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Language serves as a bridge between ideas and communication, influencing how individuals perceive and interact with the world. Psychologists have long debated whether language shapes thought or vice versa. This discussion gained grip with Edward Sapir and Benjamin Lee Whorf in the 1940s, who proposed that language determines thought, a concept known as linguistic determinism. They suggested that the vocabulary and structure of a language influence how its speakers think and perceive reality.
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Surprisal From Language Models Can Predict ERPs in Processing Predicate-Argument Structures Only if Enriched by an

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Artificial neural network language models capture human sentence processing. However, human language comprehension also requires an Agent Preference principle, especially for verb-final sentences across languages like German, Basque, and Hindi.

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Area of Science:

  • Cognitive Science
  • Computational Linguistics
  • Neuroscience

Background:

  • Artificial neural network (ANN) language models increasingly model human sentence processing, predicting neural signals like N400 amplitudes.
  • The success of these models raises the question of whether human language processing relies solely on general ANN architecture and linguistic input.
  • N400 effects, reflecting semantic or syntactic processing difficulty, are a key neural measure in psycholinguistics.

Purpose of the Study:

  • To test the hypothesis that ANNs alone explain human sentence processing by examining N400 effects in verb-final languages.
  • To investigate the necessity of an 'Agent Preference' principle in human language comprehension.
  • To explore cross-linguistic variations in the influence of surprisal and the Agent Preference principle.

Main Methods:

  • Analysis of N400 effects (amplitudes and topographies) during the processing of verb-final sentences in German, Basque, and Hindi.
  • Utilizing stacked Bayesian generalised additive models to predict N400 data.
  • Comparing the predictive power of model-based surprisal against a combination of surprisal and the Agent Preference principle.

Main Results:

  • N400 effects in verb-final sentences across German, Basque, and Hindi are best predicted by combining model-based surprisal with an Agent Preference principle.
  • The Agent Preference principle, which biases interpretation of ambiguous noun phrases as agents, is necessary for explaining reanalysis effects.
  • The influence of the Agent Preference principle varies cross-linguistically, being weakest in German, stronger in Hindi, and strongest in Basque, correlating with grammatical features allowing unmarked NPs to function as patients.

Conclusions:

  • Language models require the incorporation of an Agent Preference principle to enhance their neurobiological plausibility.
  • Human language processing theories benefit from integrating surprisal estimates with principles like Agent Preference, which may have distinct evolutionary origins.
  • Cross-linguistic structural properties influence the strength of processing principles like Agent Preference.