|
| |
|
|
|
|
||||
| Home Help Feedback Subscriptions Archive Search Table of Contents | ||||
First published online December 14, 2005
Journal of Experimental Biology 209, 141-151 (2006)
Published by The Company of Biologists 2006
doi: 10.1242/jeb.01981
Hunting archer fish match their take-off speed to distance from the future point of catch
Universität Erlangen-Nürnberg, Institut für Zoologie II, Staudtstrasse 5, 91058 Erlangen, Germany
* Author for correspondence (e-mail: sschuste{at}biologie.uni-erlangen.de)
Accepted 9 November 2005
 |
Summary |
|---|
 TOP Summary IntroductionMaterials and methodsResultsDiscussionReferences |
|---|
Key words: prediction, motor planning, open-loop start, animal cognition, archer fish
|
Introduction |
|---|
|
TOPSummaryIntroductionMaterials and methodsResultsDiscussionReferences |
|---|
;
Dill, 1977). Thereby the fish
fire from positions not directly below their target but displaced laterally
from it (e.g. Dill, 1977;
Timmermans, 2001;
Rossel et al., 2002;
Schuster et al., 2004) so that
their shot transfers horizontal momentum to their target and makes a small
prey item fall on a ballistic path towards the water surface. It then takes
only about 100 ms from the start of the prey's ballistic motion till the
shooter as well as other school members can initiate a rapid turn and then
head off straight in the direction of the later point of impact of the falling
prey (Rossel et al., 2002).
The rapid turn and subsequent take-off in the chosen direction are performed
`open-loop', i.e. based only on information that the fish had sampled during
less than 100 ms of the prey's initial motion. That the fish can operate
without immediate sensory feedback is shown by experiments in which (i) a
filament connecting a prey item to its starting position swung the prey back
after an initial free flight phase and (ii) the fish responded to purely
horizontal target motion with turns that were directed towards the virtual
point of impact, which would be predicted on the basis of the initial values
of motion (Rossel et al.,
2002). Though restricted to the plane of the water surface, to choose the appropriate turning angle requires the fish to solve a three-dimensional problem, in which three independent variables need to be correctly taken into account: the initial speed, direction and height of the falling prey. While there is convincing evidence that the fish solve this problem and predict the bearing of the future point of impact, it is not easy to prove our conjecture that the fish truly pinpoints the location of this point. This is illustrated in Fig. 1A. In principle, the fish's `internal estimate' of the fly's later point of impact P could be anywhere within the area indicated in green (e.g. at P' or P''), and yet the fish is led to the point of impact. Here we provide direct evidence against this view by showing that (i) archer fish also predict the distance of the later point of impact and (ii) use this information to adjust their take-off speed to distance. The fish select a take-off speed that would allow them to arrive within a narrow time slot slightly after the prey's impact after travelling at constant speed. In their initial open-loop response to falling prey, hunting archer fish thus start with a predetermined turn size and take-off speed, which they can set independently to match the solution of a complex three-dimensional predictive problem.
|
|
Materials and methods |
|---|
|
TOPSummaryIntroductionMaterials and methodsResultsDiscussionReferences |
|---|
). Briefly, stationary prey items (Lucilia; killed by
freezing) were wetted (wet mass was about 50 mg) and stuck on the bottom side
of a heavy white disk (diameter 20 mm) hung in the middle of the tank, so that
prey could be presented at a height H of either 20 or 40 cm above the
water surface. Captures were videotaped from above (with 20 ms intervals
between successive half-frames). At each actual height of the falling prey its
position was projected to the water surface, the plane of the hunting fish,
taking into account the known distortion in perspective for that height. The
motion of dislodged prey followed simple ballistics so that the actual height
of the falling prey could be derived from the time it had been falling.
Responses and analysis
We restricted the analysis to responses that fulfilled all of the following
criteria. (1) Prey had to be still falling when the fish started to take off.
This criterion ensured that the response was predictive and driven by visual
input (and not e.g. mechanosensory input from the splashing prey). (2) The
path to the later point of impact had to be free of obstacles such as fellow
group members. This criterion is needed because obstacles strongly influence
the fish's take-off (S. Wöhl and S. Schuster, manuscript in preparation).
(3) Cases were excluded in which the fish could simply follow the prey's
motion. This criterion was needed to check whether the fish also predicted
bearing. A minimum angle of 10° was required between the path a responding
fish would have to take towards the point of impact and the horizontal path of
the prey. (4) To ensure that responses of sufficiently motivated fish are
evaluated and to be able to exclude extremely slow responses on the basis of
rigid criteria, the 5% responses with the largest arrival times after prey
impact (some in the order of seconds) were not included in the present
analysis.
