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Is Popper�s Falsificationist Heuristic a Helpful Resource for Developing Critical Thinking?

CHI-MING LAM

The University of Hong Kong

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Three Core Concepts of Critical Rationalism

Formulated fundamentally by Popper as an attitude of admitting that �I may be wrong and you may be right, and by an effort, we may get nearer to the truth� (1966, p. 225), critical rationalism is an attitude of readiness to listen to critical arguments and to learn from our mistakes. Near the end of his life, Popper revealed that he owes the idea of this formulation to what a young Carinthian member of the National Socialist Party, not long before the year in which Hitler came to power in Germany (1933), said to him: �What, you want to argue? I don�t argue: I shoot!� (1996, p. xiii). The young man�s readiness to shoot rather than to argue may indeed have planted the seeds of three core concepts of Popper�s critical rationalism, viz. fallibilism (�I may be wrong�), criticism (the required �effort�), and verisimilitude (�we may get nearer to the truth�).

�� By fallibilism Popper (1966) intends the view that we are fallible and that the quest for certainty is mistaken. While the former view can be substantiated historically by the fact that what we once thought to be well-established may later turn out to be false, the latter can be understood theoretically by the problem that what we can explain or know is limited. One such limitation concerns the power of our brain to explain: according to Hayek (1952), any apparatus of classification must possess a structure of a higher degree of complexity than that possessed by the objects which it classifies; it implies that no explaining agent can ever explain objects of its own kind or own degree of complexity, and thus that the human brain can never fully explain its own operations. Another limitation arises from our inability to predict the future course of history, not least because of our inability to predict the future growth of human knowledge: as Popper puts it, �if there is such a thing as growing human knowledge, then we cannot anticipate today what we shall know only tomorrow� (2002a, p. xii). Accordingly, his fallibilism denies the possibility of certain knowledge and of authoritative sources of knowledge. Instead, he asserts that nothing is secure and that our knowledge remains conjectural and fallible.

�� However, because we can learn from our mistakes, fallibilism need not cause any keptical or relativist conclusions. And criticism, he claims, �is the only way we have of detecting our mistakes, and of learning from them in a systematic way� (1966, p. 376). It includes criticizing the theories or conjectures not only of others but also of our own. Since, for Popper (1989), criticism invariably consists in pointing out some contradiction (within the theory criticized, or between the theory and another theory which we have some reason to accept, or between the theory and certain statements of facts), deductive logical reasoning is suggested as the method of criticism: only by purely deductive reasoning can we discover what our theories imply, and thus where contradictions lie. More specifically, the importance of deductive or formal logic to criticism lies in the fact that it adopts the rules by which truth is transmitted from premises to conclusions while falsity is re-transmitted from conclusions to premises. It is this re-transmission of falsity that �makes formal logic the Organon of rational criticism�that is, of refutation� (ibid., p. 64). In fact, rejecting all attempts at the justification of theories, Popper replaces justification with criticism in his non-justificationist or falsificationist view of rationality: �Previously, most philosophers had thought that any claim to rationality meant rational justification (of one�s beliefs); my thesis was, at least since my Open Society, that rationality meant rational criticism (of one�s own theory and of competing theories)� (2002b, p. 173). However, considering a theory may stand up to criticism better than its competitors, he concedes that we can sometimes �justify� our preference for a theory in the negative sense that a theory receives some kind of support if it has, rather than secured positive evidence, withstood severe criticism.

�� The idea of getting nearer to the truth or achieving greater verisimilitude is crucial to Popper�s concept of critical rationalism, for it is only the idea of truth that allows us to speak sensibly of fallibilism and criticism: the purpose of searching for mistakes and eliminating as many of them as we possibly can through critical discussion is to get nearer to the truth. Criticizing subjective theories of truth for conceiving truth as something we are justified in believing or in accepting in accordance with some criterion of well-foundedness, Popper (1989) adopts Tarski�s correspondence theory of objective truth that a statement is true if and only if it corresponds to the facts. For one thing, Tarski�s objective theory of truth allows us to make certain assertions that appear obviously correct to Popper but selfcontradictory within those subjective theories of truth: for example, a theory may be true even if nobody believes it, and even if we have no reason to think it true; another theory may be false even if we have comparatively good reasons for accepting it; we search for truth, but may not know when we have found it; and we have no criterion of truth, but are guided by the idea of truth as a regulative principle. To allay suspicions about the idea of getting nearer to the truth, or of the growth of knowledge, Popper (1979) introduces a logical idea of verisimilitude by combining two notions from Tarski, viz. truth and content. Defining the class of all true statements and false statements following from a statement p as the truth content and falsity content of p respectively, Popper explain that:

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Intuitively speaking, a theory T₁ has less verisimilitude than a theory T₂ if and only if (a) their truth contents and falsity contents (or their measures) are comparable, and either (b) the truth content, but not the falsity content, of T₁ is smaller than that of T₂, or else (c) the truth content of T₁ is not greater than that of T₂, but its falsity content is greater. (Ibid., p. 52)

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�� He accordingly regards the search for verisimilitude rather than truth as a more realistic aim of science in that while we can never have sufficiently good arguments for claiming that we have actually attained the truth, we can have reasonably good arguments for claiming that we may have made progress towards the truth (i.e. That the theory T₂ is nearer to the truth and thus preferable to its predecessor T₁).

