Outlier responses reflect sensitivity to statistical structure in the human brain

Abstract

We constantly look for patterns in the environment that allow us to learn its key regularities. These regularities are fundamental in enabling us to make predictions about what is likely to happen next. The physiological study of regularity extraction has focused primarily on repetitive sequence-based rules within the sensory environment, or on stimulus-outcome associations in the context of reward-based decision-making. Here we ask whether we implicitly encode non-sequential stochastic regularities, and detect violations therein. We addressed this question using a novel experimental design and both behavioural and magnetoencephalographic (MEG) metrics associated with responses to pure-tone sounds with frequencies sampled from a Gaussian distribution. We observed that sounds in the tail of the distribution evoked a larger response than those that fell at the centre. This response resembled the mismatch negativity (MMN) evoked by surprising or unlikely events in traditional oddball paradigms. Crucially, responses to physically identical outliers were greater when the distribution was narrower. These results show that humans implicitly keep track of the uncertainty induced by apparently random distributions of sensory events. Source reconstruction suggested that the statistical-context-sensitive responses arose in a temporo-parietal network, areas that have been associated with attention orientation to unexpected events. Our results demonstrate a very early neurophysiological marker of the brain's ability to implicitly encode complex statistical structure in the environment. We suggest that this sensitivity provides a computational basis for our ability to make perceptual inferences in noisy environments and to make decisions in an uncertain world.

Figure 1. Experimental design.

Tone pulse sequences were presented in blocks. The frequencies of the majority of tones in each block (grey) were drawn from a contextual distribution that could be narrow (left) or broad (right). The distribution densities are shown in blue and red shading; both were centred at 500 Hz and had standard deviations of 0.5 and 1.5 octaves respectively. Embedded in both sequences were probe tones whose frequencies were either equal to the distribution centres (standard, black), or 2 octaves above (odd, blue and red). Yellow rectangles indicate frequency exclusion windows used for the local adaptation analysis of Figure 5; the associated values of give the number of preceding tones that fell outside the adaptation window. Block lengths are indicated for Experiment 2. Blocks in Experiment 1 were shorter and repeated more often. Inset: The timing of tones and MEG epochs. Tone pulse waveforms (black) lasted 50 ms with ramped onsets and offsets. MEG responses (blue) were extracted from 100 ms before to 350 ms after tone onset. The evoked response shown is the average response to odd probes in the narrow context, spatially filtered as in Figure 5.

Figure 2. Behavioural results.

Displayed are responses to standard and odd probe tones under the narrow (in blue) and broad (in red) contexts. Reaction time was significantly shorter when it was accompanied by an odd probe tone rather than a standard tone regardless of the context (p = 0.002, ANOVA main effect), and more so when luminance changes were paired with odd probe tones in the narrow compared to the broad context (p = 0.043, ANOVA interaction, p = 0.0076 post-hoc t-test).

Figure 3. Sensitivity to statistical contexts in brain scalp data.

Spatio-temporal statistical analysis reveals significant effects over bilateral temporo-parietal areas (displayed at p<0.05, FWE whole-volume corrected) for (a) the main effect of surprise - deviance from the mean (odd vs. standard, MMN-like response) peaking at about 160, 190, and 310 ms and (b) the interaction, i.e., differences between MMNs under the low and high variance contexts, peaking at about 120 ms.

Figure 4. Sensitivity to statistical contexts revealed at cortical sources.

Source reconstruction analysis reveals: (a) main effect of surprise (larger sensitivity to odd vs. standard probes), (b) larger MMN-like effects under the narrow than the broad distribution and (c) a simple main effect of surprise. (All effects are displayed at p<0.05, uncorrected).

Figure 5. Local adaptation effects.

(a) Pattern of weights of the spatial filter used to extract the maximal signal-to-noise spatial projection of the MEG data. (b) The implied spatial pattern of the signal extracted by the filter shown in (a). (c) ERFs obtained by averaging responses to odd probes (solid lines) selected by threshold value of N_a (number of preceding tones falling outside a frequency window of width , here 1/3 octave). ERFs are separated by context (blue narrow; red broad). Shading for curves show regions averaged to obtain peak values in (d). Adaptation is evident for odd probes in the broad context but small or absent in the narrow context. ERFs for standard probes (dashed lines) are also shown for reference, and are not grouped by N_a. (d) Adaptation effects for a range of windows. Curves show ERF peaks (averaged as indicated in (c)) for odd probes in narrow (blue) and broad (red) context as a function of threshold value of N_a, calculated for different frequency exclusion windows (colour saturation, see legend at top). Error bars show standard errors. Grey stars indicate pairs of ERFs that were significantly different at the p<0.05 level according to a random permutation test. Lines at the bottom show the number of probe tones (combined across all subjects) that contribute to each ERF. Numbers fall as threshold N_a grows, contributing to greater uncertainty in measurements.