A set of 90 responses (26 of the group of 8 fish, 64 of the group of 6 fish) was obtained that satisfied all criteria. Responses came from bystanders (N=36) and shooters (N=54).
The deinterlaced frames (50 s-1) were analyzed with the public domain program NIH Image (developed at the US National Institute of Health) using custom-written software to extract coordinates, derive angles and distances and plot traces such as shown in Figs 8, 9, 10. Speed was determined from the change in position of the tip of the fish's mouth between successive frames and the speed value is assigned to the start of the second of the two frames. It should be noted that this is not a generally valid method as it confounds translational and angular speed during phases of turning. If independent estimates of translational speed had been required during extended phases of turning, then they would have been better based on the displacement of the center of mass. However, because the fish turn and accelerate and then take off in the chosen direction, our more convenient measure could safely be used. From the distance between neighbouring pixels in the deinterlaced images we expect our speed estimates to be accurate to about 0.05 m s-1. Additional errors could occasionally result when the fish were not moving horizontally. Because archer fish take off so fast, these errors are of a size that does not affect the conclusions of this paper.
|
|
|
|
Results |
|---|
|
TOPSummaryIntroductionMaterials and methodsResultsDiscussionReferences |
|---|
Fig. 2A introduces the error
angles used to check whether the responding fish predicted the correct
bearing. In the responses, the errors 
made with respect to the point of
impact are distributed around a zero mean
(Fig. 2B). In other words, the
responses were so as to minimize the bearing error with respect to the later
point of impact. This is, however, not yet proof that the responses were
indeed predictive. An approximating strategy in which the fish turned to align
themselves with the prey's position at the end of the turn could in principle
mimic a true prediction. The findings reported in
Fig. 2C exclude this view. In
this figure the bearing error is evaluated not with respect to the later point
of impact but with respect to the fly's horizontal position at the end of
turning. The distribution of the respective errors ' is
systematically shifted towards positive values. Thus the responses we have
gathered were indeed predictive, i.e. were directed at the later point of
impact but not towards the actual sighting at the end of the initial turn.
|
(in degrees) were
-0.04±0.93 for bystanders (N=36) and -0.78±0.73 for
shooters (N=54). Also, if the errors are quantified by the minimum
distance the fish's course has from the point of the later point of impact (as
was done in Rossel et al.,
2002), bystanders and shooters perform equally well. The distances responding fish had to cover varied over a broad range from about 30 mm to 380 mm (Fig. 2D). Fig. 2E shows the distribution of time till prey impact that remained for the responding fish after they had finished their rapid turn and were ready to take off. In most cases the remaining time was long enough to potentially allow the fish to select an adapted motor program. The sizes of the turns responding fish made were also broadly distributed (Fig. 2F).
Take-off speed scales with distance
After it has finished its turn and is aligned towards the later point of
impact a responding fish accelerates very rapidly and reaches a high and
approximately constant take-off speed already in the first video frame after
take-off. Compared to the speed gained immediately at the end of the turning
phase, the later speed changes in the first 60 ms of translation are
negligible. This is shown in Fig.
3, which reports the differences in speed between subsequent
intervals of 20 ms duration. A slight but systematic increase in speed of 0.05
m s-1 (Fig. 3A,
20-40 ms after start) and 0.07 m s-1
(Fig. 3B, 40-60 ms after start)
occurred. Though these speed changes were significant (P<0.01 in
each case, t-test), they are small compared to the speed of about 1 m
s-1 on top of which they occurred. It therefore appears well
justified to treat the take-off as a start with constant speed, with speed
acquired rapidly at the end of the turning phase. This view is confirmed by
current high-speed video analyses in our laboratory. Maximum take-off speed
was in the range of 1.2-1.6 m s-1 or about 15-25 fish lengths
s-1, which is remarkably fast and clearly in the upper range of
values reported for other teleost fish (e.g.
