Learning and recognition of tactile temporal sequences by mice and humans

Abstract

The world around us is replete with stimuli that unfold over time. When we hear an auditory stream like music or speech or scan a texture with our fingertip, physical features in the stimulus are concatenated in a particular order. This temporal patterning is critical to interpreting the stimulus. To explore the capacity of mice and humans to learn tactile sequences, we developed a task in which subjects had to recognise a continuous modulated noise sequence delivered to whiskers or fingertips, defined by its temporal patterning over hundreds of milliseconds. GO and NO-GO sequences differed only in that the order of their constituent noise modulation segments was temporally scrambled. Both mice and humans efficiently learned tactile sequences. Mouse sequence recognition depended on detecting transitions in noise amplitude; animals could base their decision on the earliest information available. Humans appeared to use additional cues, including the duration of noise modulation segments.

Keywords: head-fixed; human; mouse; neuroscience; somatosensory; streaming; temporal coding; vibrissae; whiskers.

Figure 1.. Design of sequence recognition task for mice and humans.

(A) Illustration of the treadmill-based behavioural and stimulus delivery setup for head-fixed mice. (B) Block diagram representing the structure of the GO/NO-GO paradigm. On GO trials, a mouse licking the water spout within the response period (hit) was rewarded with a water droplet. If the mouse licked on a NO-GO trial (false alarm), the next trial was delayed by 2–5 s. (C) Illustration of the stimulus delivery setup for human experiments. (D) Stimulus sequences for GO/NO-GO discrimination and intermediate ‘shaping’ sequences used for training. DOI: http://dx.doi.org/10.7554/eLife.27333.002

Figure 2.. Progression through sequence learning in mice.

(A) Number of training sessions needed to learn different training stages (75% performance criterion). Each dot, one mouse. Dots are jittered for visualisation. (B) Performance metrics for an example training session (discrimination of the GO sequence from square wave). Correct trials are green dots, incorrect are red dots. Performance quantified as % correct over a 50-trial moving window (bottom). Whisker dependency on the task was verified by removing the actuator at trial 220 (fuchsia bar) and reinserting (blue bar) at trial 270. Apparent delay in performance is caused by 50-trial averaging. (C) Performance for an example session with stimulus rotation (GO sequence versus square wave discrimination). Main symbols as for B. Stimuli were delivered, as normal, first in the rostro-caudal axis (RC; blue bar) but following a brief period of stimulator removal (fuchsia bar), in the dorso-ventral (DV; cyan) axis for 50 trials. Stimulation then returned to RC for the remainder of the session. DOI: http://dx.doi.org/10.7554/eLife.27333.003

Figure 3.. Performance of a trained mouse in a sequence recognition session.

(A) Lick time raster plot for GO trials in an example session on the final stage of training (GO vs ‘full’ NO-GO). Licks are referenced to the start of the sequence, which lasts 800 ms (grey bar at top). The response period (fuchsia bar) opens after the end of stimulation. Water was only available in the response period. Hit and miss trials (rows) marked with green and red dots, respectively. (B) Lick time raster plot for NO-GO trials of same session as A. Green dots denote correct rejection trials, red dots false alarm trials. Other symbols as for A. (C) Smoothed average of lick rate signals per trial from A and B. Dark blue, GO trials. Teal, NO-GO. (D) Difference of lick rate plots. Continuous thick line is true difference between lick rates on GO and NO-GO trials from the best 50-trial moving window from C. Broken line is average of 50 repeats of differences between randomly drawn trials. Dashed threshold line is 95% confidence interval of lick rate values from the 50 repeats. Asterisk denotes the time when the true lick rate difference surpassed the 95% CI, termed discriminative lick latency. This is an upper bound on the animal’s time to discriminate sequence identity. DOI: http://dx.doi.org/10.7554/eLife.27333.005

Figure 4.. Sequence recognition performance across mice.

(A) Performance on full sequence recognition task across 8 successive behavioural sessions for one mouse. Performance is averaged over a 50-trial moving window. Colour corresponds to colour of same mouse in panels B-D. (B) Hit rate plotted against % correct for all sessions on full task. Each dot, one session on GO vs full NO-GO; each colour depicts a different mouse (n = 6). (C) False alarm rate plotted against % correct. Symbols as for B. (D) Discriminative lick latency plotted against % correct. DOI: http://dx.doi.org/10.7554/eLife.27333.007

Figure 5.. Sequence recognition performance across humans.

(A) Performance over the course of the first training session, measured as % correct averaged over 50-trial moving window. Grey lines, individual participants. Black line, average over participants. (B) Performance for best 50-trial window on initial test session after training and (on a subset of participants) upon retesting after a minimum of one week. Each grey dot, one human participant; black dots, participants tested on both sessions. Coloured dots, performance on individual sessions for mice, replotted from Figure 4 for comparison (each mouse, one colour; colours as for Figure 4). Dots are jittered along x axis for visualisation. DOI: http://dx.doi.org/10.7554/eLife.27333.009

Figure 6.. Variants of task design to test for behavioural use of cues.

(A) Cues within the GO sequence (black line) that could allow recognition of sequence identity. An example of a local cue (within green box) is the large isolated transient ‘landmark’ (green arrow) immediately followed by a low amplitude syllable. In contrast, global cues involve changes in integrated stimulus amplitude over time, as reflected in the amplitude modulation envelope (black dotted line). (B) Binary sequences distinguishable based on syllable ordering or the durations of small-amplitude epochs. Different colours indicate epochs of large and small noise amplitude. (C) Performance on task variants using binary sequences, compared to original GO vs full NO-GO design (leftmost data point, replotted from Figure 4 and 5 with same colour assignment). Each colour depicts a mouse. Black dots and error bars, grand average and SD for each task. Grey dots in labelled circle depict performance of individual human participants on [4 1 4 1 1 1 4 1] vs [4 1 1 1 4 1 4 1]. (D) Sequences used to test effect of fixed ‘landmarks’. Frozen GO used an identical waveform across syllables, trials and sessions. Unfrozen GO maintained the same sequence of noise amplitudes (indicated by colour coding) but varied the detailed waveform across syllables, trials and sessions. NO-GO scrambled the order of syllables, i.e. the sequence of noise amplitudes. (E) Hit rate of humans on frozen and unfrozen GO trials. Each dot, one participant and session. DOI: http://dx.doi.org/10.7554/eLife.27333.011