Zoology 117 (2014) 104–111
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Visual discrimination abilities in the gray bamboo shark (Chiloscyllium griseum) Theodora Fuss ∗ , Horst Bleckmann, Vera Schluessel Department of Comparative Sensory Biology and Neurobiology, Institute of Zoology, University of Bonn, Meckenheimer Allee 169, 53115 Bonn, Germany
a r t i c l e
i n f o
Article history: Received 11 June 2013 Received in revised form 24 October 2013 Accepted 24 October 2013 Available online 10 December 2013 Keywords: Chiloscyllium griseum Elasmobranchs Learning ability Reversal task Visual discrimination
a b s t r a c t This study assessed visual discrimination abilities in bamboo sharks (Chiloscyllium griseum). In a visual discrimination task using two-dimensional (2D) geometric stimuli, sharks learned to distinguish between a square, being the positive (rewarded) stimulus, and several negative stimuli, such as two differently sized triangles, a circle, a rhomboid and a cross. Although the amount of sessions to reach the learning criterion and the average trial time needed to solve each new task did not vary significantly, the number of correct choices per session increased significantly with on-going experiments. The results indicate that the sharks did not simply remember the positive stimulus throughout the different training phases. Instead, individuals also seemed to learn each negative symbol and possibly had to “relearn” at least some aspects of the positive stimulus during each training phase. The sharks were able to distinguish between the 2D stimulus pairs at a learning rate corresponding to that found in teleosts. As expected, it took the sharks longer to learn a reversal task (with the positive stimulus now being the negative one) than to discriminate between the other stimulus pairs. Nevertheless, the present results suggest that bamboo sharks can learn visual discrimination tasks, succeed in a reversal task and probably retain (some) information about a previously learned task when progressing to a new one. © 2013 Elsevier GmbH. All rights reserved.
1. Introduction Organisms use the perception and discrimination of color, shape and size for many different tasks. The ability to discriminate visually between different objects with regard to shape and size has already been tested in a wide range of vertebrates (e.g., chickens: Jones and Osorio, 2003; sea lions: Schustermann and Thomas, 1966; Mauck and Dehnhardt, 1997; monkeys: Nitsch and Jarosch, 1972; Tanaka, 2000; Vonk and MacDonald, 2002; rodents: Zoccolan et al., 2009; humans: Hoffmann and Logothetis, 2009) and invertebrates (octopus: Schaller, 1926; Grasso and Basil, 2009; bees: Schnetter, 1968; Giurfa et al., 1996; Campan and Lehrer, 2002; Srinivasan, 2010). There is a variety of studies on the learning and memory abilities of teleosts available, testing the discrimination of colors, line orientations, shapes and sizes as well as object categorization skills. Herter (1929, 1930) and Meesters (1940) showed that several teleosts were able to recognize black and white patterns even when color schemes were reversed (Meesters, 1940), which was confirmed by Schluessel et al. (2012) in experiments on Malawi cichlids (Pseudotropheus sp.). Results on cichlids were complemented by another study which concluded that Pseudotropheus sp. could distinguish images from their vertical mirror image counterparts
∗ Corresponding author. Tel.: +49 228735488. E-mail address: [email protected] (T. Fuss). 0944-2006/$ – see front matter © 2013 Elsevier GmbH. All rights reserved. http://dx.doi.org/10.1016/j.zool.2013.10.009
(Gierszewski et al., 2013). Discrimination of color and line orientation were successfully tested in zebrafish (Danio rerio; Arthur and Levin, 2001; Colwill et al., 2005) and weakly electric elephantnose fish (Gnathonemus petersii; Schuster and Amtsfeld, 2002), while differentiation of color was successfully shown in goldfish (Carassius auratus; e.g. Neumeyer, 1986, 1992). Additionally, goldfish (Douglas et al., 1988; Frech et al., 2012) and Cypriniformes (Phoxinus phoxinus, Sutherland, 1962) were shown to recognize size constancy, while form constancy was recently shown to be recognized by cichlids (Pseudotropheus sp., Schluessel et al., 2013), providing evidence for advanced visual discrimination abilities in teleosts. Schluessel et al. (2012) also showed that Malawi cichlids not only perceived, remembered and discriminated selected visual stimuli, but also distinguished between two mental categories (representative objects in two mental categories: “fish” and “snail”). As predators, prey, habitat types or con-specifics may vary in size, shape and even coloring, using general features (gross similarities) provides fish with a certain degree of flexibility to react appropriately (Schluessel et al., 2012). Ambon damselfish (Pomacentrus amboinensis) even used visual stimuli as predictors for the availability of food at a certain time and place (anticipatory behavior; Siebeck et al., 2009). Although several teleosts from different habitats featuring different lifestyles and behaviors have been tested, these studies are far from reflecting the actual biological breadth of currently over 30,000 extant fish species. Sharks, rays and chimeras belong to the
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Fig. 1. The experimental setup within the experimental basin inside a white pavilion. The keyhole-shaped setup consists of a starting compartment (SC), a decision area and a frosted screen for projection with a divider to allow the projection of two 2D objects at a time and to provoke a clear, unambiguous choice (left or right). For projection, an LED beamer is used. Sharks are placed within the SC at the start of each trial. 1 = feeders, 2 = frosted screen for projection, 3 = cable pulls to release food from the feeders, 4a = guillotine door, 4b = cable pull to open guillotine door, 5 = ceiling-mounted fluorescent tubes (above the pavilion roof).
cartilaginous fishes and represent the oldest extant jawed vertebrate group. They have been extremely successful over the past 450 million years and inhabit almost every marine and freshwater environment (Compagno, 1999). Sharks still suffer from a negative public image and so far, few studies have been conducted to refute this prejudice, but the ones that have investigated selected cognitive abilities in sharks and rays indicate that they are as sophisticated as those of many other vertebrates. Graeber and Ebbesson (1972) and Graeber et al. (1973) successfully trained intact, telencephalon- or tectum-ablated nurse sharks (Ginglymostoma cirratum) to distinguish between colored and black and white striped discs. Freshwater stingrays (Potamotrygon motoro) also distinguished successfully between black and black and white striped discs (Schluessel, unpublished data). Aronson et al. (1967) compared the learning rates of nurse sharks (G. cirratum) in visual discrimination experiments (using illuminated transparent rectangles) to the learning rates of teleosts and mice and found them to be similar. Moreover, sharks and stingrays also used visual abilities to solve spatial tasks (Schluessel and Bleckmann, 2005, 2012; Fuss et al., 2013a,b). Van-Eyk et al. (2011) showed that giant shovelnose rays (Glaucostegus typus) discriminated colored (rewarded) stimuli from other colored (unrewarded) distractor stimuli of variable brightness. Juvenile blacktip sharks (Carcharhinus melanopterus) and gray sharks (C. menisorrah) solved visual discrimination tasks successfully with regard to stimulus orientation, form, differential brightness, and color (Tester and Kato, 1963). Electric shocks were used for negative reinforcement, but rigorous testing was lacking. The sharks were not subjected to a standardized training
schedule; they were given different numbers of tasks, received different amounts of electric shocks and participated in different numbers of sessions per day before training was terminated. Reversal training – as performed in the present study – has not been previously tested on any shark. Considering the various vertebrates and invertebrates which are able to distinguish visual stimuli, this ability must provide individuals with a distinct advantage with regard to recognizing and classifying different organisms (e.g., prey, predators or conspecifics) and objects (e.g., shelter or landmarks for orientation). As elasmobranchs hold a key phylogenetic position in the evolution of the brain of jawed vertebrates, information on their cognitive functions, even basic ones, such as simple visual discrimination abilities, will provide interesting insights from a biological as well as an evolutionary standpoint. Bamboo sharks (Chiloscyllium spp.) belong to the family Hemiscyllidae (order Orectolobiformes; Compagno, 1999) and are small benthic sharks which inhabit small territories in primarily shallow waters, such as lagoons and inshore environments, sea grass meadows, rocky and coral reef environments (Compagno et al., 2005). The present study aimed to investigate visual discrimination abilities in the gray bamboo shark (Chiloscyllium griseum) with regard to five two-dimensional (2D) stimuli and subsequent reversal learning. To facilitate comparison with previous studies performed on teleosts, similar 2D objects were used for the present study. Minnows (Phoxinus laevis), sunfish (Xenotis megalotis), cichlids (Pseudotropheus sp.), sticklebacks (Gasterosteus aculeatus), damselfish (Pomacentrus amboinensis) and goldfish have already been
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found to discriminate the same or very similar geometric shapes, including triangles, squares, diamonds, circles and crosses (e.g., Herter, 1953; Behrend and Bitterman, 1961; Mark, 1966; Mark and Maxwell, 1969; Wyzisk, 2005). 2. Materials and methods 2.1. Experimental animals and housing facilities Eight juvenile bamboo sharks (Chiloscyllium griseum, 4 male, 4 female, head–tail length between 25 and 40 cm) were kept in aquaria (1 m × 0.5 m × 0.5 m) connected to each other and to the experimental setup, thereby allowing for constant environmental conditions (conductivity, temperature and pH). The system was filled with aerated, filtered salt water (ca. 1.0217 kg salt/dm3 ; conductance: about 50 mS/m) at 26 ± 2 ◦ C. Food (small pieces of squid, fish or shrimp) was only available during the experimental training. The animals were kept at a 12 h light:12 h dark cycle; experiments were conducted during daylight hours. Individuals were identified by phenotypic characteristics. 2.2. Experimental setup Experiments were carried out in an octagonal experimental basin made out of transparent Perspex and featuring an opaque white covered floor (Fig. 1). The basin was set on five casters (15 cm high), allowing it to be freely rotated. During experiments, the basin was filled with water to a depth of about 0.3 m. Within the basin, a keyhole-shaped setup made out of dark gray PVC was installed (Fig. 1); it featured a starting compartment (SC; 0.51 m diameter × 0.35 m height), a decision area (1.15 m × 0.78 m × 0.35 m) and a frosted screen for projection (0.92 m × 0.35 m). Basin and setup were surrounded by a white pavilion (3.0 m × 3.0 m × 2.5 m) to exclude uncontrolled cueing as well as other potentially disturbing external influences and were evenly illuminated by ceilingmounted (above the pavilion roof) fluorescent tubes (Lumilux L 18W Coolwhite; Osram, Munich, Germany). A light gray guillotine door (0.43 m × 0.23 m) confined the SC, in which the sharks were placed before each trial. Independent of the type of trial/experiment, the experimenter was always situated behind the SC. The door was controlled manually by using a cable pull. The decision area was confined by the SC on the one side and a frosted screen for projection on the other side. The screen as well as the area in front of it was divided by a partitioning (0.33 m × 0.35 m) to allow an unambiguous decision making in response to two projected symbols on the screen (Fig. 1). The 2D symbols were bluish-green in color and were displayed on a light gray background. According to Hart et al. (2011), the maximum absorbance (max ) of cone visual pigments in the very closely related shark species Chiloscyllium punctatum was found to be 531.8 ± 6.7 nm, i.e., in the range for blue to green light. An LED projector situated at a distance of 1.3 m behind the screen was used for projections (Fig. 1). Sharks were swimming about 3 to 5 cm above ground (depending on the sharks’ size); thus, stimuli were projected at a height of 3 cm above the bottom. Right above both stimuli, feeders were installed which allowed food to be dropped into the setup manually using a cable pull at the experimenter’s discretion (Fig. 1). Both feeders were always filled with the same bait of small pieces of sepia, redfish, coalfish or brown shrimps during all trials to exclude unintentional cueing. Between trials, sharks were gently guided back into the SC, where they remained for up to 90 s before the next trial started. To preclude any olfactory cues after a reward was given (which could have biased the shark’s choice of arm in subsequent trials), the water in the maze was stirred after every trial.
