When asked to make repeated decisions we will often choose differently each time even when we are given the same information to inform our choice. A stock trader, for example, will typically be more inclined to buy on some days and sell on others even if the financial markets remain unchanged. Fluctuations in the brain’s level of alertness or excitability, otherwise known as its arousal, are thought to contribute to this variability in decision-making.

An area at the base of the brain called the brainstem – and in particular one of its subregions, the locus coeruleus – helps shape arousal levels by releasing chemicals called neuromodulators. For reasons that remain unknown, activation of the locus coeruleus also causes the pupil of the eye to suddenly increase in size. Now, de Gee et al. have exploited this link to unravel how changes in brain arousal lead to systematic changes in decision-making.

Volunteers were asked to judge whether a faint pattern was embedded in flickering noise on a computer screen, and to report their judgment by pressing one of two buttons to indicate “yes” or “no”. Although the decision was comparatively simple, it did involve evaluating changing information over time before making a choice – like when considering the stock market. As the volunteers performed the task, de Gee et al. measured their brain activity and the size of their pupils. Most of the volunteers had a tendency to respond “no” even when the pattern was present. However, whenever their locus coeruleus was particularly active, and their pupils increased in size, their decision process was changed so that this unhelpful choice bias decreased.

This suggests that by boosting arousal, the locus coeruleus reduces existing biases in our decision-making. Varying levels of locus coeruleus activity may thus explain why we can reach different conclusions when considering the same information on multiple occasions. The next challenge is to identify what it is about the decision-making process that activates the locus coeruleus on some occasions but not others.

Decision-makers often arrive at different choices in the face of repeated presentations of the same evidence (Glimcher, 2005; Gold and Shadlen, 2007; Shadlen et al., 1996; Sugrue et al., 2005; Wyart and Koechlin, 2016). This intrinsic behavioral variability is typically attributed to spontaneous fluctuations of neural activity in the brain regions computing decisions (Glimcher, 2005; Shadlen et al., 1996) (but see [Beck et al., 2012; Brunton et al., 2013]). Indeed, fluctuations of neural activity are ubiquitous in the cerebral cortex (Faisal et al., 2008; Glimcher, 2005; Lin et al., 2015).

One candidate source of these fluctuations in cortical activity is systematic variation in central arousal state. Central arousal state is controlled by the neuromodulatory systems of the brainstem, which have widespread projections to cortex and tune neuronal parameters governing the operating mode of their cortical target circuits (Aston-Jones and Cohen, 2005; Harris and Thiele, 2011; Lee and Dan, 2012). Importantly, these neuromodulatory systems operate at different timescales (Aston-Jones and Cohen, 2005; Parikh et al., 2007). Some, in particular the noradrenergic locus coeruleus (LC), are rapidly recruited, in a time-locked fashion, during elementary decisions (Aston-Jones and Cohen, 2005; Bouret and Sara, 2005; Dayan and Yu, 2006; Parikh et al., 2007). Pupil diameter, a reliable peripheral marker of central (cortical) arousal state (McGinley et al., 2015b), also increases during decisions (Beatty, 1982; de Gee et al., 2014; Gilzenrat et al., 2010; Lempert et al., 2015; Nassar et al., 2012). These observations point to an important role of phasic (i.e., fast) pupil-linked arousal signals in decision-making (Aston-Jones and Cohen, 2005; Dayan and Yu, 2006). Yet, the precise nature of this role has remained unknown.

Here, we investigated how phasic, task-related arousal interacts with decision computations in the human brain. We combined pupillometry, fMRI, and computational modeling to probe into the interplay between task-related arousal and decision computations underlying elementary sensory-motor choice tasks. Sensory-motor decisions entail the gradual accumulation of noisy ‘sensory evidence’ about the state of the world towards categorical decision states governing behavioral choice (Bogacz et al., 2006; Brody and Hanks, 2016; Gold and Shadlen, 2007; Ratcliff and McKoon, 2008). A large-scale network of regions in frontal and parietal cortex seems to accumulate stimulus responses provided by sensory cortices towards choices of motor movements (Gold and Shadlen, 2007; Siegel et al., 2011) (but see [Brody and Hanks, 2016; Katz et al., 2016]). We here aimed to elucidate the interaction between pupil-linked arousal responses, evidence accumulation, and decision processing across several (cortical and subcortical) brain regions.

Large task-evoked pupil responses were consistently accompanied by a reduction in perceptual decision bias in different sensory modalities (visual and auditory) and task protocols (detection and discrimination). Decision bias reflects the degree to which an observer’s choice deviates from the objective sensory evidence. Using fMRI for one of these tasks revealed that the bias reduction was accompanied by a modulation of choice-encoding pattern signals in prefrontal and parietal cortex. Further, the bias reduction was predicted by task-evoked, pupil-linked responses in a network of neuromodulatory brainstem nuclei controlling cortical arousal state. We conclude that phasic neuromodulatory signals reduce biases in the brain’s decision-making machinery. As a consequence, phasic arousal accounts for a significant component of the variability of choice behavior, over and above the objective evidence gathered from the outside world.

We systematically quantified the interaction between pupil-linked arousal responses and decision computations at the algorithmic and neural levels of analysis. We here operationalize ‘phasic arousal’ as task-evoked pupil responses (TPR). This operational definition is based on recent animal work, which established remarkably strong correlations between non-luminance mediated variations in pupil diameter and global cortical arousal state (McGinley et al., 2015b).

The Results section is organized as follows. First, we quantify TPRs during the main behavioral task studied in this paper. The key observation here was the substantial trial-to-trial variability of the TPR amplitude. All subsequent analyses exploited this variability to pinpoint the functional correlates of phasic arousal. We then present results from modeling TPR-dependent changes in choice behavior, identifying precise algorithmic correlates of phasic arousal. These results yielded detailed predictions for the underlying modulations of cortical signals. Third, we present tests of these predictions, focusing on functionally delineated cortical regions of interest. We conclude by establishing that the trial-to-trial fluctuations in TPR amplitude, and the associated bias reduction, were closely linked to task-evoked responses of neuromodulatory brainstem centers involved in regulating cortical arousal state.

Tracking trial-to-trial fluctuations in phasic arousal

The main task used in this study was detection (‘yes-no’, simple forced choice protocol) of a low-contrast grating (Figure 1A). The grating contrast was titrated to the 75% correct level, and subjects did not receive trial-by-trial feedback. As observed previously (de Gee et al., 2014), TPR amplitudes during this task fluctuated widely from trial to trial (Figure 1B,C; see Materials and Methods for quantification of TPR). To illustrate, pooling trials into two bins containing the lowest and highest 40% of TPR amplitudes (Figure 1B) yielded, on average, the commonly observed task-evoked pupil dilations for the high TPR bin, but pupil constrictions for the low TPR bin (Figure 1C). We used a previously established model to estimate the time course of the neural input driving the measured TPRs (GLM; see Materials and methods; Figure 1—figure supplement 1A–C). This revealed that the difference between the low and high TPR bins was primarily due to the difference in a sustained component that spanned the entire interval from cue to behavioral choice (Figure 1D). The difference of the sustained component between low and high TPR was significantly larger than the corresponding difference for two components at cue or choice, respectively (2-way repeated measures ANOVA with factors temporal component and TPR bin; interaction: F_2,26 = 79.00, p<0.001).

Figure 1 with 1 supplement see all

Download asset Open asset

In sum, TPR amplitude exhibited substantial trial-to-trial fluctuations, which were predominantly driven by changing levels in sustained input during decision formation. Given the prolonged nature of the decision (median of subject-median reaction time, RT: 2.11 s), the sustained, intra-decisional arousal boost might have interacted with the decision computation. To test for such an interaction between arousal boost and decision computation, we next modeled subjects’ choice behavior as a function of TPR amplitude.

Phasic arousal is inversely related to decision bias

We found a robust and consistent relationship between TPR and decision bias. This effect was present in two independent data sets using an analogous contrast detection task: the newly collected fMRI data set, and a re-analysis of an existing data set (de Gee et al., 2014)) (Figure 2A,D, middle and right panels). Decision bias was quantified in two ways (for details, see Materials and methods). First, we computed signal detection-theoretic (SDT) criterion (Figure 2A,D, middle panels). Second, we computed the fraction of ‘yes’-choices (right panels), after balancing the number of signal+noise and noise trials within each TPR bin. We did not find a consistent relationship between phasic arousal, as measured by TPR, and perceptual sensitivity, quantified by SDT d’ (Figure 2A,D, left panels).

