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Temporal Integration of Auditory Information Is Invariant to Temporal Grouping Cues(1,2,3).

Liu AS, Tsunada J, Gold JI, Cohen YE - eNeuro (2015)

Bottom Line: Auditory perception depends on the temporal structure of incoming acoustic stimuli.We designed a novel discrimination task that required human listeners to decide whether a sequence of tone bursts was increasing or decreasing in frequency.We manipulated temporal perceptual-grouping cues by changing the time interval between the tone bursts, which led to listeners hearing the sequences as a single sound for short intervals or discrete sounds for longer intervals.

View Article: PubMed Central - HTML - PubMed

Affiliation: Bioengineering Graduate Group.

ABSTRACT
Auditory perception depends on the temporal structure of incoming acoustic stimuli. Here, we examined whether a temporal manipulation that affects the perceptual grouping also affects the time dependence of decisions regarding those stimuli. We designed a novel discrimination task that required human listeners to decide whether a sequence of tone bursts was increasing or decreasing in frequency. We manipulated temporal perceptual-grouping cues by changing the time interval between the tone bursts, which led to listeners hearing the sequences as a single sound for short intervals or discrete sounds for longer intervals. Despite these strong perceptual differences, this manipulation did not affect the efficiency of how auditory information was integrated over time to form a decision. Instead, the grouping manipulation affected subjects' speed-accuracy trade-offs. These results indicate that the temporal dynamics of evidence accumulation for auditory perceptual decisions can be invariant to manipulations that affect the perceptual grouping of the evidence.

No MeSH data available.


Related in: MedlinePlus

Parameter values from fits of the basic DDM to the RT-task data. Each panel shows best-fitting values of bound height (A) or drift rate (B) plotted as a function of IBI for fits to data from individual subjects (black) or combined across all subjects (red). Dark lines/symbols indicate that the model fits were improved significantly by fitting the given parameter separately for each IBI condition (likelihood-ratio test, p < 0.01, Bonferroni-corrected for three parameters).
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Figure 5: Parameter values from fits of the basic DDM to the RT-task data. Each panel shows best-fitting values of bound height (A) or drift rate (B) plotted as a function of IBI for fits to data from individual subjects (black) or combined across all subjects (red). Dark lines/symbols indicate that the model fits were improved significantly by fitting the given parameter separately for each IBI condition (likelihood-ratio test, p < 0.01, Bonferroni-corrected for three parameters).

Mentions: We found that the effects of IBI on choice and signal RT primarily reflected changes in the decision boundary but not the drift rate (Fig. 5). The height of a symmetric, fixed decision boundary (i.e., the same height for both increasing and decreasing choices) declined systematically with increasing IBI for all six subjects and for data combined across subjects (likelihood-ratio test comparing a seven-parameter model with separate values of drift rate and bound height per IBI plus a non-decision time to a five-parameter model with a single value of drift rate shared across IBIs, p < 0.001b in all cases; Bonferroni-corrected for three parameters; Fig. 5A). In contrast, drift rate depended on IBI for only one of the six subjects (p < 0.01c; Bonferroni-corrected for three parameters) and not for the other subjects or combined data (Fig. 5B). These model fits were not improved by adding to the model either leaky accumulation (likelihood-ratio test, p > 0.24d across subjects and for all data combined) or collapsing bounds (likelihood-ratio test, p > 0.1e for five of the six subjects and for all data combined). There also was little evidence for slow errors that can be expected in models with collapsing bounds, with only eight of 216 conditions separated by subject/coherence/IBI showing such an effect (Mann−Whitney test comparing median correct vs error RTs, p < 0.01; Ditterich, 2006a)f. Likewise, there was little evidence for fast errors that can be expected in models with variable bounds, with only three conditions showing such an effect (Ratcliff and Rouder, 1998). Thus, changes in IBI, which affected perceptual grouping and the rate of arrival of decision-relevant signals, caused systematic, robust changes in the speed−accuracy trade-off governed by a fixed, time-independent bound. In contrast, the changes in IBI did not cause systematic changes in the efficiency with which sensory evidence was accumulated over time to form the decision.


