Maria Heitmeier
Biographic Data
| ID | 9437491 |
|---|---|
| NAME | Maria Heitmeier |
| GIVEN NAMES | Maria |
| FAMILY NAME | Heitmeier |
| SIGNATURE | HEITMEIER M |
| AFFILIATIONS | University of Tübingen |
| ORCID | 0000-0002-6515-7450 |
| VERIFIED | Yes |
| TOTAL WORKS | 3 |
| TOTAL CITATIONS | 0 |
| AUTHOR COUNT | 3 |
| EDITOR COUNT | 0 |
| FIRST PUBLICATION YEAR | 2023 |
| LATEST PUBLICATION YEAR | 2026 |
| H-INDEX | 0 |
An experimental and computational study of an Estonian single-subject word naming experiment
This study reports a large-scale single-subject experiment combining the word naming task with eye-tracking. Response variables (first fixation duration, total fixation duration, number of fixations, word naming latency, and spoken word duration) were subjected to statistical analyses with generalized additive models (GAMs). We compared GAMs with classical lexical predictors such as word frequency and inflectional paradigm size with GAMs that wer…
Is deeper always better? Replacing linear mappings with deep learning networks in the Discriminative Lexicon Model
Recently, deep learning models have increasingly been used in cognitive modelling of language. This study asks whether deep learning can help us to better understand the learning problem that needs to be solved by speakers, above and beyond linear methods. We utilize the Discriminative Lexicon Model introduced by Baayen and colleagues, which models comprehension and production with mappings between numeric form and meaning vectors. While so far, …
How trial-to-trial learning shapes mappings in the mental lexicon
Trial-to-trial effects have been found in a number of studies, indicating that processing a stimulus influences responses in subsequent trials. A special case are priming effects which have been modelled successfully with error-driven learning (Marsolek, 2008), implying that participants are continuously learning during experiments. This study investigates whether trial-to-trial learning can be detected in an unprimed lexical decision experiment.…
No prominent works on this page.
How trial-to-trial learning shapes mappings in the mental lexicon
Trial-to-trial effects have been found in a number of studies, indicating that processing a stimulus influences responses in subsequent trials. A special case are priming effects which have been modelled successfully with error-driven learning (Marsolek, 2008), implying that participants are continuously learning during experiments. This study investigates whether trial-to-trial learning can be detected in an unprimed lexical decision experiment.…
Is deeper always better? Replacing linear mappings with deep learning networks in the Discriminative Lexicon Model
Recently, deep learning models have increasingly been used in cognitive modelling of language. This study asks whether deep learning can help us to better understand the learning problem that needs to be solved by speakers, above and beyond linear methods. We utilize the Discriminative Lexicon Model introduced by Baayen and colleagues, which models comprehension and production with mappings between numeric form and meaning vectors. While so far, …
An experimental and computational study of an Estonian single-subject word naming experiment
This study reports a large-scale single-subject experiment combining the word naming task with eye-tracking. Response variables (first fixation duration, total fixation duration, number of fixations, word naming latency, and spoken word duration) were subjected to statistical analyses with generalized additive models (GAMs). We compared GAMs with classical lexical predictors such as word frequency and inflectional paradigm size with GAMs that wer…
Lexicon (3 works) · Artificial Intelligence (2 works) · Computer Science (2 works) · Discriminative model (2 works) · Lexical decision task (2 works) · Natural language processing (2 works) · Neurobiology of Language and Bilingualism (2 works) · Psychology (2 works) · Action Observation and Synchronization (1 works) · Artificial Intelligence (1 works)