The potential for scalable quantum computing depends on the viability of fault tolerance and quantum error correction, by which the entropy of environmental noise is removed during a quantum computation to maintain the physical reversibility of the computer’s logical qubits. However, the theory underlying quantum error correction applies a linguistic double standard to the words “noise” and “measurement” by treating environmental interactions during a quantum computation as inherently reversible, and environmental interactions at the end of a quantum computation as irreversible (...) measurements. Specifically, quantum error correction theory models noise as interactions that are uncorrelated or that result in correlations that decay in space and/or time, thus embedding no permanent information to the environment. I challenge this assumption both on logical grounds and by discussing a hypothetical quantum computer based on “position qubits.” The technological difficulties of producing a useful scalable position-qubit quantum computer parallel the overwhelming difficulties in performing a double-slit interference experiment on an object comprising a million to a billion fermions. (shrink)

Theories of language production propose that utterances are constructed by a mechanism that separates linguistic content from linguistic structure, Linguistic content is retrieved from the mental lexicon, and is then inserted into slots in linguistic structures or frames. Support for this kind of model at the phonological level comes from patterns of phonological speech errors. W present an alternative account of these patterns using a connectionist or parallel distributed proceesing (PDP) model that learns to produce sequences of phonological features. (...) The model's errors exhibit some of the properties of human speech errors, specifically, properties that have been attributed to the action of phonological rules, frames, or other structural generalizations. (shrink)

Because of its practical advantages, machine learning (ML) is increasingly used for decision-making in numerous sectors. This paper demonstrates that the integral characteristics of ML, such as semi-autonomy, complexity, and non-deterministic modeling have important ethical implications. In particular, these characteristics lead to a lack of insight and lack of comprehensibility, and ultimately to the loss of human control over decision-making. Errors, which are bound to occur in any decision-making process, may lead to great harm and human rights violations. It (...) is important to have a principled way of assigning responsibility for such errors. The integral characteristics of ML, however, pose serious difficulties in defining responsibility and regulating ML decision-making. First, we elaborate on these characteristics and their epistemic and ethical implications. We then analyze possible general strategies for resolving the assignment of moral responsibility and show that, due to the specific way in which ML functions, each potential solution is problematic, whether we assign responsibility to humans, machines, or using hybrid models. Then, we shift focus on an alternative approach that bypasses moral responsibility and attempts to define legal liability independently through solutions such as informed consent and the no-fault compensation system. Both of these solutions prove unsatisfactory because they leave too much room for potential abuses of ML decision-making. We conclude that both ethical and legal solutions are fraught with serious difficulties. These difficulties prompt us to re-weigh the costs and benefits of using ML for high-stake decisions. (shrink)

Free speech fallacies are errors of meta-argument. One commits a free speech fallacy when one argues that since there are apparent restrictions on one’s rights of free expression, procedural rules of critical exchange have been broken, and consequently, one’s preferred view is dialectically better off than it may otherwise seem. Free speech fallacies are meta-argumentative, since they occur at the level of assessing the dialectical situation in terms of norms of argument and in terms of meta-evidential principles of interpreting (...) how and why people follow (or fail to follow) argumentative rules. Our plan here is to begin with a brief explanation of meta-argument and meta-argumentative fallacy. We will then turn to the variety of forms of the free speech fallacy, which we will explain as meta-argumentatively erroneous. (shrink)

This essay takes the position, consistent with Ayer’s own retrospective judgments, that the philosophical significance of Language, Truth and Logic (LTL) was minimal at best, and that its real significance was socio-historical. LTL stands as one of the most influential expressions of an overzealous and simplistic scientism that swept through Western culture in the first half of the twentieth century. This scientism played a crucial role in problematizing the West’s relationship to truth in ways that contributed to the eventual emergence (...) of “post-truth” culture, with its “post-truth politics.” This in turn made possible the disastrous results of recent votes and elections in Britain and, above all, in the United States. In short, LTL’s real significance resides in the role it played as a sophisticated and successful bit of propaganda for an ideology that played a crucial role in loosing Western culture from its moral and epistemic moorings. (shrink)

We analysed research ethics committee (REC) letters. We found that RECs frequently identify process errors in applications from researchers that are not deemed “favourable” at first review. Errors include procedural violations (identified in 74% of all applications), missing information (68%), slip-ups (44%) and discrepancies (25%). Important questions arise about why the level of error identified by RECs is so high, and about how errors of different types should be handled.

