The role of hunger state and dieting history in neural response to food cues: An event-related potential study

Accepted Manuscript The role of hunger state and dieting history in neural response to food cues: An event-related potential study

Emily H. Feig, Samantha R. Winter, John Kounios, Brian Erickson, Staci Berkowitz, Michael R. Lowe PII: DOI: Reference:

S0031-9384(17)30169-5 doi: 10.1016/j.physbeh.2017.05.031 PHB 11816

To appear in:

Physiology & Behavior

Received date: Revised date: Accepted date:

13 January 2017 26 May 2017 31 May 2017

Please cite this article as: Emily H. Feig, Samantha R. Winter, John Kounios, Brian Erickson, Staci Berkowitz, Michael R. Lowe , The role of hunger state and dieting history in neural response to food cues: An event-related potential study. The address for the corresponding author was captured as affiliation for all authors. Please check if appropriate. Phb(2017), doi: 10.1016/j.physbeh.2017.05.031

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ACCEPTED MANUSCRIPT The Role of Hunger State and Dieting History in Neural Response to Food Cues: An EventRelated Potential Study Emily H. Feig, M.S. ([email protected]) Samantha R. Winter, M.S. ([email protected])

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John Kounios, Ph.D. ([email protected])

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Brian Erickson, M.S. ([email protected])

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Staci Berkowitz, M.S. ([email protected]) Michael R. Lowe, Ph.D. ([email protected])

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Drexel University

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Stratton Hall Suite 119 3141 Chestnut Street

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Philadelphia, PA 19104, USA

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Corresponding author: Emily H Feig, [email protected]

ACCEPTED MANUSCRIPT Highlights A history of dieting has been shown to predict weight gain

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Female past dieters were compared to never dieters on ERP response to food cues

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Historic dieters experienced slowed/blunted early food cue processing

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Historic dieters may inhibit sustained attention to food cues when not hungry

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ERP differences may reflect appetitive processes that contribute to weight gain

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Abstract

A history of dieting to lose weight has been shown to be a robust predictor of future weight gain.

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A potential factor in propensity towards weight gain is the nature of people’s reactions to the abundance of highly palatable food cues in the environment. Event Related Potentials (ERPs)

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have revealed differences in how the brain processes food cues between obese and normal

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weight individuals, as well as between restrained and unrestrained eaters. However, comparisons

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by weight status are not informative regarding whether differences predate or follow weight gain in obese individuals and restrained eating has not consistently been found to predict future

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weight gain. The present study compared ERP responses to food cues in non-obese historic

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dieters (HDs) to non-obese never dieters (NDs). HDs showed a blunted N1 component relative to NDs overall, and delayed N1 and P2 components compared to NDs in the hungry state, suggesting that early, perceptual processing of food cues differs between these groups, especially when food-deprived. HDs also showed a more hunger-dependent sustained ERP (LPP) compared to NDs. Future research should test ERP-based food cue responsivity as a mediator between dieting history and future weight gain to better identify those most at risk for weight gain as well as the nature of their vulnerability.

ACCEPTED MANUSCRIPT Keywords: Dieting; Event related potentials; Obesity; Food cue responsivity

Funding: This research did not receive any specific grant from funding agencies in the public,

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commercial, or not-for-profit sectors.

ACCEPTED MANUSCRIPT 1. Introduction1 1.1. Dieting as a predictor of weight gain A history of dieting to lose weight has been shown to be a robust predictor of future weight gain. While this finding could be interpreted as counterintuitive, since presumably aiming

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to lose weight would result in a lower weight, the research supports the opposite trend. In fact, a

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recent review of prospective studies [1] found that those who self-reported current or past dieting

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gained more weight over time than those who did not report dieting in 15 out of the 20 analyses reviewed. By comparison, measures of restrained eating predicted future weight gain in only one

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of 20 analyses. Although the exact mechanism underlying the prediction of weight gain by

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dieting is unknown, dieting history appears to be an excellent indicator of weight gain proneness. The obesogenic environment is pervasive, and yet in the face of so many cues to overeat,

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some are able to maintain a healthy weight. In order to better understand the predictive

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relationship of dieting with weight gain, it would be useful to examine how individuals with a history of dieting with the intent of weight loss (historical dieters; HDs) and those who have

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never gone on a diet to lose weight (never dieters; NDs) respond to such environmental food

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stimuli. Lowe [2] reviewed evidence consistent with the conclusion that the relationship between past dieting and future weight gain is due to the fact that those with a disposition toward weight

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gain in the obesogenic environment are most likely to go on weight loss diets to limit or reverse their weight gain. Dieting may suppress this predisposition temporarily but ultimately the predisposition will reassert itself, thereby explaining why past dieting predicts future weight gain. By examining neural correlates of food cue responsivity in HDs, insight can be gained into

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Abbreviations: HDs = historic dieters; NDs = never dieters; ERPs = event related potentials; TFEQ-RS = Three Factor Eating Questionnaire – Restraint Subscale; RRS = Revised Restraint Scale; DWHQ = Dieting and Weight History Questionnaire; WS = weight suppression.

ACCEPTED MANUSCRIPT this predisposition. In young adults in particular, those who have recently dieted (in the past year) may be particularly concerned with, and prone to, weight gain compared to those who dieted many years ago. Because such individuals are not currently on a diet there is no concern that current caloric restriction or weight loss could explain results obtained. Furthermore, by

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examining non-obese individuals the results will be relevant to weight gain proneness rather than

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to possible effects of existing obesity. The role of physiological hunger is also important to take

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into account, as food cue responsivity differs in fasting and full states [3–6]. HDs may respond differently to a hunger manipulation than NDs, particularly if their eating behavior tends to

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depend more on environmental food cues than internal hunger state [7].

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1.2. Measuring neural response to food cues

Differences in neural response to environmental food stimuli may play a role in what

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makes some more prone to overeating and weight gain than others. Event-related potentials

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(ERPs) are electroencephalogram (EEG) waves averaged over multiple trials that provide spatiotemporal information about the neural activity associated with a stimulus or other temporal

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event. There are several ways to interpret ERPs. While the most common method is to calculate

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the mean amplitude within a temporal range, peak latency, or the point in time at which amplitude is highest within a temporal range, is also used to examine timing of neural activity

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[8]. Most studies examining ERP responses to food cues have focused on the P300 (P3; positive peak 300-500 ms after stimulus presentation, parietal scalp distribution) and late positive potential (LPP; positive peak 500-800 ms after stimulus presentation, parietal scalp distribution) components, which are considered to reflect conscious levels of processing and voluntary strategies [9]. Earlier, preconscious components, including the P1 (positive peak 80-150 ms after stimulus onset, posterior scalp distribution), N1 (negative peak 100-150 ms after stimulus onset,

ACCEPTED MANUSCRIPT anterior and posterior scalp distribution), N2 (negative peak approximately 200 ms after stimulus onset, anterior and posterior scalp distribution), and P2 (positive peak approximately 200 ms after stimulus onset, posterior scalp distribution) also play a role in visual processing [10]. Though primarily influenced by exogenous stimulus features, these relatively early components

