High-speed Decision-making in Archerfish (Hochgeschwindigkeits-Entscheidungsfindung bei Schützenfischen)
Der Naturwissenschaftlichen Fakultät der Friedrich-Alexander-Universität Erlangen-Nürnberg zur Erlangung des Doktorgrades Dr. rer. nat.
vorgelegt von Thomas Schlegel
aus Nürnberg
Als Dissertation genehmigt von der Naturwissenschaftlichen Fakultät der Friedrich-Alexander- Universität Erlangen-Nürnberg
Tag der mündlichen Prüfung: 08. Juni 2010
Vorsitzender der Promotionskommission: Prof. Dr. Eberhard Bänsch
Erstberichterstatter: Prof. Dr. Stefan Schuster Zweitberichterstatter: Prof. Dr. Helmut Brandstätter
High-speed Decision-making in Archerfish
Abstract
1. Abstract Archerfish are famous for their ability to dislodge insects (such as flies) by spitting precisely aimed jets of water at them. Once fish manage to dislodge a prey of interest, they carefully monitor the initial movement of the prey item, precisely extracting several critical parameters of movement (such as speed and direction of prey movement, the distance to and height of prey), promptly predicting its future impact position, reacting with a swift and accurate turn. Finally fish accelerate towards that position, snatching their reward as it hits the water surface. The experiments of this thesis extensively engaged in the manipulation of the visual input cues, e.g. via changes in contrast levels, displaying two prey objects simultaneously or depriving the available visual input of moving prey spatially and temporally. The method of choice was the study of archerfish behaviour subsequent to the onset of prey movement as an amalgamation of the whole system s signal extraction, information processing, decision-making and overall performing abilities. In the process, I discovered that the archerfish s predictive turning behaviour can be elicited via prey movement alone – no preceding shooting is necessary (enabling all subsequent experimentation in the first place). The predictive behaviour is all the more remarkable, since it features the ability to instantly decide for one of two simultaneously appearing flies, applying a spatial representation of the outside world in the process. Furthermore fish keep up their turning accuracy even if prey motion will appear with a spatial offset to the fish s point of gaze. The latency of the fish s responses depends e.g. on the contrast levels between fly and background. The entire processing in between the onset of prey movement and the triggering of the fish s turn can be delivered within a time frame of 40 milliseconds, severely restricting the number of underlying neurons. Subsequent experiments revealed a visual input of less than 300 activated photoreceptors (equivalent to a retinal area of roughly 0.01 mm) completely suffices to elicit a precise predictive reaction. The accumulated results prove Archerfish to be a vertebrate system, shaped for top speed, in which a complex and plastic decision is performed by surprisingly small circuitry.
4 Tables
2. Tables
2.1 Table of contents
1. Abstract ...... 4
2. Tables ...... 5
2.1 Table of contents ...... 5 2.2 Table of figures ...... 7 2.3 Table of supplemental tables ...... 8
3.Introduction ...... 9
4. General methods...... 16
4.1 Animals and their keeping...... 17 4.2 Recording and managing behavioural data...... 19 4.3 Statistics ...... 20 4.4 Characterising the fish s performance...... 21 4.4.1 Latency ...... 21 4.4.2 Precision and Error...... 22 4.5 Seven criteria separating the analysable from the discarded reactions 24
5. The experiments ...... 25
5.1 Depriving the fish of shooting-related information ...... 26 5.1.1 Objectives and Experimental Approach...... 26 5.1.2 Results ...... 27 5.1.3 Discussion...... 30 5.2 Spatial attention ...... 34 5.2.1 Objectives and Experimental Approach...... 34 5.2.2 Results ...... 35 5.2.3 Discussion...... 37 5.3 Deciding for one of two flies ...... 38 5.3.1 Objectives and Experimental Approach...... 38 5.3.2 Results ...... 39
5 Table of contents
5.3.3 Discussion...... 42 5.4 Contrast dependency...... 44 5.4.1 Objectives and Experimental Approach...... 44 5.4.2 Results ...... 46 5.4.3 Discussion...... 48 5.5 Do the fish need a priori information on target height ...... 50 5.5.1 Objectives and Experimental Approach...... 50 5.5.2 Results ...... 52 5.5.3 Discussion...... 57 5.6 Finding the minimal integration interval...... 59 5.6.1 Objectives and Experimental Approach...... 59 5.6.2 Results ...... 64 5.6.3 Discussion...... 78 5.7 Breeding Archerfish ...... 81 5.7.1 Objectives and Experimental Approach...... 81 5.7.2 Results ...... 82 5.7.3 Discussion...... 83
6. Discussion...... 87
6.1 A conception of archerfish: from visual input to motor output ...... 88 6.2 Some closing remarks on cognition ...... 94
7. References ...... 96
9. Supplemental ...... 107
10. Acknowledgements ...... 121
11. Zusammenfassung auf Deutsch ...... 123
6 Table of figures
2.2 Table of figures
Figure 1: Distribution of participating fish species...... 17 Figure 2: Exemplary experimental setup ...... 20 Figure 3: Sequence, visualising latency determination ...... 21 Figure 4: Sequence, visualising determination of precision ...... 22 Figure 5: Sign conventions applied in error measurements ...... 23 Figure 6: Experimental differences in deprived versus natural setup ...... 27 Figure 7: Reactions to natural and deprived conditions are alike ...... 29 Figure 8: Matching fly movement in natural and deprived conditions...... 30 Figure 9: Using several platforms to test spatial attention...... 35 Figure 10: Behavioural reactions to horizontal offsets ...... 36 Figure 11: Two flies simultaneously...... 39 Figure 12: Providing two flies simultaneously ...... 40 Figure 13: Parameters of fly movement...... 41 Figure 14: Experimental setups applied to test for several visual contrasts .. 45 Figure 15: No correlation between latency and the fly s velocity ...... 46 Figure 16: Changing contrast conditions affects latency but not precision .... 47 Figure 17: Testing ten different contrast levels on two groups of fish...... 48 Figure 18: Experimental setup, testing behaviour to vertical offsets ...... 51 Figure 19: Applicability of method for fish of group B...... 52 Figure 20: Responses according to attentional presetting and height...... 54 Figure 21: The fish do not need a priori information on object height...... 55 Figure 22: Comparability of the applied conditions ...... 56 Figure 23: Setup for temporally restricting the available visual input...... 62 Figure 24: Display of the black coating of the depriving pipes ...... 63 Figure 25: Applied accuracy to measure the fly s velocity...... 65 Figure 26: In time reactions depend on input duration ...... 67 Figure 27: Projecting the flies movement onto the fish s retina ...... 68 Figure 28: Control data for reactions to fully available visual input...... 69 Figure 29: Latency of the reactions ...... 70 Figure 30: Bearing errors with respect to both of the fish s turns ...... 71 Figure 31: Duration and size of the fish s first and second turns ...... 73
7 Table of figures
Figure 32: First turns of reactions classified as too late ...... 75 Figure 33: Combining two intervals leads to longer latencies ...... 77 Figure 34: Injecting procedure...... 82 Figure 35: Exemplary images of fertilised eggs and fish larvae ...... 83 Figure 36: Visualisation of processes that lead to a predictive turn...... 93
2.3 Table of supplemental tables
Table 1: Supporting data for figures 7, 8 and 10...... 107 Table 2: Supporting data for figures 12, 13 and 16...... 108 Table 3: Supporting data for figure 17...... 109 Table 4: Supporting data for figures 19, 20, 21 and 22...... 110 Table 5: Supporting data for figures 25 and 26...... 111 Table 6: Supporting data for figures 26 and 27 A...... 112 Table 7: Supporting data for figures 27 B and 28...... 113 Table 8: Supporting data for figure 29...... 114 Table 9: Supporting data for figure 30 A...... 115 Table 10: Supporting data for figure 30 B...... 116 Table 11: Supporting data for figure 31 A...... 117 Table 12: Supporting data for figure 31 B...... 118 Table 13: Supporting data for figure 32...... 119 Table 14: Supporting data for figure 33...... 120
8
3.Introduction
9 Introduction
During the last couple of years, archerfish proved to feature a diversity of sophisticated behaviours in addition to their shooting ability. It became increasingly conceivable that this species features astonishingly fast visual processing and may provide more than understanding of its shooting mechanism. However, completely and properly characterising the archerfish s behavioural repertoire in the first place is not just bearing an inherent fascination by itself; it is also fundamentally necessary to provide a decent basis for further neurobiological studies. Knowledge of as many constraints of the natural behaviour as possible, will guide the dissection of its function. This dissection could start with multi-electrode recordings [1, 2] for instance, or a histological approach – or a combination of both [3]. Concepts about the where and when of signal computation within the fish s brain may be generated, using functional magnetic resonance imaging [4-9]. The shape and activity of single cells can be visualised with single-unit recordings with subsequent cell staining [10-13], even using multi-photon laser scanning nowadays [14-19]. With such an abundance of neurobiological methods available, what is the benefit of behavioural studies?
