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        <title>Modeling Long-Distance Cue Integration in Spoken Word Recognition</title>
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        <namePart type="given">Florian</namePart>
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            <title>Proceedings of the Workshop on Cognitive Modeling and Computational Linguistics</title>
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            <namePart type="given">Emmanuele</namePart>
            <namePart type="family">Chersoni</namePart>
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            <namePart type="given">Cassandra</namePart>
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            <namePart type="given">Alessandro</namePart>
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        <name type="personal">
            <namePart type="given">Enrico</namePart>
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    <abstract>Cues to linguistic categories are distributed across the speech signal. Optimal categorization thus requires that listeners maintain gradient representations of incoming input in order to integrate that information with later cues. There is now evidence that listeners can and do integrate cues that occur far apart in time. Computational models of this integration have however been lacking. We take a first step at addressing this gap by mathematically formalizing four models of how listeners may maintain and use cue information during spoken language understanding and test them on two perception experiments. In one experiment, we find support for rational integration of cues at long distances. In a second, more memory and attention-taxing experiment, we find evidence in favor of a switching model that avoids maintaining detailed representations of cues in memory. These results are a first step in understanding what kinds of mechanisms listeners use for cue integration under different memory and attentional constraints.</abstract>
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%0 Conference Proceedings
%T Modeling Long-Distance Cue Integration in Spoken Word Recognition
%A Bushong, Wednesday
%A Jaeger, T. Florian
%Y Chersoni, Emmanuele
%Y Jacobs, Cassandra
%Y Lenci, Alessandro
%Y Linzen, Tal
%Y Prévot, Laurent
%Y Santus, Enrico
%S Proceedings of the Workshop on Cognitive Modeling and Computational Linguistics
%D 2019
%8 June
%I Association for Computational Linguistics
%C Minneapolis, Minnesota
%F bushong-jaeger-2019-modeling
%X Cues to linguistic categories are distributed across the speech signal. Optimal categorization thus requires that listeners maintain gradient representations of incoming input in order to integrate that information with later cues. There is now evidence that listeners can and do integrate cues that occur far apart in time. Computational models of this integration have however been lacking. We take a first step at addressing this gap by mathematically formalizing four models of how listeners may maintain and use cue information during spoken language understanding and test them on two perception experiments. In one experiment, we find support for rational integration of cues at long distances. In a second, more memory and attention-taxing experiment, we find evidence in favor of a switching model that avoids maintaining detailed representations of cues in memory. These results are a first step in understanding what kinds of mechanisms listeners use for cue integration under different memory and attentional constraints.
%R 10.18653/v1/W19-2907
%U https://aclanthology.org/W19-2907
%U https://doi.org/10.18653/v1/W19-2907
%P 62-70

Markdown (Informal)

[Modeling Long-Distance Cue Integration in Spoken Word Recognition](https://aclanthology.org/W19-2907) (Bushong & Jaeger, CMCL 2019)

• Modeling Long-Distance Cue Integration in Spoken Word Recognition (Bushong & Jaeger, CMCL 2019)

ACL

• Wednesday Bushong and T. Florian Jaeger. 2019. Modeling Long-Distance Cue Integration in Spoken Word Recognition. In Proceedings of the Workshop on Cognitive Modeling and Computational Linguistics, pages 62–70, Minneapolis, Minnesota. Association for Computational Linguistics.