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Stratagems Opposed to Criticism

Yet, to put such a falsificationist theory into practice, it is necessary to identify and combat a nest of philosophical presuppositions that work against criticism and help to confine individuals to the justificationist framework. As the Chinese proverb cautions, �It is easy to dodge an open spear thrust but difficult to guard against an arrow shot from behind�, one is unlikely to circumvent or eliminate the effects of these anti-criticism presuppositions unless various hidden stratagems that reduce and eschew criticism are themselves exposed to criticism. Popper, as an advocate of falsificationism, spares no pains to reveal such protective or evasive stratagems. To begin with, he (1989) points out that the doctrine that truth is manifest runs counter to the doctrine of fallibility and thus of tolerance: if truth were manifest, we would be unlikely to make mistakes, and thus would not need to tolerate or pardon others for their mistakes committed as a result of their prejudices. Since criticism involves searching for errors of our own and of others, which assumes that we are prone to errors and consequently should be tolerant of others, the doctrine that truth is manifest is diametrically opposed to it. Another stratagem Popper combats is the demand for precision in concepts as a prerequisite for criticism or problem-solving. Affirming the non-existence of �precise� concepts, or concepts with �sharp boundary lines�, Popper (ibid.) emphasizes that words are significant only as tools for formulating theories and don�t need to be more precise than our problems demand. To deal with the problem that our problems may sometimes demand that we make new distinctions for the sake of clarity or precision, he suggests an ad hoc approach:

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If because of lack of clarity a misunderstanding arises, do not try to lay new and more solid foundations on which to build a more precise �conceptual framework�, but reformulate your formulations ad hoc, with a view to avoiding those misunderstandings which have arisen or which you can foresee. And always remember that it is impossible to speak in such a way that you cannot be misunderstood: there will always be some who misunderstand you. (Popper, 2002b, p. 29)

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Popper identifies further three approaches that work against criticism, namely essentialism, instrumentalism, and conventionalism. Essentialism assumes that science aims at ultimate explanations that describe the �essences� of things�the realities that lie behind appearances�and therefore are neither in need nor susceptible of further explanation. Popper (1989) criticizes essentialism as obscurantist in the sense that it prevents fruitful questions or further criticisms from being raised. He (ibid.) also condemns as obscurantist the instrumentalist view of theories as mere instruments for prediction, because it stresses application but neglects falsification or criticism: for instrumental purposes of practical application, a theory may continue to be used within the limits of its applicability even after its refutation�in other words, a theory cannot be falsified insofar as it is interpreted as a simple instrument, for it can always be said that different theories have different ranges of application. And with respect to conventionalist philosophy, which regards laws of nature as our own creations and arbitrary conventions rather than representations of nature, although Popper admits that it deserves credit for clarifying the relations between theory and experiment, or rather for recognizing �the importance � of the part played by our actions and operations, planned in accordance with conventions and deductive reasoning, in conducting and interpreting our scientific experiments� (1980, p. 80), he rejects its methods of protecting the theoretical systems of the natural sciences against criticism. He asserts that there are at least four conventionalist stratagems�introducing ad hoc hypotheses, modifying ostensive definitions, adopting a skeptical attitude to the reliability of the experimenter, and casting doubt on the acumen of the theoretician�which make it impossible to falsify these systems.

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A Bias Towards Confirmation

The Pervasiveness and Various Guises of the Confirmation Bias

Apart from exposing to criticism the various hidden stratagems that work against it, it is also important to combat what appears a common psychological tendency of humans to be biased towards confirmation, or against disconfirmation, a tendency that reflects a conflict between falsificationism and apparently deep-rooted psychological mechanisms. Unfortunately, Popper did not give much attention to this. According to Nickerson (1998), confirmation bias connotes an unwitting process of seeking or interpreting evidence in ways that are partial to existing beliefs or hypotheses. A great deal of empirical evidence supports the view that the confirmation bias not only is extensive and strong but also appears in various guises: reflected in the tendency of people, for example, to demand less hypothesis-consistent evidence for accepting a hypothesis than hypothesis-inconsistent information for rejecting a hypothesis (Pyszczynski & Greenberg, 1987); to recall or produce reasons supporting the side they favour rather than the other side on a controversial issue (Baron, 1995); and, when assessing the validity of a conditional �if p then q�, to seek for the presence of p and q so as to confirm the conditional rather than for the presence of p and not-q so as to disconfirm the conditional (Wason, 1966).

�� Although Polya (1954) has argued that what distinguishes scientists from ordinary people is their disposition to seek disconfirmatory evidence for their hypotheses, instances of confirmation bias still abound in the history of science. This can be illustrated at two�personal and institutional�levels. At the personal level, Michael Faraday advocated ignoring disconfirmatory evidence when dealing with a novel hypothesis until the hypothesis was well-confirmed (Tweney, 1989), while Robert Millikan reported only those observations that fitted his hypothesis when publishing experimental work on determining the electric charge of a single electron (Henrion & Fischhoff, 1986). At the institutional level, just as Newton�s concept of universal gravity was rejected by Huygens and Leibniz due to their resistance to the idea of a force not reducible to matter and motion extending throughout space, scientific discoveries have often met with resistance from scientists themselves, especially from those whose theoretical positions were challenged by the discoveries. The typical reaction of scientists to the challenge of anomalous data to an existing theory is in fact to challenge the data first and, if the data prove reliable, then to complicate the theory just enough to accommodate the anomalous result (Nickerson, 1998). Perhaps Polya�s characterization of individual scientists as being inclined to disconfirm their own hypotheses is half correct at most: they appear eager to criticize or disconfirm other scientists� hypotheses rather than theirs.

