During the first two years of human life a common neural substrate underlies the hierarchical organization of elements in the development of speech as well as the capacity to combine objects manually, including tool use. Subsequent cortical differentiation, beginning at age two, creates distinct, relatively modularized capacities for linguistic grammar and more complex combination of objects. An evolutionary homologue of the neural substrate for language production and manual action is hypothesized to have provided a foundation for the evolution of language (...) before the divergence of the hominids and the great apes. Support comes from the discovery of a Broca's area homologue and related neural circuits in contemporary primates. In addition, chimpanzees have an identical constraint on hierarchical complexity in both tool use and symbol combination. Their performance matches that of the two-year-old child who has not yet developed the neural circuits for complex grammar and complex manual combination of objects. (shrink)

Understanding the nature of errors in a simple, rule‐based task—programming a VCR—required analyzing the interactions among human cognition, the artifact, and the task. This analysis was guided by least‐effort principles and yielded a control structure that combined a rule hierarchy task‐to‐device with display‐based difference‐reduction. A model based on this analysis was used to trace action protocols collected from participants as they programmed a simulated VCR. Trials that ended without success (the show was not correctly programmed) were interrogated to yield insights (...) regarding problems in acquiring the control structure. For successful trials (the show was correctly programmed), steps that the model would make were categorized as matches to the model; steps that the model would not make were violations of the model. The model was able to trace the vast majority of correct keystrokes and yielded a business‐as‐usual account of the detection and correction of errors. Violations of the model fell into one of two fundamental categories. The model provided insights into certain subcategories of errors; whereas, regularities within other subcategories of error suggested limitations to the model. Although errors were rare when compared to the total number of correct actions, they were important. Errors were made on 4% of the keypresses that, if not detected, would have prevented two‐thirds of the shows from being successfully recorded. A misprogrammed show is a minor annoyance to the user. However, devices with the approximate complexity of a VCR are ubiquitous and have found their way into emergency rooms, airplane cockpits, power plants, and so on. Errors of ignorance may be reduced by training; however, errors in the routine performance of skilled users can only be reduced by design. (shrink)

Continuous culture techniques were developed in the early twentieth century to replace cumbersome studies of cell growth in batch cultures. In contrast to batch cultures, they constituted an open concept, as cells are forced to proliferate by adding new medium while cell suspension is constantly removed. During the 1940s and 1950s new devices have been designed—called “automatic syringe mechanism,” “turbidostat,” “chemostat,” “bactogen,” and “microbial auxanometer”—which allowed increasingly accurate quantitative measurements of bacterial growth. With these devices cell growth came under the (...) external control of the experimenters and thus accessible for developing a mathematical theory of growth kinetics—developed mainly by Jacques Monod, Aron Novick and Leo Szilard in the early 1950s and still in use today. The paper explores the development of continuous culture devices and claims that these devices are simulators for standard cells following specific requirements, in particular involving mathematical constraints in the design and setting of the devices as well as experiments. These requirements have led to contemporary designs of continuous culture techniques realizing a specific event-based flow algorithm able to simulate directed evolution and produce artificial cells and microorganisms. This current development is seen as an alternative approach to today’s synthetic biology. (shrink)

In the debate between simple inference heuristics and complex decision mechanisms, we take a position squarely in the middle. A decision making process that extends to both naturalistic and novel settings should extend beyond the confines of this debate; both simple heuristics and complex mechanisms are cognitive skills adapted to and appropriate for some circumstances but not for others. Rather than ask `Which skill is better?'' it is often more important to ask `When is a skill justified?'' The selection and (...) application of an appropriate cognitive skill for a particular problem has both costs and benefits, and therefore requires the resolution of a tradeoff. In revisiting satisficing, we observe that the essence of satisficing is tradeoff. Unlike heuristics, which derive their justification from empirical phenomena, and unlike optimal solutions, which derive their justification by an evaluation of alternatives, satisficing decision-making derives its justification by an evaluation of consequences. We formulate and present a satisficing decision paradigm that has its motivation in Herbert Simon''s work on bounded rationality. We characterize satisficing using a cost–benefit tradeoff, and generate a decision rule applicable to both designing intelligent machines as well as describing human behavior. (shrink)

