The Recognition of Compounds: A Computational Account

Abstract

This paper investigates the processes in comprehending spoken noun-noun compounds, using data from the BALDEY database. BALDEY contains lexicality judgments and reaction times (RTs) for Dutch stimuli for which also linguistic information is included. Two different approaches are combined. The first is based on regression by Dynamic Survival Analysis, which models decisions and RTs as a consequence of the fact that a cumulative density function exceeds some threshold. The parameters of that function are estimated from the observed RT data. The second approach is based on DIANA, a processoriented computational model of human word comprehension, which simulates the comprehension process with the acoustic stimulus as input. DIANA gives the identity and the number of the word candidates that are activated at each 10 ms time step.Both approaches show how the processes involved in comprehending compounds change during a stimulus. Survival Analysis shows that the impact of word duration varies during the course of a stimulus. The density of word and non-word hypotheses in DIANA shows a corresponding pattern with different regimes. We show how the approaches complement each other, and discuss additional ways in which data and process models can be combined.

DIANA, a Process-Oriented Model of Human Auditory Word Recognition

“…This model has been developed over a number of years. Implementation details of the model and specific simulations have been described in [1][2][3][4][5][6][7]. The current paper presents DIANA at the conceptual level and explains how its features are inspired by psycholinguistic and neurophysiological data.…”

Word Competition: An Entropy-Based Approach in the DIANA Model of Human Word Comprehension

“…DIANA (e.g., [14]) has been used to simulate RTs in the Dutch BALDEY database [15] and the large-scale North American English data (MALD) [16], for compounds [17], to differentiate predictors in regression models [18], and for modelling the cross-entropy as a significant predictor for modeling ERP components during comprehension of continuous speech in a recent EEG experiment [19].…”

“…of three contributions: (a) the word duration as a result of the unfolding of the auditory stimulus, (b) the choice-RT, i.e., the time it takes to make a choice from the set of hypotheses eligible at stimulus offset, and (c) the time it takes to execute a command such as a button press. In[18],[17] is it stipulated that the choice-RT equals a certain constant times the entropy at word offset, in line with Hick's law[29]. The model presented in table1shows this effect.…”