Figure 2 with 1 supplement see all

Download asset Open asset

Figure 2—source data 3

Table with variable identifiers used in Figure 2—source data 1 and 2.

https://doi.org/10.7554/eLife.23232.006

Download elife-23232-fig2-data3-v4.txt

Figure 2—source data 1

This csv table contains the data for Figure 2 panel A.

https://doi.org/10.7554/eLife.23232.007

Download elife-23232-fig2-data1-v4.csv

Figure 2—source data 2

This csv table contains the data for Figure 2 panel D.

https://doi.org/10.7554/eLife.23232.008

Download elife-23232-fig2-data2-v4.csv

The negative association between TPR and decision bias (SDT criterion) was approximately linear across a range of five TPR-defined bins (Figure 2B,E, right panels). In all cases, here and below, we tested whether fits of second-order polynomials, reflecting non-monotonic relationships between TPR and behavior, were superior to the linear fits (via sequential polynomial regression analysis; Materials and methods). We found a non-monotonic relationship between TPR and sensitivity in the behavioral data set from de Gee et al. (2014), but not in the fMRI dataset (Figure 2B,E, left panels). This non-monotonic (inverted U-shape) relationship between pupil diameter and sensitivity is consistent with previous animal work on correlations between baseline arousal and behavior (Aston-Jones and Cohen, 2005; McGinley et al., 2015a). However, it was less consistent across the data sets analyzed in this paper than the negative linear effect of TPR on decision bias. The consistent effect of TPR on decision bias has not been reported before in previous studies of slow fluctuations of baseline pupil diameter. In what follows, we focus on the negative effect of TPR on decision bias.

Most subjects were overall (i.e., without splitting trials by TPR) intrinsically biased to respond ‘no’: 10 out of 14 subjects exhibited a significantly conservative criterion (within-subject permutation tests; p<0.05) in the fMRI data set, and 14 out of 21 subjects in the data set from de Gee et al. (2014). Because signal+noise and noise trials were equally frequent in both experiments, this bias was always maladaptive. Critically, this maladaptive bias was particularly pronounced under low TPR; but under high TPR the bias was nearly neutralized, especially in the fMRI data set (criterion around zero, and fraction of ‘yes’-choices around 0.5 for highest TPR bins, Figure 2A,B).

Pupil-linked bias reduction is a general phenomenon

Fourth, the effect of TPR on decision bias shown in Figure 2 generalized to other perceptual choice tasks, which differed on several dimensions from the main contrast detection task used in this paper (Figure 3). In one follow-up experiment, we measured pupil-linked behavior during an auditory yes-no (tone-in-noise) detection task near psychophysical threshold using the same stimuli as in (McGinley et al., 2015a) (see Materials and methods). The only visual stimulus was a stable fixation dot. The decision interval contained only auditory noise (the same as in (McGinley et al., 2015a)) on half the trials, and a pure sine wave superimposed onto the noise on the other half of the trials. Again, TPR predicted a significant (linear) reduction in conservative decision bias, and an increased tendency to respond ‘yes’ (Figure 3A,B). TPR also exhibited a non-monotonic relationship with sensitivity, as observed in rodents for baseline pupil diameter in (McGinley et al., 2015a).

Figure 3

Download asset Open asset

Figure 3—source data 3

Table with variable identifiers used in Figure 3—source data 1 and 2.

https://doi.org/10.7554/eLife.23232.011

Download elife-23232-fig3-data3-v4.txt

Figure 3—source data 1

This csv table contains the data for Figure 3 panel A.

https://doi.org/10.7554/eLife.23232.012

Download elife-23232-fig3-data1-v4.csv

Figure 3—source data 2

This csv table contains the data for Figure 3 panel C.

https://doi.org/10.7554/eLife.23232.013

Download elife-23232-fig3-data2-v4.csv

Another follow-up experiment assessed whether the pupil-linked bias reduction observed above may have been due to the asymmetric nature of the detection tasks (i.e., discriminating the presence from the absence of a signal) or due to the absence of single-trial feedback. Symmetric two-alternative forced choice tasks are commonly associated with weaker biases than yes-no detection tasks (Green and Swets, 1966). We used a symmetric visual random dot motion (up vs. down) discrimination task near psychophysical threshold with feedback after each trial (see Materials and methods). Although many subjects exhibited clear biases for reporting one or the other direction, these were more evenly distributed around zero than in the above yes-no tasks, in which the sign of the bias was largely consistent across individuals. Therefore, we here analyzed subjects’ absolute criterion values (i.e., overall bias regardless of sign) and fraction of non-preferred choices (i.e., the choice opposite to their general bias, irrespective of TPR). Again, TPR predicted a reduction in absolute decision bias, and an increase in the fraction of non-preferred choices (Figure 3C,D), analogous to the effects observed for the detection tasks above.

In sum, a number of analyses and experiments showed that pupil-linked, phasic arousal was consistently associated with a monotonic reduction in perceptual decision biases in different sensory modalities and task protocols.

Phasic arousal predicts a reduction of evidence accumulation bias

To further pinpoint the nature of the TPR-induced bias suppression, we fitted the drift diffusion model, an established dynamic model of two-choice decision processes (Figure 4A; [Ratcliff and McKoon, 2008]) to subjects’ RT distributions from the main task (contrast detection). The drift diffusion model posits the perfect accumulation of noisy sensory evidence towards one of two decision bounds, here for ‘yes’ and ‘no’ (Figure 4A).

Figure 4 with 2 supplements see all

Download asset Open asset

Figure 4—source data 2

Table with variable identifiers used in Figure 4—source data 1.

https://doi.org/10.7554/eLife.23232.015

Download elife-23232-fig4-data2-v4.txt

Figure 4—source data 1

This csv table contains the data for Figure 4 panel D.

https://doi.org/10.7554/eLife.23232.016

Download elife-23232-fig4-data1-v4.csv

We fitted the model separately for low and high TPR trials (see Figure 4B for an individual example). Within the model, the TPR-induced reduction of conservative bias, evident in Figures 2 and 3, may have been brought about by two distinct mechanistic scenarios: (i) the evidence accumulation process started from a level closer to the ‘yes’-bound (i.e., a change in the ‘starting point’ parameter); or (ii) the accumulation process was driven more towards the ‘yes’-bound (i.e., a change in the ‘drift criterion’ parameter). The drift criterion is equivalent to an evidence-independent constant added to the drift. A non-zero drift criterion results in a bias of the decision variable that grows linearly with time. Although clearly distinct in nature, both mechanisms (starting point and drift criterion) would have resulted in an increase in the fraction of ‘yes’-choices, and thus a reduction of decision bias. Critically, both mechanisms were distinguishable through their distinct effects on the shape of the RT distribution (Figure 4—figure supplement 1). To dissociate between these alternative mechanisms we fitted the model, while allowing several model parameters (boundary separation, non-decision time, mean drift rate, starting point, and drift criterion) to vary with TPR.

The model fits (see Materials and methods and [Wiecki et al., 2013]) supported the second mechanism: a change in drift criterion. An individual example is shown in Figure 4B, and group data are shown in Figure 4C. Drift criterion was generally negative, indicating an overall conservative accumulation bias towards the bound for ‘no’-choices. But drift criterion was pushed closer towards zero under high TPR, indicating an unbiased drift, as optimal for the current task (Figure 4B,C). The other main parameters (including starting point and mean drift rate) were not significantly affected by TPR. The TPR-linked effect on drift criterion was also evident in the individual point estimates from the fMRI sample only (Figure 4D).

Again, we we found no evidence for an effect on any parameter of the drift diffusion model when comparing trials with low and high baseline pupil diameters (Figure 4—figure supplement 2A), and we obtained qualitatively identical results without removing trial-to-trial variations of RT from the TPR amplitudes (Figure 4—figure supplement 2B–D; Materials and methods).

