Animal Cognitive Studies
From Mbscientific_wiki
Lets cover some observations from the animal cognitive studies from PBS, Nova - Ape Genius:
1) In this experiment a peanut is placed in the bottom of a long glass tube that is tied up in an upright position. Never having seen this puzzle before, a chimp is to try to figure out how to get the food out. According to the Researcher Josep Call "an hour goes by and boom, they solve the problem". In the video you see the chimp going back and forth with mouth full of water, filling the tube until the peanut floats up within reach.
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Lets analyze what happened. The chimp must have memory of peanut tasting good (attractor, affirmed object representational memory). Perhaps he has the memory of things (nuts!?!) floating on water (representational action memory). Then its takes him a while to put the object and action memory together, and that Deductive Logic would provide a potential solution. (JOSEP CALL -Max Planck Institute for Evolutionary Anthropology). So you have the abstraction of object (peanut) and action (floating on water) formed in the chimp brain BEFORE he attempts the solution. And once the solution works, the chimp affirms and commits the object/action/circumstance to (procedural) memory (pictorially depicted, right). |
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2) Lets look at another chimp behavior from the same program. It is about chimps using twigs to fish for termites. Lets break down that action into its detailed constituents: a) termite tastes good (attractor, affirmed object-termite memory), b) chimp sees termites going into the mound through holes (termite-action-1 memory), c) chimp sees termites clinging to things (termite-action-2 memory), d) chimp sees twigs stuck in holes and crevices (object-twig memory). Then some genius chimp puts these abstractions together, grabs a twig with the right size for the hole (d), puts it in the hole so termites cling to it (c, b), pulls it out full of termites, and yum he has an affirmed learned behavior (compound object-action abstraction):
So here you have a set of object-action abstractions coalesced together in a hierarchy of complexity to form an affirmed object-action abstraction that itself will be committed to memory. (http://www.janegoodall.org/chimp_central/default.asp - Jane Goodall Gombe Tanzania study site)
3) Lets add on to the order of complexity. In the same TV program you see chimps dig up a termite mound with a big stick (shovel like) then try (1a..d) to fish out the termites with twigs. Lets analyze that.
Suppose chimps try (1a..d) at first and get scant results (termites are in too deep for a twig). But through play they know big sticks (object) can dig up dirt (action). So now they add this object-action sequence prior to (1). First they dig up the mound with the big stick, exposing shallow holes. Then they try (1) to get results. If we look at it diagrammatically we get:
But as you see this requires an if-then logic. There is a sequential order to the compound action, first the mound needs to be dug up, exposing shallow holes that can be fished with twigs. So the chimp strings together two distinct procedural memories, depending on the set of circumstances. Here the procedural memory has an if-ten-else intrinsic logical clause. A note of caution: when I (we) think of logic, I project my own analytical introspection, that sort of projection must be avoided.
So, in subsequent scenarios they can try different variations of the actions to get results. As you see the layers of complexity can be compounded. (http://www.wcs-congo.org/04science/02goualougo/index.html - Goualougo - Congo chimp study site)
4) To demonstrate how new behavior can be derived from previously compounded action-logic, here's another chimp trait similar to 2, 3. a) Chimp loves bush baby meet, b) chimp sees bush baby lodge in tree holes, c) in play chimp sees sticks skewer things, d) chimp fashions sticks to skewer bush babies. Actually there is more to it, they pick out the right sticks of length and girth, strip off the leaves, nibble on one end to sharpen it. For all intents and purposes they make spears. (Fongoli, Senegal, anthropologist Jill Pruetz(Iowa State University) and psychologist Andrew Whiten (University of St. Andrews).
So we can see that abstraction complexity coalesces in a hierarchical order. Yet, from what we can tell, in nature the depth of that hierarchy is relatively shallow.
In the course of studying animal cognitive strengths researchers have used a variety of techniques. One that has yielded surprising results has been the use of symbols as training tools.
First, I'll give you an example out of the same PBS Nova TV Documentary. Sally Boysen of Ohio State University asked chimps to choose between two dishes of M&Ms. One had fewer M&Ms than the other dish. The subject Sheba was to reach for the dish that was to go to another chimp Sarah, the left over dish was Sheba's. Sheba always went for the dish with more M&Ms, giving it to Sara. The impulse never allowed her to get the logic of the experiment: touch the dish with less M&Ms, giving that to Sarah and keeping the dish with more M&Ms.
The same experiment was done with the M&M dishes covered with numbers. The chimp that understood numbers got the logic of the experiment right. The dish with the smaller number goes to Sarah, the left over dish with the bigger number is the prize.
Dealing with symbols: numbers, words, pictures, etc. uses the cerebral circuits of the brain. When those control circuits are established, the complexity hierarchy can expand substantially.
Kanzi the Bonobo at The Great Ape Trust is reported to be using some 350 keyboard symbols and can understand complex instructions with a vocabulary of thousands of spoken words.
( Note: You can watch above examples, plus a whole lot more on the internet, PBS, Nova - Ape Genius (hour long movie): http://www.pbs.org/wgbh/nova/apegenius/program.html )
Alex the Talking Grey African Parrot (RIP) could similarly follow complex question/answer series- See it at Youtube: http://www.youtube.com/watch?v=XcLLk-r1aSs&feature=related. (It turns out this was (is) one famous parrot.)
