This book develops an Aristotelian account of the virtue of practical intelligence or "phronesis"--an excellence of deliberating and making choices--which ...

This paper looks at philosophical questions that arise in the context of AI alignment. It defends three propositions. First, normative and technical aspects of the AI alignment problem are interrelated, creating space for productive engagement between people working in both domains. Second, it is important to be clear about the goal of alignment. There are significant differences between AI that aligns with instructions, intentions, revealed preferences, ideal preferences, interests and values. A principle-based approach to AI alignment, which combines these elements (...) in a systematic way, has considerable advantages in this context. Third, the central challenge for theorists is not to identify ‘true’ moral principles for AI; rather, it is to identify fair principles for alignment that receive reflective endorsement despite widespread variation in people’s moral beliefs. The final part of the paper explores three ways in which fair principles for AI alignment could potentially be identified. (shrink)

This paper discusses the problem of responsibility attribution raised by the use of artificial intelligence technologies. It is assumed that only humans can be responsible agents; yet this alone already raises many issues, which are discussed starting from two Aristotelian conditions for responsibility. Next to the well-known problem of many hands, the issue of “many things” is identified and the temporal dimension is emphasized when it comes to the control condition. Special attention is given to the epistemic condition, which (...) draws attention to the issues of transparency and explainability. In contrast to standard discussions, however, it is then argued that this knowledge problem regarding agents of responsibility is linked to the other side of the responsibility relation: the addressees or “patients” of responsibility, who may demand reasons for actions and decisions made by using AI. Inspired by a relational approach, responsibility as answerability thus offers an important additional, if not primary, justification for explainability based, not on agency, but on patiency. (shrink)

Advanced AI systems are rapidly making their way into medical research and practice, and, arguably, it is only a matter of time before they will surpass human practitioners in terms of accuracy, reliability, and knowledge. If this is true, practitioners will have a prima facie epistemic and professional obligation to align their medical verdicts with those of advanced AI systems. However, in light of their complexity, these AI systems will often function as black boxes: the details of their contents, calculations, (...) and procedures cannot be meaningfully understood by human practitioners. When AI systems reach this level of complexity, we can also speak of black-box medicine. In this paper, we want to argue that black-box medicine conflicts with core ideals of patient-centered medicine. In particular, we claim, black-box medicine is not conducive for supporting informed decision-making based on shared information, shared deliberation, and shared mind between practitioner and patient. (shrink)

The concept of artificial intelligence is not new nor is the notion that it should be granted legal protections given its influence on human activity. What is new, on a relative scale, is the notion that artificial intelligence can possess citizenship—a concept reserved only for humans, as it presupposes the idea of possessing civil duties and protections. Where there are several decades’ worth of writing on the concept of the legal status of computational artificial artefacts in the USA (...) and elsewhere, it is surprising that law makers internationally have come to a standstill to protect our silicon brainchildren. In this essay, it will be assumed that future artificial entities, such as Sophia the Robot, will be granted citizenship on an international scale. With this assumption, an analysis of rights will be made with respect to the needs of a non-biological intelligence possessing legal and civic duties akin to those possessed by humanity today. This essay does not present a full set of rights for artificial intelligence—instead, it aims to provide international jurisprudence evidence aliunde ab extra de lege lata for any future measures made to protect non-biological intelligence. (shrink)

Organizations such as the EU High-Level Expert Group on AI and the IEEE have recently formulated ethical principles and (moral) values that should be adhered to in the design and deployment of artificial intelligence (AI). These include respect for autonomy, non-maleficence, fairness, transparency, explainability, and accountability. But how can we ensure and verify that an AI system actually respects these values? To help answer this question, I propose an account for determining when an AI system can be said to (...) embody certain values. This account understands embodied values as the result of design activities intended to embed those values in such systems. AI systems are here understood as a special kind of sociotechnical system that, like traditional sociotechnical systems, are composed of technical artifacts, human agents, and institutions but—in addition—contain artificial agents and certain technical norms that regulate interactions between artificial agents and other elements of the system. The specific challenges and opportunities of embedding values in AI systems are discussed, and some lessons for better embedding values in AI systems are drawn. (shrink)