Gray, 1953;
Bainbridge, 1960; Webb, 1973;
Wardle, 1975;
Nissanov and Eaton, 1989).
|
The speed measurements disclosed in a simple way that the fish must indeed have a measure of how far away the future point of impact is. This became evident when take-off speed was plotted against the distance the responding fish had to cover to the later point of their prey's impact. Fig. 4A-C report the speed values measured in the first (A) and the two subsequent (B,C) intervals (of 20 ms duration) of each take-off. Speed values determined in each of these intervals correlated highly significantly (P<0.0001) with distance.
|
This shows that the fish must have a measure of their distance from the future point of impact, which allows them to adjust their take-off speed. However, it does not exclude the possibility that take-off speed could be based on a more complex measure that takes other information besides distance and motor constraints into account. If such other factors were important in setting take-off speed and if these factors varied independently from distance then they could potentially explain the scatter in the speed-distance relation in the plots of Fig. 4. They also could offer a simple explanation for why the intercepts of the regression lines predict a non-zero take-off speed, even if a responding fish is already at the point of capture. Of the many negative findings of our extensive search to identify such other factors, two are worth mentioning. (1) The turn that the fish must make might in principle limit which speed level it can choose at start. For instance, after a large turn the fish might not be able to start slowly because the release of its strong bending could dictate a rapid start. In other words, if a responding fish had to turn much and then would have to start slowly because the point of impact were close it might not be able to set the required speed. Because turning angles of our responding fish varied largely (see Fig. 2F), we could test this notion by checking whether larger take-off speed levels tended to occur after large turns. However, we found no evidence for such a correlation. (2) We also found no convincing evidence that the fish would modulate their take-off speed in response to social factors such as `urgency' to be fast because competitors are close to the future point of impact.
How do the fish use distance information?
The search for a combined measure led to a better predictor of speed:
take-off speed scaled better with the ratio `distance/remaining time of
flight' rather than distance itself. The meaning of this ratio is intuitively
clear: it can be viewed as a `virtual speed' that the fish would have to keep
in order to arrive simultaneously with the fly after a travel at constant
speed. Fig. 5A plots take-off
speed against `virtual speed', also including those virtual speed values that
are far above the fish's (and all other known teleosts) speed limit (indicated
by an arrow). In the range of attainable speed the concentration of data
points on a line is striking. Fig.
5B plots the responses for which the remaining flight of the
target was long enough so that virtual speed was within the range of
attainable speeds. Within this region the plots describe take-off speed
remarkably well. Speed values determined in the first and third intervals of
20 ms duration after take-off (Fig.
5B,D) correlated significantly (P<0.05; Fisher
z-transformation) better with virtual speed than with distance
(Fig. 4A-C). For this analysis,
the speed values of the same 60 responses of
Fig. 5 were plotted against
distance, yielding r2=0.14, 0.24 and 0.22, respectively,
for the three intervals. For the second interval the apparent better
correlation (larger correlation coefficient) was not significant. It is
interesting to note that the large speed predicted by the regression lines of
Fig. 4 at zero distance is
greatly reduced. For the regression lines against distance the intercepts
±5% confidence intervals in m s-1 were (A) 0.56±0.12,
(B) 0.50±0.12, (C) 0.56±0.12 (using the N=60 responses
of Fig. 5).Thus, intercepts
were clearly (and significantly) larger than zero. In contrast, the regression
lines against virtual speed (shown in Fig.
5) had intercepts (m s-1) of (A) 0.17±0.16, (B)
0.20±0.20 (C) and 0.11±0.18, which are close to zero. Taken
together, virtual speed (distance per remaining time till impact) explains the
take-off speed better than distance alone.
|
Arrival times at the point of catch: predicted and actual
When did the fish actually arrive at the point of catch? Did they arrive
too early, too late or exactly just when the fly also arrived?
Fig. 6A shows when the
responding fish arrived after (which is assigned positive values of
t) their prey's impact (denoted as t=0) on the water
surface. There was not one case in which the fish overshot the point of
impact. In the majority of the responses fish managed to arrive within a
narrow time slot of 20-40 ms after their prey's impact. This is remarkable as
the responses were started from a wide variety of distances (ranging from 2.6
to 38.4 cm, see Fig. 2D) and
required very different turning angles chosen out of a broad range (see
Fig. 2F). It is interesting to
note that bystanders and shooters also performed equally well in this respect
(difference between the arrival times P>0.2, U-test).
|
;
Montgomery et al., 2002) of
the splashing prey provide, even within the remaining short time interval,
useful cues that could be used to fine tune the actual catch.
|
The course of speed and direction during the approach
To see how well fish held their initial settings of bearing and speed we
randomly selected 40 of the responses for a detailed analysis of post-start
changes. Fig. 8A shows an
approach path in which a fish initially turned by about 30°, accelerated
and took off at a speed of about 0.7 m s-1. It then approximately
kept its speed to arrive about 100 ms after the fly at full speed.
