PurposeHigher education in the past has been found to have a positive effect on the moral development of students from a variety of disciplines, decreasing conventional and increasing post‐conventional moral reasoning progressively at each level of study. This research aims to explore to what extent changes in moral judgement could be detected in students on computing degree courses, at three different stages of study, in order to establish if HE in the twenty‐first century has a similar effect and what level (...) of moral awareness computing graduates/practitioners exhibit.Design/methodology/approachThe research takes the form of an exploratory case study which aimed to investigate the current situation in one institution. The defining issues test questionnaire was used to gather data.FindingsResults showed little difference in the level of post‐conventional thinking between undergraduate students about to enter HE, final year undergraduates and students undertaking postgraduate studies.Research limitations/implicationsThis research questions if IT students moral judgement skills are at a level that enables them to be considered professionals.Practical implicationsRecommendations are made for different teaching approaches to be adopted which place greater emphasis on relating learning outcomes to professional codes of conduct, and for computing professional bodies to take a more active role in defining components of courses they accredit.Social implicationsGiven the ubiquitous nature of computers and society's high level of dependence on them it is argued that post‐conventional thinking skills are essential for people intending to work with computer technology.Originality/valueAlthough the DIT questionnaire is been used extensively, no other research has been found that has utilised it to analyse moral judgement levels of HE students within the same subject discipline, at different levels of study. (shrink)

The contents of our conscious mind can seem unpredictable, whimsical, and free from external control. When instructed to attend to a stimulus in a work setting, for example, one might find oneself thinking about household chores. Conscious content thus appears different in nature from reflex action. Under the appropriate conditions, reflexes occur predictably, reliably, and via external control. Despite these intuitions, theorists have proposed that, under certain conditions, conscious content resembles reflexes and arises reliably via external control. We introduce the (...) Reflexive Imagery Task, a paradigm in which, as a function of external control, conscious content is triggered reliably and unintentionally: When instructed to not subvocalize the name of a stimulus object, participants reliably failed to suppress the set-related imagery. This stimulus-elicited content is considered ‘high-level’ content and, in terms of stages of processing, occurs late in the processing stream. We discuss the implications of this paradigm for consciousness research. (shrink)

How should biological behaviour be modelled? A relatively new approach is to investigate problems in neuroethology by building physical robot models of biological sensorimotor systems. The explication and justification of this approach are here placed within a framework for describing and comparing models in the behavioural and biological sciences. First, simulation models – the representation of a hypothesis about a target system – are distinguished from several other relationships also termed “modelling” in discussions of scientific explanation. Seven dimensions on which (...) simulation models can differ are defined and distinctions between them discussed: 1. Relevance: whether the model tests and generates hypotheses applicable to biology. 2. Level: the elemental units of the model in the hierarchy from atoms to societies. 3. Generality: the range of biological systems the model can represent. 4. Abstraction: the complexity, relative to the target, or amount of detail included in the model. 5. Structural accuracy: how well the model represents the actual mechanisms underlying the behaviour. 6. Performance match: to what extent the model behaviour matches the target behaviour. 7. Medium: the physical basis by which the model is implemented. No specific position in the space of models thus defined is the only correct one, but a good modelling methodology should be explicit about its position and the justification for that position. It is argued that in building robot models biological relevance is more effective than loose biological inspiration; multiple levels can be integrated; that generality cannot be assumed but might emerge from studying specific instances; abstraction is better done by simplification than idealisation; accuracy can be approached through iterations of complete systems; that the model should be able to match and predict target behaviour; and that a physical medium can have significant advantages. These arguments reflect the view that biological behaviour needs to be studied and modelled in context, that is, in terms of the real problems faced by real animals in real environments. Key Words: animal behaviour; levels; models; neuroethology; realism; robotics; simulation. (shrink)

Machines and Biology have been, since antiquity, closely related. From the zoological figures present in astronomical simulacra, through renaissance mechanical imitations of animals, through Decartes' wind pipe nerves, to present day discussions on the computer and the brain, runs a continuous thread. In fact, the very name of mechanism for an attitude of inquiry throughout the history of Biology reveals this at a philosophical level. More often than not, mechanism is mentioned in opposition to vitalism, as an assertion of the (...) validity of the objectivity principle in biology: there are no purposes in animal nature; its apparent purposefulness is similar to the purposefulness of machines. Yet, the fact that one picks machines as a set of objects comparable to living systems, deserves a closer look. What in machines makes it possible to establish such a connection? (shrink)