2.3. Experimental training The behavioral experiment consisted of seven phases: phase 0 – acclimatization, phase 1 – training (square (positive stimulus) vs. empty space, i.e. only one symbol was projected), phase 2 – square vs. small triangle, phase 3 – square vs. large triangle, phase 4 – square vs. circle, phase 5 – square vs. rhomboid, phase 6 – reversal learning: cross (positive stimulus) vs. square (negative stimulus). Experiments were conducted as two-alternative forced choice experiments. After successful training of the first stimulus set (phase 1), performance was tested in the remaining pairs. During phases 1–5, the square served as the positive (rewarded) stimulus; during phase 6 (reversal learning), a cross was used as the positive stimulus and the square became the negative (unrewarded) stimulus. Before training, the sharks were gently caught manually and transferred from their home tank into the experimental setup. Acclimatization – phase 0. Before training, sharks were habituated to the transfer procedure into the experimental setup and allowed to acclimatize to the setup itself by swimming freely throughout the entire setup for up to 20 min at a time. The guillotine door was open, both divisions displayed the same 2D object (circle) and feeders were in place. Once a shark swam freely throughout the setup and looked for food being dropped from the feeders (i.e., close to the 2D objects), training commenced (Fig. 1). Training – phase 1. Before each trial, both feeders were baited and the water was stirred. Only one stimulus (square, 9.5 cm × 9.5 cm) was displayed (switched randomly between the left and the right side of the screen); the other division remained empty (Fig. 1). At the beginning of each regular trial the shark was placed in the SC. To start, the shark had to push against the guillotine door with its snout. Once the door opened, the shark had to make a choice within 2 min. A choice was recorded as soon as the shark touched the screen with its snout. A correct choice was rewarded with food. During the inter-trial interval, the shark was allowed to swim freely throughout the entire setup for 30 s before it was guided back into the SC and the door closed. The next trial started as soon as the shark pushed against the guillotine door. If a shark did not push against the door within 90 s or choose within the allocated 2 min, the trial was terminated. Training sessions were carried out five days per week; each session consisted of ten trials. Training was completed as soon as a learning criterion of 70% correct choices in three subsequent sessions was reached (2 (1) ≤ 0.05; to prove statistical significance). Animals that did not reach the criterion within 30 training sessions were excluded from further experiments. Training – phases 2–5. Training trials during phases 2–5 were performed similarly to phase 1, but now two stimuli (one in every division) were displayed in random order but never more than twice in a row in the same division. Five rotational schemes were used so as to alter the succession of the stimuli shown on a particular side between sessions. A small triangle (8 cm × 10 cm), a large triangle (12.5 cm × 15 cm) and a rhomboid (10.5 cm × 10.5 cm) served as negative (unrewarded) stimuli. If an animal did not reach the learning criterion within 30 training sessions, it proceeded with the next phase. All other criteria remained the same as outlined for phase 1. Reversal learning – phase 6. Training trials during this phase were performed similarly to phases 2–5, but now the square (formerly the positive stimulus) served as the negative stimulus while a cross (10 cm × 10 cm) became the positive stimulus. All other criteria remained the same as outlined for phase 1. 2.4. Data analysis The average trial time in seconds, the percentage of correct choices and the percentage of right and left choices were recorded
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for each session for each individual. To prove statistical significance, the learning criterion was established to be 70% correct choices in three consecutive sessions (2 (1) ≤ 0.05). A sign and binomial test was run to determine if those sharks who did not reach the learning criterion within 30 sessions in phases 2–5 chose the positive (rewarded) stimulus significantly more often than the negative (unrewarded) stimulus. A Mann–Whitney U-test was applied to determine if the average trial times differed significantly between phases for each individual as well as for the group. A Mann–Whitney U-test was also used to determine if group performance differed significantly between phases. For all tests p ≤ 0.05 was considered significant, p ≤ 0.001 highly significant.
Table 1 Results of visual discrimination tests in juvenile bamboo sharks. Number of sessions needed to reach the learning criterion (LC: 70% correct choices in three consecutive sessions) and average trial times [s] per discrimination task for every individual as well as for the group. Subject
Sessions to reach LC
Acclimatization
Shark 1 Shark 2 Shark 3 Shark 4 Shark 5 Shark 6 Shark 7 Shark 8 Group
14 10 17 8 9 8 8 8 10.25 ± 3.41
Shark 1 Shark 2 Shark 3 Shark 4 Shark 5 Shark 6 Shark 7 Shark 8 Group
18 28 8 24 14 23 19 12 18.25 ± 6.69
21.93 15.53 21.09 19.57 12.76 10.67 7.68 12.45 14.98
± ± ± ± ± ± ± ± ±
7.88 4.99 6.09 10.13 8.20 3.41 2.28 4.63 7.91
Shark 1 Shark 2 Shark 3 Shark 4 Shark 5 Shark 6 Shark 7 Shark 8 Group
13 6 9 12 20 6 6 6 9.75 ± 5.04
30.81 14.80 22.16 13.36 22.72 10.53 9.47 18.72 19.69
± ± ± ± ± ± ± ± ±
12.30 5.64 6.01 5.02 6.59 1.25 1.07 6.52 9.67
Shark 1 Shark 2 Shark 3 Shark 4 Shark 5 Shark 6 Shark 7 Shark 8 Group
4 16 4 30 30 24 5 12 10.83 ± 8.11
20.12 13.10 17.06 11.31 14.14 12.20 11.44 14.40 13.56
± ± ± ± ± ± ± ± ±
7.02 4.83 5.68 2.91 3.95 2.57 4.65 4.40 4.58
Shark 1 Shark 2 Shark 3 Shark 4 Shark 5 Shark 6 Shark 7 Shark 8 Group
13 6 3 23 10 30 7 9 10.14 ± 6.49
17.97 15.43 13.30 12.30 11.38 10.54 9.61 9.61 13.69
± ± ± ± ± ± ± ± ±
8.16 2.87 2.33 4.71 2.01 3.21 3.57 3.57 5.84
Shark 1 Shark 2 Shark 3 Shark 4 Shark 5 Shark 6 Shark 7 Shark 8 Group
30 5 14 19 6 19 6 3 10.28 ± 6.87
12.91 14.07 18.61 10.91 10.68 9.65 9.67 13.17 12.26
± ± ± ± ± ± ± ± ±
3.48 4.06 5.18 2.33 0.79 3.59 2.23 4.09 4.76
Shark 1 Shark 2 Shark 3 Shark 4 Shark 5 Shark 6 Shark 7 Shark 8 Group
22 12 22 9 6 6 28 9 14.25 ± 8.50
10.59 14.77 12.46 8.31 9.63 8.04 10.86 8.97 10.99
± ± ± ± ± ± ± ± ±
3.95 5.25 4.76 1.60 1.45 2.54 3.52 1.66 4.12
Phase 1
vs.empty space
3.1. Acclimatization – phase 0 The sharks needed on average 10.25 ± 3.41 sessions to acclimatize to the maze, understand the starting procedure as well as food retrieval from the feeders. No initial side preference was observed in any individual.
Phase 2
vs. 3.2. Training Phase 1. The sharks needed on average 18.25 ± 6.69 sessions to complete the first training phase successfully (Table 1). They needed on average 14.98 ± 7.91 s per trial to make a decision. Phases 2–5. The sharks needed on average 9.75 ± 5.04 sessions to discriminate a square from a small triangle (phase 2; Table 1). All sharks reached the learning criterion and thus showed a significant preference for the correct stimulus. Compared to phase 1, average trial times increased significantly for five individuals as well as for the group (Mann–Whitney U-test: ZGroup = –4.005, pGroup < 0.001; Table 2). The sharks needed on average 10.83 ± 8.11 sessions to discriminate a square from a large triangle (phase 3; Table 1). Two sharks (sharks 4 and 5) were not able to reach the learning criterion within the scheduled 30 sessions. Nevertheless, both showed a significant preference for the correct stimulus (sign and binomial test: one-tail pShark 4 = 0.009, two-tail pShark 4 = 0.018; onetail pShark 5 = 0.006, two-tail pShark 5 = 0.013; Table 3). Compared to phase 2, average trial times did not differ significantly for all but one shark (Mann–Whitney U-test: ZShark 5 = 4.496, pShark 5 < 0.001; Table 2) and the group (Mann–Whitney U-test: ZGroup = 4.962, pGroup < 0.001; Table 2). The sharks needed on average 10.14 ± 6.49 sessions to discriminate a square from a circle (phase 4; Table 1). One shark (shark 6) was not able to reach the learning criterion within the scheduled 30 sessions and did not show a significant preference for the correct stimulus (sign and binomial test: onetail pShark 6 = 0.193, two-tail pShark 6 = 0.386; Table 3). Compared to phase 3, average trial times did not differ significantly for all but two sharks (Mann–Whitney U-test: ZShark 5 = 2.172, pShark 5 = 0.029, ZShark 6 = 2.824, pShark 6 = 0.005; Table 2) and did not differ significantly for the group (Mann–Whitney U-test: ZGroup = −0.766, pGroup = 0.444; Table 2). The sharks needed on average 10.28 ± 6.87 sessions to discriminate a square from a rhomboid (phase 5; Table 1). One shark (shark 1) was not able to reach the learning criterion within the scheduled 30 sessions. Nevertheless, it showed a significant preference for the correct stimulus (sign and binomial test: one-tail pShark 1 = 0.016, two-tail pShark 1 = 0.033; Table 3). Compared to phase 4, average trial times did not differ significantly for all but one shark (Mann–Whitney U-test:
Average trial time (s)
Discrimination task
3. Results Eight sharks started and finished the experimental training procedure. The following section will summarize the results for those individuals as well as for the group. Group results include only sharks which finished a phase successfully.