Syntactic and structural models specify relationships between their constituents but cannot show what outcomes their interaction would produce over time in the world. Simulation consists in iterating the states of a model, so as to produce behaviour over a period of simulated time. Iteration enables us to trace the implications and outcomes of inference rules and other assumptions implemented in the models that make up a theory. We apply this method to experiments which we treat as models of the particular (...) aspects of reality they are designed to investigate. Scientific experiments are constantly designed and re-designed in the context of implementation and use. They mediate between theoretical understanding and the practicalities of engaging with the empirical and social world. In order to model experiments we need to identify and represent features that all experiments have in common. We treat these features as parameters of a general model of experiment so that by varying these parameters different types of experiment can be modelled. (shrink)

: Faraday is often described as an experimentalist, but his work is a dialectical interplay of concrete objects, visual images, abstract, theoretically-informed visual models and metaphysical precepts. From phenomena described in terms of patterns formed by lines of force he created a general explanation of space-filling systems of force which obey both empirical laws and principles of conservation and economy. I argue that Faraday's articulation of situated experience via visual models into a theory capable of verbal expression owed much to (...) his strategy of moving—via conjectural visual models—between the phenomenology of particulars (often displayed as patterns) and the general features of dynamical phenomena which he depicted as structures. (shrink)

The presence of intelligence and rationality in Artificial Intelligence and the Internet requires a new context of analysis in which Herbert Simon’s approach to the sciences of the artificial is surpassed in order to grasp the role of information in our contemporary setting. This new framework requires taking into account some relevant aspects. In the historical endeavor of building up AI and the Internet, minds and machines have interacted over the years and in many ways through the interrelation between scientific (...) creativity and technological innovation. Philosophically, minds and machines can have epistemological, methodological and ontological differences, which are based on the distinct configuration of human intelligence and artificial intelligence. Their comparison with rationality and its various forms is particularly relevant. Scientifically, AI and the Internet belong to the sciences of the artificial, because they work on designs that search for specific aims, following selected processes in order to achieve expected results. Technologically, AI and the Internet require the support of information and communication technologies. These have an instrumental role regarding the existence of AI and the Internet. Also ICT shape their diverse forms of configuration over the years.Within this framework, this paper offers a new context of analysis that goes beyond Simon’s and follows four main steps: the interaction between scientific creativity and technological innovation as the philosophico-methodological setting for Artificial Intelligence and the Internet; artificial intelligence and human intelligence as epistemological basis for machines and minds, where the differences between artificial intelligence and human intelligence are made explicit and the analysis of minds and machines is made from the perspective of rationality ; intention and its difference with design of machine learning are considered to distinguish human intelligence from artificial intelligence; and the internal and external aspects of artificial designs in contemporary society are considered through the perspective of rationality, which leads to the transition from intelligence to rationality in the Internet as well as to the historicity of information. (shrink)

We argue that Anderson's (MRH) needs further development in several directions. First, a thoroughgoing criticism of the several alternatives is required. Second, the course between the Scylla of full holism and the Charybdis of structural-functional modularism must be plotted more distinctly. Third, methodologies better suited to reveal brain circuits must be brought in. Finally, the constraints that naturalistic settings provide should be considered.

