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Lettris is a curious tetris-clone game where all the bricks have the same square shape but different content. Each square carries a letter. To make squares disappear and save space for other squares you have to assemble English words (left, right, up, down) from the falling squares.
Boggle gives you 3 minutes to find as many words (3 letters or more) as you can in a grid of 16 letters. You can also try the grid of 16 letters. Letters must be adjacent and longer words score better. See if you can get into the grid Hall of Fame !
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Tips: browse the semantic fields (see From ideas to words) in two languages to learn more.
Animal cognition is the name given to the study of the mental capacities of non-human animals. It has developed out of comparative psychology, including the study of animal conditioning and learning, but has also been strongly influenced by research in ethology, behavioral ecology, and evolutionary psychology. The alternative name cognitive ethology is therefore sometimes used; much of what used to be considered under the title of animal intelligence is now thought of under this heading.
Research in animal cognition mostly concerns mammals, especially primates, cetaceans, and elephants, as well as dogs, cats, and rodents. However, research also extends to non-mammalian vertebrates such as birds including parrots, corvids, and pigeons, as well as lizards, snakes, and fish, even to invertebrates such as cephalopods, spiders, and insects.
The behavior of non-human animals has captivated human imagination from antiquity, and over the centuries many writers have speculated about the animal mind, or its absence, as Descartes would have it. Speculation about animal intelligence gradually yielded to scientific study after Darwin placed humans and animals on a continuum, although Darwin’s largely anecdotal approach to the topic would not pass scientific muster later on. Unsatisfied with the anecdotal method of Darwin and his protégé Romanes, E. L. Thorndike brought animal behavior into the laboratory for objective scrutiny. Thorndike’s careful observations of the escape of cats, dogs, and chicks from puzzle boxes led him to conclude that intelligent behavior may be compounded of simple associations and that inference to animal reason, insight, or consciousness is unnecessary and misleading. At about the same time, I. P. Pavlov began his seminal studies of conditioned reflexes in dogs. Pavlov quickly abandoned attempts to infer canine mental processes; such attempts, he said, led only to disagreement and confusion. He was, however, willing to propose unseen physiological processes that might explain his observations.
The work of Thorndike, Pavlov and a little later of the outspoken behaviorist John B. Watson set the direction of much research on animal behavior for more than half a century. During this time there was considerable progress in understanding simple associations; notably, around 1930 the differences between Thorndike’s instrumental (or operant) conditioning and Pavlov’s classical (or Pavlovian) conditioning were clarified, first by Miller and Kanorski, and then by B. F. Skinner. Many experiments on conditioning followed; they generated some complex theories, but they made little or no reference to intervening mental processes. Probably the most explicit dismissal of the idea that mental processes control behavior was the radical behaviorism of Skinner. This view seeks to explain behavior, including "private events" like mental images, solely by reference to the environmental contingencies impinging on the human or animal.
Despite the predominantly behaviorist orientation of research before 1960, the rejection of mental processes in animals was not universal during those years. Influential exceptions included, for example, Wolfgang Köhler and his insightful chimpanzees and Edward Tolman whose proposed cognitive map was a significant contribution to subsequent cognitive research in both humans and animals.
Beginning around 1960, a “cognitive revolution” in research on humans gradually spurred a similar transformation of research with animals. Inference to processes not directly observable became acceptable and then commonplace. An important proponent of this shift in thinking was Donald O. Hebb, who argued that “mind” is simply a name for processes in the head that control complex behavior, and that it is both necessary and possible to infer those processes from behavior Animals came to be seen as “goal seeking agents that acquire, store, retrieve, and internally process information at many levels of cognitive complexity.”. However, it is interesting to note that many cognitive experiments with animals made, and still make, ingenious use of conditioning methods pioneered by Thorndike and Pavlov.