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)

In the paper we examine the use of non-classical truth values for dealing with computation errors in program specification and validation. In that context, 3-valued McCarthy logic is suitable for handling lazy sequential computation, while 3-valued Kleene logic can be used for reasoning about parallel computation. If we want to be able to deal with both strategies without distinguishing between them, we combine Kleene and McCarthy logics into a logic based on a non-deterministic, 3-valued matrix, incorporating both options as (...) a non-deterministic choice. If the two strategies are to be distinguished, Kleene and McCarthy logics are combined into a logic based on a 4-valued deterministic matrix featuring two kinds of computation errors which correspond to the two computation strategies described above. For the resulting logics, we provide sound and complete calculi of ordinary, two-valued sequents. (shrink)

An experimental study was conducted on children aged 2;6–3;0 and 3;6–4;0 investigating the priming effect of two WANT-constructions to establish whether constructional competition contributes to English-speaking children's infinitival to omission errors. In two between-participant groups, children either just heard or heard and repeated WANT-to, WANT-X, and control prime sentences after which to-infinitival constructions were elicited. We found that both age groups were primed, but in different ways. In the 2;6–3;0 year olds, WANT-to primes facilitated the provision of to in (...) target utterances relative to the control contexts, but no significant effect was found for WANT-X primes. In the 3;6–4;0 year olds, both WANT-to and WANT-X primes showed a priming effect, namely WANT-to primes facilitated and WANT-X primes inhibited provision of to. We argue that these effects reflect developmental differences in the level of proficiency in and preference for the two constructions, and they are broadly consistent with “priming as implicit learning” accounts. The current study shows that children as young as 2;6–3;0 years of age can be primed when they have only heard particular constructions, children are acquiring at least two constructions for the matrix verb WANT, and that these two WANT-constructions compete for production. (shrink)

If connectionist computational models explain the acquisition of complex cognitive skills, errors in such models would also help explain unusual brain activity such as in creativity – as well as in mental illness, including childhood onset problems with social behaviors in autism, the inability to maintain focus in attention deficit and hyperactivity disorder (ADHD), and the lack of motivation of depression disorders.

The causal and simulation theories are often presented as very distinct views about declarative memory, their major difference lying on the causal condition. The causal theory states that remembering involves an accurate representation causally connected to an earlier experience. In the simulation theory, remembering involves an accurate representation generated by a reliable memory process. I investigate how to construe detailed versions of these theories that correctly classify memory errors as misremembering or confabulation. Neither causalists nor simulationists have paid attention (...) to memory-conjunction errors, which is unfortunate because both theories have problems with these cases. The source of the difficulty is the background assumption that an act of remembering has one target. I fix these theories for those cases. The resulting versions are closely related when implemented using tools of information theory, differing only on how memory transmits information about the past. The implementation provides us with insights about the distinction between confabulatory and non-confabulatory memory, where memory-conjunction errors have a privileged position. (shrink)

Multiple simultaneous independent discoveries, so well enunciated by Robert K. Merton in the early 1960s but already discussed for several hundreds of years, is a classic concept in the sociology of science. In this paper, the concept of multiple simultaneous independent errors is proposed, analyzed, and discussed. The concept of Selective Pessimistic Induction is proposed and used to connect MIDs with MIEs. Five types of MIEs are discussed: multiple errors in the interpretation of experimental data or computational results; (...) multiple misjudgments of the value of another’s research results or conclusions; multiple cases of false anticipation of achieving a certain experimental result; multiples of ignoring or omitting relevant precedents; and multiple instances of failure due to a not-yet-conceived scientific concept or principle. Causal MIDs and MIEs are those that can be traced directly to antecedent knowledge. Acausal MIDs and MIEs are those involving a consequential and identifiable leap from antecedent knowledge. Examples of causal and acausal MIEs are provided, mostly but not exclusively from the discipline of chemistry. Comparisons are made between MIDs and MIEs. Topics for future research are discussed and implications of these concepts are proposed. (shrink)

