This article takes off from Johan van Benthem’s ruminations on the interface between logic and cognitive science in his position paper “Logic and reasoning: Do the facts matter?”. When trying to answer Van Benthem’s question whether logic can be fruitfully combined with psychological experiments, this article focuses on a specific domain of reasoning, namely higher-order social cognition, including attributions such as “Bob knows that Alice knows that he wrote a novel under pseudonym”. For intelligent interaction, it is important that the (...) participants recursively model the mental states of other agents. Otherwise, an international negotiation may fail, even when it has potential for a win-win solution, and in a time-critical rescue mission, a software agent may depend on a teammate’s action that never materializes. First a survey is presented of past and current research on higher-order social cognition, from the various viewpoints of logic, artificial intelligence, and psychology. Do people actually reason about each other’s knowledge in the way proscribed by epistemic logic? And if not, how can logic and cognitive science productively work together to construct more realistic models of human reasoning about other minds? The paper ends with a delineation of possible avenues for future research, aiming to provide a better understanding of higher-order social reasoning. The methodology is based on a combination of experimental research, logic, computational cognitive models, and agent-based evolutionary models. (shrink)

The claim defended in the paper is that the mechanistic account of explanation can easily embrace idealization in big-scale brain simulations, and that only causally relevant detail should be present in explanatory models. The claim is illustrated with two methodologically different models: Blue Brain, used for particular simulations of the cortical column in hybrid models, and Eliasmith’s SPAUN model that is both biologically realistic and able to explain eight different tasks. By drawing on the mechanistic theory of computational explanation, I (...) argue that large-scale simulations require that the explanandum phenomenon is identified; otherwise, the explanatory value of such explanations is difficult to establish, and testing the model empirically by comparing its behavior with the explanandum remains practically impossible. The completeness of the explanation, and hence of the explanatory value of the explanatory model, is to be assessed vis-à-vis the explanandum phenomenon, which is not to be conflated with raw observational data and may be idealized. I argue that idealizations, which include building models of a single phenomenon displayed by multi-functional mechanisms, lumping together multiple factors in a single causal variable, simplifying the causal structure of the mechanisms, and multi-model integration, are indispensable for complex systems such as brains; otherwise, the model may be as complex as the explanandum phenomenon, which would make it prone to so-called Bonini paradox. I conclude by enumerating dimensions of empirical validation of explanatory models according to new mechanism, which are given in a form of a “checklist” for a modeler. (shrink)

Replying to Queen Elizabeth II who in November 2008 asked why so few economists had warned about the emerging financial crisis, a group of eminent economists of the British Academy, claimed that while this failure had many causes, the most important was principally a failure of the collective imagination of many bright people, to understand the risks to the system as a whole. The paper suggests that this failure is due also to the still heavy influence of the paradigm of (...) rational expectations, that did not provide the intellectual tools to grasp the signs of the impending financial crisis. The paper explores the development of Muth’s rational expectations hypothesis and the parallel development of Simon’s idea of bounded rationality—two opposite views about economic behavior that originated in the same context, during the early 1960s at Carnegie Mellon. It is shown that the latter, while marginalized for decades from the core economic ideas, generated a wide development in cognitive psychology, problem solving and experimental economics that grew up in the last decades and now permit a more profound analysis of human behavior in economic and organizational contexts, that can be the seed of a new thinking in economics. The challenge for economists is to use this new toolbox to incorporate in their models real human behaviors, getting out from the economy of wishful thinking. (shrink)

Consensus plays an ambiguous role in deliberative democracy. While it formed the horizon of early deliberative theories, many now denounce it as an empirically unachievable outcome, a logically impossible stopping rule, and a normatively undesirable ideal. Deliberative disagreement, by contrast, is celebrated not just as an empirically unavoidable outcome but also as a democratically sound and normatively desirable goal of deliberation. Majority rule has generally displaced unanimity as the ideal way of bringing deliberation to a close. This article offers an (...) epistemic perspective on this question of consensus versus disagreement. For ensuring the production of better decisions, we argue, the normative appeal of consensus varies depending on the deliberative task – whether it entails problem solving or prediction. We argue that in pure problem-solving contexts, consensus retains a strong normative appeal and forms the ideal deliberative outcome of deliberation. In contrast, on predictive tasks, consensus should generally not be used as a stopping rule noris it likely to be epistemically desirable as an outcome. Instead deliberators may be better served by ending the deliberation with a form of deliberative disagreement we call ‘positive dissensus’, which paves the way for more accurate aggregate predictions. (shrink)

