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What is the mechanism behind cats' geolocating homing behavior?

What is the mechanism behind cats' geolocating homing behavior?


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Some cats, which are separated from their home, have the ability to travel back to their original home - even over long distances and land that they haven't encountered before. E.g., see the Time article on the mystery of the geolocating cat or the following blog entry.

Based on the material above there seem to have only been some suspected mechanisms a few years ago.


It is explained with path integration in behavioral neuroscience. Not only cats, but other mammals, birds and even insects use path integration to return to a starting point.

Here is a relevant excerpt from the book Beyond the Cognitive Map: From Place Cells to Episodic Memory (by A. David Redish):

Path integration is the ability to return directly to a starting point (sometimes called a home base or reference point) from any location in an environment, even in the dark or after a long circuitous route (Barlow, 1964; Gallistel, 1990; Maurer and Seguinot, 1995). Sometimes called dead reckoning, this ability has been shown in gerbils (Mittelstaedt and Mittelstaedt, 1980; Mittelstaedt and Glasauer, 1991), hamsters (Etienne, 1987, 1992; Chapuis and Scardigli, 1993), house mice (Alyan and Jander, 1994), rats (Tolman, 1948; Alyan et al., 1997; Whishaw and Maaswinkel, 1997), birds (Mittelstaedt and Mittelstaedt, 1982; von Saint Paul, 1982), and even insects (Wehner and Srinivasan, 1981) and arthropods (Mittelstaedt, 1983), as well as dogs, cats, and humans (Beritashvili, 1965).

Path integration in animals has been the subject of argument for more than a century, including a notable debate in 1873 between Alfred Wallace and Charles Darwin in which Wallace suggested that animals find their way back via sequences of smells and Darwin argued that animals must be using dead reckoning (see Wallace, 1873a, 1873b; Darwin, 1873a, 1873b; Nature, 1873; Forde, 1873; Murphy, 1873). The carefully controlled experiments of Mittelstaedt and Mittelstaedt (1980) and Etienne (1987) have demonstrated conclusively that this ability is a consequence of integrating internal cues from vestibular signals and motor efferent copy.

emphasis mine

Cats, dogs and rodents can use taxon navigation as well along with path integration. Here is a relevant excerpt from Neural compass or epiphenomenon? Experimental and theoretical investigations into the rodent head direction system (by Matthijs van der Meer):

“Internal allocentric” navigation. This type of navigation relies on a mapping of praxic commands to an allocentric spatial representation, allowing the animal to make a direct return to a home base after a complex outward path in the absence of cues, an ability referred to as path integration, discussed in detail in the next section. Returning by following an odour trail back, or navigating to a cue indicating the home base would be taxon navigation, but behavioural experiments have provided convincing evidence that rodents are able to do this without using external cues. As mentioned above, this ability requires some mechanism of continuously updating at least one's directional heading (a “homing vector”) relative to the home base. This is an allocentric representation which does not need to be related to any external cues, unlike the next class of strategies; the reference point or direction can in principle be set to any location the animal desires.

emphasis mine

Further reading:

  • A connectionist model of path integration with and without a representation of distance to the starting point (ROLAND MAURER, University of Geneva, Geneva, Switzerland) - Copyright 1998 Psychonomic Society, Inc.

Selective breeding

Selective breeding (also called artificial selection) is the process by which humans use animal breeding and plant breeding to selectively develop particular phenotypic traits (characteristics) by choosing which typically animal or plant males and females will sexually reproduce and have offspring together. Domesticated animals are known as breeds, normally bred by a professional breeder, while domesticated plants are known as varieties, cultigens, cultivars, or breeds. [1] Two purebred animals of different breeds produce a crossbreed, and crossbred plants are called hybrids. Flowers, vegetables and fruit-trees may be bred by amateurs and commercial or non-commercial professionals: major crops are usually the provenance of the professionals.

In animal breeding, techniques such as inbreeding, linebreeding, and outcrossing are utilized. In plant breeding, similar methods are used. Charles Darwin discussed how selective breeding had been successful in producing change over time in his 1859 book, On the Origin of Species. Its first chapter discusses selective breeding and domestication of such animals as pigeons, cats, cattle, and dogs. Darwin used artificial selection as a springboard to introduce and support the theory of natural selection. [2]

The deliberate exploitation of selective breeding to produce desired results has become very common in agriculture and experimental biology.

Selective breeding can be unintentional, e.g., resulting from the process of human cultivation and it may also produce unintended – desirable or undesirable – results. For example, in some grains, an increase in seed size may have resulted from certain ploughing practices rather than from the intentional selection of larger seeds. Most likely, there has been an interdependence between natural and artificial factors that have resulted in plant domestication. [3]


Cognitive mechanisms

Cognitive psychology proposes yet another way to study the causal mechanisms of animal behaviour. The aim of cognitive psychology is to explain an animal’s behaviour in terms of its mental organization for information processing (that is, how the animal acquires, stores, and acts on information present in its world). By studying cognitive mechanisms of an animal, one may study how the animal perceives, learns, memorizes, and makes decisions.

Consider, for example, crows (Corvus brachyrhynchos) that crack walnuts open by dropping them from heights of 5 to 10 metres (about 16 to 33 feet) or more onto rocks, roads, or sidewalks. The birds generally avoid dropping the nuts onto soil, where they would be unlikely to break open. Remarkably, the crows can discriminate between black and English walnuts, for they drop the harder black walnuts from greater heights. In addition, when a crow drops a nut, it takes into account the likelihood that a fellow crow might steal the contents before it can be retrieved. If fewer competing crows are perched nearby, the crow carries a nut higher into the air before releasing it. Thus, numerous processes of perception, learning, and decision-making activity underlie the crows’ nut-cracking behaviour.

Each of these processes may be analyzed. For example, how do crows judge the height from which to drop nuts? Do they have to learn to adjust the dropping height in relation to the type of walnut? When faced with the conflicting conditions of having a hard-shelled black walnut and seeing a number of other crows nearby, how do they decide what drop height to use?

Until the 1970s, students of animal cognition eschewed speculation about the unobservable processing of information, limiting themselves to explaining behaviours in terms of quantifiable relationships between stimuli and responses. Today, however, they make use of behaviour as a window into how an animal’s nervous system processes information. Students of cognition also emphasize the investigation of behaviours in which the animal does not simply respond to immediate stimuli but relies on stored representations of objects and events. For some investigators, mental representations of the environment are the essence of cognition. According to this view, known as the computational-representational approach, the experience of an animal results in the formation in the brain of isomorphisms between brain processes and events in the world. The brain then performs computations on these representations that are ultimately converted to behavioral outputs. For example, a bird assessing the availability of berries on a bush might store information about the time at which it finds each berry as it searches the bush. It might then convert this information, through a brain process equivalent to division, into a representation of the rate of berry collection.

It is possible, however, that the computational-representational approach exaggerates the richness and detail of animals’ representations and the complexity of the brain processes operating on them. A good illustration comes from studies of the mechanisms by which ants (Cataglyphis fortis) living in the Sahara desert navigate home after conducting a circuitous search for food (mainly dead insects). Such a search can take these ants 100 metres (about 330 feet) or more (equivalent to 10,000 body lengths) from the entrance of their underground nest. To get back home, the ants rely on landmarks as visual signposts to show the way. Originally, it was assumed that these ants and other insects that orient using landmarks are able to store their knowledge of the nest environs in maplike internal representations called “cognitive maps.” Doing so would give an ant tremendous flexibility in homing: equipped with a bird’s-eye knowledge of the terrain over which it travels, an ant could return even from points where it had never before been. The mental representation used by these ants in landmark guidance is, however, actually somewhat simpler. Experiments have revealed that each ant stores a two-dimensional visual template—a kind of snapshot—of the landmark array it saw when it left its nest. When returning to its nest, the ant moves so as to match the current visual image as closely as possible with the memorized template. The snapshot-matching mechanism, unlike the cognitive-map one, enables an ant to steer its way home only from points it has recently visited, as opposed to novel sites to which it might be displaced by an experimenter. Although this mental mechanism provides a less complete and less flexible solution to the problem of finding home, it is entirely sufficient for the problems that desert ants routinely face.

An unseen and therefore largely unappreciated aspect of behaviour is the use of decision-making rules or “ Darwinian algorithms.” Organisms rely on these rules to process information from their physical and social environments and result in particular behavioral outputs that guide key behavioral and life-history decisions. Darwinian algorithms are made up of the sensory and cognitive processes that perceive and prioritize cues within an individual’s perceptual range. These inputs are then translated into motor outputs. A Darwinian algorithm may involve a stimulus threshold (such as “when the day-length exceeds 10 hours, migrate north”) or may depend on the occurrence of a cue that is normally associated with a fitness-enhancing outcome (such as “build nests in dense vegetation where chick survival is predictably high”). Darwinian algorithms are shaped through evolutionary time by the specific selective regime of each population. Which cues are relied upon depends on the certainty with which a cue can be recognized, the reliability of the relationship between the cue and the anticipated environmental outcome, and the fitness benefits of making a correct decision versus the costs of making an incorrect decision. In general, Darwinian algorithms underlying behavioral and life-history decisions are only as complex as is necessary to yield adaptive outcomes under a species’ normal environmental circumstances but not so complex as to cover all experimentally or anthropogenically induced contingencies.

An intriguing question in the study of animal cognition is the role of consciousness. Humans easily distinguish between merely responding to objects and being conscious of them. For example, while driving along a highway deep in thought or conversation, the driver may suddenly realize that he has not been conscious of the road for the past several miles. Indeed, it is well documented that humans can effectively perceive, memorize, process, and even act on objects and events without the kind of awareness that underlies a verbal report of consciousness. It is possible, therefore, that the behaviour of animals occurs without conscious awareness. However, given that humans have consciousness, it seems reasonable to suppose that individuals in other species, especially social species (such as primates), also experience at least a rudimentary form of consciousness. To think otherwise would be to presume an evolutionary discontinuity between humans and all other forms of life. Thus, the possibility that at least some of the behaviour of animals is accompanied by conscious thinking seems reasonable.

Although most students of animal behaviour accept the idea that animal consciousness is a likely possibility, some argue that it is not yet possible to know whether any particular animal experiences consciousness because it is a private, subjective, and, ultimately, unknowable state. In contrast, cognitive ethologists (a separate group of animal behaviourists), most notably American biophysicist and animal behaviourist Donald Griffin, argue that animals are undoubtedly conscious, since individuals from a wide variety of species behave with apparent intentions of achieving certain goals. For example, chimpanzees (Pan troglodytes) stalking a monkey high above them in the treetops will distribute themselves among the trees that would otherwise provide the monkey with an escape route and attack the creature simultaneously. Similarly, groups of female lions (Panthera leo) fan out widely and then coordinate their attacks on ungulate prey. In another example, a raven (Corvus corax), when presented with the novel situation of a meat morsel dangling from a long string tied to a perch, will study the situation briefly before it acts. Subsequently, the raven will quickly procure the meat by repeatedly pulling up a length of the string with its beak and clamping each length pulled up with its feet while sitting on the perch. Studies of the states and mechanisms of animal consciousness represent important frontiers of future research.


Purring to Help Others

There are countless reports of cats coming over to ailing humans and purring for them, sometimes for extended periods of time. In addition, there are many touching accounts of cats maintaining purring vigils for fellow companion animals, including dogs and rabbits, who are in ill health or dire straits. When dispensing therapeutic purrs to their "patients," cats usually snuggle next to them as well, heightening the effect.

Cats may have a reputation for being aloof, but these experiences (among many others) show that cats not only possess deep empathy, but also act on that empathy to make a positive difference.


Contents

In animal biology Edit

Jean Henri Fabre (1823-1915), an entomologist, considered instinct to be any behavior which did not require cognition or consciousness to perform. Fabre's inspiration was his intense study of insects, some of whose behaviors he wrongly considered fixed and not subject to environmental influence. [2]

Instinct as a concept fell out of favor in the 1920s with the rise of behaviorism and such thinkers as B. F. Skinner, which held that most significant behavior is learned.