Software vulnerabilities are the root cause for a multitude of security problems in computer systems. Owing to their efficiency and tight control over low-level system resources, the C and C++ programming languages are extensively used for a myriad of purposes, from implementing operating system kernels to user-space applications. However, insufficient or improper memory management frequently leads to invalid memory accesses, eventually resulting in memory corruption vulnerabilities. These vulnerabilities are used as a foothold for elaborated attacks that bypass existing defense methods. (...) In this paper, we summarise the main memory safety violation types (i.e. memory errors), and analyse how they are exploited by attackers and the main mitigation methods proposed in the research community. We further systematise the most relevant techniques with regards to memory corruption identification in current programs. (shrink)

This paper is concerned with the problem of experimental error. The prevalent view that experimental errors can be dismissed as a tiresome but trivial blemish on the method of experimentation is criticized. It is stressed that the occurrence of errors in experiments constitutes a permanent feature of the attempt to test theories in the physical world, and this feature deserves proper attention. It is suggested that a classification of types of experimental error may be useful as a heuristic (...) device in studying the nature of these errors. However, the standard classification of systematic and random errors is mathematically based does not focus on the causes of the errors, their origins, or the contexts in which they arise. A new typology of experimental errors is therefore proposed whose criterion is epistemological. This typology reflects the various stages that can be discerned in the execution of an experiment, each stage constituting a category of a certain type of experimental error. The proposed classification consists of four categories which are illustrated by historical cases. (shrink)

This article argues that errors in audio data processing should be examined to explore and expose the underlying components that enable linguistic communication and cross-cultural understanding. Examples of errors in the transcription of a Mexican social network’s conversations are analyzed to demonstrate the potential of such data in the development of sociocognitive language-processing theories. It is suggested that researchers working with audio-recorded data should expand the scope of what is considered useful data for the sake of both methodological (...) reflexivity and identifying the underlying cognitive processes enabling linguistic understanding. (shrink)

Background: Discussions about medical errors facilitate professional learning for physicians and may provide emotional support after an error, but little is known about physicians’ attitudes and practices regarding error discussions with colleagues.Methods: Survey of faculty and resident physicians in generalist specialties in Midwest, Mid-Atlantic and Northeast regions of the US to investigate attitudes and practices regarding error discussions, likelihood of discussing hypothetical errors, experience role-modelling error discussions and demographic variables.Results: Responses were received from 338 participants . In all, (...) 73% of respondents indicated they usually discuss their mistakes with colleagues, 70% believed discussing mistakes strengthens professional relationships and 89% knew at least one colleague who would be a supportive listener. Motivations for error discussions included wanting to learn whether a colleague would have made the same decision , wanting colleagues to learn from the mistake and wanting to receive support . Given hypothetical scenarios, most respondents indicated they would likely discuss an error resulting in no harm , minor harm or major harm . Fifty-seven percent of physicians had tried to serve as a role model by discussing an error and role-modelling was more likely among those who had previously observed an error discussion .Conclusions: Most generalist physicians in teaching hospitals report that they usually discuss their errors with colleagues, and more than half have tried to role-model discussions. However, a significant number of these physicians report that they do not usually discuss their errors and some do not know colleagues who would be supportive listeners. (shrink)

This commentary questions Elqayam & Evans' (E&E's) claims that thinking tasks are doomed to have multiple normative readings and that only applied research allows normative evaluations. In fact, some tasks have just one undisputed normative reading, and not only pathological gamblers but also normal individuals sometimes need normative guidance. To conclude, normative evaluations are inevitable in the investigation of human thinking.

Predictive processing is an increasingly popular approach to cognition, perception and action. It says that the brain is essentially a hierarchical prediction machine. It is typically construed in a representationalist and inferentialist fashion so that the brain makes contentful inferences on the basis of representational models. In this paper, I argue that the predictive processing framework is inconsistent with this epistemic position. In particular, I argue that the combination of hierarchical modeling, contentful inferentialism and representationalism entail an internal inconsistency. Specifically, (...) for a particular set of states, there will be both a representation requirement and not. Yet a system cannot both be required to represent a certain set of states and not be required to represent those states. Due to this contradiction, I propose to reject the standard view. I suggest that predictive processing is best interpreted in terms of reliable covariation instead, entailing an instrumentalist approach to the statistical machinery. (shrink)