Similarly Dolphins can follow complex instructions(on Youtube: http://www.youtube.com/watch?v=ZwJaUFHs-C4 )
So there is evidence that animals can receive symbolic instructions and act on them at high levels of complexity. Still, the hierarchy of complexity that they can demonstrably generate is relatively shallow. This ability to think symbolically, communicate symbolically, is what sets our complex hierarchy of abstraction in motion. And that is what we shall cover in the next chapter, Evolution of the Mind. But before that I want to make one very important point.
Memories that are occasionally affirmed form short term memory. Those are manifested as internal connection strengths of the neurons in the memory circuits. However, if an action is continually affirmed, it ends up as permanently imprinting on the memory circuit, by the way of switching on a "Long Term Memory Gene" in the neurons of the memory circuit, thereby establishing long term memory. We need this to establish the next point.
Take the example of driving a stick-shift car. Many may not remember, but before automatic transmissions you had to shift gears with a stick shift. Think about the logical controls that go into that operation. (1) Your right foot has to let off the gas petal. (2) Next, your left foot has to press down the clutch. (3) Then your right hand has to shift the stick from one correct gear to the next. (4) Then your left foot is to lift off the clutch, and (5) your right foot has to step back on the gas. While all of that is going on (6) your left hand has to do the driving, corresponding to the (7) sight information that is to (8) lead you to your destination and hopefully avoid crashing the car in the process. 9) And you have to continually hone into sound information coming from the car, telling you whether to shift gears or not. So all of these controlled actions are distinct yet associated, some in succession, others simultaneous. Further, they are hierarchical in the context of getting you to your destination, i.e. the first decision is "I want to go from here to there", then "I'm going to drive along this route" and then all of the above decision/action kicks in.
One last point on this subject, heuristic behavior, when repeated and affirmed ad infinitum can appear as instinctive as pre-wired behavior. Why? Because of the switching on of the long term memory gene in the memory neurons. In fact, as daunting as driving a stick-shift car may be for a while, after years of doing the controlled actions, it becomes instinctive. One does not think about it, is not even conscious of it, he just does it. In this way, memory can turn in to instinct. Memory that is affirmed to the point of becoming literally instinctive (genetically driven) can itself serve as the affirming factor in decisions of a control circuit, i.e. behaving as and sometimes overriding externally affirmed memories.
Key
Chapter Key:
Morphological Flows, entities going through functional constructs thereby creating more complex entities with more complex functionalities:
Cell Stimulus-Response == cell differentiation ==> sensory and motor neurons
sensory neurons +motor neurons +controller neurons == life actions ==> instinctive behavior
Sensory neurons + neural memory circuits +affirming neurons == training ==> learning
Sensory neurons + memory circuits + integrator/arbitrators + controllers + motor cells == life actions ==> heuristic behavior
Inherent Reality (abstract entities and phenomenon) + heuristic networks ==imprinting ==> Perceived Reality (abstractions and an internal notion of what is physical)
Courses
http://ocw.mit.edu/OcwWeb/Brain-and-Cognitive-Sciences/index.htm - The entire MIT OpenCourseware curriculum on Brain and Cognitive Sciences, including:
http://ocw.mit.edu/OcwWeb/Brain-and-Cognitive-Sciences/9-373Somatosensory-and-Motor-SystemsSpring2002/CourseHome/index.htm - MIT - 9.373 Somatosensory and Motor Systems
http://ocw.mit.edu/OcwWeb/Brain-and-Cognitive-Sciences/9-01Fall-2003/CourseHome/index.htm - MIT - 9.01 Neuroscience and Behavior
http://ocw.mit.edu/OcwWeb/Brain-and-Cognitive-Sciences/9-03Fall-2003/CourseHome/index.htm - MIT - 9.03 / 9.031 Neural Basis of Learning and Memory
http://ocw.mit.edu/OcwWeb/Brain-and-Cognitive-Sciences/9-10Spring-2004/CourseHome/index.htm - MIT - 9.10 / 9.100 Cognitive Neuroscience
http://ocw.mit.edu/OcwWeb/Brain-and-Cognitive-Sciences/9-14Spring-2005/CourseHome/index.htm - MIT - 9.14 Brain Structure and its Origins
http://ocw.mit.edu/OcwWeb/Brain-and-Cognitive-Sciences/9-66JFall-2004/CourseHome/index.htm - MIT - 9.66J / 9.660J / 6.804J Computational Cognitive Science
links
http://www.youtube.com/watch?v=2Ei6wFJ9kCc - Hour talk on Neurocomputational models for understanding the brain circuits for learning - From GoolgleTechTalks, Dr. Mark Gluck, Rutgers U. (at YouTube)
http://www.pbs.org/wgbh/nova/apegenius/program.html - Ape cognitive and problem solving skills - hour program from PBS NOVA
- New Caledonian Crow Intelligence ( research website: http://users.ox.ac.uk/~kgroup/tools/introduction.shtml)
- Cetacean Intelligence on wikipedia: http://en.wikipedia.org/wiki/Cetacean_intelligence
- Elephant Intelligence (http://en.wikipedia.org/wiki/Elephant_intelligence)
- Dog Intelligence (http://en.wikipedia.org/wiki/Dog_intelligence).
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