The clear benefit of behavioural studies originates within the chance to study animals as a whole and entirely intact system, flexibly moving in familiar territory. In such a system, the single components are precisely co-operating to perform the sound symphony of animal behaviour. Harmony within that symphony is the ultimate verification that the system is operating properly, as a whole. But at the same time – having to cope with a whole symphony can become a tough challenge. Unless we have the possibility to identify the functional role of each single unit ( how does a viola sound, compared to a violin ), the whole issue can become fairly puzzling. But at the same time, exactly distinguishing each single instrument will probably prevent us from having a satisfactory musical enjoyment. The same applies for a certain behavioural pattern – watching animals behave will not inevitably result in better understanding of the underlying neuronal circuits. Such an understanding requires plausible concepts about the number and nature of
10 Introduction units involved, and the way these units are interacting. Finally, drawing conclusions about the function of a particular unit involved in generating certain behaviour, makes it necessary to trace that unit within the whole – a delicate matter. The archerfish, as the animal of choice in this thesis, features a hunting strategy that depends on high-speed visual processing. The way that archerfish behave during hunting is not just extraordinary in itself – it enables to extract information about the units involved. I will first describe the behaviour these fish are famous for, before describing which substrate may underlie their behaviour.
Archerfish are famous for their ability to dislodge stationary as well as moving insect prey by spitting precisely aimed jets of water [20-28]. These jets are generated by a blast pipe within the fish s mouth, formed between a slot in its palatine and its brawny tongue and are powerful enough to carry water at remarkable heights of up to twenty times the fish s own body length [20, 25, 29-31]. Moreover these jets are aimed with top precision and fish are able to fire them from apparently any viewing angle [26], revealing their superior skill to accurately evaluate not only the visual offset produced by water refraction [24, 32, 33], but also the distance to, and the absolute size of prey [34]. Evolutionary reasonable these fish also modify the amount of water spat, always providing their shots with a force security factor of about ten times more force, than prey animals of a certain size can maximally attain to attach themselves to vegetation [31]. Typical forces that fish reach with ease suffice to dislodge prey sizes of up to small lizards [20], but their usual diet consists of tiny to medium sized insects – like flies – commonly enriched by aquatic arthropods [20, 21].
Once fish manage to dislodge an aerial prey of interest, they are confronted with another major challenge: Catching those fast-moving objects. Archerfish are schooling fish and a dislodged insect will instantly gain attention within the school. So in order to be rewarded for the shooting investment, the shooter
11 Introduction has to be really fast to catch his reward. However, depending on its initial height, the prey might just be in the air for a few 100 milliseconds until it impacts onto the water surface. So waiting for the lateral line organ to provide information about the correct impact position would drastically reduce the time left, reducing the chance to snatch the reward just as much. So in order to receive a worthwhile payback for the shooting investment, the best strategy would be to arrive simultaneously with the prey at its impact position, making the catch the very moment the prey touches the water surface. According to former research in our lab, fish always reach that impact position either at the same time with the prey, or slightly subsequent to prey impact – never earlier than the prey [35, 36], which would be quite disastrous by the way, for fish do not slow down when they get near the impact position (for energetic reasons), but rather snatch the prey at full speed [35, 36]. So when they arrived late, they couldn t simply turn around and try again – they would just miss that particular prey. Besides, fish cannot increase their swimming speed infinitely [37], so the only chance to save time is to trigger the start towards the prey as soon as possible. Now I will describe the kind of behaviour directly following a successful dislodgement.
Fish carefully monitor the initial movement of the prey item, and then promptly react with a swift and accurate turn towards its future impact position, before they finally start to speed up in that direction [35, 36, 38]. In the process, precisely monitoring the moving prey is a crucial requirement and former research reported a very tight time frame of about 100 milliseconds as sufficient for that [39]. This time frame contains several processes, most likely happening in three spatially separable areas: (1) the retina, handling all visual input, probably already extracting important movement parameters (2) the brain, relaying and perhaps further processing that extracted information and finally (3) the motor system, not only taking finished orders, but probably further processing the information. According to published data, the duration for retinal processing might very well be the most time consuming in this chain of events [40-42], as it includes the absorption of light by the visual pigment,
12 Introduction leading to a cascade of biochemical reactions within the photoreceptor cells [43]. These processes precede downstream computational processes [44-47], in higher brain areas like the optic tectum for instance. Although lacking evidence about whether the extracted visual information is computed within the brain at all, the optic tectum is most likely a candidate to be involved in these processes, at least as a major input target for retinal ganglion cells [48]. Finally a well-defined and fine-grained order must be released, being conducted via a pair of huge nerve cells well known as Mauthner cells (named after the ophthalmologist Ludwig Mauthner), fine-tuning and mediating the movement orders [49-53]. Although we are again lacking strict knowledge about whether these cells are actually involved or not, former research in our lab proves a striking similarity between the predictive turns fish use to aim at their prey of interest and their C-shaped escape turns [38] evidentially evoked and conducted by a pair of Mauthner cells, characteristic for all teleost fish – and as recently discovered fish also feature voluntary control of the onset and fine-tuning of these characteristic C-shaped body bends [54]. The giant medullary Mauthner cells may readily distribute the appropriate activation signal among the primary motorneurons, leading to well-dosed muscular contraction and hence to accurate turning of the fish s body. At last the archerfish now aims towards the future impact position of its prey, ready to speed up to make the catch.
