Shuttling Between Depictive Models and Abstract Rules: Induction and Fallback

A productive way to think about imagistic mental models of physical systems is as though they were sources of quasi-empirical evidence. People depict or imagine events at those points in time when they would experiment with the world if possible. Moreover, just as they would do when observing the world, people induce patterns of behavior from the results depicted in their imaginations. These resulting patterns of behavior can then be cast into symbolic rules to simplify thinking about future problems and to reveal higher order relationships. Using simple gear problems, three experiments explored the occasions of use for, and the inductive transitions between, depictive models and number-based rules. The first two experiments used the convergent evidence of problem-solving latencies, hand motions, referential language and error data to document the initial use of a model, the induction of rules from the modeling results, and the fallback to a model when a rule fails. The third experiment explored the intermediate representations that facilitate the induction of rules from depictive models. The strengths and weaknesses of depictive modeling and more analytic systems of reasoning are delineated to motivate the reasons for these transitions.     

How Experts Solve a Novel Problem in Experimental Design

Research on expert-novice differences has mainly focused on how experts solve familiar problems. We know far less about the skills and knowledge used by experts when they are confronted with novel problems within their area of expertise. This article discusses a study in which verbal protocols were taken from subjects of various expertise designing an experiment in an area with which they were unfamiliar. The results showed that even when domain knowledge is lacking, experts solve a novel problem within their area of expertise by dividing the problem into a number of subproblems that are solved in a specified order. The lack of domain knowledge is compensated for by using abstract knowledge structures and domain-specific heuristic strategies. However, the quality of their solutions is considerably lower than the quality attained by experts who were familiar with the type of problem to be solved. The results suggest that when experts are confronted with novel problems as compared with familiar problems, their form of reasoning remains intact, but the content of their reasoning suffers due to lack of domain knowledge.     

Windows on the 21st century

Behavior analysis is a cultural system of which the Association for Behavior Analysis is a component cultural system. As cultural systems, they are composed of interlocking behavioral contingencies that constitute their cultural practices. Critical to the survival of both cultural systems is the frequency of interaction with and the nature of the content of the behavioral contingencies composing those cultural practices. The strengths of behavior analysis as a cultural system include its disciplinary character and its worldwide community of scientists and practitioners; its ability to be integrated into a scientific worldview; its track record in providing effective solutions to problems of importance to society; and the high levels of intellect, competence, and commitment that are characteristic of its participants. Weaknesses of behavior analysis are its status as an academic orphan, its relatively small size and its underdeveloped professional identity, and a lack of sociopolitical sophistication among many of its members. Behavior analysis will need to maximize its strengths and mitigate its weaknesses if it is to take advantage of the many opportunities available for growth in the modern world.     

Solving probabilistic and statistical problems: a matter of information structure and question form

Is the human mind inherently unable to reason probabilistically, or is it able to do so only when problems tap into a module for reasoning about natural frequencies? We suggest an alternative possibility: naive individuals are able to reason probabilistically when they can rely on a representation of subsets of chances or frequencies. We predicted that naive individuals solve conditional probability problems if they can infer conditional probabilities from the subset relations in their representation of the problems, and if the question put to them makes it easy to consider the appropriate subsets. The results of seven studies corroborated these predictions: when the form of the question and the structure of the problem were framed so as to activate intuitive principles based on subset relations, naive individuals solved problems, whether they were stated in terms of probabilities or frequencies. Otherwise, they failed with both sorts of information. The results contravene the frequentist hypothesis and the evolutionary account of probabilistic reasoning.     

Dispossessing Defeat

Higher-order evidence can make an agent doubt the reliability of her reasoning. When this happens, it seems rational for the agent to adopt a cautious attitude towards her original conclusion, even in cases where the higher-order evidence is misleading and the agent's original reasons were actually perfectly good. One may think that recoiling to a cautious attitude in the face of misleading self-doubt involves a failure to properly respond to one's reasons. My aim is to show that this is not so. My proposal is that (misleading) higher-order evidence can undermine the agent's possession of her first-order reasons, constituting what I call a dispossessing defeater. After acquiring the higher-order evidence, the agent is no longer in a position to rely competently on the relevant first-order considerations as reasons for her original conclusion, so that such reasons stop being available to her (even if they remain as strong as in the absence of the higher-order evidence). In this way, an agent with misleading higher-order evidence can adopt a cautious stance towards her original conclusion, while properly responding to the set of reasons that she possesses–a set that is reduced due to the acquisition of higher-order dispossessing defeaters.     