The scientific status of “consciousness” in animals continues to be hotly debated. Serious consideration of conscious thought in animals has been advocated by some (e.g., Donald Griffin), but the larger research community has been notably cool to such suggestions
The acceleration of research on animal cognition in the last 50 years has led to a rapid expansion in the variety of species studied and methods employed. The remarkable behavior of large-brained animals such as primates and cetacea has claimed special attention, but all sorts of mammals large and small, birds, fish, ants, bees, and others have been brought into the laboratory or observed in carefully controlled field studies. In the laboratory, animals push levers, pull strings, dig for food, swim in water mazes, or respond to images on computer screens in discrimination, attention, memory, and categorization experiments. Careful field studies explore memory for food caches, navigation by stars, communication, tool use, identification of conspecifics, and many other matters. Studies often focus on the behavior of animals in their natural environments and discuss the putative function of the behavior for the propagation and survival of the species. These developments reflect an increased cross-fertilization from related fields such as ethology and behavioral biology. Also, contributions from behavioral neuroscience are beginning to clarify the physiological substrate of some inferred mental process.
Several long term research projects have captured a good deal of attention. These include ape-language experiments such as the Washoe project and project Nim. Other animal projects include Irene Pepperberg's extended series of studies with the African Gray Parrot Alex, Louis Herman's work with bottlenosed dolphins, and studies of long-term memory in pigeons in which birds were shown to remember pictures for periods of several years.
Some researchers have made effective use of a Piagetian methodology, taking tasks which human children are known to master at different stages of development, and investigating which of them can be performed by particular species. Others have been inspired by concerns for animal welfare and the management of domestic species: for example Temple Grandin has harnessed her unique expertise in animal welfare and the ethical treatment of farm livestock to highlight underlying similarities between humans and other animals. From a methodological point of view, one of the main risks in this sort of work is anthropomorphism, the tendency to interpret an animal's behavior in terms of human feelings, thoughts, and motivations.
Human and animal cognition have much in common, and this is reflected in the research summarized below; most of the headings found here might also appear in an article on human cognition. Of course, research in the two also differs in important respects. Notably, much research with humans either studies or involves language, and much research with animals is related directly or indirectly to behaviors important to survival in natural settings. Following are summaries of some of the major areas of research in animal cognition.
Like humans, non-human animals process the data from eyes, ears, and other sensory organs to achieve a useful picture of the environment. Perceptual processes have been studied in many species, with results that are often similar to those in humans. Equally interesting are those perceptual processes that differ from, or go beyond those found in humans, such as echolocation in bats and dolphins, or skin sensitivity in fish.
Much of what is happening in the world at any moment is irrelevant to current behavior. Attention refers to mental processes that select relevant information, inhibit irrelevant information, and switch among these as the situation demands. Often the selective process is tuned before relevant information appears; such expectation makes for rapid selection of key stimuli when they become available. A large body of research has explored the way attention and expectation affect the behavior of non-human animals, and much of this work suggests that attention operates in birds and mammals in much the same way that it does in humans.
The following paragraphs contain brief accounts of several experiments. These are intended to give the reader a bit of the flavor of research on attention, but they barely scratch the surface, and readers should consult the references for many other experiments. Also, one must interpret putative “attentional” effects with caution, because they can often be accounted for in several different ways. For example, lack of response to a current stimulus might reflect inattention, but it might also reflect lack of motivation, or result from past learning that suppresses response to that stimulus or promotes an alternative response. Most experiments include control conditions intended to exclude as many alternative interpretations as possible.
Animals trained to discriminate between two stimuli, say black versus white, can be said to attend to the “brightness dimension,” but this says little about whether this dimension is selected in preference to others. More enlightenment comes from experiments that allow the animal to choose from several alternatives. For example, several studies have shown that performance is better on, for example, a color discrimination (e.g. blue vs green) after the animal has learned another color discrimination (e.g. red vs orange) than it is after training on a different dimension such as an X shape versus and O shape. The reverse effect happens after training on forms. Thus, the earlier learning appears to affect which dimension, color or form, the animal will attend to.