Some recent developments in Artificial Intelligence—especially the use of machine learning systems, trained on big data sets and deployed in socially significant and ethically weighty contexts—have led to a number of calls for “transparency”. This paper explores the epistemological and ethical dimensions of that concept, as well as surveying and taxonomising the variety of ways in which it has been invoked in recent discussions. Whilst “outward” forms of transparency may be straightforwardly achieved, what I call “functional” transparency about the (...) inner workings of a system is, in many cases, much harder to attain. In those situations, I argue that contestability may be a possible, acceptable, and useful alternative so that even if we cannot understand how a system came up with a particular output, we at least have the means to challenge it. (shrink)

Purpose The purpose of this paper is clearly illustrate this convergence and the prescriptive recommendations that such documents entail. There is a significant amount of research into the ethical consequences of artificial intelligence. This is reflected by many outputs across academia, policy and the media. Many of these outputs aim to provide guidance to particular stakeholder groups. It has recently been shown that there is a large degree of convergence in terms of the principles upon which these guidance documents (...) are based. Despite this convergence, it is not always clear how these principles are to be translated into practice. Design/methodology/approach In this paper, the authors move beyond the high-level ethical principles that are common across the AI ethics guidance literature and provide a description of the normative content that is covered by these principles. The outcome is a comprehensive compilation of normative requirements arising from existing guidance documents. This is not only required for a deeper theoretical understanding of AI ethics discussions but also for the creation of practical and implementable guidance for developers and users of AI. Findings In this paper, the authors therefore provide a detailed explanation of the normative implications of existing AI ethics guidelines but directed towards developers and organisational users of AI. The authors believe that the paper provides the most comprehensive account of ethical requirements in AI currently available, which is of interest not only to the research and policy communities engaged in the topic but also to the user communities that require guidance when developing or deploying AI systems. Originality/value The authors believe that they have managed to compile the most comprehensive document collecting existing guidance which can guide practical action but will hopefully also support the consolidation of the guidelines landscape. The authors’ findings should also be of academic interest and inspire philosophical research on the consistency and justification of the various normative statements that can be found in the literature. (shrink)

This paper focuses on the use of ‘black box’ AI in medicine and asks whether the physician needs to disclose to patients that even the best AI comes with the risks of cyberattacks, systematic bias, and a particular type of mismatch between AI’s implicit assumptions and an individual patient’s background situation.Pacecurrent clinical practice, I argue that, under certain circumstances, these risks do need to be disclosed. Otherwise, the physician either vitiates a patient’s informed consent or violates a more general obligation (...) to warn him about potentially harmful consequences. To support this view, I argue, first, that the already widely accepted conditions in the evaluation of risks, i.e. the ‘nature’ and ‘likelihood’ of risks, speak in favour of disclosure and, second, that principled objections against the disclosure of these risks do not withstand scrutiny. Moreover, I also explain that these risks are exacerbated by pandemics like the COVID-19 crisis, which further emphasises their significance. (shrink)

The presence of general intelligence poses a major evolutionary puzzle, which has led to increased interest in its presence in nonhuman animals. The aim of this review is to critically evaluate this question and to explore the implications for current theories about the evolution of cognition. We first review domain-general and domain-specific accounts of human cognition in order to situate attempts to identify general intelligence in nonhuman animals. Recent studies are consistent with the presence of general intelligence (...) in mammals (rodents and primates). However, the interpretation of a psychometricgfactor as general intelligence needs to be validated, in particular in primates, and we propose a range of such tests. We then evaluate the implications of general intelligence in nonhuman animals for current theories about its evolution and find support for the cultural intelligence approach, which stresses the critical importance of social inputs during the ontogenetic construction of survival-relevant skills. The presence of general intelligence in nonhumans implies that modular abilities can arise in two ways, primarily through automatic development with fixed content and secondarily through learning and automatization with more variable content. The currently best-supported model, for humans and nonhuman vertebrates alike, thus construes the mind as a mix of skills based on primary and secondary modules. The relative importance of these two components is expected to vary widely among species, and we formulate tests to quantify their strength. (shrink)

