Journal of Experimental Psychology: Human Perception and Performance

Transcription

1 Journal of Experimental Psychology: Human Perception and Performance Walkable Distances Are Bioenergetically Scaled Jonathan R. Zadra, Arthur L. Weltman, and Dennis R. Proffitt Online First Publication, August 24, CITATION Zadra, J. R., Weltman, A. L., & Proffitt, D. R. (2015, August 24). Walkable Distances Are Bioenergetically Scaled. Journal of Experimental Psychology: Human Perception and Performance. Advance online publication.

2 Journal of Experimental Psychology: Human Perception and Performance 2015, Vol. 41, No. 5, American Psychological Association /15/$ Walkable Distances Are Bioenergetically Scaled Jonathan R. Zadra, Arthur L. Weltman, and Dennis R. Proffitt University of Virginia In perceiving spatial layout, the angular units of visual information are transformed into linear units appropriate for specifying size and extent. This derivation of linear units from angular ones requires geometry and a ruler. Numerous studies suggest that the requisite perceptual rulers are derived from the observer s body. In the case of walkable extents, it has been proposed that people scale distances to the bioenergetic resources required to traverse the extents relative to the bioenergetic resources currently available. The current study sought to rigorously test this proposal. Using methods from exercise physiology, a host of physiological measures were recorded as participants engaged in exercise on 2 occasions: once while provided with a carbohydrate supplement and once with a placebo. Distance estimates were made before and after exercise on both occasions. As in previous studies, the carbohydrate manipulation caused decreased distance estimates relative to the placebo condition. More importantly, individual differences in physiological measures that are associated with physical fitness predicted distance estimates both before and after the experimental manipulations. Results suggest that walkable distances are bioenergetically scaled. Keywords: perception of spatial layout, bioenergetics, behavioral ecology, perceptual scaling Jonathan R. Zadra, Arthur L. Weltman, and Dennis R. Proffitt, Department of Psychology, University of Virginia. Jonathan R. Zadra is now at the Department of Psychology, University of Utah. The reported research comprised a part of the primary author s doctoral dissertation at the University of Virginia. Committee members were Dennis Proffitt (chair), Gerald Clore, Michael Kubovy, and Arthur Weltman. Correspondence concerning this article should be addressed to Jonathan R. Zadra, Department of Psychology, University of Utah, 380 S 1530 E Beh S 502, Salt Lake City, UT j.zadra@utah.edu Visual information is comprised exclusively of angular units. The locations of visual contrasts are specified by their angular displacement from the 0 direction of gaze; moreover, ocularmotor adjustments and binocular disparities scale as angles as well. And yet we perceive spatial layout in linear units appropriate for measuring such extents as size and distance. For this to occur, the angular units of visual information must be transformed into the linear units of perception. Such transformations require geometry and a ruler. Much is known in the perceptual literature about the geometries employed (Proffitt & Caudek, 2013), but what are the perceptual rulers? Consistent with Gibson s (1979) theory of affordances, a large and growing body of research is showing that our visual perceptions of the world are scaled to our bodies (for recent reviews, see Dotov, Nie, & De Wit, 2012; Proffitt & Linkenauger, 2013). As Gibson proposed, spatial layout is perceived in terms of body-based units, with the effective unit being determined by what is relevant to an intended action. Actions directed toward objects within near space (roughly, the area within immediate reach) are primarily scaled by action boundaries determined by morphology; for example, the distance to an object to which one is reaching is scaled to arm length, and the size of an object that one is intending to grasp is scaled to hand size (Linkenauger, Witt, & Proffitt, 2011; Linkenauger, Witt, Stefanucci, Bakdash, & Proffitt, 2009). For actions in far space, morphologically specified action boundaries become increasingly less useful and less relevant. It makes little sense to attempt to scale the extent of a meadow to arm s length or hand size. What, then, are the most relevant units for perceiving such extents? Evidence from prior studies suggests that for walkable distances, the energy required to walk the extent relative to the energy currently available may be the effective scale (Proffitt, Stefanucci, Banton, & Epstein, 2003). Energy is a currency of life, as failure to obtain more energy (food) than is expended ultimately results in starvation and death. For most animals, and for most of human evolutionary history, food energy is and has been scarce, so maintaining an economy of action is a biological imperative. As will be discussed in more detail in the Discussion, deciding where and how much to walk is the primary determinant of controllable daily energy expenditure (Levine et al., 2005). Previously, we have proposed that walkable distances are scaled in part by the bioenergetic costs associated with traversing an extent relative to the bioenergetic resources available (Proffitt, 2006b). Although there is considerable evidence that visual perception of spatial layout is scaled to the perceiver s body, alternative explanations for these findings have been suggested. These alternatives include the possibility that manipulations influence people s responses but not perception itself (Hutchison & Loomis, 2006; Loomis & Philbeck, 2008), that results are because of demand characteristics (Durgin et al., 2009; Durgin, Klein, Spiegel, Strawser, & Williams, 2012; Shaffer, McManama, Swank, & Durgin, 2013), or that the theory and data interpretation are unconvincing (Firestone, 2013). These issues have been addressed in prior experimental and analytical publications (Proffitt, 2013; Proffitt, Stefanucci, Banton, & Epstein, 2006a, 2006b; Proffitt & Zadra, 2011; Witt, Proffitt, & Epstein, 2010), and the current study addresses these issues as well. In addition, a growing number of 1

