accuracy tradeoff
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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.


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.


2021 ◽  
Author(s):  
Ma Chunyu ◽  
Noha Mohsen Zommara ◽  
Kajornvut Ounjai ◽  
Xi Ju ◽  
Johan Lauwereyns

Abstract In human perceptual decision-making, the speed-accuracy tradeoff establishes a causal link between urgency and reduced accuracy. Less is known about how urgency affects the moral evaluation of visual images. Here, we asked participants to give ratings for a diverse set of real-world images on a continuous scale from -10 (“very immoral”) to +10 (“very moral”). We used a cueing procedure to inform the participants on a trial-by-trial basis whether they could make a Self-Paced (SP) evaluation or whether they had to perform a Time-Limited (TL) evaluation within 2 seconds. In the SP condition, fast responses were associated with more extreme evaluations. Compared to the SP condition, the responses in the TL condition were much faster, indicating that our urgency manipulation was successful. However, comparing the SP versus TL conditions, we found no significant differences in the moral evaluation of the real-world images. The data indicated that, while speed is associated with polarization, urgency does not cause participants to make more extreme evaluations. Instead, the correlation between speed and polarization likely reflects the ease of processing. Images that are obviously moral or immoral are categorized faster and given more extreme evaluations than images for which the moral interpretation is uncertain.


2021 ◽  
Vol 4 (2) ◽  
pp. 102-108
Author(s):  
Walid Amin Mahmoud

A novel fast and efficient algorithm was proposed that uses the Fast Fourier Transform (FFT) as a tool to compute the Discrete Wavelet Transform (DWT) and Discrete Multiwavelet Transform. The Haar Wavelet Transform and the GHM system are shown to be a special case of the proposed algorithm, where the discrete linear convolution will adapt to achieve the desired approximation and detail coefficients. Assuming that no intermediate coefficients are canceled and no approximations are made, the algorithm will give the exact solution. Hence the proposed algorithm provides an efficient complexity verses accuracy tradeoff.   The main advantages of the proposed algorithm is that high band and the low band coefficients can be exploited for several classes of signals resulting in very low computation.


2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Jongsoo Baek ◽  
Hae-Jeong Park

AbstractMost psychological experiments measure human cognitive function through the response time and accuracy of the response to a set of stimuli. Since response time and accuracy complement each other, it is often difficult to interpret cognitive performance based on only one dependent measurement and raises a speed-accuracy tradeoff (SAT) problem. In overcoming this problem, SAT experimental paradigms and models that integrate response time and accuracy have been proposed to understand information processing in human cognitive function. However, due to a lengthy SAT experiment for reliable model estimation, SAT experiments' practical limitations have been pointed out. Thus, these limitations call for an efficient technique to shorten the number of trials required to estimate the SAT function reliably. Instead of using a block's stimulus-onset asynchrony (SOA) accuracy with long block-based task trials, we introduced a Bayesian SAT function estimation using trial-by-trial response time and correctness, which makes SAT tasks flexible and easily extendable to multiple trials. We then proposed a Bayesian adaptive method to select optimal SOA by maximizing information gain to estimate model parameters. Simulation results showed that the proposed Bayesian adaptive estimation was highly efficient and robust for accuracy and precision of estimating SAT function by enabling "multiple-step ahead search."


2021 ◽  
Author(s):  
Pavel Logacev

A number of studies have found evidence for the so-called ambiguity advantage, i.e., a speed-up in processing ambiguous sentences compared to their unambiguous counterparts. While a number of proposals regarding the mechanism underlying this phenomenon have been made, the empirical evidence so far is far from unequivocal. It is compatible with several theories, including strategic underspecification (Swets et al., 2008), race models (Van Gompel et al., 2000; Logacev and Vasishth, 2016), and a more recentcoactivation-based account (Dillon et al., 2019). While all three classes of theories make matching predictions for the average time to complete RC attachment in ambiguous compared to unambiguous sentences, their predictions diverge with regard to theminimum completion times. I used the speed-accuracy tradeoff procedure to test the predictions of all three classesof theories. According to a hierarchical Bayesian model, the speed-accuracy tradeoff functions (SATFs) for different RC attachment conditions (high, low or ambiguous) show an earlier departure from chance performance in the ambiguous condition than in either of the unambiguous conditions. The results further indicate increased asymptotic accuracy but no increase in processing rate in the ambiguous condition. Taken together, this pattern of results is compatible with the strategic underspecification model, and to a lesser degree with coactivation based accounts.


2021 ◽  
Author(s):  
EDWIN CHAU ◽  
Carolyn A. Murray ◽  
ladan shams

Studies of accuracy and reaction time in decision making often observe a speed-accuracy tradeoff, where either accuracy or reaction time is sacrificed for the other. While this effect may mask certain multisensory benefits in performance when accuracy and reaction time are separately measured, drift diffusion models (DDMs) are able to consider both simultaneously. However, drift diffusion models are often limited by large sample size requirements for reliable parameter estimation. One solution to this restriction is the use of hierarchical Bayesian estimation for DDM parameters. Here, we utilize hierarchical drift diffusion models (HDDMs) to reveal a multisensory advantage in auditory-visual numerosity discrimination tasks. By fitting this model with a modestly sized dataset, we also demonstrate that large sample sizes are not necessary for reliable parameter estimation.


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