The Nature of Representation of Information: Inductive Reasoning - Essay Example

Running Head: representation f information Evaluate research on the nature of the representation of information and how it significantly impacts thinking in inductive reasoning [Name of the writer] [Name of the institution] Evaluate research on the nature of the representation of information and how it significantly impacts thinking in inductive reasoning Psychological studies f formal reasoning have fallen largely into two domains: deductive reasoning and statistical inference. These two endeavours have much in common and some researchers work in both areas. In both cases, participants are presented with what problem-solving researchers call well-defined problems. A well-defined problem can be solved by use f the information provided and no other; in fact, the correct solution to these problems often requires the reasoners to use only the information provided in the premises, and to avoid adding background information and knowledge to the problem domain. Instead, a correct solution is achieved by applying a normatively appropriate rule f inference. Normative systems are often applied to formal reasoning problems in order to define solutions as right or wrong, such that these problems are then construed as tests f correct and fallacious reasoning. Hence, these problems are designed to measure the extent to which participants bring to the laboratory an understanding - and ability to apply - the relative normative principles. In the case f deductive reasoning research, the relevant normative system is formal logic. Participants are given some premises and asked whether a conclusion follows. Under strict deductive reasoning instructions, they are told (a) to assume that the premises are true and (b) to draw or approve only conclusions that necessarily follow. As observed elsewhere (Evans, 2002), this widely used method was developed over 40 years ago when belief in logic as a normative and descriptive system for human reasoning was very much higher than it is today. In spite f the method, much evidence has emerged to support the conclusion that pragmatic factors play a large part in human reasoning. We say "in spite of" because standard deductive instructions aim to suppress precisely those factors that dominate informal reasoning: the introduction f prior belief and the expression f uncertainty in premises and conclusions. In research on statistical inference, a similar story is found. People are asked to make statistical inference on the basis f well-defined problems, in which relevant probabilities or frequency distributions are provided, and their answers are assessed for correctness against the norms provided by the probability calculus. Research in this tradition has been mostly conducted by researchers in the "heuristics and biases" tradition inspired by the work f Danny Kahneman and Amos Tversky (Gilovich, Griffin, & Kahneman, 2002; Kahneman, Slovic, & Tversky, 1982). This results in an arguably negative research strategy that is similar to much work on deductive reasoning. That is, researchers show primarily what people cannot do (conform to the principles f logic or probability theory) and only secondarily address what people actually do. Indeed, one f the most common explanations for why intelligent, educated individuals often fail to reason normatively is that they use informal reasoning processes to solve formal reasoning tasks. For example, notwithstanding instructions to the contrary, reasoners often supplement the information they are provided with background knowledge and beliefs, and make inferences that are consistent with, rather than necessitated by, the premises. If this is the case, it is reasonable to suggest that we study these processes directly, by giving our participants tasks that allow them to express these types f behaviours freely, rather than indirectly, via the observation f poor performance on a formal task. (Vallee-Tournageau 2005) The argument for doing so becomes even more compelling when it is understood that performance on any given reasoning task is uninterruptible in the absence f a theory f how reasoners interpret the information and supplement it with background knowledge. This is because interpretive processes overlay or moderate the output f any putative inferential processes, such that a reasoner's endorsement, or failure to endorse an inference (valid or otherwise), could arise from either logically driven processes, semantically driven processes, or a combination f both. Take, for example, a straightforward conditional inference problem: Assume it is true that "if the car runs out f gas, then it will stall." Assume that the car has not stalled. What follows The logically valid response is that "the car has not run out f gas." However, it is possible to derive this answer either through a logical analysis (i.e., if the rule were true, then it is a logical impossibility for the car to run out f gas and not stall; thus, the fact that it has not stalled necessarily means that it did not run out f gas) or by applying world knowledge (i.e., internal combustion engines do not work unless they have a fuel supply, so that the fact that the engine is still running suggests that there must be fuel). Thus, because both "logical" and "illogical" thinking can arise from interpretation, even theories designed to explain formal reasoning competence cannot succeed without an