Figures and data in Individual differences in selective attention predict speech identification at a cocktail party

Figures
Tables

7 figures and 3 tables

Figures

Figure 1

Download asset Open asset

Average audiometric hearing thresholds (in dB HL), at octave frequencies between 125 Hz and 4 kHz (*N =* 50).

Left panel: left ear. Right panel: right ear. Error bars represent 95% confidence intervals.

https://doi.org/10.7554/eLife.16747.005

Figure 2

Download asset Open asset

Average individual proportion correct (speech recognition score; SRS) in the simulated cocktail-party listening task with two spatially separated interfering speakers (*N =* 50).

This measure served as the criterion variable in the regression analyses. The mean (M) and the standard deviation (SD) are displayed.

https://doi.org/10.7554/eLife.16747.006

Figure 3

Download asset Open asset

Schematic depiction of the two-interval intensity discrimination task used to measure auditory selective attention.

Green: target tones. Red: backward maskers ('distractors'). The standard (S) was a 1 kHz tone presented at 60 dB SPL. An intensity increment (I) was presented in either the first or the second interval, with equal a-priori probability. The task was to select the interval containing the louder target (that is, standard-plus-increment). The maskers were 1 kHz tones presented at 90 dB SPL. The same temporal configuration was used in the in-quiet condition, except that the maskers were not presented.

https://doi.org/10.7554/eLife.16747.007

Figure 4

Download asset Open asset

Individual elevation of the intensity difference limen caused by the backward maskers (DL_elev), defined as the difference between the DL under masking and the DL in quiet.

Lower values represent a better capability of directing auditory selective attention to the target tones. *N =* 50.

https://doi.org/10.7554/eLife.16747.008

Figure 5

Download asset Open asset

Individual IPD thresholds in the TFS-LF test (TFS_th).

Lower values represent better binaural sensitivity for the temporal fine structure. *N =* 50.

https://doi.org/10.7554/eLife.16747.009

Figure 6

Download asset Open asset

Example stimuli from the flanker task (Kramer and Jacobson, 1991) used to measure spatial visual selective attention.

The participants' task was to decide whether the central *target line* was dotted or dashed. In the *neutral condition* (left column), the neighboring flanker lines were solid and therefore not associated with a response. In the *incompatible condition* (right column), the flanker lines were associated with the incompatible response. The horizontal lines and the colors promoted the perceptual grouping of the target line and the flankers as either belonging to the *same object* (upper row), or into *different objects* (lower row).

https://doi.org/10.7554/eLife.16747.010

Figure 7

Download asset Open asset

Individual flanker interference (Int_Flanker) in the visual attention task.

Lower values represent a better capability of directing visual selective attention to the target line. *N =* 50.

https://doi.org/10.7554/eLife.16747.011

Tables

Table 1

Results of the multiple regression analysis. Criterion variable: speech recognition score (SRS; proportion correct) in the simulated cocktail-party listening task. Predictors: age, masker-induced elevation of the intensity difference limen (DL_elev), the amount of flanker interference in the flanker task (Int_Flanker), IPD threshold in the TFS-LF task (TFS_th), pure-tone average thresholds on the better ear (PTA_BE), average asymmetry in the hearing thresholds between left and right ear (HL_diff), intensity-DL in quiet (DL_quiet), response time in the neutral condition of the flanker task (RT_neutral), and proportion of correctly recalled consonants in the working memory task (SS_Pcorr). All variables were z-standardized.

https://doi.org/10.7554/eLife.16747.002

Predictor	β	SE	t	p	GDW	β_Lasso
Intercept	0.068	0.096	0.710	0.480		0.081
Age	0.194	0.110	1.760	0.086	0.020	−
DL_elev	−0.347	0.107	3.240	0.003	0.152	−0.220
Int_Flanker	−0.233	0.103	2.270	0.029	0.052	−0.081
TFS_th	−0.383	0.103	3.730	0.001	0.204	−0.286
PTA_BE	0.137	0.102	1.350	0.186	0.016	−
HL_diff	−0.088	0.106	0.830	0.413	0.007	−
DL_quiet	−0.007	0.114	0.070	0.948	0.021
RT_neutral	−0.037	0.129	0.280	0.778	0.015	−
SS_Pcorr	0.193	0.111	1.740	0.091	0.085	0.089
					R² =0.57 p<0.001	R² =0.44

Note: N = 45. β: estimated ordinary least-squares (OLS) regression coefficient. SE: standard error of the estimate. t: t-statistic. Bold font indicates a β significantly different from 0 (p<0.05). GDW: general dominance weight. β_Lasso: regression coefficients for predictors selected by the Lasso procedure (model selection via four-fold cross-validation).

