Posts tagged ‘LL’

19/02/2010

Re: Uwagi do tekstu: “L.Lazarz, Ignorance is bless. Ograniczenia wpływu nauk poznawczych na dziedziny normatywne.”

Jeszcze przed spotkaniem chciałbym odnieść się do głównego zarzutu wobec tekstu. Jak rozumiem chodzi o zarzut związany charakterystyką wiedzy i przypisaniem jej deterministycznego charakteru. Chciałbym rozróżnić wstępnie dwie kwestie.

(1)   Recenzent płynnie zamienia wywody o charakterze wiedzy na wywody dotyczące sposobu funkcjonowania umysłu. Specjalnie staram się pisać o wiedzy a nie o świecie jakim on jest, ponieważ są to dwie różne rzeczy. Nie piszę o funkcjonowaniu umysłu, czy szerzej świata (przypisując mu deterministyczny charakter) tylko o charakterystyce wiedzy, a postrzegam te dwie rzeczy jako zupełnie różne. Nie przesądzam w tej pracy jaki świat jest (deterministyczny czy nie) tylko o tym jak nam się ten świat jawi w tym co nazywamy wiedzą o świecie. Czyli nie traktuje o (świecie) tylko o (świecie + sposób ekstrakcji uogólnionej informacji o świecie z perspektywy poznawczych mechanizmów budowania wiedzy = reprezentacja świata w naszych umysłach). TO ostatnie jest ntak naprawdę jedynym dostępnym nam światem. Od poznawczej soczewki nie ma ucieczki.

(2)   Po drugie nieudolnie unikam pojęcia determinizmu (staram się pisać gdzie tylko  możliwe o STOPNIU WYMUSZANIA STANÓW RZECZY) ponieważ nawet jeśli traktuję nie o świecie a tylko o jego umysłowej reprezentacji to niekoniecznie używam terminu determinizm w klasycznym rozumieniu tego słowa. Nie chcę tracić miejsca na wywodu związane z determinizem, ale tak jak klasycznie go rozumiem, bardziej dotyczy koncepcji, z którą wiąże się przypisanie danym przyczynom jednoznacznych skutków. Np. Spinoza wykluczył w świecie istnienie przypadku i wolności. Używając pojęcia determinizm mam na myśli sytuację, w której dopuszczam, że nie potrafimy przewidzieć określonego skutku, ale jeśli posiadamy jakąś wiedzę to przynajmniej potrafimy szacowac pradopodobieńśtwo jego wystąpienia. W tym znaczeniu fundamentalne znaczenia ma właśnie przypadek, co stoi w sprzeczności z poglądami głoszonymi przez wielu deterministów (przynajmniej ich część)  a nie stoi w moim przekonaniu z interpretacjami wynikającymi z mechaniki kwantowej (zasad nieoznaczoności Heisenberga). Konia z rzędem dla tego kto poda mi przypadek uogólnionej wiedzy, które nie będzie mówiła, że pewien stan rzeczy wymusza (w takim czy innym stopniu) na inny stan rzeczy.

Taka interpretacja wiedzy powoduje, że jeśli mówimy o wiedzy o umyśle, to wiemy, że jakieś stany rzeczy określają przynajmniej prawdoponieńśtwo wystąpienia innych stanów rzeczy (np. zachowania). CO się dzieje między tymi „widełkami”, czy w ramach tych widełek jest wolna wola to sprawa wtórna, ale jeśli mamy wiedzę o mechanizmach podejmowania decyzji to jest ona (w świetle posiadanej wiedzy) jakoś tam determinowana (przynajmniej w części). Konkluzją artykułu jest stwierdzenie, że nawet taka sytuacja jest dla nas nie do zaakceptowania z przyczyn stojącących u podstaw systemów normatywnych. Nie stać nas na takie rozmycie kwestii wolnejwoli (ale niekoniecznie jej wykluczenie) bo prowadziłoby to to rozmycia min. kwestii odpowiedzialności.

ŁŁ

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19/02/2010

Short review: Greene, J.D. 2003. From neural “is” to moral “ought”: what are the moral implications of neuroscientific moral psychology? Nature Reviews Neuroscience, Vol. 4, 847-850.

Text:

Greene, J.D. 2003. From neural “is” to moral “ought”: what are the moral implications of neuroscientific moral psychology? Nature Reviews Neuroscience, Vol. 4, 847-850.

Thesis:

More involvement of “emotional” structures of the brain when deciding about personal moral dilemmas is explained with evolutionary perspective. We evolved altruistic instincts that direct us to help others in dire need, but mostly when the ones in need are presented in an ‘up-close-and-personal’way.

