Previous research has suggested that women’s food intake is influenced by increases in progesterone, most notably during the post-ovulatory period. Additionally, body mass index and high-density foods play a contributing role in the amount of attention given to food. Although research has primarily focused on women’s self-reported ratings across the menstrual cycle, whether women’s attention to food is influenced by progesterone levels, menstrual status, and BMI has been unexplored. The current study investigated women’s visual attention to high and low-caloric food items. Across two lab visits, women’s progesterone levels were tracked while they completed an eye-tracking task. The results demonstrated that high-caloric foods were viewed longer irrespective of BMI, and there was tentative support for women’s progesterone influencing their first fixation durations to high-caloric foods. Overall, the findings suggest that high caloric foods were visually salient, which may be indicative of how humans allocate attention to food high in energy density, and it suggests that women’s early attentional processes are partly influenced by sex hormones (i.e., progesterone).
Obesity is a public health issue that is influenced by psychological, biological, and behavioral factors (Crane et al., 2020). Sex differences also contribute significantly to obesity. Women are more likely to develop eating disorders than men (Hsu, 1989; Sharan & Sundar, 2015), and their eating behaviors are also affected by hormonal fluctuations, such as normal fluctuating hormones throughout the menstrual cycle and hormones throughout pregnancy (Herman & Polivy, 2010). High caloric foods contribute to weight gain; however, their impact on obesity may have to do with environmental mismatches that were not present during ancestral history. For instance, large quantities of energetically dense foods and sedentary lifestyles make it easier to develop obesity, as calories consumed are not expended during food acquisition (Rantala et al., 2020). However, in ancestral conditions, it would have been beneficial to consume and store fat when possible due to variable periods of food availability and food storage (King, 2013).
Due to periods of food uncertainty, humans have evolved psychological adaptations that aid and guide decision rules (Li et al., 2018). These decision rules influence cognition, more specifically perceptual systems (i.e., attention) (Krupp. 2008). A selective detection of foods high in calories and fat is an adaptive trait in humans (Nijs et al., 2010). One method of investigating attention to important stimuli is an eye-tracking paradigm to measure if specific features are visually important. Moreover, assessing other contributing factors to the way individuals process food, such as body mass index (BMI), menstrual cycle status, and progesterone should allow for a more thorough investigation of the factors contributing to over-eating and obesity. Therefore, the current paper investigates if the visual saliency of food items in women is influenced by food type, BMI, and hormonal fluctuations throughout their menstrual cycle.
Environmental Mismatch Hypothesis
Human environments are now extensively different than those from which we evolved (Li et al., 2018). In our ancestral past, a considerable amount of energy and attention would have been used in the search and acquisition of high-quality foods dense in calories (Rozin & Schull, 1988). Indeed, humans have an evolved taste preference for sweet foods (Birch, 1999), and hunter gatherer populations prefer food items that contain a high number of calories (Babesque and Marlowe, 2009). The change from ancestral to modern day environments has influenced the psychological mechanisms to which they were designed to solve (Tooby & Cosmides, 1990). One example is modern day preferences for high-caloric foods, despite their overall abundance. Consumption of high-caloric foods would have been beneficial in environments where food scarcity was common and modern-day refrigeration did not exist (Goetz et al., 2019). However, the motivation to seek out and consume high-caloric foods has contributed to current health problems, such as obesity and overeating (Power, 2012; Rantala et al., 2020).
Although a motivation to seek out high-caloric foods was adaptive during environments where food uncertainty was common, it has become maladaptive during modern environments where food is abundant. According to the thrifty gene hypothesis, individuals with excessive adipose tissue or high body mass indices (BMI) would have been more likely to survive during alternating periods of food availability and famine (Genne-Bacon, 2014; Neel, 1962). However, in times where famines are rare, excess body fat becomes problematic, and individuals are more succeptable to cardiovascular diseases and metabolic syndromes like diabetes mellitus. BMI is a predictor of cardiovascular disease (Taylor et al., 2010), and research has suggested that BMI is also associated with behaviors associated with overeating, such as being highly attentive to high-caloric food items (Castelanos et al., 2009). Nonetheless, excess adipose tissue is associated with elevated levels of sex hormones (e.g., estradiol) (Power, 2012), and research has suggested that sex hormones contribute to body fat percentage increases in women (Ziomkiewicz et al., 2008).