107
Phase 3
vs.
Phase 4
vs.
Phase 5
vs.
Phase 6
vs.
shark did not reach the learning criterion within the allocated 30 sessions and was excluded from group results.
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Table 2 Results of the Mann–Whitney U-test used to determine if average trial times differed significantly between phases for every individual shark as well as for the group. Mann–Whitney U-test
Subject
Z-Value
p-Value
Phase 1 vs. phase 2
Shark 1 Shark 2 Shark 3 Shark 4 Shark 5 Shark 6 Shark 7 Shark 8 Group
−2.102 −0.226 −0.337 2.014 −3.256 −0.458 −2.005 −2.295 −4.005
0.036* 0.821 0.736 0.044* 0.0011** 0.647 0.045* 0.022* 0.05 not significant. * p ≤ 0.05 significant.
−2.073
0.038*
−2.323
0.021*
−1.848
0.064
−1.958
0.050*
−1.646
0.099
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Fig. 2. Learning performance of shark 7: percentage of correct choices (symbolized by black boxes; left ordinate) as well as average trial times in seconds (symbolized by gray bars; right ordinate) per phase until reaching the learning criterion.
sessions to discriminate between a square and a small triangle, thus significantly improving performance compared to phase 1 (Table 4). Moreover, they were comparably faster than goldfish (20–35 days to reach a rate of 75% correct choices; Wyzisk and Neumeyer, 2007) and pumpkinseed sunfish (Lepomis gibbosus, 10.5 and 11.3 sessions; Königs, 2003), but 28% slower than Pseudotropheus sp. (7.6 ± 6.6 sessions; Schluessel et al., 2012). In phase 3, they needed 10.83 ± 8.11 sessions to discriminate a square from a large triangle; two sharks (sharks 4 and 5) did not reach the learning criterion. Nevertheless, both sharks chose the correct stimulus significantly more often than the alternative (2Shark 4 (1) = 0.015, 2Shark 5 (1) = 0.011, Table 3), indicating some kind of learning effect even during this phase. Those six sharks that did learn the task were about as fast as cichlids. Seven out of eight sharks distinguished the square from a circle in 10.14 ± 6.49 sessions (phase 4), making them about 20% slower than cichlids (8.1 ± 3.8 sessions; Schluessel et al., 2012). In training phase 5 the sharks needed 10.28 ± 6.87 sessions to differentiate the square from a rhomboid. In this phase they needed about 31% more sessions than Pseudotropheus sp. (7.1 ± 3.6 sessions; Schluessel et al., 2012). Although the sharks were slower than cichlids, which are known to have excellent visual capabilities (e.g., Carleton, 2009; Sabbah et al., 2010), their learning rates for every single phase fell within the range of other teleosts (goldfish, pumpkinseed sunfish). Surprisingly, performance did not increase with increasing numbers of stimulus pairs being shown (i.e., they did not need fewer sessions to reach the learning criterion, as might have been expected), despite the positive stimulus remaining the same throughout all phases. Instead, the sharks needed about the same number of sessions to reach the learning criterion in all tasks except for phase 1, which took significantly longer than the others. Two factors should be considered when interpreting the results. First, phase 1 was the first task to solve and the sharks had to get used to the setup and the type of experimental design. This could have extended the learning process as opposed to subsequent phases (2–5). The second factor concerns the potential recognition
mechanisms the fish could have used to identify the positive stimulus which always remained the same. Instead of seeing the image of the positive stimulus as a whole, one way to remember it could have been to recognize the presence of four straight lines in a regular setup or to simply remember four corners. Theoretically, applying such a mechanism could have led to confusion when the rhomboid was shown in conjunction with the square, as it features the same line arrangement and the same number of corners as the square, with the only exception of it being 45◦ rotated. Similarly, if the sharks had only remembered any number of corners and straight lines in a regular setup this could have also led to confusion with the triangles. Contradicting this possibility, however, was the fact that not even a circle, which shares no common features with the square, was recognized any faster than the other symbols. As potential mechanisms were not investigated any further it is impossible to decide which strategies may have been used. Generally, though, vertical and horizontal ends of an object seem to play a crucial role for the discrimination of objects in octopus (Sutherland, 1964), nurse sharks (Graeber and Ebbesson, 1972; Graeber et al., 1973) and several teleosts (e.g., Herter, 1929; Meesters, 1940; Schulte, 1957; Mackintosh and Sutherland, 1963; Fetsko, 2002). After successful conditioning, gudgeons (Gobio gobio) distinguished triangles and other shapes of different orientation, using the relative positions of the corners or edges of these shapes in space (Herter, 1929). Particularly the characteristic vertical ends of the figures seemed to be a decisive factor in distinguishing the shapes (e.g., Schulte, 1957). Similar results were found in studies on the organization of the visual field of crucian carps (Carassius carassius), showing that both the horizontal and vertical patterns (Herter, 1953) as well as the outlines of figures are used for distinction (Meesters, 1940). Of course, another option could have been that the sharks learned to avoid the negative symbols rather than preferring the positive stimulus or may have learned a combination of both; however, as mentioned previously, this was not determined here. In any case, the presence of a new negative stimulus seems to have been perceived as a new experimental situation, for which previously
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gained knowledge was either not exploited, or if it was, may rather have caused confusion due to shared features between the positive and the negative stimulus. Average trial times also remained fairly constant throughout phases 2–5 after an initial significant increase following phase 1. The sharks seemed to be confused by the introduction of a second stimulus in phase 2 (Table 2); therefore, they needed significantly longer to complete a trial during phase 2 than in phase 1. With increasing training routine, though, trial times significantly decreased again (Mann–Whitney U-test phase 2 vs. phase 3: ZGroup = 4.962, pGroup < 0.001; Table 2) and finally stabilized at 13.69 s and 12.26 s (phases 4 and 5, respectively; Table 1), which is similar to the times that were observed in phase 1 (14.98 ± 7.91 s; Table 1). The trial time therefore provides some further indication that the sharks actually had to “re-learn” (aspects of) each new stimulus pair and did not just exploit previously gained knowledge. The possibility of reversal of a spatial and/or visual discrimination task has been demonstrated in mammals (e.g., Sutherland, 1964; Hamilton et al., 2004), birds (e.g., Range et al., 2008), amphibians and reptiles (e.g., Day et al., 2003; Jenkin and Laberge, 2010) and several fish species (e.g., Lopez et al., 1999; Hughes and Blight, 2000; Colwill et al., 2005). Animals facing a reversal task following repetitive training in a discrimination task will initially persist in their learned strategy before trying other strategies. Accordingly, learning in a reversal task will proceed slower than in the preceding tasks (Sutherland, 1964). Phase 6, which featured the previously positive stimulus as the negative one, examined reversal learning in the bamboo sharks. The sharks needed on average 14.25 ± 8.50 sessions (Table 1) for successful completion of this task. As expected, it took them significantly longer to learn the reversal task than the tasks in phases 2–5, but the session number did not differ significantly from the initial training phase 1 (Table 4). Average trial times did also not differ significantly from any of the other phases (Table 2). Interestingly, if the sharks had indeed, as discussed above, completely “re-learned” the positive as well as the negative stimulus in phases 2–5, there should have been no difference in the number of sessions for successful discrimination in phase 6 and phases 2–5. In phase 6, there would have simply been a “new” stimulus pair yet again. The fact that the sharks took longer to solve the last task compared to the previous ones therefore contradicts the idea that the positive stimulus was completely “re-learned” in phases 2–5. As mentioned above, further experiments would be needed to elucidate the mechanism(s) applied by each shark. Nevertheless, the present results suggest that bamboo sharks can learn visual discrimination tasks, succeed in a reversal task and probably retain (some) information about a previously learned task when progressing to a new one. The present results not only reveal bamboo sharks’ ability to visually discriminate geometric stimuli. Moreover, they also provide insights into the behavioral variability found among individuals trained in the same procedure using the same training schedule. The often observed, apparently erratic nature of individual learning success is part of this variability, which appears to depend on the sharks’ motivation to cooperate day by day and possibly on a preference or a clear “non-preference” for single 2D objects (Table 1). The latter can clearly be seen in sharks 2, 3 and 8, who showed a relatively constant performance during all but one phase (shark 2 and 8: large triangle (phase 3), shark 3: rhomboid (phase 5)). In conclusion it may be stated that juvenile Chiloscyllium griseum can distinguish between various geometric symbols. Additionally, the learning rates of the sharks in the tasks presented here correspond to the rates determined for Malawi cichlids and pumpkinseed sunfish (Königs, 2003; Schluessel et al., 2012). Successful discrimination and reversal learning in bamboo sharks provides evidence from yet another phylogenetic group for the general