adequate explanation f the informal processes by which information is derived from the problem environment, retrieved from long-term memory, and applied to a given task. (Eysenck 2005) Finally, there is a genuine concern that the processes, both logical and interpretive, that are recruited to solve formal problems may differ quite substantially from those recruited to solve informal problems. Indeed, there is good reason to doubt that one can generalize from one formal task to another. For example, Thompson (2002) observed that the interpretive variables that explain variance on one type f logical task did not predict performance on another, logically identical task. Thus, our focus on formal reasoning problems may well tell us little about informal reasoning, leaving us with theories that are restricted to explaining only a few artificial laboratory tasks. Methods f Studying Informal Reasoning In recent years, researchers in the deductive reasoning tradition have begun to allow the influence f both prior knowledge and uncertainty by modifying or relaxing the kinds f instructions normally employed. Some studies no longer require participants to assume the premises or to draw only necessary conclusions. People may be given premises and simply asked: What follows Some studies go further and allow participants to express degrees f probability, belief or confidence in the conclusions that they draw - something that makes little sense in the original deduction paradigm as conclusions are either necessary or they are not. (Vallee-Tournageau 2005) Studies using the relaxed or modified version f the deduction paradigm have led to some very interesting findings (for some recent examples, see Dieussaert, Schaeken, & d'Ydewalle, 2002; George, 1995, 1997; Liu, Lo, & Wu, 1996; Stevenson & Over, 1995, 2001). With relaxed instructions, for example, the effects f participants' prior belief in the premises and conclusions have much more marked effects on their reasoning than are observed under strict instructions. Where degrees f belief can be expressed in conclusions, these may relate fairly directly to degrees f belief in the premises. Although not highly naturalistic, such studies do provide more insight into everyday informal reasoning. The method can be augmented by presenting reasoning problems in scenarios that provide a context for the premises used (e.g., Newstead, Ellis, Evans, & Dennis, 1997). However, the deductive paradigm, and all its subsequent modifications, is limited to studying a particular type f inferential process: those that are used to derive a conclusion from a fixed set f premises. In contrast, informal reasoning is rarely based on a well-identified set f premises (Shaw, 1996). Moreover, many f the inferences drawn from a set f premises are not those that are typically measured on deductive tasks, and which therefore may be missed if only deductive-type tasks are used (Thompson & Byrne, 2002; Thompson, Evans, & Handley, 2004). Thus, in addition to modifying our current tasks, we need to develop new tasks and paradigms that are designed to reflect the complexities and subtleties f people's ordinary reasoning. Implications for Theory Researchers in the decision and judgment tradition have arguably less well-developed cognitive theories than reasoning researchers, but on the other hand have taken a lot more trouble to demonstrate the generalization f the heuristics and biases they have identified to expert groups and real-world reasoning (see, for example, Bueler, Griffin, & Ross, 2002; Fischoff, 2002). In recent years, reasoning theorists have also made an effort to develop theories that explain the competence f ordinary reasoners, as opposed to explaining away their failure to perform according to normative ideals. Both traditions, however, have been limited in their development by the original emphasis on normative standards f reasoning. As space is limited, we will confine our comments to theories f reasoning that have developed from formal studies f deduction, although analogous arguments can be made about theories f judgment and decision-making. (Eysenck 2005) The study f deductive reasoning has been a major field f cognitive psychology for the past 40 years or so and was set in place by a strong emphasis on logicism - the idea that logic is the basis for rational human thought (Henle, 1962; Inhelder & Piaget, 1958). This set an agenda in which the assessment f deductive competence and the ability to provide psychological accounts f it became the major focus f the field (Evans, 2002). Consequently, much theoretical effort was expended in promoting and debating rival accounts f competence based upon theories f mental logic (Braine & O'Brien, 1998; Rips, 1994) or upon mental model theory (Johnson-Laird, 1983; Johnson-Laird & Byrne, 1991). Although the posited inferential mechanisms differed, they were nonetheless geared to producing normatively correct responses, and assumed that under optimal circumstances, reasoners would draw only valid inferences and reject invalid ones. In recent years, theorists from both the mental logic and mental models camp have tried to account for the numerous pragmatic influences that appear in reasoning tasks, even when strict deductive reasoning instructions are employed. This involves adding pragmatic mechanisms to a theory that is ostensibly deductive. As one example, mental logic theorists claim that valid inferences follow from their rules f