Surprising Threats Accelerate Evidence Accumulation for Conscious Perception

Abstract

15Our survival depends on how well we can rapidly detect threats in our environment. To facilitate this, the brain is 16 faster to bring threatening or rewarding visual stimuli into conscious awareness than neutral stimuli. Unexpected 17 events may indicate a potential threat, and yet we tend to respond slower to unexpected than expected stimuli. It 18 is unclear if or how these effects of emotion and expectation interact with one's conscious experience. To 19 investigate this, we presented neutral and fearful faces with different probabilities of occurance in a breaking 20 continuous flash suppression (bCFS) paradigm. Across two experiments, we discovered that fulfilled prior 21 expectations hastened responses to neutral faces but had either no significant effect (Experiment 1) or the opposite 22 effect (Experiment 2) on fearful faces. Drift diffusion modelling revealed that, while prior expectations accelerated 23 stimulus encoding time (associated with the visual cortex), evidence was accumulated at an especially rapid rate 24 for unexpected fearful faces (associated with activity in the right inferior frontal gyrus). Hence, these findings 25 demonstrate a novel interaction between emotion and expectation during bCFS, driven by a unique influence of 26 surprising fearful stimuli that expedites evidence accumulation in a fronto-occipital network. 27 Like predictable events, threatening stimuli are also prioritised for conscious access (Otten et al., 2017). For 63 example, fearful faces , snakes and spiders (Gomes et al., 2017), and fear-conditioned stimuli (Gayet et al., 2016) 64 are consciously perceived earlier than neutral stimuli during breaking continuous flash suppression (bCFS; 65 Tsuchiya and Koch, 2005, Jiang et al., 2007). Fearful stimuli have been shown to increase the rate of evidence 66To test the three hypotheses above, we conducted two bCFS experiments and one control experiment (see 101 Supplementary Materials).In Experiment 1, we established how expectation interacts with threat in bringing 102 stimuli into conscious perception. In Experiment 2, we adapted the design of Experiment 1 to incorporate EEG so 103 that we could observe the spatio-temporal maps of neural activity underlying the effects of emotion and expectation 104 during bCFS. We also conducted drift diffusion modelling (DDM) to examine which parameters of the decision-105 making process explained the differences in response time between conditions and how this was reflected in neural 106 activity. DDM has been used in previous studies investigating consciousness that equate the upper decision 107 boundary to the threshold for awareness (De Loof et al., 2016, Kang et al., 2017. Here, response times in the 108 bCFS paradigm reflected a perceptual decision (whether the face was rotated clockwise or anticlockwise) that 109 required conscious perception (Kang et al., 2017). We investigated how the rate of evidence accumulation (drift 110 rate; v), sensory processing and/or motor execution (non-decision time; t0), and the decision bounda...

Neural and computational processes of accelerated perceptual awareness and decisions: A 7T fMRI Study

“…Our findings demonstrate that participants can perceptually discriminate fearful faces faster than neutral faces in a breaking continuous flash suppression paradigm, replicating a previous finding from our lab (McFadyen et al, 2019) and supporting findings from previous psychophysical experiments (Jiang et al, 2009; Yang et al, 2007). Drift diffusion modelling revealed that the faster response times to fearful faces could be explained by an increased rate of evidence accumulation prior to response.…”

“…The copyright holder for this preprint (which this version posted July 23, 2021. ; https://doi.org/10.1101/2021.07.22.453451 doi: bioRxiv preprint stimuli become consciously accessible earlier than improbable stimuli -i.e., "we see what we expect to see" (McFadyen et al, 2019;Melloni et al, 2011;Pinto et al, 2015;Sterzer et al, 2008). Thus, we sought to investigate whether the degree of surprise (or expectation) about a visual stimulus might modulate neural activity relating to neutral and fearful face processing during bCFS.…”

“…Secondly, we manipulated expectations about emotional expression of the upcoming suppressed faces by using probabilistic cues. Previous research has found that more probable stimuli become consciously accessible earlier than improbable stimuli – i.e., “we see what we expect to see” (McFadyen et al, 2019; Melloni et al, 2011; Pinto et al, 2015; Sterzer et al, 2008). Thus, we sought to investigate whether the degree of surprise (or expectation) about a visual stimulus might modulate neural activity relating to neutral and fearful face processing during bCFS.…”

Mice tune out not in: violation of prediction drives auditory saliency

“…Both primates and carnivores might use their auditory senses to tune in and follow potential prey or conspecific communication signals. Interestingly and consistent with the strategy reported here for mice, threatening stimuli in humans are best perceived if they occur with relatively low-probability [32]. By contrast, implicit cueing in rewardbased tasks usually enhances perception of high-probability signals [6,10,11].…”

Great Expectations: The Neural Mechanisms of Prediction and Attention in Visual Awareness

“…Behavioural modelling (e.g., the drift diffusion model; see Alternatively, prediction may bias the drift rate of evidence accumulation after stimulus onset (the 'gain model'; Ratcliff, 1985). This hypothesis has also been supported by behavioural modelling studies (Dunovan et al, 2014;McFadyen, Smout, Tsuchiya, Mattingley, & Garrido, 2019;White et al, 2016) and is consistent with a single-unit study in monkeys that found predicted stimuli evoked greater rates of change in decision-related LIP neurons than surprising stimuli (Hanks, Mazurek, Kiani, Hopp, & Shadlen, 2011). Importantly, however, the origin and gain models are not necessarily mutually exclusive -prediction might bias both the starting point and drift rate of the evidence accumulation process.…”