The first one will appear in the Cognitive Science proceedings:
Download: https://psyarxiv.com/76aex/
Title: Feature encoding modulates cue-based retrieval: Modeling interference effects in both grammatical and ungrammatical sentences
Abstract: Studies on similarity-based interference in subject-verb number agreement dependencies have found a consistent facilitatory effect in ungrammatical sentences but no conclusive effect
in grammatical sentences. Existing models propose that interference is caused either by a faulty representation of the input
(encoding-based models) or by difficulty in retrieving the subject based on cues at the verb (retrieval-based models). Neither
class of model captures the observed patterns in human reading time data. We propose a new model that integrates a feature encoding mechanism into an existing cue-based retrieval
model. Our model outperforms the cue-based retrieval model
in explaining interference effect data from both grammatical
and ungrammatical sentences. These modeling results yield a
new insight into sentence processing, encoding modulates retrieval. Nouns stored in memory undergo feature distortion,
which in turn affects how retrieval unfolds during dependency
completion.
The second paper will appear in the International Conference on Cognitive Modeling (ICCM) proceedings:
Download: https://psyarxiv.com/3et95/
Title: Is similarity-based interference caused by lossy compression or cue-based retrieval? A computational evaluation
Abstract: The similarity-based interference paradigm has been widely used to
investigate the factors subserving subject-verb agreement processing. A
consistent finding is facilitatory interference effects in ungrammatical
sentences but inconclusive results in grammatical sentences. Existing
models propose that interference is caused either by misrepresentation
of the input (representation distortion-based models) or by
mis-retrieval of the interfering noun phrase based on cues at the verb
(retrieval-based models). These models fail to fully capture the
observed interference patterns in the experimental data. We implement
two new models under the assumption that a comprehender utilizes a lossy
memory representation of the intended message when processing
subject-verb agreement dependencies. Our models outperform the existing
cue-based retrieval model in capturing the observed patterns in the data
for both grammatical and ungrammatical sentences. Lossy compression
models under different constraints can be useful in understanding the
role of representation distortion in sentence comprehension.
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