Abstract
A number of studies indicate that the use of digital technologies has an effect on human cognitive functions such as memory (Sparrow et al., 2011) and attention (Ward et al., 2017). However, to our knowledge, research is not available on how visual word recognition transforms if aided by word prediction features as in texting. We hypothesized that people who continually use digital autocorrection, spell-checkers, text prediction, and autocompletion for typing may be less successful in visual discrimination of words and non-words. Conversely, people who do not use digital word-processing features, or use them to a lesser extent, would distinguish words from non-words faster. We used a lexical decision task (LDT) to test visual word recognition in groups of self-reported users/heavy-users and non-users of the digital word-processing. Each group included 17 participants and performed 168 LDT trials in an online experiment, differentiating words from non-words by pressing a corresponding key on a keyboard. Our visually presented stimuli included 84 Russian nouns, length-matched by an equal number of pseudowords. To make sure that the groups did not differ in literacy, we conducted a literacy test after the experiment. Statistical analysis of the reaction times of both groups revealed a significant difference in the RT means between the groups. The high accuracy rates for both groups (97.7% for users and 97.1% for non-users) back up the validity of our RT data-driven findings. At the same time, in an ANOVA test examining literacy scores and reaction times interaction, we found no evidence that high literacy levels correlate with higher reaction times — a positive indication in favor of the effects of digital word-processing. Exercising cautious optimism in interpreting our results, we call for a greater sample size with participants of more diverse backgrounds and speakers of other languages in future studies.