逻辑－信念冲突与推理难度对逻辑和信念判断的影响

双加工理论框架下的平行竞争模型和默认干涉模型对推理过程中启发式和分析式加工的启动次序和相互作用的性质提出了不同假设。本研究通过考察被试在信念判断和逻辑判断两种指导语下解决不同难度和不同类型推理问题的正确率和反应时,对两个模型的预测进行了检验。实验结果表明,解决简单问题时,问题类型对信念判断有显著影响,对逻辑判断无显著影响;解决复杂问题时,问题类型对逻辑判断的影响显著大于对信念判断的影响。实验结果支持平行竞争模型的假设,即两类加工在推理过程中同时启动,共同竞争最终的推理反应。     

Is case-based reasoning a source of knowledge generalisation?

This study examines the link between case-based reasoning and knowledge generalisation. Experiment 1 showed that case-based reasoning is not in itself a source of generalisation. In this experiment, subjects were able to adapt knowledge acquired during analysis of an example in order to successfully solve a very similar problem. But immediately afterwards, they failed on a problem symmetrical to the one they had just solved. Experiments 2A and 2B showed, in this same situation, that it is possible to lead subjects to generalise simply by stating that the practice problems were similar to the examples. These findings suggest that knowledge generalisation accompanying case-based reasoning is the result of a process that must be actively implemented by the subject, and not an automatic outcome of the source-to-target transfer.     

Minimum Muscle-Tension Change Trajectories Predicted by Using a 17-Muscle Model of the Monkey's Arm

Four computational problems to be solved for visually guided reaching movements, hand path, and trajectory formations, coordinate transformation, and calculations of muscle tensions are ill-posed in redundant biological control systems. These problems are ill-posed in the sense that there exist an infinite number of possible solutions. In this article, it is shown that the nervous system can solve those problems simultaneously by imposing a single global constraint: finding the smoothest muscle- tension trajectory that satisfies the desired final hand position, velocity, and acceleration. Horizontal trajectories were simulated by using a l7-muscle model of the monkey's arm as the controlled object. The simulations predicted gently curved hand paths for lateral hand movements and for movements from the side of the body to the front, and a roughly straight hand path for anterioposterior movements. The tangential hand velocities were roughly bell shaped. The simulated results were in agreement with the actual biological movements.     

Children's Reasoning in Solving Relational Problems of Deduction

This article reports on a study of children's deductive reasoning in solving novel relational problems. Detailed protocols were obtained from 264 children (aged 9- 12 years) who verbalised their thinking as they solved the problems. The study included the development of a three-phase theory based on Johnson-Laird and Byrne's mental models perspective, but with some distinct modifications. These include a focus on the relational complexity entailed in model construction and in premise integration, and the advancement of four reasoning principles that are applied throughout problem solution (in contrast to Johnson-Laird's falsification processes as the hallmark of deductive reasoning). The reported case studies and the results of statistical analyses supported predictions arising from the proposed theory, including the key role of the reasoning principles. The results also showed that problem difficulty is a function of relational complexity, not of the number of models to be constructed, as argued by Johnson-Laird and Byrne.     

Philosophical methodology and strikes.

...how do we train residents to employ ethical reasoning? This is a good question, not only for the problem of strikes, but also for all medical training. The best method is inductive, since that most closely parallels the clinical reasoning processes that define the reality of medical practice. The strengths of inductive reasoning are that it most closely matches the realities of practice, it arises from the particular circumstances of the case, and it leads to a casuistic conclusion that applies more directly than abstract reasoning models to the problem at hand. The weaknesses, though, require that inductive models include a check and balance.     

