Wilfrid Sellars Philosophy and the Scientific Image of Man Sellars in his article has a closer look at phylosophical problem related to ways of describing the perciving of the world. Two kinds of image: The Manifest Image and the Scientific Image are the two conceptions which philosophers are concerned with when trying to understand the status of man-in-the-world. They are both explanatory constructs for understanding the world and our place in it, though there are some clearly visible differences between them. The Manifest Image is the understanding of itself and the world that humankind has developed throughout history – it is the framework in terms of which man encountered himself. The Scientific Image is the understanding of the world that has arisen through new techniques of inquiry and through the modern revolutions in science. In this conception, the world can be described with terms of science – and there we have reached the main point: is the manifest image really useful? After all, every proces can be described in scientific terms! Sellars claims that we can not reduce the MI to the SI. His argument refers to our need to find ourselves in the community. We percive ourselves and others as the human beings – to do that we need to use terms which will describe us in the system of categories. That means, we need to use the manifest image – only by finding other people as an intencional beings, we can see the similarities to our behavior and, moreover, predict their behaviour by recognising their mental states. That is when we find our place in the world. The key word in this matter is normativity – consider ourselves as intentional beings give us an oportunity to enforce the social rules which depens on community that establish them. Those rules are form of social agreement. The community establish norms, consisting of the words, which have defined intention. By the Manifest Image we can correctly interprate them and try to enforce them in our social live. This strong arguments lead us to the statement that the Manifest Image is very important and can not be reduced to Scientific Image. Daniel Dennett Personal and Sub-personal Levels of Explanation Dennett’s artice contains similar considiration as Sellar’s paper – differences between ways of explanation. He defends the sharp distinction between the personal and subpersonal levels of explanation. Personal level refers to horizontal explanation, which can be presented as story telled step by step. It is used when we talk about the person. The Subpersonal level of explanation is vertical – it is used when we talk about sever states of mind (a process represented by a certain state). His primary example to illustrate the need for this distinction is the phenomenon of pain. For Dennett, the subpersonal level of explanation for pain is pretty obvious - it involves a scientific account of the various neurophysiological activities triggered by afferent nerves responding to damage which would negatively affect the evolutionary condition of an organism. The question is if we explain the brain processes which occure when we feel pain, do we really explain the feeling of suffering? Dennett considers three questions about pain that a mechanical explanation might attempt to answer: How does a person distinguish pains from other sensations? How does a person locate pain? Why do we abhor pain? In each case, Dennett attempts to show that the mechanical explanation fails. The answers never refers to the cause of the state of mind, while on the subpersonal level you can’t refer to the phenomenon of pain. You simply account for the physical behavior of the system in whatever scientific vocabulary is appropriate. On the personal level you acknowledge that the term “pain” does not directly refer to any neurophysiological mechanism. Dennett claims that it references the phenomena of “just knowing you are in pain”, in virtue of the immediate sensation of painfulness. The explanation can only makes sense in terms of being the pain of a person (not a brain) who “just knows” he is in pain, when he is in pain. Dennett shows an example of the differences between those two explanations - when a person touches a hot stove, the personal explanation is that he had a sensation of pain in a speciﬁc place, and that sensation caused him to withdraw his hand. No further explanation is possible, and these facets of the explanation are “brute facts”. You simply can not talk about the feeling of pain only with Sub-personal explanations – it concludes that you can not get on without the Personal level of explanation. Jerry Fodor The Persistence of the Attitudes Fodor in his article claims that commonsense belief “is worth saving” and what he means is that folk psychology does in fact exist as a science and, moreover, it is useful for understanding how the mind operates. This paper was written at the time when folk psychology was a target of other papers and discussions and it was critisied. Fodor gives us 3 seperate arguments in order to save folk psychology. The first of these acknowledges the high probability that folk psychology is an accurate predictor of human behavior. It is so commonly used and accepted that it is practically unnoticed by people. In fact, people use it in ordinary conversation persistently and constantly, for example when they settle the meeting. The initial attitudes of these people then result in a predictable reality. The second argument is that folk psychology has “depth”. Analyzing a situation using folk psychology involves describing a person’s attitudes and the process these attitudes go through to create new states of mind and possibly new states of the real world. The power of attitudes to cause change in the mind is combined with folk psychology’s similarity to the “most powerful etiological generalizations” to create an expansive but predictable complex system. Final argument states that we have no alternative to explaine human behavior and the process of its causes without folk psychology. Fodor points out that without folk psychology there will be no science to generalize over human behaviour using the counter-factual evidence. Folk psychology is essential for