As a control of the significance of the TPR-dependent effect on drift criterion, we re-fitted the model, but now fixing drift criterion with TPR, while still allowing all other of the above parameters to vary with TPR. In this variant of the model, we again found no TPR-dependent change in any of the other parameters (boundary separation: p=0.428; non-decision time: p=0.370; starting point: p=0.117; mean drift rate: p=0.361). Critically, model comparison favored the complete version of the model with TPR-dependent variation in drift criterion (deviance information criterion, 50437 vs. 50528, respectively; see Materials and methods). This implies that the TPR-dependent variability in accumulation bias was essential to account for the TPR-dependent effects on behavior.

The individual changes in drift criterion between low vs. high TPR trials established by means of diffusion modeling accounted for a substantial fraction of the individual differences in TPR-predicted changes in the fraction of ‘yes’-choices (Figure 4E) obtained in the model-free analyses (Figure 2A,D, right panels). TPR-related changes in starting point had a weaker, and statistically not significant, effect on the fraction of ‘yes’-choices (fMRI data set: r = −0.345, p=0.227; de Gee et al. (2014) data set: r = −0.419, p=0.059).

In sum, in the decision task studied here, pupil-linked, phasic arousal predicted a reduction of conservative bias, specifically in the evidence accumulation, and was neither reflected in the baseline level of the decision variable at the start of the accumulation nor its mean drift. In other words, TPR accounted for a portion of the trial-to-trial variability in the drift unrelated to the objective sensory evidence. This correlate of phasic arousal at the algorithmic level was in line with the notion that phasic arousal shapes decision outcome by interacting with the evidence accumulation computation that lies at the heart of the decision process.

Taken together, the behavioral modeling results reported in Figures 2–4 put strong constraints on the expected changes in cortical decision processing due to phasic arousal. Specifically, changes in the encoding of the incoming evidence by sensory cortical areas, as observed in previous work on fluctuations in baseline arousal levels (McGinley et al., 2015a; Reimer et al., 2014; Vinck et al., 2015), would be associated with changes in perceptual sensitivity. However, we found that TPR was not associated with any robust change in sensitivity (measured as d’ or as mean drift rate) in the fMRI dataset, thus, predicting no TPR-linked modulation of sensory responses in visual cortex. Instead, the observed effect of TPR on choice bias (criterion, drift criterion) predicted a directed shift (towards ‘yes’) in neural signals encoding subjects’ choices, in downstream cortical regions. We next tested these predictions by assessing the relationship between TPR and (i) stimulus-specific responses in early visual cortex, and (ii) choice-specific responses in downstream cortical regions.

Phasic arousal does not boost sensory responses in visual cortex

The fMRI response in early visual cortex (areas V1, V2, and V3) during near-threshold visual tasks is made up of distinct components, including a (weak and focal) stimulus-specific component and a (large and global) task-related, but stimulus-independent, component (Cardoso et al., 2012; Donner et al., 2008; Ress et al., 2000). We used an approach based on multi-voxel pattern analysis analogous to previous work (Choe et al., 2014; Pajani et al., 2015) to isolate the stimulus-specific response component. Because the majority of visual cortical neurons encoding stimulus contrast are also tuned to stimulus orientation, orientation-tuning could serve as a ‘filter’ to separate the cortical stimulus response from stimulus-unrelated signals. Specifically, the low contrast signal in our task should have evoked a small response in each visual cortical neuron selective for the orientation of the target signal (45° or 135°, on different experimental runs, Figure 1A) across a substantial part of the retinotopic map. Thus, the presence or absence of the target signal should be reliably encoded in the orientation-specific component of the cortical population response, within the retinotopic sub-region corresponding to the signal. We first individually delineated these retinotopic sub-regions within each of V1-V3 (see Figure 5A for an example subject) and then quantified the orientation-specific response component therein as the spatial correlation of multi-voxel response patterns with an orientation-specific ‘template’ (Materials and methods).

Figure 5 with 1 supplement see all

Download asset Open asset

As expected, this orientation-specific response component differed robustly between signal+noise and noise trials (Figure 5B). A 2-way repeated measures ANOVA with factors stimulus and TPR bin yielded a highly significant main effect of stimulus for V1, V2, and V3 (V1: F_1,13 = 303.5, V2: F_1,13 = 646.3, V3: F_1,13 = 316.6; all p<0.001).

The orientation-specific response component also reliably discriminated between signal+noise and noise trials on a single-trial basis (Figure 5—figure supplement 1). Consequently, we henceforth refer to this component as the ‘stimulus-specific response’. However, the stimulus-specific response was not boosted under high TPR (Figure 5B, no significant main effect of TPR, nor stimulus x TPR interaction in any of V1-V3).

No evidence for arousal-dependent boost of sensory responses in any cortical area

The above analysis focused on the stimulus-specific response in early visual cortex. To avoid missing TPR-dependent modulations of sensory responses in higher cortical regions, we also mapped out modulations of fMRI responses by TPR across cortex (see Materials and methods). Various regions including visual, parietal, prefrontal, and motor cortices exhibited robust task-evoked overall fMRI responses (i.e., difference between the decision interval and baseline; Figure 6A), as well as robust modulations by TPR (Figure 6B), whereby TPR-induced boosts only partly overlapped with the task-positive responses.

Figure 6

Download asset Open asset

However, in no single region did the overall fMRI responses differ between signal+noise and noise trials (Figure 6C). This indicates that our multi-voxel pattern approach described above was, in fact, essential for detecting the weak cortical response to the near-threshold target signals. Critically, in no region did we find a significant interaction between the factors stimulus (signal+noise vs. noise) and TPR (low vs. high TPR; Figure 6D).

Taken together, both complementary analyses showed that phasic, task-evoked arousal signals did not modulate cortical responses encoding the presence of the low-contrast signal. This is in line with the lack of TPR-linked change in perceptual sensitivity in the fMRI dataset (Figure 2A, Figure 4D).

Phasic arousal modulates choice-specific signals in frontal and parietal cortex

We then sought to test for directed shifts in neural signals encoding subjects’ choices under high TPR, which would be in line with the changes in decision biases identified by behavioral modeling. Here, we use the term ‘choice-specific’ to refer to fMRI-signals that reliably discriminated between subjects’ choice (‘yes’ vs. ‘no’). Two complementary approaches delineated several cortical regions that exhibited such choice-specific signals (Figure 7). The first approach (Figure 7A) was based on the lateralization of fMRI responses with respect to the motor effector used to report the choice (i.e., response hand; see (de Lange et al., 2013; Donner et al., 2009) and Materials and methods). In addition to the hand area of primary motor cortex (henceforth referred to as M1), this approach yielded reliable effector-specific lateralization also in two regions of posterior parietal association cortex: the junction of the intraparietal and postcentral sulcus (IPS/PostCeS) and the anterior intraparietal sulcus (aIPS1; Figure 7A and Figure 7—figure supplement 1A,B). The second approach (Figure 7B) was based on multi-voxel pattern classification of choice, using a ‘searchlight’ procedure that scanned the entire cortex for choice information (see (Hebart et al., 2012, 2016) and Materials and methods). The underlying rationale was to identify cortical regions encoding choice in other formats (e.g., in terms of more fine-grained patterns) than the hemispheric lateralization of response amplitudes. The second approach revealed robust (and reproducible) choice-specific response patterns in a number of additional regions in bilateral posterior parietal cortex and (right) prefrontal cortex: superior and inferior parietal lobule (SPL and IPL, respectively), a second region within aIPS (aIPS2), posterior insula (pIns), the junction of precentral sulcus and right inferior frontal gyrus (PreCeS/IFG) and right medial frontal gyrus (MFG; Figure 7B and Figure 7—figure supplement 1C,D). In both approaches, choice specific regions were delineated after factoring out the physical stimulus (see Materials and methods).

Figure 7 with 1 supplement see all

Download asset Open asset

In all the above choice-encoding regions, responses (estimated in a cross-validated fashion, see Materials and methods) reliably differentiated between ‘yes’- and ‘no’-choices – both on average (Figure 7—figure supplement 1E,F) and at the single-trial level (Figure 7C, see also Figure 7—figure supplement 1G). As expected, the single-trial reliability of the choice-specific responses differed between cortical regions (1-way repeated measures ANOVA with factor region of interest (9 levels): F_8,104 = 30.20, p<0.001), with the strongest reliability for M1 (dashed horizontal line in Figure 7C), the region closest to the subjects’ motor output.