This article focuses on defective conditionals ? namely indicative conditionals whose antecedents are false and whose truth-values therefore cannot be determined. The problem is to decide which formal connective can adequately represent this usage. Classical logic renders defective conditionals true whereas traditional mathematics dismisses them as irrelevant. This difference in treatment entails that, at the propositional level, classical logic validates some sentences that are intuitively false in plane geometry. With two proofs, I show that the same flaw is shared by (...) a family of trivalent logics. I go on to examine the strict conditional and its derivatives. This family is the only one to avoid the faulty inference but it does so without addressing the status of the truth-value assigned to defective conditionals. (shrink)

How do people make deductions? The orthodox view in psychology is that they use formal rules of inference like those of a “natural deduction” system.Deductionargues that their logical competence depends, not on formal rules, but on mental models. They construct models of the situation described by the premises, using their linguistic knowledge and their general knowledge. They try to formulate a conclusion based on these models that maintains semantic information, that expresses it parsimoniously, and that makes explicit something not directly (...) stated by any premise. They then test the validity of the conclusion by searching for alternative models that might refute the conclusion. The theory also resolves long-standing puzzles about reasoning, including how nonmonotonic reasoning occurs in daily life. The book reports experiments on all the main domains of deduction, including inferences based on prepositional connectives such as “if” and “or,” inferences based on relations such as “in the same place as,” inferences based on quantifiers such as “none,” “any,” and “only,” and metalogical inferences based on assertions about the true and the false. Where the two theories make opposite predictions, the results confirm the model theory and run counter to the formal rule theories. Without exception, all of the experiments corroborate the two main predictions of the model theory: inferences requiring only one model are easier than those requiring multiple models, and erroneous conclusions are usually the result of constructing only one of the possible models of the premises. (shrink)

This paper is a commentary on the target article by Lokendra Shastri & Venkat Ajjanagadde [S&A]: “From simple associations to systematic reasoning: A connectionist representation of rules, variables and dynamic bindings using temporal synchrony” in same issue of the journal, pp.417–451. -/- It puts S&A's temporal-synchrony binding method in a broader context, comments on notions of pointing and other ways of associating information - in both computers and connectionist systems - and mentions types of reasoning that are a challenge to (...) S&A's system (amongst many other connectionist systems, recognizing that S&A's is much more capable as regards reasoning than most other contemporary systems). (shrink)

According to a simple form of consequentialism, we should base decisions on our judgments about their consequences for achieving our goals. Our goals give us reason to endorse consequentialism as a standard of decision making. Alternative standards invariably lead to consequences that are less good in this sense. Yet some people knowingly follow decision rules that violate consequentialism. For example, they prefer harmful omissions to less harmful acts, they favor the status quo over alternatives they would otherwise judge to be (...) belter, they provide third-party compensation on the basis of the cause of an injury rather than the benefit from the compensation, they ignore deterrent effects in decisions about punishment, and they resist coercive reforms they judge to be beneficial. I suggest that nonconsequentialist principles arise from overgeneralizing rules that are consistent with consequentialism in a limited set of cases. Commitment to such rules is detached from their original purposes. The existence of such nonconsequentialist decision biases has implications for philosophical and experimental methodology, the relation between psychology and public policy, and education. (shrink)

I consider the role of cognitive modeling in cognitive science. Modeling, and the computers that enable it, are central to the field, but the role of modeling is often misunderstood. Models are not intended to capture fully the processes they attempt to elucidate. Rather, they are explorations of ideas about the nature of cognitive processes. In these explorations, simplification is essential—through simplification, the implications of the central ideas become more transparent. This is not to say that simplification has no downsides; (...) it does, and these are discussed. I then consider several contemporary frameworks for cognitive modeling, stressing the idea that each framework is useful in its own particular ways. Increases in computer power (by a factor of about 4 million) since 1958 have enabled new modeling paradigms to emerge, but these also depend on new ways of thinking. Will new paradigms emerge again with the next 1,000‐fold increase? (shrink)

