Alpha oscillations and event-related potentials reflect distinct dynamics of attribute construction and evidence accumulation in dietary decision making

Neuroeconomic theories of decision making suggest that value-based decisions can be captured by a process involving several distinct computations, including both attribute valuation and evidence accumulation (EA; Rangel et al., 2008). For example, when we decide what to eat for lunch, we might consider attributes like how tasty or healthy a food is, our dieting goals, current hunger levels, and so forth. Each of these attributes constitutes evidence for or against eating the food, with different attributes given differential weight depending on a person’s momentary goals. To choose, current models assume that noisy neural representations of this attribute-based evidence may serve as the input to a process of sequential EA over time until sufficient evidence has accumulated to pass a decision threshold (Forstmann et al., 2016; Gold and Shadlen, 2007).

Based on this model of choice, recent work has argued that attempts to regulate dietary behaviour may operate in part by altering the weights given to specific attributes during food choice (Hutcherson et al., 2012; Sullivan et al., 2015; Tusche and Hutcherson, 2018). For example, dieting goals could be accomplished either by down-regulating the influence of hedonic attributes like tastiness, up-regulating the influence of attributes like healthiness, or both. Yet the precise computational route by which this up- and down-regulation occurs, and why some people succeed or fail to regulate effectively, remains unclear. Here, we took a computational decision neuroscience approach to this problem, linking computational predictions to observed neural data, to ask two questions. First, how do the computations contributing to a choice change when people regulate their behaviour by increasing or decreasing the influence of different attributes? Second, which of these changes predict success or failure in altering food consumption?

Sequential accumulation models provide a behaviourally and biologically plausible approach to study these questions (Forstmann et al., 2016) that appears to capture patterns of choice, response time, and neural activity with remarkable accuracy. For example, in the perceptual decision-making domain, electroencephalogram (EEG) studies have shown that decrements in the power of alpha (9–12 Hz) and theta (4–8 Hz) oscillations correlate with the predicted build-up of evidence (van Vugt et al., 2012; Werkle-Bergner et al., 2014). Analyses of event-related potentials (ERPs) also show that the P300 component elicited by oddball stimuli in auditory and visual paradigms satisfies the characteristics of an EA signal (decision variable) (O'Connell et al., 2012; Twomey et al., 2015). Parallel to this, in the value-based decision-making domain, EEG and MEG studies have found that model predictions about the temporal dynamics of EA during decisions correlate with both parietal and frontal theta and alpha (Hunt et al., 2012), and with beta (18–20 Hz) and gamma (40–80 Hz) oscillations (Polanía et al., 2014). Given that value-based decision making engages multiple cognitive processes and involves communication across different regions, these diverse results are perhaps not surprising. However, they raise important questions about what precise computations these EEG dynamics represent, and whether and how these signals might be altered by up- and down-regulating the influence of food attributes on choice.

These questions are vital for understanding the underlying regulatory mechanisms in value-based decision making since it could target multiple distinct phases of the EA process. Although considerable evidence suggests that self-regulation during decision making modifies choices by changing the influence of attributes on behaviour (Hare et al., 2009; Hare et al., 2011; Hutcherson et al., 2012; Sullivan et al., 2015; Tusche and Hutcherson, 2018), it is unclear how such changes are accomplished neurally. For example, observed decreases in the influence of food tastiness on behaviour could be accomplished either by abolishing attribute representations of tastiness or by preventing the incorporation of such representations into the EA process. If the former is true, regulation should directly influence the neural representation of an attribute such as tastiness (perhaps early on in the decision process), whereas if the latter is true, the neural representation of an attribute might remain intact and only the later stages of decision making, when the attribute is incorporated into EA, should change. Unfortunately, fMRI studies (Hare et al., 2009; Hutcherson et al., 2012) cannot dissociate the neural representations of attributes and EA with the necessary temporal resolution for testing these distinct processes.

EEG studies provide some clues in this regard, although they also leave open a number of questions. For example, some research suggests that when instructed to attend to food healthiness, the amplitude of a fronto-central ERP component around 300–500 ms post-food stimulus correlated more strongly with food healthiness (Harris et al., 2013). However, it is unclear whether this ERP component represents changes to early attribute representations or changes to their subsequent weight in the EA process. Moreover, while regulation reduced behavioural sensitivity to tastiness considerations, ERP amplitude in the same time window correlated with tastiness regardless of regulatory effort (Harris et al., 2013). This leaves open the question of how the influence of tastiness on behaviour was suppressed.