This study examined 4-year-olds’ problem-solving under different social conditions. Children had to use water in order to extract a buoyant object from a narrow tube. When faced with the problem ‘cold’ without cues, nearly all children were unsuccessful. But when a solution-suggesting video was pedagogically delivered prior to the task, most children succeeded. Showing children the same video in a non-pedagogical manner did not lift their performance above baseline and was less effective than framing it pedagogically. The findings support ideas (...) central to natural pedagogy, 148–153, 2009). They also challenge the Cultural Intelligence hypothesis, according to which only humans’ social, but not their physical, cognition differs qualitatively from that of great apes. A more radical, transformative variant of the Cultural Intelligence hypothesis is suggested according to which humans’ physical cognition is shaped by their social nature and must therefore be recognized as equally distinctive as their social cognition. (shrink)

This study examines factors impacting ethical behavior of 103 hospital nurses. The level of emotional intelligence and ethical behavior of peers had a significant impact on ethical behavior of nurses. Independence climate had a significant impact on ethical behavior of nurses. Other ethical climate types such as professional, caring, rules, instrumental, and efficiency did not impact ethical behavior of respondents. Implications of this study for researchers and practitioners are discussed.

What kind of activity are non-human animals capable of? A venerable tradition insists that lack of language confines them to ‘mere behaviour’. This article engages with this ‘lingualism’ by developing a positive, bottom-up case for the possibility of animal agency. Higher animals cannot just act, they can act intelligently, rationally, intentionally and for reasons. In developing this case I draw on the interplay of behaviour, cognition and conation, the unduly neglected notion of intelligence and its connection to rationality, the (...) need to recognize that reasons are objective conditions, and the difference between the ability to act for reasons and the capacity to reflect on reasons. (shrink)

Artificial Neural Networks have reached “grandmaster” and even “super-human” performance across a variety of games, from those involving perfect information, such as Go, to those involving imperfect information, such as “Starcraft”. Such technological developments from artificial intelligence (AI) labs have ushered concomitant applications across the world of business, where an “AI” brand-tag is quickly becoming ubiquitous. A corollary of such widespread commercial deployment is that when AI gets things wrong—an autonomous vehicle crashes, a chatbot exhibits “racist” behavior, automated credit-scoring (...) processes “discriminate” on gender, etc.—there are often significant financial, legal, and brand consequences, and the incident becomes major news. As Judea Pearl sees it, the underlying reason for such mistakes is that “... all the impressive achievements of deep learning amount to just curve fitting.” The key, as Pearl suggests, is to replace “reasoning by association” with “causal reasoning” —the ability to infer causes from observed phenomena. It is a point that was echoed by Gary Marcus and Ernest Davis in a recent piece for theNew York Times: “we need to stop building computer systems that merely get better and better at detecting statistical patterns in data sets—often using an approach known as ‘Deep Learning’—and start building computer systems that from the moment of their assembly innately grasp three basic concepts: time, space, and causality.” In this paper, foregrounding what in 1949 Gilbert Ryle termed “a category mistake”, I will offer an alternative explanation for AI errors; it is not so much that AI machinery cannot “grasp” causality, but that AI machinery (qua computation) cannot understand anything at all. (shrink)

Why are computers so smart these days? And why are humans apparently still a bit smarter? Does this have something to do with the difference between data and meaning? Does this in turn mean that at least some abstract entities, such as numbers, exist independently of human thought? Wouldn’t that require an expansion of our scientific world view? And would that at all be compatible with what we know about our world from physics and chemistry, philosophy, psychology, neuroscience and the (...) theory of evolution? Finally, what would this tell us about ethical and aesthetic value theories? These and related questions will be discussed in this book. We will find that the difference between data and meaning, i.e. quantitative and qualitative information, does indeed appear to be of central importance for understanding both artificial and natural intelligence. And then the independent existence of abstract entities not only appears to be a particularly promising hypothesis, but also one that is entirely compatible with the sum of our scientific knowledge, especially with regard to value theories. The book thus arrives at the exploration of a scientifically tenable, panpsychistically inspired, objective idealism that can be derived from our most fundamental intuitions as subjects that perceive qualities, but that can also take into account the structuring of the world already at the micro-scale, found in the modern natural sciences. The result is a Platonic, but in a second step also a scientific realism and a naturalism in the sense that it is informed by the natural sciences in terms of an inductive metaphysics. An objective idealism, not in a rationalistic maximum form, but in a pragmatic minimum form; without eternal truths, but dependent on the continued philosophical-scientific and also philosophicalsocial dialog. The proposed model could offer interesting solutions to a number of problems at and near the mind/matter boundary: Proposals are being considered for the interpretation of quantum mechanics, the problem of molecular symmetry, the neuronal code and the binding problem in neuroscience, mental causation, a more holistic understanding of mental processes, and so on and so forth. However, the extent to which the model threatens to promise far too much is also being discussed. In sum, the core question is how we can imagine human thinking beyond physically conceived information processing. An alternative model of human thinking is then put up for discussion, for which not only machine-like cognitive performance, but above all the intentional perception of qualitative information, i.e. of abstract entities, would be central, as well as the free, ultimately creative linking of patterns of quantitative information (signals, data) with such qualities (meanings). (shrink)