Starting from John MacFarlane's recent survey of answers to the question ‘What is assertion?’, I defend an account of assertion that draws on elements of MacFarlane's and Robert Brandom's commitment accounts, Timothy Williamson's knowledge norm account, and my own previous work on the normative status of logic. I defend the knowledge norm from recent attacks. Indicative conditionals, however, pose a problem when read along the lines of Ernest Adams' account, an account supported by much work in the psychology of reasoning. (...) Furthermore, there seems to be no place for degrees of belief in the accounts of belief and assertion given here. Degrees of belief do have a role in decision‐making, but, again, there is much evidence that the orthodox theory of subjective utility maximization is not a good description of what we do in decision‐making and, arguably, neither is it a good normative guide to how we ought to make decisions. (shrink)

This paper outlines a theory and computer implementation of causal meanings and reasoning. The meanings depend on possibilities, and there are four weak causal relations: A causes B, A prevents B, A allows B, and A allows not‐B, and two stronger relations of cause and prevention. Thus, A causes B corresponds to three possibilities: A and B, not‐A and B, and not‐A and not‐B, with the temporal constraint that B does not precede A; and the stronger relation conveys only the (...) first and last of these possibilities. Individuals represent these relations in mental models of what is true in the various possibilities. The theory predicts a number of phenomena, and, contrary to many accounts, it implies that the meaning of causation is not probabilistic, differs from the meaning of enabling conditions, and does not depend on causal powers or mechanisms. The theory also implies that causal deductions do not depend on schemas or rules. (shrink)

Abstract How do people find arguments while engaged in a discussion? Following an analogy with visual search, a mechanism that performs this task is described. It is a metarepresentational device that examines representations in a mostly serial manner until it finds a good enough argument supporting one’s position. It is argued that the mechanism described in dual process theories as ‘system 2’, or analytic reasoning fulfills these requirements. This provides support for the hypothesis that reasoning serves an argumentative function. Content (...) Type Journal Article Pages 1-20 DOI 10.1007/s10503-011-9256-1 Authors Hugo Mercier, Philosophy, Politics and Economics Program, University of Pennsylvania, 313 Cohen Hall, 249 South 36th Street, Philadelphia, PA 19104, USA Journal Argumentation Online ISSN 1572-8374 Print ISSN 0920-427X. (shrink)

Short abstract (98 words). Reasoning is generally seen as a means to improve knowledge and make better decisions. However, much evidence shows that reasoning often leads to epistemic distortions and poor decisions. This suggests that the function of reasoning should be rethought. Our hypothesis is that the function of reasoning is argumentative. It is to devise and evaluate arguments intended to persuade. Reasoning so conceived is adaptive given humans’ exceptional dependence on communication and vulnerability to misinformation. A wide range of (...) evidence in the psychology of reasoning and decision making can be reinterpreted and better explained in the light of this hypothesis. (shrink)

Unified, all-purpose, philosophical models of reduction in science lack resources for capturing varieties of cross-scientific relations that have proven critical to understanding some scientific achievements. Not only do those models obscure the distinction between successional and cross-scientific relations, their preoccupations with the structures of both theories and things provide no means for accommodating the contributions to various sciences of theories and research about long-term diachronic processes involving large-scale, distributed systems. Darwin's theory of evolution by natural selection is the parade case. (...) Explanatory pluralism accommodates a wider range of connections between theories and inquiries in science than all-purpose models of reduction do. Consequently, it provides analytical tools for understanding the roles of the theoretical proposals about the evolution of the human mind/brain that have proliferated over the last two decades. Those proposals have testable implications pertaining to both structure and processing in the modern human mind/brain. An example of such research illustrates how those proposals and investigative tools and experiments cut across both explanatory levels and modes of analysis within the cognitive sciences and how those studies can yield evidence that bears on the assessment of competing theories and models. (shrink)

The paper argues that the two best known formal logical fallacies, namely denying the antecedent (DA) and affirming the consequent (AC) are not just basic and simple errors, which prove human irrationality, but rather informational shortcuts, which may provide a quick and dirty way of extracting useful information from the environment. DA and AC are shown to be degraded versions of Bayes’ theorem, once this is stripped of some of its probabilities. The less the probabilities count, the closer these fallacies (...) become to a reasoning that is not only informationally useful but also logically valid. (shrink)