Role of Sex Hormones on Food Behavior
Sex hormones play an influential role in eating behavior (Hirschberg, 2012). In many animals, including primates, the hypothalamic-pituitary-gonadal axis regulates hormonal fluctuations. This results in a decrease in food intake around the estrous or ovulatory phase when they are most sexually receptive (Asarian & Geary, 2006). In rodent research, ovariectomized rats overeat, and this can be restored by administering doses of estradiol (Butera, 2010). Food intake rates are reduced during the estrous phase, which is associated with increased estradiol and decreased progesterone (Asarian & Geary, 2006; Richard et al., 2017). Although progesterone alone does not stimulate food intake, when combined with estradiol, it does influence eating behaviors in rats (Wade & Schneider, 1992). In humans, food intake fluctuates with normally fluctuating hormones in the menstrual cycle. Food intake decreases during the ovulatory period (i.e., high estradiol) and increases during the premenstrual periods (i.e., high progesterone) (Buffenstein et al., 1995; Hirschberg, 2012; Webb, 1986). Studies have shown that in women with bulimia nervosa, decreases in estradiol and increases in progesterone influence binge eating behaviors (Edler et al., 2007; Klump et al., 2008). Furthermore, women taking hormonal based contraceptives often report weight gain as a reason for discontinued use (Westhoff et al., 2007); however, a meta-analytic study on hormonal contraceptives and weight gain showed that the evidence for progestin based contraceptives and weight gain is weak (Lopez et al., 2007).
Evolutionary explanations for women’s food intake during different phases of the menstrual cycle have suggested that there is a trade-off between reproductive and foraging efforts. Food intake has been shown to decrease during the periovulatory phase of the menstrual cycle and increase during the post-ovulatory phase (for a review, see Fessler, 2003).
Decreases in food intake during the fertile window may demonstrate that women trade-off foraging efforts (i.e., eating behavior) in pursuit of mating related goals. A vast literature has found evidence for women’s mating psychology to be influenced during the fertile phase of the menstrual cycle (Gildersleeve et al., 2014); therefore, it is suggested there are psychological mechanisms that are important in reducing eating behavior during peak ovulation and instead focus on activities that can lead to mating or facilitate looking for alternative mates (Fessler, 2003). Further, food intake during the post-ovulatory phase can be explained by the increase in metabolic activity needed to prepare for the thickening of the endometrium and preparation for possible pregnancy (Strassman, 1996). In fact, behavioral studies investigating women’s food desires and purchases of beautification products have supported this trade-off. In looking at food desires and money spent on clothing, women’s food desires, hunger, and eating behavior were all significantly higher during the post-ovulatory period, while appearance related desires and clothing purchases increased during the fertile period (Saad & Stenstrom, 2012).
Eye Movement Research in Food Stimuli
Eye-tracking has been used in past research to measure attentional bias to food stimuli.
Eye-tracking is a non-invasive procedure that can examine natural eye movements and can provide researchers with a direct measure of attentional bias (Conklin et al., 2018; Graham et al., 2011) as opposed to indirect measures, such as relying on reaction time or ratings. In fact, a person’s gaze behavior is indicative of their thoughts, emotions, and intentions (Kleinke, 1986). Food studies on eye-tracking have primarily focused on comparing visual attention to food items across weight groups. The rationale stems from “gaze bias theory” (Schotter et al., 2010), which suggests that gaze is associated with preferences or chosen items. Chosen foods have been shown to be gazed at longer, as well as high-caloric food items (Manippa et al., 2019). In examining attentional biases to food images across different weight groups (i.e., normal vs. obese) in different hunger states (i.e., fasted vs. satiated), normal weight individuals are more likely to view food images longer compared with non-food images when in a fasted state (Castelanos et al., 2009). Furthermore, regardless of their hunger state, obese individuals display greater visual attention to food images compared with non-food images (Castelanos et al., 2009). Other studies have found similar findings. In comparing high-fat food stimuli with non-food items, overweight individuals have longer first fixation durations to food-items than non-food items compared with healthy individuals (Werthmann et al., 2011).
However, others studies have not found differences in attentional bias across weight groups (Doolan et al., 2014; Graham et al., 2011). Children display an automatic visual orientation towards food items vs. non-food items irrespective of weight category (Werthmann et al., 2015). In comparing across BMI (i.e., low vs. high), no differences were reported in attentional bias when viewing high-caloric sweet, high-caloric salty, and low-caloric food items, albeit, there was a trending effect for individuals to view low-caloric foods longer (Graham et al., 2011). Similar findings were noted when showing high-energy dense vs. low-energy dense foods. Participants viewed high-energy dense foods longer compared with low-energy dense foods regardless of BMI (Doolan et al., 2014). In contrast, when comparing foods that differed in sugar, high-sugar foods were gazed longer compared with low-sugar foods independent of BMI (Wang et al., 2018). Overall, eye-tracking studies using food item cues provides some evidence that food gazing is influenced by differences in weight groups and types of food (i.e., fat and sugar content).