The most famous challenge to computational cognitive science and artificial intelligence is the philosopher John Searle's "Chinese Room" argument.

The moral enhancement of human beings is a constant theme in the history of humanity. Today, faced with the threats of a new, globalised world, concern over this matter is more pressing. For this reason, the use of biotechnology to make human beings more moral has been considered. However, this approach is dangerous and very controversial. The purpose of this article is to argue that the use of another new technology, AI, would be preferable to achieve this goal. Whilst several (...) proposals have been made on how to use AI for moral enhancement, we present an alternative that we argue to be superior to other proposals that have been developed. (shrink)

J P White; Intelligence and the Logic of the Nature-Nurture Issue, Journal of Philosophy of Education, Volume 8, Issue 1, 30 May 2006, Pages 30–51, https://doi.

In this series of lectures, a distinguished group of international contributors from a variety of disciplines debate the current position of the recent achievements in engineering and computer science. (Technology & Industrial).

Linguistics. Artificial intelligence. Related fields. Computation.

I ventured to think during the war. I wrote several blogs on the topic "War and Intelligence". Everything seems out of place here. War requires action (participation, protection, assistance), and thought wants concentration in peace. I am not engaged in scientific research, I just think about what I can, what I have, and what I carry with me, without referring to sources and reference books. I'm thinking about this very: how does the event of the war relate to the (...) ability — and maybe even the duty — to think. I wrote two posts. They consider intelligence (intellectus) as a kind of responsibility. All knowledge, awareness, concepts, sciences, discursive practices, etc. — are secondary. They make sense in the primary context of responsibility. All intellectual pursuits have meaning and meaning as aspects of the answer to the question that constitutes a human being as human. This is being-in-question. (shrink)

Emotional intelligence (EI) stands at the nexus between intelligence and emotion disciplines, and we outline how EI research might be better integrated within both theoretical frameworks. From the former discipline, empirical research focused upon whether EI is an intelligence and what type of intelligence it constitutes. It is clear that ability-based tests of EI form a group factor of cognitive abilities that may be integrated into the Cattell–Horn–Carroll framework; less clear is the lower order factor structure (...) of EI. From the latter discipline, research linking EI with theoretical frameworks from emotion research remain relatively sparse. Emotion regulation and appraisal theory may be key to explain how EI may reflect different processes. We propose a research agenda to advance the EI study. (shrink)

Why are some people more mentally able than others? In an authoritative, critical and intergrated series of review essays Professor Ian Deary inquires after the cognitive and biological foundations of human mental ability differences. Many accounts of intelligence have examined the structure and number of human mental ability differences and whether they can predict sucess in education,work and social life. Few books have taken psychometric intelligence differences as a starting point and brought together the reductionistic attempts to explain (...) them.New to the highly acclaimed Oxford Psychology Series, Looking Down on Human Intelligence appraises the search for the origins of psychometric intelligence differences in terms of brain function parameters. The book provides an original and thought provoking guide to ancient and modern research on one of the most compelling questions in human psychology. (shrink)

This chapter presents an overview of the issues that arise when logic is used in helping to understand problems in intelligent reasoning and to guide the design of mechanized reasoning systems. It provides some historical and technical details concerning nonmonotonic logic and reasoning about action and change, a topic that is not only central in artificial intelligence but that is normally of considerable interest to philosophers. The remaining sections provide brief sketches of selected topics, with references to the primary (...) literature. (shrink)