importance of this ability in all animal species using the visual system, vertebrates as well as invertebrates. As seen here, these successful marine predators have the anatomical prerequisites as well as the cognitive skills to use this capability, regardless of the importance of the visual system compared to other sensory systems, such as olfaction or mechanoreception. As the stimuli used in this study were quite artificial, but nonetheless recognized, it would be even more interesting to determine in future studies whether learning proceeds more rapidly when using biologically relevant stimuli are used or when experiments are performed under more pressing conditions. Acknowledgements We would like to thank Slawa Braun for animal caretaking, maintenance and repairs. We are specifically grateful to “Haus des Meeres” in Vienna for supplying the animals used during this study. The research reported herein was performed under the guidelines established by the current German animal protection law. References Aronson, L.R., Aronson, F.R., Clark, E., 1967. Instrumental conditioning and light–dark discrimination in young nurse sharks. Bull. Mar. Sci. 17, 249–256. Arthur, D., Levin, E., 2001. Spatial and non-spatial visual discrimination learning in zebra fish (Danio rerio). Anim. Cogn. 4, 125–131. Behrend, E.R., Bitterman, M.E., 1961. Probability-matching in the fish. Am. J. Psychol. 74, 542–551. Campan, R., Lehrer, M., 2002. Discrimination of closed shapes by two species of bee, Apis mellifera and Megachile rotundata. J. Exp. Biol. 205, 559–572. Carleton, K., 2009. Cichlid fish visual systems: mechanisms of spectral tuning. Integr. Zool. 4, 75–86. Colwill, R.M., Raymond, M.P., Ferreira, L., Escudero, H., 2005. Visual discrimination learning in zebra fish (Danio rerio). Behav. Proc. 70, 19–31. Compagno, L.J.V., 1999. Systematics and body form. In: Hamlett, W.C. (Ed.), Sharks, Skates, and Rays: The Biology of Elasmobranch Fishes. Johns Hopkins University Press, Baltimore, pp. 1–42. Compagno, L.J.V., Dando, M., Fowler, S., 2005. A Field Guide to the Sharks of the World. Collins, London. Day, L., Ismail, N., Wilczynski, W., 2003. Use of position and feature cues in discrimination learning by the whiptail lizard (Cnemidophorus inornatus). J. Comp. Psychol. 117, 440–448. Douglas, R.H., Eva, J., Guttridge, N., 1988. Size constancy in goldfish (Carassius auratus). Behav. Brain Res. 30, 37–42. Fetsko, L.A., (Ph.D. Thesis) 2002. What can be learned from a fish: an analysis of visual discrimination in the zebrafish, Danio rerio. Temple University, Philadelphia, PA. Frech, B., Vogtsberger, M., Neumeyer, C., 2012. Visual discrimination of objects differing in spatial depth by goldfish. J. Comp. Physiol. A 198, 53–60. Fuss, T., Bleckmann, H., Schlüssel, V., 2013a. The shark Chiloscyllium griseum can orient using turn responses before and after partial telencephalon ablation. J. Comp. Physiol. A, http://dx.doi.org/10.1007/s00359-013-0858-y. Fuss, T., Bleckmann, H., Schlüssel, V., 2013b. Place learning prior to and after telencephalon ablation in bamboo and coral cat sharks (Chiloscyllium griseum and Atelomycterus marmoratus). J. Comp. Physiol. A, http://dx.doi.org/10.1007/s00359-013-0859-x. Gierszewski, S., Bleckmann, H., Schluessel, V., 2013. Cognitive abilities in Malawi cichlids (Pseudotropheus sp.): matching-to-sample and image/mirror-image discriminations. PLoS ONE 8, e57363. Giurfa, M., Eichmann, B., Menzel, R., 1996. Symmetry perception in an insect. Nature 382, 458–461. Guttridge, T.L., Myrberg, A.A., Porcher, I.F., Sims, D.W., Krause, J., 2010. The role of learning in shark behavior. Fish Fish. 10, 450–469. Graeber, R.C., Ebbesson, S.O.E., 1972. Visual discrimination learning in normal and tectal-ablated nurse sharks (Ginglymostoma cirratum). Comp. Biochem. Physiol. A 42, 131–139. Graeber, R.C., Ebbesson, S.O., Jane, J.A., 1973. Visual discrimination in sharks without optic tectum. Science 180, 413–415. Grasso, F.W., Basil, J.A., 2009. The evolution of flexible behavioral repertoires in cephalopod molluscs. Brain Behav. Evol. 74, 231–245. Hamilton, D.A., Rosenfelt, C.S., Whishaw, I.Q., 2004. Sequential control of navigation by locale and taxon cues in the Morris water task. Behav. Brain Res. 154, 385–397. Herter, K., 1929. Dressurversuche an Fischen. Z. Vergl. Physiol. 10, 688–711. Herter, K., 1930. Weitere Dressurversuche an Fischen. Z. Vergl. Physiol. 11, 730–748. Herter, K., 1953. Die Fischdressuren und ihre sinnesphysiologischen Grundlagen. Akademie Verlag, Berlin. Hoffmann, K.L., Logothetis, N.K., 2009. Cortical mechanisms of sensory learning and object recognition. Philos. Trans. R. Soc. B 364, 321–329. Hughes, R., Blight, C., 2000. Two intertidal fish species use visual association learning to track the status of food patches in a radial maze. Anim. Behav. 59, 613–621.
T. Fuss et al. / Zoology 117 (2014) 104–111 Jenkin, S., Laberge, F., 2010. Visual discrimination learning in the fire-bellied toad Bombina orientalis. Learn. Behav. 38, 418–425. Jones, C.D., Osorio, D., 2003. Discrimination of oriented visual textures by poultry chicks. Vis. Res. 44, 83–89. Königs, K., 2003. Visual discrimination experiments concerning cognitive abilities in teleost fish (Lepomis gibbosus). Diploma Thesis, University of Bonn, Germany. Lopez, J.C., Broglio, C., Rodriguez, F., Thinus-Blanc, C., Salas, C., 1999. Multiple spatial learning strategies in goldfish (Carassius auratus). Anim. Cogn. 2, 109–120. Mackintosh, J., Sutherland, N.S., 1963. Visual discrimination by the goldfish: the orientation of rectangles. Anim. Behav. 11, 135–141. Mark, R.F., 1966. The tectal commissure and interocular transfer of pattern discrimination in cichlid fish. Exp. Neurol. 16, 215–225. Mark, R.F., Maxwell, A., 1969. Circle size discrimination and transposition behavior in cichlid fish. Anim. Behav. 17, 155–158. Mauck, B., Dehnhardt, G., 1997. Mental rotation in a California sea lion (Zalophus californianus). J. Exp. Biol. 200, 1309–1316. Meesters, A., 1940. Über die Organisation des Gesichtsfeldes der Fische. Z. Tierpsychol. 4, 84–148. Neumeyer, C., 1986. Wavelength discrimination in goldfish. J. Comp. Physiol. A 158, 203–213. Neumeyer, C., 1992. Tetrachromatic color vision in goldfish. Evidence from color mixture experiments. J. Comp. Physiol. A 171, 639–649. Nitsch, F., Jarosch, E., 1972. Individuelle Lernstrategien von Totenkopfäffchen bei der Diskriminierung von visuellen Sequenzen. Psychol. Res. 35, 335–354. Range, F., Bugnyar, K., Kotrschal, K., 2008. The performance of ravens on simple discrimination tasks: a preliminary study. Acta Ethol. 11, 34–41. Sabbah, S., Laria, R.L., Gray, S.M., Hawryshyn, C.W., 2010. Functional diversity in the color vision of cichlid fishes. BMC Biol. 8, 133. Schaller, A., 1926. Sinnesphysiologische und psychologische Untersuchungen an Wasserkäfern und Fischen. Z. Vergl. Physiol. 4, 370–464. Schluessel, V., Bleckmann, H., 2005. Spatial memory and orientation strategies in the elasmobranch Potamotrygon motoro. J. Comp. Physiol. A 191, 695–706. Schluessel, V., Bleckmann, H., 2012. Memory retention in the grey bamboo shark Chiloscyllium griseum. Zoology 115, 346–353. Schluessel, V., Fricke, G., Bleckmann, H., 2012. Visual discrimination and object categorization in the cichlid Pseudotropheus sp. Anim. Cogn. 15, 525–537.
111
Schluessel, V., Kraniotakes, H., Bleckmann, H., 2013. Visual discrimination of rotated 3D objects in Malawi cichlids (Pseudotropheus sp.): a first indication for form constancy in fishes. Anim. Cogn., http://dx.doi.org/10.1007/s10071-013-0667-2. Schnetter, B., 1968. Visual form discrimination of the honey bee within the range of 6- and 4-pointed stars. Z. Vergl. Physiol. 59, 90–109. Schulte, A., 1957. Transfer- und Transpositionsversuche mit monokular dressierten Fischen. Z. Vergl. Physiol. 39, 432–476. Schuster, S., Amtsfeld, S., 2002. Template-matching describes visual patternrecognition tasks in the weakly electric fish Gnathonemus petersii. J. Exp. Biol. 205, 549–557. Schustermann, R.J., Thomas, T., 1966. Shape discrimination and transfer in the California sea lion. Psychon. Sci. 5, 21–22. Siebeck, U.E., Litherland, L., Wallis, G.M., 2009. Shape learning and discrimination in reef fish. J. Exp. Biol. 212, 2113–2119. Srinivasan, M.V., 2010. Honey bees as a model for vision, perception, and cognition. Annu. Rev. Entomol. 55, 267–284. Sutherland, N.S., 1962. The Methods and Findings of Experiments on the Visual Discrimination of Shape by Animals. Heffner, Cambridge. Sutherland, N.S., 1964. Visual discrimination of animals. Br. Med. Bull. 20, 54–59. Tanaka, K., 2000. Mechanisms of visual object recognition studied in monkeys. Spat. Vis. 13, 147–163. Tester, A., Kato, S., 1963. Visual target discrimination in blacktip sharks (Carcharhinus melanopterus) and grey sharks (C. menisorrah). Pac. Sci. 20, 461–471. Van-Eyk, S., Siebeck, U., Champ, C., Marshall, J., Hart, N., 2011. Behavioral evidence for color vision in an elasmobranch. J. Exp. Biol. 214, 4186–4192. Vonk, J., MacDonald, S.E., 2002. Natural concepts in a juvenile gorilla (Gorilla gorilla gorilla) at three levels of abstraction. J. Exp. Anal. Behav. 78, 315–332. Wyzisk, K., 2005. Experimente zur Formen- und Größenwahrnehmung beim Goldfisch (Carassius auratus) unter Verwendung von Scheinkonturen und Größentäuschungen. Ph.D. Thesis, University of Mainz, Germany. Wyzisk, K., Neumeyer, C., 2007. Perception of illusionary surfaces and contours in goldfish. Vis. Neurosci. 24, 291–298. Zoccolan, D., Oertelt, N., DiCarlo, J.J., Cox, D.D., 2009. A rodent model for the study of invariant visual object recognition. Proc. Natl. Acad. Sci. U. S. A. 106, 8748–8753.
