Cognitive/Affective Psychology - Research Paper Example

? Cognitive/Affective Psychology: Units 6-10 Essay Examination Waliah Abdulrazaq Capella Essay I: Human and Artificial Intelligence We live in a world that is dominated by the advances of technology that we hardly notice how it has influenced the manner, by which we think. One major area where this is most evident is in the field of artificial intelligence. Adherents of this view are primarily interested in the construction of what they call intelligent machines. John Searle (1980) presents a view called strong artificial intelligence, where it is assumed that human minds are themselves, computer programs. My purpose for choosing this topic is significant in my study of psychology, for in this light, I gain a better understanding of what is going on in the human brain, i.e. how we think. Indeed, by understanding how a computer program works, we are given an avenue to better understand how human thinking works. However, what exactly does it mean to say that computers can think? More so, what does it mean to say that machines are intelligent? Sternberg (2009, p. 565) claims “computers cannot actually think.” In my discussion, I shall consider the current theories and researches presented within the field of artificial intelligence that focus on the issue of what enables us to ascribe intelligence to a computer program or machine in relation to that of human intelligence. Furthermore, I will discuss the competing views and objections that question the intelligence of computer programs. Intelligent Programs: The Turing Test A popular formulation of the thesis of artificial intelligence is the computational theory, i.e. “the mind is to the brain as the program is to the hardware” (Searle, 1994, p. 200). With regards to the claim concerning machine intelligence, Alan Turing’s (1995) Turing test, is used as a justification for such. The Turing test is thought experiment devised in order to deal with the issue as to whether it is meaningful to ascribe intelligence to machines. There are many ways of describing this test. Here is one simplified version: imagine a human interrogator communicating with two respondents, one of which is a human being while the other is a machine. A wall physically separates the interrogator and the two respondents, and the interrogator communicates with the respondents only through text messages using computers. Let us say that there are two computer terminals, one for each respondent, and the interrogator, though he knows that he is communicating with a human being and a machine, does not know in which terminal he is communicating with the human respondent or with the machine. Now, if, after a series of questions and answers, and the human interrogator could not tell, on the basis of the respondents’ answers, which is the human and which is the machine, then the machine is said to have passed the Turing test. This means that we can now meaningfully say that the machine is intelligent. This emphasis on the indistinguishability of the machine from the human respondent is “commonly used in assessing the intelligence of a computer program” (Sternberg, 2009, p. 565). What is worth emphasizing here is how well the machine or computer program can simulate or perform the task at hand. A dominant model of the computational theory is the symbolic model wherein, with respect to the notion of representation, the brain performs the task at hand by using symbols (like 1 and 0 of computer programs) whose structure is primarily linguistic in nature. While with regards to how the computational process proceeds, it subscribes to serial processing (one operation at a time). The main theses of this view has been articulated by Fodor’s (1979) mentalese hypothesis, the empirical hypothesis that our mind or brain uses an internal system of representation, called the language of thought, as it carries out its computational processes. As Fodor explains, “computation presupposes a medium of computation: a system of representation” (1979, p. 27). This view was greatly influenced by Noam Chomsky’s (1968) idea that we are born with the same linguistic categories that enable us to learn various natural languages. This view actually constitutes Chomsky’s alternative to the claim of B. F. Skinner (1991) that the learning of natural languages is a matter of conditioning or an association between stimuli and responses. Accordingly, Chomsky shows that the behaviorist explanation of language acquisition, most especially by children, is flawed. Chomsky’s innate linguistic categories, in the hands of Fodor, make up a language of thought. This representational system is believed to be that, which makes some features of human intelligence possible, such as its capacity to produce new thoughts by combining previous thoughts, and the generality of certain structures in its processes. The language of thought may be compared to the internal language of computers, or better yet, of our mobile phones. As Fodor explains, on this view, “what happens when a person understands a sentence must be a translation process basically analogous to what happens when a machine understands a sentence in its programming language” (1979, p. 67). Notice that we can easily change the natural language of our mobile phones, say from English to German, for these languages are