Given previous research that clearly links oscillatory dynamics to EA (Hunt et al., 2012; Polanía et al., 2014; van Vugt et al., 2012; Werkle-Bergner et al., 2014), and more ambiguous evidence linking value representations to fronto-central ERPs (Harris et al., 2013), we took a model-based approach to determine whether part of the answer to these questions might lie in the functional role of oscillations in incorporating attribute values into the EA process. More specifically, we asked subjects to make food choices naturally or under two regulation conditions. In the HEALTH condition, designed to increase the influence of the healthiness attribute and, secondarily, decrease the influence of the tastiness attribute, we asked subjects to explicitly focus on healthy eating. In the DECREASE condition, designed to decrease the influence of the tastiness attribute on choice without strongly modifying the influence of the healthiness attribute, we asked participants to decrease their desire and craving for foods generally.

We used a computational model of choice to first fit the behavioural data, and then simulated the predicted dynamics of both attribute representation and EA during food choice under the three conditions. These simulations suggested that the computations associated with attribute values and EA follow distinct temporal trajectories. Then, we identified both ERP and oscillatory correlates of tastiness and healthiness representations during self-regulation and asked whether and how the time course of these correlates resembled the simulated computational dynamics of both attribute representation and EA during natural and regulated choices. Finally, we asked whether and how changes in these oscillatory dynamics correlated with individual differences in regulatory success, as measured by changes in model parameters. Our results suggested that the time course of ERPs correlated more strongly with model-based predictions for attribute representations, while suppression of alpha oscillatory activity correlated better with model-based predictions for EA signals. Importantly, regulation had a larger effect on the alpha-correlate of tastiness compared to the ERP-correlate of tastiness, and this effect appeared to be mediated by an increase in the power of theta oscillations in the early stages of decision formation.

Subjects performed a food choice task (Figure 1) in which they decided whether or not they wanted to consume different foods, while focusing on one of three goals: respond naturally to all foods (NATURAL), focus on healthy eating (HEALTH), or focus on decreasing desire for all foods (DECREASE; see Materials and methods for a more detailed description of the task). On each trial of the choice task, subjects made one of four responses (Strong No, No, Yes, or Strong Yes) to indicate whether they wanted to eat the food that was displayed on the screen. After the choice task, subjects rated each food stimulus for its tastiness and healthiness on a 1–6 scale, allowing us to examine how these attributes were encoded in neural activation at the time of choice.

Figure 1

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Effects of regulatory focus on computational model parameters

To examine the computational bases of these effects, we estimated a drift-diffusion model (DDM) with six parameters: weights on tastiness (w_tastiness) and healthiness (w_healthiness), a generic value constant (ValConst; that is, a general tendency to assign positive or negative values to foods, unrelated to tastiness or healthiness), threshold (trs), non-decision time (ndt), and starting point bias (spbias; see Materials and methods for details; Figure 2 and Figure 2—figure supplement 1).

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Figure 2—source data 1

Group hierarchical multi-attribute drift diffusion model (HDDM) parameter estimates for each condition.

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As expected, we found that regulation had a significant effect on the weight given to tastiness (w_tastiness: F(2,98) = 33.17, p<0.001) and healthiness (w_healthiness: F(2,98) = 62.50, p<0.001) and on the value constant (F(2,98) = 22.45, p<0.001). Post-hoc paired t-tests showed that weight on tastiness decreased to a similar extent in HEALTH (t (49) = −7.42, p<0.001) and DECREASE (t (49) = −6.18, p<0.001) compared to NATURAL. By contrast, while the weight on healthiness (w_healthiness) significantly increased in both HEALTH and DECREASE compared to NATURAL (HEALTH: t (49) = 9.38, p<0.001; DECREASE: t(49) = 3.56, p<0.001), it was significantly larger in HEALTH compared to DECREASE (t(49) = 7.12, p<0.001). In addition, the value constant (ValConst) was more negative in DECREASE compared to NATURAL (t(49) = −4.11, p<0.001) and HEALTH (t(49) = −6.09, p<0.001), and more negative in NATURAL compared to HEALTH (t(49) = −2.38, p=0.02). Thus, while both strategies decreased the influence of tastiness on choice, HEALTH focus specifically increased the influence of healthiness, and DECREASE resulted in a decrease in value that is non-specific to tastiness or healthiness.

Regulation also influenced other parameters of the model beyond attribute weights. In particular, it changed the decision threshold (trs: F(2,98) = 25.45, p<0.001) and non-decision time (ndt: F(2,98) = 4.69, p=0.01). Notably, we found little evidence that regulation affected starting point biases (spbias: p>0.05), suggesting that most of the effects of regulation occurred during post-stimulus processing rather than in pre-stimulus preparation. Post-hoc paired t-tests on significant effects showed that decision threshold was higher in DECREASE compared to NATURAL (t(49) = 6.0, p<0.001) and HEALTH (t(49) = 3.67, p<0.001) and in HEALTH compared to NATURAL (t(49) = 4.87, p<0.001). Non-decision time was also shorter in DECREASE compared to NATURAL (t(49) = −2.32, p=0.02) and HEALTH (t(49) = −2.72, p=0.009) but was not different in HEALTH compared to NATURAL (p>0.05). These results indicate that regulation more strongly changes value components as opposed to creating a value-independent bias.

