Neurally constrained modeling of speed-accuracy tradeoff during visual search: gated accumulation of modulated evidence

Differential Formation of Motor Cortical Dynamics during Movement Preparation According to the Predictability of Go Timing

The motor cortex not only executes but also prepares movement, as motor cortical neurons exhibit preparatory activity that predicts upcoming movements. In movement preparation, animals adopt different strategies in response to uncertainties existing in nature such as the unknown timing of when a predator will attack-an environmental cue informing "go." However, how motor cortical neurons cope with such uncertainties is less understood. In this study, we aim to investigate whether and how preparatory activity is altered depending on the predictability of "go" timing. We analyze firing activities of the anterior lateral motor cortex in male mice during two auditory delayed-response tasks each with predictable or unpredictable go timing. When go timing is unpredictable, preparatory activities immediately reach and stay in a neural state capable of producing movement anytime to a sudden go cue. When go timing is predictable, preparation activity reaches the movement-producible state more gradually, to secure more accurate decisions. Surprisingly, this preparation process entails a longer reaction time. We find that as preparatory activity increases in accuracy, it takes longer for a neural state to transition from the end of preparation to the start of movement. Our results suggest that the motor cortex fine-tunes preparatory activity for more accurate movement using the predictability of go timing.

Neural mechanisms for executive control of speed-accuracy trade-off

The medial frontal cortex (MFC) plays an important but disputed role in speed-accuracy trade-off (SAT). In samples of neural spiking in the supplementary eye field (SEF) in the MFC simultaneous with the visuomotor frontal eye field and superior colliculus in macaques performing a visual search with instructed SAT, during accuracy emphasis, most SEF neurons discharge less from before stimulus presentation until response generation. Discharge rates adjust immediately and simultaneously across structures upon SAT cue changes. SEF neurons signal choice errors with stronger and earlier activity during accuracy emphasis. Other neurons signal timing errors, covarying with adjusting response time. Spike correlations between neurons in the SEF and visuomotor areas did not appear, disappear, or change sign across SAT conditions or trial outcomes. These results clarify findings with noninvasive measures, complement previous neurophysiological findings, and endorse the role of the MFC as a critic for the actor instantiated in visuomotor structures.

Secondary motor integration as a final arbiter in sensorimotor decision-making

Sensorimotor decision-making is believed to involve a process of accumulating sensory evidence over time. While current theories posit a single accumulation process prior to planning an overt motor response, here, we propose an active role of motor processes in decision formation via a secondary leaky motor accumulation stage. The motor leak adapts the "memory" with which this secondary accumulator reintegrates the primary accumulated sensory evidence, thus adjusting the temporal smoothing in the motor evidence and, correspondingly, the lag between the primary and motor accumulators. We compare this framework against different single accumulator variants using formal model comparison, fitting choice, and response times in a task where human observers made categorical decisions about a noisy sequence of images, under different speed-accuracy trade-off instructions. We show that, rather than boundary adjustments (controlling the amount of evidence accumulated for decision commitment), adjustment of the leak in the secondary motor accumulator provides the better description of behavior across conditions. Importantly, we derive neural correlates of these 2 integration processes from electroencephalography data recorded during the same task and show that these neural correlates adhere to the neural response profiles predicted by the model. This framework thus provides a neurobiologically plausible description of sensorimotor decision-making that captures emerging evidence of the active role of motor processes in choice behavior.

Relating a Spiking Neural Network Model and the Diffusion Model of Decision-Making

Many models of decision making assume accumulation of evidence to threshold as a core mechanism to predict response probabilities and response times. A spiking neural network model (Wang, 2002) instantiates these mechanisms at the level of biophysically-plausible pools of neurons with excitatory and inhibitory connections, and has numerous model parameters tuned by physiological measures. The diffusion model (Ratcliff, 1978) is a cognitive model that can be fitted to a range of behaviors and conditions. We investigated how parameters of the cognitive-level diffusion model relate to the parameters of a neural-level spiking model. In each simulated "experiment", we generated "data" from the spiking neural network by factorially combining a manipulation of choice difficulty (via the input to the spiking model) and a manipulation of one of the core parameters of the spiking model. We then fitted the diffusion model to these simulated data to observe how manipulation of each core spiking model parameter mapped on to fitted drift rate, response threshold, and non-decision time. Manipulations of parameters in the spiking model related to input sensitivity, threshold, and stimulus processing time mapped on to their conceptual analogues in the diffusion model, namely drift rate, threshold, and non-decision time. Manipulations of parameters in the spiking model with no direct analogue to the diffusion model, non-stimulus-specific background input, strength of recurrent excitation, and receptor conductances, mapped on to threshold in the diffusion model. We discuss implications of these results for interpretations of fits of the diffusion model to behavioral data.

The time-course of distractor-based activation modulates effects of speed-accuracy tradeoffs in conflict tasks

The cognitive processes underlying the ability of human performers to trade speed for accuracy is often conceptualized within evidence accumulation models, but it is not yet clear whether and how these models can account for decision-making in the presence of various sources of conflicting information. In the present study, we provide evidence that speed-accuracy tradeoffs (SATs) can have opposing effects on performance across two different conflict tasks. Specifically, in a single preregistered experiment, the mean reaction time (RT) congruency effect in the Simon task increased, whereas the mean RT congruency effect in the Eriksen task decreased, when the focus was put on response speed versus accuracy. Critically, distributional RT analyses revealed distinct delta plot patterns across tasks, thus indicating that the unfolding of distractor-based response activation in time is sufficient to explain the opposing pattern of congruency effects. In addition, a recent evidence accumulation model with the notion of time-varying conflicting information was successfully fitted to the experimental data. These fits revealed task-specific time-courses of distractor-based activation and suggested that time pressure substantially decreases decision boundaries in addition to reducing the duration of non-decision processes and the rate of evidence accumulation. Overall, the present results suggest that time pressure can have multiple effects in decision-making under conflict, but that strategic adjustments of decision boundaries in conjunction with different time-courses of distractor-based activation can produce counteracting effects on task performance with different types of distracting sources of information.

