New #preprint out on @biorxiv! \w @neuromurphy, @kobedesender, Nicole de Ru & Sander Nieuwenhuis, we show that temporal expectation enhances perception by speeding up the the decision process onset without affecting its evolution! #tweeprint below (1/10)http://bit.ly/2vL0ovX
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Temporal expectation could enhance perception though (1) expedited sensory evidence encoding, or (2) an increase in the quality of sensory evidence (the latter is equivalent to the mean accumulation rate under most computational frameworks for decision-making). (2/10)
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Previous work has arrived at different conclusions about account 1 versus 2, potentially due to systematic discrepancies between conclusions based on sequential-sampling models and neural signatures of decision formation. Here, we thus used both approaches. (3/10)
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We manipulated expectation about the onset of a dim visual target using the temporal cueing paradigm: when an auditory cue validly signaled target onset after a short delay, people responded faster compared to when the cue invalidly signaled a long delay (4/10)pic.twitter.com/6YCBjdS3rM
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When we applied the drift diffusion model to the behavioral data, we found evidence for account 1: non-decision time (Ter) was reduced when people expected a target. Drift rate (v) was affected by task difficulty (as predicted), but not by expectation. (5/10)pic.twitter.com/YoGndSW4ZB
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We then looked at an EEG marker of evidence accumulation during decision making tasks: the CPP. Its onset and slope showed expected scaling with RT (faster onset and steeper slope for fast responses). We could then examine the effect of expectation on CPP onset and slope (6/10)pic.twitter.com/XtKdh3d7xC
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Again supporting account 1, at the single-trial level, temporal expectation reduced onset, but did not affect slope. Difficulty did affect slope, as expected (so results are not just due to a difference in sensitivity between slope/onset). (7/10)pic.twitter.com/KQ5axzx4Ne
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These results strongly support an account in which temporal expectation enhances task performance specifically by shortening the time needed for sensory encoding, and not by affecting the decision process (besides its onset) (8/10)
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Where might fluctuations in sensory encoding originate? Anticipatory alpha power was modulated by expectations, and tracked both response-time and CPP onset. This may reflect fluctuations in top-down signaling in accordance with expectations about target onset (9/10)pic.twitter.com/fo5VHWxhiO
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Our findings thus highlight how temporal predictions optimize our interaction with unfolding sensory events: by reducing sensory encoding time. Thanks to @k_tsetsos for helpful comments, and @AvHStiftung and @ERC_Research for funding! (10/10)
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