Bayesian analogy with relational transformations

How can humans acquire relational representations that enable analogical inference and other forms of high-level reasoning? Using comparative relations as a model domain, we explore the possibility that bottom-up learning mechanisms applied to objects coded as feature vectors can yield representations of relations sufficient to solve analogy problems. We introduce Bayesian analogy with relational transformations (BART) and apply the model to the task of learning first-order comparative relations (e.g., larger, smaller, fiercer, meeker) from a set of animal pairs. Inputs are coded by vectors of continuous-valued features, based either on human magnitude ratings, normed feature ratings (De Deyne et al., 2008), or outputs of the topics model (Griffiths, Steyvers, & Tenenbaum, 2007). Bootstrapping from empirical priors, the model is able to induce first-order relations represented as probabilistic weight distributions, even when given positive examples only. These learned representations allow classification of novel instantiations of the relations and yield a symbolic distance effect of the sort obtained with both humans and other primates. BART then transforms its learned weight distributions by importance-guided mapping, thereby placing distinct dimensions into correspondence. These transformed representations allow BART to reliably solve 4-term analogies (e.g., larger:smaller::fiercer:meeker), a type of reasoning that is arguably specific to humans. Our results provide a proof-of-concept that structured analogies can be solved with representations induced from unstructured feature vectors by mechanisms that operate in a largely bottom-up fashion. We discuss potential implications for algorithmic and neural models of relational thinking, as well as for the evolution of abstract thought.     

Reducing cognitive biases in probabilistic reasoning by the use of logarithm formats

Research on probability judgment has traditionally emphasized that people are susceptible to biases because they rely on “variable substitution”: the assessment of normative variables is replaced by assessment of heuristic, subjective variables. A recent proposal is that many of these biases may rather derive from constraints on cognitive integration, where the capacity-limited and sequential nature of controlled judgment promotes linear additive integration, in contrast to many integration rules of probability theory (Juslin, Nilsson, & Winman, 2009). A key implication by this theory is that it should be possible to improve peoples’ probabilistic reasoning by changing probability problems into logarithm formats that require additive rather than multiplicative integration. Three experiments demonstrate that recasting tasks in a way that allows people to arrive at the answers by additive integration decreases cognitive biases, and while people can rapidly learn to produce the correct answers in an additive formats, they have great difficulty doing so with a multiplicative format.     

Beyond subgoaling: A dynamic knowledge generation framework for creative problem solving in cognitive architectures

In this paper we propose a computational framework aimed at extending the problem solving capabilities of cognitive artificial agents through the introduction of a novel, goal-directed, dynamic knowledge generation mechanism obtained via a non monotonic reasoning procedure. In particular, the proposed framework relies on the assumption that certain classes of problems cannot be solved by simply learning or injecting new external knowledge in the declarative memory of a cognitive artificial agent but, on the other hand, require a mechanism for the automatic and creative re-framing, or re-formulation, of the available knowledge. We show how such mechanism can be obtained trough a framework of dynamic knowledge generation that is able to tackle the problem of commonsense concept combination. In addition, we show how such a framework can be employed in the field of cognitive architectures in order to overcome situations like the impasse in SOAR by extending the possible options of its subgoaling procedures.     

Heuristic thinking and human intelligence: a commentary on Marewski,Gaissmaier and Gigerenzer

Marewski, Gaissmaier and Gigerenzer (2009) present a review of research on fast and frugal heuristics, arguing that complex problems are best solved by simple heuristics, rather than the application of knowledge and logical reasoning. We argue that the case for such heuristics is overrated. First, we point out that heuristics can often lead to biases as well as effective responding. Second, we show that the application of logical reasoning can be both necessary and relatively simple. Finally, we argue that the evidence for a logical reasoning system that co-exists with simpler heuristic forms of thinking is overwhelming. Not only is it implausible a priori that we would have evolved such a system that is of no use to us, but extensive evidence from the literature on dual processing in reasoning and judgement shows that many problems can only be solved when this form of reasoning is used to inhibit and override heuristic thinking.     

