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Wie intelligent sind Katzen Copyright 2004-2014, Sarah Hartwell Katzebesitzer behaupten häufig, dass Katzen zu intelligent sind, um die Art der Tricks zu tun, die Hunde bereitwillig tun. Andere glauben, Katzen sind unintelligent, weil es schwieriger ist, sie zu trainieren, um Tricks zu tun. In diesem Artikel (auf 2 Seiten) möchte ich erklären, einige dieser Unterschiede und erkunden feline Intelligenz und die Einschränkungen auf feline Intelligenz. Dies bedeutet auch, wie Katzen sehen die Welt und an einigen Aspekten der natürlichen Katze Verhalten. Leider für Katzen, sie sind oft nicht-einwilligende Teilnehmer in chirurgisch aufdringliche Experimente zu beurteilen, Lernen und Intelligenz. Die Menschen scheinen zu fühlen, es notwendig, um die Intelligenz der Tiere als eine Möglichkeit zur Stärkung unseres eigenen Gefühl der Überlegenheit zu bewerten und die Katze wurde ein beliebtes Thema für das Studium der Lern-und Gehirnfunktion für mehr als ein Jahrhundert. Viele Tests setzen Elektroden in Katzen Gehirne entweder zur Überwachung der Hirnaktivität oder stimulieren bestimmte Verhalten andere mit bewußter Verletzung des Gehirns zu sehen, ob Lernfähigkeit oder Intelligenz betroffen ist. Die meisten dieser Testpersonen werden getötet und ihre Gehirne weiter seziert, um nach Beweisen für Veränderungen des Gehirns, die aus dem Lernen zu suchen. Ich persönlich halte diese Experimente grausam und unentgeltlich (deren medizinischer Nutzen für den Menschen ist zu oft zweifelhaft), und obwohl einige dieser Experimente hier verwiesen wird, unterstützt Messybeast diese Form des Experimentierens nicht. In den letzten Jahren gab es eine Zunahme der Tests in einer natürlicheren Heimat-Umgebung und nicht in einem künstlichen Laborumfeld. Während Laborbedingungen leichter manipuliert werden, bringen sie nicht das Beste in Testpersonen und geben irreführende Ergebnisse. Bessere Tests berücksichtigen auch die angeborenen Verhaltensweisen und Instinkte von Tieren, die zuvor in klassischen Labortests gegen Katzen gezählt haben. Dieser Artikel berücksichtigt auch einige der anecdotal Beweise für Intelligenz von den Eigentümern berichtet, aber häufig von Laborforschern entlassen. Da Katzen in der Natur wirken, ist es sinnvoll, sie in ihrer eigenen Umgebung und nicht nur in hoch kontrollierten, künstlichen Laborumgebungen zu beobachten. Die Wahrheit über Katzen und Hunde Hunde wurden ausgebildet, um Schutz zu schützen, Herde, Jagd, Suchrescue, Unterstützung (z. B. Blindenhunde) und führen Zirkustricks, Gehorsam oder Agility-Klassen. Für viele ist dies ein klares Zeichen für ihre Intelligenz und die Überlegenheit der Hunde-Intellekt über katzenhafte Intelligenz. Katzen wurden ausgebildet, um Tricks ausführen, wie auf Filmen oder TV-Werbung gesehen, haben aber nicht das gleiche Repertoire wie Hunde. Dies führt zu den offensichtlichen Schlussfolgerungen, dass Katzen weder intelligent genug noch kooperativ genug sind, um trainiert zu werden. Zum Beispiel, in Experimenten, wo Katzen und Hunde erwartet wurden, um Labyrinthe zu navigieren, die meisten Katzen schlecht durchgeführt. Hunde lernten bald, das Labyrinth zu navigieren und die Belohnung zu erreichen. Katzen setzten sich und wusch sich. Sie untersuchten Sackgassen. Sie vervollständigten das Labyrinth in der zugewiesenen Zeit nicht und wurden daher als nicht bestanden angesehen. Eifrig-to-please Hunde gelernt, dass sie eine Belohnung für das Lernen. Katzen sind nicht so motiviert. Als Opportunisten, untersucht jede Sackgasse Sinn für die Katze - schließlich, wer weiß, wo Beute könnte in der realen Welt verstecken Sitting und Waschen ist eine Verschiebung Aktivität, wenn eine Katze unsicher ist. Die meisten der Eckzähne Aktivitäten zitiert früher auf manipulierende Eckzahn soziale Instinkte. Hunde leben, jagen und spielen in hierarchischen sozialen Paketen unter der Leitung von einem Alpha-Männchen und Alpha-Weibchen. Sie kooperieren häufig bei der Aufzucht der Alpha-Paare jung und kooperieren, um große Beute zu jagen. Jugendliche betteln submissiv nach Nahrung von Erwachsenen. Sie sind bestrebt, die Packmate zufrieden zu stellen, um ein Teil ihrer Packung zu bleiben, und sie zeigen die Unterordnung gegenüber höherrangigen Tieren. Inländische Hunde Ansichtmenschen als dominierende Satzmitglieder, also sind sie eifrig, uns zu gefallen. Zusätzlich wurden Hunde selektiv über Hunderte von Jahren gezüchtet, um einige Eigenschaften zu erhöhen und andere zu verringern oder zu beseitigen. Katzen haben mittlerweile eine andere soziale Struktur. Wo Nahrung reichlich vorhanden ist, sind sie größtenteils einsam, obwohl Frauen, normalerweise in Verbindung stehende, soziale Gruppen bilden können. Männer neigen dazu, auf der Suche nach Weibchen zu wandern, anstatt als Teil einer Gruppe zu bleiben. Wo Nahrungsquellen lokalisiert sind (z. B. ein Müllkippe), bilden sie Kolonien, aber die soziale Struktur ähnelt eher der von Löwen - Gruppen von Frauen, die gemeinsam kooperativ aufziehen können. Anders als Löwen, Katzen nicht in der Regel jagen Beute größer als sich selbst und selten jagen in Paaren oder Gruppen. Katzen sind daher unabhängig und nicht wirklich sozial und haben wenig oder keine Notwendigkeit, mit anderen Katzen zusammenzuarbeiten. Feline Zusammenarbeit mit Menschen ist begrenzt, es sei denn, es dient den einzelnen Katzen Interessen eine Aufgabe zu erfüllen. Während Hunde für Nützlichkeit gezüchtet worden sind, wurden Katzen nur für Aussehen gezüchtet. Hunde sind weitgehend durch den Packungslebensinstinkt motiviert, d. H. Sie werden lediglich für das Lob und die Akzeptanz, die von dem dominierenden Packungsmitglied (d. h. dem Besitzer oder Trainer) ausgegeben werden, durchgeführt. Sie werden auch durchführen, weil sie in der Wildnis riskieren, aus einer Packung vertrieben oder zur paria Position degradiert zu werden. Katzen werden nicht durch soziale Statusfaktoren motiviert. Um eine Katze zu trainieren, müssen Sie herausfinden, was sie motiviert. Normalerweise bedeutet dies Lebensmittel, oder zumindest Konditionierung, dass es das Versprechen der Nahrung am Ende der Sitzung. Selbst dann, Katzen sind nicht von Lebensmitteln in der gleichen Weise wie Hunde motiviert - wenn die Erreichung der Nahrung Belohnung ist zu viel harte Arbeit, Katzen häufig schneiden ihre Verluste und gehen auf der Suche nach leichter Beute. In der Wildnis, macht es keinen Sinn für einen Solo-Jäger, mehr Energie auf das Finden oder Töten von Beute aufzuwenden, als es vom Essen dieser Beute erhält. Während Hunde verfolgen und verfolgen über große Entfernungen und verschleißen ihren Steinbruch, Katzen Jagd durch Warten im Hinterhalt und Verfolgung Beute für kurze Entfernungen nur. Verhungern einer Katze macht es nicht einfacher, entweder zu trainieren, sind Katzen besser als Hunde bei der Ignorierung von Hungerstörungen. Für junge Katzen, obwohl Nahrung eine leistungsfähige Belohnung ist, können Tätigkeiten wie Manipulation der einfachen Gegenstände wie ein Ball oder scrunched herauf Papier oder die Wahrscheinlichkeit, einen unbekannten Raum zu erforschen, angemessene Belohnungen in einigen Aufgaben sein. Es gibt immer einige Katzen, die nicht nur leicht lernen, sondern scheinen Lernen zu genießen, obwohl dies die Ausnahme eher als die Regel sind. Weil wir Intelligenz beurteilen, indem wir andere Kreaturen mit uns vergleichen, beschreiben viele populäre Berichte über Katzenverhalten das Lernen, als ob Katzen geistig defekte Menschen sind, anstatt hochspezialisierte Fleischfresser. Im Jahr 1915 schrieb L T Hobhouse (Professor für Soziologie an der University of London): Ich hatte einmal eine Katze, die an der Tür klopfte, indem sie die Matte draußen anhob und sie fallen ließ. Die gemeinsame Rechnung dieses Vorgangs wäre, dass die Katze es getan, um zu bekommen. Es geht davon aus, dass die Katzen Aktion durch sein Ende bestimmt werden. Ist das gemeinsame Konto falsch Wir wollen es testen, indem wir Erklärungen versuchen, die wir bei den primitiveren Operationen der Erfahrung gefunden haben. Erstens, können wir erklären, die Katzen Aktion durch die Assoziation der Ideen Die offensichtliche Schwierigkeit hier ist, die Idee oder Wahrnehmung, die den Prozess geht zu finden. Der Anblick einer Tür oder einer Matte war, soweit ich weiß, in der Katzenerfahrung mit der Handlung verbunden, die sie bis zu ihrer Ausführung durchführte. Wenn es Vereinigung, es muss gesagt werden, um rückläufig zu arbeiten. Die Katze assoziiert die Idee, sich mit der von jemandem an die Tür zu kommen, und dies wieder mit der Herstellung von einem Klang, um Aufmerksamkeit zu erregen, und so weiter. Solch eine Reihe von Assoziationen so gut angepasst bedeutet in Wirklichkeit eine Reihe von verwandten Elementen durch das Tier ergriffen und verwendet, um seine Handlung zu bestimmen. Ideen von Personen, Türen öffnen, Aufmerksamkeit erregen und so weiter, hätten keine Wirkung, wenn sie nicht an die vorhandenen Umstände gebunden sind. Wenn die Katze überhaupt so abstrakte Vorstellungen hat, muß sie etwas mehr haben - nämlich die Kraft, sie auf die gegenwärtige Wahrnehmung anzuwenden. Die Vorstellungen, Aufmerksamkeit zu erregen und die Matte fallen zu lassen, müssen irgendwie zusammengebracht werden. Wenn es sich bei dem Prozess um eine Assoziation handelt, ist es ein seltsamer Zufall, dass die richtigen Assoziationen gewählt werden. Wenn die Katze auf eine Reihe von Assoziationen anfing, die von den Leuten im Raum begannen, konnte sie so leicht gehen, auf den Freuden des Einsteigens zu wohnen, von, wie sie einen Bissen Fische von einem oder von einer Saucerful Creme von einem anderen koaxieren würde , Und ihre Zeit in der müßigen Träumerei zu verbringen. Aber sie vermeidet diese Assoziationen und wählt diejenigen, die zu ihrem Zweck geeignet sind. Kurz, wir finden Zeichen auf der einen Seite der Anwendung von Ideen, auf der anderen der Auswahl. Beide Merkmale zeigen eine höhere Stufe als diejenige der bloßen Assoziation. Hobhouse deutete seine Katzen Verhalten als mit zielgerichteten Elemente, aber er bietet eine alternative Verhaltens-Erklärung: eine Verbindung zwischen der Motivation und Freude, durch die Tür, und die Aktion des Hebens und Fallenlassen der Matte. Frühe Stimulus-Response-Theorien Frühe Psychologen glaubten, dass alle Verhalten aus Stimulus-Response-Assoziationen. Ihre Theorien hatten keinen Raum für Denken, Bewusstsein, Instinkt, angeborene Verhaltensweisen oder eine Prädisposition für bestimmte Verhaltensweisen. Auf ihrer einfachsten Ebene beinhaltet das Lernen die Verknüpfung (Assoziierung) bisher unzusammenhängender Stimuli oder Aktionen und die Konsequenzen dieser Aktionen. Viele wirbellose Tiere sind in der Lage, solche Assoziationen zu bilden. Frühe Forscher hatten festverdrahtete Verhaltensweisen entdeckt, aber extrapoliert, dass alle Verhaltensweisen einfache Stimulus-Response-Reflexe waren. Im Jahr 1966 schrieb Fernand Mery: Amerikanische Neurophysiologen der Yale University sind auf einem anderen Gebiet erfolgreich. Dr. Joseacute Delgado installierte eine komplette Reihe von Elektroden im Gehirn einer Katze. Die Operation fand unter vollständiger Anästhesie statt, und als die Katze aufwachte, wußte er nichts über das Geschehene. Experimente begannen erst, wenn alles perfekt geheilt war. Es ist unmöglich, nicht für diese Laborkatze zu fühlen, aber diejenigen, die anwesend waren und an dem Experiment teilnahmen, bestätigen, dass er keinen Versuch gemacht hat, zu entkommen. Er schien die Situation zu schätzen, als würdigte er das Interesse, das in ihm genommen wurde. Er wußte nichts von dem chirurgischen Eingriff, dem er vorgelegt worden war, und verhielt sich so, als gehorchte er einem einfachen freundlichen Drill: er wurde ein Roboter. Um den Hals herum kann man einen kleinen Kragen unterscheiden, an dem ein Empfängersatz mit winzigen Sendern befestigt ist, an denen ordentliche Silberdrähte befestigt sind, die jeweils einer zerebralen Lokalisation entsprechen und in seinem Pelz verschwinden. Auf diese Weise kann die Katze, wenn sie im selben Raum oder Hunderte von Kilometern entfernt ist, und durch ein Funkgespräch die Notwendigkeit zu trinken erleben (und er hat Wasser und Milch zur Verfügung gestellt), um zu essen (er kann wählen Was er will), um zu juckeln (und kann sich so viel kratzen, wie er will). Es ist sogar möglich, einen solchen und einen solchen Theil der Frontallappen zu stimulieren, in ihm eine überwältigende Zuneigung oder eine aggressive Antipathie zu provozieren und im nächsten Augenblick diese Zustände zu reduzieren. Die Wichtigkeit dieses Experiments ist nicht, daß man die Katze dazu zwingen kann, eine solche und eine solche Bewegung durchzuführen, sondern man kann einfach, indem man einen elektrischen Strom leitet, in ihm den Wunsch wecken, in einer bestimmten Richtung zu handeln. Gegenwärtig werden solche Experimente zur besseren Kenntnis der Katzenpsychologie nicht regelmäßig verfolgt, obwohl sie mit Affen und seit einiger Zeit mit Menschen erneuert wurden. Diese selben Minute Elektroden sind in speziell ausgewählten Punkten gepflanzt, die sich auf die psychischen Erkrankungen, die von den Probanden. Auf diese Weise lassen sich Tests durchführen, deren Ergebnisse für Psychiater äußerst aufschlussreich sind. Diese Ergebnisse werden derzeit von der New York Academy of Science veröffentlicht. Es versteht sich von selbst, dass sie uns einige erschreckende Perspektiven auf den menschlichen Geist geben können. In der Vergangenheit glaubten die Psychologen, alles Lernen sei eine einfache Assoziation. Die Stimulus-Response-Reflexwirkungstheorie wurde auch für den Menschen als wahr angesehen. Es wird nun angenommen, dass viele Säugetiere zu komplexeren geistigen Prozessen fähig sind. Die meisten höheren Tiere haben eine Art geistige Repräsentation