3 2 ZADRA, WELTMAN, AND PROFFITT researchers are independently reporting evidence of body-based perceptual scaling. One such study, which eliminated previously proposed possibilities for response biases or demand characteristics was conducted by White, Shockley, and Riley (2013). Using a treadmill and a virtual environment, they created situations with altered relationships between energy expenditure and the optically specified distance that was traversed (individually changing either the walking rate, grade of inclination, or optic flow rate while keeping the other two variables constant). They found that these manipulations influenced subsequent distance judgments in the predicted directions. Increasing energy expenditure while keeping the optically specified distance constant, for example, led to larger distance judgments. Previous research in our lab has demonstrated that manipulating blood glucose levels (glucose carried in the blood and stored in the liver and muscles being the primary source of energy for physical activity) influences the perceived slant of a hill, such that individuals who are glucose-depleted perceive hills to be steeper (Schnall, Zadra, & Proffitt, 2010). 1 The current research extends the bioenergetic account to distance perception over flat ground. We address two questions: Does a manipulation of blood glucose under conditions of physical fatigue influence distance perception? And, do individual differences in physiological factors related to physical fitness influence distance perception, not only after fatiguing physical activity, but even before it has taken place? In the following experiment, we make use of methods from the discipline of exercise physiology to look more directly at bioenergetic factors and how they relate to distance perception. The field of exercise physiology is expressly focused on understanding physical performance and how it is affected by bioenergetics, and, therefore, its practitioners have well-developed manipulations and measurement techniques. The bioenergetic factors that explain the differences in physical performance and the proposed bioenergetic perceptual scale for walkable distances are theoretically one and the same. In many areas of sport, increasing performance depends on either reducing the energetic cost of the activity (for instance, by making performance more economical) or increasing the energy available to the athlete (perhaps by consuming a carbohydratecontaining drink). If spatial layout is perceptually scaled by the limits and costs of our physical performance, as determined by bioenergetic factors, then it follows that bioenergetic factors that affect physical performance will similarly affect perception of distances. Additionally, individual differences in physical ability should predict individual differences in distance perception: An athlete in top physical condition should perceptually scale the environment differently from someone of low physical fitness, just as the two would have different abilities to act on the world. We hypothesized that Experiment Overview 1. Participants with higher fitness levels would perceive distances to be shorter than participants of lower fitness levels; 2. Participants will have higher blood glucose levels following exercise if they have received caloric supplementation compared with when they did not; and 3. Caloric supplementation will be associated with shorter posttest distance judgments. In this experiment we show that, as in previous studies, manipulating available energy via glucose ingestion affects visual perception. Of greater interest, however, we are able to refine this by taking direct measurements of a host of physiological factors: While inducing physical fatigue, we continuously measured blood glucose, blood lactate, heart rate (HR), caloric expenditure, power output, and several other performance factors. With these data, we establish the existence of multiple direct relationships between individual differences in fitness-relevant physiology and individual differences in distance perception. When participants arrived, baseline distance estimates were obtained, and then participants cycled on a stationary bike for 45 min while their physiology was continuously monitored and blood samples were repeatedly taken. On one occasion, participants consumed a carbohydrate drink during the cycling, whereas on the other occasion, they consumed an artificially sweetened placebo. Following the cycling, participants made a second set of distance estimates. The perceptual measures of this experiment were an addition to an exercise physiology study that was designed to assess the effect of carbohydrate supplementation on the relationship between ratings of perceived exertion (RPE; Borg & Noble, 1974) and blood lactate levels. Details not directly relevant to the perception hypothesis have been omitted (for complete details of all procedures, see Steiner, Curmaci, Patrie, Gaesser, & Weltman, 2009). The study made use of the Borg RPE scale, which is a commonly used measure of subjective exercise intensity. It has been found to be closely associated with blood lactate concentrations across individuals (Henritze, Weltman, Schurrer, & Barlow, 1985), suggesting that it is a valid self-report measure of the physiological changes that occur with increasing exercise intensity. Lactate accumulates in the blood during intense exercise when the amount of lactate produced by the muscle exceeds that body s ability to clear it. Steiner et al. (2009) were interested in whether carbohydrate supplementation would affect the RPE/blood-lactate relation- 1 Durgin et al. (2012) attribute these results to demand characteristics. They present as evidence an experiment that involved telling some participants to ignore the backpack, and oddly concluded that the no-instruction condition induced a demand characteristic, whereas the instructions to ignore potential demand characteristics did not. In the case of Schnall et al. (2010), they suggest that putting a backpack on participants (as Schnall et al. did to every participant) created a demand characteristic that made participants more susceptible to other demand characteristics such as blood glucose levels so that those who ingested glucose were less susceptible to the backpack demand characteristic. To our knowledge, there is nothing in social psychology literature to support this claim about susceptibility to demand characteristics. As Orne s (1962) classic article on demand characteristics explicitly noted, there is no way to design an experimental manipulation that is completely free from demand characteristics because participants will always have some expectations or intuitions about the research. It would not seem possible to eliminate presumed demand characteristics by introducing a whole new set of demand characteristics. Regardless, the concerns raised by Durgin are not relevant to the current study. Participants do not wear a backpack, and we make extensive use of individual differences data that, by their very nature, are free from potential demand characteristics.

