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, 2015. http://dx.doi.org/10.1037/xhp0000107
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. http://dx.doi.org/10.1037/xhp0000107
Journal of Experimental Psychology:
Human Perception and Performance
2015, Vol. 41, No. 5, 000
© 2015 American Psychological Association
0096-1523/15/$12.00 http://dx.doi.org/10.1037/xhp0000107
Walkable Distances Are Bioenergetically Scaled
Jonathan R. Zadra, Arthur L. Weltman, and Dennis R. Proffitt
This document is copyrighted by the American Psychological Association or one of its allied publishers.
This article is intended solely for the personal use of the individual user and is not to be disseminated broadly.
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
nauger, 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
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 (Linke-
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 84112. E-mail: j.zadra@utah.edu
1
ZADRA, WELTMAN, AND PROFFITT
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2
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.
Experiment Overview
We hypothesized that
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.
BIOENERGETIC SCALING
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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 ⫽ 26.38 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) to 20 (very, very hard). RPE was assessed at
the end of each minute during the production trials.
Physiological measurements.
Aerobic capacity (VO2). 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
3
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 VO2 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 VO2 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).
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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 (VO2)
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 VO2 versus power output were used to determine HR,
RPE, and VO2 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, VO2, 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 mEqⴱL-1), sodium (⬃19.0 mEqⴱL-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.
BIOENERGETIC SCALING
5
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Figure 1. The experimental design of the production trials. BGL ⫽ blood glucose; HLA ⫽ blood lactate;
VO2 ⫽ 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.
ipant’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
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 partic-
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.
ZADRA, WELTMAN, AND PROFFITT
6
Table 1
Coefficients of Fixed Effects for Main Model
Estimate
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Intercept (Pre, Placebo)
Target
Occasion (Post)
Condition (Carb)
Day
Trial number (w/in Occ)
OccPost:ConditionCarb
⫺2.91
1.11
⫺0.55
1.18
0.43
0.23
⫺0.84
HPD95
MCMC
mean Lower Upper pMCMC
⫺2.91
1.11
⫺0.54
1.20
0.44
0.23
⫺0.84
⫺4.36
0.97
⫺1.40
0.61
0.02
0.01
⫺1.61
⫺1.50
1.25
0.36
1.76
0.84
0.47
⫺0.08
⬍.001
⬍.001
.233
⬍.001
.041
.047
.032
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): VO2 Max and VO2 at LT.
VO2 Max is an indicator of overall aerobic fitness but is
especially relevant to the maximum potential for all-out activities, whereas VO2 at LT is generally considered to be one of
the best physiological indicators of aerobic fitness for submaximal
endurance activities, as it represents the percentage of VO2 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 VO2 at LT, with a subject
level random intercept nested within drink condition. As hypothesized, participants’ aerobic fitness, as indicated by higher VO2 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 VO2 at lactate threshold (LT) for pre- and postexercise
distance estimates. Higher values of VO2 at LT indicate higher levels of aerobic fitness.
BIOENERGETIC SCALING
Table 2
VO2 at LT as a Predictor of Distance Perception
Intercept (Pre)
Target
VO2 at LT
Occasion (Post)
VO2 at LT:OccPost
HPD95
Estimate
MCMC
mean
Lower
Upper
pMCMC
2.10
1.07
⫺0.12
⫺0.72
0.02
2.07
1.07
⫺0.12
⫺0.69
0.02
⫺1.56
0.93
⫺0.20
⫺3.06
⫺0.08
5.60
1.22
⫺0.03
1.73
0.10
.196
⬍.001
.009
.558
.722
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This article is intended solely for the personal use of the individual user and is not to be disseminated broadly.
Note. MCMC ⫽ Markov chain Monte Carlo; HPD95 ⫽ 95% of the
highest probability density; VO2 at LT ⫽ volume of oxygen consumption
at lactate threshold.
occasion as a factor showed no significant interaction between occasion and VO2 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 VO2 Max and/or VO2 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 (␤ ⫽ 262.09, 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 be-
7
tween 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, VO2 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.
VO2 (ml/kg/min) during cycling. VO2 (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 to 6
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 VO2 Max and HR Max
(McArdle et al., 2010). For an RPE of 16, VO2 should be about 85% of
VO2 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.