Abstract:

The Author distinguish two type moral dilemmas;  the personal one (that it would be deeply wrong to abandon a bleeding stranger by the side of the road in order to preserve one’s leather car seats) and impersonal (that it is morally acceptable to spend money on luxuries when that money could be used to save the lives of impoverished people).

To explore difference between this two type of dilemmas the author,conducted a brain imaging study in which participants responded to the above moral dilemmas. The dilemma with the bleeding hiker is a ‘personal’ moral dilemma, in which the moral violation in question occurs in an ‘upcloseand-personal’ manner. The donation dilemma is an ‘impersonal’ moral dilemma, in which the moral violation in question does not have this feature. To make a long story short, he found that judgements in response to ‘personal’moral dilemmas,  compared with ‘impersonal’ ones, involved greater activity in brain areas that are associated with emotion and social cognition. Why should this be?

The Author uses an evolutionary perspective. As he noticed over the last four decades, it has become clear that natural selection can favour altruistic instincts under the right conditions, and many believe that this is how human altruism came to be. If that is right, then our altruistic instincts will reflect the environment in which they evolved rather than our present environment. With this in mind, consider that our ancestors did not evolve in an  environment in which total strangers on opposite sides of the world could save each others’ lives by making relatively modest material sacrifices. Consider also that our ancestors did evolve in an environment in which individuals standing face-to-face could save each others’ lives, sometimes only through considerable personal sacrifice. Given all of this, it makes sense that we would have evolved altruistic instincts that direct us to help others in dire need, but mostly when the ones in need are presented in an ‘up-close-and-personal’way.

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16/02/2010

L.Lazarz, Ignorance is bless. Ograniczenia wpływu nauk poznawczych na dzieidziny normatywne.

Ignorance is bless_Ograniczenia wpływu nauk poznawczych na dzieidziny normatywne.

Witam,

Tekst jest może nieco “publicystyczny”, ale inspirowany ostatnią rozmową i omawianiem tekstu Radka. W skrócie wyraża on pewien sceptycyzm co do ewentualnych rewolucji na gruncie prawa w związku z posiadaniem przez nas coraz większej wiedzy o ludzkim umyśle.  W skrócie próbuje wykazac dlaczego neurolaw jest skazana na pewną “niszowość”. Wybaczcie trochę uogólnień, ale nie miałem wiele czasu:)

Ł.

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12/02/2010

Pei Wang “Cognitive Logic versus Mathematical Logic Department of Computer and Information Sciences, Temple University, pei.wang@temple.edu, http://www.cis.temple.edu/_pwang/

Abstract:

The Author considers what is the deference between a “cognitive logic” as used in everyday life and a “mathematical logic” as used in meta-mathematics? A key difference is their assumptions on whether their knowledge and resources are sufficient to solve the problems they face. On this aspect, the author distinguish three types of reasoning systems:

1.  Pure-axiomatic systems

As the author says pure-axiomatic systems  are designed under the assumption that both knowledge and resources are sufficient. A typical example is the notion of “formal system” suggested by Hilbert (and many others), in which all answers are deduced from a set of axioms by a deterministic algorithm. The axioms and answers get their meaning by being mapped into a concrete domain using model-theoretical semantics. Such a system is built on the idea of sufficient knowledge and resources, because all relevant knowledge is assumed to be fully embedded in the axioms, and because questions have no time constraints, as long as they are answered in finite time. If a question requires information beyond the scope of the axioms, it is not the system’s fault but the questioner’s, so no attempt is made to allow the system to improve its capacities and to adapt to its environment.

2.  Semi-axiomatic systems

These systems are designed under the assumption that knowledge and resources are insufficient in some, but not all, aspects. Consequently, adaptation is necessary. Most current non-classical logics fall into this category. For example, non-monotonic logics draw tentative conclusions (such as “Tweety can fly”) from defaults (such as “Birds normally can fly”) and facts (such as “Tweety is a bird”), and revise such conclusions when new facts (such as “Tweety is a penguin”) arrive. However, in these systems, defaults and facts are usually unchangeable, and time pressure is not taken into account. Fuzzy logic treats categorical membership as a matter of degree, but does not accurately explain where the degree come from.