In Study 1, we test a theoretical model involving temptation, monetary intelligence (MI), a mediator, and unethical intentions and investigate the direct and indirect paths simultaneously based on multiple-wave panel data collected in open classrooms from 492 American and 256 Chinese students. For the whole sample, temptation is related to low unethical intentions indirectly. Multi-group analyses reveal that temptation predicts unethical intentions both indirectly and directly for male American students only; but not for female American students. For Chinese students, (...) both paths are non-significant. Love of money contributes significantly to MI for all students. In Study 2, using money as a temptation and giving them opportunities to cheat on a matrix task, most Chinese students (78.4 %) do not cheat in open classrooms; supporting survey and structural equation modeling (SEM) results in Study 1. However, students in private cubicles cheat significantly more (53.4 %) than those in open classrooms (21.6 %). Finally, students’ love of money attitude predicts cheating. Factor rich predicts the cheating amount, whereas factor motivator predicts the cheating percentage. Our results shed new light on the impact of temptation and love of money as dispositional traits, money as a temptation, and environmental context (public vs. private) on unethical intentions and cheating behaviors. (shrink)

This multilevel meta-analysis examines whether emotional intelligence can be enhanced through training and identifies training effects’ determinants. We identified 24 studies containing 28 samples aiming at increasing individual-level EI among healthy adults. The results revealed a significant moderate standardized mean change between pre- and post-measurement for the main effect of EI training, and a stable pre- to follow-up effect. Additionally, the type of EI model, dimensions of the four branch model, length, and type of publication turned out to be (...) significant moderators. The results suggest that EI trainings should be considered effective interventions. (shrink)

The present paper aims at contributing to the ethical debate on the impacts of artificial intelligence (AI) systems on human autonomy. More specifically, it intends to offer a clearer understanding of the design challenges to the effort of aligning driving automation technologies to this ethical value. After introducing the discussion on the ambiguous impacts that AI systems exert on human autonomy, the analysis zooms in on how the problem has been discussed in the literature on connected and automated vehicles (...) (CAVs). On this basis, it is claimed that the issue has been mainly tackled on a fairly general level, and mostly with reference to the controversial issue of crash-optimization algorithms, so that only limited design insights have been drawn. However, integrating ethical analysis and design practices is critical to pursue the implementation of such an important ethical value into CAV technologies. To this aim, it is argued, a more applied approach targeted at examining the impacts on human autonomy of current CAV functions should also be explored. As an example of the intricacy of this task, the case of automated route planning is discussed in some detail. (shrink)

Even though tools for identifying and analyzing arguments are now in wide use in the field of argumentation studies, so far there is a paucity of resources for evaluating real arguments, aside from using deductive logic or Bayesian rules that apply to inductive arguments. In this paper it is shown that recent developments in artificial intelligence in the area of computational systems for modeling defeasible argumentation reveal a different approach that is currently making interesting progress. It is shown how (...) these systems provide the general outlines for a system of argument evaluation that can be applied to legal arguments as well as everyday conversational arguments to assist a user to evaluate an argument. (shrink)

Artificial intelligence (AI) is a digital technology that will be of major importance for the development of humanity in the near future. AI has raised fundamental questions about what we should do with such systems, what the systems themselves should do, what risks they involve and how we can control these. - After the background to the field (1), this article introduces the main debates (2), first on ethical issues that arise with AI systems as objects, i.e. tools made (...) and used by humans; here, the main sections are privacy (2.1), manipulation (2.2), opacity (2.3), bias (2.4), autonomy & responsibility (2.6) and the singularity (2.7). Then we look at AI systems as subjects, i.e. when ethics is for the AI systems themselves in machine ethics (2.8.) and artificial moral agency (2.9). Finally we look at future developments and the concept of AI (3). For each section within these themes, we provide a general explanation of the ethical issues, we outline existing positions and arguments, then we analyse how this plays out with current technologies and finally what policy conse-quences may be drawn. (shrink)

Despite there being little consensus on what intelligence is or how to measure it, the media and the public have become increasingly preoccupied with the concept owing to recent accomplishments in machine learning and research on artificial intelligence (AI). Governments and corporations are investing billions of dollars to fund researchers who are keen to produce an ever‐expanding range of artificial intelligent systems. More than 30 countries have announced such research initiatives over the past 3 years 1. For example, (...) the EU Commission pledged to increase the investment in AI research to €1.5 billion by 2020 (from €500 million in 2017), while China has committed $2.1 billion towards an AI technology park in Beijing alone 1. This global investment in AI is astonishing and prompts several questions: What are the true possibilities and limitations of AI? What do AI researchers and developers mean by “intelligence”? How does this compare to the everyday concept of intelligence and how the term is other branches of cognitive science? And can machine learning produce anything that is truly “intelligent”? (shrink)