ERP-correlates of model-predicted signals

Having identified putative time courses for both tastiness and healthiness attribute representations, as well as EA signals, we began by comparing model-simulated results against observed single-trial ERPs time-locked to food presentation (see Materials and methods). Using Model 2, in which we regressed the trial-level ERP signal at each time point on tastiness and healthiness (i.e. ${\displaystyle ERP\left(t\right)\cong b{\left(t\right)}_0+b{\left(t\right)}_{tastiness}\ast tastiness+b{\left(t\right)}_{healthiness}\ast healthiness+b{\left(t\right)}_{rt}\ast rt}$), we asked whether there were any ERP signatures that correlated with taste-AC or taste-EA when no self-regulation was applied, and whether self-regulation in HEALTH and DECREASE conditions modulated these signatures. Based on previous results (Harris et al., 2011; Harris et al., 2013), we expected to observe a fronto-central ERP component that correlated with tastiness and healthiness, and sought to determine whether this component might match the predicted dynamics of attribute valuation or EA.

ERPs correlate with the simulated influence of tastiness on the taste-attribute signal

Based on our significance criteria (see Materials and methods for details), we found that ERPs in fronto-central (t(49) = 3.45, p=0.004) channels were correlated with tastiness in the NATURAL condition. We refer to this as the ERP-correlate of tastiness (Figure 3a). However, this effect showed no significant differentiation between NATURAL and regulated conditions (p>0.05; but see Figure 3c for other differences in the scalp maps across NATURAL and DECREASE). The time course of the ERP-correlate of tastiness was closely predicted by the simulated influence of tastiness on the taste-AC signals (r = 0.89, p<0.001) and, somewhat less clearly but still significantly, the taste-EA signal (r = 0.77, p<0.001). It was also a significantly closer match for the taste-AC than the taste-EA signal (z = 2.55, p=0.005; Figure 3b). The magnitude of the ERP-correlate of tastiness was not correlated with weight on taste in the NATURAL condition or behavioural regulation (i.e. the difference in the weight on taste between regulation conditions) across subjects. See Figure 3—figure supplement 1d, e for the parietal component of ERP-correlate of tastiness. We also looked for signals matching the healthiness attribute signal, but there were no ERP signals that met our joint criterion for healthiness (i.e. correlated significantly with model-predicted time courses for healthiness and independently correlated with trial-by-trial variation in food healthiness).

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Early ERPs correlate with healthiness when subjects focus on health

Because we failed to find ERP signals for healthiness that matched our model-predicted time course, we also conducted an exploratory (i.e. not model-based) analysis of ERPs, which revealed that trial-by-trial ERPs in the HEALTH condition were correlated significantly with healthiness ~200–400 ms post-food in occipital channels (t(49) = 3.12, p=0.007; Figure 3—figure supplement 1a). We refer to this as the ERP-correlate of healthiness (see Figure 3—figure supplement 1c for scalp distribution and Figure 3—figure supplement 1b for frontal component time course). However, this early ERP signature of healthiness did not correlate with the behavioural increase in healthiness weight across subjects. We thus do not consider it further here.

Time-frequency power correlates of model-predicted signals

To compare the model-simulated results against observed oscillatory neural responses, we extracted trial-by-trial event-related changes in power of all oscillatory ranges from 200 ms before to 1000 ms after the presentation of the food stimulus. We then regressed averaged power in each trial on trial-by-trial tastiness ratings (Model 2; ${\displaystyle tfPower\left(t\right)\cong b{\left(t\right)}_0+b{\left(t\right)}_{tastiness}\ast tastiness+b{\left(t\right)}_{healthiness}\ast healthiness+b{\left(t\right)}_{rt}\ast rt}$), separately for each of 25 time bins beginning 200 ms prior to food onset and ending 1000 ms after food presentation (duration ~49 ms). We asked whether there were any oscillatory signatures that correlated with the model-predicted taste-AC or taste-EA signal when no self-regulation was applied, and whether self-regulation in HEALTH and DECREASE conditions modulated these signatures.

Alpha power correlates with the simulated influence of tastiness on EA

Based on our significance criteria (see Materials and methods for details), we identified an alpha power signature showing a regulation-modulated sensitivity to tastiness that was in line with model predictions (Figure 4a). In particular, in the NATURAL condition, we found a significant change in alpha power over left-frontal and right parietal-occipital channels (Figure 4c, top left) that negatively correlated with tastiness ratings from 500 to 1000 ms after presentation of food (t(49) = −3.78, p<0.001). By contrast, alpha power during the two regulation conditions (HEALTH and DECREASE) was not significantly associated with tastiness. The difference in correlation between tastiness and alpha power in the window from 500 to 1000 ms was significant for the contrast of DECREASE vs. NATURAL (t (49) = 2.40, p=0.02) and marginally significant for HEALTH vs. NATURAL (t (49) = 2.01, p=0.05; Figure 4c, bottom). For simplicity, we call this effect the alpha-correlate of tastiness.