Fig. 8B shows that speed
increases slightly during the course but the major acceleration to take-off
speed occurred at the rapid start. Fig.
8C shows that the bearing was kept to throughout the approach.
A high precision in the fish's initial choice of bearing is typical of all examples. The chosen direction was kept with deviations less than about 5-10°. In 3 of the 40 paths, corrections to the chosen bearing were made but were likely due to the fact that other responding fish were about to come into the way. In contrast to direction, speed was not nearly as constant. As far as our speed resolution permits us to say, there appears to be no fixed pattern according to which take-off speed is changed in an approach. Approaches with approximately constant speed were seen in 25 of the 40 cases. In several cases (about 7 of 40) the responding fish kept an approximately constant acceleration throughout its course. An example of this is shown in Fig. 9. In other cases the fish first accelerated to high speed and then decelerated, or first reduced their speed and then accelerated during the final phase that preceeded the catch. An example of this latter situation is shown in Fig. 10.
|
Discussion |
|---|
|
TOPSummaryIntroductionMaterials and methodsResultsDiscussionReferences |
|---|
;
Sobel, 1990). The ability of
archer fish to fine tune an open-loop start on the basis of three-dimensional
movement prediction so far is unique. It differs remarkably from what
sportsmen, such as baseball outfielders, do in comparable three-dimensional
interceptive tasks in which the direction of target motion is not restricted
within a narrow range (e.g. Chapman,
1968; McBeath et al.,
1995; Regan, 1997;
van der Camp et al., 1997;
Shaffer and McBeath, 2002).
Outfielders have been convincingly shown to employ closed-loop strategies in
which their movement is under continuous sensory control so that no precise
match between the start direction and speed to the spatial location of the
later point of catch is required.
How to set take-off speed?
What could be an advantage for the fish to adjust take-off speed to
distance rather than simply heading off at maximum speed? This point is
perhaps best addressed by considering two hypothetical strategies to reach the
future point of catch. In the first strategy, the fish would accelerate very
rapidly to a speed that then is kept throughout the approach and selected such
as to make the fish arrive simultaneously with the prey at the point of
impact. A fish that follows this strategy would choose a constant velocity
v0=d/
, where d is its distance from
the future point of impact and the remaining time of falling. An
alternative second strategy would be to move at maximum speed, stop at the
point of impact and wait for the prey to arrive. If the fraction of the prey's
falling time during which the fish moves is denoted as the `duty interval'
, then a fish that follows this strategy would select a speed
v=v0/. To choose the second strategy would,
however, be more costly. The power a fish must invest in order to keep its
speed v is expected to vary roughly proportional to
v3. In a beautiful experimental arrangement, Webb
(1971) has experimentally
shown the proportionality to be v2.8 in freely moving
trout. The roughly cubic dependency of power on speed explains why the second
strategy is not clever: the saving made during the resting phase at the point
of catch is far outweighed by the costs of moving faster during the previous
translatory phase. Generally, the power P required for a path with
duty interval <1 increases very rapidly as decreases below
=1 (i.e. the first, constant-speed, strategy), according to the
relation P()/P(=1)=1/2. For
instance, moving fast for the first half of the prey's falling time and
waiting at the later point of impact for the remaining other half (i.e.
=0.5) causes fourfold higher costs than following the constant speed
motor program (=1).
This argument does not, however, clarify whether moving at constant
acceleration a would be better than moving with constant speed. It is
easy to see that moving at constant speed is superior in terms of work lost to
hydrodynamic friction. The argument goes as follows. In order to cover the
distance d in the remaining time by means of a
constant-acceleration motor program, the fish would have to set its
acceleration to a=2d/2. The power loss to
frictional forces, Pa, would then be:
 | (1) |
where 
is a constant. In contrast, to cover the distance d in
time at constant speed v requires v=d/
and the frictional power loss is:
 | (2) |
which is only one half of the loss Pa. Thus using a constant acceleration rather than a constant velocity strategy would double frictional costs.
Sensory and motor requirements
To profit from an optimal strategy requires that the fish is able to
predict with sufficient accuracy its distance d from the future point
of catch and the time that remains till prey impact. This prediction is
not at all trivial (see Fig. 1)
and the algorithm the fish uses is presently unknown. Deriving absolute speed,
for instance, requires rather precise knowledge of the spatial relations
between eye and target in order to correct for the strongly position-dependent
refractional errors at the water-air interface
(Schuster et al., 2004).
