We present a simple example that disproves the universality principle. Unlike previous counter-examples to computational universality, it does not rely on extraneous phenomena, such as the availability of input variables that are time varying, computational complexity that changes with time or order of execution, physical variables that interact with each other, uncertain deadlines, or mathematical conditions among the variables that must be obeyed throughout the computation. In the most basic case of the new example, all that is used is (...) a single pre-existing global variable whose value is modified by the computation itself. In addition, our example offers a new dimension for separating the computable from the uncomputable, while illustrating the power of parallelism in computation. (shrink)

The central aim of this paper is to shed light on the nature of explanation in computational neuroscience. I argue that computational models in this domain possess explanatory force to the extent that they describe the mechanisms responsible for producing a given phenomenon—paralleling how other mechanistic models explain. Conceiving computational explanation as a species of mechanistic explanation affords an important distinction between computational models that play genuine explanatory roles and those that merely provide accurate descriptions or predictions of phenomena. It (...) also serves to clarify the pattern of model refinement and elaboration undertaken by computational neuroscientists. (shrink)

This article examines three candidate cases of non-causal explanation in computational neuroscience. I argue that there are instances of efficient coding explanation that are strongly analogous to examples of non-causal explanation in physics and biology, as presented by Batterman, Woodward, and Lange. By integrating Lange’s and Woodward’s accounts, I offer a new way to elucidate the distinction between causal and non-causal explanation, and to address concerns about the explanatory sufficiency of non-mechanistic models in neuroscience. I also use this framework to (...) shed light on the dispute over the interpretation of dynamical models of the brain. _1_ Introduction _1.1_ Efficient coding explanation in computational neuroscience _1.2_ Defining non-causal explanation _2_ Case I: Hybrid Computation _3_ Case II: The Gabor Model Revisited _4_ Case III: A Dynamical Model of Prefrontal Cortex _4.1_ A new explanation of context-dependent computation _4.2_ Causal or non-causal? _5_ Causal and Non-causal: Does the Difference Matter? (shrink)

We characterize abstraction in computer science by first comparing the fundamental nature of computer science with that of its cousin mathematics. We consider their primary products, use of formalism, and abstraction objectives, and find that the two disciplines are sharply distinguished. Mathematics, being primarily concerned with developing inference structures, has information neglect as its abstraction objective. Computer science, being primarily concerned with developing interaction patterns, has information hiding as its abstraction objective. We show that abstraction through information hiding is a (...) primary factor in computer science progress and success through an examination of the ubiquitous role of information hiding in programming languages, operating systems, network architecture, and design patterns. (shrink)

Computer science only became established as a field in the 1950s, growing out of theoretical and practical research begun in the previous two decades. The field has exhibited immense creativity, ranging from innovative hardware such as the early mainframes to software breakthroughs such as programming languages and the Internet. Martin Gardner worried that "it would be a sad day if human beings, adjusting to the Computer Revolution, became so intellectually lazy that they lost their power of creative thinking" (Gardner, 1978, (...) p. vi-viii). On the contrary, computers and the theory of computation have provided great opportunities for creative work. This chapter examines several key aspects of creativity in computer science, beginning with the question of how problems arise in computer science. We then discuss the use of analogies in solving key problems in the history of computer science. Our discussion in these sections is based on historical examples, but the following sections discuss the nature of creativity using information from a contemporary source, a set of interviews with practicing computer scientists collected by the Association of Computing Machinery’s on-line student magazine, Crossroads. We then provide a general comparison of creativity in computer science and in the natural sciences. (shrink)

This paper is a contribution to that part of science studies known as 'the cognitive study of science'. The general goal of such studies is to understand cogni-.