We do know about the archerfish s ability to identify the absolute size of prey items [34], but what about height, direction and velocity? These parameters are quite variable amongst different hunting situations. So assuming, that fish are very well capable to instantly react to virtually any falling insect, plus the assumption that these parameters have to be extracted within a fraction of 100 milliseconds, appoints the fish s retina most likely as the unit to extract the parameters named above. In recent years it has been shown that retinae can extract parameters like relative velocity and direction – all within their cell layers already [55-57]. The information is conducted through the optic nerve
13 Introduction and into the brain where it will be transformed into a matching nerve activation pattern, finally fine-tuning the activation of muscle.
To analyse the conditions that must be fulfilled for top performance two criteria were used that are of main importance for the fish to successfully make a catch: the time to trigger the turning (latency), and the turning precision (accuracy). These criteria were analysed in all of the experiments covered by this thesis, when appropriate, extended to turn duration and the angular size of the fish s turns. The flies velocities and trajectory lengths were closely analysed to ensure the comparability of setups, whenever required. With these criteria at hand, I challenged the fish with various experimental setups, altering the character of visual input available, checking the fish s resulting reactions. The type of input that triggers the predictive turn of fish is a most important point to know: Is another fish s shooting necessary, or does the mere movement of a prey object suffice as a trigger? Since prey movement suffices (as I will show subsequently), a whole world of experiments come into reach that would otherwise be impossible to realise. With prey items starting anytime on my command and from a starting position defined by me and not by a shooting fish, I am in a position to check if the fish s turning behaviour can only be triggered if fish gazed at the point of expected prey movement, or if and how they would react to a movement event, starting in the periphery of their visual field, for instance. Starting the fly s movement with a vertical offset from the position expected by the fish would also become possible, just as expanding or decreasing the length of the flies trajectory. How if at all, the fish s reaction depended on certain contrast levels would then be just as accessible, as testing the fish s reaction to more than one prey objects simultaneously starting and moving in contrary directions. Altogether, defining the character of the parameters and the duration of their availability, enabled me to challenge the fish with setups that would never be possible in natural surroundings, thus enabling understanding of the fish s behavioural mechanisms via their modified reactions to completely unexpected and novel visual input.
14 Introduction
When fish decide to react to a falling prey item, performing a predictive turn, they should carefully decide for the most effective trade-off between the accuracy of their turn and the speed with which to elicit that turn. This is probably critical, because inaccurate performance either in extracting the prey s movement parameters, or in computing the proper motor reaction, will most likely lead to the missing of that prey. This trade-off may be the basis for making the archerfish a high-speed predator with unfailing aiming accuracy. Although archerfish will perhaps not make it into the squad of model-systems, lacking the availability of easily generated genetic mutants, research on this species will certainly and significantly contribute to our understanding of the mechanisms of visual processing and the essential transformation of the generated information into proper behaviour, subsequent to computation. At least these fish will remain a rich source for behavioural studies, since their willingness to participate in novel and perfectly challenging setups equals their appetite for just the next fly.
15
4. General methods
16 4.1. Animals and their keeping
4.1 Animals and their keeping
Archerfish belong to the family of Toxotidae (order Perciformes) consisting of seven species all occurring in fresh, brackish and marine waters from India to the Philippines, Australia and Polynesia [58, 59] (see figure 1). Toxotes jaculatrix (PALLAS) and Toxotes chatareus (HAMILTON) are the most widespread representatives of the genus and all experiments are conducted on these two species. They commonly inhabit mangrove-lined estuaries where they can be found hiding amongst the numerous roots or hunting for insect prey resting on overhanging vegetation. In the laboratory fish were kept and all experiments were done in large tanks filled with brackish water (conductivity 3.5 mS/cm; temperature 28 °C; tank measurements either 1.6 m x 0.6 m x 0.6 m for experiments 5.1 - 5.4; or 1.0 m x 1.0 m x 0.6 m for experiments 5.5 - 5.6; each filled to a height of 30 cm). Animals were subjected to a 12:12 light regime and all experiments started no earlier than five hours after light onset.
Figure 1: Distribution of participating fish species According to Allen (1978) the distribution of the two archerfish species Toxotes jaculatrix (B) and Toxotes chatareus (C) reaches from India to the Philippines, Australia and Polynesia, as displayed (A). Red dots on the globe (A) represent the distribution of Toxotes jaculatrix; black dots that of Toxotes chatareus.
17 4.1. Animals and their keeping
The behavioural studies were performed with a group of either five Toxotes jaculatrix (standard length 12 cm (this is the length without the length of the caudal fin); experiments 5.1 to 5.4, referred to as group A ), or a mixed group consisting of three Toxotes jaculatrix and seven Toxotes chatareus. These are likely to have participated uniformly throughout these experiments, as they did in similar experiments by my colleague Caro Reinel (personal communication. The fish s standard lengths was 12 cm; experiment 5.5 to 5.6, referred to as group B ). An additional third group, consisting of four adult and “retired” Toxotes chatareus participated in the breeding experiment (standard length 12 to 15 cm; experiment 5.7, group C ). Fish were purchased from tropical fish importer (“Stimex Corporation”, “Aquarium Glaser”), caught in the wild (supposedly in Thailand) and trained in the laboratory for at least one year previously to all described experiments. Performing precise shooting and fast predictive reactions were all part of the training, as well as adapting to being fed by an experimenter. During all behavioural experiments fish were exclusively rewarded with flies (Calliphora spec. average body lengths of 11.0 mm and fresh weight of roughly 57.0 mg each), receiving an additional handful of “Cichlid Sticks” (SERA, Heinsberg, Germany) at the end of each week s experiments, supplying the fish with vitamins and nutrients to maintain their health at a standardized high level.
18 4.2. Recording and managing behavioural data
4.2 Recording and managing behavioural data
All behavioural data was obtained by imaging behaving fish at a frame rate of 500 frames per second (resulting in a temporal resolution of 2 ms; shutter 1/500), via digital high-speed video (HotShot 1280, NAC Image Technology, California, USA). In all experiments the camera was positioned above the tank, generating a top view of the scenery, using either 20 mm (Nikkor 1:2.8 or Sigma 1:1.8) or 35 mm lenses (Nikkor 1:1.4). If necessary, a second camera of the same type recorded an additional side view, using a MASTER-8 (A.M.P.I., Jerusalem, Israel) for synchronous triggering of both of the cameras. The videos were obtained with HotShot software (version 1.2.2.3), using *.avi file extensions (resolution 1280 x 1024 pixels), then converted to *.dv using iMovie (version 4.0.1, resolution 720 x 576 pixels) and finally analysed via Object-Image (version 2.12). Analyzing the videos, generally means breaking down the images of behaving animals into 2-D coordinates of distinctive fish and fly positions, as well as frame numbers essentially necessary for further evaluation of data, like gaining latencies and errors (for details see 4.4). Although the camera system allowed recordings with regular room illumination, the tank was diffusely illuminated from below with one or two halogen lamps, as appropriate (500 W each) for increased contrast in the recording. One or multiple additional halogen lamps illuminated a white cloth (cotton), spanned above the tank to increase contrast between the moving fly and its background, as seen by the fish. Contrast values were gained using a luminance meter (luminance meter LS-110, Minolta camera, Japan), averaging luminance values (measured as cd/m2) for background and fly I I respectively and converting these values to Michelson contrast ( max min ). All Imax + Imin initial plotting of data was done using OriginPro (version 7.5) and all plots were further refurbished using CorelDRAW (version 11 .6 33 for Macintosh). For a general illustration of the behavioural setup, see figure 2.