An interest in innate behaviors arose again in the 1950s with Konrad Lorenz and Nikolaas Tinbergen, who made the distinction between instinct and learned behaviors. Our modern understanding of instinctual behavior in animals owes much to their work. For instance, there exists a sensitive period for a bird in which it learns the identity of its mother. Konrad Lorenz famously had a goose imprint on his boots. Thereafter the goose would follow whoever wore the boots. This suggests that the identity of the goose's mother was learned, but the goose's behavior towards what it perceived as its mother was instinctive.

In psychology Edit

The term "instinct" in psychology was first used in the 1870s by Wilhelm Wundt. By the close of the 19th century, most repeated behavior was considered instinctual. In a survey of the literature at that time, one researcher [ who? ] chronicled 4,000 human "instincts," having applied this label to any behavior that was repetitive. [ citation needed ] In the early twentieth century, there was recognized a "union of instinct and emotion". [3] William McDougall held that many instincts have their respective associated specific emotions. [4] As research became more rigorous and terms better defined, instinct as an explanation for human behavior became less common. In 1932, McDougall argued that the word 'instinct' is more suitable for describing animal behaviour, while he recommended the word 'propensity' for goal directed combinations of the many innate human abilities, which are loosely and variably linked, in a way that shows strong plasticity. [5] In a conference in 1960, chaired by Frank Beach, a pioneer in comparative psychology, and attended by luminaries in the field, the term 'instinct' was restricted in its application. [ citation needed ] During the 1960s and 1970s, textbooks still contained some discussion of instincts in reference to human behavior. By the year 2000, a survey of the 12 best selling textbooks in Introductory Psychology revealed only one reference to instincts, and that was in regard to Sigmund Freud's referral to the "id" instincts. [ citation needed ] In this sense, the term 'instinct' appeared to have become outmoded for introductory textbooks on human psychology.

Sigmund Freud considered that mental images of bodily needs, expressed in the form of desires, are called instincts. [6]

In the 1950s, the psychologist Abraham Maslow argued that humans no longer have instincts because we have the ability to override them in certain situations. He felt that what is called instinct is often imprecisely defined, and really amounts to strong drives. For Maslow, an instinct is something which cannot be overridden, and therefore while the term may have applied to humans in the past, it no longer does. [7]

The book Instinct: an enduring problem in psychology (1961) [8] selected a range of writings about the topic.

In a classic paper published in 1972, [9] the psychologist Richard Herrnstein wrote: "A comparison of McDougall's theory of instinct and Skinner's reinforcement theory — representing nature and nurture — shows remarkable, and largely unrecognized, similarities between the contending sides in the nature-nurture dispute as applied to the analysis of behavior."

F.B. Mandal proposed a set of criteria by which a behavior might be considered instinctual: a) be automatic, b) be irresistible, c) occur at some point in development, d) be triggered by some event in the environment, e) occur in every member of the species, f) be unmodifiable, and g) govern behavior for which the organism needs no training (although the organism may profit from experience and to that degree the behavior is modifiable). [10]

In Information behavior: An Evolutionary Instinct (2010, pp. 35–42), Amanda Spink notes that "currently in the behavioral sciences instinct is generally understood as the innate part of behavior that emerges without any training or education in humans." She claims that the viewpoint that information behavior has an instinctive basis is grounded in the latest thinking on human behavior. Furthermore, she notes that "behaviors such as cooperation, sexual behavior, child rearing and aesthetics are [also] seen as 'evolved psychological mechanisms' with an instinctive basis." [11] [12] [13] Spink adds that Steven Pinker similarly asserts that language acquisition is instinctive in humans in his book The Language Instinct (1994). In 1908, William McDougall wrote about the "instinct of curiosity" and its associated "emotion of wonder", [14] though Spink's book does not mention this.

M.S. Blumberg in 2017 examined the use of the word instinct, and found it varied significantly. [15]

The existence of the simplest instincts in humans is a widely debated topic. [ citation needed ] Among possible examples of instinct-influenced behavior in humans are the following.


The Evolution of Search Strategies

The fact that biological entities of many kinds must overcome what appear, at least on the surface, to be similar challenges in their search processes raises a question: Has evolution led these entities to solve their respective search problems in similar ways? Clearly the molecular and biomechanical mechanisms a bacterium uses to climb a chemical gradient are different from the neural processes a moth uses to search for a potential mate. But at a more abstract level, it is tempting to speculate that the two organisms have evolved strategies that share a set of properties that ensure effective search. This leads to our first question: Do the search strategies that different kinds of organisms have evolved share a common set of features? If the answer to this question is “yes,” many other questions follow. For example, what are the selective pressures that lead to such convergent evolution? Do common features of search strategies reflect common features of search environments? Can shared features of search strategies inform the design of engineered searchers, for example, synthetic microswimmers for use in human health applications (14) or searching robots (15)?

An example of a common feature of many search strategies is the use of spatial gradients in the strength or timing of sensory cues. In Box 1, we describe parallels between the responses of single cells to chemical gradients, and the responses of single animals and animal groups to environmental gradients. The important point is that the use of spatial gradients is a fundamental part of the search strategies of cells like neutrophils (16) and bacteria (17), solitary animals like mice and fruit flies (18, 19), and large animal groups like fish schools (20). In all cases, the searching individual or group has a means of measuring a signal differential (e.g., difference in signal strength or the timing of signal arrival between sensors) over space, and responding to that differential by altering locomotory behavior in a way that causes the individual or group to climb the gradient (Fig. 2A).

Box 1

Shared features of search: Sensing and climbing spatial gradients.

Detecting and navigating using environmental gradients is a common feature of the strategies used by a wide range of searchers, from single cells to groups of social animals (Fig. 2A). The mechanisms through which organisms measure and respond to gradients are diverse, but the outcome of these processes—ascending or descending local signal gradients—is highly conserved. Eukaryotic cells detect chemical gradients by sensing concentration differentials across their length (45), and respond through a variety of motility mechanisms. Bacteria such as E. coli and Vibrio spp. are too small to directly perceive concentration gradients, but instead detect gradients by measuring changes in concentration over time as they swim and delaying reorientations when swimming in a favorable direction (23). Searching animals, including humans, use information from paired sensory organs (eyes, ears, nares, antennae) to detect differentials in the strength of signals or the timing of signal arrival (11, 100, 101), which they use to decide which way to turn in response to a signal gradient (19, 100). Groups of animals also respond to environmental gradients and do so in a coordinated fashion. Schools of minnows are capable of collectively descending and tracking dynamic light gradients, even amid considerable noise (20). Individuals within these schools respond not only to measurements of the environment, but also to social cues from nearby group members (Fig. 2A), allowing the group to act as a spatially distributed sensor that collectively “computes” the signal gradient (63, 102).

Although single cells, solitary animals, and animal groups all use spatial gradient sensing as a component of their search strategies, a quantitative framework for exploring these similarities is lacking. In examining similarities more deeply, it will be important to consider how gradient climbing fits into the broader set of rules that constitute an organism’s search strategy (Fig. 2B). For example, mice use spatial gradients in scent concentration as well as exploratory movements to locate odor targets in novel environments, but over time, rely increasingly on learned information about the location of targets to navigate more efficiently (18). Bacteria combine random search with directed gradient climbing and rescale their responses by adapting to prevailing conditions, which increases the dynamic range of their search capabilities (42, 103). Developing a common mathematical formalism that can connect gradient climbing and other components of search strategies with the structure of real search environments will facilitate more rigorous comparisons between microbes, animals, and animal groups, and help uncover shared features.

Spatial gradient climbing, behavioral modules, and natural search algorithms. (A) Single cells, solitary animals, and animal groups are capable of measuring and responding to spatial gradients in the environment. These responses involve measuring a signal (e.g., chemical concentration, light), processing multiple measurements to calculate a spatial differential in signal strength or timing, and responding by altering locomotory behavior to ascend or descend the gradient. (B) At larger scales, organisms combine responses to spatial gradients with other behavioral modules [e.g., periods of exploration, memory-based homing to known locations (21, 22)] to generate flexible sequences of search behavior. We define this set of modules and the rules a searcher uses to transition between them as a natural search algorithm. By developing mathematical descriptions of natural search algorithms, search behavior can be studied using the mathematical tools applied to analyze engineered search algorithms.

Modern experimental techniques have been crucial for identifying the ingredients that enable these gradient-climbing behaviors. Studies of biochemical receptors in single cells (23, 24) and sensory receptor neurons in animals (11) have identified the raw input available to inform search decisions. In well-studied microorganisms, such as Escherichia coli, the biochemical pathways involved in decision-making are understood thoroughly enough that models of gradient-climbing can be formulated directly from the knowledge of intracellular signaling pathways that govern the gradient response (23 ⇓ –25). In the case of animals, the neural processes involved in integrating and making decisions using measurements of a gradient are not as well understood however, the key features of the signal integration and decision-making process can be inferred using experiments that provide known sensory input and map this input to observed searcher motions (18, 20, 26). In this way, researchers are beginning to understand how measurements of spatial gradients lead to gradient-climbing behavior in a wide variety of model systems.

Although the biophysical and behavioral mechanisms cells, animals, and animal groups use to respond to gradients differ, these entities are all capable of readily climbing spatial gradients. Responding to gradients is, however, just one component of the set of rules organisms use to guide search behavior (Fig. 2B). Indeed, orienting using gradients alone suffers from well-known pitfalls when the environment contains many signal sources (27) or signals are highly intermittent, as is the case in turbulent chemical plumes (4). Accordingly, most species likely use gradient-climbing as one behavioral “module” (21) in a larger set of sensory-motor responses that, together, generate the long sequences of search behavior needed to locate targets in large spatial landscapes. Other modules may include exploratory behaviors that increase the likelihood that the searcher will encounter sensory cues emitted by a target—for example, the cross-wind casting of insects (21) and seabirds (28)—and memory-based mechanisms for returning to previously visited locations: for example, the path integration-based homing of the desert ant, Cataglyphis bicolor (22). We define the set of behavioral modules a searcher employs and the rules the searcher uses to transition between modules as a natural search algorithm (Fig. 2B). We expect natural selection to drive the evolution of algorithms that yield high search performance, while balancing fitness costs, such as exposure to predation risk (29). By developing mathematical descriptions of natural search algorithms and a more thorough understanding of the environments in which search takes place, we can begin to compare the strategies of searchers as diverse as human cells, bacteria, flies, and schooling fish in quantitative terms.

Mapping the Search Environment.

What are the salient features of the search problems organisms solve in nature? What do target landscapes look like? What sensory cues do searchers have access to? These seem like straightforward questions, but even in the case of well-studied species like E. coli, we know surprisingly little about their answers (30 ⇓ –32). Without knowing more about the landscapes in which organisms search, experiments risk being arbitrary and the connection between experimental findings and search behavior in natural systems is liable to be loose. Recently, some important progress has been made toward characterizing the physical and chemical environment that marine microbes experience in the ocean (17), the flow environment that influences search decisions of planktonic predators (33), and the physical structure and chemical composition of the odor plumes to which moths and other olfactory searchers respond (5, 10, 11, 34, 35). These studies notwithstanding, we have little quantitative information about the structure and spatiotemporal dynamics of the environments in which most organisms search, largely because of the difficulty of quantifying spatially and temporally varying cues in natural settings.

Mapping the structure of the search environment is particularly crucial in field studies of animal search behavior (36), where the processes that generate an animal’s movement trajectory are difficult or impossible to infer without some knowledge of the sensory cues the animal has access to as it makes movement decisions (37). Combining technologies for measuring the structure and dynamics of sensory cues, such as geographical information system (GIS)-based and computational modeling of water currents or winds (28, 38), acoustic methods for quantifying the locations of prey aggregations (39), and onboard sensors that measure real-time conditions at the animal’s location (40), will dramatically increase what we can learn about search strategies using animal tracking data from the field.

Cross-disciplinary collaborations could further increase the value of field data by using direct measurements or simulations to characterize sensory cues in the field, and then recreating these sensory landscapes in the laboratory. This will not only help make experiments more realistic, but may also reveal how organisms distinguish relevant environmental signals from irrelevant ones. For example, when approaching flowers from short distances, moths in the wild encounter filaments of high odor concentration that arrive at time intervals within a relatively narrow frequency range (10). In wind-tunnel experiments with moths, odors that are pulsed in this natural frequency range cause strong firing responses in central olfactory neurons, and elicit stereotyped search behavior, whereas odors outside this range elicit weak responses at both the neural and behavioral levels. At greater distances from an odor source, pulses of odor are far more intermittent, yet moths and other insects are still able to distinguish relevant olfactory cues from noise and to use these cues to navigate (5). The neural mechanisms that underpin this long-range response to odors are still poorly understood, but quantitative characterization of odor landscapes has provided crucial clues (5, 11).