Temporal Integration of Auditory Information Is Invariant to Temporal Grouping Cues(1,2,3).

Liu AS, Tsunada J, Gold JI, Cohen YE - eNeuro (2015)

Parameter values from fits of the basic DDM to the RT-task data. Each panel shows best-fitting values of bound height (A) or drift rate (B) plotted as a function of IBI for fits to data from individual subjects (black) or combined across all subjects (red). Dark lines/symbols indicate that the model fits were improved significantly by fitting the given parameter separately for each IBI condition (likelihood-ratio test, p < 0.01, Bonferroni-corrected for three parameters).
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4596088&req=5

Figure 5: Parameter values from fits of the basic DDM to the RT-task data. Each panel shows best-fitting values of bound height (A) or drift rate (B) plotted as a function of IBI for fits to data from individual subjects (black) or combined across all subjects (red). Dark lines/symbols indicate that the model fits were improved significantly by fitting the given parameter separately for each IBI condition (likelihood-ratio test, p < 0.01, Bonferroni-corrected for three parameters).
Mentions: We found that the effects of IBI on choice and signal RT primarily reflected changes in the decision boundary but not the drift rate (Fig. 5). The height of a symmetric, fixed decision boundary (i.e., the same height for both increasing and decreasing choices) declined systematically with increasing IBI for all six subjects and for data combined across subjects (likelihood-ratio test comparing a seven-parameter model with separate values of drift rate and bound height per IBI plus a non-decision time to a five-parameter model with a single value of drift rate shared across IBIs, p < 0.001b in all cases; Bonferroni-corrected for three parameters; Fig. 5A). In contrast, drift rate depended on IBI for only one of the six subjects (p < 0.01c; Bonferroni-corrected for three parameters) and not for the other subjects or combined data (Fig. 5B). These model fits were not improved by adding to the model either leaky accumulation (likelihood-ratio test, p > 0.24d across subjects and for all data combined) or collapsing bounds (likelihood-ratio test, p > 0.1e for five of the six subjects and for all data combined). There also was little evidence for slow errors that can be expected in models with collapsing bounds, with only eight of 216 conditions separated by subject/coherence/IBI showing such an effect (Mann−Whitney test comparing median correct vs error RTs, p < 0.01; Ditterich, 2006a)f. Likewise, there was little evidence for fast errors that can be expected in models with variable bounds, with only three conditions showing such an effect (Ratcliff and Rouder, 1998). Thus, changes in IBI, which affected perceptual grouping and the rate of arrival of decision-relevant signals, caused systematic, robust changes in the speed−accuracy trade-off governed by a fixed, time-independent bound. In contrast, the changes in IBI did not cause systematic changes in the efficiency with which sensory evidence was accumulated over time to form the decision.

Bottom Line: Auditory perception depends on the temporal structure of incoming acoustic stimuli.We designed a novel discrimination task that required human listeners to decide whether a sequence of tone bursts was increasing or decreasing in frequency.We manipulated temporal perceptual-grouping cues by changing the time interval between the tone bursts, which led to listeners hearing the sequences as a single sound for short intervals or discrete sounds for longer intervals.

View Article: PubMed Central - HTML - PubMed

Affiliation: Bioengineering Graduate Group.

ABSTRACT
Auditory perception depends on the temporal structure of incoming acoustic stimuli. Here, we examined whether a temporal manipulation that affects the perceptual grouping also affects the time dependence of decisions regarding those stimuli. We designed a novel discrimination task that required human listeners to decide whether a sequence of tone bursts was increasing or decreasing in frequency. We manipulated temporal perceptual-grouping cues by changing the time interval between the tone bursts, which led to listeners hearing the sequences as a single sound for short intervals or discrete sounds for longer intervals. Despite these strong perceptual differences, this manipulation did not affect the efficiency of how auditory information was integrated over time to form a decision. Instead, the grouping manipulation affected subjects' speed-accuracy trade-offs. These results indicate that the temporal dynamics of evidence accumulation for auditory perceptual decisions can be invariant to manipulations that affect the perceptual grouping of the evidence.

No MeSH data available.


Related in: MedlinePlus