establishing certain components of theory of the mind, such as the stipulations for propositional attitudes. Representational theory of mind refers to states of mind as representations to which we apply as language representations, which can have different meanings. We need to use Ceteris Paribus clause when we draw inferences about each other. According to this theory, language of thoughts is comprised of tokens that are the objects of propositional attitudes. This tokens allows to transitions between mental states to preserve the truth. Our beliefs are used to describe the rules of the folk psychology, they are the components of our world. As we describe behaviour, we can’t refer only to changes of the state in our brain - we have to assume an intencional attitude. A chess-playing computer doesn’t token dispositional beliefs that might become occurrent, though we can still use intentional explanation to describe what it does. After Fodor statement, I completely agree that commonsense belief “is worth saving”. Daniel Dennett Real Patterns Dennett in his paper is considering the issue about the existance of our beliefs. When it comes to beliefs, there are two opposite stances: realism or eliminationism. However, Dennett has claimed to be a mild realist about the ontological status of beliefs. He is presenting a completely new ontology of mental states: the fact that we can predict behaviours is not conditioned by respecting the laws of Folk Psychology, but rather using the patterns of behaviour. We use folk psychology to interpret each other as believers, wanters, intenders, and to predict what people will do next. Ability to interpret the actions of others depends on our ability to predict them. Our ability to predict behaviour is a function of the pattern that becomes discernible from the perspective of the intentional stance. But prediction is impossible from genuinely random, patternless noise. If we can make useful predictions, there is a discernible pattern underneath. But what exacly are those patterns? They consist compressed information. If we can’t compress the inforamtion, there is only a random information which doesn’t give us a posibility to predict correctly. Pattern recognition requires the transfer of information which is possible in two ways: efficiency and accuracy. It may be appeled to Dennett’s three levels of stances. The physical stance is the least efficient, but yields the most accurate predictions. The design stance is more efficient, but sacrifices accuracy. Intentional stances are those which let us see the patterns. Higher level stances offer scales of compression and this is there the importance of patterns enters. Mild realism is the doctrine that makes the most sense when what we are talking about is real patterns from the intentional stance. When can we talk about “real patterns”? Dennett claims, that a pattern existing in some data is real if there is a description of the data that is more efficient than the bit map. Sometimes we may not recognize a pattern in the very same data, but it may genuinely be there to be recognized by other systems, even though we are unable to do so. Moreover, that pattern, though genuinely there, may not be recognizable by any currently devised system. That demonstrates the ontological independence of patterns - patterns may exist independently of whether anyone recognizes it. Pattern happens in time and space, not nessecerly in mind. While there are no belief-like structure in the head, there are patterns of behaviour, and these are the referents attribution in folk psychology. David Rosenthal Explaining Consciousness David Rosenthal in his paper considers a disputable issue about consciousness itself. At the beginning he distinguishes the various concepts which we are called consciousness: creature consciousness – biological matter - organism is conscious when is awake and when its sensory systems are receptive normally, transitive consciousness – relation between an organism and the world – when an organism is conscious of something and state consciousness – matter of phenomenology – distinguish whether the state is conscious or not. It is one thing to say of an individual person or organism that it is conscious and it is quite another thing to say of one of the mental states of a creature that it is conscious. And it is recognized that not all mental states are conscious (such as certain desires, emotions or some bodily sensation such as pain). But what it is for a mental state to be conscious? According to Rosenthal’s theory, mental state is conscious if it is accompanied by a specific type of thought: Higher-Order Thought. A conscious mental state M, of mine, is a state that is actually causing an activated belief (generally a non-conscious one) that I have M, and causing it non-inferentially. An account of phenomenal consciousness can then be generated by stipulating that the mental state M should have a causal role or content of a certain distinctive sort in order to count as an experience and that when M is an experience it will be phenomenally conscious when suitably targeted. Mental state is conscious just in case it is accompanied by a noninferential, nondispositional, assertoric thought to the effect that one is in that very state. Other issue which is worth to mention is the explanation of the connection between consciousness and sensory states. Sensory states occur when a mental state has two properties: the sensory quality and the property of the state consciousness. What it is like to be a particular conscious individual is a matter of the sensory qualities of that individual's conscious experiences. The consciousness of those experiences, by contrast, is simply that individual's being aware of having the experiences. The HOT one determines what it’s like to be in relevant sensory state. The HOT hypothesis deals satisfactorily with the phenomenon of state consciousness, even for the special case of sensory state. Robert Cumins “How does it work?” vs. “What are the laws” Cummins in his article considers different ways of psychological explanation and its language. Special science like psychology differs from more fundamental, “normal” science