ihrer Welt und wie die Welt arbeitet, die sie konsultieren, wann immer sie eine Entscheidung treffen müssen. Es kann niemals möglich sein, wirklich zu verstehen, wie eine Katze die Welt wahrnimmt und versteht. Virtuelle Realität kann uns eine Vorstellung davon vermitteln, wie die Welt aussieht und wie eine Katze klingt, indem sie die Signale, die unsere Augen und Ohren erreichen, und durch Filmen auf Katzenaugenebene anpasst, aber wie viele Wissenschaftler Elektroden in die Hirne unglücklicher Katzen stoßen, Sie können nicht wirklich in ihren Köpfen erhalten. Um feline Intelligenz und Lernfähigkeit zu untersuchen, müssen wir besser geeignete und humane Tests entwickeln. Um dies zu tun, müssen wir verstehen, wie Katzen entwickelt haben, um ihre Umgebung und Lebensstil, Dinge, die sie in bestimmter Weise Verhaltensweisen anzupassen entwickelt. Eine der einfachsten Formen des Lernens ist Pavlovian Konditionierung (Pavlovian Learning). Dies beinhaltet die Zuordnung eines Reizes mit einem Ereignis. Ein Stimulus, der so genannte Unconditioned Stimulus, ist normalerweise mit einem bestimmten Motivationszustand verbunden und führt zu einer angeborenen Reaktion, die Unbedingte Antwort genannt wird. Wenn zum Beispiel der Unbedingte Stimulus der Geruch nach Nahrung ist und der Motivationszustand Hunger ist, dann sinkt der UCR. Wenn ein Conditioned Stimulus wie ein Buzzer unmittelbar vor oder gleichzeitig mit dem Unconditioned Stimulus auftritt, ergibt sich daraus In der Unconditioned Response auch auf eigene Faust. Die Unconditioned Response wird eine Conditioned Response und die konditionierten Probanden sabbern beim Klang des Summers. In einer Katzen natürlichen Umgebung, könnte ein Unconditioned Stimulus die Schmerzen durch eine aggressive Tom Katze zugefügt werden. Die Unconditioned Response wird wahrscheinlich Flug sein, um eine Wiederholung der Schmerzen zu vermeiden. In der Zukunft könnte der bloße Anblick des Aggressors (jetzt ein Conditioned Stimulus) zu einem Flug führen, d. h. eine Conditioned Response, weil die Katze motiviert ist, Schmerzen zu vermeiden. Wenn der Conditioned Stimulus (die aggressive Tomkatze) in der Entfernung ist, ist die Katze motiviert, eine Erkennung zu vermeiden, und die Conditioned Response soll statt fliehen. Pavlovianische Konditionierung bildet eine Verbindung zwischen dem ursprünglichen Reiz und dem konditionierten Reiz, aber die tatsächliche Antwort hängt vom Katzenmotivzustand ab. Konditioniertes Lernen wird durch ein angeborenes Verhalten der Tiere kompliziert. Katzen Ohren sind entworfen, um nach Hause in auf Geräusche wie kleine raschelnde Beute im langen Gras. In einem Experiment wurde die Ankunft von Nahrungsmitteln durch 10 Sekunden eines Klickens eines Lautsprechers signalisiert, der 2 Meter von dem Nahrungsmittelausgabegerät entfernt war. Die Katzen liefen zum Klang, durchsuchten den Lautsprecher oder griffen ihn sogar an. Einige ignorierten die eigentliche Nahrung und konzentrierten ihre Aufmerksamkeit auf den Lautsprecher. Es dauerte hunderte von Versuchen, die Katzen zu konditionieren, um zum Nahrungsmittelverteiler zu gehen, als sie die Klicken hörten. In demselben Experiment untersuchte Ratten den Lautsprecher nicht, sondern verband den Klang schnell mit der Ankunft der Nahrung. Das war nicht, weil die Katzen dumm waren. Für Katzen, Ton zeigt die scheinbare Lage der Beute und sie reagierten nach ihren Instinkten. Hochadäquaten Raubtiere erwarten, dass die Beute Raubtiere und die Beute selbst (das Essen) an der gleichen Stelle zu finden. Katzen schnell lernen, wenn ein Conditioned Stimulus ist unzuverlässig und sie können un-lernen eine unzuverlässige Conditioned Response, ignorieren Glocken, Buzzer, Klicks oder was auch immer als irrelevant. Die Menschen sind voreingenommen, wenn sie die Intelligenz anderer Spezies beurteilen und sie nach ihrer Ähnlichkeit mit uns selbst beurteilen. Tiere, die gutes Sehvermögen und dextrous Hände haben, werden konsequent als intelligenter bewertet als Tiere, denen diese Merkmale fehlen. Wir sind voreingenommen gegenüber Tieren, die auf ähnliche Weise zu sehen, zu reagieren und zu manipulieren. Tiere, die lernen, Dinge zu tun, die für den Menschen nützlich sind, werden auch als intelligenter bewertet als weniger kooperative Kreaturen. Dies ist ein Defizit in der menschlichen Weltanschauung, nicht in der Tierintelligenz. Tiere, die weitgehend auf Instinkt oder sehr kontextsensitives Lernen angewiesen sind (d. H. Nur Dinge lernen, die mit der Umgebung zusammenhängen, in der sie sich entwickelt haben) können nur in einem Tempo, das durch evolutionäre Mechanismen bestimmt wird, Diejenigen mit umfangreicheren Lernfähigkeiten können ihre Verhaltensmuster schnell ändern. Katzen haben auch ökologisch überschüssige Fähigkeit, d. H. Die Fähigkeit, Probleme außerhalb ihrer spezifischen Anpassungen an ihre Umweltnische zu lösen. Ökologisch überschüssige Fähigkeiten erlauben es den Tieren, mit einer raschen oder unerwarteten Veränderung der Umwelt umzugehen, sind aber schwer zu messen. Die Katzen ökologisch überschüssige Fähigkeiten werden durch ihre Fähigkeit, von verwöhntem Haustier zu feral Katze und wieder zurück bewegen, innerhalb einer sehr wenigen Generationen oder sogar innerhalb der Lebensdauer einer einzigen Katze gezeigt. Menschen definieren oft Intelligenz als IQ. Dies ist irreführend, weil es verschiedene Scoring-Systeme für IQ und es ist möglich zu lernen, wie man gut bei IQ-Tests durchzuführen. Es gibt auch intelligente Leute, die nicht gut bei IQ-Tests, weil die Tests auf bestimmte Arten von Intelligenz voreingenommen sind (z. B. logische Argumentation) und sind kulturell schief. Andere Tests beinhalten die Fähigkeit zu lernen und zu erinnern. Ist die Fähigkeit, durch rote ein Zeichen der Intelligenz zu lernen Wenn ja, jede Avian Mimik ist intelligent. Intelligenz umfasst viele Dinge - die Fähigkeit zu verstehen und zu nutzen eine Umgebung die Fähigkeit lernen und erinnern Fakten (speichern Wissen) die Fähigkeit, Fakten zu verknüpfen die Fähigkeit, Wissen anzuwenden und an neue Situationen anpassen die Fähigkeit, zu überschreiben oder eine instinktive Antwort anzupassen. Eine Katze oder Hund muss nicht nukleare Physik zu lernen oder zu verstehen, Shakespeare, um zu überleben. Tier-Intelligenz ist mit den Tieren natürliche Umwelt und ihre Überlebensbedürfnisse verbunden. Um ihre Intelligenz zu messen, müssen wir unsere Wahrnehmung der Intelligenz an ihre Weltanschauung anpassen und entsprechende Tests formulieren. Wenn der Test auf dem Lernen beruht, müssen wir herausfinden, was motiviert einen Hund oder eine Katze zu lernen oder zu tun Unterschiedliche Tiere Ökologie bedeutet verschiedene motivierende Faktoren Wir brauchen Tests, die für die Tiere physikalische und Verhaltensmerkmale und Zwänge, nicht zu unseren eigenen Zwängen gelten . Wir brauchen auch eine Möglichkeit, ihre ganz unterschiedlichen Verhaltensweisen zu vergleichen. Verschiedene Tiere haben unterschiedliche angeborene Verhaltensweisen. Zum Beispiel werden eine nicht trainierte Katze und ein nicht trainierter Border-Collie-Hund mit einer Gruppe von Entenküken präsentiert. Der Hund zütet die Enten und schützt sie. Die Katze sticht die Enten und isst eine oder mehrere von ihnen. Ist die Katze unintelligent, weil es doesd Herd die Entenküken Ist der Hund unintelligent, weil es fehlschlägt, diese Entlein als Beute zu identifizieren und es nutzt Vorteil einer Mahlzeit Gelegenheit Keiner Kreatur ist mehr oder weniger intelligent als die anderen, wenn von diesem Test beurteilt. Beide spielten nach ihrem Instinkt. Der Hund kam aus einer Rasse mit einem starken Hüten Instinkt Verbesserung durch menschliche Auswahl über Generationen es tut, was natürlich kommt zu Border Collies. Die Katze tut, was natürlich kommt zu Katzen und identifiziert eine einfache Mahlzeit, aber scheitert die Hüte-Test. Der Test ist entweder schlecht gewählt oder ist in Richtung Hütehunde voreingenommen, die Ergebnisse sind offen für Interpretation und die Schlussfolgerungen sind wertlos. Solche Tests werden manchmal von Forschern mit versteckten Tagesordnungen verwendet, d. h. diejenigen, die einfach Statistiken benötigen, um eine Haustiertheorie oder eine ausgemachte Sache zu beweisen. Schließlich sind die Menschen sehr schützend für die Intelligenz. Indikationen der Intelligenz in anderen Tieren werden oft als listig oder als Instinkt abgeschrieben. Als Rasse wollen wir nicht zugeben, dass Intelligenz nicht ausschließlich ein menschliches Merkmal ist. Ähnliches gilt auch für die menschliche Geschichte, in der weiße Europäer nicht-weiße Menschen (so genannte Klein-Rassen) als schlau und fähig, geschult, aber nicht intelligent zu sein scheinen. Menschen, sowie Katzen, haben einen Grad von fest verdrahteten Verhaltensweisen. Diese hartverdrahteten Verhaltensweisen ermöglichen es uns, Routineaufgaben auf Autopilot zu machen und mehr Gehirn für die Lösung anderer Herausforderungen freizugeben. Pferde für Kurse und Tests für Spezies Die Fähigkeit eines Tieres, eine experimentelle Aufgabe zu meistern, hat oft weniger mit Intelligenz zu tun als mit Einschränkungen, die durch physikalische Eigenschaften und Verhaltensvoraussetzungen auferlegt werden. Spezies unterscheiden sich darin, wie sie die visuellen oder akustischen Hinweise sehen, auf die sie gelehrt werden, um darauf zu reagieren, so wie ein menschlicher Mensch nicht lernen kann, auf ein Ultraschall - oder Ultraviolett-Cue zu reagieren, da diese außerhalb unserer Hör - und Sehbereiche liegen. Tiere unterscheiden sich in der Art der Belohnungen sind sie bereit zu arbeiten. Sie unterscheiden sich in den Dingen, von denen sie vorsichtig sind oder sogar Angst haben, und die das Lernen beeinträchtigen oder ein Experiment, z. B. Eine Katze lernt nicht, eine bestimmte Plastikform auszuwählen, wenn der Kunststoff einen beleidigenden Geruch hat. Tiere sind auch prädisponiert (vorbereitet), um bestimmte Arten von Assoziationen zu lernen, und sind prädisponiert, andere nicht zu lernen (kontraproduziert). Es ist eine Frage, wie ihre Gehirnverdrahtung entwickelt hat, prädisponiert sie, um mit ihrer Umgebung in gewisser Weise zu interagieren. Wenn ein Test oder die Art der Belohnung nicht irgendwie in was eine Katze prädisponiert zu tun passt (zB Manipulation einer bestehenden Verhaltensmerkmal), dann wird die Katze es nicht tun Beim Versuch, die relative Intelligenz der verschiedenen Arten zu messen (ein Verhalten, das besessen ist Menschliche Spezies), manche Tiere schlecht lernen, bestimmte Dinge zu lernen, aber wenn das Experiment neu gestaltet wird, um besser auf eine Art Verhaltens-oder Wahrnehmungsmerkmale, und es berücksichtigt, was die Spezies ist prädisponiert tun nicht tun, tun die gleichen Tiere viel besser. Trotz seiner Lieblings-Forschung Themen für mehr als ein Jahrhundert, Katzen sind besonders anspruchsvolle Themen für Intelligenztests. Es ist schwer, sie zu zeigen, wie sie lernen, oder was sie wissen, vor allem in einem Labor. Während soziale Tiere wie Hunde und Pferde auf soziale Belohnungen und Strafe reagieren, sind diese für Katzen fast bedeutungslos. Obwohl Katzen genießen können, petted, hat es nicht die Bedeutung der Akzeptanz von einem Vorgesetzten in der gleichen Weise, wie es für Hunde. Sie sind gleichgültig gegenüber dem Konzept des Streichens als Belohnung und Verweigerung Streicheln als Strafe in der Tat ignorieren eine Katze kann kontraproduktiv sein, da dies ein Zeichen der Höflichkeit in der Katze ist Bestrafung ein soziales Tier (indem man es ignoriert, sprechen hart oder durch Physische Bestrafung) einer sozialen Mißbilligung oder Ausgrenzung aus der sozialen Gruppe gleichkommt. Whiles dieses arbeitet für Hunde, Katzen sind entweder nicht-soziale oder haben eine lose Sozialstruktur und reagieren auf die gleiche Bestrafung mit der Kampf - oder Flugreaktion. Nachdem sie sich entwickelt haben, um autark zu sein, fehlt ihnen der Drang, soziale Vorgesetzte zu beruhigen oder Akzeptanz in eine Packung oder Herde zu gewinnen - sie sind eher zu gehen für ein paar Stunden und warten, bis der menschliche Teilnehmer sich zu beruhigen. Hunde, Ratten und andere Forschungsthemen erlernen spezifische, fokussierte Aufgaben, um eine Nahrungsmittelbelohnung zu gewinnen. Katzen sind autarke, einsame, opportunistische Jäger und haben sich entwickelt, um mit den Hungersnöten fertig zu werden, denn nur etwa eine von drei Jagden führt zu einer Mahlzeit. In den Experimenten, in denen Katzen, die nicht für einen ganzen Tag gefüttert worden waren, auf ihre Fähigkeit geprüft wurden, einen Gegenstand zu lokalisieren, der hinter einem Schirm verborgen ist, stellten die Forscher fest, daß die Katzen Suchen langsam oder lackadaisical waren, obwohl die Belohnungen für das Finden des Gegenstandes die Katzenliebling waren Essen Leckereien. In der Wildnis sind Katzen Opportunisten und untersuchen ihr Territorium für Orte, die die Beute verbergen sollen, so dass die fehlenden Versuchspersonen weniger motiviert waren von der Nahrungsaufgabe als durch die Überprüfung aller potenziellen Beutetierlöcher. Es ist offensichtlich, Haustierbesitzer und Naturforscher beobachten wilde Katzen, dass Katzen innewohnlich neugierig sind und sie können und lernen. Im häuslichen oder naturnahen Umfeld passen sich Katzen ihren Verhaltensweisen und Strategien entsprechend den Umständen an. Es gibt Katzen, die abholen, öffnen Türgriffe oder brechen in Pakete alle so teuflisch wie Labor Puzzle-Boxen. Gut gestaltete Experimente, die die körperlichen Fähigkeiten der Katzen und die angeborenen Verhaltensmerkmale der Katze berücksichtigen, zeigen, dass Katzen neugierig, intelligent und fähig zu lernen sind. Reflexe und konditioniertes Lernen sind für einige Verhaltensweisen gut, aber ein anderes Lernmodell ist für ein flexibleres Verhalten erforderlich, das es der Katze