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Theoretical Explanations for the Confirmation Bias

With regard to the question of how to account for the confirmation bias, apart from what Matlin and Stang (1978) dub the Pollyanna principle, which explains in a commonsensical way that people tend to be partial towards pleasant thoughts and memories rather than unpleasant ones and thus to believe propositions they would like to be true rather than those they would prefer to be false, there are at least four theoretical explanations that various researchers have proposed. First, according to Nickerson, people are basically limited to consideration of only one thing�and inclined to gather information about only one hypothesis�at a time. However, restricting attention to a single hypothesis might strengthen that hypothesis even if it is false:

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An incorrect hypothesis can be sufficiently close to being correct that it receives a considerable amount of positive reinforcement, which may be taken as further evidence of the correctness of the hypothesis in hand and inhibit continued search for an alternative. (Nickerson, 1998, p. 198)

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Hence the confirmation bias.

�� Second, discounting the possibility that people seek deliberately to confirm rather than disconfirm their hypotheses, Evans (1989) attributes the confirmation bias not to their motivation to confirm but to their failure to think in explicitly disconfirmatory terms. His argument accords with much evidence that people find it more difficult to deal with negative than positive information. For example, it is more difficult to decide the truth or falsity of negative sentences than of positive ones (Wason, 1961); and inferences from negative premises need more time to evaluate and are more likely to be evaluated wrongly than those from positive premises (Fodor, Fodor, & Garrett, 1975).

�� Third, just as Friedrich asserts that �our inference processes are first and foremost pragmatic, survival mechanisms and only secondarily truth detection strategies� (1993, p. 298), the judgements people make in many real-life situations are motivated more by a desire to achieve success and survival�and thus to balance potential rewards against perceived risks�than by the objective of determining the truth or falsity of hypotheses. This explains why confirmation bias may result when the undesirable consequences of considering a true hypothesis as false are greater than those of considering a false hypothesis as true.

�� Last, stressing the importance of being able to justify what one believes at all levels of education can establish or strengthen a tendency to seek confirmatory evidence selectively: if one is always stimulated to adduce reasons for opinions that one holds and is not urged also to articulate reasons that could be given against them, one is being trained to exercise a confirmation bias (Nickerson, 1998). To make matters worse, some educational practices fail to distinguish explicitly between case-building (i.e. seeking selectively or giving undue weight to evidence that supports one�s position while neglecting to seek or discounting evidence that would tell against it) and evidence-weighing (i.e. seeking evidence on all sides and evaluating it as objectively as one can) so that what is in reality case-building passes for the impartial evaluation of evidence: hence the ubiquity and strength of the confirmation bias among educated adults. A typical example of such case-building educational practices is debate, in which debaters give their primary attention to arguments that support the positions they are defending�even if they might advance potential counter-arguments, their intention is only to reveal the shortcomings of these counter-arguments. After all, debaters aim to win, and the way to do so is to make the strongest possible case for their own position while countering, discounting, or simply ignoring any evidence that might be brought against it.

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The Teacher�s Role in Undermining the Strength and Spread of the Confirmation Bias

Although it can be argued that the confirmation bias helps both to protect our sense of self by rendering our preferred beliefs less vulnerable than they otherwise would be (Greenwald, 1980) and to guard science against indiscriminate acceptance of alleged new discoveries that fail to stand the test of time (Price, 1963), the bias is still generally regarded as a human failing: it can contribute to the formation of various delusions, the development and survival of superstitions, and the perpetuation of hostility and strife between people with conflicting views of the world (Nickerson, 1998). It is probably a good idea to start with the education of children if the strength and spread of the confirmation bias are to be undermined and checked. What then are the implications for educational practice? First, teachers themselves should be aware of the confirmation bias�its pervasiveness and the various guises in which it appears. Such awareness could help students to be more cautious in making decisions about important issues and more open to opinions that differ from their own.

�� Considering, moreover, that the confirmation bias is partly attributed to the tendency of people to consider only one hypothesis at a time, teachers should encourage their students to think of several alternative hypotheses simultaneously in attempting to explain a phenomenon. The discovery by Tweney et al. that individuals seldom employ this thinking strategy successfully�for they prefer �to evaluate several pieces of data against a single hypothesis, rather than one datum against several hypotheses� (1980, p. 119)�demonstrates the superiority of working in groups in learning to avoid the bias: having each individual work on a different hypothesis, groups can keep track of several hypotheses at the same time.

�� Teachers should also realize the significance of making explicit the distinction between case-building and evidence-weighing, and encourage their students to evaluate evidence objectively in the formation and evaluation of hypotheses. Here it is vital to cultivate in students a critical mindset that prompts them to think of reasons both for and (especially) against any judgement that is to be made. And they should be made aware that the motivation to find support for preferred beliefs �often leads a person to overlook even glaring faults in the data, because it is difficult to find what is not sought� (Dawson, Gilovich, & Regan, 2002, p. 1386).

�� Despite the inclination of scientists to discount data inconsistent with their theory, Fugelsang et al. (2004) found that scientists began to modify their original theory when repeated observations of inconsistent data occurred. Indeed, the initial reluctance of scientists to accept inconsistent data and their subsequent re-theorization through repeated experimentation can be considered as a practical heuristic device: it prevents them from prematurely accepting findings that may be spurious while permitting the revision of theories and thus the growth of knowledge. In the realm of science teaching, this heuristic device should be introduced to students, particularly for fostering an appropriate attitude towards inconsistent data.

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Can Students Be Taught to Falsify?