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can also be affected by top-down endogenous factors [11], including emotions [12–15]. In

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particular, the P1 and N1 have been shown to be larger in response to both appetitive and

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aversive stimuli [16–18] and the P2 has been found to be larger in response to emotional than neutral stimuli [19]. The N2 has been thought to reflect cognitive control [14,15], increasing its

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relevance to food-related studies in which a participant may be both drawn to and wanting to

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avoid the same food stimulus. 1.3. Food cue responsivity

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Although the ERP responses of HDs and NDs have not previously been directly

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compared, ERPs to visual food cues have been shown to differ between obese and normalweight individuals [20–22]. Obese, but not normal-weight individuals, showed an increased

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preconscious (P2) response to high calorie food, compared to office words [22], and obese

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individuals had a smaller conscious (P3) response to high calorie food images compared to normal weight individuals, suggesting they may down-regulate their conscious processing of

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food-related stimuli [21]. However, group comparisons based on weight status are limited because it is not possible to distinguish changes that occur with weight gain from those that predated obesity onset. ERPs have also been recorded from non-obese restrained and unrestrained eaters. Cognitive restraint measures such as the Three Factor Eating Questionnaire- Restraint Subscale (TFEQ-RS) [23] capture efforts at eating less than desired in order to avoid or reverse weight

ACCEPTED MANUSCRIPT gain [24]. Restrained eating may or may not co-occur with dieting, and while dieting predicts future weight gain, cognitive restraint generally does not [1]. Among non-obese adults, cognitive restraint has not been associated with modulation of ERPs to visual food cues [15], but a reduced preconscious (N2) response has been seen in restrained eaters (as measured with the TFEQ-RS)

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in response to a chocolate odor cue compared to unrestrained eaters [25]. A suppression of early

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attention towards a palatable odor may be part of why restrained eaters, as measured with the

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TFEQ-RS, are not prone to weight gain.

Restrained eating, as measured with the Revised Restraint Scale (RRS) [26], differs from

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measures of cognitive restraint in that it captures individuals who report a history of frequent

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dieting and weight fluctuations, likely similar to HDs. To our knowledge, ERPs have only been used in one study to compare participants based on their RRS score [27]. When participants were

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told that the food in presented images would be available for consumption later, high RRS

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scorers showed a smaller LPP mean amplitude relative to low scorers, suggesting a downregulation of attention late in the processing stream. Unfortunately, interpretation of these

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findings is limited by the fact that hunger was not manipulated and the high-scoring group had a

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significantly higher BMI than the low-scoring group. 1.4. The Present Study

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Because of the wide variety in methodologies and sampling in the extant literature, no single conclusion can be drawn about brain responses to food cues in individuals prone to weight gain. The present study aimed to study a new model of weight gain proneness based on historic dieting to determine, in an exploratory manner, if this conceptualization leads to more robust results. ERPs were recorded from HDs and NDs while they viewed a series of food images with

ACCEPTED MANUSCRIPT high or moderate hedonic value, once when hungry and once when full. They rated each image as either “delicious” or “not delicious.” Based on prior work showing a decreased late (P3, LPP) response to food cues in obese individuals [21] and RRS-measured restrained eaters [27], we hypothesized a smaller P3 and

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LPP in HDs compared to NDs. We also posited that, because HDs may be more reliant on

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external than internal food cues [7], that they would respond differently to the hunger

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manipulation than NDs in both early (N2, P2) and late (P3, LPP) components. The lack of prior research testing a hunger manipulation in food-cue dependent ERPs limited our ability to form

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specific directional hypotheses about the hunger by dieting history interaction. Because of the

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lack of prior food cue research on the P1 and N1 components, group differences were examined in an exploratory manner. BMI was included as a covariate in all analyses as ERP response to

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food cues has been found to vary by weight status [20–22].

2. Materials and Methods

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2.1. Participants

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2.1.1. Recruitment

Women aged 18 to 30 were recruited for this study from Drexel University’s student

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population as well as from the surrounding Philadelphia community with flyers, announcements in undergraduate psychology classes, and on a Drexel website that allows undergraduate students to participate in research for extra credit. All participants signed consent forms. The study was described as recording the brain’s responses to viewing images of food. In order to have sufficient power to compare HDs to NDs and to ensure a wide range of weights, we recruited similar numbers of normal-weight and overweight participants, and of HDs and NDs.

ACCEPTED MANUSCRIPT Participants were compensated $10 for completing the screening visit, plus their choice of $30 or $10 plus extra credits to a psychology course for completing the EEG visits. Drexel University’s Institutional Review Board approved the study. 2.1.2. Inclusion/Exclusion criteria

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In order to be eligible for the study, participants must have been right handed and have a

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body mass index (BMI) between 20kg/m2 and 30kg/m2 . Only non-obese individuals were

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recruited, as the goal was to study factors that might contribute to weight gain and eventual obesity. NDs had never been on a diet to lose weight. HDs reported at least one weight loss diet

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in the past year, but no current diet. HDs were determined based on two screening questions: (1)

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Have you ever been on a diet to lose weight?; and (2) How long ago (in months) were you most recently on a diet to lose weight? Exclusion criteria included use of a medication that affects

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body weight, energy expenditure, or brain function; presence of a medical or psychiatric

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condition that may have limited one’s ability to comply with the study procedure; history of an eating disorder (Anorexia Nervosa, Bulimia Nervosa, or Binge Eating Disorder); being pregnant

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or planning to become pregnant in the next year; current dieting to lose weight; most recent

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weight loss diet more than 1 year prior; and being unable to consent. Participants were asked about exclusion criteria in an initial online screening questionnaire and then again at the in-

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person screening visit. Information about whether participants adhered to a specific diet (e.g. vegetarianism or veganism) was not collected. 2.2. Procedure Interested potential participants completed a brief online screening questionnaire where they provided information based on the study’s inclusion/exclusion criteria. Eligible participants attended an in-person screening session where informed consent was obtained, height and weight

ACCEPTED MANUSCRIPT were measured, and the participant completed a series of screening questionnaires to further determine eligibility. If eligible, the participant was scheduled for two EEG sessions (one fasting, one fed), both at the same time of day (either morning or afternoon). Sessions were held 8.67 days apart on average (SD = 4.83, range: 1 – 28). Session order was counterbalanced. For the

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fasting session, participants were asked to only consume water starting six hours prior to the

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session. For the fed session, they were asked to eat a meal consisting of 400-600 calories one

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hour before the session. Participants were given a number of examples of meals with the requested caloric value. Participants were asked to abstain from tobacco, alcohol, or recreational

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drug use for 24 hours prior to each EEG session. They were reminded to follow instructions 24

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hours prior to each session, and compliance was ensured prior to each EEG session. The EEG procedure was the same for each session. Skin on the forehead and behind the

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ears was cleaned with rubbing alcohol. Then the electrode cap was placed on the head and all