Moreover, the time period that the fish have to sample visual information and
to carry out the computations is very short (<100 ms) and would be expected
to seriously limit the precision the fish can attain (e.g. see
Bialek, 1990). It is thus
likely that the fish can only use rough estimates of d and ,
which may even contain systematic errors. This will translate into deviations
from a constant-speed program. For instance, if the fish underestimated the
distance d from the later point of impact then it would start too
slow and would have to speed up later during the approach in order not to
arrive too late.
However, despite these difficulties, archer fish must be able to derive at least some of the variables quite accurately. Otherwise they could not have set their bearing so well (e.g. see Figs 8, 9, 10). It is clear that the pressures on the yet unknown algorithm that predicts bearing are high because even slight bearing errors at take-off can cause large errors in the target region. For instance, to arrive within a region that lies not more than 10% of the true distance apart from the later point of impact already requires a precision of at least 5.7°, or about the angle a minute hand of a watch covers in 1 min.
On the other hand the motor system of the fish must also be able to
translate the available estimates of distance, direction and remaining time
into extremely rapid and well set angular- and translational acceleration
phases that rapidly turn the fish precisely by the required angle and push it
off at the required take-off speed. Clearly, failures to do so will also
result in errors that the fish might have to correct later as it comes close
to the point of catch. It is impressive how well the fish can set angle and
speed independently from each other within an extremely short time interval of
approximately 60 ms, and the linear and rotational accelerations involved are
remarkable. This remarkable performance of the fish's motor system is
currently analyzed using high-speed video techniques and our analysis so far
strongly suggests that archer fish recruit their fast life-saving C-start
network of identified reticulospinal neurones, common in teleost fish (e.g.
Nissanov and Eaton, 1989), to
drive their fine-tuned and extremely rapid open-loop starts to the point of
catch (S. Wöhl and S. Schuster, manuscript in preparation).
The deviation between actual and expected arrival times
If a constant velocity strategy minimizes frictional losses, why then do
the fish not simply keep their take-off speed constant throughout the approach
and arrive exactly as expected? Is it because they do not intend to follow a
constant speed approach because other constraints are more important than
minimizing friction? Or is it due to sensory or motor limitations as discussed
above? As shown in Fig. 6, fish
started slightly too slowly, so that most take-offs would lead the fish to
arrive about 20 ms later than they actually did. However, despite this the
correspondence between expected and actual arrival times is rather close. The
scatter in the expected arrival times (expected on the basis of constant
speed) is not much larger than that observed in the actual arrival times and
would still enable the fish to arrive within a narrow time slot, despite the
broad range of preceeding turns and distances to cover (shown in
Fig. 2D-F). This suggests that
speed corrections are not so much needed to compensate an initial sloppiness,
i.e. a broad distribution of take-off speed and hence of expected arrival
times, but rather to correct for a systematic `error'. The alternative
interpretation, that the fish did not intend to follow a constant speed
profile but another, for instance a slightly accelerated velocity profile,
seems not to fit two findings: (i) the close correspondence of actual and
expected arrival, and (ii) the failure to observe a clear pattern of
post-start speed changes that would have been indicative of another strategy
the fish might be trying to use. We therefore suggest that the fish set a
slightly erroneous take-off speed, which they correct later. This small
`error' need not be viewed as a shortcoming, but can be of adaptive value for
an open-loop start that enables the fish to catch its prey at full speed. If a
chance occurs to view the falling target during the actual approach, then the
slow initial speed can readily be corrected. And in case no useful cues
occurred after the start it is safe to set the initial speed slightly too slow
- slow enough to minimize the risk of overshooting the target.
Conclusion: the potential of open-loop solutions
Archer fish can not only predict the bearing but also the distance of the
later point of impact of their dislodged prey, and use this ability to
predetermine an appropriate turn size and take-off speed in their open-loop
start. This makes archer fish an attractive model to study how hunting animals
use predictive abilities to match extremely rapid open-loop responses to
non-trivial tasks. Given that such responses remove the heavy computational
demand of continuous sensory-processing and -feedback to the motor system, we
hope that this work will provide novel impetus on how open-loop strategies
could help to make autonomous robots a bit faster than they currently are.
|
Acknowledgments |
|---|
|
References |
|---|
|
TOPSummaryIntroductionMaterials and methodsResultsDiscussionReferences |
|---|
Bainbridge, R. (1960). Speed and stamina in three fish. J. Exp. Biol. 37,129 -153.[Abstract]
Bialek, W. (1990). Theoretical physics meets experimental neurobiology. Lectures in Complex Systems, SFI Studies in the Sciences of Complexity, Lect. Vol.II (ed. E. Jen), pp. 513-595. Menlo Park, CA, USA: Addison-Wesley.
