scholarly journals Parsing Model and a Rational Theory of Memory

2021 ◽  
Vol 12 ◽  
Author(s):  
Jakub Dotlačil ◽  
Puck de Haan
Keyword(s):  
Adaptive Control ◽  
Sentence Processing ◽  
Qualitative Data ◽  
Cognitive Architecture ◽  
Memory Systems ◽  
Data Driven ◽  
Garden Path ◽  
Rational Theory

This paper explores how the rational theory of memory summarized in Anderson (1991) can inform the computational psycholinguistic models of human parsing. It is shown that transition-based parsing is particularly suitable to be combined with Anderson's theory of memory systems. The combination of the rational theory of memory with the transition-based parsers results in a model of sentence processing that is data-driven and can be embedded in the cognitive architecture Adaptive Control of Thought-Rational (ACT-R). The predictions of the parser are tested against qualitative data (garden-path sentences) and a self-paced reading corpus (the Natural Stories corpus).

scholarly journals Children do not overcome lexical biases where adults do: the role of the referential scene in garden-path recovery

2010 ◽  
Vol 38 (1) ◽  
pp. 222-234 ◽  
Author(s):  
EVAN KIDD ◽  
ANDREW J. STEWART ◽  
LUDOVICA SERRATRICE
Keyword(s):  
Eye Tracking ◽  
Sentence Processing ◽  
Test Sentence ◽  
Visual World ◽  
Garden Path ◽  
Initial Analysis ◽  
Adults And Children ◽  
Scene Information ◽  
Lexical Cues

ABSTRACTIn this paper we report on a visual world eye-tracking experiment that investigated the differing abilities of adults and children to use referential scene information during reanalysis to overcome lexical biases during sentence processing. The results showed that adults incorporated aspects of the referential scene into their parse as soon as it became apparent that a test sentence was syntactically ambiguous, suggesting they considered the two alternative analyses in parallel. In contrast, the children appeared not to reanalyze their initial analysis, even over shorter distances than have been investigated in prior research. We argue that this reflects the children's over-reliance on bottom-up, lexical cues to interpretation. The implications for the development of parsing routines are discussed.

scholarly journals The interaction between structure and meaning in sentence comprehension: Recurrent neural networks and reading times

2019 ◽  
Author(s):  
Stefan L. Frank ◽  
John Hoeks
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Sentence Processing ◽  
Recurrent Neural Networks ◽  
Sentence Comprehension ◽  
Semantic Relations ◽  
Garden Path ◽  
Word Predictability ◽  
Syntactic Ambiguities ◽  
Potential Object

Recurrent neural network (RNN) models of sentence processing have recently displayed a remarkable ability to learn aspects of structure comprehension, as evidenced by their ability to account for reading times on sentences with local syntactic ambiguities (i.e., garden-path effects). Here, we investigate if these models can also simulate the effect of semantic appropriateness of the ambiguity's readings. RNNs-based estimates of surprisal of the disambiguating verb of sentences with an NP/S-coordination ambiguity (as in `The wizard guards the king and the princess protects ...') show identical patters to human reading times on the same sentences: Surprisal is higher on ambiguous structures than on their disambiguated counterparts and this effect is weaker, but not absent, in cases of poor thematic fit between the verb and its potential object (`The teacher baked the cake and the baker made ...'). These results show that an RNN is able to simultaneously learn about structural and semantic relations between words and suggest that garden-path phenomena may be more closely related to word predictability than traditionally assumed.

Data-Driven Retrospective Cost Adaptive Control for Flight Control Applications

2021 ◽  
pp. 1-27
Author(s):  
Syed Aseem Ul Islam ◽  
Tam W. Nguyen ◽  
Ilya V. Kolmanovsky ◽  
Dennis S. Bernstein
Keyword(s):  
Adaptive Control ◽  
Flight Control ◽  
Data Driven ◽  
Control Applications

A Scene-Based Episodic Memory System for a Simulated Autonomous Creature

2013 ◽  
Vol 4 (1) ◽  
pp. 32-64 ◽  
Author(s):  
Elisa C. Castro ◽  
Ricardo R. Gudwin
Keyword(s):  
Episodic Memory ◽  
Navigation System ◽  
Cognitive Architecture ◽  
Human Memory ◽  
Memory Systems ◽  
Memory Module ◽  
The Past ◽  
3D Environment ◽  
Main Ideas

In this paper the authors present the development of a scene-based episodic memory module for the cognitive architecture controlling an autonomous virtual creature, in a simulated 3D environment. The scene-based episodic memory has the role of improving the creature’s navigation system, by evoking the objects to be considered in planning, according to episodic remembrance of earlier scenes testified by the creature where these objects were present in the past. They introduce the main background on human memory systems and episodic memory study, and provide the main ideas behind the experiment.

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