Original: Sensory complexity and global gain in a DCNN codetermine optimal arousal state
Revised: Sensory complexity and global gain in a DCNN codetermine optimal arousal state

Original: AfﬁliationAddress

Original: Abstract
Revised: Abstract

Original: Arousal deeply impacts behaviour and sensory processing.
Revised: Arousal deeply impacts behaviour and sensory processing.

Original: Whereas for an easytask, perceptual performance linearly increases with higher arousal levels, fora more challenging task, an inverted-U-shaped relationship has been described.
Revised: Whereas for an easy task, perceptual performance linearly increases with higher arousal levels, for a more challenging task, an inverted-U-shaped relationship has been described.

Original: These ﬁndings are commonly referred to as the Yerkes-Dodson law (1908).
Revised: These ﬁndings are commonly referred to as the Yerkes-Dodson law (1908).

Original: Yet, itremains unclear why perceptual performance decays with high levels of arousalin difﬁcult but not in simple tasks.
Revised: Yet, it remains unclear why perceptual performance decays with high levels of arousal in difﬁcult but not in simple tasks.

Original: Based on recent studies linking a global gainchange in sensory processing to changes in arousal state, we augmented a deepconvolutional neural network with a global gain mechanism to mimic the effectsof cortical arousal.
Revised: Based on recent studies linking a global gain change in sensory processing to changes in arousal state, we augmented a deep convolutional neural network with a global gain mechanism to mimic the effects of cortical arousal.

Original: With this approach, we show that the Yerkes-Dodson law canbe accounted for by this global gain mechanism, acting on a hierarchical sensorysystem that processes stimuli of varying sensory complexity.
Revised: With this approach, we show that the Yerkes-Dodson law can be accounted for by this global gain mechanism, acting on a hierarchical sensory system that processes stimuli of varying sensory complexity.

Original: By leveraging the fullobservability of our model, we reconcile conﬂicting ﬁndings from previous studieson sensory processing, by showing that both linear as well inverted-U-shaped gainproﬁles emerge in the interaction of hierarchical sensory processing and globalarousal changes.
Revised: By leveraging the full observability of our model, we reconcile conﬂicting ﬁndings from previous studies on sensory processing, by showing that both linear as well inverted-U-shaped gain proﬁles emerge in the interaction of hierarchical sensory processing and global arousal changes.

Original:  Introduction
Revised:  Introduction

Original: The effects of neuromodulation are pervasive in the brain.
Revised: The effects of neuromodulation are pervasive in the brain.

Original: Recent estimates are that around 11% of thevariance of spontaneous activity in visual cortex is related to arousal state [1].
Revised: Recent estimates are that around 11% of the variance of spontaneous activity in visual cortex is related to arousal state [1].

Original: For behaviour, alreadyin 1908, Yerkes and Dodson [2] established that an animal’s ability to learn a sensory discriminationtask was deeply affected by its arousal state (which they manipulated by electrical stimulation).
Revised: For behaviour, already in 1908, Yerkes and Dodson [2] established that an animal’s ability to learn a sensory discrimination task was deeply affected by its arousal state (which they manipulated by electrical stimulation).

Original: Thiseffect took the shape of an inverted U for challenging perceptual discriminations, indicating thatthe animals learnt the discrimination best at intermediary levels of stimulation.
Revised: This effect took the shape of an inverted U for challenging perceptual discriminations, indicating that the animals learnt the discrimination best at intermediary levels of stimulation.

Original: Interestingly, thisrelationship became linear for perceptually easy tasks:
Revised: Interestingly, this relationship became linear for perceptually easy tasks:

Original: The animals were fastest at learning whenconfronted with the strongest stimulation intensities.
Revised: The animals were fastest at learning when confronted with the strongest stimulation intensities.

Original: This interaction between arousal state andperceptual task difﬁculty and its effect on performance has been replicated many times (e g. [3]).
Revised: This interaction between arousal state and perceptual task difﬁculty and its effect on performance has been replicated many times (e.g. [3]).

Original: Yet,the origin of this interaction is much less clear.
Revised: Yet, the origin of this interaction is much less clear.