merely programmed into the internal language of the main program of our mobile phones. As such, the rules governing the computational states of our mobile phones are not the grammatical rules of the natural language, but the rules of their internal language. In the same way, our mental states do not follow the rules of our natural languages—which, in a way, are just programmed into our minds, but the rules of the language of thought. Sternberg enumerates various intelligent programs or expert systems that simulate and perform the way an expert does in a specific domain (2009, pp. 568-570). Questioning the Intelligence of AI Programs: Chinese Room However, intelligent programs do have their own failures in accounting for certain aspects of human intelligence. One major flaw is the absence of intuition. Sternberg explains that computers, though excellent in objective and deductive aspects of thinking, lack an integral aspect of human intelligence, i.e. intuition (2009, p. 571). Adherents of this view claim that this flaw is what differentiates genuine experts from expert systems. This is akin to a machine’s failure to account for intentionality, the property of mental states to be directed at some object or state of affair. For instance, one cannot have a belief without this belief being about something. John Searle (1980) offers an objection against the claim of ascribing intelligence to machines, which he calls the Chinese Room problem. This thought-experiment specifically challenges the claim that all there is in having a mind is the implementation of a computer program, and that, as a consequence, mental states of humans are no different in kind from the computational states of a computer program. The Chinese Room problem challenges the computational view of the mind by showing that, unlike humans, computers do not know what the contents of their computational states (or the symbols they manipulate) are about or represent in the world. What computers only know of these symbols are their shapes and the ways in which they should be combined according to the rules of their programs. Here is a simple version of the Chinese Room problem (Searle, 1980): imagine that a native English speaker, who does not understand Chinese, is locked in a room with only two outlets. Outside of this room are native Chinese speakers who do not know who or what is inside the room. In one outlet, the person inside the room is given several manuscripts bearing Chinese symbols and a manual of English instructions for manipulating the Chinese symbols. The person inside the room does not know that the symbols are Chinese; he only recognizes and individuates the symbols according to their shapes or formal properties. Now imagine that the manual says that if he recognizes certain combinations of symbols in the manuscripts given to him in one outlet, then he should arrange certain combinations of symbols and send them to the persons outside the room through the other outlet. Suppose that what the person inside the room sends to the persons outside the room are correct answers to the questions that the persons outside the room ask him through the manuscripts that they send him. In this case, in so far as the persons outside the room are concerned, the person inside the room understands Chinese. However, the fact is that the person inside the room does not understand the symbols; he does not even know that they are Chinese or what they represent; and he simply manipulates them according to the instructions in the manual. Technically speaking, he does not know the semantics or the meaning of those symbols; he only knows their syntax or logical form. An important thought experiment that is used to defend the views of artificial intelligence is the Turing test, as discussed in the previous chapter. It will be recalled that according to this test, if after a series of questions and answers, the human interrogator could not tell, on the basis of the respondents’ answers, which is the human and which is the machine, then the machine is said to have passed the test. This, consequently, means that the machine is considered to be intelligent. However, Searle’s Chinese Room has shown that even when the computing machine passes the Turing test, intelligence still cannot be attributed to it. The reason is that attributing intelligence means attributing consciousness, which in turn means attributing intentionality. It is in this light that Searle argues that a computing machine can never have intrinsic intentionality, for it does its computational processes in a way that considers only the syntactic features of symbols or representations, i.e. it only knows the syntax and not the semantics. Essay II: Decision Making and Reasoning We are constantly faced to make decisions in most of our daily affairs in that we tend to forget whether our reasoning is valid or invalid. For instance, one day, as you turn on your TV, you hear the host of a talk show discussing a very important issue. He reminds the audience that a well-known politician, Mr. J, has recently been accused of corruption and bribery, and if found guilty, Mr. J could face death penalty. Before the host asks the members of the panel to discuss the issue, he asks the viewers of the show to respond to a question that is flashed on the screen. The question is, “do you think Mr. J