Recent progress in artificial intelligence has renewed interest in building systems that learn and think like people. Many advances have come from using deep neural networks trained end-to-end in tasks such as object recognition, video games, and board games, achieving performance that equals or even beats that of humans in some respects. Despite their biological inspiration and performance achievements, these systems differ from human intelligence in crucial ways. We review progress in cognitive science suggesting that truly human-like learning and thinking (...) machines will have to reach beyond current engineering trends in both what they learn and how they learn it. Specifically, we argue that these machines should build causal models of the world that support explanation and understanding, rather than merely solving pattern recognition problems; ground learning in intuitive theories of physics and psychology to support and enrich the knowledge that is learned; and harness compositionality and learning-to-learn to rapidly acquire and generalize knowledge to new tasks and situations. We suggest concrete challenges and promising routes toward these goals that can combine the strengths of recent neural network advances with more structured cognitive models. (shrink)

Perhaps there has been no greater opportunity than in this “VOLUME FIFTEEN of our Death And Anti-Death set of anthologies” to write about how might think about life and how to avoid death. There are two reasons to discuss “life”, the first being enhancing our understanding of who we are and why we may be here in the Universe. The second is more practical: how humans meet the physical challenges brought about by the way they have interacted with their environment. (...) Many persons discussing “life” beg the question about what “life” is. Surely, when one discusses how to overcome its opposite, death, they are not referring to another “living” thing such as a plant. There seems to be a commonality, though, and it is this commonality is one needing elaboration. It ostensibly seems to be the boundary condition separating what is completely passive (inert) from what attempts to maintain its integrity, as well as fulfilling other conditions we think “life” has. In our present discussion, there will be a reminder that it by no means has been unequivocally established what life really is by placing quotes around the word, namely, “life”. Consider it a tag representing a bundle of philosophical ideas that will be unpacked in this paper. (shrink)

Plant and animal species are information-processing entities of such complexity, integration, and adaptive competence that it may be scientifically fruitful to consider them intelligent. The possibility arises from the analogy between learning and evolution, and from recent developments in evolutionary science, psychology and cognitive science. Species are now described as spatiotemporally localized individuals in an expanded hierarchy of biological entities. Intentional and cognitive abilities are now ascribed to animal, human, and artificial intelligence systems that process information adaptively, and that manifest (...) problem-solving abilities. The structural and functional similarities between such systems and species are extensive, although they “are usually obscured by population-genetic metaphors that have nonetheless contributed much to our understanding of evolution.In this target article, I use Sewall Wright's notion of the “adaptive landscape” to compare the performances of evolving species to those of intelligent organisms. With regard to their adaptive achievements and the kinds of processes by which they are achieved, biological species compare very favorably to intelligent animals by virtue of interactions between populations and their environments, between ontogeny and phylogeny, and between natural, interdemic, organic, and species selection. Addressing the question of whether species are intelligent could help to refine our ideas about species, evolution, and intelligence, and could open new lines of empirical and theoretical inquiry in many disciplines. (shrink)

Some scholars suspect that thought experiments have something in common with jokes. Moreover, Thomas Kuhn has suggested that what happens to someone who thinks through a thought experiment “is very similar to what happens to a man, like Lavoisier, who must assimilate the result of a new unexpected experimental discovery” (1964: 321). In this paper, I pinpoint the presumed commonalities. I identify, more specifically, what cognitive linguists call “incongruity-resolution” as the problem-solving process not only involved in humor comprehension, but in (...) scientific discovery and thought experimentation as well. (shrink)