Original: Some have proposed that an involvement of frontalcortices (e.g. prefrontal cortex) is the decisive factor for an inverted U-shaped performance proﬁle[4], while other suggest that the saturation of activation levels in the locus coeruleus – the centralsource of noradrenaline – is at the core of this transition [5].
Revised: Some have proposed that an involvement of frontal cortices (e.g. prefrontal cortex) is the decisive factor for an inverted U-shaped performance proﬁle [4], while other suggest that the saturation of activation levels in the locus coeruleus – the central source of noradrenaline – is at the core of this transition [5].

Original: Yet, these broad proposals still do notspecify how performance degrades with increasing levels of arousal for a perceptually difﬁcult task.
Revised: Yet, these broad proposals still do not specify how performance degrades with increasing levels of arousal for a perceptually difﬁcult task.

Original: Studies examining the effects of arousal (indexed by pupil dilation) on processing in sensory corticesand perceptual performance provide conﬂicting answers to this question [6].
Revised: Studies examining the effects of arousal (indexed by pupil dilation) on processing in sensory cortices and perceptual performance provide conﬂicting answers to this question [6].

Original: For early visual cortex,a number of studies report a linear increase in ﬁring rates with increases in arousal during sensorystimulation [7–10].
Revised: For early visual cortex, a number of studies report a linear increase in ﬁring rates with increases in arousal during sensory stimulation [7–10].

Original: One of these studies also reported that the animals performed best at a go/no-gotask with perceptual discrimination at the highest levels of arousal, identiﬁed through locomotion [8].
Revised: One of these studies also reported that the animals performed best at a go/no-go task with perceptual discrimination at the highest levels of arousal, identiﬁed through locomotion [8].

Original: Yet, another study in auditory cortex that also used a go/no go-task discovered an inverted U-shaped link for both ﬁring rates during sensory stimulation and performance levels [11].
Revised: Yet, another study in auditory cortex that also used a go/no go-task discovered an inverted U-shaped link for both ﬁring rates during sensory stimulation and performance levels [11].

Original: In contrast tostudies in visual cortex, this ﬁnding thus suggests an optimum for both a gain in ﬁring rates as wellperformance at intermediate levels of arousal.
Revised: In contrast to studies in visual cortex, this ﬁnding thus suggests an optimum for both a gain in ﬁring rates as well performance at intermediate levels of arousal.

Original: The differences across these studies have been mainly attributed to the difference in sensory modality[6], yet more recent studies suggest a high degree of similarity for the effects of arousal on both visualand auditory processing [12].
Revised: The differences across these studies have been mainly attributed to the difference in sensory modality [6], yet more recent studies suggest a high degree of similarity for the effects of arousal on both visual and auditory processing [12].

Original: Beyond the sensory modality, another striking difference betweenthese studies is the complexity of sensory stimuli that were presented to the animals.
Revised: Beyond the sensory modality, another striking difference between these studies is the complexity of sensory stimuli that were presented to the animals.

Original: While the studywith the inverted U-shaped relationship used quite complex sensory stimuli for their go/no go task(presence of pure tones embedded in sequences of structured noise, [11]), the studies suggesting alinear link used rather simple stimuli (presence of a large Gabor patch on a grey screen, [8, 10]).
Revised: While the study with the inverted U-shaped relationship used quite complex sensory stimuli for their go/no go task (presence of pure tones embedded in sequences of structured noise, [11]), the studies suggesting a linear link used rather simple stimuli (presence of a large Gabor patch on a grey screen, [8, 10]).

Original: Thisprompted us to ask whether the different ﬁndings of arousal on evoked sensory processing could bedue to a difference in experimental stimuli instead.
Revised: This prompted us to ask whether the different ﬁndings of arousal on evoked sensory processing could be due to a difference in experimental stimuli instead.

Original: To answer this question, we implemented a global gain model to mimic the effects of cortical arousalon sensory processing in a deep convolutional neural network (DCNN) and tested it on a range oftasks with varying degrees of sensory complexity.
Revised: To answer this question, we implemented a global gain model to mimic the effects of cortical arousal on sensory processing in a deep convolutional neural network (DCNN) and tested it on a range of tasks with varying degrees of sensory complexity.

Original: With our approach, we show that long establishedeffects of arousal on perceptual performance, as captured by the Yerkes-Dodson law, can be accountedfor by a global gain mechanism acting on a hierarchical sensory system processing stimuli of varyingcomplexity.
Revised: With our approach, we show that long established effects of arousal on perceptual performance, as captured by the Yerkes-Dodson law, can be accounted for by a global gain mechanism acting on a hierarchical sensory system processing stimuli of varying complexity.

