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Causal contribution of primate auditory cortex to auditory perceptual decision-making.

Tsunada J, Liu AS, Gold JI, Cohen YE - Nat. Neurosci. (2015)

Bottom Line: However, the specific and causal contributions of different brain regions in this pathway, including the middle-lateral (ML) and anterolateral (AL) belt regions of the auditory cortex, to auditory decisions have not been fully identified.Both ML and AL neural activity was modulated by the frequency content of the stimulus.Together, these findings suggest that AL directly and causally contributes sensory evidence to form this auditory decision.

View Article: PubMed Central - PubMed

Affiliation: Department of Otorhinolaryngology, University of Pennsylvania, Philadelphia, Pennsylvania, USA.

ABSTRACT
Auditory perceptual decisions are thought to be mediated by the ventral auditory pathway. However, the specific and causal contributions of different brain regions in this pathway, including the middle-lateral (ML) and anterolateral (AL) belt regions of the auditory cortex, to auditory decisions have not been fully identified. To identify these contributions, we recorded from and microstimulated ML and AL sites while monkeys decided whether an auditory stimulus contained more low-frequency or high-frequency tone bursts. Both ML and AL neural activity was modulated by the frequency content of the stimulus. But, only the responses of the most stimulus-sensitive AL neurons were systematically modulated by the monkeys' choices. Consistent with this observation, microstimulation of AL, but not ML, systematically biased the monkeys' behavior toward the choice associated with the preferred frequency of the stimulated site. Together, these findings suggest that AL directly and causally contributes sensory evidence to form this auditory decision.

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Relationship between neurometric and psychometric sensitivity for ML (top) and AL (bottom)a, Example neurometric curves. Different curves and their corresponding neurometric slopes (an index of sensitivity) are shown in different colors. The curves were obtained from neural data elicited at the time of the peak of the correlation between neurometric and behavioral sensitivity (see panel c). b, Time courses of average neurometric slopes. The neurometric slope was calculated using 300-ms bins (with 10-ms increments) relative to stimulus onset. This relatively large bin size was needed to obtain reliable slope measurements and likely exaggerates the apparently gradual rise in sensitivity following stimulus onset. Thick/dashed lines show median/95%-confidence intervals across individual neurons. Insets show the distributions of neurometric slopes calculated from individual neurons using firing rates measured between stimulus onset and the inferred time of the decision commitment for each trial (i.e., the end of the decision time plus an additional 50 ms to account for the sensory latency), per monkey; black bars indicate H0: slope>0, p<0.05 (one-tailed permutation test). c, d, e, Time-dependent correlations between neuron-by-neuron neurometric slope and the simultaneously measured psychometric slope, plotted relative to stimulus onset (c), the inferred time of the decision commitment (d), and the inferred time of movement initiation (e). Significant regression coefficients are colored red (Spearman correlation coefficient, p<0.05). In b and c, the horizontal bars represent the range of the inferred times of decision commitment, for high (<−80% and >+80%, black), middle (−80% to −20% and +80% to +20%, dark gray), and low (−20% to +20%, light gray) coherence.
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Figure 5: Relationship between neurometric and psychometric sensitivity for ML (top) and AL (bottom)a, Example neurometric curves. Different curves and their corresponding neurometric slopes (an index of sensitivity) are shown in different colors. The curves were obtained from neural data elicited at the time of the peak of the correlation between neurometric and behavioral sensitivity (see panel c). b, Time courses of average neurometric slopes. The neurometric slope was calculated using 300-ms bins (with 10-ms increments) relative to stimulus onset. This relatively large bin size was needed to obtain reliable slope measurements and likely exaggerates the apparently gradual rise in sensitivity following stimulus onset. Thick/dashed lines show median/95%-confidence intervals across individual neurons. Insets show the distributions of neurometric slopes calculated from individual neurons using firing rates measured between stimulus onset and the inferred time of the decision commitment for each trial (i.e., the end of the decision time plus an additional 50 ms to account for the sensory latency), per monkey; black bars indicate H0: slope>0, p<0.05 (one-tailed permutation test). c, d, e, Time-dependent correlations between neuron-by-neuron neurometric slope and the simultaneously measured psychometric slope, plotted relative to stimulus onset (c), the inferred time of the decision commitment (d), and the inferred time of movement initiation (e). Significant regression coefficients are colored red (Spearman correlation coefficient, p<0.05). In b and c, the horizontal bars represent the range of the inferred times of decision commitment, for high (<−80% and >+80%, black), middle (−80% to −20% and +80% to +20%, dark gray), and low (−20% to +20%, light gray) coherence.