The current study investigated women’s eye movements to food stimuli. In addition, we looked at the moderating role of progesterone and BMI in evaluating food stimuli, as sex hormones have been shown to influence eating behavior (Hirschberg, 2012) and research on eye-tracking has revealed differences in attention when comparing BMI (Castelanos et al., 2009; Werthmann et al., 2011). A prevailing gap in the literature on visual attention to food items is how normal fluctuating hormones, such as progesterone, influence visual attention to different types of food items. Although food behavior has been studied extensively in animal models, in humans, it remains unclear if sex hormones influence visual attention and thereby behavior. By examining eye movements in women, we can provide a direct measure of interest in food items, and we can determine if BMI and sex hormones moderate those behaviors. The use of eye tracking provides insight into the processes that occur when evaluating food items. Given that high-caloric foods would have been beneficial during ancestral history, a direct measure of attention could highlight if those processes are important today in the presence of modern food abundance. Further, food consumption is expected to decrease during the ovulatory period of the menstrual cycle and increase during the post-ovulatory period, and by measuring attention during different phases of the menstrual cycle, we can determine if women’s attention to food items is different across those different time points. Research has shown that individuals display an attentional bias to high-caloric or high-dense foods (Doolan et al., 2014; Graham et al., 2011), so it is predicted that women will display an attentional bias to high-caloric food items. Attentional bias to high-caloric food is also attributed to BMI (Castelanos et al., 2009; Werthmann et al., 2011); therefore, it is predicted that BMI will positively predict visual attention to high-caloric foods. Lastly, research on women’s hormonal levels has suggested that progesterone is associated with increases in food intake (Hirschberg, 2012); we predict that progesterone will positively predict visual attention to high-caloric food items.
A G*Power analysis detecting a moderate effect size with 80% power indicated a sample size of 36 participants for a within-subjects design. Forty-four undergraduate women (Mage = 19.61, SDage = 2.80) from a Midwestern University participated in the study in exchange for course credit. Participants signed up using the university’s SONA system. The study was announced so that they signed up for two sessions, one session during the follicular phase of their menstrual cycle (days: 6–14 from the onset of menstruation) and one session that was outside the follicular phase window (days: 0–5; 15–28). As part of a pre-screener, participants were excluded from signing up if they were pregnant, on any hormonal based contraceptives, or smoked.
Measures and Materials
As in previous research, participants viewed two categories of food stimuli which consisted low-caloric (LC) and high-caloric (HC) food items. Examples of low-caloric food items were images of broccoli, green peas, oranges, and strawberries. High-caloric foods consisted of images of hamburgers, pizza, spaghetti, and donuts. The images were presented in quadrants depicting 4 squares of food stimuli where high-caloric and low-caloric food items were in opposite diagonal presentation and always showing two images of high-caloric and low-caloric food items. Using the interest area creator in Tobii Pro Studio, four regions of interest were created to collect visual attention to the food items and means were computed for the quadrants to include a mean for low-caloric food and high-caloric food.
Menstrual Cycle Status
Menstrual cycle status was determined by following the forward counting method, where we counted the days from the onset of menstrual bleeding to their current cycle day. Women who were in the follicular phase of their menstrual cycle (i.e., 6–14 days from the onset of menstruation) signed up for session 1, and those who not in the follicular phase (i.e., outside the 6–14 day window period) signed up for session 2. As part of the sociodemographic questionnaire, participants indicated what day they were on by answering the following question, “Please select the first day of your last menstrual cycle?” This created two menstrual cycle statuses, the follicular and non-follicular phase.