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electrodes were filled with conductive gel. Baseline EEG recordings were recorded for 10 minutes (results presented elsewhere [28]). Next, participants completed a short questionnaire

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assessing hunger level. Then they completed the food rating task (programmed with E-Studio 2.0

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and displayed on an 18” monitor with a resolution of 640x480 pixels). Participants viewed a fixation cross for 750 ms, followed by a larger cross shown for 500 ms, which alerted them that

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the picture was about to be shown. The image was then presented for 2000 ms, after which “Respond now” flashed for 250 ms. Participants were instructed to click the left mouse button if they found the food delicious, and the right mouse button if they did not find the food delicious. This task was designed to provide information to compare foods based on participants’ own ratings in order to test for a “hedonic effect” of neural response. A fixation cross remained on the screen for up to 2500 ms, or until a response was given. Between each trial, “OK to blink” was

ACCEPTED MANUSCRIPT presented on the screen for a variable amount of time, between 3200 and 3500 ms. Prior to the experimental trials, 16 practice trials were shown. The room in which EEG sessions were conducted was quiet and sparsely decorated to reduce distractions. Alertness and task adherence was monitored throughout the study with a video camera and through their response pattern. If

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participants appeared to be falling asleep or distracted, they were provided with a reminder to

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stay awake and follow instructions during the next break in testing. Participants were asked to

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complete several questionnaires following each EEG recording (measures relevant to this manuscript are discussed in detail below).

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2.2.1. Stimuli

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Eighty food images were used in the paradigm. Half were chosen to have high hedonic value (e.g. brownie, cheeseburger) and half were chosen to have moderate hedonic value (e.g.

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cucumber, plain rice), based on ratings by a separate sample (n = 39, demographic information

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unknown, recruited via online survey shared on social media) prior to the study. This sample was shown, via online questionnaire, a series of 123 images of food and asked to rate each one based

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on the question “How good does this food taste?” on a 5-point Likert scale ranging from “not

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good at all” to “very good.” The 40 foods with mean ratings in the lowest quartile and the 40 foods with mean ratings in the highest quartile were selected for inclusion in the present study.

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The purpose of this distinction was to introduce a range of food stimuli with varying levels of palatability within two categories of hedonic value. All stimuli were matched on size, angle, and brightness. Each image included the food item on a plate that was 16 cm in diameter. At a viewing distance of approximately 50 cm, each picture occupied approximately 18 degrees of the visual angle horizontally and vertically. Examples of foods with high and moderate hedonic value can be seen in Fig. 1. The stimulus order was randomized and all stimuli were shown in the

ACCEPTED MANUSCRIPT same order and with the same orientation for every participant. The same images were shown during each session, but were presented in their mirror image form during the second session,

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and in a new random order.

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Fig. 1. Examples of food images used. The top row represents items with high hedonic value and

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the bottom row represents items with moderate hedonic value. 2.3. Measures

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Diet and Weight History Questionnaire [29]. The DWHQ is designed to gain a detailed

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history of one’s attempts at dieting and weight fluctuations. Information about current dieting status and number of past diets was used to categorize participants as HDs, NDs, or ineligible.

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Additionally, the DWHQ measures weight suppression (WS), the difference between current weight and highest weight since reaching adult height, which has been found to predict a wide variety of characteristics in eating disordered populations [29]. WS is one marker of how successful one’s diet and weight loss maintenance has been. Hunger Assessment [30]. Participants were asked to self-report current hunger level, desire to eat, fullness, and the amount they currently felt able to eat on a 9-point scale at both

ACCEPTED MANUSCRIPT EEG sessions. This measure was used to ensure the experimental manipulation of hunger state had its intended effect. Menstrual Status and Medication Questionnaire. A series of questions were developed for this study to gain information about participants’ typical menstrual cycles, the dates of their

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most recent periods, and any medications they were taking. A subset of participants (n = 46)

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completed this questionnaire. Menstrual information was collected because EEG has been found

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to vary based on phase of the menstrual cycle and use of oral contraceptive [31]. Body Mass Index. BMI was assessed at the screening session. Weight (in pounds) and

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height (in inches) was measured twice with shoes removed, using a calibrated digital scale with

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an attached stadiometer. Weight and height averages were calculated from the two measurements, and BMI was calculated using the average values. BMI was used to determine

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eligibility.

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Positive and Negative Affect Schedule - State (PANAS)[32]. The PANAS is a 20-item measure of positive and negative affect. It has been shown to have high internal consistency and

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reliability [32,33]. Participants completed the PANAS – State version, rating aspects of their

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mood in the present moment, immediately following each EEG session. Stimulus ratings. Stimulus categories were determined based on ratings during the ERP

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paradigm. For each image, participants categorized it as either delicious or not delicious. They were instructed to consider only their own opinion rather than what they imagine others would think about that food. 2.4. Electrophysiological recording Continuous EEG data were collected using a Sensorium Inc. amplifier (Charlotte, VT) and Ag-AgCL electrode array in an elastic cap (Brain Products, Gilching, Germany). Twenty-

ACCEPTED MANUSCRIPT one electrodes (plus left/right mastoid and vertical/horizontal eye electrodes) from the International 10-20 System were used with a left mastoid reference. EEG recordings were digitized at a sampling rate of 256 Hz without online filtering. Vertical and horizontal eye movements were recorded from two electrodes placed on the face next to and below the left eye.

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Impedances were tested and adjusted to below 10 kOhm prior to data collection. All

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preprocessing was completed in EEGLAB [34]. Data were re-referenced to an average reference

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calculated from all electrodes. Bad channels were removed by visual inspection and replaced by interpolation from surrounding electrodes. Data were filtered using a 0.1 Hz high-pass and 30 Hz

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low-pass FIR filter. Data were epoched from -200 ms to 1000 ms around stimulus presentation.

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Epochs with gross movement artifacts were detected by visual inspection and removed. If fewer than 50 epochs remained for analysis after this step from either session, that participant was

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excluded. Next, channels by epoch were identified for interpolation with an individually tuned

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µV threshold filter ranging from 35 to 60 µV to capture variation across participants in overall artifact magnitude [35]. Channels that contained peak amplitudes larger than that participant’s

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filter were replaced by interpolating from the surrounding electrodes. On average, each channel

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was interpolated for 0.81% of trials (SD = 0.04%, range: 0.01% - 3.51%), and 75.21 trials per session were used to calculate ERPs.

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In order to calculate ERPs, the 200 ms prestimulus served as baseline. ERPs were averaged separately for HDs and NDs in the hungry and full conditions. Mean amplitudes in six temporal windows were used to quantify the ERP components (See Table 1) [10,15,22,36,37]. Epochs and electrodes were based both on previous research and visual inspection of the ERP waves [38]. The N1 and N2 components were tested in both anterior and posterior electrodes because the progression of neural stimulus processing differs based on the scalp region being

ACCEPTED MANUSCRIPT measured (see Figs. 2 and 4) [10].

