The elusive tradeoff: Speed vs accuracy in visual discrimination tasks

Swensson, Richard G.

doi:10.3758/bf03212837

Cited by 165 publications

(146 citation statements)

References 43 publications

(67 reference statements)

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“…Boundary separation indirectly affects the balance between trial-to-trial variability in starting point and trial-to-trial variability in drift rate: when boundary separation is relatively small, the impact of trial-to-trial variability in starting point is relatively strong, and this prominent role of starting point variability results in relatively fast errors. Swensson (1972) and Ratcliff and Rouder (1998, Experiment 1) also used this particular experimental design to study the RT difference between correct responses and error responses. Swensson used an intricate pay-off system to influence the tradeoff between speed and accuracy, whereas Ratcliff and Rouder used instructions that stressed either accurate or speedy performance.…”

Section: Discussionmentioning

confidence: 99%

“…MROM and DRC use the same mechanism for lexical decision; in particular, both models assume that "nonword" responses are given when a temporal deadline is exceeded (cf. Swensson, 1972;Yellott, 1971). The temporal deadline mechanism marks a major conceptual divide between MROM and DRC on the one hand and the diffusion model on the other.…”

Section: The Deadline Model For Lexical Decisionmentioning

confidence: 99%

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A diffusion model account of criterion shifts in the lexical decision task

Wagenmakers

Ratcliff

Gómez

et al. 2008

Journal of Memory and Language

264

479

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Performance in the lexical decision task is highly dependent on decision criteria. These criteria can be influenced by speed versus accuracy instructions and word/nonword proportions. Experiment 1 showed that error responses speed up relative to correct responses under instructions to respond quickly. Experiment 2 showed that that responses to less probable stimuli are slower and less accurate than responses to more probable stimuli. The data from both experiments support the diffusion model for lexical decision . At the same time, the data provide evidence against the popular deadline model for lexical decision. The deadline model assumes that "nonword" responses are given only after the "word" response has timed out -consequently, the deadline model cannot account for the data from experimental conditions in which "nonword" responses are systematically faster than "word" responses.

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Section: Discussionmentioning

confidence: 99%

Section: The Deadline Model For Lexical Decisionmentioning

confidence: 99%

A diffusion model account of criterion shifts in the lexical decision task

Wagenmakers

Ratcliff

Gómez

et al. 2008

Journal of Memory and Language

264

479

View full text Add to dashboard Cite

show abstract

“…Slow errors are often observed especially when accuracy is emphasized during more difficult conditions. Slow errors have become an important hurdle for any model of decision times to overcome (Estes & Wessel, 1966;Luce, 1986;Swensson, 1972;Townsend & Ashby, 1983). We have added the result of slow errors as the sixth empirical hurdle in Table 1 that any complete model of cognitive performance must explain.…”

Section: Fit Of 2dsd Interrogation Model To Choices Decision Times mentioning

confidence: 99%

Two-stage dynamic signal detection: A theory of choice, decision time, and confidence.

Pleskac¹,

Busemeyer²

2010

Psychological Review

669

947

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The 3 most often-used performance measures in the cognitive and decision sciences are choice, response or decision time, and confidence. We develop a random walk/diffusion theory-2-stage dynamic signal detection (2DSD) theory-that accounts for all 3 measures using a common underlying process. The model uses a drift diffusion process to account for choice and decision time. To estimate confidence, we assume that evidence continues to accumulate after the choice. Judges then interrupt the process to categorize the accumulated evidence into a confidence rating. The model explains all known interrelationships between the 3 indices of performance. Furthermore, the model also accounts for the distributions of each variable in both a perceptual and general knowledge task. The dynamic nature of the model also reveals the moderating effects of time pressure on the accuracy of choice and confidence. Finally, the model specifies the optimal solution for giving the fastest choice and confidence rating for a given level of choice and confidence accuracy. Judges are found to act in a manner consistent with the optimal solution when making confidence judgments.

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“…To address this problem, as noted earlier, short outliers (RTs shorter than 300 ms for young subjects and shorter than 350 ms for older subjects) were trimmed out by examining the time at which accuracy began to rise above chance (e.g., Swensson, 1972) and long outliers (responses longer than 3,000 ms for young subjects and 4,000 ms for older subjects here) were also eliminated from analyses. Ratcliff and Tuerlinckx showed that any remaining long contaminant RTs can be explicitly modeled.…”

Section: Fits Of the Diffusion Model To The Datamentioning

confidence: 99%

A Diffusion Model Analysis of the Effects of Aging in the Lexical-Decision Task.

Ratcliff¹,

Thapar²,

Gómez³

et al. 2004

Psychology and Aging

278

329

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The effects of aging on response time (RT) are examined in 2 lexical-decision experiments with young and older subjects (age 60-75). The results show that the older subjects were slower than the young subjects, but more accurate. R. diffusion model provided a good account of RTs, their distributions, and response accuracy. The fits show an 80-100-ms slowing of the nondecision components of RT for older subjects relative to young subjects and more conservative decision criterion settings for older subjects than for young subjects. The rates of accumulation of evidence were not significantly different for older compared with young subjects (less than 2% and 5% higher for older subjects relative to young subjects in the 2 experiments).Across a wide variety of cognitive tasks, research has shown that processing slows with age. For some tasks, especially those like letter discrimination that depend heavily on peripheral processes, this is not surprising (e.g., . However, for other tasks it might be expected that performance would improve with age. One such task is lexical decision, the task of interest in this article. Over a lifetime of 60 to 70 years, the number of encounters with many words must greatly exceed the number of encounters in the first 20 years. Yet despite so many years of practice, lexical-decision response times (RTs) increase with age. For example, Allen, Madden, and Crozier (1991) found average RTs of 800 ms for older adults compared with 500 ms for young adults. Word frequency effects, longer RTs with lower frequency words, are also larger for older adults (see Allen et al., 1991;Allen, Madden, Weber, & Groth, 1993;Allen, Sliwinski, & Bowie, 2002;Lima, Hale, & Myerson, 1991).Recently, Ratcliff, Gomez, and McKoon (2004) have applied the diffusion model for twochoice decisions (Ratcliff, 1978(Ratcliff, , 1981(Ratcliff, , 1985(Ratcliff, , 1988(Ratcliff, , 2002Ratcliff & Rouder, 1998Ratcliff & Smith, 2004;Ratcliff, Van Zandt, & McKoon, 1999) to lexical-decision data. The model allows processing to be separated into several components: the rate at which information about the stimulus string of letters accumulates in the decision system (which reflects the goodness of match between the test string and lexical memory), the criteria that determine the amounts of information that must be accumulated before a decision can be made, nondecision

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The elusive tradeoff: Speed vs accuracy in visual discrimination tasks

Cited by 165 publications

References 43 publications

A diffusion model account of criterion shifts in the lexical decision task

A diffusion model account of criterion shifts in the lexical decision task

Two-stage dynamic signal detection: A theory of choice, decision time, and confidence.

A Diffusion Model Analysis of the Effects of Aging in the Lexical-Decision Task.

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