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Zoology journal homepage: www.elsevier.com/locate/zool
Visual discrimination abilities in the gray bamboo shark (Chiloscyllium griseum) Theodora Fuss ∗ , Horst Bleckmann, Vera Schluessel Department of Comparative Sensory Biology and Neurobiology, Institute of Zoology, University of Bonn, Meckenheimer Allee 169, 53115 Bonn, Germany
a r t i c l e
i n f o
Article history: Received 11 June 2013 Received in revised form 24 October 2013 Accepted 24 October 2013 Available online 10 December 2013 Keywords: Chiloscyllium griseum Elasmobranchs Learning ability Reversal task Visual discrimination
a b s t r a c t This study assessed visual discrimination abilities in bamboo sharks (Chiloscyllium griseum). In a visual discrimination task using two-dimensional (2D) geometric stimuli, sharks learned to distinguish between a square, being the positive (rewarded) stimulus, and several negative stimuli, such as two differently sized triangles, a circle, a rhomboid and a cross. Although the amount of sessions to reach the learning criterion and the average trial time needed to solve each new task did not vary significantly, the number of correct choices per session increased significantly with on-going experiments. The results indicate that the sharks did not simply remember the positive stimulus throughout the different training phases. Instead, individuals also seemed to learn each negative symbol and possibly had to “relearn” at least some aspects of the positive stimulus during each training phase. The sharks were able to distinguish between the 2D stimulus pairs at a learning rate corresponding to that found in teleosts. As expected, it took the sharks longer to learn a reversal task (with the positive stimulus now being the negative one) than to discriminate between the other stimulus pairs. Nevertheless, the present results suggest that bamboo sharks can learn visual discrimination tasks, succeed in a reversal task and probably retain (some) information about a previously learned task when progressing to a new one. © 2013 Elsevier GmbH. All rights reserved.
1. Introduction Organisms use the perception and discrimination of color, shape and size for many different tasks. The ability to discriminate visually between different objects with regard to shape and size has already been tested in a wide range of vertebrates (e.g., chickens: Jones and Osorio, 2003; sea lions: Schustermann and Thomas, 1966; Mauck and Dehnhardt, 1997; monkeys: Nitsch and Jarosch, 1972; Tanaka, 2000; Vonk and MacDonald, 2002; rodents: Zoccolan et al., 2009; humans: Hoffmann and Logothetis, 2009) and invertebrates (octopus: Schaller, 1926; Grasso and Basil, 2009; bees: Schnetter, 1968; Giurfa et al., 1996; Campan and Lehrer, 2002; Srinivasan, 2010). There is a variety of studies on the learning and memory abilities of teleosts available, testing the discrimination of colors, line orientations, shapes and sizes as well as object categorization skills. Herter (1929, 1930) and Meesters (1940) showed that several teleosts were able to recognize black and white patterns even when color schemes were reversed (Meesters, 1940), which was confirmed by Schluessel et al. (2012) in experiments on Malawi cichlids (Pseudotropheus sp.). Results on cichlids were complemented by another study which concluded that Pseudotropheus sp. could distinguish images from their vertical mirror image counterparts
∗ Corresponding author. Tel.: +49 228735488. E-mail address: [email protected] (T. Fuss). 0944-2006/$ – see front matter © 2013 Elsevier GmbH. All rights reserved. http://dx.doi.org/10.1016/j.zool.2013.10.009
(Gierszewski et al., 2013). Discrimination of color and line orientation were successfully tested in zebrafish (Danio rerio; Arthur and Levin, 2001; Colwill et al., 2005) and weakly electric elephantnose fish (Gnathonemus petersii; Schuster and Amtsfeld, 2002), while differentiation of color was successfully shown in goldfish (Carassius auratus; e.g. Neumeyer, 1986, 1992). Additionally, goldfish (Douglas et al., 1988; Frech et al., 2012) and Cypriniformes (Phoxinus phoxinus, Sutherland, 1962) were shown to recognize size constancy, while form constancy was recently shown to be recognized by cichlids (Pseudotropheus sp., Schluessel et al., 2013), providing evidence for advanced visual discrimination abilities in teleosts. Schluessel et al. (2012) also showed that Malawi cichlids not only perceived, remembered and discriminated selected visual stimuli, but also distinguished between two mental categories (representative objects in two mental categories: “fish” and “snail”). As predators, prey, habitat types or con-specifics may vary in size, shape and even coloring, using general features (gross similarities) provides fish with a certain degree of flexibility to react appropriately (Schluessel et al., 2012). Ambon damselfish (Pomacentrus amboinensis) even used visual stimuli as predictors for the availability of food at a certain time and place (anticipatory behavior; Siebeck et al., 2009). Although several teleosts from different habitats featuring different lifestyles and behaviors have been tested, these studies are far from reflecting the actual biological breadth of currently over 30,000 extant fish species. Sharks, rays and chimeras belong to the
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Fig. 1. The experimental setup within the experimental basin inside a white pavilion. The keyhole-shaped setup consists of a starting compartment (SC), a decision area and a frosted screen for projection with a divider to allow the projection of two 2D objects at a time and to provoke a clear, unambiguous choice (left or right). For projection, an LED beamer is used. Sharks are placed within the SC at the start of each trial. 1 = feeders, 2 = frosted screen for projection, 3 = cable pulls to release food from the feeders, 4a = guillotine door, 4b = cable pull to open guillotine door, 5 = ceiling-mounted fluorescent tubes (above the pavilion roof).
cartilaginous fishes and represent the oldest extant jawed vertebrate group. They have been extremely successful over the past 450 million years and inhabit almost every marine and freshwater environment (Compagno, 1999). Sharks still suffer from a negative public image and so far, few studies have been conducted to refute this prejudice, but the ones that have investigated selected cognitive abilities in sharks and rays indicate that they are as sophisticated as those of many other vertebrates. Graeber and Ebbesson (1972) and Graeber et al. (1973) successfully trained intact, telencephalon- or tectum-ablated nurse sharks (Ginglymostoma cirratum) to distinguish between colored and black and white striped discs. Freshwater stingrays (Potamotrygon motoro) also distinguished successfully between black and black and white striped discs (Schluessel, unpublished data). Aronson et al. (1967) compared the learning rates of nurse sharks (G. cirratum) in visual discrimination experiments (using illuminated transparent rectangles) to the learning rates of teleosts and mice and found them to be similar. Moreover, sharks and stingrays also used visual abilities to solve spatial tasks (Schluessel and Bleckmann, 2005, 2012; Fuss et al., 2013a,b). Van-Eyk et al. (2011) showed that giant shovelnose rays (Glaucostegus typus) discriminated colored (rewarded) stimuli from other colored (unrewarded) distractor stimuli of variable brightness. Juvenile blacktip sharks (Carcharhinus melanopterus) and gray sharks (C. menisorrah) solved visual discrimination tasks successfully with regard to stimulus orientation, form, differential brightness, and color (Tester and Kato, 1963). Electric shocks were used for negative reinforcement, but rigorous testing was lacking. The sharks were not subjected to a standardized training
schedule; they were given different numbers of tasks, received different amounts of electric shocks and participated in different numbers of sessions per day before training was terminated. Reversal training – as performed in the present study – has not been previously tested on any shark. Considering the various vertebrates and invertebrates which are able to distinguish visual stimuli, this ability must provide individuals with a distinct advantage with regard to recognizing and classifying different organisms (e.g., prey, predators or conspecifics) and objects (e.g., shelter or landmarks for orientation). As elasmobranchs hold a key phylogenetic position in the evolution of the brain of jawed vertebrates, information on their cognitive functions, even basic ones, such as simple visual discrimination abilities, will provide interesting insights from a biological as well as an evolutionary standpoint. Bamboo sharks (Chiloscyllium spp.) belong to the family Hemiscyllidae (order Orectolobiformes; Compagno, 1999) and are small benthic sharks which inhabit small territories in primarily shallow waters, such as lagoons and inshore environments, sea grass meadows, rocky and coral reef environments (Compagno et al., 2005). The present study aimed to investigate visual discrimination abilities in the gray bamboo shark (Chiloscyllium griseum) with regard to five two-dimensional (2D) stimuli and subsequent reversal learning. To facilitate comparison with previous studies performed on teleosts, similar 2D objects were used for the present study. Minnows (Phoxinus laevis), sunfish (Xenotis megalotis), cichlids (Pseudotropheus sp.), sticklebacks (Gasterosteus aculeatus), damselfish (Pomacentrus amboinensis) and goldfish have already been
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found to discriminate the same or very similar geometric shapes, including triangles, squares, diamonds, circles and crosses (e.g., Herter, 1953; Behrend and Bitterman, 1961; Mark, 1966; Mark and Maxwell, 1969; Wyzisk, 2005). 2. Materials and methods 2.1. Experimental animals and housing facilities Eight juvenile bamboo sharks (Chiloscyllium griseum, 4 male, 4 female, head–tail length between 25 and 40 cm) were kept in aquaria (1 m × 0.5 m × 0.5 m) connected to each other and to the experimental setup, thereby allowing for constant environmental conditions (conductivity, temperature and pH). The system was filled with aerated, filtered salt water (ca. 1.0217 kg salt/dm3 ; conductance: about 50 mS/m) at 26 ± 2 ◦ C. Food (small pieces of squid, fish or shrimp) was only available during the experimental training. The animals were kept at a 12 h light:12 h dark cycle; experiments were conducted during daylight hours. Individuals were identified by phenotypic characteristics. 2.2. Experimental setup Experiments were carried out in an octagonal experimental basin made out of transparent Perspex and featuring an opaque white covered floor (Fig. 1). The basin was set on five casters (15 cm high), allowing it to be freely rotated. During experiments, the basin was filled with water to a depth of about 0.3 m. Within the basin, a keyhole-shaped setup made out of dark gray PVC was installed (Fig. 1); it featured a starting compartment (SC; 0.51 m diameter × 0.35 m height), a decision area (1.15 m × 0.78 m × 0.35 m) and a frosted screen for projection (0.92 m × 0.35 m). Basin and setup were surrounded by a white pavilion (3.0 m × 3.0 m × 2.5 m) to exclude uncontrolled cueing as well as other potentially disturbing external influences and were evenly illuminated by ceilingmounted (above the pavilion roof) fluorescent tubes (Lumilux L 18W Coolwhite; Osram, Munich, Germany). A light gray guillotine door (0.43 m × 0.23 m) confined the SC, in which the sharks were placed before each trial. Independent of the type of trial/experiment, the experimenter was always situated behind the SC. The door was controlled manually by using a cable pull. The decision area was confined by the SC on the one side and a frosted screen for projection on the other side. The screen as well as the area in front of it was divided by a partitioning (0.33 m × 0.35 m) to allow an unambiguous decision making in response to two projected symbols on the screen (Fig. 1). The 2D symbols were bluish-green in color and were displayed on a light gray background. According to Hart et al. (2011), the maximum absorbance (max ) of cone visual pigments in the very closely related shark species Chiloscyllium punctatum was found to be 531.8 ± 6.7 nm, i.e., in the range for blue to green light. An LED projector situated at a distance of 1.3 m behind the screen was used for projections (Fig. 1). Sharks were swimming about 3 to 5 cm above ground (depending on the sharks’ size); thus, stimuli were projected at a height of 3 cm above the bottom. Right above both stimuli, feeders were installed which allowed food to be dropped into the setup manually using a cable pull at the experimenter’s discretion (Fig. 1). Both feeders were always filled with the same bait of small pieces of sepia, redfish, coalfish or brown shrimps during all trials to exclude unintentional cueing. Between trials, sharks were gently guided back into the SC, where they remained for up to 90 s before the next trial started. To preclude any olfactory cues after a reward was given (which could have biased the shark’s choice of arm in subsequent trials), the water in the maze was stirred after every trial.