For analysis of the association with TPR, we pooled the choice-specific signals of these different regions into three groups (Figure 7—figure supplement 1A): the motor end stage of the decision process M1, the combined ‘lateralization signal’ (i.e., regions from Figure 7A excluding M1), and the combined ‘searchlight signal’ (i.e., all regions from Figure 7B). Critically, as predicted, the combined choice-specific signals, but not the M1 response, were significantly pushed towards the ‘yes’-choice (i.e., more positive in Figure 7D) for high compared to low TPR. The effect of TPR differed by cortical signal (2-way repeated measures ANOVA with factors signal type (3 levels) and TPR bin (2 levels); interaction: F_2,26 = 7.30, p=0.003). Specifically, the difference of the choice-specific signals between low and high TPR was significantly larger for the combined lateralization signal and the combined searchlight signal than for M1 (combined lateralization signal vs. M1: p=0.015; combined searchlight signal vs. M1: p=0.004; permutation tests).

Because subjects’ mean accuracy was about 74% correct, their choices were partially correlated with the physical stimulus (i.e., signal+noise vs. noise trials). Consequently, the choice-specific cortical responses were also (weakly) predictive of the stimulus (Figure 7—figure supplement 1H). To isolate variations in the amplitude of the choice-specific response that were independent of the stimulus, we removed (via linear regression) components explained by the stimulus and quantified the effect of TPR on the residual choice-specific cortical signals. Fitting the linear model to the combined choice-specific responses yielded highly significant TPR coefficients, for both the combined lateralization and combined searchlight signals (Figure 7E, middle and right panel). By contrast, the TPR-linked modulation was absent in the end stage region M1 (Figure 7E, left panel).

In sum, a number of fronto-parietal cortical regions exhibited signals that reliably encoded subjects’ behavioral choice and were robustly modulated by phasic arousal, with a larger tendency towards the ‘yes’-choice under high TPR. This was true even when factoring out the effect of the sensory evidence (i.e. presence of the target signal).

Task-evoked pupil response are predicted by responses in a network of brainstem centers

Finally, we aimed to identify brainstem regions whose task-evoked responses were (i) linked to the trial-to-trial fluctuations of TPR, and (ii) accounted for the trial-to-trial modulation of subjects’ evidence accumulation bias, and the resulting tendency to choose ‘yes’. Previous work from monkey physiology has implicated three brainstem nuclei in particular in the control of TPR: the locus coeruleus (LC), the inferior colliculus (IC), and the superior colliculus (SC), respectively (Joshi et al., 2016; Varazzani et al., 2015; Wang et al., 2012). Here, we exploited the wide coverage of our fMRI measurements to concurrently monitor responses across a wider brainstem network, including a number of other nuclei implicated in central arousal: the dopaminergic substantia nigra (SN) and ventral tegmental area (VTA), as well as the (partly) cholinergic basal forebrain (BF). We further sub-divided the BF region into the part including cell groups within the septum and the horizontal limb of the diagonal band (BF-sept) and the sublenticular part (BF-subl). BF-subl contains cholinergic neurons with widespread ascending projections (Zaborszky et al., 2008), which are involved in the regulation of cortical arousal state (Lee and Dan, 2012; McGinley et al., 2015b). Our analysis approach minimized the effect of physiological noise on the brainstem fMRI responses, including removal of the fourth ventricle signal (see Materials and methods). We also verified that the fourth ventricle signal was unrelated to TPR (Figure 8—figure supplement 1D,E). The LC region of each subject was delineated through independent structural scans (Figure 8A, and Figure 8—figure supplement 1A; for details see Materials and methods).

Figure 8 with 1 supplement see all

Download asset Open asset

The LC region exhibited a robust positive response on high TPR trials and a trend towards deactivation on low TPR trials (Figure 8B–D, and Figure 8—figure supplement 1C). The same pattern was evident for both signal+noise and noise trials separately (Figure 8C). The association to TPR was also highly significant in the most spatially specific definition of the LC region afforded by our measurements: evaluating only the two fMRI voxels with the largest probability of containing the individual LC region (Figure 8D, and see Materials and methods). Fluctuations of task-evoked fMRI responses measured in the LC were also robustly coupled to fluctuations in TPR amplitude at the single trial level (Figure 8F,H).

Similar to the LC region, we found a robust difference between low and high TPR conditions for fMRI responses in the SC and VTA regions (Figure 8E,F, and Figure 8—figure supplement 1B,C). Mapping the trial-to-trial correlations between TPR and brainstem fMRI responses at the single-voxel level yielded robust coupling to TPR in the LC, SC, VTA and as well as in BF-subl regions (Figure 8F).

As expected from the anatomical connectivity between brainstem centers (España and Berridge, 2006; Sara, 2009; Wang and Munoz, 2015), the trial-to-trial fluctuations of the task-evoked responses were significantly correlated among a number of these brainstem nuclei (Figure 8G). Removing components of the trial-to-trial fluctuations in TPR and fMRI responses shared with the other ROIs yielded significant residual (i.e., partial) correlations between TPR and responses in SC, LC region, VTA and BF-subl (Figure 8H). This indicates robust and unique contributions of these four nuclei to TPR.

Phasic brainstem responses during decision tasks might be driven by top-down signals from anterior cingulate cortex (ACC), which sends descending projections to the LC (Aston-Jones and Cohen, 2005) and other brainstem nuclei. In line with this notion, trial-to-trial fluctuations of both LC responses and TPR were robustly correlated to trial-to-trial fluctuations of task-evoked responses of the ACC (Figure 8I).

Task-evoked responses in neuromodulatory centers, but not the colliculi, predict suppression of evidence accumulation bias

The task-evoked responses in the neuromodulatory nuclei, but not the colliculi, were tightly linked to the inferred decision computation and subjects’ overt choice behavior. We computed the combined ‘neuromodulatory brainstem signal’ as the linear combination of responses from LC, VTA, SN, and BF that maximized the correlation to TPR (Materials and methods; correlation coefficient across subjects, 0.146 (±0.014 s.e.m.)). The amplitude of this combined signal predicted a significant reduction in conservative decision bias (Figure 9A), and an increased tendency to choose ‘yes’ (Figure 9B), but no change in sensitivity (Figure 9—figure supplement 1A). This pattern of effects was absent for the combined ‘colliculi signal’ (Figure 9A,B), a linear combination of responses from SC and IC that maximized the correlation to TPR (correlation coefficient across subjects, 0.092 (±0.011 s.e.m.)). Further, the trial-to-trial variations in the strength of the combined neuromodulatory (but not colliculi) response robustly pushed the trial-to-trial drift towards the ‘yes’-boundary, in effect reducing the overall negative drift criterion (Figure 9D, see Materials and methods for details).

Figure 9 with 1 supplement see all

Download asset Open asset

In sum, trial-to-trial fluctuations in TPR were predicted by fluctuations in the task-evoked responses of a network of brainstem regions, most notably the LC, VTA and SC. Despite the expected coupling between these and other brainstem regions (Figure 8G), TPR carried robust LC-, SC-, and (less strongly) VTA-specific components (Figure 8H). But only the responses of the neuromodulatory ROIs, not of the colliculi, accounted for the concomitant reduction of the bias in evidence accumulation and the resulting behavioral choice patterns. These results establish a tight link between phasic neuromodulator release and the dynamics of evidence accumulation.

Intrinsic variability in the face of uncertain evidence is a pervasive feature of decision-making (Glimcher, 2005; Gold and Shadlen, 2007; Shadlen et al., 1996; Sugrue et al., 2005; Wyart and Koechlin, 2016). Most current models of choice treat this intrinsic behavioral variability as a nuisance to be accounted for by additional ‘noise parameters’ (Bogacz et al., 2006; Ratcliff and McKoon, 2008). Other theories have proposed that the behavioral variability may be due to hidden, but systematic, biases in the decision process (Beck et al., 2012; Wyart and Koechlin, 2016). Here, we present evidence that helps reconcile these ideas. We found that a significant component of choice variability was explained by trial-to-trial variations in the amplitude of task-evoked, pupil-linked arousal responses. Specifically, pupil-linked arousal responses accounted for trial-to-trial variations in the bias of the evidence accumulation process as well as decision-related cortical population signals: under large phasic arousal conservative biases were reduced. The implication is that, without monitoring arousal responses, the associated, systematic variations in accumulation bias would appear as random trial-to-trial variability in the accumulation process (i.e., drift). Going further, we established that the dynamic bias suppression was explained by responses in a network of neuromodulatory brainstem systems controlling cortical arousal state. Taken together, our results are consistent with a scenario in which phasic neuromodulatory activity during decision-making optimizes choice behavior through a suppression of maladaptive biases in the evidence accumulation process.