This essay is, primarily, a discussion of four results about the principle of maximizing entropy (MAXENT) and its connections with Bayesian theory. Result 1 provides a restricted equivalence between the two: where the Bayesian model for MAXENT inference uses an "a priori" probability that is uniform, and where all MAXENT constraints are limited to 0-1 expectations for simple indicator-variables. The other three results report on an inability to extend the equivalence beyond these specialized constraints. Result 2 established a sensitivity of (...) MAXENT inference to the choice of the algebra of possibilities even though all empirical constraints imposed on the MAXENT solution are satisfied in each measure space considered. The resulting MAXENT distribution is not invariant over the choice of measure space. Thus, old and familiar problems with the Laplacian principle of Insufficient Reason also plague MAXENT theory. Result 3 builds upon the findings of Friedman and Shimony (1971; 1973) and demonstrates the absence of an exchangeable, Bayesian model for predictive MAXENT distributions when the MAXENT constraints are interpreted according to Jaynes's (1978) prescription for his (1963) Brandeis Dice problem. Lastly, Result 4 generalizes the Friedman and Shimony objection to cross-entropy (Kullback-information) shifts subject to a constraint of a new odds-ratio for two disjoint events. (shrink)

Ethologists and others studying animal behavior in a spirit are in need of a descriptive language and method that are neither anachronistically bound by behaviorist scruples nor prematurely committed to particular Just such an interim descriptive method can be found in intentional system theory. The use of intentional system theory is illustrated with the case of the apparently communicative behavior of vervet monkeys. A way of using the theory to generate data - including usable, testable data - is sketched. The (...) underlying assumptions of this approach can be seen to ally it directly with theorizing in evolutionary biology, which has recently come under attack from Stephen Gould and Richard Lewontin, who castigate it as the Their arguments, which are strongly analogous to B. F, Skinner's arguments against point to certain pitfalls that attend the careless exercise of such thinking (and rival varieties of thinking as well), but do not constitute a fundamental objection to either adaptationist theorizing or its cousin, intentional system theory. (shrink)

Applied Evolutionary Epistemology is a scientific-philosophical theory that defines evolution as the set of phenomena whereby units evolve at levels of ontological hierarchies by mechanisms and processes. This theory also provides a methodology to study evolution, namely, studying evolution involves identifying the units that evolve, the levels at which they evolve, and the mechanisms and processes whereby they evolve. Identifying units and levels of evolution in turn requires the development of ontological hierarchy theories, and examining mechanisms and processes necessitates theorizing (...) about causality. Together, hierarchy and causality theories explain how biorealities form and diversify with time. This paper analyzes how Applied EE redefines both hierarchy and causality theories in the light of the recent explosion of network approaches to causal reasoning associated with studies on reticulate and macroevolution. Causality theories have often been framed from within a rigid, ladder-like hierarchy theory where the rungs of the ladder represent the different levels, and the elements on the rungs represent the evolving units. Causality then is either defined reductionistically as an upward movement along the strands of a singular hierarchy, or holistically as a downward movement along that same hierarchy. Upward causation theories thereby analyze causal processes in time, i.e. over the course of natural history or phylogenetically, as Darwin and the founders of the Modern Synthesis intended. Downward causation theories analyze causal processes in space, ontogenetically or ecologically, as the current eco-evo-devo schools are evidencing. This work demonstrates how macroevolution and reticulate evolution theories add to the complexity by examining reticulate causal processes in space–time, and the interactional hierarchies that such studies bring forth introduce a new form of causation that is here called reticulate causation. Reticulate causation occurs between units and levels belonging to different as well as to the same ontological hierarchies. This article concludes that beyond recognizing the existence of multiple units, levels, and mechanisms or processes of evolution, also the existence of multiple kinds of evolutionary causation as well as the existence of multiple evolutionary hierarchies needs to be acknowledged. This furthermore implies that evolution is a pluralistic process divisible into different kinds. (shrink)

This paper aims to justify the concept of natural intelligence in the biosemiotic context. I will argue that the process of life is (i) a cognitive/semiotic process and (ii) that organisms, from bacteria to animals, are cognitive or semiotic agents. To justify these arguments, the neural-type intelligence represented by the form of reasoning known as anthropic reasoning will be compared and contrasted with types of intelligence explicated by four disciplines of biology – relational biology, evolutionary epistemology, biosemiotics and the systems (...) view of life – not biased towards neural intelligence. The comparison will be achieved by asking questions related to the process of observation and the notion of true observers. To answer the questions I will rely on a range of established concepts including SETI (search for extraterrestrial intelligence), Fermi’s paradox, bacterial cognition, versions of the panspermia theory, as well as some newly introduced concepts including biocivilisations, cognitive/semiotic universes, and the cognitive/semiotic multiverse. The key point emerging from the answers is that the process of cognition/semiosis – the essence of natural intelligence – is a biological universal. (shrink)