Another advantage of quantitatively characterizing natural search environments is that doing so may help answer longstanding questions about the features of sensory systems. Many chemosensory organs are exquisitely sensitive to variations in chemical concentration. Dictyostelium cells can detect concentration gradients amounting to differences in the occupancy of only five chemoattractant receptors between the up-gradient and down-gradient sides of the cell (41). Silkmoths detect and respond to as few as 170 molecules of sex pheromone by temporally integrating subthreshold activity of olfactory receptor neurons (8). Sensitivity often comes at the cost of susceptibility to noise, raising the question of what kinds of environmental conditions could have selected for such extreme sensitivity. Some species cope with the sensitivity–susceptibility trade-off by rescaling their responses to stimuli via adaptation, allowing them to respond to cues across a wide dynamic range bacteria, for example, retain high sensitivity to chemical gradients over several orders-of-magnitude in chemical concentration (42). However, this too comes at a cost. Rescaling their response to gradients by overall chemical concentration means that bacteria respond to high- and low-concentration sources in the same way, an outcome that may or may not be desirable depending on the environmental and ecological context in which these species search (42) (see also ref. 32 for further discussion of the drawbacks of sensory adaptation). Quantitatively studying the signal and noise landscapes that searching organisms navigate (43) may help us understand why evolution has selected for sensory systems that both adapt and, in some cases, operate close to the physical limits of sensitivity (44, 45).

Convergent Evolution and Shared Features of Effective Search Strategies.

One might expect to find convergent search strategies across a diverse array of biological systems if some general features distinguish good search strategies from poor ones. Several studies have proposed statistical properties that could serve this role. One hypothesis posits that searchers can achieve robust search performance by using a strategy that is “maximally informative” (46), in the sense that the searcher makes decisions that maximize the rate at which it reduces uncertainty about the location of its target (4). Maximizing the rate of information gain would appear to require fairly sophisticated neural or biochemical machinery however, at least in simple environments, a heuristic that approximates a maximally informative search can be implemented by a decision circuit containing a surprisingly small number of components (46). Recent research efforts have begun to explore whether search strategies and behavioral circuits can be understood in more general terms by studying how they affect information acquisition from the environment (31, 47).

A second feature that may prove to be shared among search strategies is risk-aversion. For example, the chemotactic search algorithm used by E. coli does not maximize the rate at which cells reach local hotspots of high chemical concentration, but instead maximizes the minimum chemical concentration the cell will experience over a wide range of possible environmental conditions (48). Similarly, it has been argued that some animal foraging strategies do not maximize the rate of resource acquisition, but instead ensure that the animal acquires a minimum required quantity of resources (often called “satisficing”) (49). Such strategies serve to minimize the risk of bad outcomes in uncertain environments, often at the cost of underperforming when the environment is favorable. Whether a strategy appears risky or risk-averse, however, may depend on how risk is measured. For example, in the ocean, male copepods search for mates by adopting a swimming pattern that makes them conspicuous to predators (50). This strategy may lower the risk of not finding a mate but it increases the risk of predation.

Determining whether different kinds of organisms use strategies that maximize the rate of information gain, minimize risk, or involve other shared properties requires formal methods of comparison. By studying search behavior through an algorithmic lens, we can begin to apply existing frameworks from the rich field of engineered search algorithms to better understand the features of search strategies in nature. Mathematical tools from reinforcement learning (51), information theory (47), and operations research (52) are commonly used to design and evaluate engineered search algorithms, but they may also provide a way of making quantitative comparisons between natural search algorithms. To take advantage of these tools, we propose a systematic approach (Fig. 3), in which field and laboratory studies of model organisms are used to identify the salient features of natural search environments and constraints on sensing and decision-making in real biological systems. These features can then be mapped onto an appropriate algorithmic framework for modeling search behavior and for comparing the search strategies of different kinds of organisms (Fig. 3).

Combining models and data to study shared features and evolution of natural search algorithms. Quantitative information from laboratory and field studies of model organisms can be combined to determine the essential features and constraints that are common to search problems faced by different kinds of organisms. These features are then used to select a modeling framework (e.g., MAB) and to build a model that retains the essential features of the search problem. The model can then be used to understand sensitivity to differences in biophysical, physiological and environmental constraints across systems, and to operationally define shared features. Tools from evolutionary theory can be used to identify the selective pressures that lead to convergent or divergent search strategies.

A particularly promising mathematical tool, often used in operations research, is the multiarmed bandit framework (MAB) (53). This framework models a sequence of actions taken by a decision-maker or set of decision-makers, in this case the searchers. Each action yields a reward and the success of different decision-making strategies can be compared over time. The term “bandit” is an analogy to a slot machine, and the “arms” refer to the set of choices the decision maker has available to it. This framework is powerful because it allows one to compare search strategies to one another and to theoretical performance bounds (54, 55). MAB models are among the simplest models of decision-making that capture the essential challenges associated with making choices amid uncertainty about the state of the environment however, a crucial feature of the MAB framework is that it can be extended to include important physical and neural constraints on the search capabilities of real organisms (e.g., limited memory, limited perception of space, and so forth). For example, this framework has recently been extended to accommodate spatial constraints on the choices an individual can make, and transition costs incurred when moving from one choice to another, key characteristics of search problems in spatial landscapes (56), where moving costs energy (57) and can expose a searcher to predation risk (29). Mapping natural search algorithms onto the MAB framework would constitute a major step toward a quantitative understanding of the similarities and differences between search strategies. Recent studies of the neural and biophysical mechanisms involved in search behavior (6, 7, 9, 10, 46, 58) provide the quantitative information needed to define such models in a way that respects the constraints on search strategies in real biological systems.

To better understand how evolution shapes search strategies over the long term, modeling approaches like MAB models can be combined with tools, such as game theory (48, 52, 59 ⇓ –61) and simulated evolution (30, 62, 63) (Fig. 3). The most complex of such models combine behavioral simulations of entire populations of heterogeneous individuals with computational evolution of populations of searchers over time (30, 62, 63). Such models are extremely flexible and allow one to include detailed knowledge of environmental structure, biophysics and neuroscience of sensing, decision-making, and motility specific to particular model systems. This flexibility may come at the cost of generalizability, but simpler approaches from evolutionary game theory can help distill the results of detailed computational models into a form that can be more readily applied to other systems (64).

To realize the connection between experimental studies of search behavior and the theoretical tools described above, collaborations between behaviorists, neuroscientists, and ecological and evolutionary modelers are needed. Many organisms studied in the laboratory readily execute search behaviors and research groups are already using techniques like computer vision, virtual reality, microfabricated landscapes, and optogenetics to measure and manipulate search behavior and to link sensory input to behavioral output (8, 65 ⇓ –67). The high-resolution data such studies generate provide the kind of information needed to build and parameterize mathematical models of search strategies. Theoreticians and experimentalists could benefit by working together to develop and test such models. In addition to revealing underlying similarities and differences among search strategies, models that are carefully linked to experimental systems could identify steps in the search process that strongly influence performance, but are poorly studied experimentally. More generally, the approach described in Fig. 3 may help reveal the broader biological relevance of search behaviors identified in individual model systems. An active feedback between mathematical models and data will be crucial if we are to understand, at a more fundamental level, how evolutionary forces structure the search strategies in the world around us.


Introduction

Stem cell homing is a controlled recruitment of stem cells within the vascular endothelium that leads to trans-endothelial and directional migration. Damaged tissues in heart, liver, and other organs can be regenerated by stem cell homing through well-directed migration of stem cells. The directional migration of stem cell is precisely regulated by the homing factors released from the injury sites. The released soluble cytokines, homing factors, contribute to generating the cytokine gradient that determines the direction of stem cell migration. Consequently, the bio-chemical gradient induces stem cells to migrate to the injury site for regeneration.

Although the healing process by stem cells has not been elucidated, it has been shown that homing factors have a pivotal role in tissue regeneration [1]. After tissue damage, homing factors such as SDF-1α also known as the C-X-C motif chemokine 12 (CXCL12) is released from the damaged site. A predominant receptor for the SDF-1α is CXCR4 which is a seven transmembrane G protein-coupled receptor widely expressed in cells and tissues taking a role in vasculogenesis and organogenesis [2, 3]. More importantly, down regulation of CXCR4 and SDF-1α significantly decreased the invasiveness of cancer cells, meaning that expression of CXCR4 is responsible for the cell recruitment [4, 5]. CXCR7 is also a protein known as the receptor of SDF-1α [2, 6]. The released homing factors form a chemical gradient from the injury site to the surrounding area, which initiates the transmigration of stem cells through the endothelium and directional migration into the stromal tissue (Fig 1a) [7]. Dar et al have shown enhanced trans-endothelial migration under a gradient of SDF-1α [8]. Cheng et al showed that stem cells overexpressing CXCR4, contributes to the improvement of cardiac performance in myocardial infarction [9], illustrating that SDF-1α is a key homing factor for stem cells [10]. However, the mechanism behind the directional migration of mesenchymal stem cells (MSC) through the endothelium due to a chemokine gradient has not been clearly elucidated in in vivo or conventional in vitro experimental systems.

(a) Theoretical schematic of peripheral MSC homing process. (b) Illustration of the microfluidic device and stem cell homing on-chip.

Directional migration of stem cells during stem cell homing is a key mechanism of homing from the blood vessels to injury sites based on the gradient of homing factors. Peripheral MSCs expressing CXCR4 are trafficked by the gradient of SDF-1α. Binding of SDF-1α leads to activation of signaling pathways related to migratory mechanisms such as Rho-ROCK, Rac, and Cdc42 [11]. Rho-ROCK and Rac pathways are known for their roles in the synthesis of migratory machineries for the cells and are mediated by SDF-1α ligand binding [12, 13].

Although there are limitations in the study of microfluidics [14], the device used in this study is a fascinating system that is able to mimic numerous in vivo microenvironments generating gradients of soluble cytokines. Directional migration was incorporated into a collagen matrix-integrated microfluidic device, which could be used for the assessment of stem cell homing [15] (Fig 1b). Chung et al developed the basic three-channel-based microfluidic chips with collagen matrix as a barrier of fluid on a vascular structure [16]. Also, the capability of this device to form endothelial cell (EC) monolayers allowed the observation of EC migration and sprouting through the collagen matrix by Chung et al and Jeong et al [16, 17].

To better understand the homing mechanism, Boyden chambers and transwells have been used as tools to observe the increased migration of MSCs due to the chemokine effect of SDF-1α [1, 18]. However, none of these devices showed the chemotaxis of MSCs through the endothelial barrier or the ECM conditions, meaning that the devices were not able to mimic the in vivo spatial environment. The proposed device has a geometric set up for building perfusable vessel structures as well as the ECM environment and has both biological and technical advantages over the Boyden chamber and the transwell systems. Furthermore, the previous studies did not focus on the effect of migratory inhibitors during cellular migration or on the quantification of migration distance or directional migration.

In this study, we describe the directional migration of MSCs under a gradient of homing factors using a microfluidic channel. To identify the behaviors of homing factors, directional movement and transmigration of MSCs were observed. To construct an in-vivo-mimicking microenvironment, an EC barrier was constructed by forming an EC monolayer along the center channel of the microfluidic device. Directionality and migratory ability of MSCs were assessed in the presence of different inhibitors. Five conditions including the control environment were created by exposing inhibitors to MSCs undergoing migration (i) Control group without SDF-1α gradient, (ii) SDF-1α condition, (iii) AMD3100 (CXCR4 antagonist) treatment condition, (iv) Y-27632 (Rho-ROCK inhibitor) treatment condition, and (v) NSC23766 (Rac inhibitor) treatment condition. The inhibitor AMD3100 was used for blocking SDF-1α binding to CXCR4 in order to disrupt the directionality of MSC and Y-27632, and NSC23766 were used to disable the migratory mechanism of the stem cells. Treatment with Y-27632, an inhibitor of the Rho-ROCK signaling pathway, and NSC23766, an inhibitor of the Rac signaling pathway, resulted in decreased migration distance of MSCs without loss of directionality. In contrast, the CXCR4 antagonist AMD3100 disrupted the directionality of the MSCs but did not affect the migration ability of the stem cells resulting in near average migration distances.