Original:  Global gain as a model for cortical arousal
Revised:  Global gain as a model for cortical arousal

Original: Cortical arousal is linked to increased ﬁring rates in response to sensory stimulation in sensorycortices, i.e. increased gain [6].
Revised: Cortical arousal is linked to increased ﬁring rates in response to sensory stimulation in sensory cortices, i.e. increased gain [6].

Original: These increases in gain have been argued to be implemented viaa global release of noradrenaline [13], making them spatially unspeciﬁc and temporally slow.
Revised: These increases in gain have been argued to be implemented via a global release of noradrenaline [13], making them spatially unspeciﬁc and temporally slow.

Original: Forcreating an analogous situation in a DCNN, we used a ResNet18 architecture [14] with sigmoidal-likeactivation functions [15] that was pretrained on ImageNet [16].
Revised: For creating an analogous situation in a DCNN, we used a ResNet18 architecture [14] with sigmoidal-like activation functions [15] that was pretrained on ImageNet [16] and achieved a top-1 accuracy of 64.04% on the validation set.

Original:  To simulate the effects of arousal, wetargeted all activation functions in the network simultaneously with one global gain parameter. Thisparameter multiplicatively scaled the output of the activation functions, thus giving rise to responsegain everywhere in the network.  Results
Revised: We here chose to use a sigmoidal-like function instead of the more commonly used rectiﬁed linear unit (ReLU) as our activation function because it more accurately captures the saturating property of biological neurons at extreme values, a characteristic we deemed to be central for studying the effects of gain [17]. To simulate the effects of arousal, we scaled all activation functions in the network simultaneously with one global gain parameter γ :

Original: [Figure]
Revised: [Equation]

Revised: where f ( S ) describes the outgoing activation and S is the incoming activation.

Revised: The second part of the equation describes the sigmoidal-like properties of the activation function where the constants h , c 0 , c 1 , c 2 , c 3 and c 4 were derived from a spiking neuron model capturing the mapping between the incoming current and the average post-synaptic potential over inﬁnite time steps (please see [15] for more details and the full derivation from the spiking neuron model).

Revised: In brief, changing the global gain parameter thus scaled the output of the activation functions, thereby increasing or reducing response gain everywhere in the network.

Original:   3.1 Yerkes–Dodson law – A global gain model accounts for behavioural effects of arousal
Revised:  Results 3.1 Yerkes–Dodson law – A global gain model accounts for behavioural effects of arousal

Original: To evaluate the link between global gain and perceptual difﬁculty, we developed a task in whichwe could parametrically alter the visual complexity required to solve a binary discrimination task(Figure 1A).
Revised: To evaluate the link between global gain and perceptual difﬁculty, we developed a task in which we could parametrically alter the visual complexity required to solve a binary discrimination task (Figure 1A).

Original: The network was tasked to distinguish a natural scene from an average image.
Revised: The network was tasked to distinguish a natural scene from an average image.

Original: Theperceptual difﬁculty was determined by the number of images that were used to compute the averageimages, with average images that consisted of fewer images being more difﬁcult to distinguish fromthe natural scenes.
Revised: The perceptual difﬁculty was determined by the number of images that were used to compute the average images, with average images that consisted of fewer images being more difﬁcult to distinguish from the natural scenes.

Original: Next, we ﬁne-tuned a single sigmoidal neuron connected to the fully-connectedlayer of the ResNet18.
Revised: Next, we ﬁne-tuned a single sigmoidal neuron connected to the fully-connected layer of the ResNet18.

Original: The remaining network weights were kept unchanged.
Revised: The remaining network weights were kept unchanged.

Original: For all degrees ofvisual complexity, a separate model was  ﬁne-tuned and tested with varying degrees of global gain(Figure 1B).
Revised: For all degrees of perceptual difﬁculty, a separate model was ﬁne-tuned. A single ﬁne-tuned model per perceptual difﬁculty was then tested with varying degrees of global gain (Figure 1B).

Original: For the most difﬁcult task, based on an average of two images, we observed an inverted U-shaperelationship between global gain and performance.
Revised: For the most difﬁcult task, based on an average of two images, we observed an inverted U-shape relationship between global gain and performance.

Original: The best task performance was achieved close toa gain of 1, corresponding to the evaluation of the original model.
Revised: The best task performance was achieved close to a gain of 1, corresponding to the evaluation of the original model.

