How Noisy is Lexical Decision?

How Noisy is Lexical Decision?

Lexical decision is one of the most frequently used tasks in word recognition research. Theoretical conclusions are typically derived from a linear model on the reaction times (RTs) of correct word trials only (e.g., linear regression and ANOVA). Although these models estimate random measurement error for RTs, considering only correct trials implicitly assumes that word/non-word categorizations...

How lexical decision is affected by recent experience: symmetric versus asymmetric frequency-blocking effects.

In a lexical decision task (LDT) in which list composition is manipulated, a typical finding to date has been a slowdown for easy items (e.g., high-frequency words) but little speedup for hard items (e.g., low-frequency words) when they are mixed together. This asymmetric frequency-blocking effect contrasts with the symmetric pattern (both a speedup for hard items and a slowdown for easy items ...

Economic irrationality is optimal during noisy decision making.

According to normative theories, reward-maximizing agents should have consistent preferences. Thus, when faced with alternatives A, B, and C, an individual preferring A to B and B to C should prefer A to C. However, it has been widely argued that humans can incur losses by violating this axiom of transitivity, despite strong evolutionary pressure for reward-maximizing choices. Here, adopting a ...

Robust Lexical Acquisition Despite Extremely Noisy Input

How Hard is Computing Parity with Noisy Communications?

We show a tight lower bound of Ω(N log logN) on the number of transmissions required to compute the parity of N input bits with constant error in a noisy communication network of N randomly placed sensors, each having one input bit and communicating with others using local transmissions with power near the connectivity threshold. This result settles the lower bound question left open by Ying, S...