2.3. Experimental training The behavioral experiment consisted of seven phases: phase 0 – acclimatization, phase 1 – training (square (positive stimulus) vs. empty space, i.e. only one symbol was projected), phase 2 – square vs. small triangle, phase 3 – square vs. large triangle, phase 4 – square vs. circle, phase 5 – square vs. rhomboid, phase 6 – reversal learning: cross (positive stimulus) vs. square (negative stimulus). Experiments were conducted as two-alternative forced choice experiments. After successful training of the first stimulus set (phase 1), performance was tested in the remaining pairs. During phases 1–5, the square served as the positive (rewarded) stimulus; during phase 6 (reversal learning), a cross was used as the positive stimulus and the square became the negative (unrewarded) stimulus. Before training, the sharks were gently caught manually and transferred from their home tank into the experimental setup. Acclimatization – phase 0. Before training, sharks were habituated to the transfer procedure into the experimental setup and allowed to acclimatize to the setup itself by swimming freely throughout the entire setup for up to 20 min at a time. The guillotine door was open, both divisions displayed the same 2D object (circle) and feeders were in place. Once a shark swam freely throughout the setup and looked for food being dropped from the feeders (i.e., close to the 2D objects), training commenced (Fig. 1). Training – phase 1. Before each trial, both feeders were baited and the water was stirred. Only one stimulus (square, 9.5 cm × 9.5 cm) was displayed (switched randomly between the left and the right side of the screen); the other division remained empty (Fig. 1). At the beginning of each regular trial the shark was placed in the SC. To start, the shark had to push against the guillotine door with its snout. Once the door opened, the shark had to make a choice within 2 min. A choice was recorded as soon as the shark touched the screen with its snout. A correct choice was rewarded with food. During the inter-trial interval, the shark was allowed to swim freely throughout the entire setup for 30 s before it was guided back into the SC and the door closed. The next trial started as soon as the shark pushed against the guillotine door. If a shark did not push against the door within 90 s or choose within the allocated 2 min, the trial was terminated. Training sessions were carried out five days per week; each session consisted of ten trials. Training was completed as soon as a learning criterion of 70% correct choices in three subsequent sessions was reached (2 (1) ≤ 0.05; to prove statistical significance). Animals that did not reach the criterion within 30 training sessions were excluded from further experiments. Training – phases 2–5. Training trials during phases 2–5 were performed similarly to phase 1, but now two stimuli (one in every division) were displayed in random order but never more than twice in a row in the same division. Five rotational schemes were used so as to alter the succession of the stimuli shown on a particular side between sessions. A small triangle (8 cm × 10 cm), a large triangle (12.5 cm × 15 cm) and a rhomboid (10.5 cm × 10.5 cm) served as negative (unrewarded) stimuli. If an animal did not reach the learning criterion within 30 training sessions, it proceeded with the next phase. All other criteria remained the same as outlined for phase 1. Reversal learning – phase 6. Training trials during this phase were performed similarly to phases 2–5, but now the square (formerly the positive stimulus) served as the negative stimulus while a cross (10 cm × 10 cm) became the positive stimulus. All other criteria remained the same as outlined for phase 1. 2.4. Data analysis The average trial time in seconds, the percentage of correct choices and the percentage of right and left choices were recorded
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for each session for each individual. To prove statistical significance, the learning criterion was established to be 70% correct choices in three consecutive sessions (2 (1) ≤ 0.05). A sign and binomial test was run to determine if those sharks who did not reach the learning criterion within 30 sessions in phases 2–5 chose the positive (rewarded) stimulus significantly more often than the negative (unrewarded) stimulus. A Mann–Whitney U-test was applied to determine if the average trial times differed significantly between phases for each individual as well as for the group. A Mann–Whitney U-test was also used to determine if group performance differed significantly between phases. For all tests p ≤ 0.05 was considered significant, p ≤ 0.001 highly significant.
Table 1 Results of visual discrimination tests in juvenile bamboo sharks. Number of sessions needed to reach the learning criterion (LC: 70% correct choices in three consecutive sessions) and average trial times [s] per discrimination task for every individual as well as for the group. Subject
Sessions to reach LC
Acclimatization
Shark 1 Shark 2 Shark 3 Shark 4 Shark 5 Shark 6 Shark 7 Shark 8 Group
14 10 17 8 9 8 8 8 10.25 ± 3.41
Shark 1 Shark 2 Shark 3 Shark 4 Shark 5 Shark 6 Shark 7 Shark 8 Group
18 28 8 24 14 23 19 12 18.25 ± 6.69
21.93 15.53 21.09 19.57 12.76 10.67 7.68 12.45 14.98
± ± ± ± ± ± ± ± ±
7.88 4.99 6.09 10.13 8.20 3.41 2.28 4.63 7.91
Shark 1 Shark 2 Shark 3 Shark 4 Shark 5 Shark 6 Shark 7 Shark 8 Group
13 6 9 12 20 6 6 6 9.75 ± 5.04
30.81 14.80 22.16 13.36 22.72 10.53 9.47 18.72 19.69
± ± ± ± ± ± ± ± ±
12.30 5.64 6.01 5.02 6.59 1.25 1.07 6.52 9.67
Shark 1 Shark 2 Shark 3 Shark 4 Shark 5 Shark 6 Shark 7 Shark 8 Group
4 16 4 30 30 24 5 12 10.83 ± 8.11
20.12 13.10 17.06 11.31 14.14 12.20 11.44 14.40 13.56
± ± ± ± ± ± ± ± ±
7.02 4.83 5.68 2.91 3.95 2.57 4.65 4.40 4.58
Shark 1 Shark 2 Shark 3 Shark 4 Shark 5 Shark 6 Shark 7 Shark 8 Group
13 6 3 23 10 30 7 9 10.14 ± 6.49
17.97 15.43 13.30 12.30 11.38 10.54 9.61 9.61 13.69
± ± ± ± ± ± ± ± ±
8.16 2.87 2.33 4.71 2.01 3.21 3.57 3.57 5.84
Shark 1 Shark 2 Shark 3 Shark 4 Shark 5 Shark 6 Shark 7 Shark 8 Group
30 5 14 19 6 19 6 3 10.28 ± 6.87
12.91 14.07 18.61 10.91 10.68 9.65 9.67 13.17 12.26
± ± ± ± ± ± ± ± ±
3.48 4.06 5.18 2.33 0.79 3.59 2.23 4.09 4.76
Shark 1 Shark 2 Shark 3 Shark 4 Shark 5 Shark 6 Shark 7 Shark 8 Group
22 12 22 9 6 6 28 9 14.25 ± 8.50
10.59 14.77 12.46 8.31 9.63 8.04 10.86 8.97 10.99
± ± ± ± ± ± ± ± ±
3.95 5.25 4.76 1.60 1.45 2.54 3.52 1.66 4.12
Phase 1
vs.empty space
3.1. Acclimatization – phase 0 The sharks needed on average 10.25 ± 3.41 sessions to acclimatize to the maze, understand the starting procedure as well as food retrieval from the feeders. No initial side preference was observed in any individual.
Phase 2
vs. 3.2. Training Phase 1. The sharks needed on average 18.25 ± 6.69 sessions to complete the first training phase successfully (Table 1). They needed on average 14.98 ± 7.91 s per trial to make a decision. Phases 2–5. The sharks needed on average 9.75 ± 5.04 sessions to discriminate a square from a small triangle (phase 2; Table 1). All sharks reached the learning criterion and thus showed a significant preference for the correct stimulus. Compared to phase 1, average trial times increased significantly for five individuals as well as for the group (Mann–Whitney U-test: ZGroup = –4.005, pGroup < 0.001; Table 2). The sharks needed on average 10.83 ± 8.11 sessions to discriminate a square from a large triangle (phase 3; Table 1). Two sharks (sharks 4 and 5) were not able to reach the learning criterion within the scheduled 30 sessions. Nevertheless, both showed a significant preference for the correct stimulus (sign and binomial test: one-tail pShark 4 = 0.009, two-tail pShark 4 = 0.018; onetail pShark 5 = 0.006, two-tail pShark 5 = 0.013; Table 3). Compared to phase 2, average trial times did not differ significantly for all but one shark (Mann–Whitney U-test: ZShark 5 = 4.496, pShark 5 < 0.001; Table 2) and the group (Mann–Whitney U-test: ZGroup = 4.962, pGroup < 0.001; Table 2). The sharks needed on average 10.14 ± 6.49 sessions to discriminate a square from a circle (phase 4; Table 1). One shark (shark 6) was not able to reach the learning criterion within the scheduled 30 sessions and did not show a significant preference for the correct stimulus (sign and binomial test: onetail pShark 6 = 0.193, two-tail pShark 6 = 0.386; Table 3). Compared to phase 3, average trial times did not differ significantly for all but two sharks (Mann–Whitney U-test: ZShark 5 = 2.172, pShark 5 = 0.029, ZShark 6 = 2.824, pShark 6 = 0.005; Table 2) and did not differ significantly for the group (Mann–Whitney U-test: ZGroup = −0.766, pGroup = 0.444; Table 2). The sharks needed on average 10.28 ± 6.87 sessions to discriminate a square from a rhomboid (phase 5; Table 1). One shark (shark 1) was not able to reach the learning criterion within the scheduled 30 sessions. Nevertheless, it showed a significant preference for the correct stimulus (sign and binomial test: one-tail pShark 1 = 0.016, two-tail pShark 1 = 0.033; Table 3). Compared to phase 4, average trial times did not differ significantly for all but one shark (Mann–Whitney U-test:
Average trial time (s)
Discrimination task
3. Results Eight sharks started and finished the experimental training procedure. The following section will summarize the results for those individuals as well as for the group. Group results include only sharks which finished a phase successfully.