Original: Further in- or decreases in gainonly hampered performance, thus resulting in an inverted U-shaped proﬁle.
Revised: Further in- or decreases in gain only hampered performance, thus resulting in an inverted U-shaped proﬁle.

Original: Interestingly, the easierthe task became the more this inverted U transformed into a monotonically increasing performanceproﬁle that had its optimum at very large gain values.
Revised: Interestingly, the easier the task became the more this inverted U transformed into a monotonically increasing performance proﬁle that had its optimum at very large gain values.

Original: This means that the model performed best in astate that was very different from training.
Revised: This means that the model performed best in a state that was very different from training.

Original: Across all perceptual difﬁculties, performance decreasedback to chance performance at a similar global gain value.
Revised: Across all perceptual difﬁculties, performance decreased back to chance performance at a similar global gain value.

Original: This pattern of ﬁndings directly replicates the Yerkes-Dodson law [2], assuming that very low andvery high levels of global gain are not accessible experimentally or plausible to occur in a livingorganism.
Revised: This pattern of ﬁndings directly replicates the Yerkes-Dodson law [2], assuming that very low and very high levels of global gain are not accessible experimentally or plausible to occur in a living organism.

Original: Moreover, we show that the transition from a linear to an inverted U-shape proﬁle is acontinuum and could arise directly from applying a global gain factor to a hierarchical sensory systemsuch as a DCNN.
Revised: Moreover, we show that the transition from a linear to an inverted U-shape proﬁle is a continuum and could arise directly from applying a global gain factor to a hierarchical sensory system such as a DCNN.

Original: 3.2 From binary to multi-class tasks - The Yerkes Dodson law also holds for objectrecognition
Revised: 3.2 From binary to multi-class tasks - The Yerkes Dodson law also holds for object recognition

Original: A commonality of many studies describing the Yerkes-Dodson law is the usage of sensory discrimination tasks with artiﬁcial stimuli that typically involve the detection of a single target class[2, 3, 8, 11].
Revised: A commonality of many studies describing the Yerkes-Dodson law is the usage of sensory discrimination tasks with artiﬁcial stimuli that typically involve the detection of a single target class [2, 3, 8, 11].

Original: Yet despite this commonality, these ﬁndings have been interpreted to apply to taskdifﬁculty more broadly [4, 5].
Revised: Yet despite this commonality, these ﬁndings have been interpreted to apply to task difﬁculty more broadly [4, 5].

Original: To test the generalization of our ﬁrst experiment that the inverted
Revised: To test the generalization of our ﬁrst experiment that the inverted

Original: U-shaped performance proﬁle arises in tasks with complex visual stimuli, we therefore reasoned thatit should also hold for other visual recognition tasks with more than one target class.
Revised: U-shaped performance proﬁle arises in tasks with complex visual stimuli, we therefore reasoned that it should also hold for other visual recognition tasks with more than one target class.

Original: To this end, we ﬁne-tuned the same ResNet18 on a 8-way multi-label object detection task with a shared context(Figure 2A).
Revised: To this end, we ﬁne-tuned the same ResNet18 on a 8-way multi-label object detection task with a shared context (Figure 2A).

Original: As in the binary task, we again only trained an output layer consisting of eight sigmoidal outputnodes.
Revised: As in the binary task, we again only trained an output layer consisting of eight sigmoidal output nodes.

Original: The networks were ﬁrst trained with multi-label images, but evaluated on single target images.
Revised: The networks were ﬁrst trained with multi-label images, but evaluated on single target images.

Original: We ﬁnd that also here the same pattern of results holds: Across both contexts, we observe an invertedU-shaped performance proﬁle (Figure 2B).
Revised: We ﬁnd that also here the same pattern of results holds: Across both contexts, we observe an invertedU-shaped performance proﬁle (Figure 2B).

Original: Comparing the datasets shows that networks performedless well on the food compared to the street scenes at baseline, a gain of 1.
Revised: Comparing the datasets shows that networks performed less well on the food compared to the street scenes at baseline, a gain of 1.

Original: In the data by Yerkesand Dodson [2] more difﬁcult tasks resulted in a narrower inverted U-shape than less difﬁcult task.
Revised: In the data by Yerkes and Dodson [2] more difﬁcult tasks resulted in a narrower inverted U-shape than less difﬁcult task.

