scholarly journals Post-error slowing reflects the joint impact of adaptive and maladaptive processes during decision making

2021 ◽  
Author(s):  
Fanny Fievez ◽  
Gerard Derosiere ◽  
Frederick Verbruggen ◽  
Julie Duque

Errors and their consequences are typically studied by investigating changes in decision speed and accuracy in trials that follow an error, commonly referred to as "post-error adjustments". Many studies have reported that subjects slow down following an error, a phenomenon called "post-error slowing" (PES). However, the functional significance of PES is still a matter of debate as it is not always adaptive. That is, it is not always associated with a gain in performance and can even occur with a decline in accuracy. Here, we hypothesized that the nature of PES is influenced by one's speed-accuracy tradeoff policy, which determines the overall level of choice accuracy in the task at hand. To test this hypothesis, we investigated post-error adjustments in subjects performing the same task while they were required to either emphasize speed (low accuracy) or cautiousness (high accuracy) in two distinct contexts (hasty and cautious contexts, respectively) experienced on separate days. Accordingly, our data indicate that post-error adjustments varied according to the context in which subjects performed the task, with PES being solely significant in the hasty context. In addition, we only observed a gain in performance after errors in a specific trial type, suggesting that post-error adjustments depend on a complex combination of processes that affect the speed of ensuing actions as well as the degree to which such PES comes with a gain in performance.

2020 ◽  
Author(s):  
Kobe Desender ◽  
Luc Vermeylen ◽  
Tom Verguts

AbstractHumans differ in their capability to judge the accuracy of their own choices via confidence judgments. Signal detection theory has been used to quantify the extent to which confidence tracks accuracy via M-ratio, often referred to as metacognitive efficiency. This measure, however, is static in that it does not consider the dynamics of decision making. This could be problematic because humans may shift their level of response caution to alter the tradeoff between speed and accuracy. Such shifts could induce unaccounted-for sources of variation in the assessment of metacognition. Instead, evidence accumulation frameworks consider decision making, including the computation of confidence, as a dynamic process unfolding over time. We draw on evidence accumulation frameworks to examine the influence of response caution on metacognition. Simulation results demonstrate that response caution has an influence on M-ratio. We then tested and confirmed that this was also the case in human participants who were explicitly instructed to either focus on speed or accuracy. We next demonstrated that this association between M-ratio and response caution was also present in an experiment without any reference towards speed. The latter finding was replicated in an independent dataset. In contrast, when data were analyzed with a novel dynamic measure of metacognition, which we refer to as v-ratio, in all of the three studies there was no effect of speed-accuracy tradeoff. These findings have important implications for research on metacognition, such as the question about domain-generality, individual differences in metacognition and its neural correlates.


2021 ◽  
Author(s):  
Jeff Larson ◽  
Guy Hawkins

A fundamental aspect of decision making is the speed-accuracy tradeoff (SAT): slower decisions tend to be more accurate, but since time is a scarce resource people prefer to conclude decisions more quickly. The current research adds to the SAT literature by documenting two previously unrecognized influences on the SAT: perception shifts and goal activation. Decision makers' perceptions of what constitutes a fast or a slow decision, and what constitutes an accurate or inaccurate decision, are based on prior experience, and these perceptions influence decision speed. Similarly, previous experience in a decision context associates the context with a particular decision goal. Thus, in later decisions the decision context will activate this goal, and thereby influence decision speed. Both of these mechanisms contribute to a specific decision bias: decision speeds are biased toward original decision speeds in a decision context. Four experiments provide evidence for the bias and the two contributing mechanisms.


