Thanks Junjie!

Also, I apologise for the poor figure quality in the HTML version of the article. Elsevier’s typesetting team made some nonsense changes that I did not consent to, which have somehow proved to be frustrating to fix on their end.

The PDF version is fine though!

I’d like to thank my co-authors (particularly Naohide and Jonny) and reviewers for helping me elevate the quality of this work 😊

This suggests that visual surprise may operate at the bound object level and/or is a domain-general response, which is identical to the conclusion drawn from our previous work! www.sciencedirect.com/science/arti...

Perhaps the coolest result was that these surprise signals were *shared across attributes*. That is, classifiers trained to decode surprise for shape could reliably do so for colour (and vice versa), after accounting for latency shifts.

Interestingly, we were still able to decode multivariate whole-scalpe representations of surprise (neutral vs. violation) separately for each attribute. Moreover, these signals were reliable from ~250 ms, suggesting that surprise is predominantly signalled after the initial feedforward sweep.

We first looked at the evoked responses and found classic effects of adaptation via the constant vs. change sequence comparisons.

This said, we found no evidence for visual surprise after controlling for cortical adaptation (i.e., when comparing surprising changes to neutral changes).

Here, we recorded EEG from participants who viewed sequences of a bound object that changed in either colour or shape over four steps. Crucially, the contexts of these changes were designed to appear random (and unsurprising) or violate the established trajectory (and cause surprise).

But when does the visual system signal surprise? And do the dynamics of a surprise signal depend on which attributes (features) violate a prediction? This is important to think about, given the functionally segregated organisation of the visual system.

Predictive coding theories assert that the brain uses prior knowledge when resolving percepts. Deviations between what is predicted and sensed generate surprise signals (so-called ‘prediction errors’), which calibrate the relevant erroneous predictions.

This suggests that visual surprise may operate at the bound object level and/or is a domain-general response.

This is identical to the conclusions drawn from our previous work :)

doi.org/10.1016/j.co...

Perhaps the coolest result was that surprise signals were *shared across attributes*. That is, classifiers trained to decode surprise for shape could reliably do so for colour (and vice versa), after accounting for latency shifts.

Interestingly, we were still able to decode whole-scalp multivariate representations of surprise (neutral vs. violation) separately for each attribute. Moreover, these signals were reliable from ~250 ms, suggesting that surprise is predominantly signalled after the initial feedforward sweep.

We first looked at the evoked responses and found classic effects of adaptation via the constant vs. change sequence comparisons.

This said, we found no evidence for visual surprise after controlling for cortical adaptation (i.e., when comparing surprising changes to neutral changes).

Here, we recorded EEG from participants who viewed sequences of a bound object that changed in either colour or shape over four steps. Crucially, the contexts of these changes were designed to appear random (and unsurprising) or violate the established trajectory (and cause surprise).

But when does the visual system signal surprise? And do the dynamics of a surprise signal depend on which attributes (features) violate a prediction? This is important to think about, given the functionally segregated organisation of the visual system.

Predictive coding theories assert that the brain uses prior knowledge to predict upcoming visual events when resolving percepts. Deviations between what is predicted and sensed generate surprise signals (so-called ‘prediction errors’), which calibrate the relevant erroneous predictions.