4 BIOENERGETIC SCALING 3 ship. 2 They examined the relationship both under incrementally increasing exercise intensity (cycle resistance was increased by a set amount every 3 min), and under constant exercise intensity (participants adjusted the cycle resistance to maintain a constant RPE of 16). An RPE of 16 was chosen because it is known to produce blood lactate levels of about 4.0 mm. Elevated blood lactate levels were necessary in order to evaluate the blood-lactate/ RPE relationship. In the larger study, participants were tested on four occasions overall, once with each combination of exercise intensity and carbohydrate/placebo supplementation, but perceptual measures were collected only on the two constant exercise intensity occasions. Method Participants Eight participants from the Charlottesville, Virginia, community (five males, mean age years, SD 5.53) participated in exchange for physiological data reports of their performance (although nine participants were included in the larger study, one did not participate in the perceptual measures). Participants were regularly exercising cyclists (minimum 30 min per day, at least 3 days per week on bicycle). The Institutional Review Board, Human Investigation Committee, of the University of Virginia Health System approved this study, and each participant completed a medical history and physical examination and provided written informed consent. Females were tested during the early follicular phase of their menstrual cycles (Days 1 to 7). All testing was conducted at the University of Virginia General Clinical Research Center (GCRC). Perception measures for one participant were obtained for only one of the 2 days, and this participant was omitted from certain analyses as appropriate, as specified in the Results section. Two participants had a single procedural error on one of the six distance estimates on one of the days. These estimates were excluded, leaving each of these two participants with a total of 11 out of the normal 12 estimates. Measures RPE. Before each trial, participants were read specific instructions describing how to use the Borg Ratings of Perceived Exertion scale (Borg & Noble, 1974). The scale is used to measure how hard a person feels they are working, and therefore is relative to the individual s fitness level. The scale of exertion ratings ranges from 6 (none) to20(very, very hard). RPE was assessed at the end of each minute during the production trials. Physiological measurements. Aerobic capacity (VO 2 ). The amount of oxygen that can be taken up and used by the body is one factor that determines fitness. These metabolic measurements were collected during each exercise trial using standard open-circuit spirometric techniques (Sensor-Medics Model 229 metabolic measurement cart; Sensor Medics, Yorba Linda, CA), in which a mask is placed over the nose and mouth to measure the oxygen and carbon dioxide content of each inspiration and expiration, as well as the volume of each respiration. HR. The amount of blood that can be pumped throughout the body in 1 min is cardiac output. Cardiac output is determined by stroke volume (how much blood is pumped for each beat) and HR. Individuals who are more fit typically have stronger hearts and thus greater stroke volumes. As a result, relative to a less-fit individual, they can exercise at the same intensity while maintaining a lower HR. HR was determined electrocardiographically (Quinton Q 4500, Botella, WA). Blood lactate (HLA). Lactic acid is a result of anaerobic metabolism. When an individual exercises beyond their aerobic capacity (i.e., aerobic metabolism does not meet the energy demands of the muscles), anaerobic metabolism supplements the energy supply. The lactate threshold (LT) is the point at which HLA levels begin to increase beyond baseline. Thus, HLA is an indicator of how far beyond aerobic capacity the individual was exercising. More-fit individuals will reach LT at higher exercise intensity levels than less-fit individuals because of their greater aerobic capacity. The VO 2 at LT for an individual is an excellent indicator of aerobic capacity for submaximal exercise such as the exercise RPE of 16 of the production trials. Both LT and VO 2 at LT were measured during incremental trials (see Trial Types section). A lactate elevation of at least 0.2 mmol (mm; the error associated with the lactate analyzer) was required for LT determination. Blood samples were analyzed for blood lactate and blood glucose concentration during each test (YSI Instruments 2700, Yellow Springs, OH). Air displacement plethysmography. Body composition (body fat and muscle mass percentages) was measured using air displacement plethysmography (Bod Pod Life Measurement Instruments, Concord, CA; Dempster & Aitkens, 1995). Estimated distances. Two orthogonal hallways near the testing room were used to make distance estimates. Two sets of three distances were used, each set having a mean of 7.16 m (Set A: 5, 7.5, and 9 m; Set B: 6, 7, and 8.5 m), such that participants were never exposed to the same distance more than once on a single day. The order of sets on a given day was randomized and counterbalanced, and the orders of distances presented within a set were randomized. Trial Types Participants were seen on four occasions. Two consisted of incremental trials from which physical fitness data was obtained. The other two were production trials in which assessments of distance perception were made before and after strenuous exercise. Incremental trials. Incremental trials occurred on Visits 1 and 2. Perceptual measures were not collected on these visits, but fitness data from these trials were used in later analyses. In the context of the larger study, these trials were intended to examine the blood-lactate/rpe relationship under increasing exercise intensity, whereas the later production trials examined the relationship under constant exercise intensity. The two continuous incremental protocols were initiated at a power output (PO) of 65 W, and PO increased in 15-W increments at the end of each 3-min stage. 2 The hypothesis was that blood lactate levels at a given RPE would be lower after ingestion of the carbohydrate beverage relative to those at the same RPE after a placebo beverage. They did not find any difference in the RPE/blood-lactate relationship for incremental cycling, and although there was also no evidence of the hypothesized relationship for constant intensity trials, they did find that total work production was higher with a carbohydrate beverage despite maintaining the same RPE (Steiner et al., 2009).