ZADRA, WELTMAN, AND PROFFITT
8
Table 3
Physiological Measure Correlations
VO2@LT
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VO2 Max
HR
BGL
HLA
Kcal total
VO2/Kg
Power output
VO2 Max
HR
BGL
HLA
Kcal
VO2/Kg
⫺0.88ⴱⴱⴱ
⫺0.40ⴱⴱⴱ
⫺0.73ⴱⴱⴱ
⫺0.71ⴱⴱⴱ
⫺0.64ⴱⴱⴱ
⫺0.68ⴱⴱⴱ
0.43ⴱⴱⴱ
0.55ⴱⴱⴱ
0.53ⴱⴱⴱ
0.68ⴱⴱⴱ
0.68ⴱⴱⴱ
0.16
0.03
0.01
⫺0.04
0.73ⴱⴱⴱ
0.31ⴱⴱ
0.66ⴱⴱⴱ
0.60ⴱⴱⴱ
0.78ⴱⴱⴱ
.70ⴱⴱⴱ
ⴱⴱ
0.33
⫺0.01
⫺0.02
⫺0.69ⴱⴱⴱ
⫺0.56ⴱⴱⴱ
0.16
⫺0.19
Note. The correlations of physiological variables within subjects. VO2@LT ⫽ volume of oxygen consumption
at lactate threshold; VO2 Max ⫽ maximal oxygen consumption; HR ⫽ heart rate; BGL ⫽ blood glucose; HLA ⫽
blood lactate. Aerobic measures are expected to be highly correlated.
ⴱ
p ⬍ .05. ⴱⴱ p ⬍ .01. ⴱⴱⴱ p ⬍ .001.
own; however, the percent of VO2 relative to each individual’s
VO2 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 VO2 at LT and
total Kcal expended yielded a near-significant model in the expected direction (␤ ⫽ ⫺0.0001, pMCMC ⫽ .058; BGL AUC
values range from 3267.90 to 4378.95). 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
MCMC
Estimate mean
HPD95
Lower
HLA
0.0059 0.0058
0.0019
Power output
0.0004 0.0004
0.0000
Heart rate
0.0005 0.0005
0.0000
Kcal total
0.0068 0.0068
0.0028
0.0011 0.0010 ⫺0.0011
VO2 per Kg
VO2/Kg of VO2@LT
0.0530 0.0524
0.0235
BGL
⫺0.0001 ⫺0.0001 ⫺0.0003
Upper
pMCMC
0.0099
0.0007
0.0009
0.0107
0.0031
0.0822
0.0000
.005
.031
.030
.002
.330
.002
.058
Note. Each row is from an independent model. BGL is unique in including additional predictors for total calories burned and VO2 at LT.
MCMC ⫽ Markov chain Monte Carlo; HPD95 ⫽ 95% of the highest
probability density; HLA ⫽ blood lactate; VO2 ⫽ volume of oxygen
consumption; VO2@LT ⫽ 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 VO2 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).
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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 VO2 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-
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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
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):
MSEE ⫽
Energy Expenditure
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),
MSEE ⫽
B
Optically Specified Distance
where B is energetic cost relative to energy availability:
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.
B ⫽
Energy Cost
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.
BIOENERGETIC SCALING
↑B ⫽
↑ energetic cost
energy available
or ↑ B ⫽
energetic cost
↓ energy available
and
↓B ⫽
↓ energetic cost
energy available
or ↓ B ⫽
energetic cost
↑ energy available
Consequently, distances are perceived as greater when the bioenergetic scaler is larger, and vice versa.
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Individual Differences
Both components of B— energy costs and availability—are going to be different for every individual. Beyond shorter term state
differences, such as fluctuations in stored energy resulting from
energy depletion or ingesting food and drink, more stable traits that
can be considered as components of physical fitness have multiple
bioenergetic consequences— both for the bioenergetic cost of
walking and for the bioenergetic resources available to walk. With
higher levels of fitness come (a) a greater energy storage capacity
(in the form of glucose stored as glycogen in the muscles), so that
there is more energy available for action; (b) increased movement
efficiency, so that less energy is required to perform a given action;
and (c) faster and more efficient metabolism of ingested glucose
into adenosine triphosphate (the ultimate currency for biological
work), so that a given quantity of glucose carries a greater benefit
(McArdle et al., 2010). These are but a few of the different
bioenergetic consequences, but all have the ultimate effect of
influencing the bioenergetic scaler. Even the consequences of
ingesting glucose will be different for a fit individual, as it will be
processed faster, more efficiently, and ultimately can produce
more biological work than the same amount would in the body of
a less-fit individual.
It is these bioenergetic consequences of what is termed
“fitness” that allow athletes to run farther, faster, and longer, or
generally outperform less-fit individuals in a physical activity.
They have more stored energy available, they use less energy to
do the same things, and they get more energy and can produce
more biological work out of the food they eat. As stated
previously, 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. This is precisely what we have observed in the results
of the current study. Not only did the glucose manipulation
affect distance scaling by changing the amount of available
energy, but individual differences in fitness that would affect
both the cost of walking and the energy available for walking
predicted interindividual variability in distance scaling in the
predicted directions. Individual fitness was inversely correlated
with perceived distance both on the occasion of the pretest,
which was prior to any experimental manipulation, and for the
posttest, which followed strenuous exercise.
