Calloway, Regina
(2016)
Integrative and predictive processes in text reading: The N400 across a sentence boundary.
Master's Thesis, University of Pittsburgh.
(Unpublished)
Abstract
In the present study we used two experiments to test whether readers use integrative (retrospective), predictive (prospective), or both processes when reading words across a sentence boundary. We used Experiment 1 to determine whether prediction and integration could be measured as distinct processes. Response times (RTs) to determining whether probe words occurred in a previous sentence were measured. Critical probes were either high or low predictable words, given a context sentence. Both word types were easy to integrate, fitting well with the previous sentence. Results showed high predictable words had longer RTs than low predictable words, demonstrating that prediction and integration are distinct processes. In Experiment 2 we aimed to determine which processes were used when reading across a sentence boundary using event-related potentials (ERPs). The ERP component of interest was the N400, an indicator of semantic fit. We measured processing differences for high and low predictable words that were matched for integrability in sentence pairs. In a control condition, words were unpredictable and difficult to integrate. There was no difference in word processing (indicated by N400 amplitudes) between high and low predictable words across a sentence boundary. However, both word types were easier to process (reduced N400s) than control conditions. Findings show semantic overlap from word- and sentence-level activations facilitate integration in cross-sentence boundary reading.
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Details
| Item Type: |
University of Pittsburgh ETD
|
| Status: |
Unpublished |
| Creators/Authors: |
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| ETD Committee: |
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| Date: |
20 June 2016 |
| Date Type: |
Publication |
| Defense Date: |
14 December 2015 |
| Approval Date: |
20 June 2016 |
| Submission Date: |
16 March 2016 |
| Access Restriction: |
1 year -- Restrict access to University of Pittsburgh for a period of 1 year. |
| Number of Pages: |
133 |
| Institution: |
University of Pittsburgh |
| Schools and Programs: |
Dietrich School of Arts and Sciences > Psychology |
| Degree: |
MS - Master of Science |
| Thesis Type: |
Master's Thesis |
| Refereed: |
Yes |
| Uncontrolled Keywords: |
ERP, prediction, integration, text processing |
| Date Deposited: |
20 Jun 2016 20:54 |
| Last Modified: |
20 Jun 2017 05:15 |
| URI: |
http://d-scholarship.pitt.edu/id/eprint/27256 |
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