Other experiments have shown that after animals have learned to respond to one aspect of the environment responsiveness to other aspects is suppressed. In “blocking”, for example, an animal is conditioned to respond to one stimulus (“A”) by pairing that stimulus with reward or punishment. After the animal responds consistently to A, a second stimulus (“B”) accompanies A on additional training trials. Later tests with the B stimulus alone elicit little response, suggesting that learning about B has been blocked by prior learning about A . This result supports the hypothesis that stimuli are neglected if they fail to provide new information. Thus, in the experiment just cited, the animal failed to attend to B because B added no information to that supplied by A. If true, this interpretation is an important insight into attentional processing, but this conclusion remains uncertain because blocking and several related phenomena can be explained by models of conditioning that do not invoke attention.
Casual observation suggests that attention is a limited resource and is not all-or-none: the more attention is devoted to one aspect or dimension of the environment, the less is available for others. In preparing a meal you may divide your attention among a number of things, but a sudden spill may distract you from a falling souffle. A number of experiments have studied this sort of thing in animals. For example, in one experiment, a tone and a light came on simultaneously. The pigeon subjects gained reward only by choosing the correct combination of the two dimensions (a high pitch together with a yellow light). The birds did fairly well at this task, presumably by dividing attention between the two dimensions. When only one of the stimulus dimensions varied, while the other was held at its rewarded value, discrimination improved on the variable stimulus, and later tests showed that discrimination had also gotten worse on the alternative stimulus dimension. These outcomes are consistent with the idea that attention is a limited resource that can more or less focused among incoming stimuli.
As noted above, attention functions to select information that is of special use to the animal. Visual search typically calls for this sort of selection, and search tasks have been used extensively in both humans and animals to determine the characteristics of attentional selection and the factors that control it.
Experimental research on visual search in animals was initially prompted by field observations published by Luc Tinbergen (1960). Tinbergen observed that birds are selective when foraging for insects. For example, he found that birds tended to catch the same type of insect repeatedly even though several types were available. Tinbergen suggested that this prey selection was caused by an attentional bias that improved detection of one type of insect while suppressing detection of others. This “attentional priming” is commonly said to result from a pretrial activation of a mental representation of the attended object, which Tinbergen called a “searching image.”
Tinbergen’s field observations on priming have been supported by a number of experiments. For example, Pietrewicz and Kamil (1977, 1979)  presented blue jays with pictures of tree trunks upon which rested either a moth of species A, a moth of species B, or no moth at all. The birds were rewarded for pecks at a picture showing a moth. Crucially, the probability with which a particular species of moth was detected was higher after repeated trials with that species (e.g. A, A, A,…) than it was after a mixture of trials (e.g. A, B, B, A, B, A, A…). These results suggest again that sequential encounters with an object can establish an attentional predisposition to see the object.
Another way to produce attentional priming in search is to provide an advance signal that is associated with the target. For example, if you hear a song sparrow you may be predisposed to detect a song sparrow in a shrub, or among other birds. A number of experiments have reproduced this effect in animal subjects.
Still other experiments have explored nature of stimulus factors that affect the speed and accuracy of visual search. For example, the time taken to find a single target increases as the number of items in the visual field increases. This rise in RT is steep if the distracters are similar to the target, less steep if they are dissimilar, and may not occur if the distracters are very different in from the target inform or color.
Fundamental but difficult to define, the concept of “concept” was discussed for hundreds of years by philosophers before it became a focus of psychological study. Concepts enable humans and animals to organize the world into functional groups; the groups may be composed of perceptually similar objects or events, diverse things that have a common function, relationships such as same versus different, or relations among relations such as analogies. Extensive discussions on these matters together with many references may be found in Shettleworth (2010) Wasserman and Zentall (2006)  and in Zentall et al. (2008). The latter is freely available online 
Most work on animal concepts has been done with visual stimuli, which can easily be constructed and presented in great variety, but auditory and other stimuli have been used as well. Pigeons have been widely used, for they have excellent vision and are readily conditioned to respond to visual targets; other birds and a number of other animals have been studied as well. In a typical experiment, a bird or other animal confronts a computer monitor on which a large number of pictures appear one by one, and the subject gets a reward for pecking or touching a picture of a category item and no reward for non-category items. Alternatively, a subject may be offered a choice between two or more pictures. Many experiments end with the presentation of items never seen before; successful sorting of these items shows that the animal has not simply learned many specific stimulus-response associations. A related method, sometimes used to study relational concepts, is matching-to-sample. In this task an animal sees one stimulus and then chooses between two or more alternatives, one of which is the same as the first; the animal is then rewarded for choosing the matching stimulus.
