Temporal statistical regularity results in a bias of perceived timing

Statistical regularity in the stimulus leads to learned expectations that give rise to intrinsic biases affecting the processing of subsequent stimulus information. One unresolved question in timing research is how learned temporal regularity affects the perceived timing of subsequent stimuli. The prevailing hypothesis is that expectations should bias any deviant intervals to be more similar to the regular intervals on which the expectations are learned. Precisely, expectations will have a symmetric performance effect on early and late stimuli.

In a series of experiments, Di Luca and Rhodes (2016) presented subjects with a sequence of isochronous stimuli followed by a test stimulus of varying asynchrony, and subjects are required to report if they perceive the test stimulus as isochronous or anisochronous compared to the initial sequence. They find that when subjects are presented with isochronous stimulus sequence, the expectations learned from the sequence give rise to a bias, termed Bias by expected timing (BET). Under such conditions, the minimum detectable anisochrony in the test stimulus should be greater than the actual BET. This is because BET counteracts the improved detectability of stimuli presented later than expected i.e., stimuli following a long sequence that are presented later than expected are perceptually accelerated against the detectability of asynchrony. However, behavioural results show that perceptual delay is only present at large anisochronies for stimuli presented earlier than expected. Thus, the effect of BET for early stimuli is insufficient to counteract the effect of improved detectability, leading to an asymmetric distribution of responses. To summarise, BET leads to acceleration of stimuli presented at the expected time point or later and a perceptual delay for stimuli presented earlier than expected (figure 1 below). Di Luca and Rhodes (2016) used novel experimental paradigm to obtain a less biased measure of BET in different conditions.



Figure 1: A: Example trial sequences where participants judged the temporal order of the audiovisual pair presented at the end of a sequence. Top: An audio sequence with the final stimulus presented earlier than expected (negative anisochrony) and light presented before the final audio stimulus (positive stimulus-onset asynchrony). Bottom: A visual sequence with the final stimulus presented later than expected (positive anisochrony) and audio presented before the final visual stimulus (negative stimulus-onset asynchrony). B: Average perception of subjective simultaneity (PSS) corresponding to the stimulus-onset asynchrony at which the audio and visual stimulus are perceived to be simultaneous. The difference between the PSS values on the two curves indicates the BET. If there was no change in perceived timing across presented anisochronies, the PSS curves should be horizontal. Instead, the BET changes as a function of anisochrony as stimuli presented at -80ms are perceptually delayed while stimuli presented at 0 ms and +40ms are perceptually accelerated (from Di Luca and Rhodes (2016)).

Different classes of models that explain how the brain deals with temporal regularities (interval-based models and entrainment models) predict a symmetric performance of BET and cannot explain the asymmetry in performance seen above. Di Luca and Rhodes (2016) explain this counter-intuitive effect using a Bayesian model of perceived timing. A Bayesian framework usually requires two distributions: apriori probability and likelihood function that combine together to estimate the posterior probability distribution. In experimental paradigms described in the paper, subjects could perform the task by comparing the perceived timing of the test stimuli to the expected timing (the apriori probability). The probability of sensing a stimulus after it has occurred is given by the likelihood function and is modelled as a monophasic impulse response function resulting from an exponential low pass filter. As the isochronous sequence progresses in the trial, the initially flat prior updates dynamically after the posterior distribution and becomes increasingly similar to the asymmetric likelihood function (figure 2 below). The asymmetric prior represents the learned expectation and when combined with the asymmetric likelihood pushes the posterior away from the likelihood and towards the prior distribution. The perceived timing is given by the mean of the posterior probability distribution. As shown in figure 2, stimulus presented after or before the expected time leads to an acceleration or delay of perceived time respectively.



Figure 2: Bayesian model of perceived timing. A: Likelihood probability distributions for audio and visual stimuli presented at t = 0. B: The apriori distribution curves for the next stimulus as the trial progresses. C: Posterior probability distributions obtained by integrating the prior and likelihood distributions for stimuli presented before, at and later than the expected time-point respectively.Perceived timing is the mean of the posterior distribution. Due to the asymmetric shape of prior and likelihood distributions, the posterior is pushed towards the prior distribution resulting in a bias of perceived timing (from Di Luca and Rhodes, 2016).

Now that we have a model that explains the data, the next step is to identify the neural correlates for the Bayesian model proposed above. It has been shown that temporal expectations lead to a desynchronization of alpha-band activity, where the neural response to stimuli is amplified at the expected time point (Rohenkohl and Nobre, 2011). As a result, stimulus presented before the expected time point is thus not amplified (leading to a perceptual delay) whereas stimuli presented at the expected time point or later are accelerated. Other studies have shown that alpha-beta band activity mediates feedback projections in human visual processing (Michalareas et al., 2016) and in a predictive coding framework, feedback connections subserve the signalling of predictions from higher cortical areas to lower cortical areas in the decision-making hierarchy. Taken together, these studies suggest alpha-band activity as a strong candidate for the neurophysiological correlate of BET.

Source article: Di Luca, M. & Rhodes, D. (2016). Optimal perceived timing: Integrating sensory information with dynamically updated expectations. Scientific Reports 6: 28563.

 Articles cited:

Rohenkohl, G. & Nobre, A. C. (2011). Alpha oscillations related to anticipatory attention follow temporal expectations. J. Neurosci. 31, 14076 – 14084.

Michalareas, G., Vezoli, J., van Pelt, S., Schoffelen, J.-M., Kennedy, H. & Fries, P. (2016). Alpha-beta and gamma rhythms subserve feedback and feedforward influences among human visual cortical areas. Neuron, 89, 384 – 397.

Author: Argie