How to discriminate conclusively among different models of decision making?

Modeling evidence accumulation decision processes using integral equations: Urgency-gating and collapsing boundaries

Diffusion models of evidence accumulation have successfully accounted for the distributions of response times and choice probabilities from many experimental tasks, but recently their assumption that evidence is accumulated at a constant rate to constant decision boundaries has been challenged. One model assumes that decision-makers seek to optimize their performance by using decision boundaries that collapse over time. Another model assumes that evidence does not accumulate and is represented by a stationary distribution that is gated by an urgency signal to make a response. We present explicit, integral-equation expressions for the first-passage time distributions of the urgency-gating and collapsing-bounds models and use them to identify conditions under which the models are equivalent. We combine these expressions with a dynamic model of stimulus encoding that allows the effects of perceptual and decisional integration to be distinguished. We compare the resulting models to the standard diffusion model with variability in drift rates on data from three experimental paradigms in which stimulus information was either constant or changed over time. The standard diffusion model was the best model for tasks with constant stimulus information; the models with time-varying urgency or decision bounds performed similarly to the standard diffusion model on tasks with changing stimulus information. We found little support for the claim that evidence does not accumulate and attribute the good performance of the time-varying models on changing-stimulus tasks to their increased flexibility and not to their ability to account for systematic experimental effects. (PsycInfo Database Record (c) 2022 APA, all rights reserved).

Competition, Conflict and Change of Mind: A Role of GABAergic Inhibition in the Primary Motor Cortex

Deciding between different voluntary movements implies a continuous control of the competition between potential actions. Many theories postulate a leading role of prefrontal cortices in this executive function, but strong evidence exists that a motor region like the primary motor cortex (M1) is also involved, possibly via inhibitory mechanisms. This was already shown during the pre-movement decision period, but not after movement onset. For this pilot experiment we designed a new task compatible with the dynamics of post-onset control to study the silent period (SP) duration, a pause in electromyographic activity after single-pulse transcranial magnetic stimulation that reflects inhibitory mechanisms. A careful analysis of the SP during the ongoing movement indicates a gradual increase in inhibitory mechanisms with the level of competition, consistent with an increase in mutual inhibition between alternative movement options. However, we also observed a decreased SP duration for high-competition trials associated with change-of-mind inflections in their trajectories. Our results suggest a new post-onset adaptive process that consists in a transient reduction of GABAergic inhibition within M1 for highly conflicting situations. We propose that this reduced inhibition softens the competition between concurrent motor options, thereby favoring response vacillation, an adaptive strategy that proved successful at improving behavioral performance.

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.

Optimal Readout of Correlated Neural Activity in a Decision-Making Circuit

The neural correlates of decision making have been extensively studied with tasks involving a choice between two alternatives that is guided by visual cues. While a large body of work argues for a role of the lateral intraparietal (LIP) region of cortex in these tasks, this role may be confounded by the interaction between LIP and other regions, including medial temporal (MT) cortex. Here, we describe a simplified linear model of decision making that is adapted to two tasks: a motion discrimination and a categorization task. We show that the distinct contribution of MT and LIP may indeed be confounded in these tasks. In particular, we argue that the motion discrimination task relies on a straightforward visuomotor mapping, which leads to redundant information between MT and LIP. The categorization task requires a more complex mapping between visual information and decision behavior, and therefore does not lead to redundancy between MT and LIP. Going further, the model predicts that noise correlations within LIP should be greater in the categorization compared to the motion discrimination task due to the presence of shared inputs from MT. The impact of these correlations on task performance is examined by analytically deriving error estimates of an optimal linear readout for shared and unique inputs. Taken together, results clarify the contribution of MT and LIP to decision making and help characterize the role of noise correlations in these regions.

Unbounded evidence accumulation characterizes subjective visual vertical forced-choice perceptual choice and confidence

Humans can subjectively yet quantitatively assess choice confidence based on perceptual precision even when a perceptual decision is made without an immediate reward or feedback. However, surprisingly little is known about choice confidence. Here we investigate the dynamics of choice confidence by merging two parallel conceptual frameworks of decision making, signal detection theory and sequential analyses (i.e., drift-diffusion modeling). Specifically, to capture end-point statistics of binary choice and confidence, we built on a previous study that defined choice confidence in terms of psychophysics derived from signal detection theory. At the same time, we augmented this mathematical model to include accumulator dynamics of a drift-diffusion model to characterize the time dependence of the choice behaviors in a standard forced-choice paradigm in which stimulus duration is controlled by the operator. Human subjects performed a subjective visual vertical task, simultaneously reporting binary orientation choice and probabilistic confidence. Both binary choice and confidence experimental data displayed statistics and dynamics consistent with both signal detection theory and evidence accumulation, respectively. Specifically, the computational simulations showed that the unbounded evidence accumulator model fits the confidence data better than the classical bounded model, while bounded and unbounded models were indistinguishable for binary choice data. These results suggest that the brain can utilize mechanisms consistent with signal detection theory-especially when judging confidence without time pressure.NEW & NOTEWORTHY We found that choice confidence data show dynamics consistent with evidence accumulation for a forced-choice subjective visual vertical task. We also found that the evidence accumulation appeared unbounded when judging confidence, which suggests that the brain utilizes mechanisms consistent with signal detection theory to determine choice confidence.

Modulation of Premotor and Primary Motor Cortical Activity during Volitional Adjustments of Speed-Accuracy Trade-Offs

Recent work suggests that while animals decide between reaching actions, neurons in dorsal premotor (PMd) and primary motor (M1) cortex reflect a dynamic competition between motor plans and determine when commitment to a choice is made. This competition is biased by at least two sources of information: the changing sensory evidence for one choice versus another, and an urgency signal that grows over time. Here, we test the hypothesis that the urgency signal adjusts the trade-off between speed and accuracy during both decision-making and movement execution. Two monkeys performed a reaching decision task in which sensory evidence continuously evolves over the course of each trial. In different blocks, task timing parameters encouraged monkeys to voluntarily adapt their behavior to be either hasty or conservative. Consistent with our hypothesis, during the deliberation process the baseline and gain of neural activity in decision-related PMd (29%) and M1 cells (45%) was higher when monkeys applied a hasty policy than when they behaved conservatively, but at the time of commitment the population activity was similar across blocks. Other cells (30% in PMd, 30% in M1) showed activity that increased or decreased with elapsing time until the moment of commitment. Movement-related neurons were also more active after longer decisions, as if they were influenced by the same urgency signal controlling the gain of decision-related activity. Together, these results suggest that the arm motor system receives an urgency/vigor signal that adjusts the speed-accuracy trade-off for decision-making and movement execution. Significance statement: This work addresses the neural mechanisms that control the speed-accuracy trade-off in both decisions and movements, in the kinds of dynamic situations that are typical of natural animal behavior. We found that many "decision-related" premotor and motor neurons are modulated in a time-dependent manner compatible with an "urgency" signal that changes between hasty and conservative decision policies. We also found that such modulation influenced cells related to the speed of the reaching movements executed by the animals to report their decisions. These results suggest that a unified mechanism determines speed-accuracy trade-off adjustments during decision-making and movement execution, potentially influencing both the cognitive and motor aspects of reward-oriented behavior.