Perceptual categorization is said to occur when a person or animal responds in a similar way to a range of stimuli that share common features. For example, a squirrel climbs a tree when it sees Rex, Shep, or Trixie, which suggests that it categorizes all three as something to avoid. This sorting of instances into groups is crucial to survival. Among other things, an animal must categorize if it is to apply learning about one object (e.g. Rex bit me) to new instances of that category (dogs may bite).
Many animals readily classify objects by perceived differences in form or color. For example, bees or pigeons quickly learn to choose any red object and reject any green object if red leads to reward and green does not. Seemingly much more difficult is an animal’s ability to categorize natural objects that vary a great deal in color and form even while belonging to the same group. In a classic study Richard J. Herrnstein trained pigeons to respond to the presence or absence of human beings in photographs. The birds readily learned to peck at photos containing partial or full views of humans and to avoid photos with no human, despite great differences in the form, size, and color of both the humans displayed and in the non-human pictures. In follow-up studies pigeons categorized other natural objects (e.g. trees) and after training they were are able without reward to sort photos that had never been seen before . Similar work has been done with natural auditory categories, for example, bird songs 
Perceptually unrelated stimuli may come to be responded to as members of a class if they have a common use or lead to common consequences. An oft-cited study by Vaughan (1988) provides an example. Vaughan divided a large set of unrelated pictures into two arbitrary sets, A and B. Pigeons got food for pecking at pictures in set A but not for pecks at pictures in set B. After they had learned this task fairly well, the outcome was reversed: items in set B led to food and items in set A did not. Then the outcome was reversed again, and then again, and so on. Vaughan found that after 20 or more reversals, associating reward with a few pictures in one set caused the birds to respond to the other pictures in that set without further reward, as if they were thinking “if these pictures in set A bring food, the others in set A must also bring food.” That is, the birds now categorized the pictures in each set as functionally equivalent. Several other procedures have yielded similar results.
When tested in a simple stimulus matching-to-sample task (described above) many animals readily learn specific item combinations, such as “touch red if the sample is red, touch green if the sample is green.” But this does not demonstrate that they distinguish between “same” and “different” as general concepts. Better evidence is provided if, after training, an animal successfully makes a choice that matches a novel sample that it has never seen before. Monkeys and chimpanzees do learn to do this, as do pigeons if they are given a great deal of practice with many different stimuli. However, because the sample is presented first, successful matching might mean that the animal is simply choosing the most recently seen “familiar” item rather than the conceptually “same” item. A number of studies have attempted to distinguish these possibilities, with mixed results.
The use of rules has sometimes been considered an ability restricted to humans, but a number of experiments have shown evidence of simple rule learning in primates (e.g. ) and also in other animals. Much of the evidence has come from studies of sequence learning in which the “rule” consists of the order in which a series of events occurs. Rule use is shown if the animal learns to discriminate different orders of events and transfers this discrimination to new events arranged in the same order. For example, Murphy et al. (2008)  trained rats to discriminate between visual sequences. For one group ABA and BAB were rewarded, where A=”bright light” and B=”dim light." Other stimulus triplets were not rewarded. The rats learned the visual sequence, although both bright and dim lights were equally associated with reward. More importantly, in a second experiment with auditory stimuli, rats responded correctly to sequences of novel stimuli that were arranged in the same order as those previously learned. Similar sequence learning has been demonstrated in birds and other animals as well.
The categories that have been developed to analyze human memory (short term memory, long term memory, working memory) have been applied to the study of animal memory, and some of the phenomena characteristic of human short term memory (e.g. the serial position effect) have been detected in animals, particularly monkeys. However most progress has been made in the analysis of spatial memory, partly in relation to studies of the physiological basis of spatial memory and the role of the hippocampus, and partly in relation to scatter-hoarder animals such as Clark's Nutcracker, certain jays, tits and certain squirrels, whose ecological niches require them to remember the locations of thousands of caches, often following radical changes in the environment.