This essay considers methodological aspects ofcomputer ethics and argues for a multi-levelinterdisciplinary approach with a central role forwhat is called disclosive computer ethics. Disclosivecomputer ethics is concerned with the moraldeciphering of embedded values and norms in computersystems, applications and practices. In themethodology for computer ethics research proposed inthe essay, research takes place at three levels: thedisclosure level, in which ideally philosophers,computer scientists and social scientists collaborateto disclose embedded normativity in computer systemsand practices, the theoretical level, in whichphilosophers develop and modify (...) moral theory, and theapplication level, that draws from research performedat the other two levels, and at which normativeevaluations of computer systems and practices takesplace. (shrink)

The ‘received view’ about computation is that all computations must involve representational content. Egan and Piccinini argue against the received view. In this paper, I focus on Egan’s arguments, claiming that they fall short of establishing that computations do not involve representational content. I provide positive arguments explaining why computation has to involve representational content, and how that representational content may be of any type. I also argue that there is no need for computational psychology to be individualistic. (...) Finally, I draw out a number of consequences for computational individuation, proposing necessary conditions on computational identity and necessary and sufficient conditions on computational I/O equivalence of physical systems.Keywords: Computation; Representation; Computational identity; Explanation; Narrow content; Physical computation. (shrink)

Relative to digital computation, analogue computation has been neglected in the philosophical literature. To the extent that attention has been paid to analogue computation, it has been misunderstood. The received view—that analogue computation has to do essentially with continuity—is simply wrong, as shown by careful attention to historical examples of discontinuous, discrete analogue computers. Instead of the received view, I develop an account of analogue computation in terms of a particular type of analogue representation that (...) allows for discontinuity. This account thus characterizes all types of analogue computation, whether continuous or discrete. Furthermore, the structure of this account can be generalized to other types of computation: analogue computation essentially involves analogue representation, whereas digital computation essentially involves digital representation. Besides being a necessary component of a complete philosophical understanding of computation in general, understanding analogue computation is important for computational explanation in contemporary neuroscience and cognitive science. (shrink)

Model validation in computational cognitive psychology often relies on methods drawn from the testing of theories in experimental physics. However, applications of these methods to computational models in typical cognitive experiments can hide multiple, plausible sources of variation arising from human participants and from stochastic cognitive theories, encouraging a “model fixed, data variable” paradigm that makes it difficult to interpret model predictions and to account for individual differences. This article proposes a likelihood‐based, “data fixed, model variable” paradigm in which models (...) are treated as stochastic processes in experiments with participant‐to‐participant variation that can be applied to a broad range of mechanistic cognitive architectures. This article discusses the implementation and implications of this view in model validation, with a concrete focus on a simple class of ACT‐R models of cognition. This article is not intended as a recipe for broad application of these preliminary, proof‐of‐concept methods, but as a framework for communication between statisticians searching for interesting problems in the cognitive modeling sphere, and cognitive modelers interested in generalizing from deterministic to stochastic model validation, in the face of random variation in human experimental data. (shrink)

I argue that influential purely syntactic views of computation, shared by such philosophers as John Searle and Hilary Putnam, are mistaken. First, I discuss common objections, and during the discussion I mention additional necessary conditions of implementation of computations in physical processes that are neglected in classical philosophical accounts of computation. Then I try to show why realism in regards of physical computations is more plausible, and more coherent with any realistic attitude towards natural science than the received (...) view, and distinguish computational simulation, implementation, and re-engineering. I also point out the sources of confusion about what computation is that seem to stem from disregarding the use/mention distinction. (shrink)

A basic deep neural network (DNN) is trained to exhibit a large set of input–output dispositions. While being a good model of the way humans perform some tasks automatically, without deliberative reasoning, more is needed to approach human‐like artificial intelligence. Analysing recent additions brings to light a distinction between two fundamentally different styles of computation: content‐specific and non‐content‐specific computation (as first defined here). For example, deep episodic RL networks draw on both. So does human conceptual reasoning. Combining the (...) two takes advantage of the complementary costs and benefits of each. It also offers a better model of human cognitive competence. (shrink)

On definable filters in computably enumerable degrees.