Peak Latency Window 50 – 140 ms

Electrodes

P1a

Mean Amplitude Epoch 60 – 110 ms

Anterior N1a

60 – 110 ms

50 – 140 ms

FP1, FPz, FP2

Posterior N1

110 – 170 ms

80 – 200 ms

O1, Oz, O2

Anterior N2a

150 – 240 ms

150 – 250 ms

FP1, FPz, FP2

P2a

150 – 240 ms

150 – 270 ms

Posterior N2

240 – 300 ms

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P3

300 – 500 ms

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LPP

500 – 800 ms

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O1, Oz, O2

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O1, Oz, O2

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O1, Oz, O2 P3, Pz, P4 P3, Pz, P4

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Component

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Table 1. Time period and electrodes where each component was tested. aThe P1 and Anterior N1 components, as well as the Anterior N2 and P2 components, were tested during the same epoch,

2.5. Data analysis

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in different electrodes.

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An independent samples t-test was used to compare hunger level between full and hungry

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states, to determine whether the hunger manipulation was successful. Scores on the Hunger Assessment were compared, as well as mean hours since last meal eaten between conditions. Positive and negative affect were also compared between conditions. Dieting groups were compared on demographics, menstrual timing, use of hormonal contraceptive, and weight-related variables, including weight suppression (WS; the distance between one’s highest lifetime weight and current weight). Mixed factorial analyses of covariance (ANCOVAs) tested the hunger (hungry, full) by dieting history (HD, ND) interaction for each component in that component’s

ACCEPTED MANUSCRIPT region of interest (see Table 1). The hunger by dieting history interaction was also tested on peak latency for early components (P1, Anterior N1, Posterior N1, Anterior N2, and P2) within each component’s region of interest, due to apparent latency differences in ERP waves. Slightly larger time windows were used to account for peak variability among participants (see Table 1).

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Because no significant interactions were seen between dieting history and stimulus rating, the

covariates for all analyses.

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3.1. Self-report measures and demographics

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3. Results

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stimulus-rating factor was not included in final models. Session order and BMI were included as

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Six hundred and fifty-two interested individuals completed our online screening questionnaire. Of these, 122 were eligible for and attended an in-person screening session.

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Seventy-six were eligible and invited to participate in the EEG portion of the study. Nine

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dropped out before completing both EEG sessions, leaving 67 participants who completed the entire study. Four participants were excluded from analyses due to problems with their data

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collection, and three more were excluded due to insufficient clean data.

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The final dataset included 60 participants. The sample was primarily white (54.2%) and Asian (25.4%), with 10.2% of participants identifying as Hispanic/Latino, 5.1% identifying as black, and 5.1% identifying as other. Twenty-eight reported a history of past dieting, and 32 had never been on a diet to control their weight. Within NDs, 68.8% were in the normal weight range and 31.2% were overweight. Of HDs, 64.3% were normal weight and 35.7% were overweight. HDs, relative to NDs, were similar in BMI (t(58) = 0.01, p = 0.995), younger (t(50.01) = 2.43, p = 0.02), and more weight suppressed (t(34.32) = -2.09, p = 0.04). Diet groups did not

ACCEPTED MANUSCRIPT significantly differ in percentage of participants on oral contraceptives ( χ2 = 0.01, p = 0.92) or in phase of menstrual cycle (t(44) = 0.46, p = 0.65). Descriptive information by diet group can be

BMI

23.81 (2.44)

23.81 (2.70)

WSa

3.61 (4.44)

1.74 (1.75)

Age

20.25 (1.69)

21.75 (3.00)

Oral Contraceptive Use (%)

39

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Days since start of periodb

17.59 (11.50)

19.25 (12.65)

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NDs (n = 32)

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HDs (n = 28)

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found in Table 2.

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Table 2. Participant weight and age information (M (SD)). aWS = weight suppression, measured

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in kg. bDays since last period were calculated at the first EEG session (information available for 46 participants).

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Hunger manipulation. The hunger manipulation was successful. Participants rated themselves as significantly higher on all questions of the Hunger Assessment when fasting than

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when full. For example, on a 1 – 7 scale where 1 = “nothing” and 7 = “a large amount,” in

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response to the question “how much food do you think you could eat right now?” participants gave a mean rating of 2.98 (SD = 1.43) in the fed condition and 6.95 (SD = 1.33) in the fasting condition. Additionally, in a subset of participants for which the information was collected (n = 41; time since last meal was not asked for the first several months of data collection), they reported a mean of 9.96 hours (SD = 3.96) since their last meal in the fasting condition and 0.91 hours (SD = 0.41) in the fed condition, t(40) = -14.00, p < 0.01. Hunger ratings and hours since last meal in each condition did not significantly differ between dieting groups. Participants rated

ACCEPTED MANUSCRIPT higher positive affect in the fed, compared to fasting condition (t(53) = 2.29, p = 0.03). Negative affect did not significantly differ between hunger conditions (t(53) = -1.78, p = 0.08). Stimulus ratings. On average participants rated 56.51% of the images as “delicious,” 41.81% as “not delicious,” and did not respond to 1.68% of images. However, there was wide

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variability in the way participants split their responses. Out of the 80 stimuli presented in each

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session, the number of “not delicious” responses ranged from one to 72, and the number of

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“delicious” responses ranged from seven to 76. So, some participants rated the large majority of foods as “delicious,” some rated the large majority as “not delicious,” and some had a more even

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distribution of responses. Fifty-five percent of visits included at least one trial where no response

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was given. Of these sessions, the mean number of missed trials was 2.45 (SD = 2.69, range: 1 – 16). Stimulus rating did not significantly interact with dieting history for any components.

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Participants rated a higher percentage of stimuli as delicious when in the fasting condition

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(62.7%) than the fed condition (52.2%; t(56) = 4.17, p < 0.01). Session order/timing effects. Although condition order was randomized to avoid

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systematic effects of session order on results, ANOVAs with session number and hunger as

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factors and ERP component as outcome tested for any effects of session order. The interaction was significant for the 110 – 170 ms (posterior N1), 240 – 300 ms (posterior N2), and 300 – 500

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ms (P3) epochs. Therefore, for consistency, order of hungry or full visits (dummy coded as 0 or 1) was included as a covariate in all analyses. Groups did not differ in proportion who completed the EEG sessions in the morning compared to the afternoon (χ2 (2) = 1.49, p = 0.47). 3.2. ERPs 3.2.1. Significant findings related to hunger and dieting history

ACCEPTED MANUSCRIPT Anterior N1 (60 – 110 ms). An ANCOVA tested the hunger-by-dieting history interaction in the fronto-polar region (FP1, FPz, FP2), where the anterior N1 effect is most pronounced. A significant main effect of dieting history was found (F(1,56) = 4.44, p = 0.04, partial η2 = 0.07). HDs had a smaller (less negative) mean amplitude (M = -0.22, SD = 2.00) than NDs (M = -1.30,

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SD = 1.81). Dieting groups did not differ in peak latency of the anterior N1 (F(1,56) = 2.72, p =

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0.104, partial η2 = 0.05 (see Figs. 2 and 3).