1. 1. Jan Willem de Gee
   2. Olympia Colizoli
   3. Niels A Kloosterman
   4. Tomas Knapen
   5. Sander Nieuwenhuis
   6. Tobias H Donner

   (2017) Data set: Dynamic modulation of decision biases by brainstem arousal systems

   Available at figshare under a CC0 Public Domain licence (https://figshare.com/).

   https://doi.org/10.6084/m9.figshare.4806562

1. 1. de Gee JW
   2. Knapen T
   3. Donner TH

   (2017) Data set: Decision-related pupil dilation reflects upcoming choice and individual bias

   Available at figshare under a CC0 Public Domain licence (https://figshare.com/).

   https://doi.org/10.6084/m9.figshare.4806559

1. 1. Astafiev SV
   2. Snyder AZ
   3. Shulman GL
   4. Corbetta M

   (2010) Comment on "Modafinil shifts human locus coeruleus to low-tonic, high-phasic activity during functional MRI" and "Homeostatic sleep pressure and responses to sustained attention in the suprachiasmatic area"

   Science 328:309.

   https://doi.org/10.1126/science.1177200

   • PubMed
   • Google Scholar
2. 1. Aston-Jones G
   2. Cohen JD

   (2005) An integrative theory of locus coeruleus-norepinephrine function: adaptive gain and optimal performance

   Annual Review of Neuroscience 28:403–450.

   https://doi.org/10.1146/annurev.neuro.28.061604.135709

   • PubMed
   • Google Scholar
3. 1. Beatty J

   (1982) Task-evoked pupillary responses, processing load, and the structure of processing resources

   Psychological Bulletin 91:276–292.

   https://doi.org/10.1037/0033-2909.91.2.276

   • PubMed
   • Google Scholar
4. 1. Beck JM
   2. Ma WJ
   3. Pitkow X
   4. Latham PE
   5. Pouget A

   (2012) Not noisy, just wrong: the role of suboptimal inference in behavioral variability

   Neuron 74:30–39.

   https://doi.org/10.1016/j.neuron.2012.03.016

   • PubMed
   • Google Scholar
5. 1. Beissner F

   (2015) Functional MRI of the Brainstem: common problems and their Solutions

   Clinical Neuroradiology 25 Suppl 2:251–257.

   https://doi.org/10.1007/s00062-015-0404-0

   • PubMed
   • Google Scholar
6. 1. Bogacz R
   2. Brown E
   3. Moehlis J
   4. Holmes P
   5. Cohen JD

   (2006) The physics of optimal decision making: a formal analysis of models of performance in two-alternative forced-choice tasks

   Psychological Review 113:700–765.

   https://doi.org/10.1037/0033-295X.113.4.700

   • PubMed
   • Google Scholar
7. 1. Bouret S
   2. Sara SJ

   (2005) Network reset: a simplified overarching theory of locus coeruleus noradrenaline function

   Trends in Neurosciences 28:574–582.

   https://doi.org/10.1016/j.tins.2005.09.002

   • PubMed
   • Google Scholar
8. 1. Brody CD
   2. Hanks TD

   (2016) Neural underpinnings of the evidence accumulator

   Current Opinion in Neurobiology 37:149–157.

   https://doi.org/10.1016/j.conb.2016.01.003

   • PubMed
   • Google Scholar
9. 1. Brooks JC
   2. Faull OK
   3. Pattinson KT
   4. Jenkinson M

   (2013) Physiological noise in brainstem FMRI

   Frontiers in Human Neuroscience 7:623.

   https://doi.org/10.3389/fnhum.2013.00623

   • PubMed
   • Google Scholar
10. 1. Brunton BW
    2. Botvinick MM
    3. Brody CD

    (2013) Rats and humans can optimally accumulate evidence for decision-making

    Science 340:95–98.

    https://doi.org/10.1126/science.1233912

    • PubMed
    • Google Scholar
11. 1. Cardoso MM
    2. Sirotin YB
    3. Lima B
    4. Glushenkova E
    5. Das A

    (2012) The neuroimaging signal is a linear sum of neurally distinct stimulus- and task-related components

    Nature Neuroscience 15:1298–1306.

    https://doi.org/10.1038/nn.3170

    • PubMed
    • Google Scholar
12. 1. Chang C-C
    2. Lin C-J

    (2011) LIBSVM: a library for support vector machines

    ACM Transactions on Intelligent Systems and Technology 2:1–27.

    https://doi.org/10.1145/1961189.1961199

    • Google Scholar
13. 1. Cheadle S
    2. Wyart V
    3. Tsetsos K
    4. Myers N
    5. de Gardelle V
    6. Herce Castañón S
    7. Summerfield C

    (2014) Adaptive gain control during human perceptual choice

    Neuron 81:1429–1441.

    https://doi.org/10.1016/j.neuron.2014.01.020

    • PubMed
    • Google Scholar
14. 1. Choe KW
    2. Blake R
    3. Lee SH

    (2014) Dissociation between neural signatures of stimulus and choice in population activity of human V1 during perceptual decision-making

    Journal of Neuroscience 34:2725–2743.

    https://doi.org/10.1523/JNEUROSCI.1606-13.2014

    • PubMed
    • Google Scholar
15. 1. Clayton EC
    2. Rajkowski J
    3. Cohen JD
    4. Aston-Jones G

    (2004) Phasic activation of monkey locus ceruleus neurons by simple decisions in a forced-choice task

    Journal of Neuroscience 24:9914–9920.

    https://doi.org/10.1523/JNEUROSCI.2446-04.2004

    • PubMed
    • Google Scholar
16. 1. Dale AM
    2. Fischl B
    3. Sereno MI

    (1999) Cortical surface-based analysis. I. segmentation and surface reconstruction

    NeuroImage 9:179–194.

    https://doi.org/10.1006/nimg.1998.0395

    • PubMed
    • Google Scholar
17. 1. Dayan P
    2. Yu AJ

    (2006) Phasic norepinephrine: a neural interrupt signal for unexpected events

    Network: Computation in Neural Systems 17:335–350.

    https://doi.org/10.1080/09548980601004024

    • PubMed
    • Google Scholar
18. Data

    1. de Gee JW
    2. Colizoli O
    3. Kloosterman NA
    4. Knapen T
    5. Nieuwenhuis S
    6. Donner TH

    (authors) (2017a) Data set: dynamic modulation of decision biases by brainstem arousal systems

    Figshare.

    https://doi.org/10.6084/m9.figshare.4806562
19. 1. de Gee JW
    2. Knapen T
    3. Donner TH

    (2014) Decision-related pupil dilation reflects upcoming choice and individual Bias

    PNAS 111:E618–E625.

    https://doi.org/10.1073/pnas.1317557111

    • PubMed
    • Google Scholar
20. Data

    1. de Gee JW
    2. Knapen T
    3. Donner TH

    (authors) (2017b) Data set: decision-related pupil dilation reflects upcoming choice and individual Bias

    Figshare.

    https://doi.org/10.6084/m9.figshare.4806559
21. Data

    1. de Gee JW

    (authors) (2017a) 2017_eLife

    Github.

    https://github.com/jwdegee/2017_eLife
22. Data

    1. de Gee JW

    (authors) (2017b) 2014_PNAS

    Github.

    https://github.com/jwdegee/2014_PNAS
23. 1. de Lange FP
    2. Rahnev DA
    3. Donner TH
    4. Lau H

    (2013) Prestimulus oscillatory activity over motor cortex reflects perceptual expectations

    Journal of Neuroscience 33:1400–1410.

    https://doi.org/10.1523/JNEUROSCI.1094-12.2013

    • PubMed
    • Google Scholar
24. 1. Destrieux C
    2. Fischl B
    3. Dale A
    4. Halgren E

    (2010) Automatic parcellation of human cortical gyri and sulci using standard anatomical nomenclature

    NeuroImage 53:1–15.