There is a widespread feeling that health is special; the rules that are usually used in other policy areas are not applied in health policy. Health economists, for example, tend to be reluctant to offer economists’ usual prescription of competition and consumer choice, even though they have largely failed to justify this reluctance by showing that health economics involves special features such as public goods, externalities, adverse selection, poor consumer information, or unusually severe consequences. Similarly, while some philosophers argue for (...) bioethical conclusions based on very general ethical intuitions,1 many others rely on moral intuitions that are specific to health and medicine to draw conclusions that are meant to apply mainly in health and medicine. For example, many authors appear to start from the strong moral intuition that it typically seems wrong to deny poor people access to health care, and then seek moral principles that can both account for such intuitions and justify the claim that people have some sort of right to health care.2 In metaethics, opinions on moral intuitions range from an extreme intuitionism, which accepts all case-specific moral intuitions at face value as reliable moral guides, to an extreme foundationalism, which rejects such intuitions as evidence regarding correct general moral principles. Between these extremes, opinions vary on how severe the errors in our moral intuitions are. The practice of bioethics seems to favor the extreme intuitionist end of this spectrum, and thus implicitly expects mild errors.3 In contrast, this essay will suggest that common practice in bioethics has seriously underestimated the errors in our moral intuitions. In this essay, I consider the evolutionary origin of our moral intuitions, but avoid the extreme positions of moral skepticism and “whatever evolved must be good,” both of which are commonly associated with evolution-. (shrink)

It is well known that Bayes’ theorem (with likelihood ratios) can be used to calculate the impact of evidence, such as a ‘match’ of some feature of a person. Typically the feature of interest is the DNA profile, but the method applies in principle to any feature of a person or object, including not just DNA, fingerprints, or footprints, but also more basic features such as skin colour, height, hair colour or even name. Notwithstanding concerns about the extensiveness of databases (...) of such features, a serious challenge to the use of Bayes in such legal contexts is that its standard formulaic representations are not readily understandable to non-statisticians. Attempts to get round this problem usually involve representations based around some variation of an event tree. While this approach works well in explaining the most trivial instance of Bayes’ theorem (involving a single hypothesis and a single piece of evidence) it does not scale up to realistic situations. In particular, even with a single piece of match evidence, if we wish to incorporate the possibility that there are potential errors (both false positives and false negatives) introduced at any stage in the investigative process, matters become very complex. As a result we have observed expert witnesses (in different areas of speciality) routinely ignore the possibility of errors when presenting their evidence. To counter this, we produce what we believe is the first full probabilistic solution of the simple case of generic match evidence incorporating both classes of testing errors. Unfortunately, the resultant event tree solution is too complex for intuitive comprehension. And, crucially, the event tree also fails to represent the causal information that underpins the argument. In contrast, we also present a simple-to-construct graphical Bayesian Network (BN) solution that automatically performs the calculations and may also be intuitively simpler to understand. Although there have been multiple previous applications of BNs for analysing forensic evidence—including very detailed models for the DNA matching problem, these models have not widely penetrated the expert witness community. Nor have they addressed the basic generic match problem incorporating the two types of testing error. Hence we believe our basic BN solution provides an important mechanism for convincing experts—and eventually the legal community—that it is possible to rigorously analyse and communicate the full impact of match evidence on a case, in the presence of possible errors. (shrink)

Students systematically and deliberately apply rule‐based but erroneous algorithms to solving unfamiliar arithmetic problems. These algorithms result in erroneous solutions termed rational errors. Computationally, students' erroneous algorithms can be represented by perturbations or bugs in otherwise correct arithmetic algorithms (Brown & VanLehn, 1980; Langley & Ohilson, 1984; VanLehn, 1983, 1986, 1990; Young S O'Sheo, 1981). Bugs are useful for describing how rational errors occur but bugs are not sufficient for explaining their origin. A possible explanation for this is (...) that rational errors are the result of incorrect induction from examples. This prediction is termed the “induction hypothesis” (VanLehn, 1986). The purpose of the present study was to: (a) expand on post formulations of the induction hypothesis, and (b) use a new methodology to test the induction hypothesis more carefully than has been done previously. The first step involved teaching participants a new number system called NewAbacus, a written modification of the abacus system. The second step consisted of dividing them into different groups, where each individual received an example of only one port of the NewAbacus addition algorithm. During the third and final step, participants were instructed to solve both familiar and unfamiliar types of addition problems in NewAbacus. The induction hypothesis was supported by using both empirical and computational investigations. (shrink)