19 4.2. Recording and managing behavioural data
Figure 2: Exemplary experimental setup This is an exemplary illustration, showing a typical experimental setup (in this case experiment 5.5), using two simultaneously triggered HotShot cameras. In the background, tank and fish are clearly visible with the two cameras and the testing setup mounted above the water surface. The computer screen in front shows a still image side view of the setup referring to the camera in the middle of the picture (left of the tank). Halogen lamps (not visible) diffusely illuminate the white cloth beyond the upper camera and the bottom of the tank. Several fish (group B) cruise along the tank s front pane, curious about the things to come.
4.3 Statistics
All statistical evaluation was done using SigmaStat (version 3.11.0), utilizing Mann-Whitney Rank Sum Tests (U-test), whenever a comparison of two original datasets was necessary (like latencies of natural versus deprived conditions, see figure 7 B). Checking for statistical relations of more than two datasets (like latencies of ten different contrast conditions, see figure 17), intending to detect differences or attest consistency, One-Way Analysis Of Variance On Ranks (ANOVA) was utilized, with an additional Dunn s test, if appropriate. Regression analysis, as well as statistical comparison of a whole dataset to a single value (like comparing a set of latencies to 40 ms, see figure 32 B) was done via t-test, using OriginPro (version 7.5).
20 4.4 Characterising the fish s performance
4.4 Characterising the fish!s performance
The fish s central performance subsequent to successfully dislodging its prey is a quick and immediate turn towards the prey s future point of impact. According to their anticipating nature, these turns are referred to as predictive turns , predictive reactions , or simply as predictions , characterized primarily by the elapsed time until they are initiated (latency) and the accuracy leading the fish towards the target s impact position (error).
4.4.1 Latency
The latency of the reaction is defined as the time-span beginning with the onset of the prey s movement and ending with the onset of the fish s predictive turn. Extracting this time span simply works via frame counting: At a frame rate of 500 frames per second (resulting in a time interval of 2 ms between each successive frame), the latency can simply be calculated by subtracting the frame numbers of the related frames and multiplying these figures by two (figure 3). All the other important time-spans, like turn durations, input durations and such, are determined the same way.
Figure 3: Sequence, visualising latency determination This sequence was extracted from a typical movie, showing the fish s reaction to a falling fly that was dislodged from a transparent platform (greyish circle) above water level, as indicated by a red arrow (A). The onset of the fly s movement (B), defined the starting point of latency measurement, whilst the onset of the fish s predictive turn (C) defines the ending. In this particular example the latency of this reaction was 60 ms (72 ms minus 12 ms). Movies always end as fish grab their reward (D). A red line encircles the interesting spots.
21 4.4 Characterising the fish s performance
4.4.2 Precision and Error
The accuracy of the fish s turns (referred to as error) was assessed by the angular deviation between the required direct course to the later point of prey impact (or the point where a fish grabs the fly as this occasionally happened briefly previous to the fly s impact) and the orientation assumed at the end of the fish s predictive turn. The end of the predictive turn is defined as the frame in which the fish completely finished bending (showing a straight-lined body), just before accelerating towards the fly s impact position. Extracting this angle works via simple geometry using pixel coordinates (x and y values) of marked positions such as the tip of the fish s snout, its centre of mass (see [38] for a definition of centre of mass) and starting as well as impact position of the falling fly (see figures 4 and 5). As indicated in figure 5 the assessment of the angular deviation of the two straight lines (direct course and fish s orientation) use the fish s centre of mass as intersection. All the other important angles, like turning angles, intersecting angles, are determined following the same principles. All of the fish s actual bearings in each experimental condition (e.g. deprived, see 5.1) were always compared to controls conducted and randomly interspersed in the same experimental setup (e.g. natural, see 5.1).
Figure 4: Sequence, visualising determination of precision This sequence was extracted from a typical movie, showing the fish s reaction to a falling fly that was shot down from a transparent platform (greyish circle) mounted above water level, as indicated by a red arrow (A) with the onset of the fly s movement encircled in B. Finishing its predictive turn, the fish accelerates towards the prey s future point of impact, seeking to select a most effective trade-off between speed and accuracy, bearing as precise as possible. However there might still be an angular offset (as indicated by the angle between both red lines in C), which can then be calculated via simple geometry. In this particular example the fish s error is 12,3°. Movies always end when fish grab their reward despite their initial offset (D).
22 4.4 Characterising the fish s performance
Figure 5: Sign conventions applied in error measurements These are two examples to illustrate the sign convention adopted to describe the aiming of fish after finishing their predictive turns. Although a predictive turn will always lead a fish towards the future impact position of its prey (red circle), a small angular offset may remain (red area). Two straight lines (the elongation of the fish s orientation after finishing its turn, and the direct course needed for an exact aim towards the prey), intersecting at the fish s centre of mass (CM), are utilised to assess the angular offset. According to the starting position of the fly s movement this angular offset is defined either as negative (A), when the intersection of the fish s course with the direction of fly movement lies between the fly s starting and impact position, or as positive when the intersection lies beyond the fly s impact position (B). This sign convention is applied, when it is helpful for understanding the aiming behaviour of fish and in those cases, the aiming offset is referred to as error . If knowing the sign will not contribute to understanding the aiming behaviour at hand, the offsets absolute values are utilised, referred to as precision .
23 4.5 Criteria for analysable reactions
4.5 Seven criteria separating the analysable from the discarded reactions
1. Working with schools of fish bears a formidable problem when accessing the fish s predictive turns: In cases when a school member is blocking the direct course towards the prey s later point of impact, fish tend to making the detour via the “edge” of the blocking fish [36] and therefore bearing of fish will not lead to the fly s impact position, but to the “edge” of the blocker. These reactions had to be excluded since they do not show the fish s reaction to the falling fly alone. 2. Avoiding that fish could simply respond to performing school members, generating their turns accordingly, reactions of all other than the first reacting fish were strictly excluded. Assuring to only consider reactions surely following the visual input of moving prey – and not that of moving school members. 3. To exclude responses in which the fish could simply continue along their initial direction, a minimal necessary turning angle to the later point of impact of 10° was required. 4. Only those predictive turns were to be analysed, that were led by visual input cues and not by input generated by their lateral organ, so fish had to finish their predictive turns before the flies impact on the water surface. This criterion was not a problem at all, as reactions were always initiated before the flies impact. 5. Fish had to be attentive to the task at hand, so reactions of fish chasing other fish, or being chased were excluded. Fish had to stand still and nearby the surface of the water, before initiating their turn. 6. Obtaining the positions of fish and flies obviously requires their full visibility, so reactions in which these positions were hidden also had to be excluded. 7. Deflections of the fly s trajectory through setup fittings or the tank s glass panel lead to exclusion of that particular movie, since sudden changes in prey movement may misguide the fish and interfere with their turning.
