Combining Data-Oriented and Process-Oriented Approaches to Modeling Reaction Time Data

Bosch, L.F.M. ten; Boves, L.W.J.; Ernestus, Mirjam

doi:10.21437/interspeech.2016-1072

Cited by 3 publications

(1 citation statement)

References 22 publications

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“…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.…”

Section: Introductionmentioning

confidence: 99%

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

2022

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This article presents DIANA, a new, process-oriented model of human auditory word recognition, which takes as its input the acoustic signal and can produce as its output word identifications and lexicality decisions, as well as reaction times. This makes it possible to compare its output with human listeners’ behavior in psycholinguistic experiments. DIANA differs from existing models in that it takes more available neuro-physiological evidence on speech processing into account. For instance, DIANA accounts for the effect of ambiguity in the acoustic signal on reaction times following the Hick–Hyman law and it interprets the acoustic signal in the form of spectro-temporal receptive fields, which are attested in the human superior temporal gyrus, instead of in the form of abstract phonological units. The model consists of three components: activation, decision and execution. The activation and decision components are described in detail, both at the conceptual level (in the running text) and at the computational level (in the Appendices). While the activation component is independent of the listener’s task, the functioning of the decision component depends on this task. The article also describes how DIANA could be improved in the future in order to even better resemble the behavior of human listeners.

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Section: Introductionmentioning

confidence: 99%

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

2022

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Computational Modeling of an Auditory Lexical Decision Experiment Using DIANA

2022

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We present an implementation of DIANA, a computational model of spoken word recognition, to model responses collected in the Massive Auditory Lexical Decision (MALD) project. DIANA is an end-to-end model, including an activation and decision component that takes the acoustic signal as input, activates internal word representations, and outputs lexicality judgments and estimated response latencies. Simulation 1 presents the process of creating acoustic models required by DIANA to analyze novel speech input. Simulation 2 investigates DIANA’s performance in determining whether the input signal is a word present in the lexicon or a pseudoword. In Simulation 3, we generate estimates of response latency and correlate them with general tendencies in participant responses in MALD data. We find that DIANA performs fairly well in free word recognition and lexical decision. However, the current approach for estimating response latency provides estimates opposite to those found in behavioral data. We discuss these findings and offer suggestions as to what a contemporary model of spoken word recognition should be able to do.

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

Bosch

Boves

2021

Interspeech 2021

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We discuss the role of entropy of the set of unfolding word candidates in the context of DIANA, a computational model of human auditory speech comprehension. DIANA consists of three major interacting components: Activation, Decision and Execution. The Activation component computes activations of word candidates that change over time as a function of the unfolding audio input. The resulting set of word candidate activations can be associated with an entropy that is related to difficulty of the decision when one of these candidates must be selected at time T. The paper presents the close relation between entropy measures and the between-word competition during the unfolding of the auditory stimuli, and at the end of the stimuli if no decision could be made before stimulus offset. We present a way for computing the entropy that takes into account linguistic-phonetic constraints that play a role in speech comprehension and in lexical decision experiments. Using the BALDEY data set and linear mixed effects regression models for RT, we show that entropy measures explain differences between RTs of words with different morphological structure.

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Combining Data-Oriented and Process-Oriented Approaches to Modeling Reaction Time Data

Cited by 3 publications

References 22 publications

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

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

Computational Modeling of an Auditory Lexical Decision Experiment Using DIANA

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

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