In addition to menstrual cycle statuses, we collected saliva samples to be used for progesterone analysis. Saliva vials were stored in at − 80 °C after collection. On the day of analysis, saliva vials were thawed for 1.5 h and centrifuged for 15 min at 3000 rpm. Following guidelines from Salimetrics, 200 μL of assay diluent were added to microvial tubes and 100 μL of the progesterone standard were added to the first tube and then serially diluted to the remaining microvial tubes. This resulted in our 6 standards, 2430 pg/μL, 810 pg/μL, 270 pg/μL, 90 pμ/mL, 30 pg/mL, 10 pg/mL, and the high and low controls. The high 1060 ± 265.03 pg/μL and low 72.24 ± 28.90 pg/μL were in range according to the protocol. All samples (50 μL) were pipetted into the microplate within 20 min. From there, 22.5 μL of an enzyme conjugate was diluted into 18 mL of assay diluent, in which 150 μL of the resulting mixture was pipetted using a multichannel pipette into the microplate. The microplate was placed on a Jitterbug plate rotator for 1 h, washed using a BioTek plate washer, and then treated with 200 μL of a TMB substrate solution to each well using a multichannel pipette. The microplate was covered with foil and mixed with a plate reader for 25 min, and then a stop solution (50 μL) was added and mixed for 3 more minutes. Microplates were analyzed afterwards. Intra and inter-assay coefficients were 4.14% and 2.43%.
The eye tracking apparatus used was a Tobii X2-60, which is a free viewing eye tracker that does not constrain the participant on a chinrest. It is a binocular eye tracker that measures eye movements at 60 framers per second. It is non-obtrusive which allows participants in behavioral studies to act freely while maintaining research validity. The eye-tracking measures used were total first fixation duration, total visit duration, and fixation count. First fixation duration was defined as the average fixation duration upon first view on a region of interest, and it is often used as a metric to indicate early-onset processing (Conklin et al., 2018). Total visit duration was defined as the average amount of time spent per region of interest (ROI) in milliseconds (ms) per trial. Fixation count was defined as the total amount of visual fixations made per region of interest. Both of these measures (total visit duration, fixation count) are traditionally used in the eye-tracking literature and are regarded as late measures of processing (Conklin et al., 2018). For regions of interest, we used the Tobii interest area creator to create squares that highlight the food items that were viewed in each trial.
Participants entered the laboratory on two separate sessions, once during the follicular phase of their menstrual cycle (days: 6–14), and the second session outside the follicular phase window (days 0–5; 15–28). At a minimum, participants had to wait 7 days between sessions to ensure variability in progesterone levels. Upon consent, participants were asked to submit a saliva sample for each session through passive drool collection. Saliva was collected via passive drool through a collection tube that was inserted into a 1.5-mL vial. Participants were instructed not to forcefully spit or include mucus in their saliva sample. Each participant took between 1 and 3 min to provide a saliva sample, and no participant took more than 5 min to provide a sample.
After sample collection, they were instructed to sit approximately 55 cm from the computer where an eye tracker was positioned on a magnetic strip on the desktop screen. The eye tracker was a Tobii X2-60, which does not use a chinrest so that participants were not constrained throughout the task. They were instructed to be as still as possible and minimize head movement throughout the task. They completed a calibration task to ensure that their eyes could be seen by the eye tracker and that it could adequately record their eye movements. To ensure this, a 5-point calibration was performed, where participants had to follow a red dot moving across the screen to 5 locations in random order. Once proper calibration was completed, they were instructed that the study was a participant-controlled eye tracking task where they were to view images of food items presented in quadrants and when done with the image to press the ‘spacebar’ to continue to the next image. When participants were done viewing the images, they were directed to a Qualtrics link to complete a sociodemographic questionnaire. The questionnaire included questions on age, sex, sexual orientation, height, and weight. Participants completed the measures after the eye tracking task to prevent any priming effect when viewing food. Participants were thanked for their participation once they were done with the survey and were reminded of their next session in which the same procedure was performed.
BMI and Progesterone Descriptives
Participants reported their height and weight, and we derived their BMI using BMI = kg/m2. Women’s BMI were M = 26.69, SD = 5.45, and their progesterone on time 1 was, M = 152.33, SD = 111.33, and time 2, M = 169.63, SD = 121.07. Table 1 presents the zero-order correlations between all variables.
Data were analyzed using a 2 (menstrual cycle status: follicular, non-follicular) by 2 (food: high-caloric, low-caloric) repeated measures ANOVA. All pairwise comparisons were conducted using a Bonferroni correction, and we report partial eta-square’s for significant effects. To investigate associations between BMI, progesterone, and food stimuli, linear mixed-effects models with maximum likelihood were run for each eye tracking metric. Linear mixed-effects models are robust when using time varying covariates (i.e., progesterone) to predict differences across repeated measurements or stimuli (i.e., food images). Progesterone was a time-varying covariate because it was measured at two different time points (i.e., follicular, non-follicular), and the repeated measurements of food images was nested within participants.