What is the relationship between information and representation? Dating back at least to Dretske (1981), an influential answer has been that information is a rung on a ladder that gets one to representation. Representation is information, or representation is information plus some other ingredient. In this paper, I argue that this approach oversimplifies the relationship between information and representation. If one takes current probabilistic models of cognition seriously, information is connected to representation in a new way. It enters as a (...) property of the represented content as well as a property of the vehicles that carry that content. This offers a new, conceptually and logically distinct way in which information and representation are intertwined in cognition. (shrink)

I argue that Sellars’s distinction between signifying and picturing should be taken seriously by philosophers of mind, language, and cognition. I begin with interpretations of key Sellarsian texts in order to show that picturing is best understood as a theory of non-linguistic cognitive representations through which animals navigate their environments. This is distinct from the kind of discursive cognition that Sellars called ‘signifying’ and which is best understood in terms of socio-linguistic inferences. I argue that picturing is required because reflection (...) on signifying cannot adequately explain our need for cognitive friction. I then show how the idea of picturing is further developed by Paul Churchland, Ruth Garrett Millikan, and Huw Price. I finally turn to predictive processing as a theory of cognitive representation, and in particular Andy Clark’s ‘radical predictive processing’, to further characterize picturing. However, doing so has the cost of pushing picturing and signifying further apart than Sellars intended. (shrink)

The examples and concepts that Shoemaker cites are rather heterogeneous. Some distinctions need to be drawn. An optimality thesis involves not just an ordering of options, but a value judgment about them. So let us begin by distinguishing minimality from optimality. And the concept of minimality can play a variety of roles, among which I distinguish between extremum descriptions, statements hypothesizing an optimizing process, and methodological recommendations. Finally, I consider how the three categories relate to Shoemaker’s question that “Who is (...) optimizing: the scientist or nature?” . (shrink)

I argue that Sellars’s naturalization of Kant should be understood in terms of how he used behavioristic psychology and cybernetics. I first explore how Sellars used Edward Tolman’s cognitive-behavioristic psychology to naturalize Kant in the early essay “Language, Rules, and Behavior”. I then turn to Norbert Wiener’s understanding of feedback loops and circular causality. On this basis I argue that Sellars’s distinction between signifying and picturing, which he introduces in “Being and Being Known,” can be understood in terms of what (...) I call cybernetic behaviorism. I interpret picturing in terms of cycles of cybernetic behavior and signifying in terms of coordination between cybernetic behavior systems, or what I call triangulated cybernetic behavior. This leads to a formal, naturalistic understanding of personhood as the capacity to engage in triangulated cybernetic behavior. I conclude by showing that Sellars’s thought has the resources, which he did not exploit, for introducing the concept of second-order cybernetics. This suggests that Sellars’s philosophy of mind could be developed in the direction of autopoiesis and enactivism. (shrink)

This paper argues that intelligence can be approximated by the ability to produce accurate predictions. It is further argued that general intelligence can be approximated by context dependent predictive abilities combined with the ability to use working memory to abstract away contextual information. The flexibility associated with general intelligence can be understood as the ability to use selective attention to focus on specific aspects of sensory impressions to identify patterns, which can then be used to predict events in novel situations (...) and environments. The argumentation synthesizes Godfrey-Smith’s environmental complexity theory, adding the notion of niche broadness as well as changes concerning the view of cognition and control, and Hohwy’s predictive mind theory, making explicit the significance of accuracy as a composite of trueness and precision where the nervous system acts as a distributed controller motivating actions that keep the body in homeostasis. (shrink)

The information age can be dated to the work of Norbert Wiener and Claude Shannon in the 1940s. Their work on cybernetics and information theory, and many subsequent developments, had a profound influence on reshaping the field of psychology from what it was prior to the 1950s. Contemporaneously, advances also occurred in experimental design and inferential statistical testing stemming from the work of Ronald Fisher, Jerzy Neyman, and Egon Pearson. These interdisciplinary advances from outside of psychology provided the conceptual and (...) methodological tools for what is often called the cognitive revolution but is more accurately described as the information-processing revolution. Cybernetics set the stage with the idea that everything ranging from neurophysiological mechanisms to societal activities can be modeled as structured control systems with feedforward and feedback loops. Information theory offered a way to quantify entropy and information, and promoted theorizing in terms of information flow. Statistical theory provided means for making scientific inferences from the results of controlled experiments and for conceptualizing human decision making. With those three pillars, a cognitive psychology adapted to the information age evolved. The growth of technology in the information age has resulted in human lives being increasingly interweaved with the cyber environment, making cognitive psychology an essential part of interdisciplinary research on such interweaving. Continued engagement in interdisciplinary research at the forefront of technology development provides a chance for psychologists not only to refine their theories but also to play a major role in the advent of a new age of science. (shrink)

