Q' is a posteriori.Recent work to explain the problem of Mary's seeming new knowledge relies upon a semantic theory of indexicals. !ARU and the problem of "rock paper scissors" provides a distributive computational approach to understanding the role of indexicals in the knowledge argument.
rock_paper_scissors_a.txt provides a neutral platform for understanding a rational decision process for prefering some single set of behaviors/methods for solving a best action puzzle given an array of neural stimulations. _a is a neutral platform in that before providing the model with a system of ostensive indexical identification of objects/processes it exhibits no prefered behaviors/method. The model is balanced so that it tends to settle on rejection of all actions. It has an inate sort of kantian preference to do no harm.
If the inputs to the system are put out of balance through the use of the "be forgetful" button, (without the intervention of a system for identification of indexicals) it will settle on a random prefered behavior/method contingent upon what environmental influences happen by chance to dominate.
Later we can extend the system to simulate environmental conditions for Mary inside the room vs mary outside the room. We can use this system to suggest how differences in learning conditions interact with an indexical semantics and influence what Mary has the possibility to know. We can also analyse the system to arrive at a criteria for "individuating" and "counting" Mary's knowledge and determining how much and which of it is new knowledge. We can then use that to suggest whether this adds support to the abilities hypothesis the the qualia hypothesis or some other/new? hypothesis.
One new possibility that is being explored here is that Mary will be shown to have less knowledge after leaving the room.
For those not familiar with the story:
According to Frank Jackson (2004) "Mary is a brilliant scientist who is, for whatever reason, forced to investigate the world from a black and white room via a black-and-white television monitor. She specialises in the neurophysiology of vision and acquires, let us suppose, all the physical information there is to obtain about what goes on when we see ripe tomatoes, or the sky, and use terms like 'red', 'blue', and so on . . . .
"What will happen when Mary is released from her black-and-white room or is given a color television monitor? Will she learn anything or not? It seems just obvious that she will learn something about the world and our visual experience of it. But then it is inescapable that her previous knowledge was incomplete. But she had all the physical information. Ergo there is more to have than that, and physicalism is false" (pp. 42-43).
The Chalmers Formalism
(1) 'PT Q' is a posteriori.
(2) If 'PT 
Q' is a posteriori, 
is 1-contingent.
(3) If 'PT 
Q' is 1-contingent, 
is 2-contingent or
panprotopsychism is true.
(4) If 'PT 
Q' is 2-contingent, Physicalism is false.
(5) Physicalism is false or panprotopsychism is true.
"Let P be the complete micro-physical truth about the world, and let Q be a truth stating that phenomenal redness is instantiated, deploying a pure phenomenal concept of phenomenal redness.
"...straightforwardly ... conjoin[ing] to P a "that's-all" claim T, saying that our world is a minimal world satisfying P (roughly, a world containing no more than it needs to in order to satisfy P).. .
"Let us say that a sentence S is 1-necessary when its epistemic intension is true at all centered metaphysically possible worlds, and that it is 1-contingent when its epistemic intension is false at some centered metaphysically possible world. Let us also say that a sentence S is 2-necessary when it's subjunctive intension is true at all worlds, and that it is 2-contingent when it's subjective intension is false at some world.
"2-D thesis: If S is a posteriori, S is 1-contingent" (D.J. Chalmers 2004, pp. 269 298).
A system of Ostensive Indexical Identification of Objects/Processes
Chalmers considers:
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“beliefs and concepts as psychological entities rather than as semantic entities. Beliefs and concepts have contents, but are not themselves contents. . . . . “ Mary looks at a red apple, and visually experiences its color. This experience instantiates a phenomenal property R, which we might call phenomenal redness. . . . Phenomenal redness (a property of experiences, or of subjects of experience) is a different property from external redness (a property of external objects), but both are respectable properties in their own right.” according to Chalmers (2004 p. 270). |
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"The reference of the concept expressed by ‘red’ or ‘phenomenal redness’ is fixed via a relation to red things and a relation to paradigmatic red objects ostended in learning the term. . . .The phenomenal concept plausibly designates an intrinsic property rigidly, so that there are counterfactual worlds in which red experiences are never caused by red things.
