Jens Roeser 1,
, @sentwrite.bsky.social

Pablo Aros Muñoz1 Mark Torrance1

1 Department of Psychology, Nottingham Trent University

Presented at the 31\(^\text{st}\) AMLaP conference in Prague, Czechia, Friday 5\(^\text{th}\) September 2025, 10 – 11.30 am.

Funded by UKRI ESRC (ES/W011832/1) – Project title: “Can you use it in a sentence?”: Establishing how word-production difficulties shape text formation

Poster: rpubs.com/jensroes/amlap-2025 (or use QR code)


Introduction

  • Expressing ideas in language involves a cascade of processes starting with an intended message, moving on to retrieving words, their spelling or phonology, and finally outputting them in speech, onto paper or screen.
  • Our understanding of the timecourse of linguistic planning and spelling retrieval in writing is limited.
  • Word spelling may or may not be fully retrieved at word onset (Kandel, 2023) and upcoming linguistic information may be planned at linguistic edges or in parallel to production (Roeser et al., 2025).
  • We tested to what extent difficulty with orthography delays planning of upcoming information.
  • Importantly, only the parallel view predicts that disruptions delay planning of upcoming information.

Conclusion

  • Spelling difficulty disrupts parallel planning!
  • Hesitations related to N1 spelling affect N2 retrieval when there was not enough time for parallel planning.
  • Written sentence planning unfolds in parallel to production: difficulty with spelling creates a planning bottleneck with knock-on effects on higher level planning.


Methods

Design

Task: Describe the image arrangement from left to right.

Example item [see e.g. @roeser2019advance].

Figure 1: Example item (see e.g. Roeser et al., 2019).

N1 type: easy spelling / long, easy spelling / short, difficult spelling / long – see Figure 1 – where long is \(\ge\) 2 syllables and difficult is \(H_\text{spell}\) > 0.5.

Prediction: Retrieval of N2 (clown) is delayed if N1 doesn’t allow for parallel planning when N1 is difficult to spell (broccoli) or too short (wok) .

Spelling diversity

Images were based on Rossion and Pourtois (2004). Spelling diversity was defined as

\[\begin{equation*} \small \begin{aligned} H_\text{spell} = \sum_{i = 1}^K p_i \times \text{log}_2\left(\frac{1}{p_i}\right), \end{aligned} \end{equation*}\]

where \(k\) is the number of spellings for a picture name and \(p_i\) is the proportion of participants producing the \(i^{th}\) spelling (Torrance et al., 2018).

Table 1: Example summary of “raccoon” with naming responses of 97 participants.
response \(\mathit{N}_{resp}\) \(\mathit{N}_{name}\) \(\mathit{Pr}_{name}\) \(\mathit{H}_{name}\) \(\mathit{Pr}_{spell}\) \(\mathit{H}_{spell}\)
raccoon 41 75 0.77 2.48 0.55 1.31
racoon 30 75 0.77 2.48 0.40 1.31
raccon 2 75 0.77 2.48 0.03 1.31
raccoo 1 75 0.77 2.48 0.01 1.31
racoo 1 75 0.77 2.48 0.01 1.31


Materials, Procedure & Participants

Thirty-six items with auditory prime (simple, conjoined subject NP) controlling for N1 retrieval; see Figure 2.

Example window sequence.

Figure 2: Example window sequence.

  • 24 fillers with structures different form items.
  • Sentence-picture match: YES for items; NO for fillers.

Ten ppts per counterbalancing list. Exp. 1: 56 ppts (6 lists), Exp. 2: 80 ppts (9 lists), Exp. 3: 96 ppts (12 lists).

Writing timecourse analysis

Hesitation probability \(\theta\) was inferred from inter-key intervals (iki) using Bayesian mixed-effects mixture models (Roeser et al., 2025, 2024);

\[\begin{equation*} \scriptsize \begin{aligned} \left(\log\left(\text{iki}_\text{ij}\right) - \text{shift}_\text{keyloc[i]}\right) \sim \ & \theta_\text{keyloc,n1type} \times \mathcal{N}\left(\beta_\text{keyloc} + \delta_\text{keyloc} + u_i + w_j, \sigma_{e'}^2\right)\ + \\ & \left(1 - \theta_\text{keyloc,n1type}\right) \times \mathcal{N}\left(\beta_\text{keyloc} + u_i + w_j, \sigma_{e}^2\right)\\ \end{aligned} \end{equation*}\]

also illustrated in Figure 3.

Hypothetical mixture of fluent (faded) and hesitant (solid) inter-key intervals.

Figure 3: Hypothetical mixture of fluent (faded) and hesitant (solid) inter-key intervals.

Tutorial: rpubs.com/jensroes/mixture-models-tutorial

Experiment 1

Finding: Spelling difficulty increases hesitations before, during, and immediately after typing N1.

Posterior hesitation probability with 95% probability intervals (PIs).

Figure 4: Posterior hesitation probability with 95% probability intervals (PIs).

Experiment 2

Prime without N2 determiner: The N1 and N2 are above the N3 (vs The N1 and the N2 \(\dots\))

Finding: When N1 does not allow parallel planning, “the”-omission increases post-N1 hesitations.

Posterior hesitation probability with 95% probability intervals (PIs).

Figure 5: Posterior hesitation probability with 95% probability intervals (PIs).

Experiment 3

(Auditory) prime with N2 preview: The N1 and clown are above the pipe (vs The N1 and bow)

Finding: Post-N1 hesitations are associated with N2 retrival.

Posterior hesitation probability with 95% probability intervals (PIs).

Figure 6: Posterior hesitation probability with 95% probability intervals (PIs).

Write here, write now! – When writing
the N1 and N2 … N2 is retrieved while spelling N1.