5 4 ZADRA, WELTMAN, AND PROFFITT Participants were instructed to maintain pedaling cadence between 70 and 80 revolutions per minute (rpm). During the incremental protocol, the volume of oxygen consumption in respiration (VO 2 ) was measured minute by minute, and HR, blood lactate (HLA), and blood glucose (BGL; drawn from an indwelling venous cannula) were assessed at the end of each stage. The test was terminated when the subject reached volitional exhaustion or was unable to maintain the desired pedal cadence. Individual plots of HR, RPE, and VO 2 versus power output were used to determine HR, RPE, and VO 2 associated with LT (Weltman, 1995). Production trials. Two 45-min production exercise trials on a stationary bicycle consisted of a 5-min warm-up at a power output of 50 W, followed by 45 min at power outputs self-selected to maintain an RPE of 16 when pedaling between 70 and 80 rpm. Participants were blind to the power output level and elapsed time and were allowed to adjust the power output at 1-min intervals to maintain the requested RPE of 16 ( hard exertion). During the exercise trials, VO 2, power output, and RPE were measured every minute, and blood lactate, blood glucose, HR, and blood pressure were measured every 5 min. Drinks. Participants ingested a carbohydrate beverage on one day and a placebo beverage on the other. Beverages were administered in a randomly assigned, double-blinded manner. The Gatorade Sports Science Institute (Barrington, IL) provided both beverages. The carbohydrate beverage was a 6% carbohydrate solution, and the placebo was artificially sweetened. The beverages were indistinguishable in taste, 3 appearance, potassium ( 3.0 meq L-1), sodium ( 19.0 meq L-1), and ph ( 3.0; Steiner et al., 2009). Participants ingested 240 ml ( 8 oz.) of the appropriate beverage just before the start of exercise and every 15 min thereafter (until volitional exhaustion during the incremental trials; after the warm-up and after 15 and 30 min of exercise during the production trials). Procedure Participants reported to the testing site on four separate occasions and completed four exercise tests: two incremental protocols and two production trials. The two incremental protocols were completed during exercise Visits 1 and 2, and the two production trials were completed during exercise Visits 3 and 4. Perceptual measures were taken only for Visits 3 and 4; however, fitness data from Visits 1 and 2 were used in the analyses. Prior to the testing trials, participants were asked to maintain normal dietary habits, including typical caffeine intake, for the duration for the testing period. Participants were asked to fast for a minimum of 4 hr prior to each testing trial and to refrain from any exercise or training for at least 24 hr prior to each testing trial. Participants arrived at the GCRC at the same time of day for each exercise test. A minimum of 48 hr separated testing sessions. Upon arrival at the GCRC, participants had an intravenous catheter inserted in their left arm, and participants who had any anxiety or negative reaction to the procedure were given time for any negative symptoms to subside. The design of the production trials is summarized in Figure 1. Participants next completed three trials of a distance estimation task: A target cone was placed at a set distance down a hallway while the participants backs were turned. Participants turned and viewed the distance until they felt they had a good idea of how far it was from them. Participants were assured and then reassured that the experimenter would be with them during the blind walking and would stop them if they were in danger of running into anything. Participants then lowered a blindfold over their eyes, donned sound-muffling earmuffs, and were moved several steps forward and sideways by the experimenter and pointed down a second hallway at a 90 angle relative to the hallway containing the target cone. They then blind walked until they felt that they had traversed a distance that was equal to the distance they had viewed between their original location and the target cone. Participants remained standing at their ending location, and the distance between the starting and stopping point was measured with a laser distance meter. With the blindfold and earmuffs still in place, the participants were led back to the original viewing location via a circuitous route so as not to provide any performance feedback. They then removed the blindfold and earmuffs and stood with their back to the target while it was moved to a new distance. After the distance estimation trials, participants moved to the exercise testing room and electrodes were placed to monitor HR. The face mask for open-circuit spirometry was fitted to the participant and connected to the monitoring cart. Participants then began the exercise trial with a 5-min warm-up period. After the warm-up, participants were temporarily disconnected from the spirometry hose and ingested 240 ml of the drink via a straw. The main 45-min exercise period began, and participants were instructed to pedal at between 70 and 80 rpm and give the experimenter verbal commands (e.g., up or down ) to adjust the resistance of the bicycle such that the participant experienced an RPE of 16. Participants were blind to the actual resistance setting and the elapsed time. At the end of each minute, participants were asked to give their current RPE and told to request any change in resistance necessary to maintain the 16 RPE. Every 5 min, a blood sample was withdrawn from the IV access port and analyzed for blood glucose and blood lactate levels, and a manual blood pressure reading was taken from the other arm. After 15 min had elapsed, the spirometry hose was again briefly disconnected so that participants could consume another 240 ml of the drink, and this procedure was repeated again after 30 min had elapsed. Participants did not discontinue pedaling for these measurements at any point during the 45 min. At the end of the trial, participants dismounted and were given 5 min to cool down. Finally, participants returned to the distance estimation hallway location and completed three additional estimation trials with a novel set of distances in the same manner as before. Participants were debriefed only after completing their final visit. 3 In three other studies using the same formulation of caloric and noncaloric Gatorade, a total of 157 participants were questioned as to which of the two versions of the drink they believed they had ingested. Participants responses were statistically no different than chance in each study. The studies for which these assessments were made are in manuscripts under preparation.