Linear Logic is a branch of proof theory which provides refined tools for the study of the computational aspects of proofs. These tools include a duality-based categorical semantics, an intrinsic graphical representation of proofs, the introduction of well-behaved non-commutative logical connectives, and the concepts of polarity and focalisation. These various aspects are illustrated here through introductory tutorials as well as more specialised contributions, with a particular emphasis on applications to computer science: denotational semantics, lambda-calculus, logic programming and concurrency theory. The (...) volume is rounded-off by two invited contributions on new topics rooted in recent developments of linear logic. The book derives from a summer school that was the climax of the EU Training and Mobility of Researchers project 'Linear Logic in Computer Science'. It is an excellent introduction to some of the most active research topics in the area. (shrink)

Gualtiero Piccinini articulates and defends a mechanistic account of concrete, or physical, computation. A physical system is a computing system just in case it is a mechanism one of whose functions is to manipulate vehicles based solely on differences between different portions of the vehicles according to a rule defined over the vehicles. Physical Computation discusses previous accounts of computation and argues that the mechanistic account is better. Many kinds of computation are explicated, such as digital (...) vs. analog, serial vs. parallel, neural network computation, program-controlled computation, and more. Piccinini argues that computation does not entail representation or information processing although information processing entails computation. Pancomputationalism, according to which every physical system is computational, is rejected. A modest version of the physical Church-Turing thesis, according to which any function that is physically computable is computable by Turing machines, is defended. (shrink)

Many of our cognitive capacities are shaped by enculturation. Enculturation is the acquisition of cognitive practices such as symbol-based mathematical practices, reading, and writing during ontogeny. Enculturation is associated with significant changes to the organization and connectivity of the brain and to the functional profiles of embodied actions and motor programs. Furthermore, it relies on scaffolded cultural learning in the cognitive niche. The purpose of this paper is to explore the components of symbol-based mathematical practices. Phylogenetically, these practices are the (...) result of concerted organism-niche interactions that have led from approximate number estimations to the emergence of discrete, symbol-based mathematical operations. Ontogenetically, symbol-based mathematical practices are associated with plastic changes to neural circuitry, action schemata, and motor programs. It will be suggested that these practices rely on previously acquired capacities such as subitizing and counting. With these considerations in place, I will argue that computations, understood in the sense of Turing (1936), are a specific kind of symbol-based mathematical practices that can be realized by human organisms, machines, or by hybrid organism-machine systems. In sum, this paper suggests a new way to think about mathematical cognition and computation. (shrink)

Analogicity in computer science is understood in two, not mutually exclusive ways: 1) with regard to the continuity feature (of data or computations), 2) with regard to the analogousness feature (i.e. similarity between certain natural processes and computations). Continuous computations are the subject of three methodological questions considered in the paper: 1a) to what extent do their theoretical models go beyond the model of the universal Turing machine (defining digital computations), 1b) is their computational power greater than that of the (...) universal Turing machine, 1c) under what conditions are continuous computations realizable in practice? The analogue-analogical computations lead to two other issues: 2a) in what sense and to what extent their accuracy depends on the adequacy of certain theories of empirical sciences, 2b) are there analogue-analogical computations in nature that are also continuous? The above issues are an important element of the philosophical discussion on the limitations of contemporary computer science. (shrink)

The many worlds anthropic principle is explored here from the a priori perspective of rationalist metaphysics, within the framework of modal logic. It is shown how the apparent contradictions of quantum superposition can be thought of in terms of different levels of world models. The framework of modal logic is used, but given the rationalist assumption that all possible worlds exist. There is thus no absolute distinction between possibility and necessity. To take the point of view of a conscious being (...) in a world, however, is to adopt some such distinction--something we must do in order to do physics. This paper is intended to lay the groundwork for future attempts to develop theories of what is necessarily true in a world with conscious entities. It also contains some tentative speculations on the difficult issue of death and quantum mechanics. (shrink)