is guilty?” Beside the question are two columns, one for “YES” and the other for “NO.” As the show proceeds, the votes are tallied. You notice that 90% of those who responded to the question answered YES and only 10% answered NO. Towards the end of the show, a few callers were entertained. One caller, a woman, tells the host, “Mr. J must be guilty. After all, 90% of those who responded to the question posted on the screen answered YES. Indeed, How could 90% of the population be wrong?” She asks. Given this situation, how would you assess the caller’s line of reasoning? Would you say that she reasoned out correctly? Throughout my discussion, I will emphasize the fact that the ability to reason out well is necessary for making good decisions, and an integral aspect herein includes the psychological underpinnings of the emotions behind it. In what follows, I will discuss two scholarly works on affect. After I present a summary of both, I shall assess the validity of their line of reasoning. I shall conclude by discussing their relevance to cognitive-affective psychology. Blanchette, I. & Richards, A. (2010). The influence of affect on higher-level cognition: A review of research on interpretation, judgment, decision-making and reasoning. Cognition and Emotion, 24(4), 561-595. The article studies the empirical effects of emotions on higher-level cognitive processes in the light of four main processes, namely, judgment, decision-making, interpretation and reasoning. According to the authors, one major factor is anxiety. That is to say, the manner by which we interpret things is largely affected by our feelings of anxiety. Closely related to this is our feeling of threat, which influences our mode of interpretation and attention to a rather selective preference rather than that of a neutral one. The authors provides a basis for this by claiming that “neurobiological evidence confirms that the effect of anxiety on both interpretation and attention rely on the same prefrontal amygdala network, thus adding support to the suggestion that the two processes are linked” (2010, p. 586). The article shows that emotions manifest complex effects in our process of decision-making and reasoning. Most evident is the influence of fear in our method of reasoning, and sadness in our interpretation and biases. However, most of the study focuses on anxiety and depression since these are said to be the most prevalent among psychological disorders. Among others are disgust, anger, sadness, and other negative moods. Emphasis is made on negative emotions for the study shows that negative emotions are more influential than positive emotions. Emotions affect higher-level cognitive processes through the following mechanisms, namely, attentional effects, priming of concepts, computational capacity, reflective processes, and memory. The authors suggest that memory increases uncertainty in our judgments and decision-making, for our cognitive processes heavily rely on our working memory resources. This is due to the fact that we are unconsciously blinded by our emotions, thus affecting our normal cognitive and objective perspective on the decisions we make. Moreover, the article shows that emotions highly affect the logical nature of deduction, thus resulting to an impaired performance in logical reasoning. The authors conclude that by studying other neurobiological evidences, which shows how emotions influence cognitive processes, we are thus, able to acquire new findings in the field of cognitive psychology to better understand the workings of human cognition. Dennett, D. (1988). Quining qualia. In A.J. Marcel and E. Bisiach (Eds.), Consciousness in contemporary science (pp. 42-77). New York, NY: Oxford University Press. In this work, Dennett questions and negates the existence of qualia, which in general, are subjective qualities or the experiences of our own mental states. Here, he delineates his own definition of qualia through certain attributes such as being ineffable, intrinsic, private and indirectly accessible to the consciousness of a person. Moreover, he argues that qualia is not something objectively real because he surmised that our subjective states can be explained and reduced to analyzable objective elements, particularly, reactionary dispositions. Based on this, qualia is unveiled as an extrinsic property rather than those which are internal or intrinsic. This contradicts the essential nature of qualia as something private and subjective. Dennett concludes that qualia is merely illusory. To support this claim, Dennett gave an intuition pump or thought experiment called the neurosurgical prank. Dennett tells us of a scenario: you are sleeping, and a group of evil neurosurgeons opens up your brain and alters your optic nerves so that you will see colors in an inverted manner. So, when you wake up, you will surprisingly see red things such as apples as blue, while you see blue objects like the sky as red, and so on. Anyway, this thought experiment shows that if your reactive dispositions change then it follows that there is a change in qualia. So if there is a change in qualia, then qualia is not conveyed as an intrinsic entity, but as an extrinsic phenomenon which can be explained scientifically. This, as a result, nullifies the reality of qualia. So with that in mind, Dennett, following the mechanics of the