    https://doi.org/10.1016/j.neuroimage.2010.06.010

    • PubMed
    • Google Scholar
25. 1. Donner TH
    2. Sagi D
    3. Bonneh YS
    4. Heeger DJ

    (2008) Opposite neural signatures of motion-induced blindness in human dorsal and ventral visual cortex

    Journal of Neuroscience 28:10298–10310.

    https://doi.org/10.1523/JNEUROSCI.2371-08.2008

    • PubMed
    • Google Scholar
26. 1. Donner TH
    2. Siegel M
    3. Fries P
    4. Engel AK

    (2009) Buildup of choice-predictive activity in human motor cortex during perceptual decision making

    Current Biology 19:1581–1585.

    https://doi.org/10.1016/j.cub.2009.07.066

    • PubMed
    • Google Scholar
27. Book

    1. Draper NR
    2. Smith H

    (1998) Applied Regression Analysis

    Wiley-Interscience.

    https://doi.org/10.1002/9781118625590

    • Google Scholar
28. 1. Dumoulin SO
    2. Wandell BA

    (2008) Population receptive field estimates in human visual cortex

    NeuroImage 39:647–660.

    https://doi.org/10.1016/j.neuroimage.2007.09.034

    • PubMed
    • Google Scholar
29. 1. Eckert MA
    2. Keren NI
    3. Aston-Jones G

    (2010)

    Science E-Letter

    Looking forward with the locus coeruleus, Science E-Letter.

    • Google Scholar
30. 1. Eickhoff SB
    2. Stephan KE
    3. Mohlberg H
    4. Grefkes C
    5. Fink GR
    6. Amunts K
    7. Zilles K

    (2005) A New SPM toolbox for combining Probabilistic Cytoarchitectonic maps and Functional imaging data

    NeuroImage 25:1325–1335.

    https://doi.org/10.1016/j.neuroimage.2004.12.034

    • PubMed
    • Google Scholar
31. 1. Eklund A
    2. Nichols TE
    3. Knutsson H

    (2016) Cluster failure: why fMRI inferences for spatial extent have inflated false-positive rates

    PNAS 113:7900–7905.

    https://doi.org/10.1073/pnas.1602413113

    • PubMed
    • Google Scholar
32. 1. Eldar E
    2. Cohen JD
    3. Niv Y

    (2013) The effects of neural gain on attention and learning

    Nature Neuroscience 16:1146–1153.

    https://doi.org/10.1038/nn.3428

    • PubMed
    • Google Scholar
33. 1. España RA
    2. Berridge CW

    (2006) Organization of noradrenergic efferents to arousal-related basal forebrain structures

    The Journal of Comparative Neurology 496:668–683.

    https://doi.org/10.1002/cne.20946

    • PubMed
    • Google Scholar
34. 1. Faisal AA
    2. Selen LP
    3. Wolpert DM

    (2008) Noise in the nervous system

    Nature Reviews Neuroscience 9:292–303.

    https://doi.org/10.1038/nrn2258

    • PubMed
    • Google Scholar
35. 1. Fiorillo CD
    2. Tobler PN
    3. Schultz W

    (2003) Discrete coding of reward probability and uncertainty by dopamine neurons

    Science 299:1898–1902.

    https://doi.org/10.1126/science.1077349

    • PubMed
    • Google Scholar
36. 1. Fischl B
    2. Sereno MI
    3. Dale AM

    (1999) Cortical surface-based analysis. II: inflation, flattening, and a surface-based coordinate system

    NeuroImage 9:195–207.

    https://doi.org/10.1006/nimg.1998.0396

    • PubMed
    • Google Scholar
37. 1. Forstmann BU
    2. de Hollander G
    3. van Maanen L
    4. Alkemade A
    5. Keuken MC

    (2017) Towards a mechanistic understanding of the human subcortex

    Nature Reviews Neuroscience 18:57–65.

    https://doi.org/10.1038/nrn.2016.163

    • Google Scholar
38. 1. Friston K

    (2010) The free-energy principle: a unified brain theory?

    Nature Reviews Neuroscience 11:127–138.

    https://doi.org/10.1038/nrn2787

    • PubMed
    • Google Scholar
39. 1. Gil Z
    2. Connors BW
    3. Amitai Y

    (1997) Differential regulation of neocortical synapses by neuromodulators and activity

    Neuron 19:679–686.

    https://doi.org/10.1016/S0896-6273(00)80380-3

    • PubMed
    • Google Scholar
40. 1. Gilzenrat MS
    2. Nieuwenhuis S
    3. Jepma M
    4. Cohen JD

    (2010) Pupil diameter tracks changes in control state predicted by the adaptive gain theory of locus coeruleus function

    Cognitive, Affective, & Behavioral Neuroscience 10:252–269.

    https://doi.org/10.3758/CABN.10.2.252

    • PubMed
    • Google Scholar
41. 1. Glimcher PW

    (2005) Indeterminacy in brain and behavior

    Annual Review of Psychology 56:25–56.

    https://doi.org/10.1146/annurev.psych.55.090902.141429

    • PubMed
    • Google Scholar
42. 1. Glover GH
    2. Li TQ
    3. Ress D

    (2000) Image-based method for retrospective correction of physiological motion effects in fMRI: RETROICOR

    Magnetic Resonance in Medicine 44:162–167.

    https://doi.org/10.1002/1522-2594(200007)44:1<162::AID-MRM23>3.0.CO;2-E

    • PubMed
    • Google Scholar
43. 1. Gold JI
    2. Shadlen MN

    (2007) The neural basis of decision making

    Annual Review of Neuroscience 30:535–574.

    https://doi.org/10.1146/annurev.neuro.29.051605.113038

    • PubMed
    • Google Scholar
44. Book

    1. Green DM
    2. Swets JA

    (1966)

    Signal Detection Theory and Psychophysics

    New York: John Wiley and Son.

    • Google Scholar
45. 1. Harris KD
    2. Thiele A

    (2011) Cortical state and attention

    Nature Reviews Neuroscience 12:509–523.

    https://doi.org/10.1038/nrn3084

    • PubMed
    • Google Scholar
46. 1. Hebart MN
    2. Donner TH
    3. Haynes JD

    (2012) Human visual and parietal cortex encode visual choices independent of motor plans

    NeuroImage 63:1393–1403.

    https://doi.org/10.1016/j.neuroimage.2012.08.027

    • PubMed
    • Google Scholar
47. 1. Hebart MN
    2. Schriever Y
    3. Donner TH
    4. Haynes JD

    (2016) The relationship between Perceptual Decision Variables and Confidence in the human brain

    Cerebral Cortex 26:118–130.

    https://doi.org/10.1093/cercor/bhu181

    • PubMed
    • Google Scholar
48. 1. Hoeks B
    2. Levelt WJM

    (1993) Pupillary dilation as a measure of attention: a quantitative system analysis

    Behavior Research Methods, Instruments, & Computers 25:16–26.

    https://doi.org/10.3758/BF03204445

    • Google Scholar
49. 1. Hsieh CY
    2. Cruikshank SJ
    3. Metherate R

    (2000) Differential modulation of auditory thalamocortical and intracortical synaptic transmission by cholinergic agonist

    Brain Research 880:51–64.

    https://doi.org/10.1016/S0006-8993(00)02766-9

    • PubMed
    • Google Scholar
50. 1. Jenkinson M
    2. Bannister P
    3. Brady M
    4. Smith S

    (2002) Improved optimization for the robust and accurate linear registration and motion correction of brain images

    NeuroImage 17:825–841.

    https://doi.org/10.1006/nimg.2002.1132

    • PubMed
    • Google Scholar
51. 1. Joshi S
    2. Li Y
    3. Kalwani RM
    4. Gold JI

    (2016) Relationships between Pupil Diameter and neuronal activity in the Locus Coeruleus, Colliculi, and Cingulate Cortex

    Neuron 89:221–234.

    https://doi.org/10.1016/j.neuron.2015.11.028

    • PubMed
    • Google Scholar
52. 1. Katz LN
    2. Yates JL
    3. Pillow JW
    4. Huk AC

    (2016) Dissociated functional significance of decision-related activity in the primate dorsal stream

    Nature 535:285–288.

    https://doi.org/10.1038/nature18617

    • PubMed
    • Google Scholar
53. 1. Keren NI
    2. Lozar CT
    3. Harris KC
    4. Morgan PS
    5. Eckert MA