6 BIOENERGETIC SCALING 5 Figure 1. The experimental design of the production trials. BGL blood glucose; HLA blood lactate; VO 2 volume of oxygen consumed; PO power output; HR heart rate. Results and Discussion Analysis Overview Analyses proceeded in stages in order to more parsimoniously show the independent effects of manipulations, individual differences in general long-term fitness, and individual differences in physiological state at the time of testing. First, ignoring any individual differences, the effect of the drink manipulation on distance perception was modeled. Next, data from the incremental trials was used to assess the effect of aerobic fitness on distance perception, ignoring any manipulations. (Using data obtained during the incremental trials allowed for overall fitness effects to be evaluated separately from physical state at the time of distance estimations.) Finally, physiological data collected during the production trials was used to examine the role of individual differences in both fitness and physiological state at the time of distance estimates. In the present study, the eight subjects had a large number of repeated measurements taken: six distance estimates on each day per participant, resulting in a total of 12 for each person, and between 18 and 90 total data points for each of the physiological measures across both days (the lower a result of measures taken every 5 min throughout two 45-min trials, and the higher a result of measures taken every minute). Thus, there is a large amount of data across multiple time points despite a relatively small n. For all mixedeffects models, R software package lme4 was used to obtain model fits, and with package languager, Markov chain Monte Carlo (MCMC) simulated sampling was used to obtain p values for the fixed effects (Baayen, 2011; Bates, Maechler, & Bolker, 2012; R Core Team, 2012). MCMC involves sampling from probability distributions that take small random steps away from the original distribution a large number of times. All analyses using MCMC used 10,000 simulated samples. If 95% of the highest probability density from the simulations does not include zero, it indicates that the effect (beta value) is significant. Glucose Drink and Exercise Manipulation A linear mixed-effects model was used to investigate the effect of the glucose drink on distance perception. Distance estimate data for one participant was available for only one of the 2 days, and because values were extreme in the hypothesized direction for the condition assigned that day, this participant s data was excluded for this analysis to avoid potential confounds of imbalance. Grouping factors were dummy coded for drink condition (glucose or placebo) and occasion (pre- and postcycling and drink consumption period), and blind walked distances were then predicted from the additive effects of the target distance, the condition, the occasion, the interaction between condition and occasion, the visit number, the trial number, and a subject-level random intercept. Of primary interest, there was a significant interaction between drink condition and occasion, such that compared with their initial distance estimates, participants who ingested the carbohydrate drink perceived distances to be significantly shorter after cycling than participants who ingested the placebo ( 0.84, pmcmc.033; see Figure 2). As shown in Table 1, there were several other significant effects: There was an unexpected effect of drink condition such that for the carbohydrate condition, blind-walk distances were higher overall; the target distance was of course highly predictive of blind-walked distance; the visit number predicted greater blind-walking distances on the second visit (most visits were within 2 to 3 days, so participants may not have fully recovered from the first visit and may have been slightly fatigued, or may have been more comfortable with blind walking); and there was an effect for trial number such Figure 2. Change in pre- to post-blind-walk distance by glucose drink condition. See the online article for the color version of this figure.

7 6 ZADRA, WELTMAN, AND PROFFITT Table 1 Coefficients of Fixed Effects for Main Model Estimate MCMC mean Lower HPD95 Upper pmcmc Intercept (Pre, Placebo) Target Occasion (Post) Condition (Carb) Day Trial number (w/in Occ) OccPost:ConditionCarb Note. MCMC Markov chain Monte Carlo; HPD95 95% of the highest probability density. that participants walked increasingly farther with each successive trial (it is known that blind-walking responses change with repeated trials; see Philbeck, Woods, Arthur, & Todd, 2008). The primary result of interest supports the idea that walkable distances are scaled by bioenergetics and, specifically, energy availability. The decrease from pre- to post-estimates in both conditions may have been related to one or more extraneous factors. Familiarity with blind walking may have contributed, as participants may have initially been cautious about the potential for running into something but then grew more comfortable with experience (the fact that the trial number within each pre- and postoccasion and the day predict increasing blind-walked distances supports this explanation). A warm-up effect may have also contributed (see Riener, Witt, Augustyn, & Proffitt, 2006). Especially in trained individuals, a period of exercise can serve to ready the body for physical activity, increasing both the efficiency of energy metabolism and the availability of metabolic substrates (McArdle, Katch, & Katch, 2010). Regardless, the critical finding is a difference between the two conditions showing a greater decrease on the day of carbohydrate supplementation. Further, irrespective of the between-condition manipulation effect, the most powerful evidence in support of bioenergetic scaling comes from the novel use of physiological measures that allow an examination of individual differences. Individual Differences in Overall Fitness During the incremental testing trials, several indicators of overall aerobic fitness were obtained for each participant on each of the two trials (one trial including a placebo drink, the other including a carbohydrate drink): VO 2 Max and VO 2 at LT. VO 2 Max is an indicator of overall aerobic fitness but is especially relevant to the maximum potential for all-out activities, whereas VO 2 at LT is generally considered to be one of the best physiological indicators ofaerobic fitness for submaximal endurance activities, as it represents the percentage of VO 2 Max that can be attained and maintained over time without fatigue. Because an RPE of 16 is a submaximal level of energy output, a linear mixedeffects model was used to predict blind-walked distances from the additive effects of the target distance and VO 2 at LT, with a subject level random intercept nested within drink condition. As hypothesized, participants aerobic fitness, as indicated by higher VO 2 at LT scores, was negatively associated with perceived distances, as indicated by shorter blind-walking responses ( 0.12, pmcmc.009; see Figure 3). This relationship was equally true both for preand postcycling blind-walking responses; a model that included the Figure 3. Relationship between distance perception and VO 2 at lactate threshold (LT) for pre- and postexercise distance estimates. Higher values of VO 2 at LT indicate higher levels of aerobic fitness.

