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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 post-error 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.

Choice reaction time generally increases linearly with the logarithm of the number of potential stimulus-response alternatives, a regularity known as Hick's law. Two apparent violations of this generalization, which have been reported for aimed eye movements (Kveraga, Boucher, & Hughes, Experimental Brain Research, 146, 307-314, 2002), and arm movements (Wright, Marino, Belovsky, & Chubb, Experimental Brain Research, 179, 475-496, 2007), occurred when the indicator stimulus was an abrupt change at the location that was the target of the to-be-made movement. We report two experiments that examined and rejected the hypothesis that these abrupt-onset indicator stimuli triggered a shift in exogenous attention and that this led to unusually small uncertainty effects. Each experiment compared this indicator stimulus with a single alternative: Experiment 1 tested an indicator stimulus at all locations other than the target; Experiment 2 tested a central pointer to the target. Neither alternative led to an uncertainty effect for pointing responses that was of the size typically observed for other responses using the same stimuli.

Humans extract visual information from the world through spatial frequency (SF) channels that are sensitive to different scales of light-dark fluctuations across visual space. Using two methods, we measured human SF tuning for discriminating videos of human actions (walking, running, skipping and jumping). The first, more traditional, approach measured signal-to-noise ratio (s/n) thresholds for videos filtered by one of six Gaussian band-pass filters ranging from 4 to 128 cycles/image. The second approach used SF "bubbles", Willenbockel et al. (Journal of Experimental Psychology. Human Perception and Performance, 36(1), 122-135, 2010), which randomly filters the entire SF domain on each trial and uses reverse correlation to estimate SF tuning. Results from both methods were consistent and revealed a diagnostic SF band centered between 12-16 cycles/image (about 1-1.25 cycles/body width). Efficiency on this task was estimated by comparing s/n thresholds for humans to an ideal observer, and was estimated to be quite low (>.04%) for both experiments.