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Fig. 2. ERP waves in electrodes FP1, FP2, and FPz (respectively) in NDs (hungry = black; full = red) and HDs (hungry = blue, full = green). Anterior N1 computed from 60 – 110 ms.; anterior N2 computed from 150 – 240 ms. Time (in ms) goes from left to right on the x-axis. Stimulus presentation starts at 0 ms. Amplitude (in µV) is shown on the y-axis. Negative

ACCEPTED MANUSCRIPT amplitudes are plotted up on the y-axis according to ERP convention. *Indicates significant

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difference in mean amplitude.

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Fig. 3. Scalp map demonstrating the difference in mean amplitude (in µV) between HDs and

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NDs from 60-110 ms. The scale bar on the right provides µV difference (HDs minus NDs) values for each color. The anterior N1 component was measured at electrodes FP1, FPz, and

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FP2. The top of the map indicates the front of the head, and the bottom refers to the back. Dots represent electrode locations.

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Posterior N1 (110 – 170 ms). No main effects or interactions of hunger or dieting history were

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found on mean amplitude at posterior electrodes (O1, Oz, O2). A hunger-by-dieting history interaction was found on posterior N1 peak latency (F(1,56) = 7.10, p = 0.01, partial η2 = 0.112). Analysis of simple effects of the interaction revealed a simple effect of hunger among HDs such that they showed later posterior peaks when hungry (M = 145.43 ms, SD= 29.33) than when full (M = 138.60 ms, SD = 24.94; F(1,56) = 4.69, p = 0.035, partial η2 = 0.077). NDs demonstrated similar peak latencies when hungry (M = 131.58, SD = 21.13) and full (M = 136.27, SD = 19.34; F(1,56) = 2.66, p = 0.108, partial η2 = 0.045). A simple effect of dieting history in the hungry

ACCEPTED MANUSCRIPT state (F(1,56) = 4.20, p = 0.045, partial η2 = 0.07) was also found, with HDs showing a later

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peak latency than NDs when hungry, but not full (see Fig. 4).

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Fig. 4. ERP waves from electrodes O1, O2, and Oz (respectively) in NDs (hungry = black; full = red) and HDs (hungry = blue, full = green). P1 computed from 60 – 110 ms.; posterior N1

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computed from 110 – 170 ms.; P2 computed from 150 – 240 ms.; posterior N2 computed from

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240 – 300 ms. Time (in ms) goes from left to right on the x-axis. Stimulus presentation starts at 0 ms. Amplitude (in µV) is shown on the y-axis. Negative amplitudes are plotted up on the y-axis

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according to ERP convention. ^Indicates significant difference in peak latency.

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P2 (150 – 240 ms). No main effects or interactions involving either hunger or dieting history were found on mean amplitude in the region of interest (O1, O2, and Oz). However, a

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significant dieting history-by-hunger interaction was found on P2 peak latency in O1, Oz, and O2 (F(1,56) = 4.10, p = 0.048 partial η2 = 0.068). Analysis of simple effects found a simple effect of dieting history in the hungry condition (F(1,56) = 5.18, p = 0.027, partial η2 = 0.085, with a later component peak for HDs (M = 220.71, SD = 26.79) than NDs (M = 204.75, SD= 28.33). Peak latencies were similar among HDs (M = 213.21, SD = 27.17) and NDs (M = 210.13, SD = 32.13) in the full condition (F(1,56) = 0.11, p = 0.745, partial η2 = 0.002; see Fig. 4).

ACCEPTED MANUSCRIPT LPP (500 – 800 ms). When the hunger-by-dieting history interaction was tested at electrodes P3, P4, and Pz, where the LPP is most strongly seen [27], a significant interaction was found (F(1,56) = 4.20, p = 0.045 partial η2 = 0.070. This included a simple effect of hunger within HDs (F(1,56) = 4.59, p = 0.036, partial η2 = 0.076). HDs showed a larger LPP when

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hungry (M= 0.55, SD = 0.57) than full (M = 0.37, SD = 0.68), while NDs showed similar mean

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amplitudes when hungry (M = 0.46, SD = 0.63) and full (M = 0.52, SD = 0.62; F(1,56) = 0.56, p

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= 0.46, partial η2 = 0.010); see Figs. 5 and 6.

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Fig. 5. ERP waves at electrodes P3, P4, and Pz (respectively) in NDs (hungry = black; full = red) and HDs (hungry = blue, full = green). P3 computed from 300 – 500 ms.; LPP computed from

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500 – 800 ms. Time (in ms) goes from left to right on the x-axis. Stimulus presentation starts at 0

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according to ERP convention. *Indicates significant difference in mean amplitude.

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Fig. 6. Scalp map demonstrating the difference in mean amplitude (in µV) between hungry and full state from 500 – 800 ms. The scale bar on the right provides µV difference values (hungry

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minus full) for each color. The LPP component is measured in electrodes P3, Pz, and P4. The top scalp map represents NDs and the bottom map represents HDs. The top of the map indicates the front of the head, and the bottom refers to the back. Dots represent electrode locations. 3.2.2. Marginally significant findings relating to hunger or dieting history P3 (300 – 500 ms). When the hunger-by-dieting history interaction was tested in the region of interest for the P3 component (electrodes P3, P4, Pz), a trend toward a main effect of dieting history was seen (F(1,56) = 3.96, p = 0.052 partial η2 = 0.066). HDs trended toward

ACCEPTED MANUSCRIPT showing a larger mean amplitude (M = 1.54, SD = 1.00) than NDs (M = 1.11, SD = 1.00); see

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Figs. 5 and 7.

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Fig. 7. Scalp map demonstrating the difference in mean amplitude (in µV) between HDs and

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NDs from 300 – 500 ms. The scale bar on the right provides µV difference values (HDs minus NDs) for each color. The P3 component is measured in electrodes P3, Pz, and P4. The top of the

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map indicates the front of the head, and the bottom refers to the back. Dots represent electrode

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locations.

3.2.3. Nonsignificant findings

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P1 (60 – 110 ms). When the hunger-by-dieting history interaction was tested on mean amplitude and peak latency in P1 electrodes of interest (O1, Oz, O2), it was not significant (see Figs. 3 and 4).

Anterior N2 (150 – 240 ms). At electrodes of interest (FP1, FPz, FP2), the hunger-bydieting history interaction was not significant on mean amplitude or peak latency (see Fig. 2).

ACCEPTED MANUSCRIPT Posterior N2 (240 – 300 ms). No main effects or interactions involving either hunger or dieting history were found on mean amplitude in the region of interest (O1, O2, and Oz; see Fig. 4).