8 BIOENERGETIC SCALING 7 Table 2 VO 2 at LT as a Predictor of Distance Perception Estimate MCMC mean Lower HPD95 Upper pmcmc Intercept (Pre) Target VO 2 at LT Occasion (Post) VO 2 at LT:OccPost Note. MCMC Markov chain Monte Carlo; HPD95 95% of the highest probability density; VO 2 at LT volume of oxygen consumption at lactate threshold. occasion as a factor showed no significant interaction between occasion and VO 2 at LT ( 0.02, pmcmc.722; see Table 2). Individual Differences in Exercise Performance and Physiological Response From the many sources of physiological data collected during the production trials, a number should be indicative of either the general fitness of the individual or of their present physiological state. 4 Those measures that are related to general fitness should be highly correlated with VO 2 Max and/or VO 2 at LT, and in fact many are and should therefore share a large proportion of explanatory power for variability in distance estimates (see Table 3). Nonetheless, it is useful to examine the specific relationships between each physiological factor and distance perception independently, both to show the strength of the connection between physiological state and perceptual scaling, and to demonstrate the predictive value of each measure independently for potential future use. There was no difference in physiological factors between conditions with the exception of BGL. A linear model predicting the trapezoidal area under the curve (AUC) for BGL (used in exercise physiology for physiological measures to capture change from baseline and duration of the change) from drink condition shows that BGL was raised and maintained at higher levels in the carbohydrate condition ( , p.001; for further details and statistics, see Steiner et al., 2009). In order to test whether this variability in physiological response was predictive of variability in distance perception, a series of linear mixed-effects models was used to predict blind-walking distance from physiological data collected on the day of the corresponding estimates. Blind-walk distances were predicted from the additive effects of target distance and the trapezoidal AUC for values of each physiological factor during the 45-min cycling period, with a subject level random intercept nested within condition. Coefficients and p values for each physiological factor are summarized in Table 4. (As expected, in each model the coefficient for target distance was highly significant and near 1.06, and therefore is omitted from the table.) For each physiological factor except blood glucose, the effect was the same for both preand postcycling estimates: Including an interaction term between the physiological factor and occasion in each case did not yield a significant interaction in any of the models. This suggests that the individual differences in distance perception are related primarily to differences in fitness between individuals. Relationships between the ratio of blind-walked to actual target distances and physiological measurements are shown in Figure 4. Blood lactate. Higher blood lactate levels were predictive of greater distance estimates. Higher blood lactate levels are indicative of exercise intensity beyond the individual s LT, and occur because the individual is either less fit or worked harder relative to their fitness level. Either cause would fit with the current notion of perceptual scaling of distance. Power output. Higher power output (the energy generated by cycling, as determined by the resistance set on the cycle) was predictive of greater distance estimates. Interestingly, this was predictive of both pre- and postexercise estimates, suggesting that it is a reflection of fitness. Because power output was negatively correlated with other measures of fitness, namely, VO 2 Max, this indicates that participants who were less fit tended to work harder. 5 HR. Higher HR was predictive of greater distance estimates. Higher HRs are indicative of lower levels of fitness, as more-fit individuals have higher cardiac outputs and generally more efficient hearts. Energy expenditure. Greater caloric energy expenditure during cycling was predictive of greater distance estimates. Energy expenditure is negatively correlated with other fitness indicators, suggesting that this is an indicator of efficiency (more-fit people use less energy to produce the same work), or energy depletion (less-fit people use up more of their energy to do the same work), or both. VO 2 (ml/kg/min) during cycling. VO 2 (ml/kg/min) during cycling was not a significant predictor of distance estimates on its 4 The question arises as to why there exist any individual differences in present physiological state, given that participants were all exercising at the same RPE. Participants were instructed to adjust the power output of the bike in order to maintain an RPE of 16 throughout the production trials. Although this should have, for the most part, matched the strenuousness of the exercise to each participant s individual level of fitness, and therefore yielded similar physiological responses to the exercise, it does not appear to have been entirely successful in doing so. The variability of physiological responses despite a common RPE was evidenced primarily by the finding that although an RPE of 16 is known to correspond to a blood lactate level of 4 mm, in the present study, steady-state blood lactate levels were attained that varied greatly between subjects, from 1 to6 mm, though few went much above 4 mm (Steiner et al., 2009). The variability appears to be related to individual differences rather than measurement error, as blood lactate levels were relatively stable within participants across the two production trial visits. These individual differences may have resulted from different understandings of what an RPE of 16 should be, or perhaps from aspects of fitness or physiological state that are not perceived in the experience of strenuousness. Additionally, RPE should coincide with values for percent of VO 2 Max and HR Max (McArdle et al., 2010). For an RPE of 16, VO 2 should be about 85% of VO 2 Max, yet in this sample the values during the last 15 min of cycling ranged from 44.19% to 85.84% (M 72.91%, SD 9.38%). Similarly, at an RPE of 16, HR should be at 92% of HR Max, yet in this sample the average HR values during the last 15 min of cycling ranged from 54.73% to 97.37% of HR Max (M 86.65%, SD 10.16%). Clearly, there was a large degree of variability in physiological response to the cycling despite the attempt to center the participants with a target RPE (RPE ratings assessed every 5 min show that there was little variation, M 15.84, SD 0.27). 