# A Distributional Response Time Analysis of the Perceptual Disfluency Effect

## TL;DR

Blurred text can improve memory by affecting both early and late cognitive processes, with high-blur showing the strongest effect.

## Contribution

This study uses distributional and computational modeling to reveal how perceptual disfluency affects memory encoding mechanisms.

## Key findings

- High-blur text affects both early and late cognitive processes, leading to better memory retention.
- Low-blur text mainly influences early-stage processing without significant memory benefits.
- High-frequency words with high-blur show the strongest memory enhancement.

## Abstract

Perceptual disfluency, induced by blurring or difficult-to-read typefaces, can sometimes enhance memory retention, but the underlying mechanisms remain unclear. To investigate this effect, we manipulated blurring levels (clear, low-blur, high-blur) during encoding and assessed recognition performance in a surprise memory test. In Experiments 1A and 1B, response latencies from a lexical decision task were analyzed using ex-Gaussian distribution modeling and supplemented by drift diffusion modeling. Results showed that blurring differentially influenced parameters of the model, with high-blur affecting both early and late-stage processes, while low-blur primarily influenced early-stage processes. Recognition test results further revealed that high-blur words were remembered better than both clear and low-blurred words. Experiment 2 employed a semantic categorization task with a word frequency manipulation to further examine the locus of the perceptual disfluency effect. Similar to Experiments 1A and 1B, high-blur influenced both early and late-stage processes, while low-blur primarily affected early-stage processes. Low-frequency words exhibited greater shifting and skewing in distributional parameters, yet only high-frequency, highly blurred words demonstrated an enhanced memory effect. These findings suggest that both early and late cognitive processes contribute to the mnemonic benefits associated with perceptual disfluency. Overall, this study demonstrates that distributional and computational analyses provide powerful tools for dissecting encoding mechanisms and their effects on memory, offering valuable insights into models of perceptual disfluency.