ermöglicht, die Konsequenzen ihrer eigenen Handlungen vorherzusagen und ihre Handlungen auf der Grundlage vergangener Erfolge und Misserfolge zu modifizieren. Es ist eine Überlebensanforderung, dass Tiere lernen, dass einige Lebensmittel sind giftig oder schmecken nach nur einem Fehler und wird dann vermeiden, dass Lebensmittel. Dies wird als Instrumental Learning oder Trial-and-Error bezeichnet. In Thorndikes Puzzle-Boxen, Katzen zuerst gekratzt und kratzte wahllos an den Seiten des Käfigs, bis versehentlich entdecken Sie den Hebel, Schnur usw., die sie aus. Ihre späteren Versuche waren weniger zufällig. Einige Puzzle-Boxen waren ziemlich komplex. Eine Verriegelung erforderte einen gleichzeitigen Hub und Schub, und in anderen Käfigen mussten zwei oder sogar drei Verriegelungen in der richtigen Reihenfolge geöffnet werden. Nicht alle Katzen beherrschten diese, aber einige taten es. Die Fertigkeiten wurden allmählich gewonnen, und Thorndike schloß Die allmähliche Steigung der Zeitkurve zeigt dann das Fehlen der Argumentation. Sie repräsentieren das Tragen eines glatten Weges im Gehirn, nicht die Entscheidungen eines rationalen Bewusstseins. Dies ist eine Verallgemeinerung, da einige Katzen abrupt verbesserten und keine weiteren Fehler machten, auch wenn Monate zwischen den Tests verstrichen waren. Wir beschreiben die plötzliche Verbesserung, wie der Penny gefallen ist oder etwas geklickt hat. Eine meiner Katzen, Affy, war fast unmöglich, Sänfte Zug trotz 18 Monate der Anstrengung. Eines Tages beobachtete sie eine andere Katze mit einem Katzenklo und der Pfennig sank von da an die Katze, die sie auch durch Beobachtung gelernt hatte. In frühen klassischen psychologischen Experimenten gelernt, Katzen leicht zu entkommen aus Puzzle-Boxen durch Manipulation Strings oder Hebel in bestimmten Sequenzen. Nachdem sie eine Puzzlebox gelernt hatten, beherrschten sie schnell andere, wie jeder Besitzer eines Katzensprungkünstlers bestätigen wird. Obwohl sie gelernt haben, Hebel und Streicher zu manipulieren, konnten sie nie das Geheimnis lernen, aus der Schachtel zu kommen, als der Experimentator die Tür zur Schachtel öffnete, nur wenn die Katze sich zerkratzte oder leckte. Wenn eine Katze versehentlich die Klinke mit ihrem Schwanz vertrieb, erfuhr sie nichts darüber, wo die Klinke war oder wie sie sich öffnete. Eine instinktive manipulative Handlung wie das Tupfen eines Gegenstandes mit irgendeiner äußeren Realweltfolgerung zu verbinden ist eine natürliche Tätigkeit, die das Katzengehirn prädisponiert, um sein natürliches zu lernen (weshalb so viele Katzen lernen, Nahrung von einer Dose zu schaufeln, die ihre Tatze wie das ArthursKattomeat verwendet Katze). Assoziieren eine Instinktpflege Aktion wie Lecken oder Kratzen mit einigen externen Real-World-Konsequenz ist sehr unnatürlich und Katzen können es nicht lernen. In freier Wildbahn werden die Fähigkeiten, die für das Überleben am nützlichsten sind, am leichtesten erworben. Es ist einfacher, eine Katze zu trainieren, um eine Nahrungsmittelbelohnung zu erlangen, indem sie einen normalen Teil ihres Verhaltensrepertoires verwendet, wie das Zurückziehen eines Bolzens mit seiner Pfote (die gleiche Bewegung wird verwendet, um die Beute zu entfernen, die in einer Felsspalte Zuflucht sucht), als durch Eine willkürliche, aber unkomplizierte Aktion, wie das Drücken einer identischen Schraube nach innen. Katzen treiben instinktiv die Dinge aus, um die Dinge nicht weiter zu vertreiben. Doch Katzen suchen manchmal nach anderen Lösungen: In einem Experiment, das von Professor Julius Masserman in Amerika durchgeführt wurde, scheinen zwei Katzen anscheinend die Menschen überdacht zu haben. Sie bewusst blockiert den Mechanismus, den sie waren, um jedes Mal, wenn sie Essen wollte. Die Katzen fanden heraus, dass durch die Verkeilung eines elektrischen Hebels in eine Ecke ihres Käfigs, die Feeder funktionierte kontinuierlich, Abgabe von Speisen ohne weitere Anstrengung seitens der Katzen. Ob die Katzen dies zufällig entdeckt und wiederholt, war nicht klar, in den 1950er-Bericht hatte ich. Wenn es möglich ist, eine Katze zu trainieren, um einen Hebel zu betätigen, ist es sicher für eine Katze möglich, zu lernen, wie man den Hebel deaktiviert. Ein weiteres Beispiel für die Assoziation einer manipulativen Handlung mit einer realen Konsequenz ist, wenn Ihre Katze kratzt höflich an einer Tür (oder Fenster), um Ihre Aufmerksamkeit, so dass Sie die Tür für sie zu öffnen oder zu öffnen. Nachdem Sie gelernt haben, werden Sie die Tür für sie auf mindestens einige der Gelegenheiten zu öffnen, ist es viel schwieriger für die Katze, die Lektion zu verlernen. Wenn Sie es ignorieren, wird es weggehen und dann später noch einmal versuchen, damit es nicht zu erwarten, dass die Tür zu öffnen, müssen Sie es konsequent ignorieren. Eine meiner Katzen, Squeak, lernte, dass das Ziehen eines bestimmten Zweiges und das Freigeben, so dass es die Tür mit einem lauten Schlag war noch effektiver bei immer die Tür geöffnet. Natürlich konnte Squeak nicht wissen, dass mein eigentlicher Grund, sie hereinzulassen, darin bestand, die Glasscheibe intakt zu halten. Viele Katzen lernten auch, dass Menschen sich durch Vokalisierung verständigen und ihre natürliche Handhabung (Pfoten oder Klauen) verändern und an der Tür oder dem Schrank malen stattdessen. Im Wesentlichen sind sie assoziieren 2 Lektionen (manipulative Aktionsstimme) und ändern ihr eigenes Verhalten, um die gewünschte Antwort von ihren Menschen zu bekommen. Nicht nur ein Zeichen der Intelligenz, sondern ein Fall von wer trainiert wen Jetzt zurück zu den Puzzle-Boxen. Für Ihre Katze ist eine Katze Träger eine Puzzle-Box. Katzen lernen, welche Seite die Öffnung hat und oft lernen, die Gurt-und-Schnalle Befestigung mit einem Ausgang und Hintern, Pfote oder Biss an der Tür und der Befestigung zu assoziieren. Wenn sie es locker genug, um zu entkommen, wird die Lektion schnell gelernt, oft wiederholt und schnell auf andere Katze-Träger angewendet - nachdem gelernt, gibt es eine Schließung Mechanismus, lernt die Katze für Verschlussmechanismen auf jedem anderen Träger suchen Sie es in Besitzer behaupten, dass ihre Katzen gelernt haben, in der Ecke eines Pappkartenträgers zu pinkeln und durch das entstandene Päckchen Macheacute zu entkommen - was als nervöser Unfall anfing, kann schnell zu einem gelernten Verhalten werden. Das Problem ist, dass die Katze wahrscheinlich nicht pinkelt, um sich zu öffnen Der Träger, ist es pinkeln, weil es durch den Träger erschreckt (nachdem er gelernt, den Träger mit den unerwünschten Bedienungen des Tierarztes assoziiert) und seine Flucht aus matten Karton ist eine zufällige Konsequenz. Die gleichen nervösen Katzen noch pinkeln in Kunststoff-Träger, auch nach konsequent fehlte, um aus dem Träger zu entkommen. Wie Lecken, Pissen ist ein instinktives Verhalten und es ist unnatürlich, es mit einer externen realen Welt zu verbinden. Solche Intelligenz kann auch ihr Verderben sein. Einige Katzen, wie mein eigener Scrapper (einer der felinitys helleren Funken), begreifen nie, dass Katzenklappen in beide Richtungen offen gedrückt werden können, indem sie gelernt haben, von einer Seite zu schieben, um herauszukommen, ziehen sie die Klappe unbeholfen auf der anderen Seite auf Kommen in. Katzen sind auch motiviert, um in bestimmte Arten von Puzzle-Box erhalten. A food cupboard, a carton or a fridge door is also a puzzle box and the cat soon learns which edge of the door to pull at in order to open it. One enterprising Siamese cat learned to bite a hole in a milk carton, as far down the carton as possible, to get the maximum amount of milk out of it Cats view their owners as equals and when a cat tries to please you it does so on its terms, not yours. Cats are also adept at manipulating their owners those whose cats enjoy playing fetching games might reflect on who taught whom the game. In all likelihood, the cat initiated the retrieving game and trained the owner to throw the object. One of my first cats, Scrapper, regularly retrieved his favourite wand-type toy from a bookshelf and brought it to me - but only when Scrapper wanted a game. The following series of photos are from psychological testing of cats at brooklynCollege in the early 1940s. The show cats learning to open the puzzle box to get a food reward. In one experiment, 2 cats co-opearted to haul the food towards them. In another, the cats competed to get to the food before the other. And finally, a kitten learns to navigate a maze. How Cats See the World How intelligence is expressed is largely determined by how the sense organs and motor abilities (e. g. whether it can manipulate objects) operate. Evolution is economical and an animals brain is wired up according to what sensory inputs it can receive and what its limbs are capable of doing. An animals brain is wired up according to what is important for its survival. If it relies on vision for hunting, the brain areas related to receiving and processing visual stimuli will be well developed. If it relies on smell, the region for processing smell will be well developed. An important sense gets more brain-space at the expense of a less important one. The neocortex region (grey matter) of the brain plays a crucial part in learning and is highly specialised according to species. In diurnal humans it contains a large visual area and a large area for fine motor control of our hands. We excel at intelligence tests that require visual abilities and fine manipulation of objects. Cats are crepuscular (active at duskdawn) and rely particularly on their hearing, hence a large region of neocortex is devoted to processing sounds. This is enhanced by their highly mobile ears. The importance of hearing is evident in blind cats, many of which can catch prey or chase toys, relying entirely on sound. Most humans have excellent colour vision, about 120 o of stereoscopic vision (giving good depth perception), relatively good hearing in a limited frequency range (but not mobile ears) and a comparatively poor sense of smell. We find it hard to imagine how other animals with differently tuned senses perceive the world and intelligence tests were geared towards creatures with human-like sensory abilities. Cats perceive the world quite differently. Like humans, they have forward facing eyes and stereoscopic vision and can judge size, distance and depth essential for stalking and pouncing on prey. Cats have about 90 o to 130 o of stereoscopic vision, depending on breed-specific traits such as face shape. Otherwise, they view the world quite differently. Intrusive studies measuring electrical nerve impulses in cats brains show their colour perception is very different. Animals with poor colour vision, do poorly at learning tests which require them to distinguish between different coloured objects. In brief, the human retina (back of the eye) has three types of cone cell (colour receptors) sensitive to red, green and blue. Nerve cells pick up the relative amounts of red, green and blue and our brain translates this into the various colours of the spectrum. We can distinguish around 100 distinct hues. The other type of cell in the retina are rods these are sensitive to light and dark. Because we evolved for daytime living, we have relatively few rods and hence have poor vision in dim light. Cats have cones sensitive to green and blue, but few, if any, cones for red. To a cat, red, orange, yellow and green are seen as one colour. Blue and violet are seen as another colour. Other hues are variations on these two colours (much as monochrome photos are different shades of grey). They can tell that a red object is not black, grey or white, but cannot distinguish it from a green object. Cats are more active in dim light where colour vision is less important than good night vision, so much more of the retina is given over to rod cells. They have enough colour vision to help them spot camouflaged predators, but most owners will have noticed how cats often miss toys (or prey) until the object moves. This is because rods are also very good when it comes to detecting movement (the pattern of light and shade changes when something moves). Cats have other adaptations for dim light. Behind the retina is a reflective layer called the tapetum lucidum. This bounces light back through the retinal cells, amplifying available light (like night-sight binoculars). This is what makes cats eyes glow yellow-green in car headlights or flashlit photos. Cats have different visual acuity (sharpness) to humans. Acuity is linked to the size and structure of the eye. High visual acuity give a sharper image while lower visual acuity gives a grainier image. Humans can pick out very fine patterns of stripes before the image blurs into solid grey. Testing animals visual acuity involves measuring brain-wave patterns from electrodes implanted into the brain while the animal is shown a striped image. The stripes are continually narrowed until the signal from the animals visual cortex undergoes a characteristic change, showing that it sees a grey image instead of stripes. A less intrusive method involves training the cat to pick a striped card in preference to a solid grey card, the limit of visual acuity is the point where the success rate is 5050 for picking the right card. Cats visual acuity is between 4 and 10 times worse than humans. In medical terms, cats have 2080 vision meaning that what a normally sighted human can see well at 80 feet, a cat can only see in as much detail at 20 feet. Other visual experiments show that cats can distinguish visual textures, for example they can distinguish a triangle of vertical lines from a background of horizontal lines. This helps explain why zebra have vertical stripes to blend with vertical lines of the background (trees, tall grass) - a horizontally