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4. Discussion

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The aim of the current study was to investigate the relationship of dieting history and

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hunger state with neural response to visual food cues. Prior research has examined differences in food cue responsivity between individuals of different weights and those with various levels of

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cognitive restraint. However, this is the first study examining the relationship that historical

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dieting, a robust predictor of future weight gain, has with appetitive responsivity as measured by ERP. While we expected differences between groups, because of the novelty of the question and

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methodology we were unable to make many a priori predictions about directionality of

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relationships. Therefore the results should be viewed as exploratory, with need for replication. In summary, a history of dieting was associated with differential processing of food cues

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early and late in the visual processing stream. Specifically, HDs showed decreased ERP

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activation in response to food cues in preconscious (N1) stages of visual processing compared to NDs. HDs also showed a delayed early processing stream (N1 and P2) compared to NDs,

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specifically in the hungry state. Later in the processing stream (LPP), HDs showed differences in response dependent on hunger state, whereas NDs did not. It was hypothesized that HDs would show smaller mean amplitudes in late components (P3 and LPP), which reflect conscious, sustained attention, and that HDs and NDs would respond differently to the hunger manipulation. While main effects of dieting history did not reach significance, dieting history significantly interacted with hunger state on LPP mean

ACCEPTED MANUSCRIPT amplitude, such that HDs showed larger mean amplitudes when fasting compared to fed. This difference was not seen in NDs. The LPP findings suggest that while viewing food cues in a state of hunger, the HDs in this study may have paid more attention to food cues compared to when they were not hungry, at least once processing reached a conscious time period.

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A history of dieting predicts future weight gain, suggesting an attraction to food beyond

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physiological need. As BMI in this sample did not differ by dieting history, and the HD group

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was significantly more weight suppressed than the ND group, perhaps the sample selected was in fact a group of successful dieters. Although they did not report being on a weight loss diet, they

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may have been able to continue employing strategies to keep their weight below what it once

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was, at least when it comes to sustained, conscious attention towards food cues. Differential sustained processing contingent on hunger state could be an adaptive strategy where, when not

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food deprived, successful HDs are able to down-regulate their attention to food to avoid

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overeating.

No group or condition differences were seen in N2 mean amplitude. Most past studies

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with significant N2 effects involved tasks that required response inhibition [22,25]. Our task, on

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the other hand, only involved passive viewing of food cues and rating their deliciousness. A task that involved more explicit response inhibition or other executive control processes may have

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better identified differences in N2 amplitude between groups and conditions While there were no differences in mean amplitude by condition or group for the P2 component, there was a significant group-by-hunger interaction in peak latency; while in the fed state both groups had similar peak latencies, in the fasting state HDs had a later P2 peak than NDs. Later waves may suggest less efficient processing, implying that when hungry, HDs may

ACCEPTED MANUSCRIPT be slower to process stimuli than NDs. This might reflect ambivalence whereby HDs are both more drawn to, and more avoidant of, the food cues presented [39]. Unexpectedly, differences were seen in earlier components than have previous ly been studied with respect to food cue responsivity. Upon visual inspection of the ERP difference

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wave, the P1 and N1 components appeared to be responsive to variables of interest. Thus, they

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were analyzed in an exploratory manner. NDs showed a larger anterior N1 mean amplitude than

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HDs did. The N1 is thought to reflect stimulus classification as an early mechanism to determine which stimuli require further processing [8]. This pattern suggests that, early in the visual

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processing stream, HDs had a blunted response compared to NDs. Perhaps HDs required less

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processing capacity to determine that food was worthy of further processing. In the posterior N1, group differences in mean amplitude were not seen. However, peak latency of the posterior N1

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was affected by both hunger and dieting history; HDs demonstrated a later peak when hungry

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than when full, and in the hungry state HDs showed a later posterior N1 and NDs. It appears that both hunger and dieting history may be relevant to N1 size and latency, although the nature of

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the relationship is uncertain.

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The present study’s comparison of foods rated as delicious and not delicious was designed to be an improvement upon the past practice of comparing highly caloric food to non-

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food items, in order to isolate a “hedonic effect” rather than measuring a more general “food effect.” However, dieting history and stimulus rating did not interact on any ERP mean amplitudes. Further, ratings changed as a function of hunger state, with participants rating more foods as delicious when they were fasting than fed. The lack of a non-food comparison limits our ability to ascertain that it is response to food specifically, rather than images in general, that elicit the ERP differences seen. Blechert and colleagues [27] compared passive viewing of food and

ACCEPTED MANUSCRIPT neutral cues in RRS-measured restrained eaters, a conceptually similar group to HDs, and found no difference in LPP mean amplitude to food compared to neutral cues in either restrained or unrestrained eaters. The fact that the task in the present study required imagining eating the foods presented suggests that eating was on participants’ minds. Additionally, the hunger effect on

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ratings suggests that drive to consume food was related to how they rated these foods. However,

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food-specific, or more general, differences in visual processing.

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the lack of a non-food comparison group leaves the question of whether HDs and NDs show

A particular challenge to interpreting ERP results is the lack of a basis on which to infer

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what is driving increased attention reflected by a larger ERP component. The present study used

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fairly novel categorization criteria to create dieting groups. There is little basis to inform interpretations of findings, especially for groups that by definition experience conflicting

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motivations when it comes to food cues [39]. That is, HDs presumably experience an appetitive

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drive to consume food with high hedonic value, as well as motivation to avoid consumption to meet weight and dieting goals. From this framework, an increased ERP component could reflect

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increased motivation toward either of these goals, especially if the foods are not perceived to be

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“diet-consistent.” Additionally, a smaller P3 or LPP component could reflect an innate lack of drive towards the stimulus, or, conversely, a conscious down-regulation towards the food cue in

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alignment with one’s dieting goal. Much of the prior literature attempting to explain ERP response to food cues has struggled with the same interpretive concern [20,21,25,27]. Future ERP work should correlate neural activation with more objective behavioral measures to overcome this problem. These might include measured weight loss in dieters, palatable food consumption in the lab, or real-time reports of eating in response to environmental food cues using ecological momentary assessment.

ACCEPTED MANUSCRIPT Comparing ERP results to other neuroimaging techniques can also provide complementary information to help with ERP interpretation. Weight status and obesity proneness have been found to relate to neural response to food cues as measured with functional magnetic resonance imaging (fMRI; see [40] for review). In particular, food reward-related brain signaling

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was higher in overweight compared to normal weight participants when satiated, while activation

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of inhibitory control regions was reduced, suggesting susceptibility to overeating in overweight

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individuals [41]. Additionally, obesity-resistant individuals showed attenuated activation of brain regions involved in energy intake regulation when they were satiated compared to fasting, a

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pattern not seen in obesity-prone individuals, which authors believe may represent a mechanism

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related to weight gain proneness [42]. These results differ from the present study’s findings that HDs, conceptualized similarly to obesity-prone individuals, showed hunger-dependent sustained

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attention to food cues while NDs (similar to obesity-resistant individuals) did not. The

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complexity of both fMRI and ERP measurement, as well as methodological differences between studies, highlights the difficulty in replicating a pattern of findings between different

5. Conclusions

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methodologies. More work that combines fMRI with ERP may help to clarify this issue [43].