5 It may at first seem counterintuitive that measures of work during exercise are predictive of preestimates as well as postestimates. Although the physiological effects of exercise are expected to affect postexercise estimates, both how a person exercises and their physiological response to exercise are ultimately determined by fitness.

9 8 ZADRA, WELTMAN, AND PROFFITT Table 3 Physiological Measure Correlations VO VO 2 Max HR BGL HLA Kcal VO 2 /Kg VO 2 Max 0.33 HR BGL HLA Kcal total VO 2 /Kg Power output Note. The correlations of physiological variables within subjects. VO volume of oxygen consumption at lactate threshold; VO 2 Max maximal oxygen consumption; HR heart rate; BGL blood glucose; HLA blood lactate. Aerobic measures are expected to be highly correlated. p.05. p.01. p.001. own; however, the percent of VO 2 relative to each individual s VO 2 at LT was, indicating that individuals who work harder relative to their own fitness level to maintain an RPE of 16 perceived distances to be greater. Blood glucose. As the only physiological measure that is directly affected by a manipulation, blood glucose should not be expected to be predictive of predrink estimates. Rather, the effect of the manipulation on an individual s blood glucose levels should be related to the change from pre- to postdrink estimates. For blood glucose, unlike the other physiological measures, the pre- to postchange in ratios of blind-walked to actual distances was predicted. An initial model yielded a nonsignificant negative trend; however, accounting for the fitness of the individual and the calories they burned (a portion of which were likely from the glucose being carried in the blood) by including VO 2 at LT and total Kcal expended yielded a near-significant model in the expected direction ( , pmcmc.058; BGL AUC values range from to ). Participants who had a higher BGL AUC trended toward a greater decrease in distance estimates from pre- to postoccasions. It should be noted that changes in blood glucose levels in response to carbohydrate ingestion are different under exercise and nonexercise conditions. 6 Maintaining a higher blood glucose level during exercise is indicative of better energy supply to active muscles and a delay in time to fatigue; however, as available glucose is being continually used, Table 4 Fixed Effects Coefficients for Physiological Models Estimate MCMC mean Lower HPD95 Upper pmcmc HLA Power output Heart rate Kcal total VO 2 per Kg VO 2 /Kg of VO BGL Note. Each row is from an independent model. BGL is unique in including additional predictors for total calories burned and VO 2 at LT. MCMC Markov chain Monte Carlo; HPD95 95% of the highest probability density; HLA blood lactate; VO 2 volume of oxygen consumption; VO volume of oxygen consumption at lactate threshold; BGL blood glucose. it is not surprising that the effect size of blood glucose levels is smaller than has been observed in previous experiments that did not include exercise (e.g., Schnall et al., 2010). Summary. Individual differences in physiological responses were repeatedly predictive of perceived distance. The fact that the measures are all correlated with VO 2 indicators of fitness reinforces the link between fitness and distance perception, and explains why the measures are predictive both of postcycling estimates and precycling estimates. Although participants all cycled at an RPE of 16, and physiological responses should thus have been similar, there is always going to be some degree of individual difference in physiological responses (or, for that matter, in perception of exertion or in understanding of the RPE scale). Although the RPE prescription should have generally accounted for large differences in fitness by allowing each individual to adjust the cycle resistance, it would be impossible to completely remove all variability. These results show that that variability is predictive of individual differences in distance perception. General Discussion Summary of Results The current experiment demonstrates direct links between physiology, energy, and the scaling of spatial layout. Manipulating blood glucose levels via manipulations of carbohydrate supplementation during vigorous exercise led to condition-dependent differences in distance perception. Relative to their initial estimates, when participants received a glucose drink, they perceived distances to be shorter after vigorous, sustained exercise than they did when they received an artificially sweetened placebo. More importantly, however, inherent individual differences in fitness, physiological responses to exercise, and exercise performance were predictive of individual differences in distance perception both before and after vigorous exercise. 6 Under nonexercise conditions, insulin must be released to cause cellular uptake of blood glucose, while under exercise conditions, active muscles are able to take up glucose from the blood without insulin. For higher intensity exercise with a duration beyond 10 min, blood glucose becomes a major source of energy for the active muscles. Glucose in the blood is replenished by liver glycogen stores and exogenous carbohydrates, and failure to maintain adequate blood glucose levels results in fatigue (McArdle et al., 2010).

