Surprise About Sensory Event Timing Drives Cortical Transients in the Beta Frequency Band

P3-like signatures of temporal predictions: a computational EEG study

Many cognitive processes, ranging from perception to action, depend on the ability to predict the timing of forthcoming events. Yet, how the brain uses predictive models in the temporal domain is still an unsolved question. In previous work, we began to explore the neural correlates of temporal predictions by using a computational approach in which an ideal Bayesian observer learned the temporal probabilities of target onsets in a simple reaction time task. Because the task was specifically designed to disambiguate updating of predictive models and surprise, changes in temporal probabilities were explicitly cued. However, in the real world, we are usually incidentally exposed to changes in the statistics of the environment. Here, we thus aimed to further investigate the electroencephalographic (EEG) correlates of Bayesian belief updating and surprise associated with incidental learning of temporal probabilities. In line with our previous EEG study, results showed distinct P3-like modulations for updating and surprise. While surprise was indexed by an early fronto-central P3-like modulation, updating was associated with a later and more posterior P3 modulation. Moreover, updating was associated with a P2-like potential at centro-parietal electrodes, likely capturing integration processes between prior beliefs and likelihood of the observed event. These findings support previous evidence of trial-by-trial variability of P3 amplitudes as an index of dissociable inferential processes. Coupled with our previous findings, the present study strongly bolsters the view of the P3 as a key brain signature of temporal Bayesian inference. Data and scripts are shared on OSF: osf.io/sdy8j/.

A narrative review of the interconnection between pilot acute stress, startle, and surprise effects in the aviation context: Contribution of physiological measurements

Aviation remains one of the safest modes of transportation. However, an inappropriate response to an unexpected event can lead to flight incidents and accidents. Among several contributory factors, startle and surprise, which can lead to or exacerbate the pilot's state of stress, are often cited. Unlike stress, which has been the subject of much study in the context of driving and piloting, studies on startle and surprise are less numerous and these concepts are sometimes used interchangeably. Thus, the definitions of stress, startle, and surprise are reviewed, and related differences are put in evidence. Furthermore, it is proposed to distinguish these notions in the evaluation and to add physiological measures to subjective measures in their study. Indeed, Landman's theoretical model makes it possible to show the links between these concepts and studies using physiological parameters show that they would make it possible to disentangle the links between stress, startle and surprise in the context of aviation. Finally, we draw some perspectives to set up further studies focusing specifically on these concepts and their measurement.

Implicit Versus Explicit Timing-Separate or Shared Mechanisms?

Time implicitly shapes cognition, but time is also explicitly represented, for instance, in the form of durations. Parsimoniously, the brain could use the same mechanisms for implicit and explicit timing. Yet, the evidence has been equivocal, revealing both joint versus separate signatures of timing. Here, we directly compared implicit and explicit timing using magnetoencephalography, whose temporal resolution allows investigating the different stages of the timing processes. Implicit temporal predictability was induced in an auditory paradigm by a manipulation of the foreperiod. Participants received two consecutive task instructions: discriminate pitch (indirect measure of implicit timing) or duration (direct measure of explicit timing). The results show that the human brain efficiently extracts implicit temporal statistics of sensory environments, to enhance the behavioral and neural responses to auditory stimuli, but that those temporal predictions did not improve explicit timing. In both tasks, attentional orienting in time during predictive foreperiods was indexed by an increase in alpha power over visual and parietal areas. Furthermore, pretarget induced beta power in sensorimotor and parietal areas increased during implicit compared to explicit timing, in line with the suggested role for beta oscillations in temporal prediction. Interestingly, no distinct neural dynamics emerged when participants explicitly paid attention to time, compared to implicit timing. Our work thus indicates that implicit timing shapes the behavioral and sensory response in an automatic way and is reflected in oscillatory neural dynamics, whereas the translation of implicit temporal statistics to explicit durations remains somewhat inconclusive, possibly because of the more abstract nature of this task.

Implicitly learning when to be ready: From instances to categories

There is growing appreciation for the role of long-term memory in guiding temporal preparation in speeded reaction time tasks. In experiments with variable foreperiods between a warning stimulus (S1) and a target stimulus (S2), preparation is affected by foreperiod distributions experienced in the past, long after the distribution has changed. These effects from memory can shape preparation largely implicitly, outside of participants' awareness. Recent studies have demonstrated the associative nature of memory-guided preparation. When distinct S1s predict different foreperiods, they can trigger differential preparation accordingly. Here, we propose that memory-guided preparation allows for another key feature of learning: the ability to generalize across acquired associations and apply them to novel situations. Participants completed a variable foreperiod task where S1 was a unique image of either a face or a scene on each trial. Images of either category were paired with different distributions with predominantly shorter versus predominantly longer foreperiods. Participants displayed differential preparation to never-before seen images of either category, without being aware of the predictive nature of these categories. They continued doing so in a subsequent Transfer phase, after they had been informed that these contingencies no longer held. A novel rolling regression analysis revealed at a fine timescale how category-guided preparation gradually developed throughout the task, and that explicit information about these contingencies only briefly disrupted memory-guided preparation. These results offer new insights into temporal preparation as the product of a largely implicit process governed by associative learning from past experiences.

Bayesian modeling of temporal expectations in the human brain

The brain predicts the timing of forthcoming events to optimize processes in response to them. Temporal predictions are driven by both our prior expectations on the likely timing of stimulus occurrence and the information conveyed by the passage of time. Specifically, such predictions can be described in terms of the hazard function, that is, the conditional probability that an event will occur, given it has not yet occurred. Events violating expectations cause surprise and often induce updating of prior expectations. While it is well-known that the brain is able to track the temporal hazard of event occurrence, the question of how prior temporal expectations are updated is still unsettled. Here we combined a Bayesian computational approach with brain imaging to map updating of temporal expectations in the human brain. Moreover, since updating is usually highly correlated with surprise, participants performed a task that allowed partially differentiating between the two processes. Results showed that updating and surprise differently modulated activity in areas belonging to two critical networks for cognitive control, the fronto-parietal (FPN) and the cingulo-opercular network (CON). Overall, these data provide a first computational characterization of the neural correlates associated with updating and surprise related to temporal expectation.