Table 2

Pairwise Pearson partial correlation coefficients, controlling for age. N = 50. In each row, the upper numbers are the partial correlation coefficients (ρ_partial), and the lower numbers are the p-values for the test of |ρ_partial| > 0. The rightmost column shows Pearson correlation coefficients with age. Bold font: p<0.05. Italics: p<0.10.

https://doi.org/10.7554/eLife.16747.003

	DL_elev	Int_Flanker	TFS_th	PTA_BE	HL_diff	DL_quiet	RT_neutral	SS_Pcorr	SSQ_speech	SSQ_spatial	SSQ_qualities	Age
OLSA_Pcorr	−0.374	−0.149	−0.353	0.060	−0.163	−0.244	−0.232	0.338	0.121	0.083	0.230	0.033
OLSA_Pcorr	0.008	0.307	0.013	0.683	0.263	0.091	0.109	0.018	0.407	0.570	0.112	0.819
DL_elev		−0.047	0.038	0.030	−0.083	−0.045	0.092	−0.255	−0.302	−0.049	−0.178	−0.045
DL_elev		0.748	0.793	0.838	0.570	0.759	0.529	0.077	0.035	0.740	0.220	0.754
Int_Flanker			−0.081	−0.018	−0.234	−0.032	−0.094	−0.112	−0.141	−0.098	−0.112	0.045
Int_Flanker			0.578	0.903	0.105	0.826	0.522	0.444	0.335	0.501	0.444	0.758
TFS_th				0.034	−0.023	0.399	0.312	−0.177	−0.149	−0.314	−0.352	0.027
TFS_th				0.818	0.873	0.005	0.029	0.224	0.306	0.028	0.013	0.852
PTA_BE					−0.292	−0.083	−0.010	−0.092	0.136	0.082	0.097	−0.248
PTA_BE					0.042	0.572	0.944	0.531	0.353	0.577	0.508	0.082
HL_diff						0.195	0.038	0.087	0.274	0.198	0.119	0.227
HL_diff						0.180	0.795	0.551	0.057	0.174	0.416	0.113
DL_quiet							0.383	−0.115	−0.008	−0.118	−0.130	0.082
DL_quiet							0.007	0.431	0.959	0.420	0.375	0.573
RT_neutral								−0.198	−0.087	−0.135	−0.011	0.217
RT_neutral								0.172	0.552	0.355	0.940	0.129
SS_Pcorr									−0.011	0.081	0.053	−0.380
SS_Pcorr									0.940	0.580	0.720	0.006
SSQ_speech										0.707	0.728	0.040
SSQ_speech										<0.0001	<0.0001	0.784
SSQ_spatial											0.701	0.123
SSQ_spatial											<0.0001	0.393
SSQ_qualities												0.058
SSQ_qualities												0.692

Table 3

Multiple regression analysis of the relation between the SSQ scores (Gatehouse and Noble, 2004) representing self-reported hearing abilities (predictors) and the speech recognition score in the simulated cocktail-party listening task (criterion). N = 47.

https://doi.org/10.7554/eLife.16747.004

Predictor	β	SE	t	p
Intercept	−1.150	0.988	1.160	0.251
Age	−0.006	0.034	0.170	0.864
SSQ_speech	0.006	0.116	0.050	0.958
SSQ_spatial	−0.160	0.117	1.360	0.181
SSQ_qualities	0.321	0.142	2.250	0.030

Download links

A two-part list of links to download the article, or parts of the article, in various formats.

Downloads (link to download the article as PDF)

Article PDF

Open citations (links to open the citations from this article in various online reference manager services)

Mendeley

Cite this article (links to download the citations from this article in formats compatible with various reference manager tools)

Daniel Oberfeld
Felicitas Klöckner-Nowotny

(2016)

Individual differences in selective attention predict speech identification at a cocktail party

eLife 5:e16747.

https://doi.org/10.7554/eLife.16747

Figures

Average audiometric hearing thresholds (in dB HL), at octave frequencies between 125 Hz and 4 kHz (N = 50).

Average individual proportion correct (speech recognition score; SRS) in the simulated cocktail-party listening task with two spatially separated interfering speakers (N = 50).

Schematic depiction of the two-interval intensity discrimination task used to measure auditory selective attention.

Individual elevation of the intensity difference limen caused by the backward maskers (DL_elev), defined as the difference between the DL under masking and the DL in quiet.

Individual IPD thresholds in the TFS-LF test (TFS_th).

Example stimuli from the flanker task (Kramer and Jacobson, 1991) used to measure spatial visual selective attention.

Individual flanker interference (Int_Flanker) in the visual attention task.

Tables

Download links

Downloads (link to download the article as PDF)

Open citations (links to open the citations from this article in various online reference manager services)

Cite this article (links to download the citations from this article in formats compatible with various reference manager tools)

Be the first to read new articles from eLife

Share this article

Cite this article

Average audiometric hearing thresholds (in dB HL), at octave frequencies between 125 Hz and 4 kHz (N = 50).

Average individual proportion correct (speech recognition score; SRS) in the simulated cocktail-party listening task with two spatially separated interfering speakers (N = 50).

Schematic depiction of the two-interval intensity discrimination task used to measure auditory selective attention.

Individual elevation of the intensity difference limen caused by the backward maskers (DLelev), defined as the difference between the DL under masking and the DL in quiet.

Individual IPD thresholds in the TFS-LF test (TFSth).

Example stimuli from the flanker task (Kramer and Jacobson, 1991) used to measure spatial visual selective attention.

Individual flanker interference (IntFlanker) in the visual attention task.

Downloads (link to download the article as PDF)

Open citations (links to open the citations from this article in various online reference manager services)

Cite this article (links to download the citations from this article in formats compatible with various reference manager tools)

Individual elevation of the intensity difference limen caused by the backward maskers (DL_elev), defined as the difference between the DL under masking and the DL in quiet.

Individual IPD thresholds in the TFS-LF test (TFS_th).

Individual flanker interference (Int_Flanker) in the visual attention task.