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We further asked whether changes in the alpha-correlate of tastiness in regulation conditions compared to NATURAL predicted individual differences in DDM tastiness weight changes (Δw_tastiness) in regulation compared to NATURAL, as would be expected if this signal represents a computation directly related to choice. As hypothesized, we found a correlation between the decrease in the weight on tastiness (Δw_tastiness) and the decrease in the alpha-correlate of tastiness ~500–1000 ms post-food, both for the comparison of DECREASE vs. NATURAL (Pearson r = −0.28, p=0.05) and the comparison of HEALTH vs. NATURAL (Pearson r = −0.29, p=0.04) (Figure 4d).

Finally, we tested whether alpha power in channels where the alpha-correlate of tastiness was found correlated better with the taste-AC or taste-EA time courses predicted by the DDM (Figure 2c). Importantly, we found that the alpha-correlate of tastiness was predicted closely by taste-EA (r_alpha-EA = −0.90, p<0.001) but not taste-AC (r_{alpha-tastiness} = −0.70, p>0.05), with the taste-EA signal showing a more precise relationship compared to the taste-AC signal across time (z = 4.09, p<0.001; Figure 4e). These results suggest that regulation may operate in part by altering alpha frequency representations of tastiness attributes, and that this modulation might occur in relationship to EA rather than attribute construction.

We also sought to use a second way of examining the extent to which this signal might better track EA or attribute construction processes. In particular, we reasoned that if this signal is related to EA, it should show different temporal dynamics as a function of response time: when responses terminate quickly, the sensitivity to tastiness in alpha signals should also terminate quickly. However, when choices take longer, sensitivity of alpha to tastiness should endure for longer since it takes longer for the EA process to terminate. Follow-up tests confirmed this hypothesis: we found that alpha signal was sensitive to tastiness for different durations on trials where the participant responded quickly compared to trials where they responded slowly (Figure 4—figure supplement 1a, b).

Next, we conducted a further test of the idea that the alpha-correlate of tastiness relates to EA by examining response-locked data in fast and slow decisions. Prior research suggests that signals related to EA build-up gradually and peak just prior to the time of response, regardless of whether that response occurs quickly or slowly (e.g. O'Connell et al., 2012; Twomey et al., 2015). We thus theorized that we should observe a gradual build-up in alpha sensitivity to tastiness that peaks just prior to the time of response for both fast and slow responses. As expected, we found that alpha signals at taste-sensitive channels (Figure 4c, top left) showed a build-up of correlation with tastiness in NATURAL trials that peaked just before the time of response both when decisions were made quickly and when they were made slowly (Figure 4—figure supplement 1c, d). Intriguingly, this analysis also revealed an additional significant correlation with healthiness in taste-sensitive channels prior to the response on slow trials in the NATURAL condition, but not on the fast trials (Figure 4—figure supplement 3e, f). The time courses of these signals correlated with the simulated time course of response-locked EA (r = −0.73, p>0.05), albeit failing to reach full significance based on our a priori criteria.

Finally, we asked whether the average alpha power (as opposed to correlation with tastiness) reached a fixed value just prior to the response for fast and slow RTs, as would be expected if this signal produces the response directly. Although we found that raw alpha signals in this region did build gradually, peaking in the time before response (Figure 4—figure supplement 2), they did not always reach a fully clear and consistent value, as might be predicted if these signals provided the final trigger for the motor response.

Taken together, these findings suggest that, while the alpha-correlate of tastiness shows patterns consistent with some form of EA, it may represent an intermediate stage of EA based on value that is then passed on to other mechanisms that accumulate evidence for a particular motor response. Such architecture would be consistent with observations from other work finding evidence for multiple EA mechanisms working at different stages to determine the final choice (e.g. Steinemann et al., 2018).

Alpha power correlates with the simulated influence of healthiness on EA

Next, we turned to the healthiness attribute using a similar analytical approach. Notably, in the HEALTH condition, we again found significant effects restricted to alpha power in frontal and parietal-occipital channels (Figure 4b). Alpha power in these channels was negatively correlated with healthiness from ~500–1000 ms after presentation of food (t(49) = −2.96, p=0.005; alpha-correlate of healthiness; Figure 4c, top-right). The timing of this signal correlated with the predicted time course of the health-EA signal (r_alpha-EA = −0.95, p<0.001) and, less so, to the health-AC signals (r_{alpha-healthiness} = −0.76, p=0.02) signals. The time course was significantly more strongly correlated with the health-EA signal than the health-AC signal (z = 4.17, p<0.001; Figure 4f).