striped zebra would stick out like a sore thumb to a lion Cats also see subjective contours. In the diagrams below, when the three-quarter white circles are properly aligned, an optical illusion produces a black square in the middle of them. When they are randomly aligned, there is no square. Cats can discriminate between the visual illusion and the random patterns. Cats supplement their sense of vision with extremely sensitive sense of touch thanks to their whiskers (vibrissae). It is general belief that the large cheek whiskers gauge the width of a hole so a cat can tell if it is large enough to get through. As well as the prominent cheek whiskers, cats have smaller whiskers on the muzzle, whiskers above the eyes and whiskers on their lower legs. A blind cat can feel its way over and around obstacles with great precision. The large number of nerves devoted to these whiskers occupy a disproportionately large area of the cats mental map of its own body (much as the nerves devoted to the hands and fingers dominate in humans). A cats sense of smell is far better than that of humans, but is far less than that of dogs. It is, however, good enough that smells imperceptible to us can confound experiments using cats. Hidden food is not so hidden if you are a cat and can smell it. Cats can detect food going stale (and refuse to eat it) long before we can. Smell is an important sense in animals that mark their territories with urine or faeces or that recognise places and individuals by smell. Cats have excellent hearing and can hear sounds up to about 60,000 Hz while humans (with a few unusual exceptions) can only hear up to bout 20,000 Hz. This means cats can hear the ultrasonic noises made by rats and mice. In addition, they can pinpoint a sound source to within about 8 o thanks to their swivelling ears. Cats have relatively intricate brain wiring for control of their paws compared to dogs. They are surprisingly dextrous when seizing and manipulating objects. This is most obvious in polydactyl (extra-toed) cats as these often their paws to grasp objects. Photographs and X-rays of cats paws in action show several methods of handling an object: it may be pierced with just the claws, held between a claw and pad of the paws, or sometimes held between the paw pads without the use of the claws at all. Cats have some ability to move the digits (toes) of their paws separately, again this is most evident in polydactyl cats. When a cat reaches out to grab an object, it pre-shapes its grip, much as we do, giving it a much better chance of catching and holding the object. Gripping is therefore not simply a mindless reflex action in response to something touching the paw pad. Early Learning and Slowing Seniors Psychologists originally believed that animals like cats and humans are born helpless and dependent and develop the ability to learn later in life. Even helpless human babies are learning the physical rules of the world around and their innate language module is acquiring language. Exhaustive developmental studies in kittens have found that cats also have an innate mental ability to learn that is present from the start. Cat workers often comment that kittens develop a preference for suckling from a particular nipple on their mother. Days old kittens can be trained to preferentially suckle from one of two artificial nipples distinguished by texture, location or smell. Using an artificial mother, consisting of a carpeted surface with two rubber nipples, a 2 day old kitten can learn to distinguish between a nipple that delivers milk, and one that does not, based on its texture alone. Discrimination based on odour is possible just one day later. Kittens in pooled litters can also discriminate between its own mother and other lactating females if it is in a pooled litter and between its mother and an artificial nipple. Despite their mothers protectiveness, kittens have to learn quickly. Orientation develops in the first week. For the first few days, if a kitten is removed from the nest it simply crawls in circles wherever it is. Six day old kittens (i. e. eyes not yet opened) can orient themselves towards the nest in response to the smell of their mother or littermates. By the end of their first week, they have learnt to distinguish by scent the home region of their cage or pen from other parts of the cage. At 2 weeks old, they can orient themselves over a distance of about 3 metres and they begin to explore. Visual cues take over from scent cues at around 3 - 4 weeks. The innate behaviours displayed by kittens are based on inherited patterns, but these behaviours are modified, supplemented and altered, in both the long and short term, by learning. What determines learning ability is not so much innate brainpower as behavioural development i. e. the ability to take in and process information so it does something useful in the real world. Right from birth, animals, are predisposed to find certain things and certain associations important. They are motivated to explore and learn these important things (or at the very least not to shun those things, even if the exploration stage doesnt happen until they are more mature). Early experiences interact with natural instincts and shape the ability to learn later on. Cats also have different personality types which both affect their willingness to learn and which are affected by early experiences in life. Kittens brought up with other animals, a vacuum cleaner, plenty of people and other household objects are more confident in later life than kittens brought up in a quiet home with only one person. Just as you cant teach an old dog new tricks, elderly cats are less able to learn. Many geriatric cats suffer a cognitive dysfunction syndrome similar to Alzheimers disease and often referred to as feline senility. They are easily disoriented, forgetful, they show compulsive behaviours (one of my senile cats had to be confined because she compulsively walked in a more-or-less straight line until she grew tired and simply sat down), sleep erratically, may forget their litter-training or become incontinent. On a molecular level feline senility resembles Alzheimers: plaques of a chemical called beta-amyloid appear in the brain. This interferes with the normal action of neurotransmitters (brain chemicals that relay nerve signals) and is also toxic to nerve cells so that nerves are killed off. Even those cats which dont become senile become slower to learn new things. Studies have found that cats over the age of 10 years are often incapable of learning the basic Pavlovian associations that young cats learn easily. Pavlovian associations are named after the famous Pavlovs dogs experiment where dogs learned to associate a ringing bell with getting a meal and automatically salivated when the bell was rung. Though the older cats were awake and fully alert and their perceptual nerves were supplying the right inputs to their brains, their brains didnt process things as efficiently compared to younger cats. There is a link between learning, brain activity and sleeping. Researchers have found that different patches of the brain can be in different sleep states at the same time. Sleep regulatory biochemicals build up in the brain during wakefulness and help trigger the transition into sleep. They build up faster in parts of the brain that are most active during wakefulness. The harder a brain region works during the day, perhaps learning a task, the harder that brain region has to sleep at night. Cats that are kept in the dark during wakeful hours have to rely heavily on their whiskers to find their way around they have unusually shallow non-REM sleep in the visual cortex, but much deeper non-REM sleep in the part of the cortex dealing with touch. Self-Centred Mental Maps Some of the apparently stupid things that cats do can be explained by how they mentally map out their world. A cats world is three-dimensional (includes shelves, tree branches) and is partly mapped by smells which represent territorial boundaries or signposts. The apparently circuitous route a cat might take to get from A to B is not due to stupidity it is due to the cat avoiding other cats territories or stopping to check out (or deposit) scents which announce its presence, age, health and breeding status to other cats. These are things to be taken into account when understanding how cats map out their world. The simplest type of orientation relies on directly seeing the goal, or a step-by-step route based on landmarks (turn left at the tree, turn right at the fence etc). Simple orientation systems are error-prone - if a landmark is removed, the is animal immediately lost something owners of blind cats are well aware of (although blind cats will attempt to find another landmark so they can reorient themselves). Cats use a mix of these methods and construct mental maps of their surroundings, the more thoroughly they have explored, the better their mental map. Cats can also construct mental maps based on a brief view of relevant features, but these are not remembered for more than a few minutes. Mental maps allow cats to take short cuts, cutting across fields instead of sticking to the edges. If given a choice, cats opt for the shortest route to an out-of-sight goal. If there are several equally short routes, they tend to use the one that starts off by pointing in the direction of the goal - something we ourselves do. Minimising the number of twists and turns in a route affects the choice, but is less important than distance and initial direction. When it comes to finding its way around, a cat learns best by doing, not just by seeing. French comparative psychologists, influenced by the theories of the developmental psychologist Jean Piaget, are interested in how (and whether) various species develop object permanence. Piaget noted that human infants go through various stages of understanding the physical laws of the world. At first, they lose interest when a toy is hidden or taken out of sight and they make little effort to search for it. Once it is out of sight, it has ceased to exist. Older infants will search for something that partially or completely disappears but may not understand where to look. If they see someone hide the object behind a screen, they will not know to look behind the screen but may instead look in a place they previously found it. As they grow older, they will know to look behind the screen and at around 18 months of age they can follow a series of invisible displacements: Invisible displacements are when someone hides the ball in a cup, takes the cup behind the screen and takes the ball out of it, then takes the cup back to the infant and shows that it is empty. The infant reasons that the ball is behind the screen. Piaget termed this Stage 6 object permanence. Object permanence is a useful skill for animals that need to be aware of the most likely location of prey that has gone to ground. If prey becomes temporarily invisible, a cat first searches for it under or behind the place where it disappeared, but if this is unsuccessful it starts searching the nearest available cover. Cats familiar with their territories know and search the most likely hiding places. Cats sometimes appear unable to solve simple invisible displacement using hidden toys because the apparatus used to hide the toy is equally interesting to the cat Even though it knows the toy is under a cloth, many cats will play with the cloth (regarded as a new and therefore more interesting toy) rather than hunt the hidden toy. If you roll a ball under a floor-length drape, many cats get distracted and end up playing with the moving drape because it is a new game. Early experiments suggested cats never reach Stage 6 object permanence. Owners often disputed this finding, based on games with cat toys being lost, hidden or retrieved behind sofas More recent and better designed studies show that they do reach Stage 6. The cats were tested in their familiar home surroundings and the screens were left around for a week in advance so the cat got used to them and also so they learnt there were no toys hidden behind them. The cats were first taught that whenever they touched their noses to a particular toy they got a food reward. For the actual test, a cat was lightly restrained by its owner and two screens were positioned in front of it. In full view of the cat, the experimenter put the toy in a cup, secretly removed the toy behind one of the screens, and then placed the empty cup in front of the cat. The at was released and, in nearly every trial, went straight behind the screen where the toy had been hidden. The screens were moved from trial to trial and were replaced with new screens of a different appearance, but the cats still got the right answer, proving that they had not just learned a local rule but had generalised the solution. Objects do not simply cease to exist and if the object was in the cup before it went behind the screen, but was not in the cup when it emerged again, then the object must logically be behind the screen. In another test, a cat watched food being hidden in a cup, and the cup was then hidden in turn under three covers, after which the empty cup was shown to the cat. To eliminate scents, the food was not actually deposited under the last cover, but was palmed by the researcher. In one test as soon as the cup was removed from under the final cover and shown to be empty, the cat hurried to this cover (not to the researchers hand). It persistently pushed back the cover until the place where the food should have been was entirely revealed. Not finding any food, it pawed at the cover and tried to push its face underneath for several more minutes. When confronted by prey that has gone to ground, it pays to be persistent (within