The contributions to this special issue on cognitive development collectively propose ways in which learning involves developing constraints that shape subsequent learning. A learning system must be constrained to learn efficiently, but some of these constraints are themselves learnable. To know how something will behave, a learner must know what kind of thing it is. Although this has led previous researchers to argue for domain-specific constraints that are tied to different kinds/domains, an exciting possibility is that kinds/domains themselves can be (...) learned. General cognitive constraints, when combined with rich inputs, can establish domains, rather than these domains necessarily preexisting prior to learning. Knowledge is structured and richly differentiated, but its “skeleton” must not always be preestablished. Instead, the skeleton may be adapted to fit patterns of co-occurrence, task requirements, and goals. Finally, we argue that for models of development to demonstrate genuine cognitive novelty, it will be helpful for them to move beyond highly preprocessed and symbolic encodings that limit flexibility. We consider two physical models that learn to make tone discriminations. They are mechanistic models that preserve rich spatial, perceptual, dynamic, and concrete information, allowing them to form surprising new classes of hypotheses and encodings. (shrink)

It is proposed that there are important generalizations about problem solving in design activity that reach across specific disciplines. A framework for the study of design is presented that (a) characterizes design as a radial category and fleshes out the task environment of the prototypical cases; (b) takes the task environment seriously; (c) shows that this task environment occurs in design tasks, but does not occur in every nondesign task; (d) explicates the impact of this task environment on the design (...) problem space; and (e) demonstrates that, given the structure of the information-processing system, the features noted in the problem spaces of design tasks will not all occur in problem spaces where the task environment is vastly different. This analysis leads to the claim that there are a set of invariant features in the problem spaces of design situations that collectively constitute a design problem space. Protocol studies are reported in which the problem spaces of three design tasks in architecture, mechanical engineering, and instructional design are explored and compared with several protocols from nondesign problem-solving tasks. (shrink)

In this essay we argue that the notion of machine necessarily includes its being designed for a purpose. Therefore, being a mechanical system is not enough for being a machine. Since the experimental scientific method excludes any consideration of finality on methodological grounds, it is then also insufficient to fully understand what machines are. Instead in order to understand a machine it is first required to understand its purpose, along with its structure, in clear parallel with Aristotle’s final and formal (...) causes. Obviously, purpose and structure are not machine components that can physically interact with other components; nonetheless they are essential to understanding their operation. This casts an interesting light on the relationship between mind and body: for just as an artifact’s finality and structure explain its operation, so also consciousness is the explanation—not the efficient cause—of specifically human behavior. What machines and human beings have in common is that, in order to understand them, it is necessary to appeal to the principle of finality. Yet while finality is given and extrinsic in the case of machines, we human beings are characterized by the ability to self-propose our own ends. Since the principle of finality is essential to understanding the production of machines, the traditional view in modern Western philosophy that finality lies beyond the scope of objective/scientific knowledge should be rectified to allow for a genuine science of the artificial. We think a correct understanding of final causality will overcome current resistance to this principle. (shrink)

Religion persists, even within enlightened secular society, because it has adaptive functions. In particular, Ralph Wendell Burhoe's theory holds that religion is the repository of cultural wisdom that most encourages mutual altruism among nonkin, long-term social survival, and human progress. This article suggests a variant of Burhoe's rationalized naturalistic view. Cognitive theism is a proposal that secularists sometimes take religion on its own terms by suspending disbelief about God. If we consider particular human capacities and limitations in memory, perception, personality, (...) and motivation, the regulated “mind expansion” of cognitive theism may help us to evaluate, coordinate, and invigorate things in a modern environment. In this environment, communicative and travel technologies have led to a high loading of consciousness with a historically unusual diverse range of experiences and responsibilities, a high rate of cultural change relative to biological evolution, and a tendency to factionalize. Burhoe's extension of the concept of symbiosis to the coevolution of culture and genes is modified here in recognition of individual differences and of individuals' potential for choosing strategies, recombining in groups, and learning. In human biocultural symbiont pools, cultural phenomena can evolve while changing partners in a dance with genetic substrates, a dance that broadly supports these substrates. In the context of diversity and incessant change in a large predominantly secular community, Judeo-Christian monotheism can have a valuable advisory unifying function. (shrink)

