Academic journal article Attention, Perception and Psychophysics

Testing Theories of Post-Error Slowing

Academic journal article Attention, Perception and Psychophysics

Testing Theories of Post-Error Slowing

Article excerpt

Published online: 22 November 2011

© The Author(s) 2011. This article is published with open access at

Abstract People tend to slow down after they make an error. This phenomenon, generally referred to as post-error slowing, has been hypothesized to reflect perceptual distraction, time wasted on irrelevant processes, an a priori bias against the response made in error, increased variability in a priori bias, or an increase in response caution. Although the response caution interpretation has dominated the empirical literature, little research has attempted to test this interpretation in the context of a formal process model. Here, we used the drift diffusion model to isolate and identify the psychological processes responsible for posterror slowing. In a very large lexical decision data set, we found that post-error slowing was associated with an increase in response caution and-to a lesser extent-a change in response bias. In the present data set, we found no evidence that post-error slowing is caused by perceptual distraction or time wasted on irrelevant processes. These results support a response-monitoring account of post-error slowing.

Keywords Response caution . Response time distributions . Cognitive control and automaticity . Diffusion model decomposition . Lexical decision

"What does a man do after he makes an error?" This question is just as valid as when it was first articulated by Rabbitt and Rodgers (1977), over 30 years ago. One answer to this question is that, after making an erroneous decision, one slows down on the next decision-an empirical regularity known as post-error slowing (PES; Laming, 1968, 1979a, 1979b; Rabbitt, 1966, 1979; Rabbitt & Rodgers, 1977). However, this answer raises a new and more interesting question: Namely, why does one slow down after making an error? Various answers have been proposed, and one of the main goals of this article is to implement these answers in a formal model of decision making so as to compare their adequacy in a precise and quantitative fashion.

The competing explanations for PES, detailed in the next section, are (1) increased response caution, (2) an a priori bias away from the response that was just made in error, (3) an overall decrease in the across-trial variability of a priori bias, (4) distraction of attention, and (5) delayed startup due to irrelevant processes (e.g., overcoming disappointment). We propose that these five explanations map uniquely onto parameters in a drift diffusion model for response time (RT) and accuracy (Ratcliff, 1978; Ratcliff & McKoon, 2008). As we will explain below, this one-to-one mapping between psychological processes and model parameters allows for an informative diffusion model decomposition of PES and a rigorous assessment of the extent to which each explanation (or, indeed, any combination of them) holds true.

A major practical obstacle that we needed to overcome was that the drift diffusion model requires relatively many observations to produce informative parameter estimates; as a rule of thumb, the model requires at least 10 error RTs in each experimental condition. Because the interest here centered on trials that follow an error, this means that the model required at least 10 errors that immediately followed an error. With an error rate of 5% throughout, the minimum number of observations would already be 4,000. Thus, a reliable diffusion model decomposition of PES would require a relatively large data set (or a data set with many errors). Here we fit the model to a lexical decision data set featuring 39 participants who each completed 28,074 trials of speeded two-choice decisions (Keuleers, Brysbaert, & New, 2010).

In the next sections, we will briefly discuss the different explanations for PES and formalize these predictions in the context of the drift diffusion model. We then test the different explanations by fitting the model to the lexical decision data from Keuleers, Brysbaert, & New (2010). …

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