This paper deals with the question: how is computation best individuated? -/- 1. The semantic view of computation: computation is best individuated by its semantic properties. 2. The causal view of computation: computation is best individuated by its causal properties. 3. The functional view of computation: computation is best individuated by its functional properties. -/- Some scientific theories explain the capacities of brains by appealing to computations that they supposedly perform. The reason for (...) that is usually that computation is individuated semantically. I criticize the reasons in support of this view and its presupposition of representation and semantics. Furthermore, I argue that the only justified appeal to a representational individuation of computation might be that it is partly individuated by implicit intrinsic representations. (shrink)

Purpose– This paper aims to introduce the concept of ETHICOMP as “community mentor” – the role that the ETHICOMP conference plays outside the standard conference fare, in which it nurtures and supports up-and-coming researchers in the field of computer ethics.Design/methodology/approach– This paper uses an auto-ethnographic methodology to reflexively explore the author’s career from PhD student to early career researcher spanning the years 2005-2013, and how the ETHICOMP community has played a significant role as a mentor in her life. The literature (...) on mentorship is discussed, particularly focussing on the importance of mentorship for women in philosophy-related academic careers, and criteria for successful mentorship are measured against the ETHICOMP “community mentorship”. Additionally, some key philosophical concepts are introduced and reflected upon.Findings– The paper produces recommendations for other philosophical communities wishing to grow their mentorship capabilities through communities around conferences.Originality/value– This paper sheds new light on the concepts of mentorship and the practical application of mentorship within an academic community. It also provides an account of the value of the ETHICOMP conference series that is beyond the usual academic output. (shrink)

This paper constructs a cirquent calculus system and proves its soundness and completeness with respect to the semantics of computability logic. The logical vocabulary of the system consists of negation \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${{\neg}}$$\end{document}, parallel conjunction \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${{\wedge}}$$\end{document}, parallel disjunction \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${{\vee}}$$\end{document}, branching recurrence ⫰, and branching corecurrence ⫯. The article is published in two parts, with (the previous) (...) Part I containing preliminaries and a soundness proof, and (the present) Part II containing a completeness proof. (shrink)

Can scientific evidence outstretch what scientists have mentally entertained, or could ever entertain? This article focuses on the plausibility and consequences of an affirmative answer in a special case. Specifically, it discusses how we may treat automated scientific data-gathering systems—especially AI systems used to make predictions or to generate novel theories—from the point of view of confirmation theory. It uses AlphaFold2 as a case study.

Reformers urge that representation no longer earns its explanatory keep in cognitive science, and that it is time to discard this troublesome concept. In contrast, we hold that without representation cognitive science is utterly bereft of tools for explaining natural intelligence. In order to defend the latter position, we focus on the explanatory role of representation in computation. We examine how the methods of digital and analog computation are used to model a relatively simple target system, and show (...) that representation plays an in-eliminable explanatory role in both cases. We conclude that, to the extent that biologic systems engage in computation, representation is destined to play an explanatory role in cognitive science. (shrink)

A quantum algorithm succeeds not because the superposition principle allows ‘the computation of all values of a function at once’ via ‘quantum parallelism’, but rather because the structure of a quantum state space allows new sorts of correlations associated with entanglement, with new possibilities for information‐processing transformations between correlations, that are not possible in a classical state space. I illustrate this with an elementary example of a problem for which a quantum algorithm is more efficient than any classical algorithm. (...) I also introduce the notion of ‘pseudotelepathic’ games and show how the difference between classical and quantum correlations plays a similar role here for games that can be won by quantum players exploiting entanglement, but not by classical players whose only allowed common resource consists of shared strings of random numbers (common causes of the players’ correlated responses in a game). *Received October 2008. †To contact the author, please write to: Department of Philosophy, University of Maryland, College Park, MD 20742; e‐mail: [email protected]. (shrink)