“One can distinguish at least two relational phenomenal concepts, depending on whether reference is fixed by relations across a whole community of subjects, or by relations restricted to the subject in question. The first is what we can call the community relational concept, or redc. This can be glossed roughly as the phenomenal quality typically caused in normal subjects within my community of paradigmatic red things. The second is what we can call the individual relational concept, or redI. This can be glossed roughly as the phenomenal quality typically caused in me by paradigmatic red things. The two concepts redC and redI will corefer for normal subjects, but for abnormal subjects they may yield different results. . . . "Phenomenal properties can also be picked out indexically. When seeing the tomato, Mary can refer indexically to a visual quality associated with it, by saying ‘this quality’ or ‘this sort of experience’. These expressions express a demonstrative concept that we might call E. E functions in an indexical manner, roughly by picking out whatever quality the subject is currently ostending. Like other demonstratives, it has a ‘character,’ which fixes reference in a context roughly by picking out whatever quality is ostended in that context; and it has a distinct ‘content’, corresponding to the quality that is actually ostended—in this case, phenomenal redness. The demonstrative concept E rigidly designates its referent, so that it picks out the quality in question even in counterfactual worlds in which no one is ostending the quality.
“The three concepts redC, redI, and E may all refer to the same quality, phenomenal redness. . . . There is another crucial phenomenal concept in the vicinity, one that does not pick out phenomenal redness in terms of its relation to external objects or to acts of ostension, but rather picks it out in terms of its intrinsic phenomenal nature. This is what we might call a pure phenomenal concept. . . .Mary may learn (or reasonably come to believe) that red things will typically cause experiences of such-and such quality in her, and in other members of her community. She may learn (or gains the cognitively significant belief) that the experience she is now having has such-and-such quality, ant that the quality she is now ostending is such-and-such. Call Mary’s ‘such-and-such’ concept her R.” (p. 271-272).. |
Mary might come to believe:
redC = R
redI = R
E = R
Chalmers maintains that these are not a priori for Mary.
Mary Inside the Room
Mary Outside the Room
Individuation and counting Knowledge
REFERENCES
D. I Chalmers 2004 Phenomenal Concepts and the Knowledge Argument, in There's something about Mary Essays on Phenomenal Consciousness and Frank Jackson's Knowledge Argument, Eds. P. Ludlow, Y. Nagasawa, D. Stoljar, MIT Press, Cambridge, Mass.
F. Jackson 2004 Epiphenomenal Qualia, in There's something about Mary Essays on Phenomenal Consciousness and Frank Jackson's Knowledge Argument, Eds. P. Ludlow, Y. Nagasawa, D. Stoljar, MIT Press, Cambridge, Mass.
P. Thagard 2000 Coherence in Thought and Action, MIT Press, Cambridge, Mass.
P. Thagard 1992 Conceptual Revolution, Princeton University Press, Princeton, NJ.
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= = = = = = = = = = = = = ABOUT THE MODEL = = = = = = = = = = = = = = |
This model provides the user with the ability to rationally assess competing ideas on the basis of coherence using a connectionist formula to update the activation of interconnected nodes. The network is also a (PDP) Parallel Distributive Processing Model in the sense that each node acts independently and simultaneously* to update its activation with information about only those nodes with which it is immediately connected.
As activation of a node increases, the node size increases. As activation decreases, the node size decreases. Below activation 0.0 the node changes from a circle to a triangle representing rejection. Some models may have nodes that include a valence in addition to an activation. Changes in valence are indicated by color changes of the nodes.
With the exception of orange special nodes set at 1, the default activation of a node is 0.01. Changes of activation and valences is achieved via links between the nodes. Positive links, colored green, are created with a default weight of 0.04 while negative links, colored red, have a default weight of -0.06. The activation and valence of unconnected nodes will decay at a rate of 0.05 per cycle.
* the model mimics simultaneous updating by first updating each node in a network and then beginning it's next cycle.
======================== AFFECT ======================= |
This particular example models a coherence network with the influence of affect shown as a star.
======================== REASON ======================= |
The theory of explanatory coherence can be summarised in the following principles (Thagard 1992. Conceptual revolutions. Princeton, NJ: Princeton University Press, 2000. Coherence in thought and action. Cambridge, MA: MIT Press.)
======================== UTILITY ======================= |
Thagard (2000) offers the following principles to be considered in the design of PDP models of utility and which principles may be applied to !ARU:
"Principle L1: Symmetry Coherence and incoherence are semmetrical relations: if factor (action or goal) F1, coheres with factor F2, then F2 coheres with F1.
Principle L2: Facilitation Consider actions Ai, . . . , An that together facilitate the accomplishment of goal G. Then
(a) each Ai coheres with G,
(b) each Ai coheres with each other Ai, and
(c) the greater the number of actions required, the less the coherence among the actions and goals.[we may imagine this to be a way to interpert work ]
Principle L3: Incompatibility
(a) If two factors cannot both be performed or achieved, then they are strongly incoherent.