As might be expected from model predictions, alpha power during the NATURAL and DECREASE conditions was not significantly associated with healthiness, although the contrast of average b_healthiness for alpha power between HEALTH and DECREASE or NATURAL in these channels ~500–1000 ms post-food failed to reach significance. Unlike the alpha-tastiness correlate, we found no evidence that the differences in alpha-healthiness correlate across conditions correlated with individual differences in the change in model parameters for the healthiness weight.

Early theta power correlates with later suppression of tastiness influence on EA

Having shown that we observed a correspondence between model-predicted changes in attribute representations and suppression of oscillations in the alpha range, we next turned to a more exploratory analysis, similar to that conducted for ERPs. Specifically, we asked whether there were any oscillatory changes that might distinguish regulatory conditions in relation to tastiness and healthiness, but that did not conform to model predictions. Strikingly, and in contrast to alpha power, which tracks tastiness in the NATURAL condition but not regulation conditions, we found a single pattern matching our significance criterion: theta power at channels over occipital and frontal areas was positively correlated with tastiness early in the trial (200–500 ms) in both HEALTH (t(49) = 3.46, p=0.005; Figure 5a, b, top left) and DECREASE (t(49) = 2.8, p=0.01; Figure 5a, b, top-right) conditions independently. This effect was also larger compared to NATURAL in both HEALTH (t(49) = 2.90, p=0.009; occipital channels) and DECREASE (t(49) = 2.28, p=0.03; occipital and left central channels) (Figure 5b, bottom). For simplicity, we call this effect the theta-correlate of tastiness.

Figure 5

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Given the early appearance of this signal compared to the alpha-correlate of tastiness, we speculated that it might represent a ‘control’ process, rather than a representation of the tastiness attribute per se. This control process might be responsible for reducing the influence of tastiness attribute representations (which we observed in ERP signals) on the EA process (observed in alpha oscillations). This interpretation predicts that, despite theta correlating with tastiness, the magnitude of this correlation should actually be negatively associated with regulation-induced decreases in the weight on tastiness, and that theta and alpha signatures might also be related. These predictions were confirmed. We found that subjects with greater increases in the theta-correlate of tastiness between 200 and 500 ms showed greater decreases in the weight on tastiness (Δw_tastiness), and this was independently true for the contrast of both HEALTH (Pearson r = –0.36, p=0.01) and DECREASE (Pearson r = –0.29, p=0.04) vs. NATURAL (Figure 5c). Moreover, changes in the alpha- and theta-correlates of tastiness in DECREASE vs. NATURAL were correlated across subjects (Pearson r = 0.43, p<0.001; Figure 5d). In other words, the more strongly tastiness was encoded in theta power early in the trial, the less strongly it was represented in the suppression of alpha power later in the trial. This relationship was nonsignificant in the HEALTH condition (Pearson r = 0.12, p=0.39; Figure 5d).

No other frequency ranges showed any effects of attribute or condition that met our criteria.

Over the last few years, research has provided ample evidence that decision makers can modulate the influence of different attributes on behaviour using cognitive regulation strategies that focus on attending to or ignoring specific attributes (e.g. Hare et al., 2011; Hutcherson et al., 2012; Tusche and Hutcherson, 2018). However, the neurocomputational process by which they achieve this modulation remains unclear. Using a computational model of attribute valuation and EA, we found distinct correlates of attribute construction and EA that also showed differential effects of regulation. Whereas a fronto-central ERP component matched the predicted time course of attribute-related computations and tracked the tastiness of a food regardless of regulatory focus, alpha power recorded from frontal and occipital areas of the scalp correlated more strongly with the predicted time course of EA and tracked variation in the tastiness of food only when subjects naturally decided what foods to eat: the tastier the food, the lower the alpha power. When subjects regulated their responses in a way that reduced the behavioural influence of tastiness, this function was compromised, that is, alpha no longer tracked tastiness values. Moreover, changes in alpha, but not changes in the ERP, predicted individual differences in successful down-regulation of the influence of tastiness on choice across two different regulatory strategies. Finally, we found an earlier rise in theta power at frontal and occipital areas of the scalp that did not correlate with model-predicted time courses but predicted regulatory success. Importantly, this signal correlated positively with food tastiness only when subjects regulated their decisions, and the more it did so, the greater the decrease in the influence of food tastiness on subjects’ choices. This was true for both regulatory strategies. Taken together, our findings paint a nuanced portrait of the processes occurring during the regulation of value-based decision making. They suggest that regulation reduces the behavioural influence of tastiness not by disrupting early, stimulus-locked representations of tastiness, but by using a theta-related regulatory mechanism to disrupt the incorporation of tastiness into alpha-related EA processes.