reason). In a more complex series of experiments, all sorts of disorienting visual tricks were played between the time the cats saw a toy hidden behind one of several identical-looking screens and the time they were allowed to search for it. In one test, the toy was first placed behind the rightmost of 3 screens. The cats view was momentarily blocked and all the screens were slid over to the right by a distance exactly equal to the spacing between them. In another test, the cat looked into the experiment chamber from the doorway and after the toy was hidden, the cats view was blocked while he entire room (walls and all) was shifted to the right. In spite of these tricks, when the cats were released to look for the toy, they found it by using an absolute sense of position (a course and bearing from its own position) rather than a relative one. They did not look for it behind what was now the rightmost screen, instead they looked behind the screen that now occupied the precise spot in space that the rightmost screen had previously occupied when the toy was hidden. A cats sense of space is egocentric - they remembered where the toy was placed relative to their own fixed position in space, and not by the toys position relative to a landmark. When the experiment was set up to make egocentric spatial reasoning impossible, the cats were forced to orient themselves using landmarks. From a central doorway, the cats observed the toy being hidden. However, they could only enter the room by taking a detour through an L-shaped tunnel, entering the room through a door to either the left or the right of the one they had watched from. Unable to use an absolute sense of position. These cats successfully located the toy using landmarks. If the egocentric cues and the landmark cues conflicted, the cats trusted to their own cat-centred co-ordinates. Cats form a mental map of their environment, but instead of mapping landmarks (the church is 300 ft to the left of the shop, the shop is a mile north of the farm) a cats mental map has the cat in the middle and everything else is relative to the cats position. This explains why cats do some apparently stupid things, such as failing to cotton on to a moved litter tray even if they watched you move the litter tray a moment ago, and why they are such creatures of habit. It takes time to adjust the egocentric co-ordinate system, hence moving the litter tray should be done by shifting it a foot or so each day and moving the feeding station should be done by establishing two feeding stations and only removing the old one when the cat has got a co-ordinates fix on the new one. Its not that cats are stupid, its just that their internal maps is different from ours. The Feline Time-Space Continuum Many species have specialised modules of the brain for certain tasks. Species which cache nuts and seeds for the winter have a phenomenal spatial memory (and a correspondingly large hippocampus region of the brain). London taxi-drivers who have to remember lots of routes and street locations also tend to have a relatively large hippocampus. Humans have a highly developed language module and human infants can acquire language, complete with rules of grammar, just by listening to it. Border Collies instinctively herd things. Experiments to assess animal intelligence often overlook or dismiss them innate or instinctive skills as being unrelated to intelligence. Instinctive skills may still require a huge amount of brainpower by hardwiring them as instincts, the animal is spared the overhead of having to learn them from scratch, but it must still hone these skills. Cats instinctively hunt things. Even if they dont hunt prey, they show hunting behaviour when playing with toys, playing with other cats or playing with owners. Hunting involves knowing where to find prey, following the motion of fast-moving prey and co-ordinating the motion of paws and jaws to seize the prey. As kittens, a lot of feline play is geared to honing these instincts. The basic hunting skills are hard-wired into the cats brain. Even if a cat has never hunted, the pounce-and-bite behaviour can be triggered by stimulating the appropriate part of the brain with an electrode inserted into it (like the poor feline robots described by Fernand Mery). The behaviour is automatic and even if the cat is not hungry it will still react to the stimulus whether it is an electrode or the sight and sound of prey. In the wild, a cat cannot afford to pass up a chance to catch a meal (in the wild, a cat is rarely so well fed it cant manage another meal). Many owners have seen their cats watching nature programs on TV. Most cats quickly put the TV into the same mental category as a window - they can see and hear the animals, but cant reach them. After one or two investigations behind the TV or the speakers, they learn that the animals stay inside the box. After that they dont bother checking for escaped TV animals again, or at least dont expect to find anything if they do check - when you are a cat, it cant hurt to be absolutely sure there isnt a snack-sized wildebeest behind the TV The interesting thing is cats recognise TV images of wildebeest as being potential prey. The secret is they recognise how animals move. Cats can tell the difference between the motion of a living thing such as a mouse or a TV image of a wildebeest and the motion of an inanimate object such as a blown leaf or a rolled ball. In one experiment, cats were shown moving images on two computer screens. One image contained 14 dots that represented the outline of a walking or running cat. The other contained 14 randomly moving dots. The cats consistently distinguished between the interesting animal motion dots (animals food potential) and the less interesting random dots. However, if the animal motion computer screen was turned upside down, the cats could no longer distinguish it from the random motion screen. To a cat, animals running upside down make no logical sense. Modern AI programmes have problems recognising animal motion dots even when they are the right way up. A famous specialised feline instinct is that of landing on all four feet, known as the self-righting reaction. In experiments, young kittens were dropped 40 cm (16 inches) onto a cushioned surface. At 4 weeks old, they lacked the ability to right themselves. Between 4 and 6 weeks old their self-righting ability developed and improved until at 6 weeks old they consistently landed on their feet. Though the instinct is hard-wired into the cats brain, it has to be honed and the usual time for honing it is when curious kittens fall out of trees or off of furniture. In cats with normal motor abilities, but certain types of brain damage, the self-righting reaction is lost and seemingly cannot be learnt from scratch (noted through observations of pet cats). Adult cats have been trained to demonstrate their self-righting ability for time-lapse photography. Having worked out the distance they are falling (the same every time), some cats became lazy and left self-righting to the last moment These lazy cats demonstrate that cats have a remarkable sense of time as we will see later on. Some animals, such as the seed-hiding birds and fruit-eating monkeys, have excellent spatial intelligence. They can find their way to a series of fixed sites (caches or trees) using the safest or most efficient routes. In addition, some animals optimise their routes so they visit the richest food sites first. Cats are opportunist hunters and do not follow such carefully planned routes. They probably dont decide in advance what sort of prey they are going to hunt. Of those cats that rely on hunting, for example farm or feral cats, they spend only a few hours each day hunting and the typical hunting trip is less than 30 minutes. This was reflected in laboratory experiments which show that learning certain kinds of spatial relationships does not come naturally to most cats due to the egocentric mental maps (and the use of scent markers on vertical surfaces). Though complex spatial relationships may not come naturally to cats, remembering a simple location does. Having learned that prey (or cat food) is usually to be found in a particular location, cats will return to the location. Moreover, they associate the availability of food with a time of day or time interval: cats are very good at time calculations as the owners of furry feline alarm clocks with no snooze button can confirm. Cats appear to calculate how much time to invest in hunting and can discriminate time intervals with an impressive degree of precision. For a cat, the time interval between hunting trips and the energy expended on a hunting trip are more important than the spatial relationship between areas where food is obtained. Cats can tell the difference between a sound that lasts 4 seconds from one that lasts 5 seconds and can learn to delay their response to a stimulus by several seconds, down to an accuracy of one second. This means they have an internal clock, with a one second accuracy, that can be used to time both external and internal events. In one experiment, cats were placed in cages for either 5 seconds or 20 seconds. When released, they were rewarded with a food treat that would always be hidden in the left-hand feeder if they had been in the cage for 20 seconds and in the right-hand feeder if they had been in the cage for 5 seconds. If the cat went to the wrong feeder, it was counted as an error. After training 14 cats, using 400 - 1000 repetitions of the drill each (depending on the cat), all 14 cats could pick the correct feeder more than 80 of the time. The researchers then shortened the 20 second trials to see if the cats could still tell the difference between a long wait and a short wait. 7 of the cats could discriminate a 5 second interval from an 8 second interval. In another experiment cats were trained to press a bar a number of times to open a food tray having gained access, they could eat as much as they wanted at that sitting. At first it took 40 presses to gain access to the food. As the number of bar presses required for the food tray to open was increased (up to 2560), the cats responded by eating fewer meals each day, but eating more at each sitting. The cats were not counting the presses (well look at number sense later on), they simply continued pressing the bar until the food tray opened. For a cat to press a bar 2560 times shows a remarkable level of patience and persistence. The trade off was to expend less effort but more often, or expend more effort but less frequently. Researchers then varied the number of bar presses from one meal to the next, the cats calculated the average price per meal. They amount they ate at a given meal was related to the average number of times they had pressed the bar in the course of a whole day or over a period of several days, not to the number of times they had pressed it for that particular meal. According to psychology lecturer Britta Osthaus at the University of Exeter, cats do not understand cause and effect. She expert attached fish and biscuit treats to one end of a piece of string and placed these under a plastic screen to see if the cats were able to work out that pulling on the string would pull the treat closer. The cats were tested using a single baited string, two parallel strings where only one was baited, and two crossed strings where only one was baited. All cats succeeded at pulling a single string to obtain a treat (93 of the time) showing they were able to learn the connection between the string and the treat, but none of the cats consistently chose the correct string when two strings were parallel. When tested with two crossed strings one cat chose the wrong string consistently and all of the others performed at chance level. According to Osthaus, dogs were able to solve the parallel string test, but cats werent. This test was flawed. Firstly, cats are less food motivated as dogs, and are as likely to be interested in the string as a toy as in achieving a treat. Secondly, the comparison with dogs was also incorrect as another paper, co-authored by Osthaus - if the strings were placed at an angle or were crossed, the dogs tended to paw or mouth at the location closest in line with the treat. In other words, both cats and dogs understood the means-end connections involving strings, but they were both unable to understand crossed strings - something very different from failing to understand cause and effect. Dogs evolved as pack hunters that may select a single animals from a herd - not dissimilar from selecting a string that will give a food-reward. Cats evolved to stalk single prey rather than making choices in that way. If a cat has previously found a mouse at a certain mouse-hole, it makes sense for the cat to check that empty mouse-hole again as other mice may be there. In this way of thinking, it makes sense for the cat to check the empty string that previously had a food payoff. Dogs make choices when pursuing prey, cats investigate all available bolt-holes. If you design a test that favours the dogs natural behaviour and view of the world then the dog will appear to perform better. Pet cats have learnt how to open doors using door-knobs and experimental cats have learnt to dispense food using a lever both instances of cause and effect. When cats do deign to co-operate on traditional animal intelligence and learning tests, they perform quite well. As cat owners well know, cats clearly indicate when they are bored of the game, which means a lot of patience is needed on the part of the testers. Cats do not like frustration and will often give up or select random answers when faced with situations where there is no clear path to a pay-off. In the wild, a cat frustrated by elusive prey