The current paradigm of Artificial Intelligence emerged as the result of a series of cultural innovations, some technical and some social. Among them are apparently small design decisions, that led to a subtle reframing of the field’s original goals, and are by now accepted as standard. They correspond to technical shortcuts, aimed at bypassing problems that were otherwise too complicated or too expensive to solve, while still delivering a viable version of AI. Far from being a series of separate (...) problems, recent cases of unexpected effects of AI are the consequences of those very choices that enabled the field to succeed, and this is why it will be difficult to solve them. In this chapter we review three of these choices, investigating their connection to some of today’s challenges in AI, including those relative to bias, value alignment, privacy and explainability. We introduce the notion of “ethical debt” to describe the necessity to undertake expensive rework in the future in order to address ethical problems created by a technical system. (shrink)

We define a notion of the intelligence level of an idealized mechanical knowing agent. This is motivated by efforts within artificial intelligence research to define real-number intelligence levels of compli- cated intelligent systems. Our agents are more idealized, which allows us to define a much simpler measure of intelligence level for them. In short, we define the intelligence level of a mechanical knowing agent to be the supremum of the computable ordinals that have codes the (...) agent knows to be codes of computable ordinals. We prove that if one agent knows certain things about another agent, then the former necessarily has a higher intelligence level than the latter. This allows our intelligence no- tion to serve as a stepping stone to obtain results which, by themselves, are not stated in terms of our intelligence notion (results of potential in- terest even to readers totally skeptical that our notion correctly captures intelligence). As an application, we argue that these results comprise evidence against the possibility of intelligence explosion (that is, the no- tion that sufficiently intelligent machines will eventually be capable of designing even more intelligent machines, which can then design even more intelligent machines, and so on). (shrink)

Several authors have made the argument that because blind evolutionary processes produced human intelligence on Earth, it should be feasible for clever human engineers to create human-level artificial intelligence in the not-too-distant future. This evolutionary argument, however, has ignored the observation selection effect that guarantees that observers will see intelligent life having arisen on their planet no matter how hard it is for intelligent life to evolve on any given Earth-like planet. We explore how the evolutionary argument might (...) be salvaged from this objection, using a variety of considerations from observation selection theory and analysis of specific timing features and instances of convergent evolution in the terrestrial evolutionary record. We find that, depending on the resolution of disputed questions in observation selection theory, the objection can either be wholly or moderately defused, although other challenges for the evolutionary argument remain. (shrink)

According to the ‘mating intelligence’ theory by evolutionary psychologist Geoffrey Miller, human morality is a system of sexually selected traits which serve as costly signals to the other sex about one’s fitness and readiness to take care for possible offspring. Starting from the standard prediction of evolutionary psychology that sexual selection produces psychological sex differences in human mating strategies, ‘mating intelligence’ theory is analyzed for its compatibility with several psychological theories about sex differences in moral traits like moral (...) reasoning, judgment and orientation. It is argued that the ‘mating intelligence’ theory, as a theory about the the evolution of morality, comes too dangerously close to being unfalsifiable because it embodies some auxiliary hypotheses and vague definitions which make it practically immune to every possible empirical finding concerning sex differences in human moral traits. (shrink)

Overall mean performance on intelligence tests by brain-damaged patients with focal lesions can be misleading in regard to localization of intelligence. The widely used WAIS has many subtests that together recruit spatially distant neural but individually the subtests reveal localized functions. Moreover, there are kinds of intelligence that defy the localizationist approach inferred from brain damage.

Descartes’s uncompromising rejection of the possibility of animal intelligence was among his most controversial theses. That rejection is based on (1) his commitment to the doctrine of animal automatism and (2) two tests that he takes to be sufficient indicators of thought (the action and language tests). Of these two tests, only the language test is truly definitive, and Descartes is firmly of the view that no animal could demonstrate the capacity to use signs to convey meaning in “all (...) the circumstances of life.” The topic is fascinating for forcing us to ponder what exactly reason is for Descartes and the role it plays in everyday life. This article explores the tensions in Descartes’s arguments produced by an over reliance on the analogy between animals and clocks, including the question of what to make of Descartes’s recognition of the need to posit representational and information-processing subsystems in the brain. (shrink)