Influential as Popper is in the philosophy and practice of science, a question can still be raised about the effectiveness of his methodology, for there is much controversy in the psychological literature over the feasibility and utility of falsification as a strategy for solving scientific problems. To start with, many psychological studies show that many scientists have difficulty in disconfirmatory reasoning. For instance, in a survey conducted by Mahoney and Kimper (1976), a sample of physicists, biologists, sociologists and psychologists were asked to rate the validity of four forms of material implication (i.e. to judge whether it is valid, assuming that p materially implies q, to infer q from p, not-q from not-p, p from q , and not-p from, not-q ) and to identify the logically critical experiments that could test the validity of a hypothesis of the form �if p then q�. It was found that over half of these scientists failed to recognize modus tollens (i.e. the inference from not-q to not-p) as logically valid, and that fewer than 10% of them were able to select correctly the experiments that had the critical potential of falsifying the sample hypothesis. Perhaps more surprisingly, similar difficulty in recognizing the logical validity of falsification was found in a sample of statisticians who had been formally trained in testing statistical (null) hypotheses and thus in examining possible disconfirming evidence (Einhorn & Hogarth, 1978).

�� However, having difficulty in using disconfirmatory reasoning does not mean a lack of ability to do so. Indeed, some researchers have successfully taught college students to employ disconfirmatory strategies to solve such reasoning problems as Wason�s (1960) 2-4-6 problem and Gardner�s (1977) �New Eleusis�. Wason advanced the 2-4-6 problem as a test of inductive reasoning: subjects were told that the sequence of three numbers �2-4-6� was an instance of a rule that the experimenter had in mind (the rule was �any three numbers in ascending order�); they were required to discover the rule by generating their own test sequences of three numbers which the experimenter would describe to them as correct or incorrect instances of the rule. Considering Wason�s subjects displayed a strong confirmation bias and tended to generate test sequences consistent with their tentative hypotheses, Tweney et al. (1980), using the same 2-4-6 task, made an attempt to teach disconfirmatory strategies to their subjects, that is, to ask their subjects to try generating disconfirmatory instances. They found that the mean number of confirmatory and disconfirmatory instances generated by subjects in the disconfirmatory group was 1.5 and 6.6 respectively (in Experiment 1). This indicates that Tweney et al. were successful in eliminating most attempts at confirmation and thus in changing the inquiry strategy of those subjects in the disconfirmatory group.

�� New Eleusis� is a card game designed to simulate the inductive search for truth. Gorman, Gorman, Latta, and Cunningham (1984) adapted it to create a task for studying scientific reasoning: subjects were asked to guess what the underlying rule behind a sequence of cards was by playing cards one at a time (one of the rules, for instance, was �a difference of 1 must separate adjacent cards�); they would be informed by the experimenter whether their cards were right or wrong but would, not receive any feedback from the experimenter on whether their guesses were right or wrong until the end of the experiment. Using this task to study how confirmatory, disconfirmatory, and combined strategies affected group problem solving (in Experiment 2), they instructed their subjects to concentrate respectively on getting as many cards right as possible, on getting as many cards wrong as possible, and on getting cards right until they had a guess which was then tested by playing cards that would be wrong. They found that disconfirmatory groups played incorrect cards 41% of the time, combined groups 33% of the time, and confirmatory groups only 20% of the time. Again, the result shows that the instructional manipulation was successful; hence the feasibility of inducing the use of disconfirmation.

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Two Contributory Factors in Eliciting Disconfirmation

Here, two contributory factors in the higher use of disconfirmation�collaborative reasoning and lower normativity�need attention if disconfirmatory strategies are to be promoted in the classroom. To illustrate how group processes often facilitated disconfirmation, Gorman et al. (1984, p. 75) provided the following brief exchange between two subjects in one disconfirmatory group:

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One subject complained to the other group members: �I have a hard time guessing wrong�. Another subject tried to tell her how to disconfirm: �If you think the series goes like this (pointing to a sequence of cards ascending by ones), try to prove it wrong by putting down a card that doesn�t go with the series�.

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The second subject soon induced not only the first subject but also other group members to falsify more and more guesses. Such beneficial effects of peer interaction are echoed in the study of Moshman and Geil (1998), who found that while 75% of the subjects in interactive groups could apply correctly a disconfirmatory strategy in testing a hypothesis, only 9% of the individual subjects working in isolation could do so. As close examination of the videotaped group discussions revealed little evidence of passive conformity to majority views or to the views of an apparent expert but a usual attempt to co-construct a consensus solution�a structure of arguments qualitatively more sophisticated than that generated by most individuals�by sharing perspectives and reasons, they attributed the superior performance of the groups to collaborative reasoning rather than to peer pressure or imitation. Insight into the logic of falsification appears to be more readily achieved in collaborative reasoning than in individual reasoning.

�� The question whether disconfirmation is used during collaborative hypothesis testing might, however, depend upon the type of relation reasoners have with their partners and with the experimenter: the study of Butera, Caverni and Rossi (2005) showed that while confrontation with a low-competence partner rendered subjects able to learn to use disconfirmation, confrontation with a high-competence partner induced them to use confirmation, even when the partner used disconfirmation. A possible explanation is that confrontation with a high-competence partner could threaten subjects� sense of competence, thereby leading them to test their own hypotheses through confirmation as a defensive strategy that seems to support their hypotheses and thus to protect their competence; in contrast, a low-competence partner is less likely to threaten the subjects� sense of competence, thereby allowing them the opportunity to test the limits of the validity of their own hypotheses through disconfirmation (ibid.). Moreover, Butera et al. (2005) showed that subjects who were confronted with the violation of a conversational rule�i.e. were told by the experimenter in solving Wason�s (1960) 2-4-6 problem that 2-4-6 was not a good example of the rule and had been chosen only to show them what was a number triad�used a high proportion of disconfirmation, whatever the competence of the partner. They explained that disconfirmation stemmed from the possibility of diverging from not only social norms in the case of interaction and social influence (e.g. the constraining power of competence), but also conversational norms in the case of language (e.g. the constraining power of the example given by the experimenter): considering the high-status experimenter might lead subjects through conversation to focus on the given triad, to formulate a hypothesis that captured all the salient features of the triad, and to try to confirm it, telling them that 2-4-6 was not a good example of the rule might break the focused processing of the task and lead them to use disconfirmation. It appears therefore that people use confirmation in constraining reasoning situations but that �the use of disconfirmation can be increased by lowering the normativity of the situation, either by a less threatening source or by less constraining conversational rules� (ibid., p. 186).