Memory has been widely investigated in foraging honeybees, Apis mellifera, which use both transient short-term working memory that is non-feeder specific and a feeder specific long-term reference memory. Memory induced in a free-flying honeybee by a single learning trial lasts for days and, by three learning trials, for a lifetime. Slugs, Limax flavus, have a short-term memory of approximately 1 min and long-term memory of 1 month.
The ability to properly navigate and search through the environment is a critical task for many animals. Much of this movement seems to be directed, in the sense that the animals in question seem to be purposely moving towards a particular spot for a reason. Purposeful navigation implies some sort of cognitive map of the external environment. Research in this area (Brown & Cook, 2006) has focused on such diffuse topics as landmark and beacon use by ants and bees, the encoding and use of geometric properties of the environment by pigeons, and the ability of rats to represent a spatial pattern in either radial arm mazes or water mazes. Sometimes included under the envelope of spatial cognition is work in humans and other animals in visual search tasks, which aim to experimentally address questions about searching through one's environment for a particular object.
The behavior of most animals is synchronized with the earth's daily light-dark cycle. Thus, many animals are active during the day, others are active at night, still others near dawn and dusk. Though one might think that these "circadian rhythms" are controlled simply by the presence or absence of light, nearly every animal that has been studied has been shown to have a "biological clock" that yields cycles of activity even when the animal is in constant illumination or darkness. Circadian rhythms are so automatic and fundamental to living things - they occur even in plants  - that they are usually discussed separately from cognitive processes, and the reader is referred to the main article (Circadian rhythms) for further information.
Survival often depends on an animal's ability to time intervals. For example, rufous hummingbirds feed on the nectar of flowers, and they often return to the same flower, but only after the flower had had enough time to replenish its supply of nectar. In one experiment hummingbirds fed on artificial flowers that quickly emptied of nectar but were refilled at some fixed time (e.g. twenty minutes) later. The birds learned to come back to the flowers at about the right time, learning the refill rates of up to eight separate flowers and remembering how long ago they had visited each one.
The details of interval timing have been studied in a number of species. One of the most common methods is the "peak procedure". In a typical experiment, a rat in an operant chamber presses a lever for food. A light comes on, a lever-press brings a food pellet at a fixed later time, say 10 seconds, and then the light goes off. Timing is measured during occasional test trials on which no food is presented and the light stays on. On these test trials the rat presses the lever more and more until about 10 sec and then, when no food comes, gradually stops pressing. The time at which the rat presses most on these test trials is taken to be its estimate of the payoff time.
Experiments using the peak procedure and other methods have shown that animals can time short intervals quite exactly, can time more than one event at once, and can integrate time with spatial and other cues. Such tests have also been used for quantitative tests of theories of animal timing, though no one theory has yet gained unanimous agreement. 
Some species, such as the Woodpecker Finch of the Galapagos Islands, use particular tools as an essential part of their foraging behavior. However, these behaviors are often quite inflexible and cannot be applied effectively in new situations. Several species have now been shown to be capable of more flexible tool use. A well known example is Jane Goodall's observation of chimpanzees "fishing" for termites in their natural environment, and captive great apes are often observed to use tools effectively; several species of corvids have also been trained to use tools in controlled experiments, or use bread crumbs for bait-fishing[unreliable source?].
Research in 2007 shows that chimpanzees in the Fongoli savannah sharpen sticks to use as spears when hunting, considered the first evidence of systematic use of weapons in a species other than humans.
Closely related to tool use is the study of reasoning and problem solving. It has been observed that the manner in which chimpanzees solve problems, such as that of retrieving bananas positioned out of reach, is not through trial-and-error. Instead, they were observed to proceed in a manner that was “unwaveringly purposeful.”
It is clear that animals of quite a range of species are capable of solving a range of problems that are argued to involve abstract reasoning; modern research has tended to show that the performances of Wolfgang Köhler's chimpanzees, who could achieve spontaneous solutions to problems without training, were by no means unique to that species, and that apparently similar behavior can be found in animals usually thought of as much less intelligent, if appropriate training is given. Causal reasoning has also been observed in rooks and New Caledonian crows.
