Signal detection in animal psychoacoustics: analysis and simulation of sensory and decision-related influences

Signal detection theory (SDT) provides a framework for interpreting psychophysical experiments, separating the putative internal sensory representation and the decision process. SDT was used to analyse ferret behavioural responses in a (yes–no) tone-in-noise detection task. Instead of measuring the receiver-operating characteristic (ROC), we tested SDT by comparing responses collected using two common psychophysical data collection methods. These (Constant Stimuli, Limits) differ in the set of signal levels presented within and across behavioural sessions. The results support the use of SDT as a method of analysis: SDT sensory component was unchanged between the two methods, even though decisions depended on the stimuli presented within a behavioural session. Decision criterion varied trial-by-trial: a ‘yes’ response was more likely after a correct rejection trial than a hit trial. Simulation using an SDT model with several decision components reproduced the experimental observations accurately, leaving only ∼10% of the variance unaccounted for. The model also showed that trial-by-trial dependencies were unlikely to influence measured psychometric functions or thresholds. An additional model component suggested that inattention did not contribute substantially. Further analysis showed that ferrets were changing their decision criteria, almost optimally, to maximise the reward obtained in a session. The data suggest trial-by-trial reward-driven optimization of the decision process. Understanding the factors determining behavioural responses is important for correlating neural activity and behaviour. SDT provides a good account of animal psychoacoustics, and can be validated using standard psychophysical methods and computer simulations, without recourse to ROC measurements

Statistical Analyses of Temporal Information in Auditory Brainstem Responses to Tones in Noise: Correlation Index and Spike-distance Metric

Gai and Carney (J Neurophysiol 96:2451–2464, 2006) previously explored the detection of tones in noise based on responses in the anteroventral cochlear nucleus; that study focused on temporal information in discharge reliability and analyses of neural responses related to the fine structure or envelope of the stimulus. Two additional temporal approaches, the correlation index (Joris et al., Hearing Res 216–217:19–30, 2006) and the spike-distance metric (Victor and Purpura, J Neurophysiol 76:1310–1326, 1996; Netw Comput Neural Syst 8:127–164, 1997), are tested in the present study. Trends in the correlation index as a function of stimulus level are similar to those of the synchronization coefficient (also called the vector strength) when the tone is presented alone. However, the present study found that trends in the correlation index did not agree with those of the synchronization coefficient for tones presented with relatively high-level background noise. Instead, trends in the correlation index generally agreed with those of the temporal reliability metric discussed in Gai and Carney (J Neurophysiol 96:2451–2464, 2006); that is, the correlation index decreased with increased tone level in the presence of relatively high-level background noise. The spike-distance metric, which was based on absolute spike times or on interspike intervals, was compared to the temporal measures described above, which were generally based on relative spike times. The results confirm that the spike-distance metric is not an optimal temporal metric. In addition, absolute spike times of primary-like responses generally contained much less temporal information than absolute spike times of chopper response types. The present study highlights the importance of relative spike-timing information as characterized by traditional and novel temporal measures

Neural population coding of sound level adapts to stimulus statistics

Mammals can hear sounds extending over a vast range of sound levels with remarkable accuracy. How auditory neurons code sound level over such a range is unclear; firing rates of individual neurons increase with sound level over only a very limited portion of the full range of hearing. We show that neurons in the auditory midbrain of the guinea pig adjust their responses to the mean, variance and more complex statistics of sound level distributions. We demonstrate that these adjustments improve the accuracy of the neural population code close to the region of most commonly occurring sound levels. This extends the range of sound levels that can be accurately encoded, fine-tuning hearing to the local acoustic environment