(b) If two factors are difficult to perform or achieve together, then they are weakly incoherent.
Principle L4: Goal priority Some goals are desirable for intrinsic or other noncoherence reasons.
Principle L5: Judgment Facilitation and competition relations can depend on coherence with judgments about the acceptability of factual beliefs.
Principle L6: Decision Decisions are made on the basis of an assessment of the overall coherence of a set of actions and goals (p. 128)."
Actions like hypotheses are evaluated with respect to their coherence with each other and with goals. In addition goals get a degreee of priority since they will connect directly with specials or else Vspecials via a valence node.
======================================================================== = = = = = = = = = = = = = USING THE INTERFACE = = = = = = == = = = = = = |
Buttons and sliders along the top of the interface provide the user with the ability to load and run the default model and to modify its operation.
Buttons and sliders along the left allow the user to add and remove components of the model.
Networks are displayed in the large center square.
Information about the status of the network is displayed on demand at in the white rectangle at the right of screen.
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================TO RUN THE DEFAULT MODEL========================================================================================= |
Click the button labeled Rock Paper Scissors
click the button near the top left labeled "balance weights"
click on he button labled valence off then
click on "the button labeled "go".
As activation of a node increases, the node size increases. As activation decreases, the node size decreases. Below activation 0.0 the node changes from a circle to a triangle representing rejection. If "valence off" is not selected, color changes will represent the flow of valence through the network.
Click the button at the top right labeled display output to see display of the current status of each node.
========================================================================== = = = = = = = = = TO RUN ADDITIONAL PRE-SCRIPTED NETWORKS = = = = = =
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========================================================================== = = = = = = = = = = = = TO MAKE CHANGES TO A NETWORK = = = = = = = = = = = |
Click the control labeled "leison nodes"
Using your mouse, click on each node to delete. All links to the node will be deleted as well.
Click the "leison nodes" control again after having deleted all nodes you selected.
Several types of nodes may be created with default settings and user interpretations. NOTE: Any nodes that are added by the user can only operate correctly if the network is reset to it's initial conditions before making additions and "Accounts" of new relationships among nodes must be done only after all new nodes have been added. To run your newly revised network, click reset, then balance weights, then go.
Look in the left hand column of buttons.
Click the button labeled "AddObervation" to add nodes representing the interface between the world and how our senses interpret observation. A user input box appears allowing you to enter a brief descriptive label of the observation to be interpreted, e.g. "the wind turns me on". ARU will then create a new node of activation 0.01 with your discriptive label and identify the number of the node, the fact that it is to be regarded as evidence, and link it to a special node. At a later stage in developing your network you can use the mouse to link your new node to others.
Click the button labeled "AddHypothesis" to add nodes representing concepts that may account for some bits of evidence. A user input box appears allowing you to enter a brief descriptive label of your hypothesis, e.g., "Because the wind is high" Another box appears allowing you to assign your hypothesis to a collection of nodes representing hypotheses that give a collective name, e.g., "T1". ARU will then create a new node of activation 0.01 with your descriptive label, identify the number of the node, and tag it as a hypothesis. At a later stage in developing your network you can use the mouse to link your new node to nodes representing supporting evidence and other concepts withwhich it may cohere or in-cohere.
Once you have created and linked all addtional nodes you can click the ACCOUNTS button to begin updating any new explanitory relations among nodes. Next, click a node you've newly created that is explained by another node or combination of nodes. An input box will appear which asks "Is this being explained by anything" Click the drop down arrow if the node has no explaination and pick "no" other wise click "OK" and a new box asks "How many other nodes will this node be explained by?" Using the AFFECT REASON UTILITY principles as your guide, count how many other nodes this node is explained by and enter the numeral in the blank provided. A new input box will now appear that asks you to provide a node number of each explaining node. Each time you enter a node number of an explaining node and click ok another input box will appear asking for the next node number until you have entered all of the node numbers that explain the node you at first clicked.
when the explainations are exhausted for the current clicked node no more input boxes will appear and you can click on the next node in need of explaination. When you're done exploring explanations click the ACCOUNTSbutton again to turn it off.
Click the control labeled "leison links"
Using your mouse point to the midpoint of the link to be deleted, click on each link to delete.
Click the "leison links" control again after having deleted all nodes you selected.
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=======================SCRIPTING RULES==================== |
go here
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