Original: Similarly in our results, the food scenes featured a narrower proﬁle compared to the street scenes.
Revised: Similarly in our results, the food scenes featured a narrower proﬁle compared to the street scenes.

Original: For high gain regimes, we observed some marked differences to the binary task for the street dataset.
Revised: For high gain regimes, we observed some marked differences to the binary task for the street dataset.

Original: While performance completely decayed in the food scenes for global gain parameters exceeding 1.2,street scenes are being solved above chance far beyond that.
Revised: While performance completely decayed in the food scenes for global gain parameters exceeding 1.2, street scenes are being solved above chance far beyond that.

Original: This could be indicative of a higherchance of guessing based on an overly driven network for the street compared to the food scenes.
Revised: This could be indicative of a higher chance of guessing based on an overly driven network for the street compared to the food scenes.

Original: In sum, we have thus shown that the Yerkes-Dodson law also holds for more complex behaviouralscenarios in our model, such as object recognition in natural scenes.
Revised: In sum, we have thus shown that the Yerkes-Dodson law also holds for more complex behavioural scenarios in our model, such as object recognition in natural scenes.

Original: This experiment also further supported our claim that the Yerkes-Dodson law in behaviour is likely moderated by sensory complexityrather than other task factors.
Revised: This experiment also further supported our claim that the Yerkes-Dodson law in behaviour is likely moderated by sensory complexity rather than other task factors.

Original: 3.3 The inverted U arises from high-level features
Revised: 3.3 The inverted U arises from high-level features

Original: In a ﬁnal analysis, we sought to connect our results for performance to observations made experimentally in sensory cortices [6–11].
Revised: In a ﬁnal analysis, we sought to connect our results for performance to observations made experimentally in sensory cortices [6–11].

Original: Speciﬁcally, arousal has been linked to both linear as wellinverted-U-shaped neural gain proﬁles for evoked processing.
Revised: Speciﬁcally, arousal has been linked to both linear as well inverted-U-shaped neural gain proﬁles for evoked processing.

Original: To obtain an intuition on how activationlevels were shaped by both stimulus complexity and global gain in our networks, we collected themean activations of the activation functions after the addition in the ResNet18 across different gainparameters and stimulus complexities.
Revised: To obtain an intuition on how activation levels were shaped by both stimulus complexity and global gain in our networks, we collected the mean activations of the activation functions after the addition in the ResNet18 across different gain parameters and stimulus complexities.

Original: A general ﬁnding across all layers is that the two most perceptually difﬁcult binary task datasets wereproducing the highest activation levels in almost all ResNet blocks (Figure 3).
Revised: A general ﬁnding across all layers is that the two most perceptually difﬁcult binary task datasets were producing the highest activation levels in almost all ResNet blocks (Figure 3).

Original: Furthermore, whilethere seems to be a linear effect of global gain for most early ResNet blocks (1 – 4), this link becomes increasingly complex in the later blocks (5 – 8).
Revised: Furthermore, while there seems to be a linear effect of global gain for most early ResNet blocks (1 – 4), this link becomes increasingly complex in the later blocks (5 – 8).

Original: Overall, this pattern is suggestive of increasinglyspeciﬁc features in later ResNet blocks, which cannot be activated to a similar extent by simpler visualstimuli such as those used for the perceptually easy task (cf.
Revised: Overall, this pattern is suggestive of increasingly speciﬁc features in later ResNet blocks, which cannot be activated to a similar extent by simpler visual stimuli such as those used for the perceptually easy task (cf.

Original: Figure 1A).
Revised: Figure 1A).

Original: Our analysis also makes itclear that both linear as well inverted-U-shaped activation proﬁles may occur in a hierarchical sensorysystem.
Revised: Our analysis also makes it clear that both linear as well inverted-U-shaped activation proﬁles may occur in a hierarchical sensory system.

Original: The gain proﬁles observed experimentally [11, 10] that were ﬁrst thought to be at odds withone another [6], are likely to co-occur when accounting for stimulus complexity and hierarchicalprocessing.
Revised: The gain proﬁles observed experimentally [11, 10] that were ﬁrst thought to be at odds with one another [6], are likely to co-occur when accounting for stimulus complexity and hierarchical processing.