All continuous predictors (BMI, progesterone) were centered, and progesterone was log-transformed due to violations of normality. We report the marginal and conditional (i.e., R2) effect sizes for the linear mixed model.
There were no significant main effects for food stimuli, F(1, 40) = 2.91, p = 0.10, or menstrual cycle status, F(1, 40) = 2.33, p = 0.13, on women’s first fixation durations. The interaction between food stimuli and menstrual cycle status was not significant, F(1, 40) = 0.28, p = 0.59. There was a significant main effect for food stimuli on women’s total visit duration, F(1, 40) = 8.53, p = 0.006, ɳ2p = 0.18. Women viewed high-caloric food longer (M = 987.66, SE = 74.50 compared with low-caloric food (M = 856.22, SE = 70.72), see Fig. 1. Menstrual cycle status was not significant, F(1, 40) = 2.68, p = 0.10. The interaction between food items and menstrual cycle status was not significant, F(1, 40) = 0.05, p = 0.82. For fixation count, there were no significant effects for food stimuli, F(1, 40) = 1.07, p = 0.30, or menstrual cycle status, F(1, 38) = 2.54, p = 0.10. The interaction between food stimuli and menstrual cycle status was not significant, F(1, 40) = 0.56, p = 0.45. Overall, data from visual movements indicate that women view high-caloric food longer irrespective of menstrual cycle status.
For individual differences in BMI, progesterone, and their interactions with food type, linear mixed-effects models were run for each eye-tracking metric. For first fixation duration, there was a significant interaction between progesterone and food stimuli, F(1, 286.91) = 10.03, p = 0.002, R2Marginal = 0.05, R2Marginal = 14. That is, progesterone positively predicted longer first fixation durations to high-caloric food (b = 87.99, t = 3.16, p = 0.002) compared with low-caloric foods, see Fig. 2. Conversely, during low progesterone, first fixation duration to low-caloric foods was higher compared with high-caloric foods. There was no interaction between BMI and food stimuli, F(1, 286.91) = 0.32, p = 0.57, suggesting that first fixation durations to food stimuli were not moderated by BMI.
There were no other significant interactions total visit duration, and number of fixations, (all F’s < 1).
The present study investigated women’s visual attention to food stimuli across the menstrual cycle. Specifically, it aimed to examine the role of sex hormones (i.e., progesterone) and BMI on visual attention to high-caloric vs. low-caloric foods. Previous research has indicated that progesterone is involved in eating behaviors during the postovulatory period (Hirschberg, 2012), and animal research has indicated that food consumption increases during estrous in rats (Asarian & Geary, 2006; Richard et al., 2017). We found that women displayed an attentional bias to high-caloric food when measuring total visit duration; however, there were no differences in the early stages of visual processing (i.e., first fixation duration) or in looking behavior (i.e., number of fixations). Support for the role of sex hormones was tentative, as women’s progesterone was positively associated with increased first fixation durations to high-caloric food items, but it was not associated with other eye tracking metrics indicative of effortful processing (i.e., total visit duration, number of fixation). We did not find support for BMI influencing visual attention to food stimuli.
Research on women’s sex hormones has shown that food intake is influenced by estradiol and progesterone. Although most studies have used animal models to detect differences in the role of estrous and eating consumption, there is some evidence to suggest that women increase their food consumption during post-ovulation, or when progesterone is high (Buffenstein et al., 1995; Hirschberg, 2012). Similarly, studies have shown that women increase food consumption during the premenstrual phase (Cross et al., 2001; Dye & Blundell, 1997; Wurtman et al., 1989), which is also indicative of high progesterone (Yen et al., 2019). In the current study, we tracked women during two phases of their menstrual cycle, the follicular phase and non-follicular phase, and we measured progesterone during those two lab visits. Women’s progesterone was positively associated with longer first fixation durations to high-caloric food items. This may indicate that progesterone influences early-onset processing, which is often measured using first fixation duration, a metric that assesses items that are salient upon first view (Conklin et al., 2018). However, we did not find any conclusive evidence that women’s visual attention in the form of effortful longer processing (i.e., total visit duration, number of fixations) was influenced by progesterone, BMI, menstrual cycle phase.
Women’s visual attention was focused primarily on high-caloric food, which is consistent with previous research using eye-tracking to measure attentional biases to food items (Castelanos et al., 2009; Doolan et al., 2014; Graham et al., 2011; Werthmann et al., 2011, 2015). Although studies have shown that BMI moderates that relationship between food and visual attention, in this study we did not find evidence of BMI influencing visual attention to food items. Overall, women displayed an attentional bias to high-caloric food items, regardless of BMI, and menstrual cycle phase.