Mentions: Despite their slightly different average frequency-dependent response profiles, ML and AL neurons had similar sensitivity to the frequency content of the stimulus (which depended on not just the average response, but also its variability) throughout stimulus presentation. We quantified neuronal sensitivity using ROC-based “neurometric functions” that described the probability that an ideal observer could use the spiking activity of an individual neuron to decide whether a given stimulus contained more high- or low-frequency tone bursts (Supplementary Fig. 2; examples are shown in Fig. 5a)21. Across our populations of AL and ML neurons, the slopes of these functions tended to increase from just after stimulus onset until around the time of decision commitment (i.e., the end of the decision time inferred from DDM fits plus an additional 50 ms to account for the sensory latency; Fig. 5b). The neurometric slopes, which were calculated from firing rates between stimulus onset and the inferred time of the decision commitment, were similar for the two brain regions and the two monkeys (ML, ; AL, ; two-tailed Wilcoxon rank-sum test for H0: median difference between ML and AL slopes=0, p=0.46 for monkey T, p=0.13 for monkey A). Neurometric slopes were slightly lower than the corresponding psychometric slopes for the two brain regions and the two monkeys (median psychometric slope [IQR] from all sessions for both , two-tailed Wilcoxon signed-rank test, monkey T: p=5.6*10−6 ML and p=1.2*10−6 AL, monkey A: p=2.0*10−4 ML and p=1.8*10−5 AL). Thus, on average, single-neuron ML and AL spiking activity was sensitive to stimulus coherence but less so than psychometric sensitivity. This finding implies that either ML or AL activity could, in principle, be pooled to improve sensitivity and provide the evidence needed to make the decision22.


Causal contribution of primate auditory cortex to auditory perceptual decision-making.

Tsunada J, Liu AS, Gold JI, Cohen YE - Nat. Neurosci. (2015)

