Analyzing recognition performance with sparse data

Sheu, Ching-Fan; Lee, Yuh Shiow; Shih, Pei Ying

doi:10.3758/brm.40.3.722

Cited by 7 publications

(6 citation statements)

References 23 publications

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“…This model has the convenient advantage that coefficient estimates are interpretable as differences in bias and sensitivity on a d 0 scale resulting from the various experimental manipulations. [29][30][31] Full model specifications are given in Eqs. 1and 3of the supplementary material; 39 the general form of this model is given here in Eq.…”

Section: Behavioral Analysismentioning

confidence: 99%

Pupillometry shows the effort of auditory attention switching

McCloy

Lau

Larson

et al. 2017

The Journal of the Acoustical Society of America

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Successful speech communication often requires selective attention to a target stream amidst competing sounds, as well as the ability to switch attention among multiple interlocutors. However, auditory attention switching negatively affects both target detection accuracy and reaction time, suggesting that attention switches carry a cognitive cost. Pupillometry is one method of assessing mental effort or cognitive load. Two experiments were conducted to determine whether the effort associated with attention switches is detectable in the pupillary response. In both experiments, pupil dilation, target detection sensitivity, and reaction time were measured; the task required listeners to either maintain or switch attention between two concurrent speech streams. Secondary manipulations explored whether switch-related effort would increase when auditory streaming was harder. In experiment 1, spatially distinct stimuli were degraded by simulating reverberation (compromising across-time streaming cues), and target-masker talker gender match was also varied. In experiment 2, diotic streams separable by talker voice quality and pitch were degraded by noise vocoding, and the time alloted for mid-trial attention switching was varied. All trial manipulations had some effect on target detection sensitivity and/or reaction time; however, only the attention-switching manipulation affected the pupillary response: greater dilation was observed in trials requiring switching attention between talkers.

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Section: Behavioral Analysismentioning

confidence: 99%

Pupillometry shows the effort of auditory attention switching

McCloy

Lau

Larson

et al. 2017

The Journal of the Acoustical Society of America

View full text Add to dashboard Cite

show abstract

“…In other words, because the model equation can be expressed as a sum of terms of the form U À1 (k), and d 0 is likewise estimable as a sum of such terms, the magnitudes of model coefficients can be interpreted as d 0 differences between conditions (assuming appropriate coding of the predictor variables). This modeling strategy is based on the formalizations in DeCarlo (1998) and Sheu et al (2008); cf. Lawrence and Klein (2013) for a recent study using a similar approach to modeling sensitivity in an audiovisual attention task.…”

Section: E Statistical Methodsmentioning

confidence: 99%

“…(2) as a mixed-effects model (i.e., estimating contributions of both population-level characteristics and individual-level effects) models hit rate and false alarm rate as consistent within-listener but potentially varying across listeners, and subject to population-level influences (Sheu et al, 2008). This design allows estimation of population-level effects (such as the effects of task design or experimental condition) to be based on data from all participants, without assuming that all participants use the same decision criterion when performing the task.…”

Section: E Statistical Methodsmentioning

confidence: 99%

Auditory attention strategy depends on target linguistic properties and spatial configuration

McCloy

Lee

2015

The Journal of the Acoustical Society of America

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Whether crossing a busy intersection or attending a large dinner party, listeners sometimes need to attend to multiple spatially distributed sound sources or streams concurrently. How they achieve this is not clear-some studies suggest that listeners cannot truly simultaneously attend to separate streams, but instead combine attention switching with short-term memory to achieve something resembling divided attention. This paper presents two oddball detection experiments designed to investigate whether directing attention to phonetic versus semantic properties of the attended speech impacts listeners' ability to divide their auditory attention across spatial locations. Each experiment uses four spatially distinct streams of monosyllabic words, variation in cue type (providing phonetic or semantic information), and requiring attention to one or two locations. A rapid button-press response paradigm is employed to minimize the role of short-term memory in performing the task. Results show that differences in the spatial configuration of attended and unattended streams interact with linguistic properties of the speech streams to impact performance. Additionally, listeners may leverage phonetic information to make oddball detection judgments even when oddballs are semantically defined. Both of these effects appear to be mediated by the overall complexity of the acoustic scene.

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“…An indicator for trial slot was not included due to issues with model convergence. This model transformed response probabilities into continuous values suitable for linear modeling using an inverse probit link function, which allows interpretation of coefficient estimates as differences on a d′ scale (DeCarlo, 1998;McCloy and Lee, 2015;Sheu et al, 2008). Significance of model coefficients was determined using Wald z-tests.…”

Section: Discussionmentioning

confidence: 99%

Auditory attention switching with listening difficulty: Behavioral and pupillometric measures

McCloy¹,

Larson²,

Lee³

2019

Preprint

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Pupillometry has emerged as a useful tool for studying listening effort. Past work involving listeners with normal audiological thresholds has shown that switching attention between competing talker streams evokes pupil dilation indicative of listening effort [McCloy et al (2017), J. Acoust. Soc. Am 141(4):2440]. The current experiment examines behavioral and pupillometric data from a two-stream target detection task requiring attention-switching between auditory streams, in two participant groups: audiometrically normal listeners who self-report difficulty localizing sound sources and/or understanding speech in reverberant or acoustically crowded environments, and their age-matched controls who do not report such problems. Three experimental conditions varied the number and type of stream segregation cues available. Participants who reported listening difficulty showed both behavioral and pupillometric signs of increased effort compared to controls, especially in trials where listeners had to switch attention between streams, or trials where only a single stream segregation cue was available.

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Analyzing recognition performance with sparse data

Cited by 7 publications

References 23 publications

Pupillometry shows the effort of auditory attention switching

Pupillometry shows the effort of auditory attention switching

Auditory attention strategy depends on target linguistic properties and spatial configuration

Auditory attention switching with listening difficulty: Behavioral and pupillometric measures

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