These findings demonstrate that a history of dieting is associated with differential

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processing of food cues early and late in the visual processing stream. Specifically, those with a history of dieting to lose weight showed decreased ERP activation in response to food cues in preconscious (N1) stages of visual processing compared to those who have never dieted. HDs also demonstrated a delayed early processing stream compared to NDs, specifically in the hungry state. Blunted and delayed early, automatic processing of food cues may predispose HDs to overeating in our food-replete environment. The hunger-by-dieting interaction on late (LPP) ERP

ACCEPTED MANUSCRIPT waves suggests that sustained attention to food cues in HDs, but not NDs, is dependent on hunger state. Strengths to the present study include a relatively large sample size, objective measures of ERP response to food cues in both the hungry and full state, and a novel classification of

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individuals based on dieting history, an empirically supported predictor of future weight gain.

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Several limitations to the current study exist. The findings related to early visual processing (N1)

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were not hypothesized, as no previous work has examined response this early with respect to food cues. Therefore replication is necessary to interpret them with confidence. Further, the

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number of trials per participant was somewhat low to detect reliable effects in early components

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[8]. The fact that group differences were seen with this smaller number of trials suggests that the effect size may be large enough to overcome the smaller number of trials; however more

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research is needed to confirm this pattern of results. Additionally, although menstrual cycle and

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oral contraceptive use has been found to affect ERPs, this information was not collected for all participants and was not controlled for in analyses. Another limitation may be the relatively

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lenient criteria for HDs. It could be that individuals with only one past weight loss diet tend to

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respond to food cues differently than those with a long history of dieting and weight cycling, and thus by including them in the HD group, between-group differences were obscured. Along these

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same lines, retrospective self-report was used to determine dieting history and for several other measures, which may be prone to errors in recall or inaccurate responses due to demand characteristics. Finally, as participants were primarily college students and were all female, results cannot be generalized to other populations with confidence. In order to further examine the early processing differences between HDs and NDs, future studies should use both food and non-food objects in order to determine whether these

ACCEPTED MANUSCRIPT effects are food-specific. Comparing ERP response to foods of high and low energy density, rather than self-reported deliciousness, could also help to better understand how HDs and NDs process food cues differently. ERPs should also be correlated with behavioral measures that are easier to interpret. Future research should also examine whether response to food cues measured

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with ERPs is a mediator between dieting history and future weight gain. If it is, ERPs may

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provide an avenue to better predict those most prone to weight gain based on their interaction

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with the food environment, thus informing obesity prevention programs.

ACCEPTED MANUSCRIPT References M.R. Lowe, S.D. Doshi, S.N. Katterman, E.H. Feig, Dieting and restrained eating as prospective predictors of weight gain., Front. Psychol. 4 (2013) 577. doi:10.3389/fpsyg.2013.00577.

[2]

M.R. Lowe, Dieting: proxy or cause of future weight gain?, Obes. Rev. 16 (2015) 19–24. doi:10.1111/obr.12252.

[3]

J. Stockburger, A.I. Weike, A.O. Hamm, H.T. Schupp, Deprivation selectively modulates brain potentials to food pictures., Behav. Neurosci. 122 (2008) 936–42. doi:10.1037/a0012517.

[4]

J. Stockburger, R. Schmälzle, T. Flaisch, F. Bublatzky, H.T. Schupp, The impact of hunger on food cue processing: an event-related brain potential study., Neuroimage. 47 (2009) 1819–29. doi:10.1016/j.neuroimage.2009.04.071.

[5]

M.A. Cornier, G.K. Grunwald, S.L. Johnson, D.H. Bessesen, Effects of short-term overfeeding on hunger, satiety, and energy intake in thin and reduced-obese individuals, Appetite. 43 (2004) 253–259. doi:10.1016/j.appet.2004.06.003.

[6]

E. Stice, K. Burger, S. Yokum, Caloric deprivation increases responsivity of attention and reward brain regions to intake, anticipated intake, and images of palatable foods, Neuroimage. 67 (2013) 322–330. doi:10.1016/j.neuroimage.2012.11.028.

[7]

C.P. Herman, J. Polivy, A boundary model for the regulation of eating., Res. Publ. Assoc. Res. Nerv. Ment. Dis. 62 (1984) 141–156.

[8]

S.J. Luck, An Introduction to the Event-Related Potential Technique, 2005. doi:10.1118/1.4736938.

[9]

G. Hajcak, J.P. Dunning, D. Foti, Motivated and controlled attention to emotion: Timecourse of the late positive potential, Clin. Neurophysiol. 120 (2009) 505–510. doi:10.1016/j.clinph.2008.11.028.

[10]

G.F. Woodman, A brief introduction to the use of event-related potentials in studies of perception and attention., Atten. Percept. Psychophys. 72 (2010) 2031–2046. doi:10.3758/BF03196680.

[11]

A. Zani, A.M. Proverbio, Is that a belt or a snake? object attentional selection affects the early stages of visual sensory processing, Behav. Brain Funct. 8 (2012) 6. doi:10.1186/1744-9081-8-6.

[12]

A. Meule, A. Kübler, J. Blechert, Time course of electrocortical food-cue responses during cognitive regulation of craving., Front. Psychol. 4 (2013) 669. doi:10.3389/fpsyg.2013.00669.

[13]

U. Toepel, J.-F. Knebel, J. Hudry, J. le Coutre, M.M. Murray, The brain tracks the energetic value in food images., Neuroimage. 44 (2009) 967–74. doi:10.1016/j.neuroimage.2008.10.005.

[14]

R.J. Huster, S. Enriquez-Geppert, C.F. Lavallee, M. Falkenstein, C.S. Herrmann, Electroencephalography of response inhibition tasks: functional networks and cognitive contributions., Int. J. Psychophysiol. 87 (2013) 217–33. doi:10.1016/j.ijpsycho.2012.08.001.

AC

CE

PT

ED

M

AN

US

CR

IP

T

[1]

ACCEPTED MANUSCRIPT T.D. Watson, K.T. Garvey, Neurocognitive correlates of processing food-related stimuli in a Go/No-go paradigm., Appetite. 71 (2013) 40–7. doi:10.1016/j.appet.2013.07.007.

[16]

B. Kuhr, J. Schomberg, T. Gruber, M. Quirin, Beyond pleasure and arousal: appetitive erotic stimuli modulate electrophysiological brain correlates of early attentional processing., Neuroreport. 24 (2013) 246–50. doi:10.1097/WNR.0b013e32835f4eba.

[17]

J.K. Olofsson, S. Nordin, H. Sequeira, J. Polich, Affective picture processing: an integrative review of ERP findings., Biol. Psychol. 77 (2008) 247–65. doi:10.1016/j.biopsycho.2007.11.006.

[18]

P. a Gable, E. Harmon-Jones, Late positive potential to appetitive stimuli and local attentional bias., Emotion. 10 (2010) 441–6. doi:10.1037/a0018425.

[19]

S.J. Thomas, S.J. Johnstone, C.J. Gonsalvez, Event-related potentials during an emotional Stroop task, Int. J. Psychophysiol. 63 (2007) 221–231. doi:10.1016/j.ijpsycho.2006.10.002.