Original: This last result indicates that the characteristic inverted U-shape observed in perceptually difﬁcult tasksmight stem from an interaction between complex sensory features that are tuned to a speciﬁc activationrange and global gain increases.
Revised: This last result indicates that the characteristic inverted U-shape observed in perceptually difﬁcult tasks might stem from an interaction between complex sensory features that are tuned to a speciﬁc activation range and global gain increases.

Original: We speculate that increases in arousal lead to activation patternsthat do not match these complex features anymore, thus leading to a decay of these features andhence to lower mean activations at these stages of processing.
Revised: We speculate that increases in arousal lead to activation patterns that do not match these complex features anymore, thus leading to a decay of these features and hence to lower mean activations at these stages of processing.

Original: For behaviour, this interaction betweenglobal gain and stimulus complexity can have direct consequences for perceptual performance: If theread-out layer learnt to capitalize on these features during training, this can either result in a linear oran inverted U-shaped performance proﬁle.
Revised: For behaviour, this interaction between global gain and stimulus complexity can have direct consequences for perceptual performance: If the read-out layer learnt to capitalize on these features during training, this can either result in a linear or an inverted U-shaped performance proﬁle.

Original:  Conclusions
Revised:  Conclusions

Original: In this paper, we implemented a global gain mechanism in a DCNN to mimic the effects of arousalon sensory processing.
Revised: In this paper, we implemented a global gain mechanism in a DCNN to mimic the effects of arousal on sensory processing.

Original: Combining performance-optimized computer vision models with recentevidence from neuroscience allowed us to address the long-standing question of why perceptualperformance decays with high levels of arousal in perceptually difﬁcult but not in simple tasks.
Revised: Combining performance-optimized computer vision models with recent evidence from neuroscience allowed us to address the long-standing question of why perceptual performance decays with high levels of arousal in perceptually difﬁcult but not in simple tasks.

Original: Speciﬁcally, we found that with our straightforward global gain model we could robustly replicatethe Yerkes-Dodson law for both binary as well as more challenging multi-label object recognitiontasks.
Revised: Speciﬁcally, we found that with our straightforward global gain model we could robustly replicate the Yerkes-Dodson law for both binary as well as more challenging multi-label object recognition tasks.

Original: Beyond that, we have provided an intuition of how the switch from linear to inverted-U shapedperformance proﬁles can arise, namely from an interaction between high-level sensory featuresand global gain increases.
Revised: Beyond that, we have provided an intuition of how the switch from linear to inverted-U shaped performance proﬁles can arise, namely from an interaction between high-level sensory features and global gain increases.

Original: Importantly, we hereby put forward that this often reported change inperformance proﬁle for perceptual discriminations is at least in part due to an interaction with sensoryprocessing, and not just the involvement of frontal cortices [4, 5].
Revised: Importantly, we hereby put forward that this often reported change in performance proﬁle for perceptual discriminations is at least in part due to an interaction with sensory processing, and not just the involvement of frontal cortices [4, 5].

Original: Based on our results, we predictthat an inverted-U-shaped gain proﬁle with complex sensory stimuli should transition to a more lineargain proﬁle with simpler sensory stimulation, both for perceptual performance as well as sensoryactivity.
Revised: Based on our results, we predict that an inverted-U-shaped gain proﬁle with complex sensory stimuli should transition to a more linear gain proﬁle with simpler sensory stimulation, both for perceptual performance as well as sensory activity.

Original: Neuromodulation and arousal in particular have been suggested to belong to different behaviouralrepertoires in the context of reinforcement learning [5, 18]: While medium levels of arousal arethought to be particularly suitable for exploitative regimes, high levels of arousal were proposed to beparticularly suitable for exploratory regimes, thus facilitating the switch to a new task.
Revised: Neuromodulation and arousal in particular have been suggested to belong to different behavioural repertoires in the context of reinforcement learning [5, 19]: While medium levels of arousal are thought to be particularly suitable for exploitative regimes, high levels of arousal were proposed to be particularly suitable for exploratory regimes, thus facilitating the switch to a new task.

Original: Our resultsprovide a starting point for addressing these types of questions more directly in a performance-orientedframework with complex sensory processing
Revised: Our results provide a starting point for addressing these types of questions more directly in a performance-oriented framework with complex sensory processing.

Revised: Acknowledgments and Disclosure of Funding

Revised: This work was funded by an NWO Talent Grant (406.17.554) awarded to LKAS, SMB, HAS and

Revised: HSS.