Written by world-leading experts, this book draws together a number of important strands in contemporary approaches to the philosophical and scientific questions that emerge when dealing with the issues of computing, information, cognition and the conceptual issues that arise at their intersections. It discovers and develops the connections at the borders and in the interstices of disciplines and debates. This volume presents a range of essays that deal with the currently vigorous concerns of the philosophy of information, ontology creation and (...) control, bioinformation and biosemiotics, computational and post-computation approaches to the philosophy of cognitive science, computational linguistics, ethics, and education. (shrink)

Computation is central to the foundations of modern cognitive science, but its role is controversial. Questions about computation abound: What is it for a physical system to implement a computation? Is computation sufficient for thought? What is the role of computation in a theory of cognition? What is the relation between different sorts of computational theory, such as connectionism and symbolic computation? In this paper I develop a systematic framework that addresses all of these (...) questions. Justifying the role of computation requires analysis of implementation, the nexus between abstract computations and concrete physical systems. I give such an analysis, based on the idea that a system implements a computation if the causal structure of the system mirrors the formal structure of the computation. This account can be used to justify the central commitments of artificial intelligence and computational cognitive science: the thesis of computational sufficiency, which holds that the right kind of computational structure suffices for the possession of a mind, and the thesis of computational explanation, which holds that computation provides a general framework for the explanation of cognitive processes. The theses are consequences of the facts that computation can specify general patterns of causal organization, and mentality is an organizational invariant, rooted in such patterns. Along the way I answer various challenges to the computationalist position, such as those put forward by Searle. I close by advocating a kind of minimal computationalism, compatible with a very wide variety of empirical approaches to the mind. This allows computation to serve as a true foundation for cognitive science. (shrink)

Andrew Boucher (1997) argues that ``parallel computation is fundamentally different from sequential computation'' (p. 543), and that this fact provides reason to be skeptical about whether AI can produce a genuinely intelligent machine. But parallelism, as I prove herein, is irrelevant. What Boucher has inadvertently glimpsed is one small part of a mathematical tapestry portraying the simple but undeniable fact that physical computation can be fundamentally different from ordinary, ``textbook'' computation (whether parallel or sequential). This tapestry (...) does indeed immediately imply that human cognition may be uncomputable. (shrink)

Computational models will play an important role in our understanding of human higher‐order cognition. How can a model's contribution to this goal be evaluated? This article argues that three important aspects of a model of higher‐order cognition to evaluate are (a) its ability to reason, solve problems, converse, and learn as well as people do; (b) the breadth of situations in which it can do so; and (c) the parsimony of the mechanisms it posits. This article argues that fits of (...) models to quantitative experimental data, although valuable for other reasons, do not address these criteria. Further, using analogies with other sciences, the history of cognitive science, and examples from modern‐day research programs, this article identifies five activities that have been demonstrated to play an important role in our understanding of human higher‐order cognition. These include modeling within a cognitive architecture, conducting artificial intelligence research, measuring and expanding a model's ability, finding mappings between the structure of different domains, and attempting to explain multiple phenomena within a single model. (shrink)