Beyond information rate, the capacity of cognitive control predicts response criteria in perceptual decision-making

Recent studies indicate that higher capacity of cognitive control (CCC) represents higher processing efficiency (i.e., high accuracy with fast speed). However, the speed-accuracy tradeoff (SAT) exists ubiquitously in decision-making, and little is known about whether and how the CCC is associated with SAT and whether the CCC-SAT relationship would be affected by changes in information entropy. In this study, fifty-nine college students performed a majority function task in which accuracy and response speed were equally emphasized. A Bayesian-based hierarchical drift diffusion modeling method was used to estimate three parameters of boundary separation, drift rate, and nondecision time for each participant in this task. In addition, the CCC of each participant was estimated. The results showed that the CCC was positively correlated with the SAT represented by jointly increasing accuracy and reaction time (RT), which was modulated by the change in task-relevant information entropy. Multiple mediation analyses indicated that drift rate served as the key mediator in the positive CCC-accuracy relationship while boundary separation played the major mediating role in the positive CCC-RT relationship. These findings suggest that the CCC reflects not only the rate of information processing but also decision strategies for achieving current goals.

Trading accuracy for speed over the course of a decision

Humans and other animals often need to balance the desire to gather sensory information (to make the best choice) with the urgency to act, facing a speed-accuracy tradeoff (SAT). Given the ubiquity of SAT across species, extensive research has been devoted to understanding the computational mechanisms allowing its regulation at different timescales, including from one context to another, and from one decision to another. However, animals must frequently change their SAT on even shorter timescales-that is, over the course of an ongoing decision-and little is known about the mechanisms that allow such rapid adaptations. The present study aimed at addressing this issue. Human subjects performed a decision task with changing evidence. In this task, subjects received rewards for correct answers but incurred penalties for mistakes. An increase or a decrease in penalty occurring halfway through the trial promoted rapid SAT shifts, favoring speeded decisions either in the early or in the late stage of the trial. Importantly, these shifts were associated with stage-specific adjustments in the accuracy criterion exploited for committing to a choice. Those subjects who decreased the most their accuracy criterion at a given decision stage exhibited the highest gain in speed, but also the highest cost in terms of performance accuracy at that time. Altogether, the current findings offer a unique extension of previous work, by suggesting that dynamic cha*nges in accuracy criterion allow the regulation of the SAT within the timescale of a single decision.NEW & NOTEWORTHY Extensive research has been devoted to understanding the mechanisms allowing the regulation of the speed-accuracy tradeoff (SAT) from one context to another and from one decision to another. Here, we show that humans can voluntarily change their SAT on even shorter timescales-that is, over the course of a decision. These rapid SAT shifts are associated with dynamic adjustments in the accuracy criterion exploited for committing to a choice.

The functional roles of neural remapping in cortex

Remapping is a property of some cortical and subcortical neurons that update their responses around the time of an eye movement to account for the shift of stimuli on the retina due to the saccade. Physiologically, remapping is traditionally tested by briefly presenting a single stimulus around the time of the saccade and looking at the onset of the response and the locations in space to which the neuron is responsive. Here we suggest that a better way to understand the functional role of remapping is to look at the time at which the neural signal emerges when saccades are made across a stable scene. Based on data obtained using this approach, we suggest that remapping in the lateral intraparietal area is sufficient to play a role in maintaining visual stability across saccades, whereas in the frontal eye field, remapped activity carries information that affects future saccadic choices and, in a separate subset of neurons, is used to maintain a map of locations in the scene that have been previously fixated.

Accumulators, Neurons, and Response Time

The marriage of cognitive neurophysiology and mathematical psychology to understand decision-making has been exceptionally productive. This interdisciplinary area is based on the proposition that particular neurons or circuits instantiate the accumulation of evidence specified by mathematical models of sequential sampling and stochastic accumulation. This linking proposition has earned widespread endorsement. Here, a brief survey of the history of the proposition precedes a review of multiple conundrums and paradoxes concerning the accuracy, precision, and transparency of that linking proposition. Correctly establishing how abstract models of decision-making are instantiated by particular neural circuits would represent a remarkable accomplishment in mapping mind to brain. Failing would reveal challenging limits for cognitive neuroscience. This is such a vigorous area of research because so much is at stake.

Designing and Interpreting Psychophysical Investigations of Cognition

Scientific experimentation depends on the artificial control of natural phenomena. The inaccessibility of cognitive processes to direct manipulation can make such control difficult to realize. Here, we discuss approaches for overcoming this challenge. We advocate the incorporation of experimental techniques from sensory psychophysics into the study of cognitive processes such as decision making and executive control. These techniques include the use of simple parameterized stimuli to precisely manipulate available information and computational models to jointly quantify behavior and neural responses. We illustrate the potential for such techniques to drive theoretical development, and we examine important practical details of how to conduct controlled experiments when using them. Finally, we highlight principles guiding the use of computational models in studying the neural basis of cognition.