�� In other words, if disconfirmation is to be taught effectively to students, merely creating the opportunity for them to collaborate with each other is not enough. The teacher should also attempt to lower the normativity of the learning environment by such means as ensuring that students interacting within the group are not threatening or dominating, and avoiding proposing as an authority a model solution to them during problem-solving. The latter is particularly noteworthy in that many teachers really see themselves as an authority in the classroom who, they think, should know the answer to every question. Such an authoritative image teachers have of their role is detrimental to the adoption of disconfirmation in two ways: first, it makes the interaction between the teacher and students more normative; and second, it makes the classroom less likely to satisfy the basic requirement for implementing falsificationism in education, that is, to become a place that values mistakes made by both teachers and students (Sankey, 1999).

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The Influence of the Complexity of the Problem

Despite the foregoing evidence in support of the argument that people can be taught to falsify their hypotheses, some studies have shown that instructional manipulations might fail to elicit falsification when the inference problem turns complex. For example, to achieve a more realistic simulation of science in their study, Mynatt, Doherty and Tweney (1977) designed a rather complex inference task: after observing a set of computer displays made up of stationary geometric figures and moving particles whose motion was influenced by the figures, subjects were asked to discover the laws that governed the motion of particles by first generating a hypothesis and then choosing the appropriate experiments to test that hypothesis. They found that their manipulation failed to induce the disconfirmatory group to seek disconfirmation. In a follow-up study, Mynatt, Doherty and Tweney (1978) gave subjects more extensive instructions to falsify and rendered the task even more complex yet more realistic by allowing them to explore it in a less constrained manner (e.g., allowing them to design their own experiments instead of forcing them to choose from the potential ones). As in Mynatt et al. (1977), however, they found that instructions to disconfirm produced little or no effect on the disconfirmatory group; indeed, �there was � almost no indication whatsoever that they (both the disconfirmatory and control groups) intentionally sought disconfirmation� (Mynatt et al., 1978, p. 400).

�� A possible explanation, they suggest, is that a disconfirmatory strategy might simply overload the cognitive capacity of most people�hence the difficulty in eliciting it from them�when they are groping for a means of dealing with complex inference problems. Accordingly, the feasibility of teaching people to falsify seems to depend on whether or not the task is complex.

�� To complicate matters still further, sometimes it is difficult to judge from the testing behaviour of people whether they have actually followed the falsificatory instruction, for it can be argued that the falsificatory instruction is not carried out successfully if people who are instructed to falsify perform what Wetherick (1962) calls negative tests�i.e. testing their hypothesis by means of test items that it predicts to be false�but at the same time expect the hypothesis to be confirmed rather than falsified by the test result. This argument is echoed and supported by the study of Poletiek (1996), who found that although 60% of subjects in the falsificatory group adopted negative tests, only 10% of them expected a hypothesis-falsifying result, concluding that:

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� when subjects are asked to behave as falsifiers in a hypothesis-testing task, their behaviour expresses the paradoxical character of this requirement by showing a preference for negative tests on the one hand, but nonetheless expecting this strategy to fail with regard to the production of hypothesis-inconsistent data on the other. (Poletiek, 1996, p. 456)

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�� In other words, it seems �paradoxical� to regard those who simultaneously use negative tests and expect confirmation of their hypotheses as following the falsificatory instruction. Leaving aside the problem of how to deal with such paradoxical situations that may arise when people are instructed to falsify, an interesting question is: why don�t they think and act in the same way? That is, why don�t they expect to falsify their hypotheses when performing negative tests? Does it reveal a disbelief at heart in the utility of falsificatory strategies? Do these strategies work in reality?

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Should Students Be Taught to Falsify?

Judging from the results of several studies conducted by Michael Gorman and his colleagues in the 1980s, there appear to be grounds for cautious optimism about the utility of falsification. To begin with, in the afore-mentioned study of how different strategies affect the performance of groups in the task adapted from �New Eleusis� (in Experiment 2), Gorman et al. (1984) found that disconfirmatory groups solved significantly more rules (72%) than combined (50%) and confirmatory (25%) groups. Together with the findings that disconfirmatory groups played the highest percentage of incorrect cards (41%) while combined and confirmatory groups played the middle (33%) and the lowest (20%) percentage respectively, and that the percentage of incorrect cards played by these three different groups was highly correlated with their success in solving the rules, this would indicate that the strategy instructions were carried out successfully and indeed accounted for the differences in performance: hence the effectiveness of disconfirmation in problem-solving.

�� Considering that scientists do not work in error-free environments, in order to model the role of disconfirmation in scientific inference more authentically, Gorman (1986) added the possibility of error to the �New Eleusis� experiment in another study (with a design virtually identical to the preceding study): subjects were told that 0�20% of the feedback on their trials from the experimenter might be in error, that is, a card that should be correct would be classified as incorrect and vice versa. He found that disconfirmatory groups did not perform significantly better than confirmatory or control (i.e. no-strategy) groups, because the possibility of error interfered with the ability of disconfirmatory groups to obtain and use disconfirmatory information in the sense that it allowed them to immunize their hypotheses against disconfirmation by classifying disconfirmatory information as error, and that it made them spend so much time checking potential errors that they failed to test their hypotheses adequately. However, this result does not mean the futility of disconfirmation under possible-error conditions. Given that the few successful groups used a strategy that combined disconfirmation with replication (i.e. replicating situations in which they thought an error might have occurred), it would imply that disconfirmation becomes even more important as a necessary, though not sufficient, strategy (ibid.).