like physics. Psychology relies on discovering effects and confirming them, not going the “axiomatic or analytic way” like fundamental science. What author is pointing out is that psychological explanations do not refer to the laws, as the model of explanation called Deductive Nomological claims. According to DN, scientific explanation is subsumption under natural law. But laws tell us simply what happens, they do not tell us why or how – for example point of physical explanations is not to explain why do leaves fall, it just refer to the basics of the universe. The laws of psychology are explananda because they specify effects (they are laws in situ). The principles of psychology do not govern nature generally, but only special sorts of systems that are their proper field of studies. Theories in such sciences are constructed to discover and specify effects and to explain them in the structure of the their systems. Psychologists faced with the task of explaining an effect generally have recourse to imitating one or another of the explanatory paradigms established in the discipline. There are five general explanatory paradigms. First paradigm is called Belief-Desire-Intention. It explains how our desires, intentions and beliefs interact. There are some problems there, such as Leibniz’ Gap - when we are inspecting machines interior, we will only find parts that push one another, and we will never find anything to explain a perception. And so, we should seek perception in the simple substance and not in the composite or in the machine. Second paradigm is computationalism – brain is compared to a computer and mind is the effect of its computations. This paradigm uses “top-down” strategy – it spots certain skill, then analyses it to find the input and output. But still if we manage to find the computation and program the device, we won’t prove that brain works the same way (biological mechanism is not a machine). Connectionism is another top-down paradigm, which apply to architecture, that is capable of doing something, not inputs and outputs. But it has to deal with the same issue that previous paradigms had. Neuroscience works similarly to connectionism by – it is also about architecture, but it focuses on brain, not on abstract objects. Last one is evolutionary psychology, which focuses more on answering “why?”. In spite of a good deal of lip service to the idea that explanation is subsumption under law, psychology, though pretty seriously disunified, is squarely on the right track. Its efforts at satisfying explanation, however, are still bedeviled by the old problems of intentionality and consciousness. Allen Bewel and Herbert A. Simon Computer Science as Empirical Inquiry: Symbols and Search Authors of this paper are trying to point out the issue about understanding of computer science by emipirical inquiry. They claim that society needs to understand that the phenomena surrounding computers are deep and obscure, requiring much experimentation to assess thair nature. As they say, the best way to show the development of this understanding is by illustrations. The authors use 2 examples of conceptions: Physical Symbol System Hypothesis and Heuristic Search Hypothesis. Laws of qualitative structure are seen everywhere in science. While such statements or laws are generic in nature, they are of great importance and often set the terms on which a whole science operates. To capture the essense statement about the representation of behaviour, we need to understand what symbols are. We will then reach the roots of inteligent action, which is the primary topic of artifical intelligence. A physical symbol system consists of a set of symbols, related to each other, which can occure as components of expression. It contains a collection of processes that operates on these expressions to produce other expressions. So physical symbol system is a machine that produces through time an evolving collection of symbol structures. It has two central notions: designation and interpretation. The Physical Symbol System Hypothesis implies that a physical symbol system has the necessary and sufficient means for general intelligent action. It is clarly the law of qualitative structure and empirical hypothesis. Research in information processing psychology consists of observations and experiments on human behaviour and programming of symbol system to it. Computer science developes scientific hypothesis which then seeks to verify by empirical inquiry. The question of how symbol systems provide intelligence behavior leads to the concept of heuristic search. The Heuristic Search Hypothesis implies that the solutions to problems are represented as symbol structures. A physical symbol system exercises its intelligence in problem solving by search - that is, by generating and progressively modifying symbol structures until it produces a solution structure. The task of intelligence is to avert the threat of exponential explosion of search by extracting and using information about the structure of the problem space. Physical symbol systems solve problems by means of heuristic search - it extracts information from a problem domain and using the information to guide the search and avoid wrong turns. Artificial intelligence researches are based on concrete experience about the behaviour of specific classes of symbol systems in specific task domains – on empirical inquiry. Daniel C. Dennett Cognitive wheels: the frame problem of AI Dennett in his article is trying to deal with the matter which is so called frame problem. To most AI researchers, the frame problem is the challenge of representing the effects of action in logic without having to represent explicitly a large number of intuitively obvious non-effects. But Dennett point out that it is a deep epistemological problem brought to light by the novel methods of AI, and still far from being solved. The puzzle is how a cognitive creature with many beliefs about the world can update those beliefs when it performs an act so that they remain roughly faithful to the world. An intelligent being learns from experience, and then uses what it has learned to guide expectation in the future. The