The findings from the current study may reflect adaptive cognitive processes that influence decision rules in food choice. Throughout history, humans have faced periods of food uncertainty (King, 2013); therefore, attending to foods that are high-caloric is an evolved psychological mechanism that aids in survival (Al-Shawaf, 2016; Li et al., 2018). Given that humans would have expended an immense amount of energy to acquire food, they would have needed to replenish the calories exhausted with dense foods (Rantala et al., 2020). One method that would have aided in the acquisition of food would have been a cognitive system, such as attention, that would have been sensitive to such foods. Even in modern environments with food abundance, we demonstrate that high-caloric foods are still attended too longer, suggesting that evolved psychological mechanisms drive women’s attention to caloric dense foods. Moreover, the saliency of high-caloric foods is influenced by progesterone to a certain degree, where attention to high-caloric foods varies across hormonal fluctuations in the menstrual cycle. One possible explanation for why there was not a strong hormonal shift and moderation effect for BMI could be because of the food-abundant environment of our sample. Women in food-abundant populations may not need to attend to caloric-rich foods across the menstrual cycle if those caloric needs are being met, compared with women from harsher ecologies or women from our ancestral past where food scarcity was a prevalent concern. Furthermore, although women in our sample were not on hormonal based contraceptives, this does not indicate that they were preparing for pregnancy, in which physiological and psychological mechanisms may play a stronger role in preparing the body to increase caloric consumption. Since there are sexually dimorphic differences in fat storage (i.e., women have more body fat than men) (Karastergiou et al., 2012), it is not clear how much of an advantage women would have by attending to high-caloric foods days before peak fertility in an already food-abundant environment. However, this does not indicate that such mechanistic approaches to food consumption do not exist. For instance, hormonal shifts may be stronger and pronounced if using a different approach, such as focusing on the olfactory system as opposed to visual attention. The current research addresses the important relationships in understanding how hormones affect eating behavior in women. The implications of the study may lead to a better understanding on how eating behaviors in women are affected by their hormone levels and their attentional bias to high-caloric food items, which can be beneficial in not only research evolutionary research but clinical based research as well. Interactions between other hormones, such as progesterone and estradiol, warrant further exploration.
The current study is limited in a few ways. Women were asked mainly to view the images of low vs. high-caloric foods and were not asked to provide any ratings. This may imply that high-caloric foods only influence the attentional processing system but not actual choice. In addition, we did not account for life history factors that may account for visual preferences to high-caloric foods. Studies using a life history approach have shown that women growing up in harsh environments exhibit greater caloric consumption (Hill et al., 2013; Laran & Salerno, 2012). Perhaps a future study using an ecological manipulation or accounting for life stressors may be able to explore if ecological harshness contributes to enhanced visual attention to high-caloric foods. Also, we only collected women’s progesterone levels at two points of the menstrual cycle (i.e., follicular vs. non-follicular). To get a precise account of ovulatory changes, research has advised to collect saliva samples from more than two sessions and to also include other salivary markers, such as estradiol (Blake et al., 2016; Gangestad et al., 2016). Perhaps, by providing both estradiol and progesterone, researchers can get a complete picture of the ovulatory processes associated with food intake and make better predictions on hormonal biomarkers and food intake. By only relying on two sessions, we were not able to get enough variability on progesterone. Further, in regard to food stimuli, it is possible that participants could have been influenced by high protein foods (i.e., hamburgers) and foods that may contain teratogens (i.e., broccoli). Lastly, it is important to note that the food items shown were not typical of foods that would have been present during the Plesitocene era. Future studies should take into consideration these food factors influencing visual attention and how ancient mechanisms influence modern food choices.
In conclusion, we find that women display an attentional bias to high-caloric foods irrespective of menstrual cycle status and BMI. The findings contribute to increasing research on the way humans process food using eye-tracking methodologies, and it adds to the literature on the mixed findings associated with menstrual cycle status and food intake. Further research is warranted in exploring the hormonal relationships in women’s menstrual cycles and women’s eating behaviors.
Data is available upon authors’ request.
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Garza, R., Clauss, N. & Byrd-Craven, J. Do BMI and Sex Hormones Influence Visual Attention to Food Stimuli in Women? Tracking Eye Movements Across the Menstrual Cycle. Evolutionary Psychological Science 7, 304–314 (2021). https://doi.org/10.1007/s40806-020-00273-7