�

A Favourable Condition for Disconfirmation to Be Effective

Gorman and Gorman (1984) showed further that the positive effect of disconfirmatory instructions found by Gorman et al. (1984) could be replicated on Wason�s (1960) 2-4-6 task with individual subjects. Specifically, they found that a significantly larger number of disconfirmatory subjects (95%) than confirmatory (48%) and control (53%) subjects solved the original rule (i.e., �any three numbers in ascending order�) of the task. Curiously enough, such positive effects of disconfirmation on performance did not appear in the afore-mentioned study by Tweney et al. (1980), the Experiment 1 of which used a design very similar to Gorman and Gorman�s and found that �while subjects did learn to seek disconfirmatory data, the possession of such strategies led neither to faster solutions, nor to a greater proportion of subjects with correct solutions� (p. 112). Later on, Gorman and his colleagues discovered that the difference between their results and those of Tweney et al. was probably caused by the fact that their subjects were given no feedback on the correctness of their guesses until the experiment was over, but Tweney et al.�s subjects were informed whether or not each of their guesses about the rule was correct and thus could rely on the experimenter for confirmation or disconfirmation (Gorman, 1992). Therefore, it appears that disconfirmation might be an effective heuristic when people cannot appeal to an outside authority to ascertain whether they are making progress towards a discovery.

�� Yet, if disconfirmation is less useful when people can appeal to such an authority, then it has little value in the case of laboratory exercises done in many high school and college classes, for, according to Gorman (1995), the objective of most of these exercises is to get the correct answer rather than to explore a novel phenomenon, and frequent appeals to authority in the form of the laboratory assistant or the instructor are not only possible but likely to be helpful. The educational implication is that another, more open-ended and exploratory kind of exercise might provide better training in the use of disconfirmation for future scientists who typically cannot appeal to any authority to test their hypotheses.

�

The Limits of Disconfirmation

Disconfirmation seems, however, not to be a universally effective strategy for solving reasoning problems. This is substantiated by the results of some 2-4-6 studies (e.g. Gorman & Gorman, 1984; Gorman, Stafford & Gorman, 1987) indicating that it does not work on very general, or more difficult, rules such as �no two numbers can be the same�. Disconfirmation seems, moreover, not to be self-sufficient either, because sometimes its utility is dependent upon confirmation in two senses. First, strategically, confirmation acts as a necessary complement to disconfirmation, especially in the early stages of a complex inference task. Here are two illustrative examples: Mynatt, Doherty and Tweney (1978) found that although no subjects solved their demanding task, the most successful one initially concentrated on accumulating confirmatory evidence for his hypothesis without regard to disconfirmatory evidence and only sought to establish whether disconfirmatory instances could be found after a relatively well-confirmed hypothesis had been developed; and Karmiloff-Smith and Inhelder (1975) found that young children presented with difficult reasoning problems were incapable of using disconfirmatory evidence�i.e. recognizing counterexample�until after their hypotheses had been sufficiently confirmed. Echoing the findings of these two studies, Vartanian, Martindale and Kwiatkowski (2003) showed that reliance on a mixed strategy of confirmation and disconfirmation in the early and later stages of hypothesis-testing respectively appeared to be quite advantageous. In fact, in order to defend Faraday against the charge that his deliberate neglect of the disconfirmatory experiments (conducted in 1831 as part of his discovery of electromagnetic induction) reflected a confirmation bias on his part, Tweney and Chitwood (1995) argue instead that what Faraday had done simply manifested a sophisticated use of such a �confirm early, disconfirm late� strategy, and explained in detail that:

�� Nature is chaotic in its character and will frequently provide false feedback to the inquirer. � [M]any of the experiments tried by Faraday were in fact producing the expected effects but the effects were small and could not be detected with [his] insensitive apparatus. The task of the scientist in such an environment is to impose order on the apparent disorder. � [O]ne of the necessary functions of a confirmation heuristic� [is that] it filters out some of the noise and may allow a signal to be detected. This is not a sure thing, which is why a disconfirmatory strategy is a necessary supplement later on. (Tweney & Chitwood, 1995, p. 255)

�� Second, essentially, a confirmatory strategy not only does not necessarily contradict the goal of seeking disconfirmation, but may be the only way to achieve it in some circumstances. To understand this, according to Klayman and Ha (1987), a confirmatory strategy is better interpreted as a positive test strategy, which means testing a hypothesis by examining instances where the target property is hypothesized to be present or is known to be present. Further, it is crucial to distinguish between two different senses of �seeking disconfirmation�. One sense, which is the focus of empirical investigations, is to examine instances that are predicted not to have the target property, or to conduct negative tests. The other sense, which is emphasized by Popper, is to examine instances that are most expected to falsify the hypothesis. Using Wason�s (1960) 2-4-6 task as an example, Klayman and Ha demonstrate graphically that although a positive test strategy cannot produce falsifications in the Popperian sense when the hypothesized rule (e.g. �increasing by 2�) is embedded within the correct rule (e.g. �increasing numbers�, as in Wason�s original task), it can do so when the hypothesized rule (e.g. �increasing by 2�) overlaps the correct rule (e.g. �three even numbers�). More importantly, indeed paradoxically, a positive test strategy is the sole strategy that can reveal conclusive falsifications�even negative tests cannot do so�when the hypothesized rule (e.g. �increasing by 2�) surrounds the correct rule (e.g. �consecutive even numbers�). We can thus conclude that it is impossible to judge the effectiveness of a confirmatory or positive test strategy in the absence of information about the nature of the task at hand.