To understand the role of expert systems as a medium for transferring knowledge and skills within organisations requires an understanding of the nature of expertise within working life contexts. Central to this issue of transfer is the debate on the nature of tacit/implicit knowledge and the problem of formalising it in explicit form. This paper considers the British approach to the development of knowledge-based systems, which is regarded as being predominantly rationalistic, and compares it with the Scandinavian approach, which is (...) regarded as being predominantly humanistic. The paper proposes that crucial to the debate on the transfer of knowledge and skills is the development of expert systems as communications media which aims at enhancing and sharing the knowledge and skills of users. A human-centred approach for the future is proposed which brings the above two traditions together thereby providing a developmental framework for knowledge based systems. (shrink)

David Marr's theory of vision has been a rich source of inspiration, fascination and confusion. I will suggest that some of this confusion can be traced to discrepancies between the way Marr developed his theory in practice and the way he suggested such a theory ought to be developed in his explicit metatheoretical remarks. I will address claims that Marr's theory may be seen as an optimizing theory, along with the attendant suggestion that optimizing assumptions may be inappropriate for cognitive (...) mechanisms just as anti-adaptationists have argued they are inappropriate for other physiological mechanisms. I will discuss the nature of optimizing assumptions and theories. Considering various difficulties in identifying and assessing optimizing assumptions, I will suggest that Marr's theory is not purely an optimizing theory and that reaction to Marr on this issue prompts interesting considerations for the development of inter-disciplinary constraints in the cognitive and brain sciences. (shrink)

What constitutes our human capacity to engage and be in the same frame of mind as another human? How do we come to share a sense of what ‘looks good’ and what ‘makes sense’? How do we handle differences and come to coexist with them? How do we come to feel that we understand what someone else is experiencing? How are we able to walk in silence with someone familiar and be sharing a peaceful space? All of these aspects are (...) part of human ‘interaction’. In designing interactive technologies designers have endeavoured to explicate, analyse and simulate, our capacity for social adaptation. Their motivations are mixed and include the desires to improve efficiency, improve consumption, to connect people, to make it easier for people to work together, to improve education and learning. In these endeavours to explicate, analyse and simulate, there is a fundamental human capacity that is beyond technology and that facilitates these aspects of being, feeling and thinking with others. That capacity, we suggest, is human entrainment. This is our ability to coordinate the timing of our behaviours and rhythmically synchronise our attentional resources. Expressed within the movements of our bodies and voices, it has a quality that is akin to music. In this paper, disparate domains of research such as pragmatics, social psychology, behaviourism, cognitive science, computational linguistics, gesture, are brought together, and considered in light of the developments in interactive technology, in order to shape a conceptual framework for understanding entrainment in everyday human interaction. (shrink)

The concepts of adaptation and fitness have such an appeal that they have been used in other scientific domains, including the social sciences. One particular aspect of this theory transfer concerns the so-called fitness landscape models. At first sight, fitness landscapes visualize how an agent, of any kind, relates to its environment, how its position is conditional because of the mutual interaction with other agents, and the potential routes towards improved fitness. The allure of fitness landscapes is first and foremost (...) that it represents a complex story about adaptation and fitness in one coherent image. Different accounts of fitness landscapes in different domains in the social sciences suggest that the properties and functions of fitness landscapes are attributed rather freely. These differences are testimony of the model’s versatility. At the same time, one will notice that the different approaches can also create ambiguity about the exact meaning and role of fitness landscapes in the social sciences. This article presents an extensive literature survey of the diverging interpretations and uses of fitness landscapes in the social sciences and discusses the implications in terms of how these models inform scientific inquiry. (shrink)

What is the nature of moral behavior? According to the study of bounded rationality, it results not from character traits or rational deliberation alone, but from the interplay between mind and environment. In this view, moral behavior is based on pragmatic social heuristics rather than moral rules or maximization principles. These social heuristics are not good or bad per se, but solely in relation to the environments in which they are used. This has methodological implications for the study of morality: (...) Behavior needs to be studied in social groups as well as in isolation, in natural environments as well as in labs. It also has implications for moral policy: Only by accepting the fact that behavior is a function of both mind and environmental structures can realistic prescriptive means of achieving moral goals be developed. (shrink)