3.  Non axiomatic systems

Pei Wang introduces the main assumptions of the non axiomatic reasoning system which absorbs non axiomatic logic that is used within his project named Non Axiomatic Reasonic System (NARS). This cognitive logic is different from a mathematical logic firstly in its assumption on the sufficiency of knowledge and resources. Because of this difference, the formal language, semantic theory, and inference rules of these two types of logic are different too, and so are the memory structure and control mechanism when

they are implemented in a computer system.

Comments:

The paper is a good perspective to consider why logic is a basic and useful tool  for legal interpretations. The problems that arise in use of logic systems while interpreting laws result from fact we do not use a cognitive logic but more or less axiomatic systems. However the way we think is the only one and its based on cognitive skills that are modeled within the Pei Wang project.

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11/02/2010

Lovett, M. C. (1998). Choice. In J. R. Anderson, & C. Lebiere (Eds.). The atomic components of thought, 255-296. Mahwah, NJ: Erlbaum.

Text:

Lovett, M. C. (1998). Choice. In J. R. Anderson, & C. Lebiere (Eds.). The atomic components of thought, 255-296. Mahwah, NJ: Erlbaum.

Abstract:

In ACT-R theory each production rule is chosen according to the probability that reflects its expected gain E(i) relative to the competitors expected gains E(j). ACT-R chooses the production with the highest expected gain, but because of the noise in evaluation the production with a highest expected gain is chosen only a certain proportion of time. The presented below Conflict Resolution Equatation describes the probability that the a production with its expected gain E(i) will have the highest noise added expected gain

where t controls the noise of the evaluation.  There evaluations of expected gain are computed as the quantity E=PG – C, where P Is estimated probability of achieving the productions goal, G is the value of the goal and C is the cost to be expected in reaching the goal. P is the estimated probability of eventual successes in attaining the goal, it is decomposed into two parts: P=qr, where q is the probability that the production under consideration will achieve intended next state, and t is the probability of achieving the production’s goal given arrival at the intended next state. For practical reasons we can takes q a 1, leaving r as the main quantity to estimate. Under this constraint the r  parameter is important for determining  the choice among competitive productions. When a production’s parameter r Is low it implies that the production tends not to lead to the goal even when it leads to its intended next state, this r low value will be represented in a low P value, which will lead the production to have a low expected gain. In contrast a production with a high likehood of leading to its goal  will have a higher estimated probability of achieving the goal and hence a higher expected gain evaluation.

In ACT-R the value of the production’s r parameter is estimated as:

r = successes/(successes + failures)

The very important improvement made within ACT-R theory of choice is also implementation of decay of successes and failures experiences used in computing expected gain. In other words: more time elapsed from last use of a production rule a r parameter shall be lower.

r(t) = successes (t) /[successes (t) + failures (t)]

where

where t(j) is defined as how long ago past success of failure was.

The author describes in the article variety of examples proving how much such implementations makes us closer for good description of the human choice.

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11/02/2010

Rumelhart, D.E. (1980) Schemata: the building blocks of cognition. In: R.J. Spiro etal. (eds) Theoretical Issues in Reading Comprehension, Hillsdale, NJ: Lawrence Erlbaum.

Text

Rumelhart, D.E. (1980) Schemata: the building blocks of cognition. In: R.J. Spiro etal. (eds) Theoretical Issues in Reading Comprehension, Hillsdale, NJ: Lawrence Erlbaum.

Abstract

One of the most influential theory on schemas and concepts which are crucial issue for a larger cognitive area regarding knowledge is the schema theory. The theory, besides the frame theory (Minksy 1975) is one of the pillars of current cognitive knowledge about schemata. As “Schemata can represent knowledge at all levels-from ideologies and cultural truths to knowledge about the meaning of a particular word, to knowledge about what patterns of excitations are associated with what letters of the alphabet. We have schemata to represent all levels of our experience, at all levels of abstraction. Finally, our schemata are our knowledge. All of our generic knowledge is embedded in schemata.” (Rumelhart 1980).

Schema theory assumes that when individuals obtain knowledge, they attempt to fit that knowledge into some structure in memory that help them make sense of that knowledge. Schema theory proposes that the individuals breakdown information into generalizable chunks which are then categorically stored in the brain for later recall. Schema theory is an active strategy coding technique necessary for facilitating the recall of knowledge. As new knowledge is perceived, it is coded into either a pre-existing schema or organized into a new script. Schemata are organized mental structures that allows the learners to understand and associate what is being presented to them.

