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1 Intelligence and reasoning are not one and the same Ira A. Noveck and Jérôme Prado Laboratoire sur le Langage, le Cerveau et la Cognition (L2C2), Institut des Sciences Cognitives, CNRS-Université de Lyon, France Abstract: Lest the conjunction intelligence and reasoning seduce the reader into supposing that the two are of a piece, we point out that analyses made at the superset level concerning intelligence do not readily align with or outperform the scientific advances made via investigations of reasoning, which at best can be viewed as a subset of intelligent behaviour. One of Piaget s better known tasks is the class-inclusion problem, in which participants are shown, for example, five daisies and three tulips and asked, Are there more flowers or more daisies? Although the task s intended normative response is flowers, many (usually younger) participants say that there are more daisies and arguably because they understand flowers to mean flowers-that-are-not-daisies (for a recent review, see Politzer 2004). The target article s reference to intelligence and reasoning harks back to 1

2 Piaget s task, because the oft-used conjunction leaves the impression that the two represent a single area of study, when in fact the domain Intelligence is very large (the target article covers chess playing, Go, IQ tests, etc.) and perhaps includes reasoning. We argue that the presentation of Intelligence in this way is infelicitous, much like the option flowers in the class inclusion problem. More specifically, we ask the two following questions: First, are there advantages in studying a large area of cognitive performance over investigating one subpart (i.e., reasoning) alone? Second, do approaches that rely on individual differences (and primarily correlations among subtests) provide conclusions and insights that have greater validity or greater predictability than those drawn from investigations of a subset (i.e., reasoning)? It is our view that the answer to both questions is negative. For better or worse, investigations into reasoning take a structural approach. The field breaks down reasoning into its component parts, both conceptually and empirically. Thus, reasoning researchers make the distinction between deductive and inductive reasoning (where the former concerns valid conclusions and the latter, conclusions that are more or less probable). Once in the deductive domain, which will remain our example, one then aims to determine the role played by factors such as logical validity, semantic content, development, as well as perceptual or belief biases that affect participants responses. Generally speaking, reasoning researchers make the assumption that findings are universal. For example, it is generally accepted that Modus Tollens (if p then q; not-q//therefore, not-p) is more difficult to carry out than Modus Ponens (if p then q; p//therefore q). The literature on the neuroimaging of reasoning, which is covering the same ground as its cognitive forebears, also aims to depict the way the 2

3 above factors play out, but with respect to the brain mechanisms or structures that are shown to be responsible for these universal effects. Neuroimaging not only has provided some specific findings that are inaccessible to classic cognitive paradigms but has informed theory making. For example, Prado and Noveck (2006;2007) demonstrated how participants are more prone to errors and are slowed down when features mentioned in a rule mismatch those in a test item (e.g., note how a test item depicting a P-in-a-circle verifies the rule If there is not an H then there is not a square while providing two mismatches). The neuroimaging experiment (Prado & Noveck, in press) revealed that an increase in mismatching leads to greater activity in the medial prefrontal cortex (PFC) and the right mid-dorsolateral PFC. This indicates that mismatching, rather than negationinterpretation, is likely critical to correct performance. Interestingly, this restricted network is basically the same as the one reported when prior beliefs interfere in evaluating logically valid conclusions (see Goel et al. 2000; Goel & Dolan 2003). This is also in line with growing evidence showing that the right lateral PFC is specifically involved in inhibiting a prepotent response (see Aron et al. 2004) and that its nonactivation in children (8 12 years) is linked to less effective attentional control when compared with adults (Bunge et al. 2002). By making distinctions between an attentional control system described above and the parietal-frontal system that is implicated in fundamental logical inferences such as Modus Ponens (Noveck et al. 2004), one gets an informed account of the way reasoning is distributed in the frontal and parietal lobes. This dual-system approach also describes some other novel findings. For example, it can explain (a) why solutions to 3

