How to optimize switch virtual keyboards to trade off speed and accuracy.
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ABSTRACT:
SUBMITTER: Zhang X
PROVIDER: S-EPMC5256434 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:Perceptual decision making has been successfully modeled as a process of evidence accumulation up to a threshold. In order to maximize the rewards earned for correct responses in tasks with response deadlines, participants should collapse decision thresholds dynamically during each trial so that a decision is reached before the deadline. This strategy ensures on-time responding, though at the cost of reduced accuracy, since slower decisions are based on lower thresholds and less net evidence later in a trial (compared to a constant threshold). Frazier and Yu (2008) showed that the normative rate of threshold reduction depends on deadline delays and on participants' uncertainty about these delays. Participants should start collapsing decision thresholds earlier when making decisions under shorter deadlines (for a given level of timing uncertainty) or when timing uncertainty is higher (for a given deadline). We tested these predictions using human participants in a random dot motion discrimination task. Each participant was tested in free-response, short deadline (800 ms), and long deadline conditions (1000 ms). Contrary to optimal-performance predictions, the resulting empirical function relating accuracy to response time (RT) in deadline conditions did not decline to chance level near the deadline; nor did the slight decline we typically observed relate to measures of endogenous timing uncertainty. Further, although this function did decline slightly with increasing RT, the decline was explainable by the best-fitting parameterization of Ratcliff's diffusion model (Ratcliff, 1978), whose parameters are constant within trials. Our findings suggest that at the very least, typical decision durations are too short for participants to adapt decision parameters within trials.
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Project description:In an attempt to simplify data analysis and to avoid confounds due to speed-accuracy trade-off, sometimes integrated measures of speed and accuracy are used. Although it has been claimed that some of these combined measures are insensitive to speed-accuracy trade-off (SAT), a systematic and broad examination of such claims has not been performed thus far. The present article reports the results of four simulation studies in which five established integrated measures were studied in different speed-accuracy trade-off contexts. All four studies used repeated measures designs crossing an experimental factor (variable of interest) with a factor representing SAT settings, with all conditions occurring randomly over the sequence of trials to avoid condition-wise SATs (mixed conditions repeated measures design). The first study used speed modulations that were balanced by accuracy changes in the opposite direction. The other studies were all based on SAT as modeled either by the drift-diffusion model, with pro-active trade-off settings (Study 2) or with reactive trade-off modulations (Study 3) or by a discontinuous two-phase model (Study 4). Only the studies based on balanced trade-offs showed that two of the measures were insensitive to SAT settings, while in all other contexts, all measures were sensitive to SAT. Nevertheless, as the mixed conditions design distributes the SAT effects over the conditions of the variable of interest, all integrated measures reliably detected the effect of this variable in all SAT conditions. Although integrated measures are sensitive to SAT, these effects can be neutralised by using a mixed conditions repeated measures design.
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Project description:[This corrects the article DOI: 10.5334/joc.154.].
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Project description:Two basic trade-offs interact while our brain decides how to move our body. First, with the cost-benefit trade-off, the brain trades between the importance of moving faster toward a target that is more rewarding and the increased muscular cost resulting from a faster movement. Second, with the speed-accuracy trade-off, the brain trades between how accurate the movement needs to be and the time it takes to achieve such accuracy. So far, these two trade-offs have been well studied in isolation, despite their obvious interdependence. To overcome this limitation, we propose a new model that is able to simultaneously account for both trade-offs. The model assumes that the central nervous system maximizes the expected utility resulting from the potential reward and the cost over the repetition of many movements, taking into account the probability to miss the target. The resulting model is able to account for both the speed-accuracy and the cost-benefit trade-offs. To validate the proposed hypothesis, we confront the properties of the computational model to data from an experimental study where subjects have to reach for targets by performing arm movements in a horizontal plane. The results qualitatively show that the proposed model successfully accounts for both cost-benefit and speed-accuracy trade-offs.
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Project description:Fitts' Law describes the speed-accuracy trade-off of human movements, and it is an elegant strategy that compensates for random and uncontrollable noise in the motor system. The control strategy during targeted movements may also take into account the rewards or costs of any outcomes that may occur. The aim of this study was to test the hypothesis that movement time in Fitts' Law emerges not only from the accuracy constraints of the task, but also depends on the perceived cost of error for missing the targets. Subjects were asked to touch targets on an iPad® screen with different costs for missed targets. We manipulated the probability of error by comparing children with dystonia (who are characterized by increased intrinsic motor variability) to typically developing children. The results show a strong effect of the cost of error on the Fitts' Law relationship characterized by an increase in movement time as cost increased. In addition, we observed a greater sensitivity to increased cost for children with dystonia, and this behavior appears to minimize the average cost. The findings support a proposed mathematical model that explains how movement time in a Fitts-like task is related to perceived risk.
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Project description:Many natural and artificial decision-making systems face decision problems where there is an inherent compromise between two or more objectives. One such common compromise is between the speed and accuracy of a decision. The ability to exploit the characteristics of a decision problem in order to vary between the extremes of making maximally rapid, or maximally accurate decisions, is a useful property of such systems. Colonies of the ant Temnothorax albipennis (formerly Leptothorax albipennis) are a paradigmatic decentralized decision-making system, and have been shown flexibly to compromise accuracy for speed when making decisions during house-hunting. During emigration, a colony must typically evaluate and choose between several possible alternative new nest sites of differing quality. In this paper, we examine this speed-accuracy trade-off through modelling, and conclude that noise and time-cost of assessing alternative choices are likely to be significant for T. albipennis. Noise and cost of such assessments are likely to mean that T. albipennis' decision-making mechanism is Pareto-optimal in one crucial regard; increasing the willingness of individuals to change their decisions cannot improve collective accuracy overall without impairing speed. We propose that a decentralized control algorithm based on this emigration behaviour may be derived for applications in engineering domains and specify the characteristics of the problems to which it should be suited, based on our new results.
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