24
5. The experiments
25 5.1 Depriving the fish – Objectives and Approach
5.1 Depriving the fish of shooting-related information
5.1.1 Objectives and Experimental Approach
To check whether the shot that normally started prey motion would be a necessary trigger for eliciting the fish s predictive response, I confronted the fish with either of two conditions in random order (figure 6): In the 'natural' condition a wetted fly was manually centred to the bottom side of a transparent disk (Plexiglas, 32 mm in diameter, mounted 30 cm above the water surface) with fish dislodging it as soon as my hand cleared the view. In the 'deprived' condition a non-transparent disk (Polyvinyl chloride, 30 mm in diameter, same height above the water surface) was attached directly on top of the first one allowing the option to place a fly on the upside, invisible from the fish s view. Centred to this top platform, a flexible tube (12 mm in diameter), equipped with eight equally spaced air-valves (3 mm diameter each), enables the direction of an air current directly at a fly placed above the rim of that platform. Flies left the platform at random angle with respect to the 10 mm rim, depending on their controlled position before take-off. The tube between my mouth, where activation of the air current took place (simply by blowing into the tube) and the platform, where the fly was launched had a length of 1 m. Comparing reactions to different setup conditions requires equal levels of attention in the fish, so in both approaches identical hand movements were adopted – actually sticking the fly to the platforms bottom in the natural condition and mimicking this hand movement in the deprived condition.
26 5.1 Depriving the fish – Objectives and Approach
Figure 6: Experimental differences in deprived versus natural setup In the 'natural' condition (A), a wetted fly was manually centred to the bottom side of a transparent disk with fish dislodging it as soon as my hand cleared the view. In the 'deprived' condition (B), a non-transparent disk was attached directly on top of the first one with a fly placed on the upside, invisible from the fish s view. Centred to this top platform, a flexible tube equipped with eight equally spaced air-valves (indicated as tiny grey dots) enables the direction of an air current (green arrow) straight at the fly placed above the rim. The colour convention with blue associated with natural and green with the deprived condition remains throughout the results section.
5.1.2 Results
The archerfish s predictive reaction can be elicited, independently from a shooting event – fish are able to react without a triggering input by their own shot, or the shooting of a school member. Having seen the preceding shot before the prey s movement does not improve the fish s performance, neither in terms of accuracy, nor in terms of latency. Their precision does not change significantly, comparing natural (with preceding shot) to deprived (without) conditions (p = 0.212; see figure 7 C), but rather matches in both conditions. Furthermore, depriving fish from shooting-related information does not lengthen the latency as one could expect, but even reduces it slightly but significantly (by 5.1 ms comparing the mean values; p = 0.034; see figure 7 B). These findings confirm that motion cues are necessary and sufficient to
27 5.1 Depriving the fish – Results trigger the archerfish s predictive reaction. The second key result is, that fish occasionally perform their turns with latencies of 40 ms. Please note that latency not only includes photo-transduction, but also processing of the visual stimuli as well as selecting and eliciting the proper motor program. Analysing three parameters of fish movement ensured otherwise comparable complexity in the responses in both natural and deprived conditions. These parameters were: the size of the fish s turning angle (the angle between the course of the fish before and after finishing its turn, see figure 7 D); the duration of the fish s turn (how long did it take the fish to perform the turn, see figure 7 F); and the angle, spanned between the elongation of the fish s orientation before its turn and the course of the fly s movement (intersecting angle, see figure 7 E). All three characteristics had equal distributions, no significant differences were found in any of them (p = 0.459; p = 0.502; p = 0.276 respectively). Additionally it was assured, that the flies movement parameters matched in both conditions, verifying that neither its speed, nor – connected to speed – its trajectory length bore any significant differences (p = 0.270 for speed and p = 0.914 for trajectory length, respectively; see figures 8 A and B). Due to compliance with the previously defined seven separating criteria (see 4.5) of the provided 426 flies only 185 (43.4 %) led to analysable data, composed of N = 91 for natural and N = 94 for deprived conditions.
28 5.1 Depriving the fish – Results
Figure 7: Reactions to natural and deprived conditions are alike Blue colour refers to the natural condition with a shot preceding prey motion, whereas green colour refers to the deprived condition, without preceding shot (A). The latency of reactions to deprived stimuli is not larger, but in contrast even slightly higher than those reactions to natural conditions (B). The fish s accuracy of aiming stayed alike in both conditions (C) and neither the sizes of the fish s turns (D), nor the turn durations (F) and the intersecting angle (i.e. the elongation of the fish s orientation before its turn and the course of the fly s movement) are significantly different (E). Respective bin sizes are 10 ms (A, F), 10 degrees (E) and 5 degrees (C, D) with blue and green bins sharing each interval. For information on total counts see supplemental table 1.
29 5.1 Depriving the fish – Results
Figure 8: Matching fly movement in natural and deprived conditions Neither the lengths of the flies trajectories (A), nor the flies velocities (B) bear significant differences, ensuring full comparability of both visual cues, delivering a good prerequisite to compare the fish s reactions. For colour conventions see figure 7 A. Respective bin sizes are 20 mm (A) and 0.2 m/s (B) with blue and green bins sharing each interval. For information on total counts see supplemental table 1.
5.1.3 Discussion
The very first observation of this thesis, and also one of its major results, is the complete independence of the fish s predictive reaction from a preceding shot as a triggering stimulus. Fish are capable of utilising the mere stimulus of moving prey, immediately predicting its future impact position onto the water surface. Using this knowledge, they instantly turn towards that impact position and speed up to make the catch, just as they would do subsequent to successful dislodgement by shooting. Comparing natural and deprived conditions does not reveal any differences in terms of turning precision. Comparing latencies even reveals that reactions to shooting deprived input are not a bit slower, than to the natural situation, i.e. if the trigger would have been a preceding shot. Even the opposite is true, for fish reacted slightly faster (about 5 ms). This may be because without a shot they don t have to
30 5.1 Depriving the fish – Discussion distinguish the moving fly amongst the expanding curtain of water droplets that are usually reflected from the food-presenting platform subsequent to the impact of the shot. It has been demonstrated that a mixture of moving stimuli (containing relevant and non-relevant information calling to be classified as such) can affect the spatial location extraction of a particularly interesting moving stimulus [60].
This first observation is actually a very central one, enabling all subsequent experiments described in this thesis, which on the other hand enables the functional and anatomical fractionalisation of principles of visual input processing and subsequent generation of motor output. My experiments solely engaged in the manipulation of the visual input available for the fish to judge the prey s future impact position, subsequently studying the fish s behaviour as an amalgamation of the whole system s processing and performing abilities. But these results now enable to continue the deconstruction of units involved in the performance. Decoding the cellular mechanisms of the featured decision-making would be complicated, if the fish still had to perform the shooting task as a trigger for its decision-making machinery, and – although possible, as previously described [61] – single-unit recordings within the fish s motor system would be severely complicated in a living, behaving fish without inducing unpredictable effects on the subsequent swimming behaviour. Finally there is no such instrument as functional magnetic resonance imaging (FMRI) of fish during unaffected swimming behaviour and multi-photon microscopy would be difficult. But now, as straight implication of the fish s independence from shooting, it seems no trouble at all to simply immobilise a living fish (e.g. via curare injection), equipping all the neurons of interest with electrodes, and displaying moving flies, or simplified visual stimuli (e.g. black dots) above the fish s eyes. This would as well result in an increased independence of the objects displayed. In a behavioural setup for instance, it might be difficult, if not impossible, to display red, yellow or blue flies of the same size, or to display moving targets, that won t impact onto the water surface, or that won t be
31 5.1 Depriving the fish – Discussion edible for the fish. Fact is, if fish aren t instantly rewarded subsequent to the performance of their predictive reactions, this will either result in a total loss of willingness to perform, or – if fish still reacted – in an unpredictable impact on the reaction (latency will certainly increase), therefore disabling promising experimental setups. But multi-electrode preparations of archerfish retina, investigating the retinal part of visual processing and computational processes in separation from the other participating units (brain, motor system), will enable to explore the retinal specialisations of archerfish retina. This could reveal the limits or prospects of information extraction in the archerfish retina, providing a more general contribution to the understanding of retinal movement computation, since retinae of different vertebrate species feature a fairly similar anatomy and neurophysiology [62]. Immobilized fish could easily be prepared for single-unit recordings of Mauthner cells, revealing the timing and character of cell activation (Mauthner cells are a key part of the motor system of any teleost fish and research on them enormously contributed to our concept of neuronal function in the past). The Mauthner cell system could also be part of the computational network itself, since its various forms of plasticity [52, 63, 64] could account for the fine-tuning of the motor response, building up an outside world induced “decisional threshold” that finally triggers movement [65-67]. Knowledge about the timing of Mauthner cell activation will allow a calculation of the time left for computational tasks and by adding information about the retinal processing time (e.g. via multi-electrode recordings, providing matching visual input), we will gain information about the scale of computational processes within the fish s brain.