4 insight tasks, which arguably benefit from having less attentional control, are more accessible to those who have lesions in the frontal cortex (Reverberi et al. 2005), and (b) why right lateral PFC activity is predictive of successful logical performance (Goel & Dolan 2003). Providing explanations for such nonintuitive findings is the hallmark of scientific advances. Most importantly, this indicates that correct performance on higherlevel tasks has little to do with the better use of normative rules; it has more to do with avoiding biases while using such rules. In contrast, studies that investigate cerebral correlates of Intelligence point to a large number of regions, but they hardly describe the role played by each. The upshot is that differences among individuals are linked to an entire system that fails to distinguish between its functionally distinguishable parts (e.g., rule access and perceptual integration). In fact, when intelligence research does aim to isolate factors, it largely confirms what is found through more structural approaches (e.g., Gray et al. 2003). The worry is that infelicitous analyses of brain function could lead to infelicitous theoretical claims (e.g., about evolution). Although reasoning research has benefited from descriptions of individual differences (e.g., see Jackson & Griggs 1988; Stanovich & West 2000), such descriptions do not amount to a central strategy in investigations of reasoning (at least as far as WAISbased tests are concerned). There are two viable reasons for this. First, quality of education and economic background are critical for better performance on standardized tests of intelligence (see Georgas et al. 2003; Shuttleworth-Edwards et al. 2004). This makes measures of intelligence unstable across populations. Second, as Georgas et al. (2003) report, of the 11 subtests that account for performance on the WISC-III, the two 4

5 subtests that imply reasoning (and not necessarily deductive reasoning) Mazes and Picture Completion are among the least predictive of variance across the 12 large populations (and 15,999 people) studied. In other words, reasoning per se has a limited impact on standardized tests of intelligence. References Aron, A. R., Robbins, T. W. & Poldrack, R. A. (2004) Inhibition and the right inferior frontal cortex. Trends in Cognitive Sciences 8(4): [IAN] Bunge, S. A., Dudukovic, N. M., Thomason, M. E., Vaidya, C. J. & Gabrieli, J. D. (2002) Immature frontal lobe contributions to cognitive control in children: Evidence from fmri. Neuron 33(2): [IAN] Georgas, J., Weiss, L. G., Van de Vijver, F. J. R. & Saklofske, D. H., eds. (2003) Culture and children s intelligence: Cross cultural analysis of the WISC III. Academic Press. [IAN] Goel, V., Buchel, C., Frith, C. & Dolan, R. J. (2000) Dissociation of mechanisms underlying syllogistic reasoning. NeuroImage 12(5): [IAN] Goel, V. & Dolan, R. J. (2003) Explaining modulation of reasoning by belief. Cognition 87(1):B [IAN] Gray, J. R., Chabris, C. F. & Braver, T. S. (2003) Neural mechanisms of general fluid intelligence. Nature Neuroscience 6(3): [IAN] Jackson, S. L. & Griggs, R. A. (1988) Education and the selection task. Bulletin of the Psychonomic Society 26(4): [IAN] Noveck, I., Goel, V. & Smith, K. (2004) The neural basis of conditional reasoning with 5

6 arbitrary content. Cortex 40: [IAN] Politzer, G. (2004) Reasoning, judgement & pragmatics. In: Experimental pragmatics, eds. I. A. Noveck & D. Sperber. Palgrave Macmillan. [IAN] Prado, J. & Noveck, I. A. (2006) How reaction times elucidate the matching bias and the way negations are processed. Thinking and Reasoning 12(3): [IAN] (2007). Overcoming perceptual features in logical reasoning: A parametric fmri study. Journal of Cognitive Neuroscience 19: [IAN] Reverberi, C., Toraldo, A., D Agostini, S. & Skrap, M. (2005) Better without (lateral) frontal cortex? Insight problems solved by frontal patients. Brain 128(12): [IAN] Shuttleworth-Edwards, A. B., Kemp, R. D., Rust, A. L., Muirhead, J. G. L., Hartman, N. P. & Radloff, S. E. (2004) Cross-cultural effects on IQ test performance: A review and preliminary normative indications on WAIS-III test performance. Journal of Clinical and Experimental Neuropsychology 26(7): [IAN] Stanovich, K. E. & West, R. F. (2000) Individual differences in reasoning: Implications for the rationality debate? Behavioral and Brain Sciences 23(5): [IAN] 6

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