    (2009) In vivo mapping of the human locus coeruleus

    NeuroImage 47:1261–1267.

    https://doi.org/10.1016/j.neuroimage.2009.06.012

    • PubMed
    • Google Scholar
54. 1. Keren NI
    2. Taheri S
    3. Vazey EM
    4. Morgan PS
    5. Granholm AC
    6. Aston-Jones GS
    7. Eckert MA

    (2015) Histologic validation of locus coeruleus MRI contrast in post-mortem tissue

    NeuroImage 113:235–245.

    https://doi.org/10.1016/j.neuroimage.2015.03.020

    • PubMed
    • Google Scholar
55. 1. Kimura F
    2. Fukuda M
    3. Tsumoto T

    (1999) Acetylcholine suppresses the spread of excitation in the visual cortex revealed by optical recording: possible differential effect depending on the source of input

    European Journal of Neuroscience 11:3597–3609.

    https://doi.org/10.1046/j.1460-9568.1999.00779.x

    • PubMed
    • Google Scholar
56. 1. Knapen T
    2. de Gee JW
    3. Brascamp J
    4. Nuiten S
    5. Hoppenbrouwers S
    6. Theeuwes J

    (2016) Cognitive and ocular factors jointly determine pupil responses under Equiluminance

    PLoS One 11:e0155574.

    https://doi.org/10.1371/journal.pone.0155574

    • PubMed
    • Google Scholar
57. 1. Kobayashi M
    2. Imamura K
    3. Sugai T
    4. Onoda N
    5. Yamamoto M
    6. Komai S
    7. Watanabe Y

    (2000) Selective suppression of horizontal propagation in rat visual cortex by norepinephrine

    European Journal of Neuroscience 12:264–272.

    https://doi.org/10.1046/j.1460-9568.2000.00917.x

    • PubMed
    • Google Scholar
58. 1. Korn CW
    2. Bach DR

    (2016) A solid frame for the window on cognition: modeling event-related pupil responses

    Journal of Vision 16:28.

    https://doi.org/10.1167/16.3.28

    • PubMed
    • Google Scholar
59. 1. Lee SH
    2. Dan Y

    (2012) Neuromodulation of brain states

    Neuron 76:209–222.

    https://doi.org/10.1016/j.neuron.2012.09.012

    • PubMed
    • Google Scholar
60. 1. Lempert KM
    2. Chen YL
    3. Fleming SM

    (2015) Relating pupil dilation and metacognitive confidence during Auditory Decision-Making

    PLoS One 10:e0126588–12.

    https://doi.org/10.1371/journal.pone.0126588

    • PubMed
    • Google Scholar
61. 1. Lin IC
    2. Okun M
    3. Carandini M
    4. Harris KD

    (2015) The nature of shared cortical variability

    Neuron 87:644–656.

    https://doi.org/10.1016/j.neuron.2015.06.035

    • PubMed
    • Google Scholar
62. 1. McDougal DH
    2. Gamlin P

    (2008)

    The Senses: A Comprehensive Reference

    Pupillary control pathways, The Senses: A Comprehensive Reference.

    • Google Scholar
63. 1. McGinley MJ
    2. David SV
    3. McCormick DA

    (2015a) Cortical membrane potential signature of optimal States for Sensory Signal detection

    Neuron 87:179–192.

    https://doi.org/10.1016/j.neuron.2015.05.038

    • PubMed
    • Google Scholar
64. 1. McGinley MJ
    2. Vinck M
    3. Reimer J
    4. Batista-Brito R
    5. Zagha E
    6. Cadwell CR
    7. Tolias AS
    8. Cardin JA
    9. McCormick DA

    (2015b) Waking state: rapid variations modulate neural and behavioral responses

    Neuron 87:1143–1161.

    https://doi.org/10.1016/j.neuron.2015.09.012

    • PubMed
    • Google Scholar
65. 1. Moran RJ
    2. Campo P
    3. Symmonds M
    4. Stephan KE
    5. Dolan RJ
    6. Friston KJ

    (2013) Free energy, precision and learning: the role of cholinergic neuromodulation

    Journal of Neuroscience 33:8227–8236.

    https://doi.org/10.1523/JNEUROSCI.4255-12.2013

    • PubMed
    • Google Scholar
66. 1. Murphy PR
    2. O'Connell RG
    3. O'Sullivan M
    4. Robertson IH
    5. Balsters JH

    (2014a) Pupil diameter covaries with BOLD activity in human locus coeruleus

    Human Brain Mapping 35:4140–4154.

    https://doi.org/10.1002/hbm.22466

    • PubMed
    • Google Scholar
67. 1. Murphy PR
    2. Vandekerckhove J
    3. Nieuwenhuis S

    (2014b) Pupil-linked arousal determines variability in perceptual decision making

    PLoS Computational Biology 10:e1003854.

    https://doi.org/10.1371/journal.pcbi.1003854

    • PubMed
    • Google Scholar
68. 1. Murty VP
    2. Shermohammed M
    3. Smith DV
    4. Carter RM
    5. Huettel SA
    6. Adcock RA

    (2014) Resting state networks distinguish human ventral tegmental area from substantia nigra

    NeuroImage 100:580–589.

    https://doi.org/10.1016/j.neuroimage.2014.06.047

    • PubMed
    • Google Scholar
69. 1. Nassar MR
    2. Rumsey KM
    3. Wilson RC
    4. Parikh K
    5. Heasly B
    6. Gold JI

    (2012) Rational regulation of learning dynamics by pupil-linked arousal systems

    Nature Neuroscience 15:1040–1046.

    https://doi.org/10.1038/nn.3130

    • PubMed
    • Google Scholar
70. 1. Nomoto K
    2. Schultz W
    3. Watanabe T
    4. Sakagami M

    (2010) Temporally extended dopamine responses to perceptually demanding reward-predictive stimuli

    Journal of Neuroscience 30:10692–10702.

    https://doi.org/10.1523/JNEUROSCI.4828-09.2010

    • PubMed
    • Google Scholar
71. 1. Pajani A
    2. Kok P
    3. Kouider S
    4. de Lange FP

    (2015) Spontaneous activity patterns in primary visual cortex predispose to Visual Hallucinations

    Journal of Neuroscience 35:12947–12953.

    https://doi.org/10.1523/JNEUROSCI.1520-15.2015

    • PubMed
    • Google Scholar
72. 1. Parikh V
    2. Kozak R
    3. Martinez V
    4. Sarter M

    (2007) Prefrontal acetylcholine release controls cue detection on multiple timescales

    Neuron 56:141–154.

    https://doi.org/10.1016/j.neuron.2007.08.025

    • PubMed
    • Google Scholar
73. 1. Pilly PK
    2. Seitz AR

    (2009) What a difference a parameter makes: a psychophysical comparison of random dot motion algorithms

    Vision Research 49:1599–1612.

    https://doi.org/10.1016/j.visres.2009.03.019

    • PubMed
    • Google Scholar
74. 1. Pouget A
    2. Beck JM
    3. Ma WJ
    4. Latham PE

    (2013) Probabilistic brains: knowns and unknowns

    Nature Neuroscience 16:1170–1178.

    https://doi.org/10.1038/nn.3495

    • PubMed
    • Google Scholar
75. 1. Ratcliff R
    2. Childers R

    (2015) Individual differences and fitting methods for the Two-Choice Diffusion Model of Decision making

    Decision 2015:237–279.

    https://doi.org/10.1037/dec0000030

    • PubMed
    • Google Scholar
76. 1. Ratcliff R
    2. McKoon G

    (2008) The diffusion decision model: theory and data for two-choice decision tasks

    Neural Computation 20:873–922.

    https://doi.org/10.1162/neco.2008.12-06-420

    • PubMed
    • Google Scholar
77. 1. Reimer J
    2. Froudarakis E
    3. Cadwell CR
    4. Yatsenko D
    5. Denfield GH
    6. Tolias AS

    (2014) Pupil fluctuations track fast switching of cortical states during quiet wakefulness

    Neuron 84:355–362.

    https://doi.org/10.1016/j.neuron.2014.09.033

    • PubMed
    • Google Scholar
78. 1. Ress D
    2. Backus BT
    3. Heeger DJ

    (2000) Activity in primary visual cortex predicts performance in a visual detection task

    Nature Neuroscience 3:940–945.

    https://doi.org/10.1038/78856

    • PubMed
    • Google Scholar
79. 1. Sara SJ

    (2009) The locus coeruleus and noradrenergic modulation of cognition

    Nature Reviews Neuroscience 10:211–223.

    https://doi.org/10.1038/nrn2573

    • PubMed
    • Google Scholar
80. 1. Shadlen MN
    2. Britten KH
    3. Newsome WT
    4. Movshon JA

    (1996)

    A computational analysis of the relationship between neuronal and behavioral responses to visual motion

    Journal of Neuroscience 16:1486–1510.