The fish s ability to predict the impact position of falling food, without preceding shot, would not be too much of a surprise by itself, being shared amongst other surface feeding fishes (e.g. the central American species Brycon guatemalensis [68], or the zebrafish Danio rerio, as recently discovered in our lab).
32 5.1 Depriving the fish – Discussion
Compared to these species, archerfish performance is superior in terms of the level of accuracy of their initial turns and in latency. The fastest though still accurate predictive reactions were elicited already 40 milliseconds after onset of the moving prey s visual stimulus [69]. This incredibly short period obviously includes everything crucially necessary to elicit a correct predictive turn: The extraction of relevant movement parameters via the fish s retinae, their computation within the fish s brain (or somewhere else in addition), leading to the generation and release of a precisely matching motor pattern, completed by activation of the appropriate muscles, most likely via the Mauthner neurons as discussed above. Just to provide an idea about the duration for a rudimentarily similar task in humans: For a comparable task of motion detection, followed by a simple hand movement (in this case, pressing a button) we need about 200 milliseconds [70]. Supposing the co-operation of retina, brain and motor system, the largest part of the 40 ms described in archerfish may be, according to published data, due to retinal processing (in humans it surely is the processing within the brain [71]). Human cones for instance, react to a light pulse stimulation with first changes in membrane current around 50 to 100 ms following light onset (with latency increasing with photon density [41]). However, ganglion cells of cat retinae generate flash light related responses 30 to 40 ms after stimulus onset [42]. These ganglion cell responses are already filtered through the cell layers of the cat s retina, but it is unclear which information these responses carry. Challenging turtle retinae with the moving stimulus of a bright bar in front of a dark background, revealed response latencies of about 100 ms for ganglion cell responses, including at least relevant information for motion processing [72].
33 5.2 Spatial attention – Objectives and approach
5.2 Spatial attention
5.2.1 Objectives and Experimental Approach
After spotting a prey item fish usually direct their full attention towards the spatial position of that prey by orienting their body in prey direction, focussing the prey to aim for a shot. To check if fish were able to improve their reaction by directing their full attention to the position of movement onset (for instance by gazing at it), compared to movement onset outside their putative centre of attention, I challenged them with a setup in which the fly s movement could be started from three platforms instead of just one. The platforms (for platform description see 5.1.1, deprived condition) were installed set distances apart from each other (10, 20 and 40 cm; figure 9), each being equipped with a blow tube, enabling each one to be the starting platform for fly movement. As fish focussed their attention to one of the platforms, supposedly expecting prey movement onset from this position, I either initiated fly movement from this platform or, randomly interspersed, from one of the other platforms, comparing the fish s reactions to both conditions. The direction of moving flies was randomised disabling fish to guess the fly s trajectory. Each test started with fish being randomly cued to one of the platforms (referred to as the cueing platform ), by mimicking the hand movement of sticking a fly to its bottom (without actually sticking a fly). Within five seconds past this signal, a fly movement started either from the cueing platform (providing a control-group dataset – the 0 cm distance) or randomly switching, from any of the other two platforms. To keep the cuing stimulus effective, I interspersed trials, in which a fly was actually stuck to the bottom of a platform and readily dislodged by the fish (at a frequency of 20 % of all provided flies). I then analysed the fish s precision and latency as they reacted to a moving stimulus appearing with a certain offset between their putative centre of attention and the movement of their prey.
34 5.2 Spatial attention – Objectives and approach
Figure 9: Using several platforms to test
spatial attention In this setup the fly s movement can be started from one of three platforms instead of one. The three platforms are installed set distances apart from each other (d could be 10, 20 or 40 cm), each being equipped with a blow tube, and each one of them being a possible starting point for a fly s course towards the water surface. Each test started with fish being cued to one of the platforms (the cueing platform, indicated by the red arrow) and by blowing into the appropriate tube (green arrow) the fly s movement started. Fly movements could either be started from the cueing platform or from any of the other two platforms.
5.2.2 Results
Substantial horizontal offsets of 10 cm and 20 cm (18.4° and 34° respectively, seen from below the cueing platform) did not affect response latency, compared to reactions to the cueing platform (p = 0.144 for 10 cm and p = 0.103 for 20 cm). Latency increased only at an offset between expected and actual takeoff of 40 cm (or about 53° of visual angle; p < 0.001; see figure 10 A). Minimum latency, observed in the fastest responses, was also only affected at this large offset (see supplemental table 1). Furthermore the precision of the fish s turns remained completely unaffected by displacing the prey s starting position (p > 0.3 in all cases; see figure 10 B). The fish s turning angles did not differ significantly throughout reactions to the tested horizontal offsets from the cuing platform (p > 0.3 each, results not shown). As these findings suggest, fish do not a priori limit the processing of target motion to a special region of interest, they are very well able to elicit precise turns in response to an object moving even from where they do not direct their full attention to. Gazing at the moving object, therefore
35 5.2 Spatial attention – Results representing it onto the fovea-like structure which archerfish feature (according to Lüling [21]), does not bear an advantage in terms of speed or accuracy of the fish s reactions. Due to compliance with the previously defined seven separating criteria (see section 4.5), of the provided 769 flies only 556 (72.3 %) were to be analysed, composed of N = 300 for 0 cm distance, N = 73 for 10 cm, N = 107 for 20 cm and N = 76 for 40 cm distance.
Figure 10: Behavioural reactions to horizontal offsets Horizontal offsets of 10 cm and 20 cm did not affect response latency, compared to 0 cm (fly movement starts from the cueing platform). Latency increased only at an offset between expected and actual takeoff of 40 cm (A). The precision of the fish s turns remained completely unaffected by displacing the prey s starting position (B). Hence, the fish did not a priori limit or enhance the processing of target motion to a region of interest. For information on total counts see supplemental table 1.