�

Conclusion

To sum up: the implementation of Popper�s falsificationist epistemology means exposing to criticism various philosophical presuppositions that work against criticism, including the doctrine that truth is manifest, the demand for precision in concepts as a prerequisite for criticism, essentialism, instrumentalism, and conventionalism; it also means combating the confirmation bias through such educational means as helping teachers and students to acquire an awareness of its pervasiveness and various guises, teaching them to think of several alternative hypotheses simultaneously in seeking explanation of phenomena, encouraging them to assess evidence objectively in the formation and evaluation of hypotheses, and cultivating in them an appropriate attitude towards inconsistent data. With regard to the feasibility of teaching students to falsify, it appears high if teachers adopt relatively simple inference tasks, while creating an opportunity for students to collaborate with each other and lowering the normativity of the learning environment. As for the utility of doing so, although disconfirmation might be an effective heuristic when students cannot appeal to an outside authority to test their hypotheses, it appears not to be a universally effective strategy for solving reasoning problems. In contrast, confirmation seems not to be completely counterproductive and might be a useful heuristic, especially in the early stages of solving by hypothesis a complex inference problem. After all, whether disconfirmation or confirmation is better often depends on the characteristics of the specific task at hand.

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2)�� ([We]) regard ([some people]) as [Y].

3)�� ([Those Ppeople]) follow the falsificatory instruction.

4)�� ([Those Ppeople]) simultaneously use negative tests and expect [Z].

~~�(People) expect Z.~~

5)�� ([~~Other~~ Those people�s own]) ~~confirm their~~ hypothesis [will be] confirm[ed].

~~�(People) follow the falsificatory instruction.~~

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�� ????????????????? 1 2)????? 1 2) ??? X ?????? 2)????? 2) ??? Y ?????? 3)????? 4) ? ?5) ?????? 23) ?? people ??????? 4 5) ??? Z ?????? 5)?~~???~~?????????????????????????????????????????~~???????~~????

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2.1.2�� ?????????????

�� ????2004?42?????�??????????????????????????????????????????????????�?~~?????2004?p. 42??????~~???????????????????????????~~??????~~????~~???????~~?????????????????????????????????????~~????????????????????????????????�??�?�??�????~~???�??�??�??�?�??�?????�??� ??�??�?�??�??~~?????????~~????????????????????????~~???~~????????????????????????????????????????????????????????????????????????

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?1?�� The typical reaction of scientists to the challenge of anomalous data to an existing theory is in fact to challenge the data first and, if the data prove reliable, then to complicate the theory just enough to accommodate the anomalous result (Nickerson, 1998).

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The typical reaction of scientists to the challenge [1] of anomalous data

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to an existing theory is in fact to challenge [2] the data first and,

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if the data prove reliable, then to complicate the theory just enough to

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accommodate the anomalous result (Nickerson, 1998).

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�� ??????????????????�??�? ~~the~~ typical reaction?~~?the~~ challenge [1] ? , prove ?????????????~~??????~~???? scientists?, anomalous data ? the data?. ?�??�? challenge [2]?, complicate??, accommodate ??????????????????????????????????~~?????????�??�~~??????????? challenge [2]?complicate ? accommodate???????�??�??? scientists?, scientists ? , ? the theory??????????????~~??????????~~??????????

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1)�� In fact Sscientists react to challenge [1] typically in [X and Y ways].

2)�� Anomalous data challenge [1] an existing theory.

3)�� ~~In fact (~~[sScientists]) challenge [2] the data first.

4)�� If the data prove reliable,. [scientists] complicate the [challenged] theory [to the Z extent]

~~(Scientists) complicate the theory.~~

5)�� ([The theory]) is just enough to accommodate the anomalous result.

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�� ??? 2) ????? 1) ? challenge [1] ??????????????????1) ?? X ? Y ??????? 3) ???? 4)?5)???? 4) ?? Z ????? 5)??????????????????????????????????????? 4) ?? [scientists]?????????????????????????? 4) ? complicate ??�??�???????? 5) ??[The theory] ??????????????�?�???????????????~~????????????????????????????????????????????????????????????????????????????????6?????~~ ~~�??�????~~??

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???� ???????????????????????~~????~~???????????????????????????????????????????????????????~~??????????~~??????????Nickerson~~???~~?1998??

???� {????????? 5) ???? [The theory] ???}�� ???????????????????????????????????Nickerson?1998??

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{KP: ?????????????????????} ?2?�� Perhaps more surprisingly, similar difficulty in recognizing the logical validity of falsification was found in a sample of statisticians who had been formally trained in testing statistical (null) hypotheses and thus in examining possible disconfirming evidence.

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~~Perhaps more surprisingly, similar difficulty in recognizing the logical validity~~

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~~of falsification was found in a sample of statisticians who had been formally~~

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~~trained in testing statistical (null) hypotheses and thus in examining possible~~

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~~disconfirming evidence (Einhorn & Hogarth, 1978).~~

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~~��~~ ~~?????????�??�????????????????????????????????????? thus in examining possible disconfirming evidence~~ ~~??thus~~ ~~???????? similar difficulty was found????????????????????????????~~

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~~1)�� ([People are]) more surprising.~~

~~2)�� ([A sample of statisticians]) found similar difficulty.~~

~~3)�� ([The sample of statisticians]) recognize the logical validity of falsification.~~