    • PubMed
    • Google Scholar
81. 1. Shibata E
    2. Sasaki M
    3. Tohyama K
    4. Otsuka K
    5. Sakai A

    (2007) Reduced signal of locus ceruleus in depression in quantitative neuromelanin magnetic resonance imaging

    NeuroReport 18:415–418.

    https://doi.org/10.1097/WNR.0b013e328058674a

    • PubMed
    • Google Scholar
82. 1. Siegel M
    2. Engel AK
    3. Donner TH

    (2011) Cortical network dynamics of perceptual decision-making in the human brain

    Frontiers in Human Neuroscience 5:21.

    https://doi.org/10.3389/fnhum.2011.00021

    • PubMed
    • Google Scholar
83. 1. Smith SM
    2. Jenkinson M
    3. Woolrich MW
    4. Beckmann CF
    5. Behrens TE
    6. Johansen-Berg H
    7. Bannister PR
    8. De Luca M
    9. Drobnjak I
    10. Flitney DE
    11. Niazy RK
    12. Saunders J
    13. Vickers J
    14. Zhang Y
    15. De Stefano N
    16. Brady JM
    17. Matthews PM

    (2004) Advances in functional and structural MR image analysis and implementation as FSL

    NeuroImage 23 Suppl 1:S208–S219.

    https://doi.org/10.1016/j.neuroimage.2004.07.051

    • PubMed
    • Google Scholar
84. 1. Spiegelhalter DJ
    2. Best NG
    3. Carlin BP
    4. van der Linde A

    (2002) Bayesian measures of model complexity and fit

    Journal of the Royal Statistical Society: Series B 64:583–639.

    https://doi.org/10.1111/1467-9868.00353

    • Google Scholar
85. 1. Sugrue LP
    2. Corrado GS
    3. Newsome WT

    (2005) Choosing the greater of two goods: neural currencies for valuation and decision making

    Nature Reviews Neuroscience 6:363–375.

    https://doi.org/10.1038/nrn1666

    • PubMed
    • Google Scholar
86. 1. Varazzani C
    2. San-Galli A
    3. Gilardeau S
    4. Bouret S

    (2015) Noradrenaline and dopamine neurons in the reward/effort trade-off: a direct electrophysiological comparison in behaving monkeys

    Journal of Neuroscience 35:7866–7877.

    https://doi.org/10.1523/JNEUROSCI.0454-15.2015

    • PubMed
    • Google Scholar
87. 1. Vinck M
    2. Batista-Brito R
    3. Knoblich U
    4. Cardin JA

    (2015) Arousal and locomotion make distinct contributions to cortical activity patterns and visual encoding

    Neuron 86:740–754.

    https://doi.org/10.1016/j.neuron.2015.03.028

    • PubMed
    • Google Scholar
88. 1. Wang CA
    2. Boehnke SE
    3. White BJ
    4. Munoz DP

    (2012) Microstimulation of the monkey superior colliculus induces pupil dilation without evoking saccades

    Journal of Neuroscience 32:3629–3636.

    https://doi.org/10.1523/JNEUROSCI.5512-11.2012

    • PubMed
    • Google Scholar
89. 1. Wang CA
    2. Munoz DP

    (2015) A circuit for pupil orienting responses: implications for cognitive modulation of pupil size

    Current Opinion in Neurobiology 33:134–140.

    https://doi.org/10.1016/j.conb.2015.03.018

    • PubMed
    • Google Scholar
90. 1. Warren CM
    2. Eldar E
    3. van den Brink RL
    4. Tona KD
    5. van der Wee NJ
    6. Giltay EJ
    7. van Noorden MS
    8. Bosch JA
    9. Wilson RC
    10. Cohen JD
    11. Nieuwenhuis S

    (2016) Catecholamine-Mediated increases in Gain enhance the precision of cortical representations

    Journal of Neuroscience 36:5699–5708.

    https://doi.org/10.1523/JNEUROSCI.3475-15.2016

    • PubMed
    • Google Scholar
91. 1. Wiecki TV
    2. Sofer I
    3. Frank MJ

    (2013) HDDM: hierarchical bayesian estimation of the Drift-Diffusion Model in Python

    Frontiers in Neuroinformatics 7:14.

    https://doi.org/10.3389/fninf.2013.00014

    • PubMed
    • Google Scholar
92. 1. Wyart V
    2. Koechlin E

    (2016) Choice variability and suboptimality in uncertain environments

    Current Opinion in Behavioral Sciences 11:109–115.

    https://doi.org/10.1016/j.cobeha.2016.07.003

    • Google Scholar
93. 1. Zaborszky L
    2. Hoemke L
    3. Mohlberg H
    4. Schleicher A
    5. Amunts K
    6. Zilles K

    (2008) Stereotaxic probabilistic maps of the magnocellular cell groups in human basal forebrain

    NeuroImage 42:1127–1141.

    https://doi.org/10.1016/j.neuroimage.2008.05.055

    • PubMed
    • Google Scholar

Author details

1. Jan Willem de Gee

   1. Department of Neurophysiology and Pathophysiology, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
   2. Department of Psychology, University of Amsterdam, Amsterdam, The Netherlands

   Contribution

   JWdG, Conceptualization, Formal analysis, Investigation, Writing—original draft, Writing—review and editing

   For correspondence

   jwdegee@gmail.com

   Competing interests

   The authors declare that no competing interests exist.

   "This ORCID iD identifies the author of this article:" 0000-0002-5875-8282

2. Olympia Colizoli

   1. Department of Neurophysiology and Pathophysiology, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
   2. Department of Psychology, University of Amsterdam, Amsterdam, The Netherlands
   3. Amsterdam Brain & Cognition, University of Amsterdam, Amsterdam, The Netherlands

   Contribution

   OC, Investigation, Writing—review and editing

   Competing interests

   The authors declare that no competing interests exist.

   "This ORCID iD identifies the author of this article:" 0000-0001-5288-2437

3. Niels A Kloosterman

   1. Department of Psychology, University of Amsterdam, Amsterdam, The Netherlands
   2. Amsterdam Brain & Cognition, University of Amsterdam, Amsterdam, The Netherlands
   3. Max Planck UCL Centre for Computational Psychiatry and Ageing Research, Max Planck Institute for Human Development, Berlin, Germany

   Contribution

   NAK, Investigation, Writing—review and editing

   Competing interests

   The authors declare that no competing interests exist.

   "This ORCID iD identifies the author of this article:" 0000-0002-1134-7996

4. Tomas Knapen

   Department of Experimental and Applied Psychology, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands

   Contribution

   TK, Resources, Writing—review and editing

   Competing interests

   The authors declare that no competing interests exist.

   "This ORCID iD identifies the author of this article:" 0000-0001-5863-8689

5. Sander Nieuwenhuis

   Institute of Psychology, Leiden University, Leiden, The Netherlands

   Contribution

   SN, Resources, Writing—review and editing

   Competing interests

   The authors declare that no competing interests exist.

   "This ORCID iD identifies the author of this article:" 0000-0003-2418-3879

6. Tobias H Donner

   1. Department of Neurophysiology and Pathophysiology, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
   2. Department of Psychology, University of Amsterdam, Amsterdam, The Netherlands
   3. Amsterdam Brain & Cognition, University of Amsterdam, Amsterdam, The Netherlands

   Contribution

   THD, Conceptualization, Resources, Supervision, Writing—original draft, Writing—review and editing

   For correspondence

   t.donner@uke.de

   Competing interests

   The authors declare that no competing interests exist.

   "This ORCID iD identifies the author of this article:" 0000-0002-7559-6019

• 5,853

  views

• 1,080

  downloads

• 244

  citations

Views, downloads and citations are aggregated across all versions of this paper published by eLife.