Do Cats Have Natural Predators?

Predators eat other organisms. The different types of predators are defined by what the predator eats, and how it harvests that food. The four main types of predators are carnivorous, herbivorous, parasitic, and mutualistic.

A carnivorous predator must hunt and kill its prey. Carnivorous predators are broken up into two further types: those that primarily scavenge carcasses, and those that primarily hunt and kill prey independently. There are many carnivorous predators, including wolves, cougars, owls, and snakes. Herbivorous predators, like krill, horses, and porcupines, consume autotrophs (plants and algae).

The final two types of predators involve small, sometimes microscopic, organisms living within another animal—flatworms living within a domestic cat, for example. Mutualism is when this smaller organism lives in harmony with its host and causes no harm. An example is the bacteria that live in the digestive tract. Parasitism, however, can impact or even kill the host. This means that the parasite deprives the host of essential nutrients to the point where its health declines.

Each of the above types of predators can be further broken down into more detailed and specific categories. For example, insectivores (animals that primarily prey on and eat insects) are a carnivore sub-type. Also, omnivores practice predation on plants and animals. The predators’ list below is primarily made up of strict carnivores.

Snakes

Snakes are found throughout most of America, so encounters with snakes are common. The majority of snake species wouldn’t consider an adult cat to be a source of food. The snakes that may include:

Pythons and Boas

Even though pythons are nonvenomous, members of the Pythonidae family still pose a danger to cats. Any snake that’s large enough to prey upon small mammals will consider cats prey. Many large, nonvenomous snakes found in the U.S., such as boa constrictors, started off life as domestic pets.

Also, the Burmese python will prey upon cats. Research published in Biological Invasions notes that Burmese pythons are established in Florida. Encounters with this snake are not uncommon, and there are concerns about the snake population spreading beyond the Everglades.

Pythons are ambush predators. A python will lay in wait for a meal to cross its path. Using its strong sense of smell and heat-sensing pits, it targets its meal. It will then latch onto its prey before wrapping the animal in its powerful coils. Constriction can kill a cat in a matter of minutes.

Diamondback Rattlesnakes

A diamondback rattlesnake is the largest venomous snake in the U.S. There are dozens of reported cases where a cat has been bitten by one of these deadly pit vipers. However, there is little evidence of rattlesnakes actively preying upon cats. However, eastern and western diamondbacks are large enough to consider smaller cats, or juvenile and sub-adult cats, as prey.

A curious cat may antagonize a rattlesnake, triggering a defensive response. If the snake is hungry and the cat is small enough, the snake may follow the ‘waste not, want not’ principle. Large adult eastern and western diamondbacks will eat fully-grown rabbits. A small domestic cat is about the same size.

Rattlesnakes are a part of the Viperidae family. As ambush predators, they will remain motionless and silent until a prey animal wanders too close. Eastern and western diamondbacks have hemotoxic venom that causes tissue damage and attacks red blood cells. So, a cat will quickly succumb to envenomation.

Dogs, both feral and domestic, will occasionally prey upon cats. How an interaction might go between a cat and a dog will depend on their personalities and upbringings. A cat and dog may be completely ambivalent towards one another. Conversely, they may attack one another.

A dog may chase a cat upon sight. This can be out of genuine aggression, territorial instincts, or the desire to play. A cat may also attack on sight, although it is more likely to puff up in a threat display first. Aggressive dogs and feral dogs may actively hunt cats.

Coyotes

Coyotes are a part of the Canidae family and are prevalent throughout the U.S. Coyotes live in packs and as individuals. The Journal of Wildlife Management found that coyotes actively prey upon cats, especially during the pup-rearing season. This study also found that both packs and individuals would successfully attack and kill cats.

Coyotes hunt using their olfactory senses and keen eyesight. Coyotes will hunt in pairs or packs to take down larger prey, such as deer. Individuals will prey upon smaller animals, such as squirrels, rodents, birds, and even domestic cats. As an opportunistic predator, a coyote will prey upon whatever it comes across.

Wolves

Wolves are the largest surviving member of the Canidae family. As noted in the Journal of Forestry Research, wolves were almost driven to extinction through habitat loss and hunting. Before total extinction, it was recognized that wolves were an essential part of the ecosystem for prey population control.

So, repopulation and conservation efforts were put into place. This included wolves being protected in the U.S. under the Endangered Species Act. Due to this, and human populations encroaching on their natural habitats, wolf-human interactions are becoming more commonplace. This also includes wolf-cat interactions.

Wolves are opportunistic hunters, so they will prey upon cats if they have the chance. This is most likely during the colder months when other prey is scarce, or while the pack is rearing young pups.

Eagles

A camera was set up to monitor a nest and captured footage of a cat being eaten by a family of bald eagles. Experts have said, previously and since this event, that eagles preying upon cats is uncommon.

It is believed that only large eagles have the ability and strength to prey upon cats. The Bureau of Land Management notes that the bald eagle is the second largest bird of prey found in North America.

Eagles hunt during the day, and will swoop down and latch onto their prey. This prey consists of fish, birds, small mammals, and rodents. Large eagles are capable of preying upon cats, but whether they would is uncertain.

Cougar

Cougars, also known as pumas and mountain lions, primarily hunt deer, coyotes, porcupines, elk, and raccoons. Livestock herds are also a temptation for cougars, which is why farmers hunted the species.

Cougars are opportunistic, nocturnal hunters. Usually, hunting will occur between dusk and dawn. Much like the domestic cat, a cougar will stealthily sneak up on its prey. At the right moment, it will lunge and aim a deadly bite for the back of its prey’s neck. Cougars have been known to prey upon pets, especially those allowed to roam outside at night.

Large owls can prey upon cats. This includes the great horned owl, which is thought to have the most diverse diet of all raptors. Also, the snowy owl.

Owls hunt from above. One will usually identify its prey while perched at a height. It will then swoop down silently. An owl will latch onto its prey using its sharp talons. Prey is usually killed by being crushed by this powerful grip, trauma caused by the talons, or a quick bite to the neck.

Snowy owls can be found in the northern U.S. when food is scarce. The great horned owl is the largest owl species found in North America. Both prey upon a variety of rodents and larger mammals, including raccoons.

Given that cats are similar in size to raccoons and like to explore when it’s dark, it puts them at risk of being preyed upon by an owl. Owls can fly silently, so a cat may not know it is being hunted until it is too late.

Hawks

Of all the hawk species found in the U.S., only the red-tailed hawk is capable of preying upon cats.

The red-tailed hawk is the most common hawk found in the U.S. This raptor hunts small animals, and it won’t distinguish between wild mammals and a small cat. As such, smaller cats and kittens can be preyed upon by hawks. This is not a common occurrence, and reported cases seem to revolve around when pets are left outside unsupervised.

Hawks will seek out prey by using their excellent eyesight. A hawk will coast along in the air and scan the ground for prey. It may also find a comfortable perch and wait for a suitable animal to cross its field of vision.

Wolverines

Although a wolverine may look like a small bear, it actually belongs to the weasel family. A wolverine will forage upon vegetation and berries, but its diet is primarily meat-based.

Wolverines have been known to attack and subdue prey many times their size. They have also been blamed for missing cats. Only a small number of missing cat cases are actually connected to wolverine attacks.


Animal Behaviour

Ethology is the study of animal behaviour to find out natural responses of animals to various environmental stimuli. Some studies are also done in laboratory conditions to elicit measured responses. Therefore, ethology involves laboratory as well as field studies and has strong relationship with other sciences such as ecology, environmental science, neurology, physiology, psychology and evolution.

The beginning of modern ethology commenced with the experimental as well as field studies done by the Dutch biologist Nikolas Tinbergen, Austrian biologists Konrad Lorenz and the German Karl von Frisch, who were jointly awarded Nobel Prize in 1973 for their contribution to this new science.

LABORATORY STUDIES

Neuroanatomical techniques

Different types of behaviour are controlled by specific regions of the brain. If a particular part of the brain is damaged, the behaviour of the animal is altered. Broca (1861) identified speech area on the cerebral cortex by the slurring of speech of a patient as a result of injury to the brain. Brain parts can be damaged by making cuts with a knife or by the neurotoxic kainic acid and behaviour is observed.

Carl Lashley (1938) conducted his studies on memory by ablation on different brain parts of rats which were trained to running maze. Which area of the body is affected by damaging which part of the brain was studied on rats by De Groot (1959) on cats by Jasper & Marsan (1954) and on dogs by Lin et al. (1961).

Stereotaxic equipment can be used to place small and precise injuries in brain. Micropipettes can be used to inject minute quantities of chemicals in precise locations of brain, such as limbic system, and behaviour can be recorded.

Studies can also be done by training the animals in skinner box, in which a lever can be pressed by the animal to get reward.

Neurophysiological techniques

Physiological studies can be done by recording electrical activity of brain by EEG or by stimulating different areas of brain by planting electrodes. Alpha, Beta, Theta and Delta waves are recorded by EEG. Alpha waves that are believed to emanate from the parietal and occipital lobes of brain reveal resting and peaceful relaxed state of brain that is otherwise alert. Beta waves are produced in frontal lobes and indicate the daily mental activity, concentration and thought. Theta waves denote emotional stress and sometimes hallucination. Delta waves are generated in deep sleep.

Neurochemical techniques

These techniques involve stimulation of parts of brain by drugs such as alcohol, opium, hashish, bhang etc. which alter the behaviour of the animal. Tranquilizers, barbiturates and drugs like calmpose, larpose etc. are phychoactive drugs which affect the brain and change the behaviour of animals.

Hormones such as estrogen and testosterone can be introduced into hypothalamus through canulation and the behaviour changes can be recorded. Adrenalin, histamine, testosterone and dopamine stimulate different parts of the limbic system. For example, stimulation of amygdala brings about aggressive behaviour and stimulation of septum pellucidum gives immense pleasure to the animal.

The modern techniques, e.g. PET scanning, CT scans, MRI etc. detect glucose utilization in different parts of brain, which is an indication of activity of that part.

Biological Rhythms – Chronobiology

Biological rhythms are self –sustaining natural cycles of animal life history which maintain themselves regardless of the environmental factors. All animals possess innate biological clocks which are driven by the biochemical mechanisms. Erwin Bunning (1936) was the first biologist to carry out extensive work on biological rhythms.

CIRCANNUAL RHYTHMS

They show one-year periodicity, e.g. a large number of animals reproduce once in a year. Flowering in plants also takes place once a year. Insects and amphibians follow a cycle of hibernation and activity. Hummingbirds in South America move to the caves and become inactive in winter in Andes. Famous migration of Monarch butterflies from North America to Mexico and back follows annual cycle. Millions of these butterflies cover a distance of 3200 km to hibernate on trees in San Francisco. Many beetle species hibernate under the snow in Himalaya. Arctic and Antarctic animals generally follow annual cycles of activity.

CIRCALUNAR RHYTHMS

These rhythms synchronise with the 28 day phases of moon and tidal rhythms. Palolo worm lives in deep sea but swims to surface on the first day of the first quarter of moon in November in Fiji. The sea hare (Aplysia) shows periodicity which is exactly half of the lunar cycle.

TIDAL RHYTHMS

They are synchronised with the periodic rise and fall in sea level due to gravitational pull of sun and moon and centrifugal force of the earth. There are daily tides due to earth’s rotation on its axis. Spring tides cause maximum rise and fall in sea level because moon and sun are on the same side of earth. Neap tides occur when sun and moon are on opposite sides of earth at full moon stage.

Circasyzygic Rhythms

They follow fortnightly cycle of 14.7 days of high tide after new moon or full moon. Molluscs exhibit egg laying behaviour according to this periodicity. Periwinkle also comes out of burrows on sea shores during high tides.

Circatidal rhythms

These follow 12.4 or 24.8 hour cycle that is synchronised with low and high tides twice a day. Animals living in burrows, such as polychaetes, planarians, crab etc. are submerged and exposed alternately and in the process get food brought by water currents. Bivalves such as Mytilus showed shell opening rhythm according to circatidal rhythms even when kept in the lab. Grunion fish spawns precisely at high tides.