Der soziotechnische Wandel bringt neue Phänomene des Zufalls hervor, die sich den üblichen Strategien des Umgangs mit Risiken und Unsicherheit entziehen. Diese Akzidenzphänomene sind prinzipiell unvorhersehbar und in ihrer komplexen Effektgesamtheit jenseits von Kalkulation und Antizipation. Eine allgemeine Verzukünftigung unseres Weltbezuges mit spezifischen weitreichenden Problemen ist die Folge und lässt klassische Agenturen des Risikomanagements und der Unsicherheitsbewältigung an ihre Leistungsgrenzen stoßen. Bruno Gransche zeigt, dass Philosophie und Zukunftsforschung gemeinsam großes Potenzial aufweisen, hier zu helfen.

We argue that in contemporary business organizations, in which fundamental purpose is construed to be increased value—especially in ‘participative’ organizations, in which non–hierarchal interaction (for example, work teams) is the norm; and in ‘adaptive’ organizations, in which unpredictable change is the rule—a process of values covenanting will be much more valueable than just espoused values or even values covenants. We propose such a process model for organizational values covenanting and argue that such covenanting reflects an anthropomorphism of the human character (...) development process, validated in terms of the theory of viable systems. (shrink)

A comment on Paul Schoemaker's target article in Behavioral and Brain Sciences, 14 (1991), p. 205-215, "The Quest for Optimality: A Positive Heuristic of Science?" (https://doi.org/10.1017/S0140525X00066140). This comment argues that the optimizing model of decision leads to an infinite regress, once internal costs of decision (i.e., information and computation costs) are duly taken into account.

This paper examines the strengths and weaknesses of one of science's most pervasive and flexible metaprinciples;optimalityis used to explain utility maximization in economics, least effort principles in physics, entropy in chemistry, and survival of the fittest in biology. Fermat's principle of least time involves both teleological and causal considerations, two distinct modes of explanation resting on poorly understood psychological primitives. The rationality heuristic in economics provides an example from social science of the potential biases arising from the extreme flexibility of (...) optimality considerations, including selective search for confirming vidence, ex post rationalization, and the confusion of prediction with explanation. Commentators are asked to reflect on the extent to which optimality is an organizing principle of nature, a set of relatively unconnected techniques of science, a normative principle for rational choice and social organization, a metaphysical way of looking at the world, or something else still. (shrink)

The naturalistic voluntary control (VC) theory explains free will and consciousness in terms of each other. It is central to free voluntary control of action that one can control both what one is conscious of, and also what one is not conscious of. Furthermore, the specific cognitive ability or skill involved in voluntarily controlling whether information is processed consciously or unconsciously can itself be used to explain consciousness. In functional terms, it is whatever kind of cognitive processing occurs when a (...) conscious state is voluntarily chosen. This leads to a bivalent view of cognitive processing in which there is voluntary choice either of non-routine (conscious) or routine (unconscious) kinds of processing. On this VC account, consciousness could not exist without its being possible to voluntarily choose a non-routine kind of processing. (shrink)

A quarter of a century has elapsed since quantum mechanics was discovered. Perhaps it is not too much to say, in retrospect, that the time was ripe for this particular development. This is attested, not only by the speed with which the edifice of the theory was completed immediately following the basic discoveries of Heisenberg and Schrödinger, but also by the rapidity, well-nigh unprecedented in the history of science with which the new results were applied to almost every branch of (...) physics and chemistry. A mental readiness on the part of the scientists involved might have had much more to do with this than the facilities of modern communication systems. (shrink)

It is not widely realised that Turing was probably the first person to consider building computing machines out of simple, neuron-like elements connected together into networks in a largely random manner. Turing called his networks unorganised machines. By the application of what he described as appropriate interference, mimicking education an unorganised machine can be trained to perform any task that a Turing machine can carry out, provided the number of neurons is sufficient. Turing proposed simulating both the behaviour of the (...) network and the training process by means of a computer program. We outline Turing's connectionist project of 1948. (shrink)