will eventually go and hunt something easier instead it makes a trade-off between time and energy spent and the likelihood of a worthwhile meal. In intelligence testing, cats learn to learn when rewarded for their efforts, but they will learn to not bother learning when faced with problems with unclear goals and no guarantee of a reward. L. T. Hobhouses experiments consisted of simple puzzles that his animals had to solve to get a food reward, though he noted that the cats innate nature made it a difficult subject. My first experiment was with my cat Tim, a small black tom, rather more than a year old. Tim is a sociable creature, who follows his friends about in the half dog-like way that some cats have, but as a psychologist he has two great defects. His attention is of the most fickle order, and what is even worse, he gets his meals at the most irregular times, and by methods known only to himself. It is therefore impossible to say beforehand whether he will take any sustained interest in the proceedings at all. Here is one of Hobhouses experiments: A piece of meat was placed on a card to which a string was tied, and then placed on a shelf beyond reach of the animal with the string dangling down. I first tried this with Tim, thinking that a young cat would very likely pull the string in play. I was surprised to find that he took no notice of it. I showed him seven times, pulling the string down before his eyes, and letting him get the meat. Neither this, nor a series of trials in which the card was placed on the table barely out of the cats reach, had the slightest effect. The kitten once grabbed the string as I was arranging the card, probably in play, and brought the card down without the meat. For the rest, he either made no attempt at all, or tried to claw at the meat directly. About a fortnight afterwards I began a long series of trials in which the string was tied to a chair leg to make it more conspicuous. Fourteen trials gave no result. Next day, eight trials passed without result, but at the ninth, the cat bit slightly at the string close by my fingers as I adjusted it, and as soon as I had got it right, pawed the string down. The biting was doubtless due to the string being slightly smeared with fish, but the effect was apparently to call the cats attention to the string for the first time in all this long series. It is clear that, in pawing it, his aim was to get the fish on the table. If he had merely been attracted by the smear on the string, he would have used his mouth. At the next trial, he sat still for a while, and then pawed the string again. At the next, he took to washing himself, and I gave up for a time but on replacing the string I saw him watching me, and he pulled it down at once. In the next trial he did the same. Next day he appeared to have forgotten, but walked under the string and knocked it down with his tail. At the second trial, he slightly brushed against the string, but walked away. I had to rearrange it. He watched me doing so, and pawed it down at once. He then pulled it five times running without hesitation. The cat, it seemed, treated the experiment as a game (although Hobhouse did not actually say this). There are reasons for its repeated failure to understand what was expected of it. It might have had difficulty recognizing the relevance of the thin string, particularly as cats are long-sighted and it might not have been able to see the string properly. Alternatively, the first time it pulled the card down there was no reward and the cat immediately lost interest it was much more interested in the smell of fish later on. On a later occasion, the reward of fish came at the first attempt and the cat was then quick to learn the trick. Hobhouse had discovered how easily cats are demotivated. In one set of experiments cats are presented with a pair of mismatched wooden figures which might differ in shape, size or colour e. g. a black square to the left of a white circle. The cat chooses one or other object by nosing it and every time he picks, for example, the black square on the left hand side, he is rewarded with food. Once the cat consistently picks the black square, the experimenters randomly switch the black square to the left or right of the white circle. After much patient repetition, the cats get the hang of picking the black square rather than whatever shape is on the left hand side (the success criteria is picking the correct shape 80 of the time since most cats occasionally check out the other shape, just in case). Later the white circle might be exchanged for a different shape such as a white triangle, or even a white square, and the cat learns to pick the original black square no matter what the other shape is. Similar object discrimination tasks have been used to assess other aspects of feline intelligence, not just whether it can tell the difference between shapes, colours and textures. Having learnt the correct solution to one such object discrimination problem, cats can learn to generalise from the experience. They catch on faster to similar object discrimination problems. To begin with, each new pair of objects requires dozens of repetitions before cats hit the magical 80 mark. After mastering about 60 different object discrimination problems, many cats will hit the 80 mark after only 10 trials. In other words, the cats have learnt that the rules of the game are to work out which of 2 objects results in a reward. Cats can extrapolate from right answers, but are not so good at extrapolating from wrong answers and end up becoming discouraged, bored and unco-operative if they keep getting a test wrong. If the test cat is lucky enough to get the right answer and its reward on the first try, he masters the problem much faster than if he picks the wrong, unrewarded answer the first try. This is not due to lack of intelligence, but is to do with a hunting animals innate behaviour. If a mouse is not found at the first location a cat visits, the cat does not automatically visit the second location - cats are opportunist hunters and do not follow fixed search patterns. By contrast, foraging animals visit a fixed set of likely food sources, starting with the most likely food source first. Cats wont tolerate frustrating situations for long and quickly give up or become indifferent when there is no clear path to a reward. So they have a harder time with a problem where they have to learn to pick an object on a given side, either the left or right, depending on which of two possible pairs of identical objects (e. g. 2 black squares versus 2 white circles) is presented. This problem has no equivalent in the cats natural world, so they have difficulty learning what is expected of them. Many cats eventually learn to solve tough problems like this, but their performance is generally only better than chance. They also have more problems extrapolating from right answers when presented with a new tough test. Cats that are given a mix of simple and tough problems catch on faster to the tough problems than do cats who are given a straight course of nothing but the tough problems. One cat who had only ever been presented with tough hard problems, never learnt to master a simple blackwhite discrimination task despite 600 trials. With no equivalent challenge in nature, cats presented with only tough tests become demotivated and appear content to get an occasional handout when they choose the right answer by chance. In certain types of test, intelligent cats are content to underachieve - a problem with the design of the test, not with the cats intelligence FELINE INTELLIGENCE PAGE 221 SQL for Analysis and Reporting To perform these operations, the analytic functions add several new elements to SQL processing. These elements build on existing SQL to allow flexible and powerful calculation expressions. With just a few exceptions, the analytic functions have these new elements. The processing flow is represented in Figure 21-1 . Figure 21-1 Processing Order The essential concepts used in analytic functions are: Query processing using analytic functions takes place in three stages. First, all joins, WHERE. GROUP BY and HAVING clauses are performed. Second, the result set is made available to the analytic functions, and all their calculations take place. Third, if the query has an ORDER BY clause at its end, the ORDER BY is processed to allow for precise output ordering. The processing order is shown in Figure 21-1 . Result set partitions The analytic functions allow users to divide query result sets into groups of rows called partitions. Note that the term partitions used with analytic functions is unrelated to the table partitions feature. Throughout this chapter, the term partitions refers to only the meaning related to analytic functions. Partitions are created after the groups defined with GROUP BY clauses, so they are available to any aggregate results such as sums and averages. Partition divisions may be based upon any desired columns or expressions. A query result set may be partitioned into just one partition holding all the rows, a few large partitions, or many small partitions holding just a few rows each. For each row in a partition, you can define a sliding window of data. This window determines the range of rows used to perform the calculations for the current row. Window sizes can be based on either a physical number of rows or a logical interval such as time. The window has a starting row and an ending row. Depending on its definition, the window may move at one or both ends. For instance, a window defined for a cumulative sum function would have its starting row fixed at the first row of its partition, and its ending row would slide from the starting point all the way to the last row of the partition. In contrast, a window defined for a moving average would have both its starting and end points slide so that they maintain a constant physical or logical range. A window can be set as large as all the rows in a partition or just a sliding window of one row within a partition. When a window is near a border, the function returns results for only the available rows, rather than warning you that the results are not what you want. When using window functions, the current row is included during calculations, so you should only specify ( n -1) when you are dealing with n items. Each calculation performed with an analytic function is based on a current row within a partition. The current row serves as the reference point determining the start and end of the window. For instance, a centered moving average calculation could be defined with a window that holds the current row, the six preceding rows, and the following six rows. This would create a sliding window of 13 rows, as shown in Figure 21-2 . Figure 21-2 Sliding Window Example Ranking Functions A ranking function computes the rank of a record compared to other records in the data set based on the values of a set of measures. The types of ranking function are: Sample Linear Regression Calculation In this example, we compute an ordinary-least-squares regression line that expresses the quantity sold of a product as a linear function of the products list price. The calculations are grouped by sales channel. The values SLOPE. INTCPT. RSQR are slope, intercept, and coefficient of determination of the regression line, respectively. The (integer) value COUNT is the number of products in each channel for whom both quantity sold and list price data are available. Linear Algebra Linear algebra is a branch of mathematics with a wide range of practical applications. Many areas have tasks that can be expressed using linear algebra, and here are some examples from several fields: statistics (multiple linear regression and principle components analysis), data mining (clustering and classification), bioinformatics (analysis of microarray data), operations research (supply chain and other optimization problems), econometrics (analysis of consumer demand data), and finance (asset allocation problems). Various libraries for linear algebra are freely available for anyone to use. Oracles UTLNLA package exposes matrix PLSQL data types and wrapper PLSQL subprograms for two of the most popular and robust of these libraries, BLAS and LAPACK. Linear algebra depends on matrix manipulation. Performing matrix manipulation in PLSQL in the past required inventing a matrix representation based on PLSQLs native data types and then writing matrix manipulation routines from scratch. This required substantial programming effort and the performance of the resulting implementation was limited. If developers chose to send data to external packages for processing rather than create their own routines, data transfer back and forth could be time consuming. Using the UTLNLA package lets data stay within Oracle, removes the programming effort, and delivers a fast implementation. Example 21-19 Linear Algebra Here is an example of how Oracles linear algebra support could be used for business analysis. It invokes a multiple linear regression application built using the UTLNLA package. The multiple regression application is implemented in an object called OLSRegression. Note that sample files for the OLS Regression object can be found in ORACLEHOMEplsqldemo . Consider the scenario of a retailer analyzing the effectiveness of its marketing program. Each of its stores allocates its marketing budget over the following possible programs: media advertisements ( media ), promotions ( promo ), discount coupons ( disct ), and direct mailers ( dmail ). The regression analysis builds a linear relationship between the amount of sales that an average store has in a given year ( sales ) and the spending on the four components of the marketing program. Suppose that the marketing