CIRCADIAN RHYTHMS

These rhythms follow 24 hour cycle of activity and sleeping synchronised with light and darkness. So, the animals can be classified as nocturnal, diurnal and crepuscular, the last ones are active at sunrise and sunset. Birds are mostly diurnal and bats nocturnal which find their way by echolocation. Body metabolism and release of hormones are synchronised with 24 hour cycle.

Honey bees are known to have time memory. In experiments, honeybees after 5 hours of freezing came to food 5 hours late. Human beings experience jet lag when their circadian rhythm is disturbed while travelling in aeroplanes.

Larvae of Wuchereria bancrofti move to peripheral blood in the night but go to deeper blood vessels in daytime, which is synchronised with the blood-sucking habit of Culex mosquito.

Brady (1969) thought that optic lobes play an important role in controlling circadian rhythms in cockroach. Corpora allata and corpora cardiaca also release hormones that control day-night cycle. Cyclic AMP and serotonin are involved in biochemical events that control circadian oscillations.

In vertebrates, neural connections exist between retina and hypothalamus and pacemaker may be located in ventromedian nucleus of hypothalamus. In amniotes, the pineal and parietal bodies regulate photoperiodism. Melatonin secreted by pineal gland has anti-gonadotropic effect. Turtles synthesize serotonin during day and melatonin at night. However, this cycle disappears during hibernation.

Kinship, Selfishness And Altruism

There are four possible types of interactions among individuals living together in a population. First, cooperation or mutualism, in which both the participants gain from the act as in the nest building by both male and female birds, or cooperation in the colony of social insects.

Second, altruism in which the actor (individual that carries out the action) pays fitness cost to the recipient that gets the benefit as in social insects.

Third is the selfishness, in which the actor gains but the recipient loses in terms of fitness. Fourth interaction, which is rather rare in nature, is spite in which both the participants lose in terms of fitness. As for example in the case of two eagles fighting in the air for the possession of a killed prey, which ultimately falls down and is taken away by a fox.

KINSHIP & ALTRUISM

Kinship is a phenomenon that occurs in social animals or in closely knit populations which are genetically related to one another. In these populations kin selection operates and traits that result in decreased personal fitness but increase the survival and reproductive fitness of the species are favoured by natural selection. Kin selection works not on individuals but on genotypes.

Altruism evolved in colonies that show kinship. An altruist by way of helping other individuals increases the fitness of its own genome. A honey bee worker is a sterile female and shares at least 50% of its genotype with its sisters even when its mother and father are unrelated. If a worker decides to breed on its own, its diploid daughters and haploid sons will never be more than 50% related to it. So, the workers choose to become sterile and ensure survival of their genetically identical sisters, because the queen can produce more offspring than workers can do individually.

Kin selection leads to altruism in a colony and fitness is direct when it gives the individual personal benefit and reproductive advantage, and indirect when the reproductive benefit goes to the colony or relatives. Kinship favours the spread of an allele that increases the indirect component of fitness of an individual and in most instances it gives rise to altruism.

The gene that favours altruism could spread when participants are related and the cost to the individuals is low as compared to the benefit to recipient. Therefore, altruism is promoted by kin selection and close genetic kinship.

In a large number of bird species, especially those in which nesting opportunities are limited, young ones help their parents in rearing their own sisters and brothers by way of nest building, nest defence and feeding the chicks, although they are themselves capable of breeding. In such birds, as for example in bee-eaters help is always given to their kin.

RECIPROCAL ALTRUISM WITHOUT KINSHIP

The theory of group selection was championed by Wynne-Edwards (1962). Altruism has evolved among the related individuals by means of kin selection. But there are also instances of cooperation among the unrelated individuals. Altruistic act towards non-kin is possible only if the recipient is likely to return the favour at a later date, in a ‘Tit for Tat’ manner.

Natural selection will favour altruism among unrelated individuals only if they reciprocate. Non-reciprocating or selfish individuals of the population are selected out. Robert Trivers (1971) proposed that reciprocal altruism can develop in the following conditions:

  1. If interacting individuals remain together for considerably longer period of time.
  2. If frequency of altruistic attempts is high.
  3. If the cost and benefit to both individuals are more or less equal.
  4. If selfish individuals that fail to reciprocate are punished in some way, such as withdrawing the benefits in future.

Species which have mutual dependence in defence, foraging, territoriality etc. are most likely to develop reciprocal altruism, as in monkeys, baboons, chimpanzees and man. Kin selection and reciprocal altruism are sometimes found to coexist in many social groups of animals and at times it is difficult to distinguish between the two or measure them independently. Altruism is promoted by group selection but when it benefits close relatives it is promoted by kin selection.

TROPHOLAXIS

This is a phenomenon in which food is offered by one individual to the other which is not its own offspring. This is very common in social insects where feeding is done by specialized individuals of the colony called workers. In chimpanzees distribution of meat among individuals after collective hunting of monkey has been recorded.

Wilkinson (1984) studied blood-sharing in vampire bats (Desmodus rotundus) in Costa Rica. The bats demonstrated altruistic behaviour by regurgitating blood meal and sharing it with others. Trophollaxis is essential for the survival of species which do not find enough food and starving individuals must be helped for the benefit of the species. Wilkinson also found that bats regurgitate food more frequently to relatives and rarely to non-relatives, since relatives are likely to reciprocate when they do not find meal themselves. This is called reciprocal altruism.

COST OF SELFISHNESS

Individuals living in groups enjoy the advantage of protection from the predators as some members out of hundreds will spot the predator and give alarm calls to alert the others. Similarly, prey hunted by a predator group can be shared by all members and sometimes even injured individuals that cannot hunt themselves can also get food. But this favour must be returned by individuals when their turn comes.

If the favour is not returned then the individuals are labelled selfish and will be selected out of the group. Herding protects the herbivore animals from predators but an individual straying away from the group will be killed by predators and eliminated from the population. Selfishness is therefore punished by natural selection. Selfishness is also found in the prides of lions where male lion kills all the cubs after dethroning a lion and taking over his pride.

This is done to bring the lionesses to oestrus so that he can have his own progeny quickly. Selfishness is also seen in protocooperation in which only one individual derives benefit, as in the case of suckerfish attached to the shark. Shark does not get any benefit but the suckerfish gets leftover food from shark’s mouth. Group selection and kin selection, therefore, demand faithfulness to the society and selfish individuals are selected out and eliminated from the population.

Orientation, Navigation and Homing in Animals

Orientation is the position of the animal with reference to gravity or resource. This is the position the animal maintains in order to reach the resource. Positional orientation is to maintain upright posture against gravity for which vertebrate have membranous labyrinth and invertebrate statocyst.

Object orientation takes place when the animal tries to approach an object which may be food or water. Aquatic animals move vertically in pond or lake which is called strato-orientation. When the animals try to move from grassland to forests, deserts or mountains it is called zonal orientation. Animals which migrate long distances generally possess topographical or geographical orientation.

Kinesis is the movement of an animal in response to stimuli. It may be oriented or undirected movement depending on the source of stimulus. The response may be proportional to the intensity of stimulus.

Klinokinesis is the change of direction during movement which may increase or decrease in the light of intensity. Generally the animal moves right and left alternately to compare the direction of stimulus to gain correct orientation. Animals having single receptor show alternate movements. Caterpillar and maggots looking for the sites of pupation vacillate while moving.

Orthokinesis depicts speed of locomotion which s related to the intensity of stimulus and accumulation of action specific energy in the animal. Whole body of the animal is involved. For example, burrowing animals such as Ammocoete larvae of lampreys burrows in sand away from light. Cockroaches move from brighter areas to darkness.

Different types of kineses are termed with respect to the stimulus, e.g. hygrokinsis is with respect to humidity as in isopods photokinesis in which stimulus is gradient of light and chemokinesis is with respect to chemical stimuli.

Taxis is the orientation of the animal with reference to the direction of stimulus in space. Movement can be towards or away from the stimulus and depending upon the stimulus it can be names as follows: hygrotaxis (humidity) geotaxis (gravity) chemotaxis (taste or odor) thermotaxis (temperature) anemotaxis (air current) rheotaxis (water current) phototaxis (light intensity) phonotaxis (sound waves) astrotaxis (sun, moon and stars) menotaxis (angle to the stimulus) mnemotaxis (based on memory).

Klinotaxis occurs in those animals which have single receptor, as in Euglena, which compares the intensity of stimulus by alternate lateral movements. Similarly in the maggots of Diptera the light sensitive organ is a cluster of cells above and behind the mouth and the negative response to light is compared by flexing movement of body.

Tropotaxis is found in animals which have paired receptors as eyes in Planaria. Animal gets equal inputs on both the receptors and hence it can move in straight line towards or away from light. If one eye of an insect is painted black it makes circling movements towards the side of painted eye.

Telotaxis is found when animal has a choice between the positive and negative stimuli or when the animal does not have a balanced input on the two receptors. Orientation is effected by fixing the image on one side by moving the head and making a choice. Honey bee seeing two light sources flies to any one by making a choice.

Menotaxis involves maintaining a constant angle in relation to the source of stimulus. Nocturnal moths have a habit of flying by keeping the light source (usually stars and moon) at right angle to the body so that they can fly parallel to the ground. But when they do the same with artificial light that is to closer, they are forced to fly in circles. Honey bees fly from their hives to the flowers by maintaining a constant angle to the sun as revealed by the wagging dace of the scout bees. The angle to the sun is remembered by foraging bees while watching the dance on the vertical surface of the comb. Foraging bees then fly towards the food source maintaining the same angle to the sun.

Mnemotaxis was first described by Kuhn (1919). This is orientation based on memory that was studied by Niko Tinbergen (1951) with his experiment on digger wasp. Wasp circles around the nest and carries a memory map of the nest and its surroundings, which helps it to accurately orient itself and return to the nest. This is also called zonal orientation and geographical orientation which involves distance, direction and landmarks that make topography of the area and help the animal in homing to its nest.

NAVIGATION & HOMING

Migratory animals which cover long distances either to reproduce or to escape from the harsh climate must find their way accurately over oceans, deserts, forest and mountains. Fishes, birds and many invertebrates possess extraordinary capabilities to cross oceans, deserts and mountains in order to reach their destination.

Invertebrates such as crustaceans, amphipods, ants, bees and wasps possess strong homing and navigational instinct and are guided by the sun, moon, stars and topography of the area in following accurate route. Monarch butterflies migrate thousands of kilometers from Canada to Mexico to escape harsh winter and return back accurately to the same place.

FISH NAVIGATION

How fishes find their way in huge expanses of sea and reach their destinations which lie thousands of kilometers away has been a mystery. It is believed that they orient by the positions of stars and moon in the night sky and sun in daytime to find the direction of swimming.

They also make use of temperature gradients and ocean currents which help them in swimming and also in navigation. However, it has been experimentally proven by A.S. Hasler that salmons are guided by the odour of their parent stream during return journey.

Odour map gets imprinted in their brains when they migrate as larvae from tributaries to the sea and they can navigate back from the sea using this odour map when they become adults. Eels can also migrate to Sargasso Sea using similar odour maps but how their larvae, leptocephali find their way back to the river mouths, crossing vast stretches of Atlantic Sea is a mystery. Probably their parents leave some kind of odour trails during their journey.

Courtship Behaviour in Animals

Courtship is a social behaviour in which there is an interaction between the male and female members of a species leading to mating and reproduction. Courtship evolved due to the fact that very large number of sperms is produced which must search and fertilise few ova leading to competition among sperms.

Since males possess sperms, they must compete with one another in order to win over the female to fertilise her ova which are a limited resource. The gametic selection has translated into sexual selection among males and females, leading to male-male competition and female choice. Courtship display is an extension of this male-male competition in which males evolved various devices and techniques to persuade female to reproduce.

IN DROSOPHILA

Courtship behaviour in vinegar fly (Drosophila) was described by Bastock & Manning (1968). Male and female come together within 2 mm of each other and then male circles around her. Female is discriminatory and wrongly approaching males are kicked off. Male vibrates one wing during circling which stimulates the female.

Vibration of the wing produces sound as well as air current which act on the antenna of female. This is followed by touching with front tarsi and genitalia licking. Mating occurs after about 3 minutes by male mounting the female. Often mounting occurs but mating is unsuccessful. Courtship of wingless male is not accepted by female.