This paper defends two initial claims. First, it argues that essentially the same cognitive resources are shared by adult creative thinking and problem-solving, on the one hand, and by childhood pretend play, on the other—namely, capacities to generate and to reason with suppositions (or imagined possibilities). Second, it argues that the evolutionary function of childhood pretence is to practice and enhance adult forms of creativity. The paper goes on to show how these proposals can provide a smooth and evolutionarily-plausible explanation (...) of the gap between the first appearance of our species in Southern Africa some 100,000 years ago, and the ‘creative explosion’ of cultural, technological and artistic change which took place within dispersed human populations some 60,000 years later. The intention of the paper is to sketch a proposal which might serve as a guide for future interdisciplinary research. 1 Introduction 2 Creativity and Pretence 3 Language and Creativity 4 Language and Cultural Accretions 5 Language, Play and Model-Building 6 Creativity, Protean Cognition and Sexual Selection 7 The Evolution of Pretence 8 The Emergence of Supposing 9 Pretence and Motivation 10 Two Objections 11 Conclusion. (shrink)

Plant and animal species are information-processing entities of such complexity, integration, and adaptive competence that it may be scientifically fruitful to consider them intelligent. The possibility arises from the analogy between learning and evolution, and from recent developments in evolutionary science, psychology and cognitive science. Species are now described as spatiotemporally localized individuals in an expanded hierarchy of biological entities. Intentional and cognitive abilities are now ascribed to animal, human, and artificial intelligence systems that process information adaptively, and that manifest (...) problem-solving abilities. The structural and functional similarities between such systems and species are extensive, although they “are usually obscured by population-genetic metaphors that have nonetheless contributed much to our understanding of evolution.In this target article, I use Sewall Wright's notion of the “adaptive landscape” to compare the performances of evolving species to those of intelligent organisms. With regard to their adaptive achievements and the kinds of processes by which they are achieved, biological species compare very favorably to intelligent animals by virtue of interactions between populations and their environments, between ontogeny and phylogeny, and between natural, interdemic, organic, and species selection. Addressing the question of whether species are intelligent could help to refine our ideas about species, evolution, and intelligence, and could open new lines of empirical and theoretical inquiry in many disciplines. (shrink)

I investigate the construction of the mathematical concept of quaternion from a methodological and heuristic viewpoint to examine what we can learn from it for the study of the advancement of mathematical knowledge. I will look, in particular, at the inferential microstructures that shape this construction, that is, the study of both the very first, ampliative inferential steps, and their tentative outcomes—i.e. small ‘structures’ such as provisional entities and relations. I discuss how this paradigmatic case study supports the recent approaches (...) to problem-solving and philosophy of mathematics, and how it suggests refinements of them. In more detail, I argue that the inferential micro-structures enable us to shed more light on the informal, heuristic side of mathematical practice, and its inferential and rational procedures. I show how they enable the generation of a problem, the construction of its conditions of solvability, the search for a hypothesis to solve it, and how these processes are representation-sensitive. On this base, I argue that: 1.the recent development of the philosophy of mathematics was right in moving from Lakatos’ initial investigation of the formal side of a mathematical proof to the investigation of the semi-formal, heuristic side of the mathematical practice as a way of understanding mathematical knowledge and its advancement. 2.The investigation of mathematical practice and discovery can be improved by a finer-grained study of the inferential micro-structures that are built during mathematical problem-solving. (shrink)

Many of the mathematicians and scientists who guided the development of digital computers in the late 1940s, such as Alan Turing and John von Neumann, saw these new devices not just as tools for calculation but as devices that might employ the same principles as are exhibited in rational human thought. Thus, a subfield of what came to be called computer science assumed the label artificial intelligence. The idea of building artificial systems which could exhibit intelligent behavior comparable to that (...) of humans was a heady prospect, and the claims made on behalf of AI during the 1950s and 1960s were impossibly ambitious. Despite some theoretical and applied successes within the field, serious problems soon became evident. Instead of fulfilling the goal of quickly producing artificial intelligent agents which could compete with or outperform human beings, by the 1970s and 1980s AI had settled into a pattern of slower but real progress in modeling or simulating aspects of human intelligence. (shrink)

it is likely that there are many different social processes occurring in different parts of science and at different times, and that these processes will impact upon the nature, quality and quantity of the knowledge that is produced in a multitude of ways and to different extents. It seems clear to me that sometimes the social processes act to increase the reliability of knowledge (such as when there is a tradition of independently reproducing experiments) but sometimes does the opposite (when (...) a closed clique act to perpetuate itself by reducing opportunity for criticism). Simulation can perform a valuable role here by providing and refining possible linkages between the kinds of social processes and its results in terms of knowledge. Earlier simulations of this sort include Gilbert et al. in [10]. The simulation described herein aims to progress this work with a more structural and descriptive approach, that relates what is done by individuals and journals and what collectively results in terms of the overall process. (shrink)