2017 ◽  
Vol 29 (8) ◽  
pp. 1433-1444 ◽  
Author(s):  
Tuğçe Tosun ◽  
Dilara Berkay ◽  
Alexander T. Sack ◽  
Yusuf Ö. Çakmak ◽  
Fuat Balcı

Decisions are made based on the integration of available evidence. The noise in evidence accumulation leads to a particular speed–accuracy tradeoff in decision-making, which can be modulated and optimized by adaptive decision threshold setting. Given the effect of pre-SMA activity on striatal excitability, we hypothesized that the inhibition of pre-SMA would lead to higher decision thresholds and an increased accuracy bias. We used offline continuous theta burst stimulation to assess the effect of transient inhibition of the right pre-SMA on the decision processes in a free-response two-alternative forced-choice task within the drift diffusion model framework. Participants became more cautious and set higher decision thresholds following right pre-SMA inhibition compared with inhibition of the control site (vertex). Increased decision thresholds were accompanied by an accuracy bias with no effects on post-error choice behavior. Participants also exhibited higher drift rates as a result of pre-SMA inhibition compared with the vertex inhibition. These results, in line with the striatal theory of speed–accuracy tradeoff, provide evidence for the functional role of pre-SMA activity in decision threshold modulation. Our results also suggest that pre-SMA might be a part of the brain network associated with the sensory evidence integration.


2020 ◽  
Author(s):  
Amélie J. Reynaud ◽  
Clara Saleri Lunazzi ◽  
David Thura

ABSTRACTA growing body of evidence suggests that decision-making and action execution are governed by partly overlapping operating principles. Especially, previous work proposed that a shared decision urgency/movement vigor signal, possibly computed in the basal ganglia, coordinates both deliberation and movement durations in a way that maximizes the reward rate. Recent data support one aspect of this hypothesis, indicating that the urgency level at which a decision is made influences the vigor of the movement produced to express this choice. Here we investigated whether conversely, the motor context in which a movement is executed determines decision speed and accuracy. Twenty human subjects performed a probabilistic decision task in which perceptual choices were expressed by reaching movements toward targets whose size and distance from a starting position varied in distinct blocks of trials. We found strong evidence for an influence of the motor context on most of the subjects’ decision policy but contrary to the predictions of the “shared regulation” hypothesis, we observed that slow movements executed in the most demanding motor blocks in terms of accuracy were often preceded by faster and less accurate decisions compared to blocks of trials in which big targets allowed expression of choices with fast and inaccurate movements. These results suggest that decision-making and motor control are not regulated by one unique “invigoration” signal determining both decision urgency and action vigor, but more likely by independent, yet interacting, decision urgency and movement vigor signals.NEW & NOTEWORTHYRecent hypotheses propose that choices and movements share optimization principles derived from economy, possibly implemented by one unique context-dependent regulation signal determining both processes speed. In the present behavioral study conducted on human subjects, we demonstrate that action properties indeed influence perceptual decision-making, but that decision duration and action vigor are actually independently set depending on the difficulty of the movement executed to report a choice.


2018 ◽  
Author(s):  
Kobe Desender ◽  
Annika Boldt ◽  
Tom Verguts ◽  
Tobias H. Donner

AbstractWhen external feedback about decision outcomes is lacking, agents need to adapt their decision policies based on an internal estimate of the correctness of their choices (i.e., decision confidence). We hypothesized that agents use confidence to continuously update the tradeoff between the speed and accuracy of their decisions: When confidence is low in one decision, the agent needs more evidence before committing to a choice in the next decision, leading to slower but more accurate decisions. We tested this hypothesis by fitting a bounded accumulation decision model to behavioral data from three different perceptual choice tasks. Decision bounds indeed depended on the reported confidence on the previous trial, independent of objective accuracy. This increase in decision bound was predicted by a centro-parietal EEG component sensitive to confidence. We conclude that the brain uses internally computed confidence signals for the ongoing adjustment of decision policies.


Author(s):  
Xiaolei Zhou ◽  
Xiangshi Ren

A tradeoff between speed and accuracy is a very common phenomenon in many types of human motor tasks. In general, the accuracy of a movement tends to decrease when its speed increases and the speed of a movement tends to decrease with an increase in its accuracy. This phenomenon has been studied for more than a century, during which several alternative performance models that account for the tradeoff between speed and accuracy have been presented. In this chapter, the authors present a critical survey of the scientific literature that discusses speed-accuracy tradeoff models of target-based and trajectory-based movement; these two types of movement are the major popular task paradigms in studies of human-computer interactions. Some of the models emerged from basic research in experimental psychology and motor control theory, whereas others emerged from a specific need to model the interaction between users and physical devices, such as mice, keyboards, and styluses in the field of Human-Computer Interaction (HCI). This chapter summarizes these models from the perspectives of spatial constraints and temporal constraints for both target-based and trajectory-based movements.