Bleckmann, H. (1993). Role of the lateral line in fish behavior. In Behavior of Teleost Fishes (ed. T. J. Pitcher), pp. 201-246. London: Chapman and Hall.
Chapman, S. (1968). Catching a baseball. Am. J. Physics 36,868 -870.
Dill, L. M. (1977). Refraction and the spitting behavior of the archerfish (Toxotes chatareus). Behav. Ecol. Sociobiol. 2,169 -184.
Gray, J. (1953). The locomotion of fishes. Essays in Marine Biology, Elmhirst Memorial Lectures. Edinburgh: Oliver and Boyd.
Heitler, W. J. and Burrows, M. (1977). The
locust jump. I. The motor programme. J. Exp. Biol.
66,203
-219.
Lüling, K. H. (1963). The archerfish. Sci. Am. 209,100 -108.
McBeath, M. K., Shaffer, D. M. and Kaiser, M. K.
(1995). How baseball outfielders determine where to run to catch
fly balls. Science 268,569
-573.
Montgomery, J. C., Macdonald, F., Baker, C. F. and Carton, A. G. (2002). Hydrodynamic contributions to multimodal guidance of prey capture behavior in fish. Brain Behav. Evol. 59,190 -198.[CrossRef][Medline]
Nissanov, J. and Eaton, R. C. (1989). Reticulospinal control of rapid escape turning maneuvers in fishes. Am. Zool. 29,103 -121.
Regan, D. (1997). Visual factors in hitting and catching. J. Sports Sci. 15,533 -558.[CrossRef][Medline]
Rossel, S., Corlija, J. and Schuster, S.
(2002). Predicting three-dimensional target motion: How archer
fish determine where to catch their dislodged prey. J. Exp.
Biol. 205,3321
-3326.
Schuster, S., Rossel, S., Schmidtmann, A., Jäger, I. and Poralla, J. (2004). Archer fish learn to compensate for complex optical distortions to determine the absolute size of their aerial prey. Curr. Biol. 14,1565 -1568.[CrossRef][Medline]
Shaffer, D. M. and McBeath, M. K. (2002). Baseball outfielders maintain a linear optical trajectory when tracking uncatchable fly balls. J. Exp. Psychol. 28,335 -348.
Sobel, E. C. (1990). The locust's use of motion parallax to measure distance. J. Comp. Physiol. A 167,579 -588.[Medline]
Timmermans, P. J. A. (2001). Prey catching in the archer fish: angles and probability of hitting an aerial target. Behav. Proc. 55,93 -105.[CrossRef][Medline]
van der Camp, J., Savelsbergh, G. and Smeets, J. (1997). Multiple information sources in interceptive timing. Hum. Mov. Sci. 16,787 -821.
Wallace, G. K. (1959). Visual scanning in the desert locust Schistocerca gregaria Forskal. J. Exp. Biol. 36,512 -525.[Abstract]
Wardle, C. S. (1975). Limit of fish swimming speed. Nature 255,725 -727.[CrossRef][Medline]
Webb, P. W. (1971). The swimming energetics of
trout. I. Thrust and power output at cruising speeds. J. Exp.
Biol. 55,489
-520.
This article has been cited by other articles:
|
|
 |
|
  T. Schlegel and S. Schuster Small Circuits for Large Tasks: High-Speed Decision-Making in Archerfish Science, January 4, 2008; 319(5859): 104 - 106. [Abstract] [Full Text] [PDF]
|
|
|
|
 |
|
 T. E. Higham The integration of locomotion and prey capture in vertebrates: Morphology, behavior, and performance Integr. Comp. Biol., July 1, 2007; 47(1): 82 - 95. [Abstract] [Full Text] [PDF]
|
|
|
|
 |
|
 S. Wohl and S. Schuster The predictive start of hunting archer fish: a flexible and precise motor pattern performed with the kinematics of an escape C-start J. Exp. Biol., January 15, 2007; 210(2): 311 - 324. [Abstract] [Full Text] [PDF]
|
|
| |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