Computing finite models by reduction to function-free clause logic

Recent years have seen considerable interest in procedures for computing finite models of first-order logic specifications. One of the major paradigms, MACE-style model building, is based on reducing model search to a sequence of propositional satisfiability problems and applying (efficient) SAT solvers to them. A problem with this method is that it does not scale well because the propositional formulas to be considered may become very large.We propose instead to reduce model search to a sequence of satisfiability problems consisting of function-free first-order clause sets, and to apply (efficient) theorem provers capable of deciding such problems. The main appeal of this method is that first-order clause sets grow more slowly than their propositional counterparts, thus allowing for more space efficient reasoning.In this paper we describe our proposed reduction in detail and discuss how it is integrated into the Darwin prover, our implementation of the Model Evolution calculus. The results are general, however, as our approach can be used in principle with any system that decides the satisfiability of function-free first-order clause sets.To demonstrate its practical feasibility, we tested our approach on all satisfiable problems from the TPTP library. Our methods can solve a significant subset of these problems, which overlaps but is not included in the subset of problems solvable by state-of-the-art finite model builders such as Paradox and Mace4.     

Choosing between hearts and minds: Children's understanding of moral advisors

Moral development research has often focused on the development of moral reasoning without considering children's understanding of moral advisors. We investigated how children construe sources of moral advice by examining the characteristics that children deem necessary for reasoning about moral or scientific problems. In two experiments, children in grades K, 2, and 4 were presented with dilemmas of a moral nature or scientific nature and chose between two advisors. Second and fourth graders chose advisors differentially based on their expertise, while kindergartners did not discriminate between advisors. In a third experiment, older children indicated that only certain characteristics are needed to solve moral or scientific problems, and they endorsed these characteristics differentially based on the problem to be solved. Thus, by middle childhood, children construe moral knowledge as distinct from scientific knowledge and select advisors in each area accordingly.     

Dual-process theories of reasoning: The test of development

Dual-process theories have become increasingly influential in the psychology of reasoning. Though the distinction they introduced between intuitive and reflective thinking should have strong developmental implications, the developmental approach has rarely been used to refine or test these theories. In this article, I review several contemporary dual-process accounts of conditional reasoning that theorize the distinction between the two systems of reasoning as a contrast between heuristic and analytic processes, probabilistic and mental model reasoning, or emphasize the role of metacognitive processes in reflective reasoning. These theories are evaluated in the light of the main developmental findings. It is argued that a proper account of developmental phenomena requires the integration of the main strengths of these three approaches. I propose such an integrative theory of conditional understanding and argue that the modern dual-process framework could benefit from earlier contributions that made the same distinction between intuition and reflective thinking, such as Piaget’s theory.     

Odd Choices: On the Rationality of Some Alleged Anomalies of Decision and Inference

This paper presents a number of apparent anomalies in rational choice scenarios, and their translation into the logic of everyday reasoning. Three classes of examples that have been discussed in the context of probabilistic choice since the 1960s (by Debreu, Tversky and others) are analyzed in a non-probabilistic setting. It is shown how they can at the same time be regarded as logical problems that concern the drawing of defeasible inferences from a given information base. I argue that initial appearances notwithstanding, these cases should not be classed as instances of irrationality in choice or reasoning. One way of explaining away their apparent oddity is to view certain aspects of these examples as making particular options salient. The decision problems in point can then be solved by ‘picking’ these options, although they could not have been ‘chosen’ in a principled way, due to ties or incomparabilities with alternative options.     

十文钱问题：中国一古老推理错觉问题初探

首先简要阐述了中国一古老推理问题——十文钱问题的学术价值,然后在研究1中通过调查法对其进行了研究。结果发现,该问题应该属于典型的推理错觉问题,且其错觉产生的原因可能是运用单式记账方式进行推理的结果;研究2和研究3分别通过口语报告法和实验法获得了支持这一假设的证据,但同时也发现记账方式问题可能也并不是问题的全部,其中财会事项发生的顺序所形成的循环结构可能也是问题困难原因之一,这一假设在研究4中得到了支持