36 5.2 Spatial attention – Discussion
5.2.3 Discussion
Archerfish are able to react to moving stimuli with incredibly short latencies, but besides that fact, they feature another striking ability: precisely predicting a prey s impact position, although the movement of that prey did not start, where the fish expected it to start, but with substantial horizontal offsets to the fish s point of gaze. This ability reveals the retina s capability to extract all the necessary information throughout a huge visual field – not just within a small and specialised retinal area (e.g. a fovea-like structure; a specialised retinal area, which the fish feature demonstrably [21]). So fish are not just able to react to movements within the periphery of their visual field, they instantly employ this ability for their predictive reaction, without having to go through a major learning process. In conclusion, it is not necessary for archerfish to focus their prey s movement within a specialised area of their retina – the extraction of information necessary to elicit a precise predictive turn is possible within a huge visual field. It also needs very large offsets of 53° of visual angle to significantly increase the fish s latency.
37 5.3 Deciding for one of two flies – Objectives and approach
5.3 Deciding for one of two flies
5.3.1 Objectives and Experimental Approach
To probe the fish s capacity to decide between conflicting visual stimuli, I confronted them with two moving flies, simultaneously released from the same platform, moving into opposing directions. The flies started from an inverted T-tube (internal diameter 8.0 mm), pivot-mounted onto the usual platform (figure 11). Randomly rotating the shaft (length 40 mm) before each run ensured that fish could not guess the course of the moving flies beforehand, since the T-tube was not visible from within the tank. Equally to the deprived condition setup (see 5.1.1), a stream of air into the inverted T- tube started the motion of the flies. Fitting the T-ends with equally sized flies ensured approximately matching speed levels and retinal object size of the two flies. I checked if the flies left the two ends of the T-tube simultaneously (applying 500 frames per second which results in a temporal resolution of 2 ms). Only those cases with confirmed synchronous appearance of both flies were analysed (46% of total) and these reactions were compared to interspersed tests with just one fly blown out of the T-tube, serving as control. In the process, I analysed and compared three different angles, taking the fish s resulting aiming subsequent to its predictive turn as a reference: (1) The angle to the impact of the centre of mass of the two flies (i.e. the centred point between the two actual points of impact; referred to as CM), (2) the angle to the later impact position of the fly the fish chooses to catch and (3) the angle to the impact position of that of the two flies the fish rejects (see figure 11).
38 5.3 Deciding for one of two flies – Objectives and approach
Figure 11: Two flies simultaneously A stream of air into an inverted T- tube (green arrow) simultaneously starts the motion of two flies and their ballistic path towards the water surface. Three angles that intersect the fish s initial course were analysed: to the impact position of the chosen fly, to the impact position of the rejected fly and to the centre of mass (CM) of both fly movements, calculated as the centre between the two actual impact positions. These positions are indicated by the red semicircles.
5.3.2 Results
Challenging the fish with two flies starting simultaneously from the same platform but in opposite directions, revealed the fish s capacity to immediately and highly selectively choose one of the two conflicting motion signals. Predictive turns were directed not at the point predicted by averaging the two motion signals (which would be the centre of mass, CM; see figure 11) or any intermediate point, but right at the impact position of the chosen fly (figure 12 B). The error to the chosen fly s impact position is not significantly different comparing one-fly with two-flies conditions (p = 0.10), but there is a significant difference comparing bearings to the centre of mass with bearings to the chosen fly in one- and two-flies conditions (p < 0.001 each). Although there is a significant difference in the fish s turning angles towards the prey in both conditions, (p = 0.004; see figure 12 D), their range is similar and very broad. Surprisingly, the added decision which of the two targets to choose did not increase latency (figure 12 A; p = 0.13), even though the decisions, which fly to attend to, were not made at random. Although trajectory lengths and associated fly velocities are significantly different comparing one-fly with two- flies conditions (p < 0.001 each; see figures 13 A and B), the choices which one of two simultaneously appearing flies the fish choose and which one they
39 5.3 Deciding for one of two flies – Discussion reject, cannot be explained by such differences in trajectory lengths (p = 0.739) or speed of the two flies (p = 0.148; see figures 13 C and D). Fish significantly preferred that of the two flies, featuring a landing position closer to the fish s own pre-start position. Chosen flies had an average distance of 266 mm, whereas rejected flies possessed significantly larger distances with an average of 353 mm (p < 0.001; figure 12 C).
Figure 12: Providing two flies simultaneously As fish are challenged with two simultaneously appearing flies, their latency is not increased compared to the usual challenge of predicting the future impact position of just one fly (A). With a precision that matches one-fly events, they choose one of the two flies and adjust their turn accordingly, completely ignoring the other fly or the centre of mass of both flies movements (B). The fish will choose that of the two flies with significantly nearer impact position (C) and their turns feature the same variability of turning angles, comparing one-fly with two-flies events (D). Respective bin sizes are 10 ms (A), 10 degrees (B, D) and 50 mm (C) with blue and grey bins sharing each interval and the red bins are centred above them (B). For information on total counts see supplemental table 2.
40 5.3 Deciding for one of two flies – Discussion
Figure 13: Parameters of fly movement Trajectory lengths (A) and associated velocities of fly movement (B) both significantly increase comparing events where two flies appeared simultaneously to events when only one fly appeared. But differences like these could not explain which of the two flies the fish chose to catch, since such differences lacked when trajectory lengths (C) and flies velocities (D) were compared between chosen and rejected flies in the experiments in which the fish were confronted with two flies. Respective bin sizes are 20 mm (A), 0.1 m/s (B, D) and 40 mm (C) with blue and grey bins sharing each interval. For information on total counts see supplemental table 2.
Due to compliance with the previously defined seven separating criteria (see section 4.5), of the provided 731 flies only 243 (33.2 %) could be analysed, composed of 163 single fly events and a total of 174 double fly events, further reduced to an analysable 80, because of the requirement of exactly simultaneous starting fly movement (at 2 ms resolution).
41 5.3 Deciding for one of two flies – Discussion
5.3.3 Discussion
Surprisingly, fish can instantly decide which one of two simultaneously appearing prey objects to attend to – while completely ignoring the other s movement. They are able to find a decision for one of the two flies, instead of being misled by averaging of both movement signals and thus bearing to an intermediate direction. Their decision for one of the flies is furthermore not made at random: Fish take their estimations for both flies future impact positions into account, significantly selecting that of the two flies that will impact nearer to the fish s initial position, revealing that their decision is based on knowledge of the two distances. This astonishingly sophisticated behaviour comes in accompanied with another surprise: Fish reach this decision completely without time delay, comparing latencies with the apparently simpler situation, of just having to attend to one moving fly. So the decision must be guided by surprisingly sophisticated feedback through the extracted knowledge of the future impact position of both prey objects. Since it is rather unlikely that the fish s retina is exclusively responsible for decision-making in addition to the already demanding computational task of extracting meaningful parameters from the moving stimuli, it makes involvement of other units very likely. These units could either be the fish s brain (which is highly probable), or the fish s Mauthner network, having the final say before the turn will be carried out.