intentional stance, concludes that qualia, or a person’s subjective state, is nothing more than a mere ascription to things which is used for purposes of explaining the behavior of a given object of reference. Suffice it to say, Dennett asserts that consciousness, or our inner mental processes are nothing but mere beliefs, which are used for the sake of imbuing calculability in the world. Comparative Analysis: Decision Making and Reasoning Based on the aforementioned articles, we may now assess the manner in which the authors utilized decision-making and reasoning. In the first article, the author argued for the influence of affective variables such as emotions, with regards to cognitive processes; various empirical evidences supported this study. However, the authors made it clear that further research is required in order to fully encompass the study. Nevertheless, based on their current findings, it is sufficient to say that emotions do influence our judgments, reasoning and decisions. The manner in which reasoning is portrayed in this article is an example of inductive reasoning. Inductive reasoning is “the process of reasoning from specific facts or observations to reach a likely conclusion that may explain the facts” (Sternberg, 2012, p. 519). Following this model of reasoning, we began by assuming a probable conclusion, and carried on with the research, for evidence to support the claim. A key feature of inductive reasoning is that the conclusion is not logically certain. At most, we can only say that the conclusion is most probably correct. In this article, it utilized the approach of causal inferences in showing that affective variables such as anxiety, influence our cognitive processes. The structure of reasoning can be explained in the following manner: it may be the case that emotions such as anxiety influence our cognitive processes, for based on the study conducted, (P1) “Extensive anxiety-congruent effects have been documented in interpretation, judgement, and decision making” (Blanchette & Richards, 2010, p. 584). Furthermore, (P2) “neurobiological evidence confirms that the effect of anxiety on both interpretation and attention rely on the same prefrontal amygdala network, thus adding support to the suggestion that the two processes are linked” (Blanchette & Richards, 2010, p. 586). Despite the need for further research in order to include an analysis of other affective variables, (P3) “the research that has been conducted generally shows effects of most emotions. In fact, there is very little evidence of affective states not affecting higher level cognitive processes” (Blanchette & Richards, 2010, p. 585). Therefore, (C) “affective variables have an important influence on cognitive processes” (Blanchette & Richards, 2010, p. 584). As we can see, the conclusion (C) can only be probably true given the premises (P1), (P2), and (P3). The correlation of the said premises to its conclusion assumes that the only cause of interpretive biases, judgments, and threatening interpretations is anxiety. This is susceptible to criticism for instances of threatening interpretations have multiple causes other than anxiety. Such room for error is what deductive reasoning eliminates. Deductive reasoning “is the process of reasoning from one or more general statements regarding what is known to reach a logically certain conclusion” (Sternberg, 2012, p. 507). The second article utilizes a deductive mode of reasoning in its approach, wherein the conclusion is logically drawn from the correlation of its premises, which must necessarily follow. In the second article, Dennett’s argument can be structured in the following deductive manner: By definition, qualia is the subjective qualities of our mental states, whose nature is something ineffable, intrinsic, private and indirectly accessible to the consciousness of a person. (P1) If our subjective states can be explained and reduced to reactionary or behavioral dispositions, then the existence of qualia is merely illusory. (P2) If the existence of qualia is merely illusory, then qualia is unveiled as an extrinsic property which can be explained scientifically. (C1) Thus, if our subjective states can be explained and reduced to reactionary or behavioral dispositions, then qualia is unveiled as an extrinsic property, which can be explained scientifically. (P3) Indeed, our subjective states can be explained and reduced to reactionary or behavioral dispositions as shown by the author in the neurosurgical prank thought-experiment. (C2) Therefore, qualia must be an extrinsic property, which can be explained scientifically. As we can see, the manner in which the argument is structured is logically valid. Its premises are propositions, which are either true of false, which follow a syllogistic form of reasoning, followed by a conditional form of reasoning. Based on the derived conclusion, we can see that it contradicts the very nature of qualia, given the definition in the beginning of the argument. This follows that qualia cannot be objectively real for it violates the law of non-contradiction where P and not-P cannot co-exist at the same time and in the same respect. Thus, Dennett claims that qualia or a person’s subjective state is nothing more than a mere ascription to things, which is used for purposes of explaining the behavior of a given object of