Mice tune out not in: violation of prediction drives auditory saliency

Abstract

Successful navigation in complex acoustic scenes requires focusing on relevant sounds while ignoring irrelevant distractors. It has been argued that the ability to track stimulus statistics and generate predictions supports the choice of what to attend and what to ignore. However, the role of these predictions about future auditory events in drafting decisions remains elusive. While most psychophysical studies in humans indicate that expected stimuli are more easily detected, most work studying physiological auditory processing in animals highlights the detection of unexpected, surprising stimuli. Here, we tested whether in the mouse, high target probability results in enhanced detectability or whether detection is biased towards low-probability deviants using an auditory detection task. We implemented a probabilistic choice model to investigate whether a possible dependence on stimulus statistics arises from short-term serial correlations or from integration over longer periods. Our results demonstrate that target detectability in mice decreases with increasing probability, contrary to humans. We suggest that mice indeed track probability over a timescale of at least several minutes but do not use this information in the same way as humans do: instead of maximizing reward by focusing on high-probability targets, the saliency of a target is determined by surprise.

Keywords: animal behaviour; auditory; expectation; statistical learning.

Figure 1.

Behavioural paradigm and stimulus protocol used in Experiment 1. (a) Go/No-Go paradigm used in this study. Mice initiated a trial by climbing on a small pedestal on the circular platform. After a variable waiting interval, a target was presented. Animals received a reward if they left the platform within 1 s after target presentation. The next trial could be initiated immediately. (b) Timeline of one experimental session. Throughout the entire session, a broadband noise stimulus was presented. Once a trial was initiated, a 500 ms pure tone was presented after a random stimulus delay. In a single session, an animal had to complete 73 or 78 trials, which typically lasted 30–45 min. (c) Different probabilities of single-frequency pure tone targets in different sessions. In single-frequency sessions, the level of the tones was varied, but only pure tones of either frequency f1 (10 kHz) or f2 (21 kHz) were presented. In mixed session, level was held constant near the behavioural threshold, but three different frequencies were presented. In any one session, either f1 or f2 was presented with 48% probability and the respective other with only 26%. In addition, a tone of the frequency close to the high-probability targets was presented in 26% of the trials. (Online version in colour.)

Figure 2.

Results for Experiment 1—tone-in-noise detection. (a) Example performance of two different animals for the tone-in-noise stimuli at a single tone frequency, presented with different probabilities. Before the mixed-frequency experiments, animals were tested individually for their thresholds at each tone frequency by presenting tones of a single-frequency (probability 100%) at different levels to construct psychometric functions (black circles, grey line). In the mixed experiments, tones with a level corresponding to a d′ of 1 (dashed line) were presented with probabilities of 48% (red circle) or 26% (blue circle). (b) Population data for all four animals at the two different frequencies used (red, 21 kHz; black, 10 kHz). The values for a probability of 100% were taken from the psychometric function obtained after the mixed experiments. Histograms above the graph visualize the probability of the tone in the respective sessions, the x-axis shows the surprise quantified as the prediction error. Note that larger numbers indicate more surprising stimuli. Total number of sessions included: 208 (152 for the mixed session, 56 for the psychometric functions). (Online version in colour.)

Figure 3.

Stimulus paradigms and results for Experiments 2 and 3. (a) Paradigm for Experiment 2: two continuous, interleaved streams of tone pips with different frequencies were presented. f1 = 10 kHz, f2 = 21 kHz. Animals had to detect a change in the frequency in either of the two streams. (b) Paradigm for Experiment 3: a short gap was inserted into one of the two narrowband noise streams (centre frequency same as f1 and f2 in (a)) as a target for detection. (c) Mean performance of all animals (n = 6) in Experiment 2 tested at different values of frequency change at either 50% (red) or 100% (blue) probability. Error bars show standard error of the mean (s.e.m.). (d) Mean performance of all animals (n = 7) in Experiment 3 tested at different gap durations at 33.3% (yellow), 66.7% (red) or 100% (blue) probability. Error bars depict s.e.m. (e) Sensitivity as a function of prediction error for Experiment 2—tone change detection—for all animals tested in both frequency streams (black, 10 kHz; red, 21 kHz). Each line joins data from an individual mouse for targets with a frequency change of 16%. (f) Sensitivity as a function of prediction error for Experiment 3—gap detection—for all animals, mean across all gap durations in either of the two frequency streams (black, 10 kHz; red, 21 kHz). (Online version in colour.)

Figure 4.

Probabilistic choice model. (a) Schematic illustration of the probabilistic choice model. The full model includes stimulus intensity, stimulus probability within the session for each stimulus, and recent history of stimuli presented in the immediately preceding trial steps t − i. Stimulus values were different in each experiment: SNR ratio in Experiment 1, frequency shift in Experiment 2 and gap duration in Experiment 3. The model was fit for each mouse and experiment, in four different versions, including either all three factors, stimulus values only, values + history or values + probability. (b) Performance of the four model versions, plotted as deviance of model output to the data, relative to the model including stimulus values only. Note that smaller numbers mean better model performance. Bars represent mean deviances from all animals in the three experiments ± s.e.m. (c) False alarm rate depending on whether stimuli of one class were presented as the only stimuli in the session (pure) or whether they were combined with other stimuli (mixed). Each line represents mean false alarm rates from a single animal. (d) Influence of immediate trial history on hit rate: x-axis: hit rate when the stimulus in the trial before was drawn from the same class as the current stimulus (repetition), y-axis: hit rate to stimuli that were preceded by a stimulus from another class (switch). (Online version in colour.)