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Found 13 papers, 5 papers with code
SentSpace: Large-Scale Benchmarking and Evaluation of Text using Cognitively Motivated Lexical, Syntactic, and Semantic Features
The modular design of SentSpace allows researchersto easily integrate their own feature computation into the pipeline while benefiting from acommon framework for evaluation and visualization.
A Deep Learning Approach to Analyzing Continuous-Time Systems
Scientists often use observational time series data to study complex natural processes, but regression analyses often assume simplistic dynamics.
CDRNN: Discovering Complex Dynamics in Human Language Processing
The human mind is a dynamical system, yet many analysis techniques used to study it are limited in their ability to capture the complex dynamics that may characterize mental processes.
Coreference information guides human expectations during natural reading
Models of human sentence processing effort tend to focus on costs associated with retrieving structures and discourse referents from memory (memory-based) and/or on costs associated with anticipating upcoming words and structures based on contextual cues (expectation-based) (Levy, 2008).
Acquiring language from speech by learning to remember and predict
Classical accounts of child language learning invoke memory limits as a pressure to discover sparse, language-like representations of speech, while more recent proposals stress the importance of prediction for language learning.
Measuring the perceptual availability of phonological features during language acquisition using unsupervised binary stochastic autoencoders
In this paper, we deploy binary stochastic neural autoencoder networks as models of infant language learning in two typologically unrelated languages (Xitsonga and English).
A large-scale study of the effects of word frequency and predictability in naturalistic reading
A number of psycholinguistic studies have factorially manipulated words{'} contextual predictabilities and corpus frequencies and shown separable effects of each on measures of human sentence processing, a pattern which has been used to support distinct mechanisms underlying prediction on the one hand and lexical retrieval on the other.
Deconvolutional Time Series Regression: A Technique for Modeling Temporally Diffuse Effects
Researchers in computational psycholinguistics frequently use linear models to study time series data generated by human subjects.
Breaking NLP: Using Morphosyntax, Semantics, Pragmatics and World Knowledge to Fool Sentiment Analysis Systems
This paper describes our {``}breaker{''} submission to the 2017 EMNLP {``}Build It Break It{''} shared task on sentiment analysis.
Speech segmentation with a neural encoder model of working memory
We present the first unsupervised LSTM speech segmenter as a cognitive model of the acquisition of words from unsegmented input.
Memory-Bounded Left-Corner Unsupervised Grammar Induction on Child-Directed Input
This paper presents a new memory-bounded left-corner parsing model for unsupervised raw-text syntax induction, using unsupervised hierarchical hidden Markov models (UHHMM).
Memory access during incremental sentence processing causes reading time latency
Studies on the role of memory as a predictor of reading time latencies (1) differ in their predictions about when memory effects should occur in processing and (2) have had mixed results, with strong positive effects emerging from isolated constructed stimuli and weak or even negative effects emerging from naturally-occurring stimuli.