Raw data are deposited on Open Science Framework, under the project DOI: 10.17605/OSF.IO/EWTVX. Raw Behavioural data: https://osf.io/yp2x9. Raw EEG data: https://osf.io/p5wd2.

1. 1. HajiHosseini A
   2. Hutcherson C

   (2021) Open Science Framework

   EEG Dynamics of Self-Regulatory Strategies in Dietary Decision Making.

   https://doi.org/10.17605/OSF.IO/EWTVX

1. 1. Axmacher N
   2. Henseler MM
   3. Jensen O
   4. Weinreich I
   5. Elger CE
   6. Fell J

   (2010) Cross-frequency coupling supports multi-item working memory in the human Hippocampus

   PNAS 107:3228–3233.

   https://doi.org/10.1073/pnas.0911531107

   • PubMed
   • Google Scholar
2. 1. Brainard DH

   (1997) The psychophysics toolbox

   Spatial Vision 10:433–436.

   https://doi.org/10.1163/156856897X00357

   • PubMed
   • Google Scholar
3. 1. Cavanagh JF
   2. Wiecki TV
   3. Cohen MX
   4. Figueroa CM
   5. Samanta J
   6. Sherman SJ
   7. Frank MJ

   (2011) Subthalamic nucleus stimulation reverses mediofrontal influence over decision threshold

   Nature Neuroscience 14:1462–1467.

   https://doi.org/10.1038/nn.2925

   • PubMed
   • Google Scholar
4. 1. Cavanagh JF
   2. Frank MJ

   (2014) Frontal theta as a mechanism for cognitive control

   Trends in Cognitive Sciences 18:414–421.

   https://doi.org/10.1016/j.tics.2014.04.012

   • PubMed
   • Google Scholar
5. 1. Delorme A
   2. Makeig S

   (2004) EEGLAB: an open source toolbox for analysis of single-trial EEG dynamics including independent component analysis

   Journal of Neuroscience Methods 134:9–21.

   https://doi.org/10.1016/j.jneumeth.2003.10.009

   • PubMed
   • Google Scholar
6. 1. Forstmann BU
   2. Ratcliff R
   3. Wagenmakers EJ

   (2016) Sequential sampling models in cognitive neuroscience: advantages, applications, and extensions

   Annual Review of Psychology 67:641–666.

   https://doi.org/10.1146/annurev-psych-122414-033645

   • PubMed
   • Google Scholar
7. 1. Foxe JJ
   2. Snyder AC

   (2011) The role of Alpha-Band brain oscillations as a sensory suppression mechanism during selective attention

   Frontiers in Psychology 2:154.

   https://doi.org/10.3389/fpsyg.2011.00154

   • PubMed
   • Google Scholar
8. 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
9. 1. Hajihosseini A
   2. Holroyd CB

   (2013) Frontal midline theta and N200 amplitude reflect complementary information about expectancy and outcome evaluation

   Psychophysiology 50:550–562.

   https://doi.org/10.1111/psyp.12040

   • PubMed
   • Google Scholar
10. Software

    1. HajiHosseini A
    2. Hutcherson C

    (2020) EEG Dynamics of Self-Regulatory Strategies in Dietary Decision Making

    EEG Dynamics of Self-Regulatory Strategies in Dietary Decision Making.

    https://osf.io/ewtvx/
11. 1. Hare TA
    2. Camerer CF
    3. Rangel A

    (2009) Self-control in decision-making involves modulation of the vmPFC valuation system

    Science 324:646–648.

    https://doi.org/10.1126/science.1168450

    • PubMed
    • Google Scholar
12. 1. Hare TA
    2. Malmaud J
    3. Rangel A

    (2011) Focusing attention on the health aspects of foods changes value signals in vmPFC and improves dietary choice

    Journal of Neuroscience 31:11077–11087.

    https://doi.org/10.1523/JNEUROSCI.6383-10.2011

    • PubMed
    • Google Scholar
13. 1. Harris A
    2. Adolphs R
    3. Camerer C
    4. Rangel A

    (2011) Dynamic construction of stimulus values in the ventromedial prefrontal cortex

    PLOS ONE 6:e21074.

    https://doi.org/10.1371/journal.pone.0021074

    • PubMed
    • Google Scholar
14. 1. Harris A
    2. Hare T
    3. Rangel A

    (2013) Temporally dissociable mechanisms of self-control: early attentional filtering versus late value modulation

    Journal of Neuroscience 33:18917–18931.

    https://doi.org/10.1523/JNEUROSCI.5816-12.2013

    • PubMed
    • Google Scholar
15. 1. Holroyd CB
    2. HajiHosseini A
    3. Baker TE

    (2012) ERPs and EEG oscillations, best friends forever: comment on Cohen et al

    Trends in Cognitive Sciences 16:192.