reference. This conclusion is logically valid, unlike the conclusion drawn from an inductive argument, wherein it could only be a mere probability. Conclusion: Cognitive/Affective Psychology The said works are of utmost significance in cognitive-affective psychology since we often make decisions and interpret data when proving certain theories to be correct. Thus, correct and valid reasoning as well as well-supported decisions are important if we are to draw valid conclusions. I agree with the first article insofar that our cognitive processes are not without its affective aspects. However, I disagree with it insofar that there may be other causes of the said phenomena in question; it is this facet that may threaten the validity of the study. The manner in which the conclusion was drawn was a bit hasty, which makes the argument weak. Moreover, the authors appealed to ignorance when claiming, “there is very little evidence of affective states not affecting higher level cognitive processes… Therefore, affective variables have an important influence on cognitive processes” (Blanchette & Richards, 2010, pp. 584-585). Moreover, the article’s mode of reasoning displays the use of heuristics, i.e. since the study confirmed to be accurate when anxiety and depression was assessed, thus, it must be true for other affective variables as well. This is akin to the hot hand effect wherein a person believes that “a certain course of events will continue based on the previous outcomes” (Sternberg, 2012, p. 499). With regards to the second article, I disagree with the view that qualia, or the subjective feel of our mental states are not real in the sense that they can be reduced to reactionary dispositions. Although the manner in which the conclusion was drawn was logically valid, it is in my contention that it is not sound, for it is not possible to reduce all mental states to reactionary or behavioral dispositions. A related argument is presented by Frank Jackson (1991), which concerns what Mary doesn’t know. Jackson imagines Mary to be an expert on neurobiology and the mechanics of colors. Mary can perfectly explain what happens to the brain, as we perceive colors of different wavelengths. The only problem with Mary is that she has lived all her life in a room where the only colors are black, white, and shades of gray. Consequently, though she is not color blind, Mary has not perceived colors other than the ones mentioned. The question now is, does Mary, who has complete knowledge of the physics involved in seeing colors but lacks the actual experience of seeing some colors already knows everything there is about in seeing colors? According to Jackson, Mary misses the phenomenal features of seeing other colors; what it is like to see red or blue, for instance. In both arguments, it is shown that knowing the physics involved in acts of consciousness will not capture something integral about such acts, namely, their phenomenal properties or qualia. Perceiving colors is a conscious act, but just knowing the physics involved in such an act without the actual experience of it will not tell us what it is like to perceive colors. What I have discussed, by far, are the building blocks of decision-making and reasoning. A person, who does not know how to reason out correctly, makes the wrong decisions. How many times have we seen women choose to marry abusive or violent men simply because these men were perceived to be exciting? How many times have we seen patients in hospitals agree to have unnecessary surgeries simply because a doctor has convinced them, using spurious reasons and technical interpretations that are too hard to understand, on how surgery is necessary? All of these could have been prevented if we deliberate carefully upon the issues that confront us. For, if we are to study how people make decisions, how they reason out, or how they interpret data, then we need to know the importance of proper decision-making and reasoning ourselves. References Blanchette, I. & Richards, A. (2010). The influence of affect on higher-level cognition: A review of research on interpretation, judgment, decision-making and reasoning. Cognition and Emotion, 24(4), 561-595. Chomsky, N. (1968). Language and mind. New York: Harcourt Brace Jovanovich. Dennett, D. (1988). Quining qualia. In A.J. Marcel and E. Bisiach (Eds.), Consciousness in contemporary science (pp. 42-77). New York, NY: Oxford University Press. Fodor, J. (1979). The language of thought. Cambridge, MA: Harvard University Press. Jackson, F. (1991). What Mary didn’t know. In D. M. Rosenthal (Ed.), The nature of mind (pp. 392-394). Oxford, UK: Oxford University Press. Nagel, T. (1974). What is it like to be a bat? Philosophical Review, 83, 435-450. Sternberg, R. J. (2012). Cognitive psychology (6th ed.). Belmont, CA: Wadsworth Cengage Learning. Sternberg, R. J. (2009). Cognitive psychology (5th ed.). Belmont, CA: Wadsworth Cengage Learning. Searle, J. (1980). Minds, brains, and programs. Behavioral and Brain Sciences, 3(3), 417-457. Searle, J. (1994). The rediscovery of the mind. Cambridge, MA: The MIT Press. Skinner, B. F. (1991). Verbal behavior. Acton, MA: Copley Publishing Group. Turing, A. (1995). Computing machinery and intelligence. In G. Luger (Ed.), Computation and intelligence: Collected readings (pp. 23-46). Cambridge, MA: The MIT Press. Read More