Screening the literature about decision-making and its underlying circuitry, will at first reveal a whole world of studies about economic decision-making in humans, mostly linked to game theory and the value of social factors such as reciprocity and equity [6, 7, 73, 74]. But the choices that participants have to cope with, commonly feature a fixed number of possibilities: Will I take the apple, or the pear – mostly within the context of social interaction. Whereas studies with a continuous array of possibilities to choose from [75] are a better parallel for the challenges an archerfish has to overcome when deciding for one of two moving preys. Although this decision may look like a simple A or B
42 5.3 Deciding for one of two flies – Discussion task at first glance, fish first have to go pass a world of processes, revealing which fly will impact nearer to their actual position – and then they still have to decide for the correct motor program to initiate the appropriate turn. This is the real challenge for the fish s decision-making network, since the computational unit (wherever it may be located) very likely just gets the retinal information about the prey s movement parameters, having to pick a motor program that exactly matches the requirements. Otherwise the fish will easily start with considerable angular offset that will not lead to the aspired reward. Decisions that have to be drawn amongst a continuous array of choices (which angle should I use for my turn), emerging from parameters of apparently any value (like direction, height and velocity of moving flies) may still be explainable using a finite number of accumulators. The resolution of the fish s visual system and the controlled fine-tuning of the fish s motor system may not require its computational unit to represent an infinite array of possibilities [76]. Turning precision will very likely still suffice if fish used a two degrees turning accuracy. This could be sufficiently reached with representing 180 different motor programs (providing a 360 degree moving ability). On the other hand, today s image of brain function is considerably different from the accumulation of inflexibly linked cogwheels, suggested by Descartes back in the 1630s. A set of few flexibly co-operating neurons could be just enough to perform all necessary computation and decision-making, superseding the need for a large number of hard-wired neuronal circuits, each representing a different motion pattern. Even if fish featured such hard-wired circuits, they still were in need for a structure to make the decision which one to activate. Their computational network therefore must have been evolutionary prepared to situations in which they instantly and flexibly had to decide which of two (and maybe even several) stimuli to attend to, ignoring the other(s).
43 5.4 Contrast dependency – Objectives and approach
5.4 Contrast dependency
5.4.1 Objectives and Experimental Approach
The aim of this experimental setup was to analyse if precision and latency of the fish s predictive turns correlated with different levels of contrast. Assessing accuracy however made top view monitoring necessary and it would also be necessary to apply exchangeable backgrounds of different luminosities. Mounting expanded backgrounds above the camera (as it was done in the other projects) and changing them several times a day however would be impractical and would moreover bear the problem of scaring the fish in the process. On the other hand, mounting smaller backgrounds at manageable height above the tank would considerably block the camera s view. The solution to this problem was to use just small rectangular plates (50 x 150 mm, Polyvinyl chloride) as backgrounds for the moving fly, blocking just a small area from the camera s view, but ensuring that fish would see the moving fly in front of this background even for large speed and from all viewing positions (placing the plate approximately 5 mm above the fly's initial path). One fly at a time was blown out of a tube in a fixed direction, leading flies straight towards the background plate (tube length 200 mm, internal diameter 13 mm; due to an opaque cardboard mounted below the tube, fish could not see the flies until they left the tube, passing the edge of the cardboard, see figure 14 A). The scene was monitored from above and just those reactions were analysed that were initiated while the fly was moving in front of the background plate. In a first run two backgrounds with largely differing luminosities were used, accompanied by a second run in which I selected ten backgrounds (figure 14 B) of ascending luminosity to further analyse the interstages. In the first run the darker background reflected 8.8 cd/m2 whereas the flies in front of this background reflected 7.8 cd/m2; the respective figures were 65.0 cd/m2 and 21.1 cd/m2 for the lighter background, resulting in a Michelson contrast of C = 0.061 (dark) and C = 0.51 (light) respectively. The fly to background contrasts
44 5.4 Contrast dependency – Objectives and approach for the backgrounds used in the second run ranged from C = 0.026 to C = 0.85 (for detailed values see supplemental table 3).
Figure 14: Experimental setups applied to test for several visual contrasts To test the fish s behaviour to variable contrasts between background and moving flies, two similar experimental setups were utilised, using either two (A), or ten background-plates (B). In these two setups, fish were monitored from above the plates, using a setup with flies closely moving underneath the background plates (A). The flies movement was elicited via blowing into a tube (green arrow in A) in which a fly was previously placed.
45 5.4 Contrast dependency – Results
5.4.2 Results
Challenging the fish with two different backgrounds, changing the visual contrast between the prey and its immediate background considerably, did not significantly affect precision (p = 0.924; figure 16 B), though strongly affecting latency (p < 0.001; figure 16 A). Trajectory lengths and associated velocities of the flies movements were significantly lower in the experiments with the darker of the two backgrounds (p < 0.001; see figure 16 C and D), which seems not to account for the significant increase in latency, since slower velocities of flies should result in faster reactions if they had any impact at all. However no correlations were found, that would be as required. Neither for flies moving in front of light nor in front of dark backgrounds (R = -0.164; p = 0.162 for light background, see figure 15 A; and R = -0.376; p = 0.004 for dark background, see figure 15 B. Although the p-value is significant for the dark background, the respective R-value does not correspond to a linear correlation and this significance may just arise because of few data points with very low latency). Due to compliance with the previously defined seven separating criteria (see section 4.5), of the provided 287 flies only 131 (45.6 %) were to be analysed, composed of 74 for high contrast and 57 for low contrast.
Figure 15: No correlation between latency and the fly!s velocity Slower velocities of flies do not result in faster reactions. This is true for flies moving in front of the light background (A) as well as in front of the dark background (B). Each black dot represents one reaction (N = 74 for A and N = 57 for B). A linear fit is displayed as dotted, black line.
46 5.4 Contrast dependency – Results
Figure 16: Changing contrast conditions affects latency but not precision Challenging the fish with two different backgrounds did significantly affect latency (A) but completely left the precision of the fish s bearing unaffected (B). Trajectory lengths (C) and velocities of the flies movements (D) are significantly decreased for the darker of the two backgrounds, which cannot account for the significant increase in latency, since slower velocities and hence shorter trajectory lengths should result in faster reactions if they had any impact at all. Respective bin sizes are 10 ms (A), 5 degrees (B), 50 mm (C) and 0.2 m/s (D) with light grey and dark grey bins sharing each interval. For information on total counts see supplemental table 2.
Challenging the fish with ten intermediate background luminosities resulted in a better understanding of the correlation of latency with background contrast. As contrast did not have significant impact on the fish s turning precision in the previous contrast setup (testing two different background luminosities), I
47 5.4 Contrast dependency – Results passed the analysis of accuracy in this setup, conducting the experiments with both of the used groups of fish for better validity. The results in both groups match, showing excellent linear correlations across the whole variability of contrasts tested (linear regression; Group A: R = -0.924 and p < 0.001; Group B: R = -0.856 and p = 0.002). Due to compliance with the previously defined seven separating criteria (see section 4.5), 177 (88.5 %) of the provided 200 flies were to be analysed in group A and 218 (87.2 %) of the provided 250 flies in group B, both numbers uniformly distributed among the ten contrasts tested. Please refer to the supplemental table 3 for detailed composition of numbers.
Figure 17: Testing ten different contrast levels on two groups of fish Two groups of fish (group A and B, see 4.1) were tested with ten intermediate background luminosities. Due to differences in the illumination levels of tanks, backgrounds and flies, different ranges of contrasts could be tested (see X- coordinate). In both groups of fish the latencies of the reactions decreased linearly as contrast increased. The linear regression is significant in both groups. Data points are mean values with the error bars showing +/- SEM. For information on total counts see supplemental table 3.
5.4.3 Discussion
As photons travel through the compartments of the archerfish eye, passing the lens, vitreous and the cell layers of the retina, they finally arrive at the membranes of the photoreceptors – the cones and rods. Retinaldehyde (or
48 5.4 Contrast dependency – Discussion