[20]

C. Babiloni, C. Del Percio, A. Valenzano, N. Marzano, M. De Rosas, A. Petito, A. Bellomo, G. Rossi, B. Lecce, C. Mundi, R. Lizio, F. Eusebi, G. Cibelli, Frontal attentional responses to food size are abnormal in obese subjects: an electroencephalographic study., Clin. Neurophysiol. 120 (2009) 1441–8. doi:10.1016/j.clinph.2009.06.012.

[21]

I.M.T. Nijs, P. Muris, A.S. Euser, I.H.A. Franken, Differences in attention to food and food intake between overweight/obese and normal-weight females under conditions of hunger and satiety., Appetite. 54 (2010) 243–54. doi:10.1016/j.appet.2009.11.004.

[22]

I.M.T. Nijs, I.H.A. Franken, P. Muris, Food-related Stroop interference in obese and normal-weight individuals: behavioral and electrophysiological indices., Eat. Behav. 11 (2010) 258–65. doi:10.1016/j.eatbeh.2010.07.002.

[23]

A.J. Stunkard, S. Messick, The three-factor eating questionnaire to measure dietary restraint, disinhibition and hunger, J. Psychosom. Res. 29 (1985) 71–83. doi:10.1016/0022-3999(85)90010-8.

[24]

Y. Chernyak, M.R. Lowe, Motivations for dieting: Drive for Thinness is different from Drive for Objective Thinness., J. Abnorm. Psychol. 119 (2010) 276–281. doi:10.1037/a0018398.

[25]

N. Kemmotsu, C. Murphy, Restrained eaters show altered brain response to food odor., 2006. doi:10.1016/j.physbeh.2005.10.008.

[26]

C. Herman, J. Polivy, Restrained eating, in: A.J. Stunkard (Ed.), Obesity, WB Saunders, Philadelphia, 1980: pp. 208–225.

[27]

J. Blechert, B. Feige, G. Hajcak, B. Tuschen-Caffier, To eat or not to eat? Availability of food modulates the electrocortical response to food pictures in restrained eaters., Appetite. 54 (2010) 262–8. doi:10.1016/j.appet.2009.11.007.

[28]

S.R. Winter, E.H. Feig, J. Kounios, B. Erickson, S. Berkowitz, M.R. Lowe, The relation of hedonic hunger and restrained eating to lateralized frontal activation, Physiol. Behav. 163 (2016) 64–69. doi:10.1016/j.physbeh.2016.04.050.

[29]

A.A. Witt, S.N. Katterman, M.R. Lowe, Assessing the three types of dieting in the ThreeFactor Model of dieting. The Dieting and Weight History Questionnaire., Appetite. 63

AC

CE

PT

ED

M

AN

US

CR

IP

T

[15]

ACCEPTED MANUSCRIPT (2013) 24–30. doi:10.1016/j.appet.2012.11.022. M.I. Friedman, P. Ulrich, R.D. Mattes, A figurative measure of subjective hunger sensations., Appetite. 32 (1999) 395–404. doi:10.1006/appe.1999.0230.

[31]

O.D. Creutzfeldt, P.M. Arnold, D. Becker, S. Langenstein, W. Tirsch, H. Wilhelm, W. Wuttke, EEG changes during spontaneous and controlled menstrual cycles and their correlation with psychological performance, Electroencephalogr. Clin. Neurophysiol. 40 (1976) 113–131. doi:10.1016/0024-3205(75)90182-4.

[32]

D. Watson, L.A. Clark, A. Tellegen, Development and validation of brief measures of positive and negative affect:, PANAS Scales. J. Personal. Soc. Psychol. 47 (1988) 1063– 1070. doi:http://dx.doi.org/10.1037/0022-3514.54.6.1063.

[33]

J.R. Crawford, J.D. Henry, The positive and negative affect schedule (PANAS): Construct validity, measurement properties and normative data in a large non-clinical sample., Br. J. Clin. Psychol. 43 (2004) 245–65. doi:10.1348/0144665031752934.

[34]

A. Delorme, S. Makeig, EEGLAB: An open source toolbox for analysis of single-trial EEG dynamics including independent component analysis, J. Neurosci. Methods. 134 (2004) 9–21. doi:10.1016/j.jneumeth.2003.10.009.

[35]

S. Hoffmann, M. Falkenstein, The correction of eye blink artefacts in the EEG: A comparison of two prominent methods, PLoS One. 3 (2008). doi:10.1371/journal.pone.0003004.

[36]

C. Hill, J. Wu, M.J. Crowley, P. Fearon, Restrictive feeding practices and adiposity are differentially related to P3b cortical responses to food stimuli in children., Appetite. 63 (2013) 7–17. doi:10.1016/j.appet.2012.11.025.

[37]

H.T. Schupp, J. Stockburger, M. Codispoti, M. Junghöfer, A.I. Weike, A.O. Hamm, Selective visual attention to emotion., J. Neurosci. 27 (2007) 1082–1089. doi:10.1523/JNEUROSCI.3223-06.2007.

[38]

L.H. Ernst, A.C. Ehlis, T. Dresler, S. V. Tupak, A. Weidner, A.J. Fallgatter, N1 and N2 ERPs reflect the regulation of automatic approach tendencies to positive stimuli, Neurosci. Res. 75 (2013) 239–249. doi:10.1016/j.neures.2012.12.005.

[39]

W. Stroebe, G.M. van Koningsbruggen, E.K. Papies, H. Aarts, Why most dieters fail but some succeed: a goal conflict model of eating behavior., Psychol. Rev. 120 (2013) 110– 38. doi:10.1037/a0030849.

[40]

K.M. Pursey, P. Stanwell, R.J. Callister, K. Brain, C.E. Collins, T.L. Burrows, Neural Responses to Visual Food Cues According to Weight Status: A Systematic Review of Functional Magnetic Resonance Imaging Studies, Front. Nutr. 1 (2014). doi:10.3389/fnut.2014.00007.

[41]

M.J.I. Martens, J.M. Born, S.G.T. Lemmens, L. Karhunen, A. Heinecke, R. Goebel, T.C. Adam, M.S. Westerterp-Plantenga, Increased sensitivity to food cues in the fasted state and decreased inhibitory control in the satiated state in the overweight1-3, Am. J. Clin. Nutr. 97 (2013) 471–479. doi:10.3945/ajcn.112.044024.

[42]

M.-A. Cornier, K.L. McFadden, E. a Thomas, J.L. Bechtell, L.S. Eichman, D.H. Bessesen, J.R. Tregellas, Differences in the neuronal response to food in obesity-resistant as compared to obesity-prone individuals., Physiol. Behav. 110–111 (2013) 122–8.

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[30]

ACCEPTED MANUSCRIPT doi:10.1016/j.physbeh.2013.01.002.

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J.J. Hendrikse, R.L. Cachia, E.J. Kothe, S. Mcphie, H. Skouteris, M.J. Hayden, Attentional biases for food cues in overweight and individuals with obesity: A systematic review of the literature, Obes. Rev. 16 (2015) 424–432. doi:10.1111/obr.12265.

AC

[43]