IN 3-SPINED STICKLEBACK FISH

Three-spined stickleback fish (Gasterosteus aculeatus) is found in ponds and rivers of Europe. Male is bluish-black in colour with bright red belly while female is silvery in colour. Male finds a place in sandy bottom where there are weeds. Male builds a tunnel-like nest in sand among weeds and defends territory around the nest. Then male swims near the surface over the nest to invite females.

Other males are attacked and chased away aggressively. Male swims upward from below and stabs the female from below with his dorsal spine. When response of female is positive both of them swim in zig-zag fashion towards the nest. If female likes the nest it enters inside and male follows.

Male places his head against the tail fin of female and quivers, which provokes the female to release eggs. Male then deposits his sperms over the eggs and female is chased away. Male then swims again to the surface to solicit another female. Up to 5 females can be made to lay eggs in his nest by the male. Male then guards the eggs and oxygenates them by fanning with fins till they hatch.

Courtship behaviour has evolved in birds to the highest level in which auditory as well as visual displays are used by males to impress females. Shape, size and colour of feathers have evolved for displaying and dancing.

Singing generally has evolved in male birds living in dense forests where there is limitation of visual distance but sound can travel to long distances. For example, males of cuckoos, starling, lapwings, larks, grackles, nightingales and bulbuls are accomplished singers and use these auditory stimuli to attract females.

Some birds imitate other animals to impress females, e.g. grackles, parakeets, starlings, magpies and shrikes. Lyre bird of Australia is a celebrated mimic whose male can imitate the sounds of mobile phones, alarm clocks, tweets of reversing vehicles and bike engines only to impress female of his extraordinary capabilities.

Nest building is also used as a means of visual stimulus to attract female. In weaver birds and bower birds male builds a nest and invites females to inspect it. If female likes the nest mating occurs. In the case of oropendolas (Zarhynchus) it is the female that builds the nest and invites males into it.

Feather display by male is a common phenomenon in birds’ courtship, e.g. birds of paradise, peacock, pheasants, grouse etc. in which length and brilliance of feathers is the deciding factor to attract female as well as to warn other intruding males.

Dancing is also a stimulus used by males to woo females in a large number of species of birds. Dancing and cooing in pigeons and doves is a courtship behaviour. Peacocks and birds of paradise males not only display their feathers but also dance and show different tactics to attract females.

Aerial displays in flight and aerobatics have been recorded in pigeons, kites, buzzards and doves. In buzzard (Buteo) male and female hook their claws and fly in circles before mating. Lek birds such as grouse clear an area of weeds in the forest where all males and females of the area gather. All males dance and display their feathers in this mating arena, while the females passively watch the proceedings. Mating takes place after several hours of dancing.

In the case of crested grebe (Podiceps cristatus) both male and female come together and do head-shaking ceremony after which both carry out diving displays. Then both rise vertically to the surface of water and do penguin dance after which nest material is exchanged. Mating takes place after a considerably long courtship display.

Jackson’s whydah (Drepanoplectes jacksoni) male prepares a display arena by clearing grasses and then dances around the central grass tuft and jumps into air frequently while the female watches. Mating takes place but female raises her family alone without any help from male.

Lesser florican jumps above the tall grasses and floats down with outstretched wings and tail, loudly calling all the time. Females are attracted by this display.

Social Life In Primates

Primates were not social animals when they evolved from the primitive insectivore ancestor in Palaeocene epoch. However, gradually primates became gregarious and social interactions developed in them leading to highly developed social life as in humans.

SOLITARY PROSIMIANS

Prosimians such as tarsiers, bush babies and lorises are mostly nocturnal and highly arboreal primates. Males are found solitary or in pairs with females in breeding. Females live with infants till they become independent. Prosimians are shy animals and hiding in foliage is their means of defence. They rarely come down from trees. The black coloured Aye-aye is nocturnal and lives singly or in pairs.

MONOGAMOUS PROSIMIANS

Tree shrews (Tupaia species) are the most primitive primates alive today and live singly or in pairs in the forests of S.E. Asia. As they are highly territorial and defend their territory with aggression, their social units are broken up into nuclear families, e.g. male, female and juveniles. Males mark their territory by urine and defend it by threat-call and tail-flicking. Intruders are quickly attacked and chased away.

Lemurs occur in the tropical rain forests of Madagascar. They are also monogamous and live in a group male, female and up to 4 young ones. Male and female marry for life and live together. Some species mark territory.

Tamarins are South American monkeys which are also monogamous and live in nuclear families of male, female and juveniles.

SINGLE MALE BISEXUAL GROUPS

Some monkeys, e.g. hanuman langur, howler monkey, red-tailed monkey and blue monkey, live in a social group in which there is a single dominant male having a harem of several females. Young males are chased away by this dominant male and hence they form all male groups outside the other groups. Harem is protected by the overlord male but is constantly attacked by males from the all male groups to unseat him from the dominant position.

MULTIMALE BISEXUAL GROUPS

Baboons (Papio) are terrestrial primates which are found in large groups that may include thousands of individuals. There are several units in which one male and several females with young ones form a small group. These small units forage together. Male protects the group and herds females together and prevents them from meeting other males. Hierarchy is maintained in females for access to male. These small units together form large bands that live together, sleep together and defend them collectively from predators. All males collect together to attack a predator which is usually a leopard.

Rhesus monkeys also form multimale bisexual groups that form large foraging units. One male is dominant and others subordinate. A foraging unit is formed by 3-8 males, each having 5-7 bonded females. Many units form large groups of hundreds of individuals for foraging and for defending.

SOLITARY APES

Orang-Utan lives in the dense forest of Sumatra and Borneo and is completely arboreal, feeding on a diet of fruits and leaves. Males are solitary, seeking females only for mating and shares no family responsibilities. Females are found with the young ones, usually only one young is found with female. For sleeping on the tree female makes a nest with branches and leaves in the fork of tree.

MONOGAMOUS APES

The lesser apes, white handed gibbon and hoolock gibbon are found in the dense forests of eastern India, China and Burma. They are highly arboreal and swing under the branches with the help of excessively long arms. Male, female and up to 4 young are found in one family unit. The units communicate with one another by loud hooting calls. Both male and female share family responsibility together.

MULTIMALE BISEXUAL APES

Gorilla is the largest ape living in the dense forests of Cameroon, Gabon, Congo and Uganda. Males are terrestrial as they are too heavy to be arboreal. Females and young make nests among branches on trees for sleeping. They are found in groups of about 20 individuals. Old dominant males are called “silver backs” which dominate over other males and females. Hierarchy is observed among males as well as females while feeding, drinking or access to females.

DIFFUSED SOCIAL APES

Chimpanzees form diffused social groups of up to 50 individuals. Females are silent and shy. There is hierarchical ranking among males as well as females but females accept several males and there is no conflict. They are omnivorous and sometimes hunt monkeys and share its meat. They also make nests in the fork of trees for sleeping. Both males and females defend their group by screaming, gesturing and by throwing sticks and stones at the intruder.

Social Organisation In Insects

In insects social life has evolved only in two orders, namely, Isoptera (termites) and Hymenoptera (bees, wasps and ants) which make a nest and live in colonies of thousands of individuals that practice division of labour and social interaction.

SOCIAL LIFE IN TERMITES

Termites were the first animals which started living in colonies and developed a well organised social system about 300 million years ago, much earlier than honey bees, ants and human beings. Although termites do not exceed 3-4 mm in size, their queen is a 4 inch long giant that lies in the royal chamber motionless, since its legs are too small to move its enormous body. Hence workers have to take care of all its daily chores.

Termite queen is an egg-laying machine that reproduces at an astonishing rate of two eggs per second. Generally the queen of a termite colony can lay 6,000 to 7,000 eggs per day, and can live for 15 to 20 years. The other castes, workers and soldiers are highly devoted to the colony, working incessantly and tirelessly, demanding nothing in return from the society.

Soldiers have long dagger-like mandibles with which they defend their nest and workers chew the wood to feed to the queen and larvae and grow fungus gardens for lean periods.

Nasutes are specialized soldiers which specialize in chemical warfare. They produce a jet of highly corrosive chemical from their bodies that can dissolve the skin of enemies and can also help in making galleries through the rocks.

SOCIAL STRUCTURE OF A BEE COLONY

The population of a healthy bee hive in spring and honey flow period may contain 40,000-80,000 individuals but the population declines in winter and extreme summer. There is remarkable order in the hive and no conflicts are seen among the members.

Queen is one and a half times larger than the workers and is the only fertile female in the hive. Queen keeps the colony together by secreting a pheromone called queen substance from its mandibular glands. In multiqueen colonies, young queens after emergence attempt to sting and kill the rival queens.

Generally only one queen stays in the hive and other queens along with their army of workers swarm out and seek new places for building their own hives. Queen takes one to several nuptial flights and after mating with drones settles in the hive and starts laying eggs.

Drones are haploid fertile males of the colony, whose only job seems to be to mate with the queen and transfer their sperms in her spermatheca. There are 2-3 dozen drones in a bee hive all of which energetically pursue a queen in her nuptial flight. Once the breeding season is over drones are driven out of the hive by workers and die of starvation, since they are unable to forage for themselves.

Workers in a hive are 20,000-80,000 in number, which are genetically sterile females that build, maintain and protect the hive. A worker attends to cleaning and maintaining the hive and feeding the larvae with honey and bee bread. It also secretes wax from the abdominal wax glands and participates in building honey comb cells. The workers function as foragers of nectar and pollen and in later part of life as water carriers, and eventually die while working.

SOCIAL ORGANISATION IN WASPS

Insects known as wasps belong to family Vespidae, about 75,000 species of wasps are known, most of them parasitic or predators. Wasps are characterized by two pairs of membranous wings, three pairs of legs and an ovipositor that may be modified as sting in sterile females.

The abdomen is narrowly attached to the thorax by a petiole. In addition to their compound eyes, wasps also have three simple eyes known as ocelli, arranged in a triangle on the top of the head. Females have diploid number of chromosomes and develop from fertilized eggs. Males are haploid and develop from unfertilized eggs.

Yellow jackets and paper wasps prey on caterpillars and other larvae that can destroy crops. Wasps feed on flower nectar and play a role in pollination. Wasps can be solitary or colonial and social insects that exist in colonies numbering up to several thousand strong and build nests.

The type of nest produced by wasps can depend on the species and location. Many social wasps produce paper pulp nests on trees, holes in the ground or in other such sheltered areas. Unlike honey bees, wasps have no wax producing glands.

Many instead create a paper-like substance primarily from wood pulp, which is gathered locally from weathered wood that is softened by chewing and mixing with saliva. The pulp is then used to make combs with cells for brood rearing. Mud daubers and pollen wasps construct mud cells in sheltered places typically on the sides of walls. Potter wasps similarly build vase-like nests from mud, often with multiple cells, attached to the twigs of trees or against walls.

ANT SOCIETIES

Ants are cousins of honeybees and wasps but while bees and wasps are diurnal and sleep in the night, ants are busy working day and night. Ants have no wings, except in sexual forms in breeding season, and therefore their job of travelling to long distances in search of food is very difficult, but addicted to work as they are and having never-say-die spirit, make them excellent foragers that work round the clock, apparently without any rest.

Ants have the highest developed social system, next only to man, with no apparent conflict seen in the society. A colony may have few thousand to over 500,000 individuals. The nests are built in various designs and are called formicaria. Extreme devotion to duty and “Work is worship” attitude binds them together.

Like honeybees, they have polyethism, which means castes are specialized to carry out specialized duties in the colony. For example, the queen has large abdomen to lay a lot of eggs (2-3 million in a year), males fertilize her, workers have broad, sharp mandibles for cutting and chewing and the soldiers have large head that bears sharp dagger-like mandibles for fighting. Workers and soldiers are sterile females.

Ants have poor eyesight and are deaf but have a highly sophisticated chemical language for communication. They possess glands that secrete pheromones for communication. The mutual attraction among the members of a colony is maintained by endless antennal caressing, licking and nuzzling during which they trade food, glandular secretions and enzymes, which is called tropholaxis.