Relationship between neurometric and psychometric sensitivity for ML (top) and AL (bottom)a, Example neurometric curves. Different curves and their corresponding neurometric slopes (an index of sensitivity) are shown in different colors. The curves were obtained from neural data elicited at the time of the peak of the correlation between neurometric and behavioral sensitivity (see panel c). b, Time courses of average neurometric slopes. The neurometric slope was calculated using 300-ms bins (with 10-ms increments) relative to stimulus onset. This relatively large bin size was needed to obtain reliable slope measurements and likely exaggerates the apparently gradual rise in sensitivity following stimulus onset. Thick/dashed lines show median/95%-confidence intervals across individual neurons. Insets show the distributions of neurometric slopes calculated from individual neurons using firing rates measured between stimulus onset and the inferred time of the decision commitment for each trial (i.e., the end of the decision time plus an additional 50 ms to account for the sensory latency), per monkey; black bars indicate H0: slope>0, p<0.05 (one-tailed permutation test). c, d, e, Time-dependent correlations between neuron-by-neuron neurometric slope and the simultaneously measured psychometric slope, plotted relative to stimulus onset (c), the inferred time of the decision commitment (d), and the inferred time of movement initiation (e). Significant regression coefficients are colored red (Spearman correlation coefficient, p<0.05). In b and c, the horizontal bars represent the range of the inferred times of decision commitment, for high (<−80% and >+80%, black), middle (−80% to −20% and +80% to +20%, dark gray), and low (−20% to +20%, light gray) coherence.
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Figure 5: Relationship between neurometric and psychometric sensitivity for ML (top) and AL (bottom)a, Example neurometric curves. Different curves and their corresponding neurometric slopes (an index of sensitivity) are shown in different colors. The curves were obtained from neural data elicited at the time of the peak of the correlation between neurometric and behavioral sensitivity (see panel c). b, Time courses of average neurometric slopes. The neurometric slope was calculated using 300-ms bins (with 10-ms increments) relative to stimulus onset. This relatively large bin size was needed to obtain reliable slope measurements and likely exaggerates the apparently gradual rise in sensitivity following stimulus onset. Thick/dashed lines show median/95%-confidence intervals across individual neurons. Insets show the distributions of neurometric slopes calculated from individual neurons using firing rates measured between stimulus onset and the inferred time of the decision commitment for each trial (i.e., the end of the decision time plus an additional 50 ms to account for the sensory latency), per monkey; black bars indicate H0: slope>0, p<0.05 (one-tailed permutation test). c, d, e, Time-dependent correlations between neuron-by-neuron neurometric slope and the simultaneously measured psychometric slope, plotted relative to stimulus onset (c), the inferred time of the decision commitment (d), and the inferred time of movement initiation (e). Significant regression coefficients are colored red (Spearman correlation coefficient, p<0.05). In b and c, the horizontal bars represent the range of the inferred times of decision commitment, for high (<−80% and >+80%, black), middle (−80% to −20% and +80% to +20%, dark gray), and low (−20% to +20%, light gray) coherence.
Mentions: Despite their slightly different average frequency-dependent response profiles, ML and AL neurons had similar sensitivity to the frequency content of the stimulus (which depended on not just the average response, but also its variability) throughout stimulus presentation. We quantified neuronal sensitivity using ROC-based “neurometric functions” that described the probability that an ideal observer could use the spiking activity of an individual neuron to decide whether a given stimulus contained more high- or low-frequency tone bursts (Supplementary Fig. 2; examples are shown in Fig. 5a)21. Across our populations of AL and ML neurons, the slopes of these functions tended to increase from just after stimulus onset until around the time of decision commitment (i.e., the end of the decision time inferred from DDM fits plus an additional 50 ms to account for the sensory latency; Fig. 5b). The neurometric slopes, which were calculated from firing rates between stimulus onset and the inferred time of the decision commitment, were similar for the two brain regions and the two monkeys (ML, ; AL, ; two-tailed Wilcoxon rank-sum test for H0: median difference between ML and AL slopes=0, p=0.46 for monkey T, p=0.13 for monkey A). Neurometric slopes were slightly lower than the corresponding psychometric slopes for the two brain regions and the two monkeys (median psychometric slope [IQR] from all sessions for both , two-tailed Wilcoxon signed-rank test, monkey T: p=5.6*10−6 ML and p=1.2*10−6 AL, monkey A: p=2.0*10−4 ML and p=1.8*10−5 AL). Thus, on average, single-neuron ML and AL spiking activity was sensitive to stimulus coherence but less so than psychometric sensitivity. This finding implies that either ML or AL activity could, in principle, be pooled to improve sensitivity and provide the evidence needed to make the decision22.

Bottom Line: However, the specific and causal contributions of different brain regions in this pathway, including the middle-lateral (ML) and anterolateral (AL) belt regions of the auditory cortex, to auditory decisions have not been fully identified.Both ML and AL neural activity was modulated by the frequency content of the stimulus.Together, these findings suggest that AL directly and causally contributes sensory evidence to form this auditory decision.

View Article: PubMed Central - PubMed

Affiliation: Department of Otorhinolaryngology, University of Pennsylvania, Philadelphia, Pennsylvania, USA.

ABSTRACT
Auditory perceptual decisions are thought to be mediated by the ventral auditory pathway. However, the specific and causal contributions of different brain regions in this pathway, including the middle-lateral (ML) and anterolateral (AL) belt regions of the auditory cortex, to auditory decisions have not been fully identified. To identify these contributions, we recorded from and microstimulated ML and AL sites while monkeys decided whether an auditory stimulus contained more low-frequency or high-frequency tone bursts. Both ML and AL neural activity was modulated by the frequency content of the stimulus. But, only the responses of the most stimulus-sensitive AL neurons were systematically modulated by the monkeys' choices. Consistent with this observation, microstimulation of AL, but not ML, systematically biased the monkeys' behavior toward the choice associated with the preferred frequency of the stimulated site. Together, these findings suggest that AL directly and causally contributes sensory evidence to form this auditory decision.

Show MeSH
Related in: MedlinePlus