In his influential paper, 'Why Was the Logic of Discovery Abandoned?', Laudan contends that there has been no philosophical rationale for a logic of discovery since the emergence of consequentialism in the 19th century. It is the purpose of this paper to show that consequentialism does not involve the rejection of all types of logic of discovery. Laudan goes too far in his interpretation of the historical shift from generativism to consequentialism, and his claim that the context of pursuit belongs (...) to neither discovery nor justification is based on narrow interpretations of the contexts of discovery and justification. As a result, Laudan draws unwarranted conclusions concerning both the early and contemporary defenders of a logic of discovery. A methodological logic of discovery - which involves self-corrective methods of hypothesis generation that promote the long-term goals of science and which require consequential support for justification - is a type of logic of discovery that survives the shift to consequentialism. (shrink)

This article describes abductions as special patterns of inference to the best explanation whose structure determines a particularly promising abductive conjecture and thus serves as an abductive search strategy. A classification of different patterns of abduction is provided which intends to be as complete as possible. An important distinction is that between selective abductions, which choose an optimal candidate from given multitude of possible explanations, and creative abductions, which introduce new theoretical models or concepts. While selective abduction has dominated the (...) literature, creative abductions are rarely discussed, although they are essential in science. The article introduces several kinds of creative abductions, such as theoretical model abduction, common cause abduction and statistical factor analysis, and illustrates them by various real case examples. It is suggested to demarcate scientifically fruitful abductions from purely speculative abductions by the criterion of causal unification. (shrink)

In recent years there has been a great deal of discussion about the prospects of developing a ‘naturalized epistemology’, though different authors tend to interpret this label in quite different ways. One goal of this paper is to sketch three projects that might lay claim to the ‘naturalized epistemology’ label, and to argue that they are not all equally attractive. Indeed, I'll maintain that the first of the three—the one I'll attribute to Quine—is simply incoherent. There is no way we (...) could get what we want from an epistemological theory by pursuing the project Quine proposes. The second project on my list is a naturalized version of reliabilism. This project is not fatally flawed in the way that Quine's is. However, it's my contention that the sort of theory this project would yield is much less interesting than might at first be thought. (shrink)

In this paper I argue for a place for logic inscientific methodology, at the same level asthat of computational and historicalapproaches. While it is well known that a awhole generation of philosophers dismissedLogical Positivism (not just for the logicthough), there are at least two reasons toreconsider logical approaches in the philosophyof science. On the one hand, the presentsituation in logical research has gone farbeyond the formal developments that deductivelogic reached last century, and new researchincludes the formalization of several othertypes of (...) reasoning, like induction andabduction. On the other hand, we call for abalanced Philosophy of Science, one inwhich both methods, the formal and thehistorical may be complementary, togetherproviding a pluralistic view of science, inwhich no method is the predominant one. (shrink)

To investigate the cognitive processes underlying creative inspiration, we tested the extent to which viewing or copying prior examples impacted creative output in art. In Experiment 1, undergraduates made drawings under three conditions: copying an artist's drawing, then producing an original drawing; producing an original drawing without having seen another's work; and copying another artist's work, then reproducing that artist's style independently. We discovered that through copying unfamiliar abstract drawings, participants were able to produce creative drawings qualitatively different from the (...) model drawings. Process analyses suggested that participants' cognitive constraints became relaxed, and new perspectives were formed from copying another's artwork. Experiment 2 showed that exposure to styles of artwork considered unfamiliar facilitated creativity in drawing, while styles considered familiar did not do so. Experiment 3 showed that both copying and thoroughly viewing artwork executed using an unfamiliar style facilitated creativity in drawing, whereas merely thinking about alternative styles of artistic representation did not do so. These experiments revealed that deep encounters with unfamiliar artworks—whether through copying or prolonged observation—change people's cognitive representations of the act of drawing to produce novel artwork. (shrink)

