Theories of perceptual decision making have been dominated by the idea that evidence accumulates in favor of different alternatives until some fixed threshold amount is reached, which triggers a decision. Recent theories have suggested that these thresholds may not be fixed during each decision but change as time passes. These collapsing…

In high-stakes, large-scale, standardized tests with certain time limits, examinees are likely to engage in either one of the three types of behavior (e.g., van der Linden & Guo, 2008; Wang & Xu, 2015): solution behavior, rapid guessing behavior, and cheating behavior. Oftentimes examinees do not always solve all items due to various…

In past decades, researchers have conducted a myriad of masked priming lexical decision experiments aimed at unveiling the early processes underlying lexical access. A relatively overlooked question is whether a masked unrelated wordlike/unwordlike prime influences the processing of the target stimuli. If participants apply to the primes the same…

For decisions between many alternatives, the benchmark result is Hick's Law: that response time increases log-linearly with the number of choice alternatives. Even when Hick's Law is observed for response times, divergent results have been observed for error rates--sometimes error rates increase with the number of choice alternatives, and…

A dominant theme in modeling human perceptual judgments is that sensory neural activity is summed or integrated until a critical bound is reached. Such models predict that, in general, the shape of response time distributions change across conditions, although in practice, this shape change may be subtle. An alternative view is that response time…

This article presents a theory of visual word recognition that assumes that, in the tasks of word identification, lexical decision, and semantic categorization, human readers behave as optimal Bayesian decision makers. This leads to the development of a computational model of word recognition, the Bayesian reader. The Bayesian reader successfully…