    https://doi.org/10.1016/j.tics.2012.02.008

    • PubMed
    • Google Scholar
16. 1. Hunt LT
    2. Kolling N
    3. Soltani A
    4. Woolrich MW
    5. Rushworth MF
    6. Behrens TE

    (2012) Mechanisms underlying cortical activity during value-guided choice

    Nature Neuroscience 15:470–476.

    https://doi.org/10.1038/nn.3017

    • PubMed
    • Google Scholar
17. 1. Hutcherson CA
    2. Plassmann H
    3. Gross JJ
    4. Rangel A

    (2012) Cognitive regulation during decision making shifts behavioral control between ventromedial and dorsolateral prefrontal value systems

    Journal of Neuroscience 32:13543–13554.

    https://doi.org/10.1523/JNEUROSCI.6387-11.2012

    • PubMed
    • Google Scholar
18. 1. Klimesch W

    (1999) EEG alpha and theta oscillations reflect cognitive and memory performance: a review and analysis

    Brain Research Reviews 29:169–195.

    https://doi.org/10.1016/S0165-0173(98)00056-3

    • PubMed
    • Google Scholar
19. 1. Klimesch W
    2. Sauseng P
    3. Gerloff C

    (2003) Enhancing cognitive performance with repetitive transcranial magnetic stimulation at human individual alpha frequency

    European Journal of Neuroscience 17:1129–1133.

    https://doi.org/10.1046/j.1460-9568.2003.02517.x

    • Google Scholar
20. 1. Klimesch W
    2. Sauseng P
    3. Hanslmayr S

    (2007) EEG alpha oscillations: the inhibition-timing hypothesis

    Brain Research Reviews 53:63–88.

    https://doi.org/10.1016/j.brainresrev.2006.06.003

    • PubMed
    • Google Scholar
21. 1. Klimesch W

    (2012) α-band oscillations, attention, and controlled access to stored information

    Trends in Cognitive Sciences 16:606–617.

    https://doi.org/10.1016/j.tics.2012.10.007

    • PubMed
    • Google Scholar
22. 1. Kloosterman NA
    2. de Gee JW
    3. Werkle-Bergner M
    4. Lindenberger U
    5. Garrett DD
    6. Fahrenfort JJ

    (2019) Humans strategically shift decision Bias by flexibly adjusting sensory evidence accumulation

    eLife 8:e37321.

    https://doi.org/10.7554/eLife.37321

    • PubMed
    • Google Scholar
23. Software

    1. Lenhard W
    2. Lenhard A

    (2014)

    Hypothesis Tests for Comparing Correlations

    Hypothesis Tests for Comparing Correlations.
24. 1. Maier SU
    2. Raja Beharelle A
    3. Polanía R
    4. Ruff CC
    5. Hare TA

    (2020) Dissociable mechanisms govern when and how strongly reward attributes affect decisions

    Nature Human Behaviour 4:949–963.

    https://doi.org/10.1038/s41562-020-0893-y

    • PubMed
    • Google Scholar
25. 1. Mas-Herrero E
    2. Marco-Pallarés J

    (2016) Theta oscillations integrate functionally segregated sub-regions of the medial prefrontal cortex

    NeuroImage 143:166–174.

    https://doi.org/10.1016/j.neuroimage.2016.08.024

    • PubMed
    • Google Scholar
26. 1. Nayak S
    2. Kuo C
    3. Tsai AC-H

    (2019) Mid-Frontal theta modulates response inhibition and decision making processes in emotional contexts

    Brain Sciences 9:271.

    https://doi.org/10.3390/brainsci9100271

    • Google Scholar
27. 1. O'Connell RG
    2. Dockree PM
    3. Kelly SP

    (2012) A supramodal accumulation-to-bound signal that determines perceptual decisions in humans

    Nature Neuroscience 15:1729–1735.

    https://doi.org/10.1038/nn.3248

    • PubMed
    • Google Scholar
28. 1. Polanía R
    2. Krajbich I
    3. Grueschow M
    4. Ruff CC

    (2014) Neural oscillations and synchronization differentially support evidence accumulation in perceptual and value-based decision making

    Neuron 82:709–720.

    https://doi.org/10.1016/j.neuron.2014.03.014

    • PubMed
    • Google Scholar
29. 1. Rangel A
    2. Camerer C
    3. Montague PR

    (2008) A framework for studying the neurobiology of value-based decision making

    Nature Reviews Neuroscience 9:545–556.

    https://doi.org/10.1038/nrn2357

    • PubMed
    • Google Scholar
30. 1. Samaha J
    2. Boutonnet B
    3. Postle BR
    4. Lupyan G

    (2018) Effects of meaningfulness on perception: alpha-band oscillations carry perceptual expectations and influence early visual responses

    Scientific Reports 8:1–14.

    https://doi.org/10.1038/s41598-018-25093-5

    • PubMed
    • Google Scholar
31. 1. Scholz S
    2. Schneider SL
    3. Rose M

    (2017) Differential effects of ongoing EEG beta and theta power on memory formation