A considerable number of areas of bioscience, including gene and drug discovery, metabolic engineering for the biotechnological improvement of organisms, and the processes of natural and directed evolution, are best viewed in terms of a ‘landscape’ representing a large search space of possible solutions or experiments populated by a considerably smaller number of actual solutions that then emerge. This is what makes these problems ‘hard’, but as such these are to be seen as combinatorial optimisation problems that are best attacked (...) by heuristic methods known from that field. Such landscapes, which may also represent or include multiple objectives, are effectively modelled in silico, with modern active learning algorithms such as those based on Darwinian evolution providing guidance, using existing knowledge, as to what is the ‘best’ experiment to do next. An awareness, and the application, of these methods can thereby enhance the scientific discovery process considerably. This analysis fits comfortably with an emerging epistemology that sees scientific reasoning, the search for solutions, and scientific discovery as Bayesian processes. (shrink)

The purpose of the two studies reported here was to develop an integrated model of the scientific reasoning process. Subjects were placed in a simulated scientific discovery context by first teaching them how to use an electronic device and then asking them to discover how a hitherto unencountered function worked. To do this task, subjects had to formulate hypotheses based on their prior knowledge, conduct experiments, and evaluate the results of their experiments. In the first study, using 20 adult subjects, (...) we identified two main strategies that subjects used to generate new hypotheses. One strategy was to search memory and the other was to generalize from the results of previous experiments. We described the former group as searching an hypothesis space, and the latter as searching an experiment space. In a second study, with 10 adults, we investigated how subjects search the hypothesis space by instructing them to state all the hypotheses that they could think of prior to conducting any experiments. Following this phase, subjects were then allowed to conduct experiments. Subjects who could not think of the correct rule in the hypothesis generation phase discovered the correct rule only by generalizing from the results of experiments in the experimental phase.Both studies provide support for the view that scientific reasoning can be characterized as search in two problem spaces. By extending Simon and Lea's (1974) Generalized Rule Inducer, we present a general model of Scientific Discovery as Dual Search (SDDS) that shows how search in two problem spaces (an hypothesis space and an experiment space) shapes hypothesis generation, experimental design, and the evaluation of hypotheses. The model also shows how these processes interact with each other. Finally, we interpret earlier findings about the psychology of scientific reasoning in terms of the SDDS model. (shrink)

Beyond modularityattempts a synthesis of Fodor's anticonstructivist nativism and Piaget's antinativist constructivism. Contra Fodor, I argue that: (1) the study of cognitive development is essential to cognitive science, (2) the module/central processing dichotomy is too rigid, and (3) the mind does not begin with prespecified modules; rather, development involves a gradual process of “modularization.” Contra Piaget, I argue that: (1) development rarely involves stagelike domain-general change and (2) domainspecific predispositions give development a small but significant kickstart by focusing the infant's (...) attention on proprietary inputs. Development does not stop at efficient learning. A fundamental aspect of human development (“representational redescription”) is the hypothesized process by which information that isina cognitive system becomes progressively explicit knowledgetothat system. Development thus involves two complementary processes of progressive modularization and progressive “explicitation.” Empirical findings on the child as linguist, physicist, mathematician, psychologist, and notator are discussed in support of the theoretical framework. Each chapter concentrates first on the initial state of the infant mind/brain and on subsequent domain-specific learning in infancy and early childhood. It then goes on to explore data on older children's problem solving and theory building, with particular focus on evolving cognitive flexibility. Emphasis is placed throughout on the status of representations underlying different capacities and on the multiple levels at which knowledge is stored and accessible. Finally, consideration is given to the need for more formal developmental models, and a comparison is made between representational redescription and connectionist simulations of development. In conclusion, I consider what is special about human cognition by speculating on the status of representations underlying the structure of behavior in other species. (shrink)