problem is AI initially knows nothing at all `about the world' and clearly some information has to be innate. Problem of instalation and storing is also mentioned – the information must be installed in a usable format. The task facing the AI researcher appears to design a system that can plan by using well-selected elements from its store of knowledge about the world it operates in. What is needed is a system that genuinely ignores most of what it knows, and operates with a well-chosen portion of its knowledge at any moment. An artificial agent with a well-stocked compendium of frames, appropriately linked to each other and to the impingements of the world via its perceptual organs would face the world with an elaborate system of what might be called habits of attention and benign tendencies to leap to particular sorts of conclusions in particular sorts of circumstances. Many critics of AI have the conviction that any AI system is and must be nothing but a gearbox of cognitive wheels - designs that are profoundly unbiological, however wizardly and elegant it is as a bit of technology. The model of AI should not be described as several implications of actions but as the network of connections which AI could use in solving problems. Key to a more realistic solution to the frame must require a complete rethinking of the semantic-level setting, prior to concern with syntactic-level implementation. Though frame problem remines unsolved, we may be a few steps further after taking under cosidiration all of our deductions. At least some problems are now pointed out – now we only need to look forward for its development. Benjamin Libet Time of conscious intention to act in relation to onset of cerebral activity In early 1980’s the neurologist Benjamin Libet performed a sequence of remarkable experiments in the early 1980's that were adopted by determinists and compatibilists to show that human free will… does not exist. Libet with other scientists have decided to conduct an experiment that will test the process of making decision empirically using EEG. The most surprising fact is that researchers recorded mounting brain activity related to the resultant action as many as three hundred milliseconds before subjects reported the first awareness of conscious will to act. In other words, apparently conscious decisions to act were preceded by an unconscious buildup of electrical activity within the brain. So brain is ready to act even before one could consciously perceive the intention to move. Libet's experiments suggest to some that unconscious processes in the brain are the true initiator of volitional acts, and free will plays no part in their initiation. If unconscious brain processes have already taken steps to initiate an action before consciousness is aware of any desire to perform it, the causal role of consciousness in volition is all but eliminated, according to this interpretation. His measurements of the time before a subject is aware of self-initiated actions have had an enormous impact on the case of human free will, despite Libet's view that his work does nothing to deny human freedom. As Libet says, the judgement of being aware of something given by subject may not be accurate, just because subject needed also to focus on the experiment itself. It has to be taken into cosideration that this results were achived in experimental environment and the decisive task was not complicated. But, nevertheless, experiment was innovatory – besides it uses the introspection which is subjective and can be not accurate, no one has tired to examine consciousness like that before. But I think that statements which were made afterwards by philosophers are misleading because of overinterpretation. Jakob Hohwy The predictive mind The author is wondering how our brain accomplishes perception, so that we percive sellected information and we are able to make proper inferentions. He remarks on additional constraints, which are needed to perform reliable causal inference about the sensory input – like prior beliefs or previous experiences which may update the inferentional thinking. The hypotheses picked by our brain have a high likelihood (probability that the cause would cause those effects) but also the prior probability (based on the frequency of the events it describes). But how does our brain can accomplish to do that? Likelihood and prior are the main ingredients of Bayes’ rule – it tells us to update the probability of given hypothesis, given some evidence by considering the product of the likehood and prior probability of the hypothesis, which gives us in result the posterior probability. To connet the inference with the Bayes’s rules it has to be shown that this approach accommodate differences in perception as such, not only the ones in categorisation. We can only understand how brains engage in probabilistic inference if we understand how neurons can realize the fuctional roles set out by forms of Bayes’ rule. And to understand that it is necessary to appreciate a hierarchical notion of perception: brain proceeds regularities in order from faster to slower – there is a massive data transfes between the levels. Lower ones produce general hypothesis. Higher levels help to make the prior belief. If the model is badly fitted to data, there comes a error in prediction. Prediction error is combination of surprise about the outcome and the difference between the model and hypothesis. Perception arises in prediction error minimization where the brain’s hypotheses about the world are stepwise brought closer to the flow of sensory input caused by things in the world.This idea gives the brain all the tools it needs to extract the causal regularities in the world and also to use them for the predictions about what comes next in a way that is sensitive to what is currently delivered to the senses. Using the notion of prediction error minimization, it is easy to see how the brain perceives and ahy this is best conceived as an inferential process: it fits with the normative constraints of Bayes. Neuronal populations are just trying to generate activity that anicipates their input. In the process of doing this they realize Bayesian inference.