Most ant species excavate nests in the ground or wood but some construct suspended nests on trees made of earth, carton, wax or silk, while some, like safari ants, do not build nests at all. Desert ants build crater-like nests or mounds in which they are able to maintain temperature much below the outside heat. The tropical ant Oecophylla makes nest by webbing the leaves with silken thread that is produced by their larvae.


Things No Amount of Learning Can Teach

QUESTION: Why do you believe that language behavior critically depends on the existence of a genetically preprogrammed language organ in the brain?

CHOMSKY: There’s a lot of linguistic evidence to support this contention. But even in advance of detailed linguistic research, we should expect heredity to play a major role in language because there is really no other way to account for the fact that children learn to speak in the first place.

QUESTION: What do you mean?

CHOMSKY: Consider something that everybody agrees is due to heredity — the fact that humans develop arms rather than wings. Why do we believe this? Well, since nothing in the fetal environments of the human or bird embryo can account for the differences between birds and men, we assume that heredity must be responsible. In fact, if someone came along and said that a bird embryo is somehow “trained” to grow wings, people would just laugh, even though embryologists lack anything like a detailed understanding of how genes regulate embryological development.

QUESTION: Is the role of heredity as important for language as it is for embryology?

CHOMSKY: I think so. You have to laugh at claims that heredity plays no significant role in language learning because exactly the same kind of genetic arguments hold for language learning as hold for embryological development. I’m very much interested in embryology but I’ve got just a layman’s knowledge of it. I think that recent work, primarily in molecular biology, however, is seeking to discover the ways that genes regulate embryological development. The gene-control problem is conceptually similar to the problem of accounting for language growth. In fact, language development really ought to be called language growth because the language organ grows like any other body organ.

QUESTION: Is there a special place in the brain and a particular kind of neurological structure that comprises the language organ?

CHOMSKY: Little enough is known about cognitive systems and their neurological basis so caution is necessary in making any direct claims. But it does seem that the representation and use of language involve specific neural structures, though their nature is not well understood.

QUESTION: But, clearly, environment plays some role in language development. What’s the relationship between heredity and environment for human language?

CHOMSKY: The language organ interacts with early experience and matures into the grammar of the language that the child speaks. If a human being with this fixed endowment grows up in Philadelphia, as I did, his brain will encode knowledge of the Philadelphia dialect of English. If that brain had grown up in Tokyo, it would have encoded the Tokyo dialect of Japanese. The brain’s different linguistic experience — English versus Japanese — would modify the language organ’s structure.

Roughly the same thing goes on in animal experiments, showing that different kinds of early visual experience can modify the part of the brain that processes visual information. As you may know, cats, monkeys, and humans have hierarchically organized brain-cell networks connected to the retina in such a way that certain cells fire only when there is a horizontal line in the visual field other hierarchies respond only to vertical lines. But early experience can apparently change the relative numbers of horizontal- and vertical-line detectors. MIT psychologists Richard Held and Alan Hein showed some time ago, for example, that a kitten raised in a cage with walls covered by bold, black vertical lines will display good sensitivity to vertical lines as an adult but poor horizontal-line sensitivity. Lack of stimulation apparently causes the horizontal-line detectors to atrophy.

An even closer analogy exists between language growth and the growth that appears in human beings after birth — for example, the onset of puberty. If someone came along and said, “Kids are trained to undergo puberty because they see other people,” once again everybody would laugh. Would we laugh because we know in great detail the gene mechanisms that determine puberty? As far as I can tell, no one knows much of anything about that. Yet we all assume that puberty is genetically determined.

QUESTION: Still, as your own example shows, environmental factors do play a major role in physiological growth.

CHOMSKY: And it goes without saying that the onset of puberty may well vary over quite a range depending on childhood diet and all kinds of other environmental influences. Nonetheless, everyone takes for granted that the fundamental processes controlling puberty are genetically programmed. This is probably true of death as well. You may be genetically programmed to die at roughly a certain point it’s a reasonable theory. Look, all through an organism’s existence, from birth to death, it passes through a series of genetically programmed changes. Plainly, language growth is simply one of these predetermined changes. Language depends upon genetic endowment that’s on par with the ones that specify the structure of our visual or circulatory systems, or determine that we have arms instead of wings.

QUESTION: What about the linguistic evidence? What have you learned from studying human languages to corroborate your biological viewpoint?

CHOMSKY: The best evidence involves those aspects of a language grammar that are so obvious, so intuitively self-evident to everyone, that they are quite rightly never mentioned in traditional grammars.

QUESTION: You mean that school grammars fill in the gaps left by heredity? They teach everything about French or Russian, for example, that can’t be taken for granted by virtue of the fact that you’re human?

CHOMSKY: That’s right. It is precisely what seems self-evident that is most likely to be part of our hereditary baggage. Some of the oddities of English pronoun behavior illustrate what I mean. Take the sentence, “John believes he is intelligent.” Okay, we all know that “he” can refer either to John or to someone else so the sentence is ambiguous. It can mean either that John thinks he, John, is intelligent, or that someone else is intelligent. In contrast, consider the sentence, “John believes him to be intelligent.” Here, the pronoun “him” can’t refer to John it can refer only to someone else.

Now, did anyone teach us this peculiarity about English pronouns when we were children? It would be hard to even imagine a training procedure that would convey such information to a person. Nevertheless, everybody knows it — knows it without experience, without training, and at quite an early age. There are any number of other examples that show that we humans have explicit and highly articulate linguistic knowledge that simply has no basis in linguistic experience.

QUESTION: There’s just no way that children can pick up this kind of information by listening to the grown-ups around them?

CHOMSKY: Precisely. But let me give you another example. English contains grammatical constructions that are called parasitic gaps. In these constructions, you can drop a pronoun and still understand the sentence in the same way as when the sentence contains a pronoun. Consider the sentence, “Which article did you file without reading it?” Notice that you can drop the pronoun “it” without changing meaning or grammaticality. You can say, “Which article did you file without reading?” But you can’t say, “John was killed by a rock falling on,” when you mean, “John was killed by a rock falling on him.” This time, omitting the pronoun destroys both meaning and grammaticality.

Constructions of this type — where you can or cannot drop the pronoun — are very rare. In fact, they are so rare that it is quite likely that during the period a child masters his native language (the first five or six years of life), he never hears any of these constructions, or he hears them very sporadically. Nonetheless, every native speaker of English knows flawlessly when you can and can’t drop pronouns in these kinds of sentences.

QUESTION: So we’re faced with a mystery. How could anyone possibly learn enough about the English language to possess the rich and exotic grammatical knowledge that we all seem to possess by the time we are five or six years old?

CHOMSKY: There’s an obvious answer to that: the knowledge is built in. You and I can learn English, as well as any other language, with all its richness because we are designed to learn languages based upon a common set of principles, which we may call universal grammar.

QUESTION: What is universal grammar?

CHOMSKY: It is the sum total of all the immutable principles that heredity builds into the language organ. These principles cover grammar, speech sounds, and meaning. Put differently, universal grammar is the inherited genetic endowment that makes it possible for us to speak and learn human languages.

QUESTION: Suppose that somewhere else in the universe intelligent life has evolved. Could we, with our specialized language organ, learn the aliens’ language if we made contact with them?

CHOMSKY: Not if their language violated the principles of our universal grammar, which, given the myriad ways that languages can be organized, strikes me as highly unlikely.

QUESTION: Maybe we shouldn’t call it “universal” then. But please explain what you mean.

CHOMSKY: The same structures that make it possible to learn a human language make it impossible for us to learn a language that violates the principles of universal grammar. If a Martian landed from outer space and spoke a language that violated universal grammar, we simply would not be able to learn that language the way that we learn a human language like English or Swahili. We should have to approach the alien’s language slowly and laboriously — the way that scientists study physics, where it takes generation after generation of labor to gain new understanding and to make significant progress. We’re designed by nature for English, Chinese, and every other possible human language. But we’re not designed to learn perfectly usable languages that violate universal grammar. These languages would simply not be within the range of our abilities.

QUESTION: How would you assess current research about universal grammar?

CHOMSKY: In the last three or four years, there’s been a major conceptual change in the underlying theory. We now assume that universal grammar consists of a collection of preprogrammed subsystems that include, for example, one responsible for meaning, another responsible for stringing together phrases in a sentence, a third one that deals, among other things, with the kinds of relationships between nouns and pronouns that I discussed earlier. And there are a number of others.

These subsystems are not genetically preprogrammed down to the last detail. If they were, there would be only one human language. But heredity does set rather narrow limits on the possible ways that the rules governing each subsystem’s function can vary. Languages like English and Italian, for example, differ in their choice of genetically permitted variations that exist as options in the universal grammar. You can think of these options as a kind of linguistic menu containing mutually exclusive grammatical possibilities.

For example, languages like Italian have chosen the “null subject” option from the universal grammar menu. In Italian, you can say “left” when you mean “He left” or “She left.” English and French have passed up this option and chosen instead the rule that requires explicit mention of the subject.

QUESTION: What are some other grammatical options on the universal grammar menu?

CHOMSKY: In English, the most important element in every major grammatical category comes first in its phrase. In simple sentences, for example, we say “John hit Bill,” not “John Bill hit.” With adjectives, we say “proud of John,” not “John of proud” with nouns, we say “habit of drinking wine,” not “drinking wine of habit” and with prepositions, we say “to John,” not “John to.” Because heads of grammatical categories always come first English is what is called a head-initial language.

Japanese is a head-final language. In Japanese, you say “John Bill hit.” And instead of prepositions, there are postpositions that follow nouns: “John to,” rather than “to John.” So here’s another parameter the child’s got to learn from experience: Is the language head-initial or head-final?

These grammatical parameters are interconnected. You can’t pick them any more freely than, say, a wine fanatic who insists on white wine with fish and red wine with meat is free to choose any main dish once he’s decided on his wine. But grammars are even more sensitive than this culinary example might suggest. A slight change in just one of the universal grammar’s parameters can have enormous repercussions throughout the language. It can produce an entirely different language. Again, there’s a close parallel to embryology, where a slight shift in the gene mechanisms regulating growth may be all that separates a fertilized egg from developing into a lion rather than a whale.

QUESTION: So what exactly would you say is the grammar of English?

CHOMSKY: The grammar of English is the collection of choices — head-initial rather than head-final, and null subject forbidden, for example — that define one of a limited number of genetically permitted selections from the universal grammar menu of grammatical options. And of course there are all the lexical facts. You just have to learn your language’s vocabulary. The universal grammar doesn’t tell you that “tree” means “tree” in English. But once you’ve learned the vocabulary items and fixed the grammatical parameters for English, the whole system is in place. And the general principles genetically programmed into the language organ just churn away to yield all the particular facts about English grammar.

QUESTION: It sounds as if your present goal is to reach the point where you can define every human language’s grammar simply by specifying its choices from the universal grammar’s menu of options.

CHOMSKY: That’s the kind of work you would hope would soon be done: to take a theory of universal grammar, fix the parameters one way or another, and then deduce from these parameters the grammar of a real human language — Japanese, Swahili, English, or whatnot. This goal is only on the horizon. But I think that it is within our conceptual grasp. Undoubtedly, the principles of universal grammar that we currently theorize are wrong. It would be a miracle if we were right this early along. But the principles are of the right type, and we can now begin to test our present system with complex examples to see what is wrong and to make changes that will improve our theory.

QUESTION: Judging from what you’ve said about language and heredity, it sounds as if you must be sympathetic to the aims of sociobiology. Is that a fair assumption?

CHOMSKY: Well, I think that in some respects the sociobiologists are on the right track. I think it’s true that a good deal of our personal behavior, social behavior, reactions, and so on are the reflection of genetic programs, and I think that it’s a worthwhile enterprise to discover what these programs are. But while I think the general idea behind sociobiology is right, I also think that sociobiologists should be extremely cautious about the specific conclusions they draw from their research. Unfortunately, they often draw conclusions that are remote from evidence or theory.

QUESTION: Many sociobiologists would dispute your note of caution. They claim that science has already gained enough information about the relationships between genes and behavior to permit some shrewd guesses about some of the ways heredity influences human social behavior. What do you say to these claims?

CHOMSKY: I’m very skeptical. I haven’t really studied the newer research in enough detail to make any informed judgment. But as for the earlier work — for example, E. O. Wilson’s Sociobiology — well, about 90% of the book was on nonprimates, and that looked interesting. There was a little bit on primates, which was more questionable. And there was a final chapter on humans that was completely empty. I don’t think Wilson understood what he was talking about in that final chapter. There were real errors in what he did describe in any detail. I don’t even understand why the chapter on humans was tacked on to the book. It didn’t seem to belong.