inference, whereas fallacies are the product f conversational implicatures external to the mental logic. In another instance, the recent revision f the mental model theory f conditionals, Johnson-Laird and Byrne (2002) defines a core semantics for "basic" conditionals that maps on the material conditional f standard logic, but adds a process f "pragmatic modulation" f these representations in the light f prior knowledge that renders them nontruth functional. More radical approaches provide accounts f reasoning that are intrinsically pragmatic, such as those based on pragmatic reasoning schemas (Cheng & Holyoak, 1985) or relevance theory (Sperber, Cara, & Girotto, 1995). Some authors argue for probabilistic and decision theoretic treatments that posit no role at all for logic and deductive processes (Oaksford & Chater, 2001). Others propose dual processing theories that posit the existence f two distinct cognitive systems, one essentially pragmatic and the other capable f deduction and abstract hypothetical thought (Evans, 2003; Sloman, 2002; Stanovich, 1999). According to the latter approach, pragmatics will dominate unless a conscious effort is made to intervene using the second system. Note that this analysis provides a mirror image f the mental logic and mental models approaches in which reasoning is in principle deductive until moderated by pragmatics. References Braine, M. D. S., & O'Brien, D. P. (1998). Mental logic. (Eds.) Mahwah, NJ: Lawrence Erlbaum Associates. Bueler, R., Griffin, D., & Ross, M. (2002). Inside the planning fallacy: The causes and consequences f optimistic time predictions. In T.Gilovich, D.Griffin, & D.Kahneman (Eds.), Heuristics and biases: The psychology f intuitive judgment (pp. 250-270). Cambridge: Cambridge University Press. Cheng, P. W., & Holyoak, K. J. (1985). Pragmatic reasoning schemas. Cognitive Psychology, 17, 391-416. Dieussaert, K., Schaeken, W., & d'Ydewalle, G. (2002). The relative contribution f content and context factors on the interpretation f conditionals. Experimental Psychology, 49, 181-195. Evans, J. St. B. (2002). The influence f prior belief on scientific thinking. In P. Carruthers, S. Stich, & m. siegal (Eds.), The cognitive basis f science (pp. 193-210). Cambridge: Cambridge University Press. Evans, J. St. B. T. (2002). Logic and human reasoning: An assessment f the deduction paradigm. Psychological Bulletin, 128, 978-996. Evans, J. St. B. T. (2003). In two minds: Dual process accounts f reasoning. Trends in Cognitive Sciences, 7, 454-459. Evans, J. St. B. T., & Over, D. E. (1996). Rationality and reasoning. Hove, UK: Psychology Press. Eysenck, M. W.,& Keane, M. (2005). Cognitive psychology, a student's handbook. (pp. 470-479, ch. 16). Hove: Psychology Press. George, C. (1995). The endorsement f the premises: Assumption based or belief-based reasoning. British Journal f Psychology, 86, 93-111. George, C. (1997). Reasoning from uncertain premises. Thinking and Reasoning, 3, 161-190. Gilovich, T., Griffin, D., & Kahneman, D. (2002). Heuristics and biases: The psychology f intuitive judgment. Cambridge: Cambridge University Press. Henle, M. (1962). On the relation between logic and thinking. Psychological Review, 69, 366-378. Inhelder, B., & Piaget, J. (1958). The growth f logical thinking. New York: Basic Books. Johnson-Laird, P. N. (1983). Mental models. Cambridge: Cambridge University Press. Johnson-Laird, P. N., & Byrne, R. M. J. (1991). Deduction. Hove & London, UK: Erlbaum. Johnson-Laird, P. N., & Byrne, R. M. J. (2002). Conditionals: A theory f meaning, pragmatics and inference. Psychological Review, 109, 646-678. Kahneman, D., Slovic, P., & Tversky, A. (1982). Judgment under uncertainty: Heuristics and biases. Cambridge: Cambridge University Press. Liu, I.-M., Lo, K.-C., & Wu, J.-T. (1996). A probabilistic interpretation f "If-Then"The Quarterly Journal f Experimental Psychology, 49A, 828-844. Newstead, S. E., Ellis, C., Evans, J. St. B. T., & Dennis, I. (1997). Conditional reasoning with realistic material. Thinking and Reasoning, 3, 49-76. Oaksford, M., & Chater, N. (2001). The probabilistic approach to human reasoning. Trends in Cognitive Sciences, 5, 349-357. Rips, L. J. (1994). The psychology f proof. Cambridge, MA: MIT Press. Shaw, V. (1996). The cognitive processes in informal reasoning. Thinking and Reasoning, 2, 51-80. Sloman, S. A. (2002). Two systems f reasoning. In T.Gilovich, D.Griffin, & D.Kahneman (Eds.), Heuristics and biases: The psychology f intuitive judgment (pp. 379-398). Cambridge: Cambridge University Press. Sperber, D., Cara, F., & Girotto, V. (1995). Relevance theory explains the selection task. Cognition, 57, 31-95. Stanovich, K. E. (1999). Who is rational Studies f individual differences in reasoning. Mahway, NJ: Lawrence Elrbaum Associates. Stevenson, R. J., & Over, D. E. (2001). Reasoning from uncertain premises: Effects f expertise and conversational context. Thinking and Reasoning, 7, 367-390. Thompson, V. A., & Byrne, R. M. J. (2002). Reasoning counterfactually: Making inferences about things that didn't happen. Journal f Experimental Psychology: Learning, Memory, and Cognition, 28, 1154-1170. Thompson, V. A., Evans, J. St. B. T., & Handley, S. (2004). Persuading and dissuading by conditional argument.Manuscript under review. Vallee-Tournageau, F., & Krusi Penney, A. (2005). The impact f external representation in a rule discovery task. European Journal f Cognitive Psychology, 17, 820-834. Read More