The modeling of human language in animals is known as animal language research. In addition to the ape-language experiments mentioned above, there have also been more or less successful attempts to teach language or language-like behavior to some non-primate species, including parrots and Great Spotted Woodpeckers. Arguing from his own results with the animal Nim Chimpsky and his analysis of others results, Herbert Terrace criticized the idea that chimps can produce new sentences. Shortly thereafter Louis Herman published research on artificial language comprehension in the bottlenosed dolphin.(Herman, Richards, & Wolz, 1984). Though this sort research has been controversial, especially among cognitive linguists, many researchers agree that many animals can understand the meaning of individual words, and some may understand simple sentences and syntactic variations, but there is little evidence that any animal can produce new strings of symbols that correspond to new sentences.
The sense in which animals can be said to have consciousness or a self-concept has been hotly debated; it is often referred to as the debate over animal minds. The best known research technique in this area is the mirror test devised by Gordon G. Gallup, in which an animal's skin is marked in some way while it is asleep or sedated, and it is then allowed to see its reflection in a mirror; if the animal spontaneously directs grooming behavior towards the mark, that is taken as an indication that it is aware of itself. Self-awareness, by this criterion, has been reported for chimpanzees and also for other great apes, the European Magpie, some cetaceans and a solitary elephant, but not for monkeys. The mirror test has attracted controversy among some researchers because it is entirely focused on vision, the primary sense in humans, while other species rely more heavily on other senses such as the olfactory sense in dogs.
It has been suggested that metacognition in some animals provides some evidence for cognitive self-awareness. The great apes, dolphins, and rhesus monkeys have demonstrated the ability to monitor their own mental states and use an "I don't know" response to avoid answering difficult questions. These species might also be aware of the strength of their memories. Unlike the mirror test, which relies primarily on body images and bodily self-awareness, uncertainty monitoring paradigms are focused on the kinds of mental states that might be linked to mental self-awareness.
A different approach to determine whether a non-human animal is conscious derives from passive speech research with a macaw (see Arielle). Some researchers propose that by passively listening to an animal's voluntary speech, it is possible to learn about the thoughts of another creature and to determine that the speaker is conscious. This type of research was originally used to investigate a child's crib speech by Weir (1962) and in investigations of early speech in children by Greenfield and others (1976). With speech-capable birds, the methods of passive-speech research open a new avenue for investigation.
Some animals are capable of distinguishing between different amounts and rudimentary counting. Elephants have been known to perform simple arithmetic, and rhesus monkeys can count. Young chimpanzees have outperformed human college students in a task requiring remembering numbers, although this task involved memory for numbers as different visual forms, not numbers as different quantities.
Ants are able to use quantitative values and transmit this information. For instance, ants of several species are able to estimate quite precisely numbers of encounters with members of other colonies on their feeding territories. Numeracy has been described in the yellow mealworm beetle, Tenebrio molitor, and the honeybee.
Some animals such as dogs, horses, great apes, and (more recently) dolphins and parrots are typically thought by laymen[clarification needed] as intelligent in ways that some other species of animal are not. For example, crows are attributed with human-like intelligence in the folklore of many cultures. A number of recent survey studies have demonstrated the consistency of these rankings between people in a given culture and indeed to a considerable extent across cultures.
A more fruitful approach has been to recognize that different animals may have different kinds of cognitive processes, which are better understood in terms of the ways in which they are cognitively adapted to their different ecological niches, than by positing any kind of hierarchy. (See Shettleworth (1998), Reznikova (2007).)
One question that can be asked coherently is how far different species are intelligent in the same ways as humans are, i.e., are their cognitive processes similar to ours. Not surprisingly, our closest biological relatives, the great apes, tend to do best on such an assessment. Among the birds, corvids and parrots have typically been found to perform well. Despite ambitious claims, evidence of unusually high human-like intelligence among cetaceans is patchy, partly because the cost and difficulty of carrying out research with marine mammals mean that experiments frequently suffer from small sample sizes and inadequate controls and replication. Octopodes have also been shown to exhibit a number of higher-level skills such as tool use, but the amount of research on cephalopod intelligence is still limited.