107
Phase 3
vs.
Phase 4
vs.
Phase 5
vs.
Phase 6
vs.
shark did not reach the learning criterion within the allocated 30 sessions and was excluded from group results.
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T. Fuss et al. / Zoology 117 (2014) 104–111
Table 2 Results of the Mann–Whitney U-test used to determine if average trial times differed significantly between phases for every individual shark as well as for the group. Mann–Whitney U-test
Subject
Z-Value
p-Value
Phase 1 vs. phase 2
Shark 1 Shark 2 Shark 3 Shark 4 Shark 5 Shark 6 Shark 7 Shark 8 Group
−2.102 −0.226 −0.337 2.014 −3.256 −0.458 −2.005 −2.295 −4.005
0.036* 0.821 0.736 0.044* 0.0011** 0.647 0.045* 0.022* 0.05 not significant. * p ≤ 0.05 significant.
−2.073
0.038*
−2.323
0.021*
−1.848
0.064
−1.958
0.050*
−1.646
0.099
T. Fuss et al. / Zoology 117 (2014) 104–111
109
Fig. 2. Learning performance of shark 7: percentage of correct choices (symbolized by black boxes; left ordinate) as well as average trial times in seconds (symbolized by gray bars; right ordinate) per phase until reaching the learning criterion.
sessions to discriminate between a square and a small triangle, thus significantly improving performance compared to phase 1 (Table 4). Moreover, they were comparably faster than goldfish (20–35 days to reach a rate of 75% correct choices; Wyzisk and Neumeyer, 2007) and pumpkinseed sunfish (Lepomis gibbosus, 10.5 and 11.3 sessions; Königs, 2003), but 28% slower than Pseudotropheus sp. (7.6 ± 6.6 sessions; Schluessel et al., 2012). In phase 3, they needed 10.83 ± 8.11 sessions to discriminate a square from a large triangle; two sharks (sharks 4 and 5) did not reach the learning criterion. Nevertheless, both sharks chose the correct stimulus significantly more often than the alternative (2Shark 4 (1) = 0.015, 2Shark 5 (1) = 0.011, Table 3), indicating some kind of learning effect even during this phase. Those six sharks that did learn the task were about as fast as cichlids. Seven out of eight sharks distinguished the square from a circle in 10.14 ± 6.49 sessions (phase 4), making them about 20% slower than cichlids (8.1 ± 3.8 sessions; Schluessel et al., 2012). In training phase 5 the sharks needed 10.28 ± 6.87 sessions to differentiate the square from a rhomboid. In this phase they needed about 31% more sessions than Pseudotropheus sp. (7.1 ± 3.6 sessions; Schluessel et al., 2012). Although the sharks were slower than cichlids, which are known to have excellent visual capabilities (e.g., Carleton, 2009; Sabbah et al., 2010), their learning rates for every single phase fell within the range of other teleosts (goldfish, pumpkinseed sunfish). Surprisingly, performance did not increase with increasing numbers of stimulus pairs being shown (i.e., they did not need fewer sessions to reach the learning criterion, as might have been expected), despite the positive stimulus remaining the same throughout all phases. Instead, the sharks needed about the same number of sessions to reach the learning criterion in all tasks except for phase 1, which took significantly longer than the others. Two factors should be considered when interpreting the results. First, phase 1 was the first task to solve and the sharks had to get used to the setup and the type of experimental design. This could have extended the learning process as opposed to subsequent phases (2–5). The second factor concerns the potential recognition
mechanisms the fish could have used to identify the positive stimulus which always remained the same. Instead of seeing the image of the positive stimulus as a whole, one way to remember it could have been to recognize the presence of four straight lines in a regular setup or to simply remember four corners. Theoretically, applying such a mechanism could have led to confusion when the rhomboid was shown in conjunction with the square, as it features the same line arrangement and the same number of corners as the square, with the only exception of it being 45◦ rotated. Similarly, if the sharks had only remembered any number of corners and straight lines in a regular setup this could have also led to confusion with the triangles. Contradicting this possibility, however, was the fact that not even a circle, which shares no common features with the square, was recognized any faster than the other symbols. As potential mechanisms were not investigated any further it is impossible to decide which strategies may have been used. Generally, though, vertical and horizontal ends of an object seem to play a crucial role for the discrimination of objects in octopus (Sutherland, 1964), nurse sharks (Graeber and Ebbesson, 1972; Graeber et al., 1973) and several teleosts (e.g., Herter, 1929; Meesters, 1940; Schulte, 1957; Mackintosh and Sutherland, 1963; Fetsko, 2002). After successful conditioning, gudgeons (Gobio gobio) distinguished triangles and other shapes of different orientation, using the relative positions of the corners or edges of these shapes in space (Herter, 1929). Particularly the characteristic vertical ends of the figures seemed to be a decisive factor in distinguishing the shapes (e.g., Schulte, 1957). Similar results were found in studies on the organization of the visual field of crucian carps (Carassius carassius), showing that both the horizontal and vertical patterns (Herter, 1953) as well as the outlines of figures are used for distinction (Meesters, 1940). Of course, another option could have been that the sharks learned to avoid the negative symbols rather than preferring the positive stimulus or may have learned a combination of both; however, as mentioned previously, this was not determined here. In any case, the presence of a new negative stimulus seems to have been perceived as a new experimental situation, for which previously
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gained knowledge was either not exploited, or if it was, may rather have caused confusion due to shared features between the positive and the negative stimulus. Average trial times also remained fairly constant throughout phases 2–5 after an initial significant increase following phase 1. The sharks seemed to be confused by the introduction of a second stimulus in phase 2 (Table 2); therefore, they needed significantly longer to complete a trial during phase 2 than in phase 1. With increasing training routine, though, trial times significantly decreased again (Mann–Whitney U-test phase 2 vs. phase 3: ZGroup = 4.962, pGroup < 0.001; Table 2) and finally stabilized at 13.69 s and 12.26 s (phases 4 and 5, respectively; Table 1), which is similar to the times that were observed in phase 1 (14.98 ± 7.91 s; Table 1). The trial time therefore provides some further indication that the sharks actually had to “re-learn” (aspects of) each new stimulus pair and did not just exploit previously gained knowledge. The possibility of reversal of a spatial and/or visual discrimination task has been demonstrated in mammals (e.g., Sutherland, 1964; Hamilton et al., 2004), birds (e.g., Range et al., 2008), amphibians and reptiles (e.g., Day et al., 2003; Jenkin and Laberge, 2010) and several fish species (e.g., Lopez et al., 1999; Hughes and Blight, 2000; Colwill et al., 2005). Animals facing a reversal task following repetitive training in a discrimination task will initially persist in their learned strategy before trying other strategies. Accordingly, learning in a reversal task will proceed slower than in the preceding tasks (Sutherland, 1964). Phase 6, which featured the previously positive stimulus as the negative one, examined reversal learning in the bamboo sharks. The sharks needed on average 14.25 ± 8.50 sessions (Table 1) for successful completion of this task. As expected, it took them significantly longer to learn the reversal task than the tasks in phases 2–5, but the session number did not differ significantly from the initial training phase 1 (Table 4). Average trial times did also not differ significantly from any of the other phases (Table 2). Interestingly, if the sharks had indeed, as discussed above, completely “re-learned” the positive as well as the negative stimulus in phases 2–5, there should have been no difference in the number of sessions for successful discrimination in phase 6 and phases 2–5. In phase 6, there would have simply been a “new” stimulus pair yet again. The fact that the sharks took longer to solve the last task compared to the previous ones therefore contradicts the idea that the positive stimulus was completely “re-learned” in phases 2–5. As mentioned above, further experiments would be needed to elucidate the mechanism(s) applied by each shark. Nevertheless, the present results suggest that bamboo sharks can learn visual discrimination tasks, succeed in a reversal task and probably retain (some) information about a previously learned task when progressing to a new one. The present results not only reveal bamboo sharks’ ability to visually discriminate geometric stimuli. Moreover, they also provide insights into the behavioral variability found among individuals trained in the same procedure using the same training schedule. The often observed, apparently erratic nature of individual learning success is part of this variability, which appears to depend on the sharks’ motivation to cooperate day by day and possibly on a preference or a clear “non-preference” for single 2D objects (Table 1). The latter can clearly be seen in sharks 2, 3 and 8, who showed a relatively constant performance during all but one phase (shark 2 and 8: large triangle (phase 3), shark 3: rhomboid (phase 5)). In conclusion it may be stated that juvenile Chiloscyllium griseum can distinguish between various geometric symbols. Additionally, the learning rates of the sharks in the tasks presented here correspond to the rates determined for Malawi cichlids and pumpkinseed sunfish (Königs, 2003; Schluessel et al., 2012). Successful discrimination and reversal learning in bamboo sharks provides evidence from yet another phylogenetic group for the general
