Skip to content

Author

Jason Tipples

5 papers indexed here

We haven’t gathered this author’s papers yet. Follow them and we’ll fetch their work.

Not the right person? Other researchers publish under this name.

#diffusion models Open access Oct 2026

Emotional valence and arousal in spoken word recognition: a hierarchical drift diffusion reanalysis of MALD

Emotional valence and arousal in spoken word recognition: a hierarchical drift diffusion reanalysis of MALD This project asks whether a spoken word’s emotional valence and arousal affect how quickly listeners accumulate evidence that it is a word. We reanalyse the Massive Auditory Lexical Decision database (MALD; Tucke...

Jason Tipples · 0 citations
#diffusion models Open access Oct 2026

Face Race, Face Sex, and Emotion Categorisation: A Bayesian Psychometric and Diffusion Model Investigation of Happy-Angry Judgements

Human observers often agree when facial expressions are clearly happy or clearly angry. However, when expressions become ambiguous, emotion judgements may be influenced by characteristics of the face itself. Previous research suggests that facial emotion recognition is affected by face sex, gender stereotypes, facial s...

Jason Tipples · 0 citations
#diffusion models Open access Oct 2026

AsPredicted pre-registration: cueing mechanisms in Schmitz et al. (2024) Experiment 2

This is a registered secondary analysis of public data from Experiment 2 of Schmitz et al. (2024, PLOS ONE), in which 39 participants identified a peripheral target letter after a central cue. The cue was an arrow, a schematic face, or a colour, and was either neutral or 100% counter-predictive (arrows and faces) or in...

Jason Tipples · 0 citations
#diffusion models Open access Oct 2026

Where in the RT distribution do cues act? A distributional (ex-Gaussian) reanalysis of arrow, gaze and colour cueing in Schmitz et al. (2024), Experiments 1 and 2

Secondary analysis of two public cueing datasets (Schmitz et al., 2024). Experiment 1 uses non-predictive arrow and face cues. Experiment 2 uses 100% counter-predictive arrow, face and colour cues. The question is where in the RT distribution cues act: a shift of the whole distribution (the ex-Gaussian location mu), a...

Jason Tipples · 0 citations
#diffusion models Book Open access Sep 2026

R Code for: "Gaze Cueing Across Correct and Error Response-Time Distributions: Constraints on Cognitive Process Accounts"

Exp3_observed_quantile_effects.R — Calculates participant-level RT quantiles separately by gaze validity, response accuracy, and SOA for Data Set 3. Computes within-participant invalid-minus-valid effects at the .10, .25, .50, .75, and .90 quantiles and produces the observed distributional-effects figure used in the ma...

Jason Tipples · 0 citations

We use cookies to run the site and, with your consent, for analytics and to show ads. See our Cookie Policy.