A review of extent business ethics research uncovered well over 200 published articles that investigated the role of job functions within a business organization as an explanatory factor of ethical or unethical behavior. While an important body of work, ethical breaches are often found to cut across job functions and involve multiple disciplines embedded in a business organization. This research seeks to explore a crossfunctional explanation for ethical reasoning by using an instrument new to business ethics research, the Wason selection (...) task, but well-grounded and validated in cognitive research and evolutionary psychology, to assess an individual's ability to detect rule-based social contract violations. A sample of 276 full-time business practitioners, enrolled in part-time M.B.A. programs, from the accounting, finance, information technology, marketing, supply chain, and human resource management job functions were compared on their ability to detect rule violators across a series of production scenarios in the Wason selection task. Rates of cheater detection were calculated to determine if substantive differences existed across job functions. This was followed by a series of pairwise comparisons of percentages of cheater detection across the job functions using z-tests for assessing statistical significance. The data analysis showed differences in cheater detection, with most of the variance due to the marketing job function group. Insights from this study for scholars, educators, and practitioners in the business ethics field are discussed. (shrink)

In everyday life it is often important to have a mental model of the knowledge, beliefs, desires, and intentions of other people. Sometimes it is even useful to to have a correct model of their model of our own mental states: a second-order Theory of Mind. In order to investigate to what extent adults use and acquire complex skills and strategies in the domains of Theory of Mind and the related skill of natural language use, we conducted an experiment. It (...) was based on a strategic game of imperfect information, in which it was beneficial for participants to have a good mental model of their opponent, and more specifically, to use second-order Theory of Mind. It was also beneficial for them to be aware of pragmatic inferences and of the possibility to choose between logical and pragmatic language use. We found that most participants did not seem to acquire these complex skills during the experiment when being exposed to the game for a number of different trials. Nevertheless, some participants did make use of advanced cognitive skills such as second-order Theory of Mind and appropriate choices between logical and pragmatic language use from the beginning. Thus, the results differ markedly from previous research. (shrink)

This paper addresses the apparent mismatch between the normative and descriptive literatures in the cognitive science of conditional reasoning. Descriptive psychological theories still regard material implication as the normative theory of the conditional. However, over the last 20 years in the philosophy of language and logic the idea that material implication can account for everyday indicative conditionals has been subject to severe criticism. The majority view is now apparently in favour of a subjective conditional probability interpretation. A comparative model fitting (...) exercise is presented that shows that a conditional probability model can explain as much of the data on abstract indicative conditional reasoning tasks as psychological theories that supplement material implication with various rationally unjustified processing assumptions. Consequently, when people are asked to solve laboratory reasoning tasks, they can be seen as simply generalising their everyday probabilistic reasoning strategies to this novel context. (shrink)

It is a commonplace that contemplation of an imaginary particular may have cognitive and motivational effects that differ from those evoked by an abstract description of an otherwise similar state of affairs. In his Treatise on Human Nature, Hume ([1739] 1978) writes forcefully of this.

This paper considers the prospects for objectivity in reasoning strategies in response to empirical studies that apparently show systematic culture‐based differences in patterns of reasoning. I argue that there is at least one modest class of exceptions to the claim that there are alternative, equally warranted standards of good reasoning: the class that entails the solution of certain well‐structured problems which, suitably chosen, are common, or touchstone, to the sorts of culturally different viewpoints discussed. There is evidence that some cognitive (...) tasks are seen in much the same way across cultures, not least by virtue of the common run of experiences with the world of material objects in early childhood by creatures with similar cognitive endowments. These tasks thus present as similarly structured sets of claims that have similar priority: what is framed, and what is bracketed, or held constant in the background, is shown to be naturally common across cultures. As a consequence, a normative view of reasoning and, by implication, critical thinking can be defended. While this might be a modest sense of objectivity, the high level of intercultural articulation that is able to occur among people of different backgrounds suggests that it provides cognitive scaffolding for a lot of other reasoning tasks as well. (shrink)