According to this theory, schemata represent knowledge about concepts: objects and the relationships they have with other objects, situations, events, sequences of events, actions, and sequences of actions. A simple example is to think of your schema for dog. Within that schema you most likely have knowledge about dogs in general (bark, four legs, teeth, hair, tails) and probably information about specific dogs, such as collies (long hair, large, Lassie) or springer spaniels (English, docked tails, liver and white or black and white, Millie). You may also think of dogs within the greater context of animals and other living things; that is, dogs breathe, need food, and reproduce. Your knowledge of dogs might also include the fact that they are mammals and thus are warm-blooded and bear their young as opposed to laying eggs. Depending upon your personal experience, the knowledge of a dog as a pet

(domesticated and loyal) or as an animal to fear (likely to bite or attack) may be a part of your schema. And so it goes with the development of a schema. Each new experience corporates more information into one’s schema.

Comments

The theory assumes how a knowledge is acquired and stored In the cognitive architecture. Such theories as the above one or the frame theory (Minsky 1975) allow to think seriously about computer simulation of larger areas of the human cognition. This ability gives us much more; we have more and more deterministic knowledge about human cognition. Independently from the above such theories are very useful for any discipline dealing with knowledge and schemas. Nowdays it is difficult to  discuss about conceptual schemes in law and ethics without referring to the above mentioned influential scheme theories.

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11/02/2010

Minsky, M. 1977. Frame theory. In P.N. Johnson-Laird and P.C.Wason, Thinking: Reasings in Cognitive Science, Cambridge: Cambridge University Press, pp. 355-376.

Text

Minsky, M. 1977. Frame theory. In P.N. Johnson-Laird and P.C.Wason, Thinking: Reasings in Cognitive Science, Cambridge: Cambridge University Press, pp. 355-376.

Abstract

As the author says: “Here is the essence of the frame theory: When one encounters a new situation (or makes a substantial change in one’s view of a problem), one selects from memory a structure called a frame. This is a remembered framework to be adapted to fit reality by changing details as necessary.” A frame is data structure for representing the stereotyped situation (for example being in the certain kind of living room). Any frame is connected with some kind of information (for example about how to use the frame, what is going to be next, etc). The frame can be imagined as a network of nodes and relations. Top levels of this frame fixed and represent the are always true about supposed situation (personally I think it shall be said not always but with the biggest probability comparing any other knowledge about supposed situation) and lower levels have many terminals – “slots”, that must be filled with specific instances or data. Each terminal can specify the conditions its assignments must meet.

As the author says: “(…) collections of related frames are linked together into frame systems. The effects of important actions are mirrored by transformations between the frames of a system. These are used to make certain kinds of calculations economical, to represent changes of emphasis and attention, and for effectivness of imaginery”.

For example for visual scene analysis, the same scene seen from different perspective is represented by different frames which however are connected with relations of the above mentioned, relevant transformations (in case of non visual kinds of frames of a system differences between frames can represent particular actions, cause effect relation or changes in conceptual viewpoint). Author underline that the crucial point that it makes possible to coordinate information gathered from different viewpoints is that different frames of the system share the same terminals.

Strong point of Minsky’s theory is that it takes into account different expectations and different presumptions. A frame’s terminals are normally already filled with default assignments. Thus a frame m ay contain a many details whose supposition is not specially warranted by the situation. These have many issues in representing general information, most likely cases, techniques for by passing logic and to make useful generalizations. These default assignments are not strongly connected with the subject frame (its terminals), they can be understood as “variables” that described rather the particular situation that the “main stream” frame.

The above main presumption of the frame theory are verified later on in the article in particular cases. The way how knowledge is acquired, represented and organized for future computation according to cognitive science is strongly associated with concepts presented in Minsky’s paper.

Comment:

The phenomena of cognitive sciences and their up going popularity is they moved forward the border where non scientific discussion starts about human being. By non scientific discussion I mean the discussion where the distance between empiric verified presumption and the philosophical conclusion is really far. Cognitive sciences have made the difference much shorter on many fields oh our knowledge about human beings, about ourselves (about cognition). Especially on the ground of two main pillars of cognitive sciences, AI and psychology, many authors have published their revolutionary articles where bigger and bigger pieces of strict, computable or almost computable, deterministic (in probabilistic way) and  causality knowledge about the mind’s abilities have been presented. Moreover many of such hypothesis are verified from empiric point of view (psychology) and many from biological point of view (neurosciences). Big spots of such knowledge has appeared after famous 1956. One of these important articles was the one presented in 1977 by Marvin Minsky “the pope of AI”. The author described the frames theory, which explained the mechanism of acquisition of knowledge and its representation in way allowing for its potential computable transformation giving the same effects as in the intelligent minds.

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