Additive and interactive effects in semantic priming: Isolating lexical and decision processes in the lexical decision task.

Abstract

The present study sheds light on the interplay between lexical and decision processes in the lexical decision task by exploring the effects of lexical decision difficulty on semantic priming effects. In 2 experiments, we increased lexical decision difficulty by either using transposed letter wordlike nonword distracters (e.g., JUGDE; Experiment 1) or by visually degrading targets (Experiment 2). Although target latencies were considerably slowed by both difficulty manipulations, stimulus quality-but not nonword type-moderated priming effects, consistent with recent work by Lupker and Pexman (2010). To characterize these results in a more fine-grained manner, data were also analyzed at the level of response time (RT) distributions, using a combination of ex-Gaussian, quantile, and diffusion model analyses. The results indicate that for clear targets, priming was reflected by distributional shifting of comparable magnitude across different nonword types. In contrast, priming of degraded targets was reflected by shifting and an increase in the tail of the distribution. We discuss how these findings, along with others, can be accommodated by an embellished multistage activation model that incorporates retrospective prime retrieval and decision-based mechanisms.

How many trials are required for parameter estimation in diffusion modeling? A comparison of different optimization criteria

“…In recent years, however, the diffusion model has increasingly attracted the attention of researchers from various other fields of psychology. Examples indicating the wide range of applications for the diffusion model include analyses of cognitive processes in such typical experimental paradigms as the lexical decision task (e.g., Yap, Balota, & Tan, 2013), sequential priming paradigms (e.g., Voss, Rothermund, Gast, & Wentura, 2013), task switching (Schmitz & Voss, 2012, or prospective memory paradigms (e.g., Boywitt & Rummel, 2012). Other applications encompass social cognitive research (e.g., Germar, Schlemmer, Krug, Voss, & Mojzisch, 2014;Klauer, Voss, Schmitz, & Teige-Mocigemba, 2007;Voss, Rothermund, & Brandtstädter, 2008), cognitive aging (e.g., McKoon & Ratcliff, 2013;Spaniol, Madden, & Voss, 2006), cognitive processes related to psychological disorders (e.g., Metin et al, 2013;Pe, Vandekerckhove, & Kuppens, 2013;White, Ratcliff, Vasey, & McKoon, 2010b), and other fields of psychology.…”

The onset and time course of semantic priming during rapid recognition of visual words.

“…This distributional shift has been interpreted to reflect a processing head start (Balota et al, 2008; Yap et al, 2013; for semantic categorization: De Wit & Kinoshita, 2014; 2015a) and as supporting the widely held belief that priming during visual word recognition is driven by the rapid prospective activation of related targets triggered by the prime. However, the current study shows that the observation, or lack thereof, of a distributional shift is strongly affected by the response time floor of the measure.…”

“…Balota and colleagues (2008) found that LDTs with a short SOA (250 ms) showed a priming effect on μ and σ, and when targets were visually degraded, semantic priming affected μ and τ (Balota et al, 2008; Yap et al, 2013, both experiments used an 800 ms SOA). De Wit and Kinoshita (2015a) also observed a combination of μ- and τ-based priming for a lexical decision task with visually intact targets at a short SOA using a high proportion of related trials.…”

Processing of Emotion Words by Patients with Autism Spectrum Disorders: Evidence from Reaction Times and EEG

“…Additionally, we excluded all trials with very slow or very fast responses. The cutoff point for slow responses was set at 2.5 standard deviations of the subject mean (this is a common cutoff threshold in psycholinguistic studies, see for example Yap et al 2013;Chwilla et al 2000;van Hell and Dijkstra 2002), and was calculated for every participant separately. The cutoff point for the fast responses was set at 100 ms after stimulus onset.…”