QUESTION: What do you think about the claim made by Wilson and others that there’s an innate incest taboo in human beings?

CHOMSKY: Sorting out what is and what is not genetically preprogrammed in human behavior is a very difficult task. As I said, I agree with the general approach of sociobiology. I think it’s a reasonable approach. But it’s important to be very cautious in making any claims about the role of heredity in human affairs — especially claims that would have social consequences if they were true. Science is held in such awe in our culture that every scientist has a special responsibility to make clear to the lay audience where his expert knowledge actually yields scientifically verifiable results and where he is guessing, indulging in sheer speculation, or expressing his own personal hopes about the success of his research. This is an important task because the lay audience is in no position to make these distinctions.

QUESTION: Moving to another controversial area in the behavioral sciences, how do you think your views differ from B. F. Skinner’s behaviorist theory of language, learning, and mind?

CHOMSKY: Skinner used to take a relatively extreme position. At one point, he held that, apart from the most rudimentary functions, essentially nothing of importance was genetically programmed in the human brain. Skinner agreed that humans were genetically programmed to see and hear, but that’s about all. Accordingly, he argued that all human behavior was simply a reflection of training and experience. This view can’t possibly be correct. And, in fact, Skinner’s approach has led absolutely nowhere in this area. It has yielded no theoretical knowledge, nontrivial principles as far as I am aware — thus far, at any rate.

CHOMSKY: Because Skinnerian behaviorism is off the wall. It’s as hopeless a project as trying to explain that the onset of puberty results from social training. But I really don’t know whether Skinner still maintains this extreme position.

QUESTION: What about the late Jean Piaget? Where do you stand on his theories of the child’s mental development?

CHOMSKY: Piaget’s position is different: it’s more complex than Skinner’s. Piaget held that the child passes through cognitive states. According to my understanding of the Piagetian literature, Piaget and his supporters were never really clear about what produced a new stage of cognitive development. What they could have said — though they seemed to shy away from it — is that cognitive development is a genetically determined maturational process like puberty, for example. That’s what the Piagetians ought to say. They don’t like this formulation but it seems right to me.

QUESTION: In other words, Piagetians place much more emphasis on the role of experience in cognitive development than you do. Are there other differences as well?

CHOMSKY: Yes. Piagetians maintain that the mind develops as a whole rather than as a modular structure with specific capacities developing in their own ways. This is a possible hypothesis but, in fact, it seems to be extremely wrong.

CHOMSKY: Well, consider the properties that determine the reference of pronouns that we talked about earlier. Once you ferret out these rules for pronouns, they seem to have nothing in common with the logical operations that Piagetians single out as being typical of the early stages of the child’s mental development.

QUESTION: In other words, a four-year-old who may not realize that the amount of water stays the same when you pour the contents of a low, wide glass into a tall, thin container nevertheless displays sophisticated logical abilities in his grasp of the complex rules of English grammar?

CHOMSKY: Yes. And these abilities are independent of the logical capacities measured by tests. There’s just no resemblance between what a child does with blocks and the kind of knowledge he displays of English grammar at the same age. In fact, I think it’s sort of quixotic to expect tight interconnections between language development and growth in other mental domains. By and large, body systems develop in their own ways at their own rates. They interact, but the circulatory system doesn’t wait until the visual system reaches a certain stage of organization before proceeding to imitate the visual system’s organizational complexity. Cognitive growth shouldn’t be different in this respect either. As far as we know, it isn’t.

QUESTION: What about the problem of free will? If genes play a crucial role in structuring the mind’s abilities, is free will an illusion?

CHOMSKY: Well, that’s interesting. Here, I think, I would tend to agree with Descartes. Free will is simply an obvious aspect of human experience. I know — as much as I know that you’re in front of me right now — that I can take my watch and throw it out the window if I feel like it. I also know that I’m not going to do that, because I want the watch. But I could do it if I felt like it. I just know this.

Now, I don’t think there’s any scientific grasp, any hint of an idea, as to how to explain free will. Suppose somebody argues that free will is an illusion. Okay. This could be the case, but I don’t believe that it’s the case. It could be. You have to be open-minded about the possibility. But you’re going to need a very powerful argument to convince me that something as evident as free will is an illusion. Nobody’s offered such an argument or even pretended to offer such an argument.

So where does that leave us? We’re faced with an overwhelmingly self-evident phenomenon that could be an illusion even though there’s no reason to believe that it is an illusion. And we have a body of scientific knowledge that simply doesn’t appear to connect with the problem of free will in any way.

QUESTION: Do you think that science will ever solve the problem of free will?

CHOMSKY: Personally, I don’t think so. People have been trying to solve the problem of free will for thousands of years and they’ve made zero progress. They don’t even have bad ideas about how to answer the question. My hunch — and it’s no more than a guess — is that the answer to the riddle of free will lies in the domain of potential science that the human mind can never master because of the limitations of its genetic structure.

QUESTION: Can you spell out what you mean?

CHOMSKY: We can laugh at a rat that always fails a complicated maze. We can say, “The rat is always going to fail because it can’t look at the maze in the right way. It’s doomed to fail this test forever.” Similarly, some other intelligence, organized along hereditary lines different from our own, could look at the human race and say, “Those humans are always formulating the problem of free will in the wrong way. And the reason they don’t understand the problem has something to do with their biological nature.” It could well turn out that free will is one maze we humans will never solve. We may be like the rat that simply is not designed to solve a certain type of maze and will never do so even if it works on it for ten million years. Look, in principle, there are almost certainly true scientific theories that our genetically determined brain structures will prevent us from ever understanding. Some of these theories may well be ones that we would like to know about.

QUESTION: That’s a discouraging prospect.

CHOMSKY: I don’t see it as much of a reason to despair. In fact, I kind of like the conclusion. I’m not sure that I want free will to be understood.

QUESTION: Do you think that any other human abilities fall into the same mysterious category as free will?

CHOMSKY: In my opinion, all of them do.

CHOMSKY: Take, for example, the aesthetic sense. We like and understand Beethoven because we are humans, with a particular, genetically determined mental constitution. But that same human nature also means there are other conceivable forms of aesthetic expression that will be totally meaningless to us. The same thing is as true for art as it is for science: the fact that we can understand and appreciate certain kinds of art has a flip side. There must be all kinds of domains of artistic achievement that are beyond our mind’s capacities to understand.

QUESTION: Do you think genetic barriers to further progress are becoming obvious in some areas of art and science?

CHOMSKY: You could give an argument that something like this has happened in quite a few fields. It was possible in the late nineteenth century for an intelligent person of much leisure and wealth to be about as much at home as he wanted to be in the arts and sciences. But forty years later that goal had become hopeless. Much of the new work in art and science since then is meaningless to the ordinary person. Take modern music — post-Schšnbergian music. Many artists say that if you don’t understand modern music it’s because you just haven’t listened enough. But modern music wouldn’t be accessible to me if I listened to it forever. Modern music is accessible to professionals, and maybe to people with a special bent, but it’s not accessible to the ordinary person who doesn’t have a particular quirk of mind that enables him to grasp modern music, let alone make him want to deal with it.

QUESTION: And you think that something similar has happened in some scientific fields?

CHOMSKY: I think it has happened in physics and mathematics, for example. There’s this idea, which goes back to the French mathematicians known collectively as Bourbaki, that the development of mathematics was originally the exploration of everyday intuitions of space and number. That is probably somewhat true through the end of the nineteenth century. But I don’t think it’s true now. As for physics, in talking to students at MIT, I notice that many of the very brightest ones, who would have gone into physics twenty years ago, are now going into biology. I think part of the reason for this shift is that there are discoveries to be made in biology that are within the range of an intelligent human being. This may not be true in other areas.

QUESTION: You seem to be saying two things. First, that whatever defines our common human nature will turn out to be a shared set of intuitions that owe much of their strength and character to our common genetic heritage — our species genotype. Second, that the exhaustion of these intuitions in many areas is producing a peculiar kind of artistic and scientific specialization. Further progress in music or mathematics, for example, requires a scientist or artist with an unusual heredity.

CHOMSKY: Well, it’s a different mental constitution — something like being a chess freak or a runner who can do a three-and-one-half minute mile. It’s almost a matter of logic that this change is going to occur sooner or later. Has it happened already? That’s a matter of judgment. It’s a matter of looking at, say, the twentieth century and seeing whether there are signs of this change. Is it the case, for example, that contemporary work in the arts and sciences is no longer part of our common aesthetic and intellectual experience? Well, there are signs. But whether the signs are realistic or whether we are just going through a sort of sea change and something will develop, who knows? Maybe a thousand years from now we’ll know.

QUESTION: Do these possibilities ever make you feel that you’re living in a time of creative stagnation?

CHOMSKY: I don’t really feel that. I think that there are too many possibilities. There’s too much human potential that hasn’t as yet been realized. And don’t forget that the vast majority of the human race hasn’t even entered into the world that we’re claiming may be finished. Who knows what the Third World will contribute to mankind’s store of science and art when it does catch up with the industrialized nations? We are well short of real stagnation or termination but that doesn’t rule out the possibility that one might be able to perceive signs of such a change, or even be able to gain some insight into the ultimate limits of our intelligence by examining these signs.

QUESTION: How do these ideas fit into your choice of linguistics as a career?

CHOMSKY: My choice of linguistics was like most people’s choice of work. It was an accident that depended on whom I met, where I was, and that sort of thing. Linguistics, however, was a fortunate choice for me because I think that linguistics is an area where it is possible to construct a very rich science.

QUESTION: How would you assess your own contributions to linguistics?

CHOMSKY: They seem sort of pre-Galilean.

QUESTION: Like physics before the scientific revolution in the seventeenth century?

CHOMSKY: Yes. In the pre-Galilean period, people were beginning to formulate problems in physics in the right way. The answers weren’t there, but the problems were finally being framed in a way that in retrospect we can see was right.

QUESTION: How “pre-” do you mean? Are you saying that linguistics is about where physics was in the sixteenth century? Or are we going back still further, to Aristotle and to other Greek ideas about physics?

CHOMSKY: We don’t know. It depends, you see, on when the breakthrough comes. But my feeling is that someday someone is going to come along and say, “Look, you guys, you’re on the right track, but you went wrong here. It should have been done this way.” Well, that will be it. Suddenly, things will fall into place.

QUESTION: And then we’ll have a scientific revolution in linguistics?

CHOMSKY: I would think so, although to speak of scientific revolutions occurring outside a small core of the natural sciences is rather misleading. In fact, there was one major scientific revolution in the seventeenth century and there have been a lot of outgrowths from it since then, including biochemistry and molecular biology. But that’s it. Nothing remotely resembling a scientific revolution has ever occurred in the social sciences.

QUESTION: How should a scientist exercise responsibility for the uses of his research?

CHOMSKY: The same way that any human does in any area of life.

QUESTION: Do you think that there are areas in science so potentially vulnerable to social misuse that they should not be pursued?

CHOMSKY: I think there are. For example, research on how to build more effective nuclear weapons. I don’t think that should be pursued.

QUESTION: What about fundamental research — say, basic research in molecular biology that might conceivably give the weapons makers of the next generation a new set of destructive tools?

CHOMSKY: There’s no simple answer to that question. Human beings are responsible for the predictable consequences of their actions. I would stop doing what I was doing if I discovered that I was engaged in an area of scientific research that I thought, under existing social conditions, would lead to, say, oppression, destruction, and pain.

QUESTION: An anachronistic question then: If you were a physicist in 1929, would you have done basic work in nuclear physics even though there was already speculation about the possibility of someday building an atom bomb?

CHOMSKY: It’s not an easy question. It’s tempting to say, “Yes, because we have to understand the world.” On the other hand, it could be that basic research in nuclear physics will lead to the extinction of the human race or to something close to that. So I don’t think a glib answer is possible. Still, if you ask me specifically, I’m sure that my answer would have been yes. I would have done the work just out of interest and curiosity and with the hope that things would somehow work out. But whether that would have been the morally responsible path is not clear.


Watch the video: Γάτες που τραγουδάνε (May 2022).