We exploit properties of certain directed graphs, obtained from the families of sets with special effective enumeration properties, to generalize several results in computable model theory to higher levels of the hyperarithmetical hierarchy. Families of sets with such enumeration features were previously built by Selivanov, Goncharov, and Wehner. For a computable successor ordinal α, we transform a countable directed graph into a structure such that has a isomorphic copy if and only if has a computable isomorphic copy.A computable structure is (...) categorical if for all computable isomorphic copies of , there is an isomorphism from onto , which is . We prove that for every computable successor ordinal α, there is a computable, categorical structure, which is not relatively categorical. This generalizes the result of Goncharov that there is a computable, computably categorical structure, which is not relatively computably categorical.An additional relation R on the domain of a computable structure is intrinsically on if in all computable isomorphic copies of , the image of R is . We prove that for every computable successor ordinal α, there is an intrinsically relation on a computable structure, which not relatively intrinsically . This generalizes the result of Manasse that there is an intrinsically computably enumerable relation on a computable structure, which is not relatively intrinsically computably enumerable.The dimension of a structure is the number of computable isomorphic copies, up to isomorphisms. We prove that for every computable successor ordinal α and every n≥1, there is a computable structure with dimension n. This generalizes the result of Goncharov that there is a structure of computable dimension n for every n≥1.Finally, we prove that for every computable successor ordinal α, there is a countable structure with isomorphic copies in just the Turing degrees of sets X such that relative to X is not . In particular, for every finite n, there is a structure with isomorphic copies in exactly the non- Turing degrees. This generalizes the result obtained by Wehner, and independently by Slaman, that there is a structure with isomorphic copies in exactly the nonzero Turing degrees. (shrink)

The essays included in the special issue dedicated to the philosophy of computer science examine new philosophical questions that arise from reflection upon conceptual issues in computer science and the insights such an enquiry provides into ongoing philosophical debates.

These are the lecture notes from a 10-hour course that the author gave at the University of Notre Dame in September 2010. The objective of the course was to introduce some basic concepts in computable structure theory and develop the background needed to understand the author’s research on back-and-forth relations.

The function of focus is to activate alternatives, and these activated alternatives are used to compute the corresponding inferences of an utterance. The experimental research reported here investigates the role of focus intonation in inference computation and its interplay with the overt focus particles only and also. In particular, I compare the mechanisms underlying the computation of exhaustivity implicatures, assertions, and additive presuppositions. A memory delay experiment revealed that contrastive intonation makes an exhaustive interpretation equally available as overt (...) only. A second experiment showed that in immediate processing, the implicature in bare focus conditions is delayed relative to the inferences associated with only and also. The findings thus indicate that L+H* accents do not conventionally encode an exhaustive meaning, but encourage implicature computation by making relevant alternatives salient and providing a strong cue that an inference should be derived. (shrink)

PurposeThe purpose of this research paper is to examine the early stages of a research project aimed at evaluating the pedagogic effectiveness of a teaching module in computing ethics.Design/methodology/approachScores of students' cognitive capabilities to make moral judgements were measured before and after they had taken the module by means of the “Defining Issues Test”. This is a standard test of students' capability to make moral judgement based on the work of Lawrence Kohlberg. Interviews were then used to help understand the (...) results of the test.FindingsResults revealed low mean scores of post‐conventional thinking skills and wide variation in overall scores of capability for moral judgement. Interviews with the students about the course and the test revealed the importance of understanding students' beliefs about the importance of ethics in interpreting the results.Research limitations/implicationsDifficulties in matching “before and after” results for each subject limited the sample size in what was an early step in the overall research project.Practical implicationsThe results point towards the importance of addressing students' own understanding of the importance of ethics when teaching computing ethics.Originality/valueThe paper reveals some of the limitations of tests of capabilities for moral judgement which rely on strongly individualistic notions of ethics. It enables a new appreciation to be made of the strengths and weaknesses of assessing student moral development in higher education in terms of cognitive factors. (shrink)

This systematic investigation of computation and mental phenomena by a noted psychologist and computer scientist argues that cognition is a form of computation, that the semantic contents of mental states are encoded in the same general way as computer representations are encoded. It is a rich and sustained investigation of the assumptions underlying the directions cognitive science research is taking. 1 The Explanatory Vocabulary of Cognition 2 The Explanatory Role of Representations 3 The Relevance of Computation 4 (...) The Psychological Reality of Programs: Strong Equivalence 5 Constraining Functional Architecture 6 The Bridge from Physical to Symbolic: Transduction 7 Functional Architecture and Analogue Processes 8 Mental Imagery and Functional Architecture 9 Epilogue: What is Cognitive Science the Science of? (shrink)