importance of this ability in all animal species using the visual system, vertebrates as well as invertebrates. As seen here, these successful marine predators have the anatomical prerequisites as well as the cognitive skills to use this capability, regardless of the importance of the visual system compared to other sensory systems, such as olfaction or mechanoreception. As the stimuli used in this study were quite artificial, but nonetheless recognized, it would be even more interesting to determine in future studies whether learning proceeds more rapidly when using biologically relevant stimuli are used or when experiments are performed under more pressing conditions. Acknowledgements We would like to thank Slawa Braun for animal caretaking, maintenance and repairs. We are specifically grateful to “Haus des Meeres” in Vienna for supplying the animals used during this study. The research reported herein was performed under the guidelines established by the current German animal protection law. References Aronson, L.R., Aronson, F.R., Clark, E., 1967. Instrumental conditioning and light–dark discrimination in young nurse sharks. Bull. Mar. Sci. 17, 249–256. Arthur, D., Levin, E., 2001. Spatial and non-spatial visual discrimination learning in zebra fish (Danio rerio). Anim. Cogn. 4, 125–131. Behrend, E.R., Bitterman, M.E., 1961. Probability-matching in the fish. Am. J. Psychol. 74, 542–551. Campan, R., Lehrer, M., 2002. Discrimination of closed shapes by two species of bee, Apis mellifera and Megachile rotundata. J. Exp. Biol. 205, 559–572. Carleton, K., 2009. Cichlid fish visual systems: mechanisms of spectral tuning. Integr. Zool. 4, 75–86. Colwill, R.M., Raymond, M.P., Ferreira, L., Escudero, H., 2005. Visual discrimination learning in zebra fish (Danio rerio). Behav. Proc. 70, 19–31. Compagno, L.J.V., 1999. Systematics and body form. In: Hamlett, W.C. (Ed.), Sharks, Skates, and Rays: The Biology of Elasmobranch Fishes. Johns Hopkins University Press, Baltimore, pp. 1–42. Compagno, L.J.V., Dando, M., Fowler, S., 2005. A Field Guide to the Sharks of the World. Collins, London. Day, L., Ismail, N., Wilczynski, W., 2003. Use of position and feature cues in discrimination learning by the whiptail lizard (Cnemidophorus inornatus). J. Comp. Psychol. 117, 440–448. Douglas, R.H., Eva, J., Guttridge, N., 1988. Size constancy in goldfish (Carassius auratus). Behav. Brain Res. 30, 37–42. Fetsko, L.A., (Ph.D. Thesis) 2002. What can be learned from a fish: an analysis of visual discrimination in the zebrafish, Danio rerio. Temple University, Philadelphia, PA. Frech, B., Vogtsberger, M., Neumeyer, C., 2012. Visual discrimination of objects differing in spatial depth by goldfish. J. Comp. Physiol. A 198, 53–60. Fuss, T., Bleckmann, H., Schlüssel, V., 2013a. The shark Chiloscyllium griseum can orient using turn responses before and after partial telencephalon ablation. J. Comp. Physiol. A, http://dx.doi.org/10.1007/s00359-013-0858-y. Fuss, T., Bleckmann, H., Schlüssel, V., 2013b. Place learning prior to and after telencephalon ablation in bamboo and coral cat sharks (Chiloscyllium griseum and Atelomycterus marmoratus). J. Comp. Physiol. A, http://dx.doi.org/10.1007/s00359-013-0859-x. Gierszewski, S., Bleckmann, H., Schluessel, V., 2013. Cognitive abilities in Malawi cichlids (Pseudotropheus sp.): matching-to-sample and image/mirror-image discriminations. PLoS ONE 8, e57363. Giurfa, M., Eichmann, B., Menzel, R., 1996. Symmetry perception in an insect. Nature 382, 458–461. Guttridge, T.L., Myrberg, A.A., Porcher, I.F., Sims, D.W., Krause, J., 2010. The role of learning in shark behavior. Fish Fish. 10, 450–469. Graeber, R.C., Ebbesson, S.O.E., 1972. Visual discrimination learning in normal and tectal-ablated nurse sharks (Ginglymostoma cirratum). Comp. Biochem. Physiol. A 42, 131–139. Graeber, R.C., Ebbesson, S.O., Jane, J.A., 1973. Visual discrimination in sharks without optic tectum. Science 180, 413–415. Grasso, F.W., Basil, J.A., 2009. The evolution of flexible behavioral repertoires in cephalopod molluscs. Brain Behav. Evol. 74, 231–245. Hamilton, D.A., Rosenfelt, C.S., Whishaw, I.Q., 2004. Sequential control of navigation by locale and taxon cues in the Morris water task. Behav. Brain Res. 154, 385–397. Herter, K., 1929. Dressurversuche an Fischen. Z. Vergl. Physiol. 10, 688–711. Herter, K., 1930. Weitere Dressurversuche an Fischen. Z. Vergl. Physiol. 11, 730–748. Herter, K., 1953. Die Fischdressuren und ihre sinnesphysiologischen Grundlagen. Akademie Verlag, Berlin. Hoffmann, K.L., Logothetis, N.K., 2009. Cortical mechanisms of sensory learning and object recognition. Philos. Trans. R. Soc. B 364, 321–329. Hughes, R., Blight, C., 2000. Two intertidal fish species use visual association learning to track the status of food patches in a radial maze. Anim. Behav. 59, 613–621.
T. Fuss et al. / Zoology 117 (2014) 104–111 Jenkin, S., Laberge, F., 2010. Visual discrimination learning in the fire-bellied toad Bombina orientalis. Learn. Behav. 38, 418–425. Jones, C.D., Osorio, D., 2003. Discrimination of oriented visual textures by poultry chicks. Vis. Res. 44, 83–89. Königs, K., 2003. Visual discrimination experiments concerning cognitive abilities in teleost fish (Lepomis gibbosus). Diploma Thesis, University of Bonn, Germany. Lopez, J.C., Broglio, C., Rodriguez, F., Thinus-Blanc, C., Salas, C., 1999. Multiple spatial learning strategies in goldfish (Carassius auratus). Anim. Cogn. 2, 109–120. Mackintosh, J., Sutherland, N.S., 1963. Visual discrimination by the goldfish: the orientation of rectangles. Anim. Behav. 11, 135–141. Mark, R.F., 1966. The tectal commissure and interocular transfer of pattern discrimination in cichlid fish. Exp. Neurol. 16, 215–225. Mark, R.F., Maxwell, A., 1969. Circle size discrimination and transposition behavior in cichlid fish. Anim. Behav. 17, 155–158. Mauck, B., Dehnhardt, G., 1997. Mental rotation in a California sea lion (Zalophus californianus). J. Exp. Biol. 200, 1309–1316. Meesters, A., 1940. Über die Organisation des Gesichtsfeldes der Fische. Z. Tierpsychol. 4, 84–148. Neumeyer, C., 1986. Wavelength discrimination in goldfish. J. Comp. Physiol. A 158, 203–213. Neumeyer, C., 1992. Tetrachromatic color vision in goldfish. Evidence from color mixture experiments. J. Comp. Physiol. A 171, 639–649. Nitsch, F., Jarosch, E., 1972. Individuelle Lernstrategien von Totenkopfäffchen bei der Diskriminierung von visuellen Sequenzen. Psychol. Res. 35, 335–354. Range, F., Bugnyar, K., Kotrschal, K., 2008. The performance of ravens on simple discrimination tasks: a preliminary study. Acta Ethol. 11, 34–41. Sabbah, S., Laria, R.L., Gray, S.M., Hawryshyn, C.W., 2010. Functional diversity in the color vision of cichlid fishes. BMC Biol. 8, 133. Schaller, A., 1926. Sinnesphysiologische und psychologische Untersuchungen an Wasserkäfern und Fischen. Z. Vergl. Physiol. 4, 370–464. Schluessel, V., Bleckmann, H., 2005. Spatial memory and orientation strategies in the elasmobranch Potamotrygon motoro. J. Comp. Physiol. A 191, 695–706. Schluessel, V., Bleckmann, H., 2012. Memory retention in the grey bamboo shark Chiloscyllium griseum. Zoology 115, 346–353. Schluessel, V., Fricke, G., Bleckmann, H., 2012. Visual discrimination and object categorization in the cichlid Pseudotropheus sp. Anim. Cogn. 15, 525–537.
111
Schluessel, V., Kraniotakes, H., Bleckmann, H., 2013. Visual discrimination of rotated 3D objects in Malawi cichlids (Pseudotropheus sp.): a first indication for form constancy in fishes. Anim. Cogn., http://dx.doi.org/10.1007/s10071-013-0667-2. Schnetter, B., 1968. Visual form discrimination of the honey bee within the range of 6- and 4-pointed stars. Z. Vergl. Physiol. 59, 90–109. Schulte, A., 1957. Transfer- und Transpositionsversuche mit monokular dressierten Fischen. Z. Vergl. Physiol. 39, 432–476. Schuster, S., Amtsfeld, S., 2002. Template-matching describes visual patternrecognition tasks in the weakly electric fish Gnathonemus petersii. J. Exp. Biol. 205, 549–557. Schustermann, R.J., Thomas, T., 1966. Shape discrimination and transfer in the California sea lion. Psychon. Sci. 5, 21–22. Siebeck, U.E., Litherland, L., Wallis, G.M., 2009. Shape learning and discrimination in reef fish. J. Exp. Biol. 212, 2113–2119. Srinivasan, M.V., 2010. Honey bees as a model for vision, perception, and cognition. Annu. Rev. Entomol. 55, 267–284. Sutherland, N.S., 1962. The Methods and Findings of Experiments on the Visual Discrimination of Shape by Animals. Heffner, Cambridge. Sutherland, N.S., 1964. Visual discrimination of animals. Br. Med. Bull. 20, 54–59. Tanaka, K., 2000. Mechanisms of visual object recognition studied in monkeys. Spat. Vis. 13, 147–163. Tester, A., Kato, S., 1963. Visual target discrimination in blacktip sharks (Carcharhinus melanopterus) and grey sharks (C. menisorrah). Pac. Sci. 20, 461–471. Van-Eyk, S., Siebeck, U., Champ, C., Marshall, J., Hart, N., 2011. Behavioral evidence for color vision in an elasmobranch. J. Exp. Biol. 214, 4186–4192. Vonk, J., MacDonald, S.E., 2002. Natural concepts in a juvenile gorilla (Gorilla gorilla gorilla) at three levels of abstraction. J. Exp. Anal. Behav. 78, 315–332. Wyzisk, K., 2005. Experimente zur Formen- und Größenwahrnehmung beim Goldfisch (Carassius auratus) unter Verwendung von Scheinkonturen und Größentäuschungen. Ph.D. Thesis, University of Mainz, Germany. Wyzisk, K., Neumeyer, C., 2007. Perception of illusionary surfaces and contours in goldfish. Vis. Neurosci. 24, 291–298. Zoccolan, D., Oertelt, N., DiCarlo, J.J., Cox, D.D., 2009. A rodent model for the study of invariant visual object recognition. Proc. Natl. Acad. Sci. U. S. A. 106, 8748–8753.
