ frequency range (8-14Hz), which are thought to reflect building decision-related motor preparation, exhibited both increased pre-trial baseline activity under speed pressure (corresponding to a static urgency effect) and a dynamic increase in activity over the course of a trial for both the choices (reflecting a time-dependent urgency effect). These observations were further supported by computational modelling showing that a version of the DDM that included an urgency signal with both static and time-dependent components, coupled with a fixed decision threshold, explained the behavioural data far better than the standard DDM without an urgency signal but with a condition-dependent, time-invariant decision threshold.
Equipped with these findings, Murphy et al., (2016) also explored whether time-dependent urgency was present in trials under mild speed-pressure (without any explicit penalty for missed deadlines) by reanalysing data from a different set of experiments. They found that a time-dependent decision policy seemed to be deployed, albeit less severely, even in contexts where speed pressure is mild. This suggests that the assumption of time-invariance may not even hold in standard perceptual decision-making tasks and that time-dependency is an important factor that cannot be ignored in studies of decision-making.
How might the flexible urgency signal described above be generated in the brain? One appealing candidate mechanism that has already received some attention from computational neuroscientists is modulation of the ‘gain’ or responsivity of the brain circuits thought to carry out neural evidence accumulation. Moreover, several studies have identified that pupil diameter seems to provide a reliable non-invasive index of the activity of low-level neuromodulatory systems that boast diffuse cortical projections and are hypothesised to control global neural gain (see Aston-Jones & Cohen (2005) for a review). Using pupillometry, Murphy et al., (2016) found in a final study that tonic pupil diameter prior to trial onset was higher when subjects performed under the temporal deadline, reflecting the static urgency effect. In addition, phasic, trial-evoked pupil fluctuations revealed a time-dependent increase in pupil size as the deadline approached, suggesting that the time-dependent urgency effect might be achieved through global gain modulation. Formal modelling of the pupil time-series showed that the input to the pupil system during decision formation is a ramping signal that increased monotonically with elapsed decision time under deadline. Lastly, simulations using a simple neural network model provided strength to the hypothesis that global gain modulation is a plausible biophysical mechanism for generating static and time-dependent urgency in the brain.
The above results, though important for decision-making researchers in general, hold equal relevance in timing research. Performing a task such as the one used by Murphy et al. requires subjects to sample and accumulate sensory evidence while also continually updating estimates of the elapsed time since trial onset, thus concurrently recruiting brain regions involved in both decision-making and time perception. The input to the neural system responsible for generating the urgency signal may thus originate from a network of brain regions involved in the estimation of elapsed time (for e.g., the dorso-medial prefrontal cortex).Perceptual decision-making experiments usually assume temporal invariance of the decision policy in a single trial level. The paper by Murphy et al., (2016) shows that this can no longer be the case. As it is well established that distributed and varied brain regions contribute to human cognition in general, it is time that more studies incorporate established theories from various domains (for e.g., time perception and decision-making as in the experiments above) to obtain better insights into the working of human brain.
Murphy, P. R., Boonstra, E. & Nieuwenhuis, S. (2016). Global gain modulation generates time-dependent urgency during perceptual choice in humans. Nat. Commun. 7, 13526. doi: 10.1038/ncomms13526.
Forstmann, B. U., Ratcliff, R. & Wagenmakers, E. J. (2016). Sequential sampling models in cognitive neuroscience: advantages, applications, and extensions. Annu. Rev. Psychol. 67, 641-666.
Aston-Jones, G. & Cohen, J. D. (2005). An integrative theory of locus coeruleus- norepinephrine function: adaptive gain and optimal performance. Annu. Rev. Neurosci. 28, 403-450.