data is stored in the following table: Then you can build the following sales-marketing linear model using coefficients: This model can be implemented as the following view, which refers to the OLS regression object: Using this view, a marketing program manager can perform an analysis such as Is this sales-marketing model reasonable for year 2004 data That is, is the multiple-correlation greater than some acceptable value, say, 0.9 The SQL for such a query might be as follows: You could also solve questions such as What is the expected base-line sales revenue of a store without any marketing programs in 2003 or Which component of the marketing program was the most effective in 2004 That is, a dollar increase in which program produced the greatest expected increase in sales See Oracle Database PLSQL Packages and Types Reference for further information regarding the use of the UTLNLA package and linear algebra. Frequent Itemsets Instead of counting how often a given event occurs (for example, how often someone has purchased milk at the grocery), you may find it useful to count how often multiple events occur together (for example, how often someone has purchased both milk and cereal together at the grocery store). You can count these multiple events using what is called a frequent itemset, which is, as the name implies, a set of items. Some examples of itemsets could be all of the products that a given customer purchased in a single trip to the grocery store (commonly called a market basket), the web pages that a user accessed in a single session, or the financial services that a given customer utilizes. The practical motivation for using a frequent itemset is to find those itemsets that occur most often. If you analyze a grocery stores point-of-sale data, you might, for example, discover that milk and bananas are the most commonly bought pair of items. Frequent itemsets have thus been used in business intelligence environments for many years, with the most common one being for market basket analysis in the retail industry. Frequent itemset calculations are integrated with the database, operating on top of relational tables and accessed through SQL. This integration provides the following key benefits: Applications that previously relied on frequent itemset operations now benefit from significantly improved performance as well as simpler implementation. SQL-based applications that did not previously use frequent itemsets can now be easily extended to take advantage of this functionality. Frequent itemsets analysis is performed with the PLSQL package DBMSFREQUENTITEMSETS. See Oracle Database PLSQL Packages and Types Reference for more information. In addition, there is an example of frequent itemset usage in Frequent itemsets . Other Statistical Functions Oracle introduces a set of SQL statistical functions and a statistics package, DBMSSTATFUNCS. This section lists some of the new functions along with basic syntax. Descriptive Statistics You can calculate the following descriptive statistics: Median of a Data Set You can calculate the following parametric statistics: Spearmans rho Coefficient Kendalls tau-b Coefficient In addition to the functions, this release has a new PLSQL package, DBMSSTATFUNCS. It contains the descriptive statistical function SUMMARY along with functions to support distribution fitting. The SUMMARY function summarizes a numerical column of a table with a variety of descriptive statistics. The five distribution fitting functions support normal, uniform, Weibull, Poisson, and exponential distributions. WIDTHBUCKET Function For a given expression, the WIDTHBUCKET function returns the bucket number that the result of this expression will be assigned after it is evaluated. You can generate equiwidth histograms with this function. Equiwidth histograms divide data sets into buckets whose interval size (highest value to lowest value) is equal. The number of rows held by each bucket will vary. A related function, NTILE. creates equiheight buckets. Equiwidth histograms can be generated only for numeric, date or datetime types. So the first three parameters should be all numeric expressions or all date expressions. Other types of expressions are not allowed. If the first parameter is NULL. the result is NULL. If the second or the third parameter is NULL. an error message is returned, as a NULL value cannot denote any end point (or any point) for a range in a date or numeric value dimension. The last parameter (number of buckets) should be a numeric expression that evaluates to a positive integer value 0, NULL. or a negative value will result in an error. Buckets are numbered from 0 to ( n 1). Bucket 0 holds the count of values less than the minimum. Bucket( n 1) holds the count of values greater than or equal to the maximum specified value. WIDTHBUCKET Syntax The WIDTHBUCKET takes four expressions as parameters. The first parameter is the expression that the equiwidth histogram is for. The second and third parameters are expressions that denote the end points of the acceptable range for the first parameter. The fourth parameter denotes the number of buckets. Consider the following data from table customers. that shows the credit limits of 17 customers. This data is gathered in the query shown in Example 21-20 . In the table customers. the column custcreditlimit contains values between 1500 and 15000, and we can assign the values to four equiwidth buckets, numbered from 1 to 4, by using WIDTHBUCKET (custcreditlimit, 0, 20000, 4). Ideally each bucket is a closed-open interval of the real number line, for example, bucket number 2 is assigned to scores between 5000.0000 and 9999.9999. sometimes denoted 5000, 10000) to indicate that 5,000 is included in the interval and 10,000 is excluded. To accommodate values outside the range 0, 20,000), values less than 0 are assigned to a designated underflow bucket which is numbered 0, and values greater than or equal to 20,000 are assigned to a designated overflow bucket which is numbered 5 (num buckets 1 in general). See Figure 21-3 for a graphical illustration of how the buckets are assigned. Figure 21-3 Bucket Assignments You can specify the bounds in the reverse order, for example, WIDTHBUCKET ( custcreditlimit. 20000. 0. 4 ). When the bounds are reversed, the buckets will be open-closed intervals. In this example, bucket number 1 is ( 15000,20000 , bucket number 2 is ( 10000,15000 , and bucket number 4, is ( 0 ,5000 . The overflow bucket will be numbered 0 ( 20000. infinity ), and the underflow bucket will be numbered 5 (- infinity. 0 . It is an error if the bucket count parameter is 0 or negative. Example 21-20 WIDTHBUCKET The followin g query shows the bucket numbers for the credit limits in the customers table for both cases where the boundaries are specified in regular or reverse order. We use a range of 0 to 20,000. User-Defined Aggregate Functions Oracle offers a facility for creating your own functions, called user-defined aggregate functions. These functions are written in programming languages such as PLSQL, Java, and C, and can be used as analytic functions or aggregates in materialized views. See Oracle Database Data Cartridge Developers Guide for further information regarding syntax and restrictions. The advantages of these functions are: Highly complex functions can be programmed using a fully procedural language. Higher scalability than other techniques when user-defined functions are programmed for parallel processing. Object datatypes can be processed. As a simple example of a user-defined aggregate function, consider the skew statistic. This calculation measures if a data set has a lopsided distribution about its mean. It will tell you if one tail of the distribution is significantly larger than the other. If you created a user-defined aggregate called udskew and applied it to the credit limit data in the prior example, the SQL statement and results might look like this: Before building user-defined aggregate functions, you should consider if your needs can be met in regular SQL. Many complex calculations are possible directly in SQL, particularly by using the CASE expression. Staying with regular SQL will enable simpler development, and many query operations are already well-parallelized in SQL. Even the earlier example, the skew statistic, can be created using standard, albeit lengthy, SQL. CASE Expressions Oracle now supports simple and searched CASE statements. CASE statements are similar in purpose to the DECODE statement, but they offer more flexibility and logical power. They are also easier to read than traditional DECODE statements, and offer better performance as well. They are commonly used when breaking categories into buckets like age (for example, 20-29, 30-39, and so on). The syntax for simple CASE statements is: Simple CASE expressions test if the expr value equals the comparisonexpr . The syntax for searched CASE statements is: You can use any kind of condition in a searched CASE expression, not just an equality test. You can specify only 255 arguments and each WHEN. THEN pair counts as two arguments. To avoid exceeding this limit, you can nest CASE expressions so that the returnexpr itself is a CASE expression. Example 21-21 CASE Suppose you wanted to find the average salary of all employees in the company. If an employees salary is less than 2000, you want the query to use 2000 instead. Without a CASE statement, you might choose to write this query as follows: Note that this runs against the hr sample schema. In this, foo is a function that returns its input if the input is greater than 2000, and returns 2000 otherwise. The query has performance implications because it needs to invoke a function for each row. Writing custom functions can also add to the development load. Using CASE expressions in the database without PLSQL, this query can be rewritten as: Using a CASE expression lets you avoid developing custom functions and can also perform faster. Example 21-22 CASE for Aggregating Independent Subsets Using CASE inside aggregate functions is a convenient way to perform aggregates on multiple subsets of data when a plain GROUP BY will not suffice. For instance, the preceding example could have included multiple AVG columns in its SELECT list, each with its own CASE expression. We might have had a query find the average salary for all employees in the salary ranges 0-2000 and 2000-5000. It would look like: Although this query places the aggregates of independent subsets data into separate columns, by adding a CASE expression to the GROUP BY clause we can display the aggregates as the rows of a single column. The next section shows the flexibility of this approach with two approaches to creating histograms with CASE . Creating Histograms With User-Defined Buckets You can use the CASE statement when you want to obtain histograms with user-defined buckets (both in number of buckets and width of each bucket). The following are two examples of histograms created with CASE statements. In the first example, the histogram totals are shown in multiple columns and a single row is returned. In the second example, the histogram is shown with a label column and a single column for totals, and multiple rows are returned. Example 21-23 Histogram Example 1 Example 21-24 Histogram Example 2 Data Densification for Reporting Data is normally stored in sparse form. That is, if no value exists for a given combination of dimension values, no row exists in the fact table. However, you may want to view the data in dense form, with rows for all combination of dimension values displayed even when no fact data exist for them. For example, if a product did not sell during a particular time period, you may still want to see the product for that time period with zero sales value next to it. Moreover, time series calculations can be performed most easily when data is dense along the time dimension. This is because dense data will fill a consistent number of rows for each period, which in turn makes it simple to use the analytic windowing functions with physical offsets. Data densification is the process of converting sparse data into dense form. To overcome the problem of sparsity, you can use a partitioned outer join to fill the gaps in a time series or any other dimension. Such a join extends the conventional outer join syntax by applying the outer join to each logical partition defined in a query. Oracle logically partitions the rows in your query based on the expression you specify in the PARTITION BY clause. The result of a partitioned outer join is a UNION of the outer joins of each of the partitions in the logically partitioned table with the table on the other side of the join. Note that you can use this type of join to fill the gaps in any dimension, not just the time dimension. Most of the examples here focus on the time dimension because it is the dimension most frequently used as a basis for comparisons. Partition Join Syntax The syntax for partitioned outer join extends the ANSI SQL JOIN clause with the phrase PARTITION BY followed by an expression list. The expressions in the list specify the group to which the outer join is applied. The following are the two forms of syntax normally used for partitioned outer join: Note that FULL OUTER JOIN is not supported with a partitioned outer join. Sample of Sparse Data A typi cal situation with a sparse dimension is shown in the following example, which computes the weekly sales and year-to-date sales for the product Bounce for weeks 20-30 in 2000 and 2001: In this example, we would expect 22 rows of data (11 weeks each from 2 years) if the data were dense. However we get only 18 rows because weeks 25 and 26 are missing in 2000, and weeks 26 and 28 in 2001. Filling Gaps in Data We