10 BIOENERGETIC SCALING 9 Figure 4. Distance ratio relationship with physiological measurements. Distance ratio perceived/actual. All values except blood glucose (BGL) are raw. BGL shows change from pre- to postratios and BGL values are fitted from a model that includes factor for total calories burned and VO 2 at lactate threshold. BGL values are in units of area under the curve (AUC). From Angles to Extents Although we perceive linear extents, visual information is entirely angular in its composition (Gibson, 1979). Optical information consists of the angular locations of luminance contrasts (see Figure 5). The rescaling of angular visual information into units appropriate for measuring linear extents requires geometry and a ruler. For example, the distance to a point on the ground a short distance in front of an observer can be scaled in units of eye height (Sedgwick, 1986). In Figure 6, the distance, d, is specified by the visual angle in units of eye height, I: d I tan Eye height is but one example of a perceptual ruler; there are many others that could potentially be used to transform angular visual information into perceived linear metrics (Proffitt & Linkenauger, 2013). For any intended action, specific aspects of the body become most relevant and useful for measuring extents; for example, hand size is relevant for grasping an object, whereas arm length is relevant for reaching to a location in near space. In such cases, hand size and arm s length have been shown to serve as perceptual rulers (Proffitt & Linkenauger, 2013). A Bioenergetic Scale As is true for all organisms, human evolution occurred under selection pressures entailing an economy of action. Survival depends on obtaining more energy than is expended. It turns out that very little of our daily energy expenditure is under volitional control. Except for athletes in training, 80% of our daily energy expenditure is consumed by resting metabolism and the energy required to eat and digest food; moreover, about 89% of the remaining energy that is under volitional control is spent on locomotion (Levine et al., 2005). Walking is the most bioenerget-

11 10 ZADRA, WELTMAN, AND PROFFITT Figure 5. Optical information is made up entirely of angles. The right side of the figure shows the optical information array inside the viewer s eye (figure originally published in Proffitt & Linkenauger, 2013). See the online article for the color version of this figure. ically expensive activity that most of us choose to pursue. Previously, we have proposed that walkable extents are scaled to the bioenergetic costs of locomotion (Proffitt, 2006a). Evidence for the bioenergetic scaling of walkable extents has been found in numerous empirical studies (see Proffitt & Linkenauger, 2013, for a review). For example, when the relationship between walking speed and optic flow is altered by having participants walk on a treadmill with or without optic flow, participants who experience no optic flow will report a distance to be greater after the treadmill adaptation than before relative to participants who experience optic flow. For the former participants, a recalibration occurs such that the visual-motor system associates a certain amount of forward walking effort with remaining stationary, and, consequently, the amount of anticipated walking effort required to walk an extent is made greater, which in turn produces an increase in perceived distance (Proffitt et al., 2003). As was discussed in the Introduction, White et al. (2013) used a treadmill in a similar manner to demonstrate that walkable extents are specifically scaled by the amount of walking energy expended relative to optic flow. They changed the relationship between optic Figure 6. The distance to an object on the ground (d) can be scaled in units of eye height (I): d I/(tan ). The subject depicted gave signed consent for their likeness to be published in this article. See the online article for the color version of this figure. flow and energy expenditure by varying either (a) the speed of optic flow (in a virtual environment viewed via a head-mounted display), (b) treadmill speed, or (c) the incline of the treadmill. Each change influenced distance judgments in the predicted direction. White et al. proposed that distance perception is a function of what they term multimodally specified energy expenditure (MSEE): Energy Expenditure MSEE Optically Specified Distance Extending White et al. s (2013) account, it is important to recognize that the energetic cost of locomotion is not the only relevant bioenergetic factor. The amount of energy required for an action becomes more meaningful when it is put in terms of how much energy is available. Just as missing a freeway exit has different consequences when driving with the Empty light on than when the gas tank is full, the cost of walking is relatively greater when physiological energy reserves are low than when reserves are higher. Similarly, changes from one occasion to the next in the amount of energy available for walking will change the bioenergetic costs, and should therefore result in differences in perceived distance. Though this has not been demonstrated with distance, manipulations of energy availability have given the expected results with perception of geographical slant. After being fatigued by a long run, participants perceive hills to be steeper than before (Bhalla & Proffitt, 1999). Thus, we propose that the scale for walkable extents is the energy cost of walking relative to the energy available for this action. The concept of bioenergetics includes both of these components. Modifying White et al. s (2013) expression, the bioenergetic scaler, MSEE, becomes a function of the relative energetic costs of walking, B, relative to optically specified distance (optically specified in terms of angles), B MSEE Optically Specified Distance where B is energetic cost relative to energy availability: Energy Cost B Available Energy As a result, the bioenergetic scaler, B, is greater for a more costly action or when less energy is available, and vice versa.