2016 ◽  
Vol 113 (45) ◽  
pp. 12868-12873 ◽  
Author(s):  
Mehdi Keramati ◽  
Peter Smittenaar ◽  
Raymond J. Dolan ◽  
Peter Dayan

Behavioral and neural evidence reveal a prospective goal-directed decision process that relies on mental simulation of the environment, and a retrospective habitual process that caches returns previously garnered from available choices. Artificial systems combine the two by simulating the environment up to some depth and then exploiting habitual values as proxies for consequences that may arise in the further future. Using a three-step task, we provide evidence that human subjects use such a normative plan-until-habit strategy, implying a spectrum of approaches that interpolates between habitual and goal-directed responding. We found that increasing time pressure led to shallower goal-directed planning, suggesting that a speed-accuracy tradeoff controls the depth of planning with deeper search leading to more accurate evaluation, at the cost of slower decision-making. We conclude that subjects integrate habit-based cached values directly into goal-directed evaluations in a normative manner.


PLoS ONE ◽  
2008 ◽  
Vol 3 (7) ◽  
pp. e2635 ◽  
Author(s):  
Jason Ivanoff ◽  
Philip Branning ◽  
René Marois

2020 ◽  
Author(s):  
Gregory Edward Cox ◽  
Gordon D. Logan ◽  
Jeffrey Schall ◽  
Thomas Palmeri

Evidence accumulation is a computational framework that accounts for behavior as well as the dynamics of individual neurons involved in decision making. Linking these two levels of description reveals a scaling paradox: How do choices and response times (RT) explained by models assuming single accumulators arise from a large ensemble of idiosyncratic accumulator neurons? We created a simulation model that makes decisions by aggregating across ensembles of accumulators, thereby instantiating the essential structure of neural ensembles that make decisions. Across different levels of simulated choice difficulty and speed-accuracy emphasis, choice proportions and RT distributions simulated by the ensembles are invariant to ensemble size and the accumulated evidence at RT is invariant across RT when the accumulators are at least moderately correlated in either baseline evidence or rates of accumulation and when RT is not governed by the most extreme accumulators. To explore the relationship between the low-level ensemble accumulators and high-level cognitive models, we fit simulated ensemble behavior with a standard LBA model. The standard LBA model generally recovered the core accumulator parameters (particularly drift rates and residual time) of individual ensemble accumulators with high accuracy, with variability parameters of the standard LBA modulating as a function of various ensemble parameters. Ensembles of accumulators also provide an alternative conception of speed-accuracy tradeoff without relying on varying thresholds of individual accumulators, instead by adjusting how ensembles of accumulators are aggregated or by how accumulators are correlated within ensembles. These results clarify relationships between neural and computational accounts of decision making.


2015 ◽  
Vol 114 (1) ◽  
pp. 650-661 ◽  
Author(s):  
Chung-Chuan Lo ◽  
Cheng-Te Wang ◽  
Xiao-Jing Wang

A hallmark of flexible behavior is the brain's ability to dynamically adjust speed and accuracy in decision-making. Recent studies suggested that such adjustments modulate not only the decision threshold, but also the rate of evidence accumulation. However, the underlying neuronal-level mechanism of the rate change remains unclear. In this work, using a spiking neural network model of perceptual decision, we demonstrate that speed and accuracy of a decision process can be effectively adjusted by manipulating a top-down control signal with balanced excitation and inhibition [balanced synaptic input (BSI)]. Our model predicts that emphasizing accuracy over speed leads to reduced rate of ramping activity and reduced baseline activity of decision neurons, which have been observed recently at the level of single neurons recorded from behaving monkeys in speed-accuracy tradeoff tasks. Moreover, we found that an increased inhibitory component of BSI skews the decision time distribution and produces a pronounced exponential tail, which is commonly observed in human studies. Our findings suggest that BSI can serve as a top-down control mechanism to rapidly and parametrically trade between speed and accuracy, and such a cognitive control signal presents both when the subjects emphasize accuracy or speed in perceptual decisions.


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