can take the sparse data of the preceding query and do a partitioned outer join with a dense set of time data. In the following query, we alias our original query as v and we select data from the times table, which we alias as t. Here we retrieve 22 rows because there are no gaps in the series. The four added rows each have 0 as their Sales value set to 0 by using the NVL function. Note that in this query, a WHERE condition was placed for weeks between 20 and 30 in the inline view for the time dimension. This was introduced to keep the result set small. Filling Gaps in Two Dimensions N-dimensional data is typically displayed as a dense 2-dimensional cross tab of (n - 2) page dimensions. This requires that all dimension values for the two dimensions appearing in the cross tab be filled in. The following is another example where the partitioned outer join capability can be used for filling the gaps on two dimensions: In this query, the WITH subquery factoring clause v1 summarizes sales data at the product, country, and year level. This result is sparse but users may want to see all the country, year combinations for each product. To achieve this, we take each partition of v1 based on product values and outer join it on the country dimension first. This will give us all values of country for each product. We then take that result and partition it on product and country values and then outer join it on time dimension. This will give us all time values for each product and country combination. Filling Gaps in an Inventory Table An inventory table typically tracks quantity of units available for various products. This table is sparse: it only stores a row for a product when there is an event. For a sales table, the event is a sale, and for the inventory table, the event is a change in quantity available for a product. For example, consider the following inventory table: The inventory table now has the following rows: For reporting purposes, users may want to see this inventory data differently. For example, they may want to see all values of time for each product. This can be accomplished using partitioned outer join. In addition, for the newly inserted rows of missing time periods, users may want to see the values for quantity of units column to be carried over from the most recent existing time period. The latter can be accomplished using analytic window function LASTVALUE value. Here is the query and the desired output: The inner query computes a partitioned outer join on time within each product. The inner query densifies the data on the time dimension (meaning the time dimension will now have a row for each day of the week). However, the measure column quantity will have nulls for the newly added rows (see the output in the column quantity in the following results. The outer query uses the analytic function LASTVALUE. Applying this function partitions the data by product and orders the data on the time dimension column ( timeid ). For each row, the function finds the last non-null value in the window due to the option IGNORE NULLS. which you can use with both LASTVALUE and FIRSTVALUE. We see the desired output in the column repeatedquantity in the following output: Computing Data Values to Fill Gaps Examples in previous section illustrate how to use partitioned outer join to fill gaps in one or more dimensions. However, the result sets produced by partitioned outer join have null values for columns that are not included in the PARTITION BY list. Typically, these are measure columns. Users can make use of analytic SQL functions to replace those null values with a non-null value. For example, the following query computes monthly totals for products 64MB Memory card and DVD-R Discs (product IDs 122 and 136) for the year 2000. It uses partitioned outer join to densify data for all months. For the missing months, it then uses the analytic SQL function AVG to compute the sales and units to be the average of the months when the product was sold. If working in SQLPlus, the following two commands will wrap the column headings for greater readability of results: Time Series Calculations on Densified Data Densificatio n is not just for reporting purpose. It also enables certain types of calculations, especially, time series calculations. Time series calculations are easier when data is dense along the time dimension. Dense data has a consistent number of rows for each time periods which in turn make it simple to use analytic window functions with physical offsets. To illustrate, let us first take the example on Filling Gaps in Data. and lets add an analytic function to that query. In the following enhanced version, we calculate weekly year-to-date sales alongside the weekly sales. The NULL values that the partitioned outer join inserts in making the time series dense are handled in the usual way: the SUM function treats them as 0s. Period-to-Period Comparison for One Time Level: Example How do we use this feature to compare values across time periods Specifically, how do we calculate a year-over-year sales comparison at the week level The following query returns on the same row, for each product, the year-to-date sales for each week of 2001 with that of 2000. Note that in this example we start with a WITH clause. This improves readability of the query and lets us focus on the partitioned outer join. If working in SQLPlus, the following command will wrap the column headings for greater readability of results: In the FROM clause of the inline view densesales. we use a partitioned outer join of aggregate view v and time view t to fill gaps in the sales data along the time dimension. The output of the partitioned outer join is then processed by the analytic function SUM. OVER to compute the weekly year-to-date sales (the weeklyytdsales column). Thus, the view densesales computes the year-to-date sales data for each week, including those missing in the aggregate view s. The inline view yearoveryearsales then computes the year ago weekly year-to-date sales using the LAG function. The LAG function labeled weeklyytdsalesprioryear specifies a PARTITION BY clause that pairs rows for the same week of years 2000 and 2001 into a single partition. We then pass an offset of 1 to the LAG function to get the weekly year to date sales for the prior year. The outermost query block selects data from yearoveryearsales with the condition yr 2001. and thus the query returns, for each product, its weekly year-to-date sales in the specified weeks of years 2001 and 2000. Period-to-Period Comparison for Multiple Time Levels: Example While the prior example shows us a way to create comparisons for a single time level, it would be even more useful to handle multiple time levels in a single query. For example, we could compare sales versus the prior period at the year, quarter, month and day levels. How can we create a query which performs a year-over-year comparison of year-to-date sales for all levels of our time hierarchy We will take several steps to perform this task. The goal is a single query with comparisons at the day, week, month, quarter, and year level. The steps are as follows: We will create a view called cubeprodtime. which holds a hierarchical cube of sales aggregated across times and products . Then we will create a view of the time dimension to use as an edge of the cube. The time edge, which holds a complete set of dates, will be partitioned outer joined to the sparse data in the view cubeprodtime . Finally, for maximum performance, we will create a materialized view, mvprodtime. built using the same definition as cubeprodtime . For more information regarding hierarchical cubes, see Chapter 20, SQL for Aggregation in Data Warehouses. The materialized view is defined in Step 1 in the following section. Step 1 Create the hierarchical cube view The materialized view shown in the following may already exist in your system if not, create it now. If you must generate it, note that we limit the query to just two products to keep processing time short: Because this view is limited to two products, it returns just over 2200 rows. Note that the column HierarchicalTime contains string representations of time from all levels of the time hierarchy. The CASE expression used for the HierarchicalTime column appends a marker (0, 1. ) to each date string to denote the time level of the value. A 0 represents the year level, 1 is quarters, 2 is months, and 3 is day. Note that the GROUP BY clause is a concatenated ROLLUP which specifies the rollup hierarchy for the time and product dimensions. The GROUP BY clause is what determines the hierarchical cube contents. Step 2 Create the view edgetime, which is a complete set of date values edgetime is the source for filling time gaps in the hierarchical cube using a partitioned outer join. The column HierarchicalTime in edgetime will be used in a partitioned join with the HierarchicalTime column in the view cubeprodtime. The following statement defines edgetime : Step 3 Create the materialized view mvprodtime to support faster performance The materialized view definition is a duplicate of the view cubeprodtime defined earlier. Because it is a duplicate query, references to cubeprodtime will be rewritten to use the mvprodtime materialized view. The following materialized may already exist in your system if not, create it now. If you must generate it, please note that we limit the query to just two products to keep processing time short. Step 4 Create the comparison query We have now set the stage for our comparison query. We can obtain period-to-period comparison calculations at all time levels. It requires applying analytic functions to a hierarchical cube with dense data along the time dimension. Some of the calculations we can achieve for each time level are: Sum of sales for prior period at all levels of time. Variance in sales over prior period. Sum of sales in the same period a year ago at all levels of time. Variance in sales over the same period last year. The following example performs all four of these calculations. It uses a partitioned outer join of the views cubeprodtime and edgetime to create an inline view of dense data called densecubeprodtime. The query then uses the LAG function in the same way as the prior single-level example. The outer WHERE clause specifies time at three levels: the days of August 2001, the entire month, and the entire third quarter of 2001. Note that the last two rows of the results contain the month level and quarter level aggregations. Note that to make the results easier to read if you are using SQLPlus, the column headings should be adjusted with the following commands. The commands will fold the column headings to reduce line length: Here is the query comparing current sales to prior and year ago sales: The first LAG function ( salespriorperiod ) partitions the data on gidp. cat. subcat. prod. gidt and orders the rows on all the time dimension columns. It gets the sales value of the prior period by passing an offset of 1. The second LAG function ( salessameperiodprioryear ) partitions the data on additional columns qtrnum. monnum. and daynum and orders it on yr so that, with an offset of 1, it can compute the year ago sales for the same period. The outermost SELECT clause computes the variances. Creating a Custom Member in a Dimension: Example In many OLAP tasks, it is helpful to define custom members in a dimension. For instance, you might define a specialized time period for analyses. You can use a partitioned outer join to temporarily add a member to a dimension. Note that the new SQL MODEL clause is suitable for creating more complex scenarios involving new members in dimensions. See Chapter 22, SQL for Modeling for more information on this topic. As an example of a task, what if we want to define a new member for our time dimension We want to create a 13th member of the Month level in our time dimension. This 13th month is defined as the summation of the sales for each product in the first month of each quarter of year 2001. The solution has two steps. Note that we will build this solution using the views and tables created in the prior example. Two steps are required. First, create a view with the new member added to the appropriate dimension. The view uses a UNION ALL operation to add the new member. To query using the custom member, use a CASE expression and a partitioned outer join. Our new member for the time dimension is created with the following view: In this statement, the view timec is defined by performing a UNION ALL of the edgetime view (defined in the prior example) and the user-defined 13th month. The gidt value of 8 was chosen to differentiate the custom member from the standard members. The UNION ALL specifies the attributes for a 13th month member by doing a SELECT from the DUAL table. Note that the grouping id, column gidt. is set to 8, and the quarter number is set to 5. Then, the second step is to use an inline view of the query to perform a partitioned outer join of cubeprodtime with timec. This step creates sales data for the 13th month at each level of product aggregation. In the main query, the analytic function SUM is used with a CASE expression to compute the 13th month, which is defined as the summation of the first months sales of each quarter. The SUM function uses a CASE to limit the data to months 1, 4, 7, and 10 within each year. Due to the tiny data set, with just 2 products, the rollup values of the results are necessarily repetitions of lower level aggregations. For more realistic set of rollup values, you can include more products from the Game Console and Y Box Games subcategories in the underlying materialized view. Scripting on this page enhances content navigation, but does not change the content in any way.