~~4)�� ([The sample of statisticians]) had been formally trained.~~

~~5)�� ([Statisticians]) test statistical ([null]) hypotheses.~~

~~6)�� Thus ([Sstatisticians found similar difficulty]).~~

~~7)�� ([Statisticians]) examine possible disconfirming evidence.~~

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~~��~~ ~~??????????????????????????????????????? 6)~~ ?~~???????????????????????????????????????????? 2)~~ ?~~???? 6)~~ ?~~???? 1)~~ ?~~???????????? 2)~~ ?~~???? 6)~~ ?~~??????????????????????? 3)~~ ?~~????? 2)~~ ?~~????????? 4)~~ ?~~????? 2)~~ ?~~? statisticians~~ ~~??????? 5)~~ ?~~????? 4)~~ ?~~??????? 7)~~ ?~~????? 6)~~ ?~~?????????????????????????????????????????? 6)~~ ?~~??????????????~~

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~~???~~� ~~??????????????????????????????????????????????????????????????????????????????????????????Einhorn & Hogarth?1978~~??

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2.2�� ???????????

�� ????????????~~????~~????????????????????~~???????????~~??????????????????????~~???~~?????????????~~?????~~????????????????????????????�~~??????????~~???????????????�????1991?p. 102??????????????????????~~??????????????~~????????????????????????~~???????~~?????????????????

�� ????????????????????????????????????????????~~????~~???????????????????????????????????????????~~????????�????????????????????????????????????�?????????1999?p. 189????????????????????????????????????????????????~~???????????????????

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?1?�� Apart from exposing to criticism the various hidden stratagems that work against it, it is also important to combat what appears a common psychological tendency of humans to be biased towards confirmation, or against disconfirmation, a tendency that reflects a conflict between falsificationism and apparently deep-rooted psychological mechanisms.

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�� ??????????????Apart from doing something, it is also important to do something??????~~????~~??????????????????????????????????????????????~~????????????~~???????????:

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1)�� ([We]) expose the various hidden stratagems to criticism.

2)�� The various hidden stratagems work against ~~the~~ criticism.

3)�� Apart from [X], combating a common psychological tendency [Y] is also important.

4)�� Humans tend to be biased towards confirmation, or against disconfirmation.

5)�� The tendency reflects a conflict between falsificationism and apparently deep-rooted [human] psychological mechanisms.

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�� ??~~???????????~~?????? 3)??X ??~~??? X~~ ~~???~~??? 1) ?? 2)??Y ????? 4) ?? 5) ~~???? tendency~~ ~~?????~~?~~?????????~~???????????????????????????????? 3)?1) ? 2) ??????????3~~?????????~~??????? 3) ????? a common psychological tendency �4~~?????~~?????????????? 4) ???~~?????~~??? 5) ???? 3) ????? a common psychological tendency ????????????????????? 4) ???????? 4) ???????????????????~~??????????~~??~~????~~?????????????

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???� ??????????????????????????????????????????????????????��?????~~???~~??????????????????~~???????~~?????????????????????????

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?2?�� Although it can be argued that the confirmation bias helps both to protect our sense of self by rendering our preferred beliefs less vulnerable than they otherwise would be (Greenwald, 1980) and to guard science against indiscriminate acceptance of alleged new discoveries that fail to stand the test of time (Price, 1963), the bias is still generally regarded as a human failing. �

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�� ?????????????~~???~~??????????????Although it can be argued that ... , the bias is still generally regarded as a human failing.??? although ???????????????? that ??????????????? ~~the confirmation bias~~ helps to ... and to ... ??????????~~?????~~?????????????????????????????????????????????????????????~~???????????????????????~~???????????????????????????????????

�

1)�� ([People]) can argue [X].

2)�� The Cconfirmation bias helps to protect our sense of self by [Y].

3)�� ([The Cconfirmation bias]) renders our preferred beliefs less vulnerable than they otherwise would be.

4)�� ([The Cconfirmation bias]) helps to guard science against indiscriminate acceptance of alleged new discoveries.

~~Science accepts alleged new discoveries.~~

5)�� ([The] alleged new discoveries) fail to stand the test of time.

6)�� [But the] Cconfirmation bias is still generally regarded as a human failing.

�

�� ~~??????????????~~??????????????? 67) ???????? 1) - 5) ?????????????????????????????????? although ??????????????X ????? 2) - 5)?Y ????? 3)~~?? 6??1?????????~~???? 2) - 5) ???2) ?? 4) ?????????? 3) ????? 2) ???? ?????5~~?? 4?????~~??? 56) ?????? 45) ?? alleged new discoveries ??????????? 1) ????? argue ??????????? ?2)?3)?4)???56) ???????????????????????�??�???????�?? ��?��?��???????��?�???????????????????????????????????????????????????????????????????????????~~?????????~~????????????????????? 1) � 5)?????????????????? 6)????????????????????????????????????????????????????????�???�?�?????�??????????????????2) ? 4) ?????????????????????~~???????????~~??????�??????????????????~~???????????~~?�??????�????��??????��????????????????????????????~~???�??�?~~???????~~????????????�????�???????????????????????????????~~

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???� ????????????????????????????????????????????????????????????~~????????~~???????????????Greenwald?, 1980????????????????????????????~~???~~???????????Price?1963??????????????????????????????? ��~~????~~

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The Pervasiveness and Various Guises of the Confirmation Bias

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Theoretical Explanations for the Confirmation Bias

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The Teacher�s Role in Undermining the Strength and Spread of the Confirmation Bias

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Can Students Be Taught to Falsify?

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Two Contributory Factors in Eliciting Disconfirmation

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The Influence of the Complexity of the Problem

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Should Students Be Taught to Falsify?

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A Favourable Condition for Disconfirmation to Be Effective

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The Limits of Disconfirmation

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Conclusion

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