    PLOS ONE 12:e0171913.

    https://doi.org/10.1371/journal.pone.0171913

    • PubMed
    • Google Scholar
32. 1. Staudigl T
    2. Hanslmayr S

    (2013) Theta oscillations at encoding mediate the context-dependent nature of human episodic memory

    Current Biology 23:1101–1106.

    https://doi.org/10.1016/j.cub.2013.04.074

    • PubMed
    • Google Scholar
33. 1. Steinemann NA
    2. O'Connell RG
    3. Kelly SP

    (2018) Decisions are expedited through multiple neural adjustments spanning the sensorimotor hierarchy

    Nature Communications 9:1–13.

    https://doi.org/10.1038/s41467-018-06117-0

    • PubMed
    • Google Scholar
34. 1. Sullivan N
    2. Hutcherson C
    3. Harris A
    4. Rangel A

    (2015) Dietary self-control is related to the speed with which attributes of healthfulness and tastiness are processed

    Psychological Science 26:122–134.

    https://doi.org/10.1177/0956797614559543

    • PubMed
    • Google Scholar
35. 1. Tesche CD
    2. Karhu J

    (2000) Theta oscillations index human hippocampal activation during a working memory task

    PNAS 97:919–924.

    https://doi.org/10.1073/pnas.97.2.919

    • PubMed
    • Google Scholar
36. 1. Tosun T
    2. Berkay D
    3. Sack AT
    4. Çakmak Y
    5. Balcı F

    (2017) Inhibition of Pre–Supplementary Motor Area by Continuous Theta Burst Stimulation Leads to More Cautious Decision-making and More Efficient Sensory Evidence Integration

    Journal of Cognitive Neuroscience 29:1433–1444.

    https://doi.org/10.1162/jocn_a_01134

    • Google Scholar
37. 1. Tusche A
    2. Hutcherson CA

    (2018) Cognitive regulation alters social and dietary choice by changing attribute representations in domain-general and domain-specific brain circuits

    eLife 7:e31185.

    https://doi.org/10.7554/eLife.31185

    • PubMed
    • Google Scholar
38. 1. Twomey DM
    2. Murphy PR
    3. Kelly SP
    4. O'Connell RG

    (2015) The classic P300 encodes a build-to-threshold decision variable

    European Journal of Neuroscience 42:1636–1643.

    https://doi.org/10.1111/ejn.12936

    • Google Scholar
39. 1. van Vugt MK
    2. Simen P
    3. Nystrom LE
    4. Holmes P
    5. Cohen JD

    (2012) EEG oscillations reveal neural correlates of evidence accumulation

    Frontiers in Neuroscience 6:1–13.

    https://doi.org/10.3389/fnins.2012.00106

    • PubMed
    • Google Scholar
40. 1. Wang YK
    2. Jung TP
    3. Lin CT

    (2018) Theta and alpha oscillations in attentional interaction during distracted driving

    Frontiers in Behavioral Neuroscience 12:3.

    https://doi.org/10.3389/fnbeh.2018.00003

    • PubMed
    • Google Scholar
41. 1. Werkle-Bergner M
    2. Grandy TH
    3. Chicherio C
    4. Schmiedek F
    5. Lövdén M
    6. Lindenberger U

    (2014) Coordinated within-trial dynamics of low-frequency neural rhythms controls evidence accumulation

    Journal of Neuroscience 34:8519–8528.

    https://doi.org/10.1523/JNEUROSCI.3801-13.2014

    • PubMed
    • Google Scholar
42. 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
43. 1. Zavala B
    2. Tan H
    3. Little S
    4. Ashkan K
    5. Green AL
    6. Aziz T
    7. Foltynie T
    8. Zrinzo L
    9. Zaghloul K
    10. Brown P

    (2016) Decisions made with less evidence involve higher levels of corticosubthalamic nucleus theta band synchrony

    Journal of Cognitive Neuroscience 28:811–825.

    https://doi.org/10.1162/jocn_a_00934

    • PubMed
    • Google Scholar

Author details

1. Azadeh HajiHosseini

   Department of Psychology, University of Toronto Scarborough, Toronto, Canada

   Contribution

   Conceptualization, Software, Formal analysis, Validation, Investigation, Visualization, Methodology, Writing - original draft, Project administration, Writing - review and editing

   For correspondence

   azadeh.haji@utoronto.ca

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0001-7621-6527

2. Cendri A Hutcherson

   1. Department of Psychology, University of Toronto Scarborough, Toronto, Canada
   2. Department of Marketing, Rotman School of Management, University of Toronto, Toronto, Canada

   Contribution

   Conceptualization, Resources, Software, Supervision, Funding acquisition, Validation, Methodology, Writing - review and editing

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0002-4441-4809

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