Learning the Scale
For morphological scaling, the relationship between what is
being viewed and the relevant aspect of morphology is easily
11
ascertained. Both are seen simultaneously. We can see our hand
approaching a to-be-grasped object; similarly, we can see the
extent of our reach.
Bioenergetic scaling, on the other hand, is slightly different.
The bioenergetic scaler cannot be visually observed. Although
it might seem more difficult to explain how the relationship
between visual and bioenergetic factors could be learned, the
mechanism need not be different. The relevant relationship can
be learned through experience. As we walk, optic flow and
bioenergetic expenditure occur simultaneously. The results
from treadmill-walking experiments provide evidence that the
visual-motor system is able to rapidly learn this relationship:
Altering the relationship between optic flow and the concomitant rate of energy expenditure influences distance perception
(Proffitt et al., 2003; White et al., 2013).
Alternative Explanations
As discussed earlier, it has been suggested that bioenergetic
perceptual scaling effects are caused by demand characteristics
(Durgin et al., 2009, 2012; Shaffer et al., 2013). Demand characteristics can never be completely ruled out whenever experimental
manipulations are involved. It should be noted that those studies
designed to show the influence of demand characteristics have
themselves employed explicit demand characteristics designed to
evoke or eliminate the demand characteristics purported to be
responsible for the perceptual effect (Durgin et al., 2009, 2012;
Shaffer et al., 2013). Whether a manipulation does or does not
evoke a demand characteristic will always be open to debate.
One way to eliminate the possibility of demand characteristics is
to take an individual differences approach. In such designs, there
are no experimental manipulations; everyone is treated the same. A
demand characteristic exists when participants alter their behavior
based on what they hypothesize is the expected effect of the
manipulation. A double-blind method and careful selection of
matching control conditions should, for the most part, prevent
participants from correctly guessing the manipulation; however,
with any experimental manipulation (i.e., when something is done
specifically with the intent of changing a participant’s behavior), it
is difficult, if not impossible, to completely hide the fact that there
was a motivated manipulation. Rather than manipulating an independent variable and looking for differences in the dependent
measure between experimental groups, an individual-differences
approach uses preexisting differences in levels of the independent
variable and asks whether the pattern of differences is predictive of
the pattern of differences in the dependent measure across individuals. The absence of a manipulation precludes the possibility
that participants could alter their behavior in response. Although in
some cases participants may be aware of what is being measured,
they are unaware of the results of these measurements—and this is
especially true for direct physiological recording.
In the current study, we found that individual differences in
physiological indices related to physical fitness predicted individual differences in perception independent of the glucose
drink manipulation. We used direct physiological measures of
fitness and exercise performance both on the same day the
perceptual measures were taken and on another completely
separate occasion. As participants cycled, a multitude of direct
physiological measurements were taken, allowing individual
ZADRA, WELTMAN, AND PROFFITT
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12
differences in energy output (work), energy usage, aerobic
fitness, blood glucose, and several other indicators to be compared with individual differences in distance perception. Not
only did results support earlier findings using similar glucose
and fatigue manipulations, but individual differences in physiology predicted individual differences in perceptual estimates
in the hypothesized direction independent of the experimental
manipulation of glucose or placebo ingestion. First and foremost, VO2 at LT, arguably the best measure of fitness for
submaximal aerobic exercise, clearly showed that more-fit individuals perceive distances to be shorter. The relationship
between physical fitness and distance perception was found not
only for the posttest but also for the pretest, which occurred
before any experimental manipulations took place. These individual difference results are strong support for the notion that
walkable extents are scaled by the bioenergetic costs of locomotion relative to the bioenergetic resources currently available.
Conclusion
Two important findings derive from the current study. First,
manipulations of bioenergetic state that increase available energy
and/or result in enhanced physical performance through other
means cause a compression of walkable distances by decreasing
the bioenergetic scaler. Second, individual differences in physiology that indicate differences in energy availability or different
bioenergetic consequences of an action also change the bioenergetic scaler, and thereby evoke differences in the apparent extent
of walkable distances.
Perception is the experience of the world as it relates to the
perceiver and, more specifically, to that aspect of the perceiver’s
body that is relevant for interacting with the world at any given
moment. Vision’s utility is in its role of supporting action; if we
cannot interact with the world, then there is little point in being
aware of it. Visual information consists of angular units that must
be transformed into linear units when perceiving extents. Such
transformations require geometry and a ruler. When measuring
walkable extents, metric rulers (meter or yard sticks) are of little
utility to an energetic organism; to have adaptive meaning, perceived extents need to be expressed in terms of the consequences
of distance-directed action. The consequence of acting over walkable distances is that it incurs an energetic cost, and the amount of
energy available for voluntary action is extremely limited. The
current research supports the proposition that walkable extents are
scaled by the bioenergetic costs of walking the extent relative to
the bioenergetic resources available.
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Received December 10, 2014
Revision received May 11, 2015
Accepted June 3, 2015 䡲