Focusing on computational studies of pericyclic reactions from the late twentieth century into the twenty-first century, this paper argues that computational diagnostics is a key methodological development that characterize the management and coordination of plural approximation methods in computational organic chemistry. Predictive divergence between semi-empirical and ab initio approximation methods in the study of pericyclic reactions has issued in epistemic dissent. This has resulted in the use of diagnostics to unpack computational greyboxes in order to critically assess the effect of (...) specific misrepresentations on predictive accuracy given that approximations and idealizations must be made to render computational models tractable. Furthermore, benchmarking is used to determine the applicability of approximation methods depending on how accurate the results need to be in a given research context. The epistemology of benchmarking consists of the co-generation of data sets in a hybrid computational–experimental form used to standardize computational methods but does not determine a unique quantitative method to be used across computational organic chemistry. (shrink)

We begin by distinguishing computationalism from a number of other theses that are sometimes conflated with it. We also distinguish between several important kinds of computation: computation in a generic sense, digital computation, and analog computation. Then, we defend a weak version of computationalism—neural processes are computations in the generic sense. After that, we reject on empirical grounds the common assimilation of neural computation to either analog or digital computation, concluding that neural computation (...) is sui generis. Analog computation requires continuous signals; digital computation requires strings of digits. But current neuroscientific evidence indicates that typical neural signals, such as spike trains, are graded like continuous signals but are constituted by discrete functional elements (spikes); thus, typical neural signals are neither continuous signals nor strings of digits. It follows that neural computation is sui generis. Finally, we highlight three important consequences of a proper understanding of neural computation for the theory of cognition. First, understanding neural computation requires a specially designed mathematical theory (or theories) rather than the mathematical theories of analog or digital computation. Second, several popular views about neural computation turn out to be incorrect. Third, computational theories of cognition that rely on non-neural notions of computation ought to be replaced or reinterpreted in terms of neural computation. (shrink)

IntroductionInformation and communication technologies are becoming an integral part of medical practice, research and administration and their use will grow as telemedicine and electronic medical record use become part of routine practice. Security in maintaining patient data is important and there is a statuary obligation to do so, but few health professionals have been trained on how to achieve this. There is no information on the use of computers and email by doctors and nurses in South Africa in the workplace (...) and at home, and whether their current computer practices meets legal and ethical requirements. The aims of this study were to determine the use of computers by healthcare practitioners in the workplace and home; the use and approach to data storage, encryption and security of patient data and patient email; and the use of informed consent to transmit data by email.MethodsA self-administered questionnaire was administered to 400 health care providers from the state and private health care sectors. The questionnaire covered computer use in the workplace and at home, sharing of computers, data encryption and storage, email use, encryption of emails and storage, and the use of informed consent for email communication.Results193 doctors and 207 nurses in the private and public sectors completed the questionnaire. Forty (10%) of participants do not use a computer. A third of health professionals were the only users of computers at work or at home. One hundred and ninety-eight respondents (55%) did not know if the data on the computers were encrypted, 132 (36.7%) knew that the data were not encrypted and 30 (8.3%) individuals knew that the data on the computers they were using were encrypted. Few doctors, 58 (16%), received emails from patients, with doctors more likely to receive emails from patients than nurses (p = 0.0025). Thirty-one percent of individuals did not respond to the emails. Emails were saved by 40 (69%) recipients but only 5 (12.5%) doctors encrypted the messages, 19 (47.5%) individuals knowingly did not encrypt and 16 (40.0%) did not know if they encrypted the data. While 20% of health professionals have emailed patient data, but only 41.7% gained consent to do so.ConclusionsMost health professionals as sampled in South Africa are not compliant with the National Health Act or the Electronic Communications Transactions Act of South Africa or guidelines from regulatory bodies when managing patient data on computers. Many appear ignorant or lack the ability to comply with simple data security procedures. (shrink)