Human agents draw a variety of inferences effortlessly, spontaneously, and with remarkable efficiency – as though these inferences were a reflexive response of their cognitive apparatus. Furthermore, these inferences are drawn with reference to a large body of background knowledge. This remarkable human ability seems paradoxical given the complexity of reasoning reported by researchers in artificial intelligence. It also poses a challenge for cognitive science and computational neuroscience: How can a system of simple and slow neuronlike elements represent a large (...) body of systemic knowledge and perform a range of inferences with such speed? We describe a computational model that takes a step toward addressing the cognitive science challenge and resolving the artificial intelligence paradox. We show how a connectionist network can encode millions of facts and rules involving n-ary predicates and variables and perform a class of inferences in a few hundred milliseconds. Efficient reasoning requires the rapid representation and propagation of dynamic bindings. Our model (which we refer to as SHRUTI) achieves this by representing (1) dynamic bindings as the synchronous firing of appropriate nodes, (2) rules as interconnection patterns that direct the propagation of rhythmic activity, and (3) long-term facts as temporal pattern-matching subnetworks. The model is consistent with recent neurophysiological evidence that synchronous activity occurs in the brain and may play a representational role in neural information processing. The model also makes specific psychologically significant predictions about the nature of reflexive reasoning. It identifies constraints on the form of rules that may participate in such reasoning and relates the capacity of the working memory underlying reflexive reasoning to biological parameters such as the lowest frequency at which nodes can sustain synchronous oscillations and the coarseness of synchronization. (shrink)

I defend the view that we should not overintellectualize the mind. Nonhuman animals can occupy islands of practical rationality: they can have contextbound reasons for action even though they lack full conceptual abilities. Holism and the possibility of mistake are required for such reasons to be the agent's reasons, but these requirements can be met in the absence of inferential promiscuity. Empirical work with animals is used to illustrate the possibility that reasons for action could be bound to symbolic or (...) social contexts, and connections are made to simulationist accounts of cognitive skills. (shrink)

The object of this paper is to show why recent research in the psychology of deductive and probabilistic reasoning does not have.

This research project seeks to discover whether certain characteristics of a moral issue facilitate individuals’ abilities to detect violators of a conditional rule. In business, conditional rules are often framed in terms of a social contract between employer and employee. Of significant concern to business ethicists is the fact that these social contracts are frequently breached. Some researchers in the field of evolutionary psychology argue that there is a biological basis to social contract formation and dissolution in business. However, although (...) it is inescapable that biological forces shaped a fixed neural structure that guides and limits humans’ abilities, we argue that characteristics of the situation in which the person finds himself or herself moderate the activation of these neural circuits in ordinary business social contract situations. Specifically, the moral intensity associated with the social contract conditional rule is likely to influence peoples’ abilities to detect violators of the rule. This study utilizes adapted versions of the Wason selection task and manipulates the issue-contingent moral intensity characteristics of magnitude of consequences, proximity, and social consensus to assess if moral intensity facilitates detection of rule violators. Results from this empirical study indicate no relationship between moral intensity characteristics and issue recognition but do provide insights into the evolutionary psychology approach. (shrink)

The social exchange theory of reasoning, which is championed by Leda Cosmides and John Tooby, falls under the general rubric “evolutionary psychology” and asserts that human reasoning is governed by content-dependent, domain-specific, evolutionarily-derived algorithms. According to Cosmides and Tooby, the presumptive existence of what they call “cheater-detection” algorithms disconfirms the claim that we reason via general-purpose mechanisms or via inductively acquired principles. We contend that the Cosmides/Tooby arguments in favor of domain-specific algorithms or evolutionarily-derived mechanisms fail and that the notion (...) of a social exchange rule, which is central to their theory, is not correctly characterized. As a consequence, whether or not their conclusion is true cannot be established on the basis of the arguments they have presented. (shrink)

We present a novel methodology for building humanlike artificially intelligent systems. We take as a model the only existing systems which are universally accepted as intelligent: humans. We emphasize building intelligent systems which are not masters of a single domain, but, like humans, are adept at performing a variety of complex tasks in the real world. Using evidence from cognitive science and neuroscience, we suggest four alternative essences of intelligence to those held by classical AI. These are the parallel themes (...) of development, social interaction, embodiment, and integration. Following a methodology based on these themes, we have built a physical humanoid robot. In this paper we present our methodology and the insights it affords for facilitating learning, simplifying the computation underlying rich behavior, and building systems that can scale to more complex tasks in more challenging environments. (shrink)

Cummins (this issue) puts the case for an innate module for deontic reasoning. We argue that this case is not persuasive. First, we claim that Cummins’evolutionary arguments are neutral regarding whether deontic reasoning is learned or innate. Second, we argue that task differences between deontic and indicative reasoning explain many of the phenomena that Cummins takes as evidence for a deontic module. Third, we argue against the suggestion that deontic reasoning is superior to indicative reasoning, either in adults or children. (...) Finally, we re‐evaluate Cummins’interpretation of differences in children's performance on deontic and indicative versions of Wason's selection task. (shrink)