In this paper I analyse different justifications for the claim that the minor premise of the libertarian argument is true, namely, intuition, van Inwagen’s argument from moral responsibility and an argument from rationality. I claim none of these is satisfactory. I conclude by suggesting a possible way of interpreting the meaning of the free will intuition libertarians claim we have.

Roughly speaking, computationalism says that cognition is computation, or that cognitive phenomena are explained by the agent‘s computations. The cognitive processes and behavior of agents are the explanandum. The computations performed by the agents‘ cognitive systems are the proposed explanans. Since the cognitive systems of biological organisms are their nervous 1 systems (plus or minus a bit), we may say that according to computationalism, the cognitive processes and behavior of organisms are explained by neural computations. Some people might prefer to (...) say that cognitive systems are ―realized‖ by nervous systems, and thus that—according to computationalism—cognitive computations are ―realized‖ by neural processes. In this paper, nothing hinges on the nature of the relation between cognitive systems and nervous systems, or between computations and neural processes. For present purposes, if a neural process realizes a computation, then that neural process is a computation. Thus, I will couch much of my discussion in terms of nervous systems and neural computation.1 Before proceeding, we should dispense with a possible red herring. Contrary to a common assumption, computationalism does not stand in opposition to connectionism. Connectionism, in the most general and common sense of the term, is the claim that cognitive phenomena are explained (at some level and at least in part) by the processes of neural networks. This is a truism, supported by most neuroscientific evidence. Everybody ought to be a connectionist in this general sense. The relevant question is, are neural processes computations? More precisely, are the neural processes to be found in the nervous systems of organisms computations? Computationalists say ―yes‖, anti-computationalists say ―no‖. This paper investigates whether any of the arguments on offer against computationalism have a chance at knocking it off.2 Ever since Warren McCulloch and Walter Pitts (1943) first proposed it, computationalism has been subjected to a wide range of objections.. (shrink)

Defending or attacking either functionalism or computationalism requires clarity on what they amount to and what evidence counts for or against them. My goal here is not to evaluate their plausibility. My goal is to formulate them and their relationship clearly enough that we can determine which type of evidence is relevant to them. I aim to dispel some sources of confusion that surround functionalism and computationalism, recruit recent philosophical work on mechanisms and computation to shed light on them, and (...) clarify how functionalism and computationalism may or may not legitimately come together. (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)

Computationalism has been the mainstream view of cognition for decades. There are periodic reports of its demise, but they are greatly exaggerated. This essay surveys some recent literature on computationalism. It concludes that computationalism is a family of theories about the mechanisms of cognition. The main relevant evidence for testing it comes from neuroscience, though psychology and AI are relevant too. Computationalism comes in many versions, which continue to guide competing research programs in philosophy of mind as well as psychology (...) and neuroscience. Although our understanding of computationalism has deepened in recent years, much work in this area remains to be done. (shrink)

According to some philosophers, computational explanation is proprietary
to psychology—it does not belong in neuroscience. But neuroscientists routinely offer computational explanations of cognitive phenomena. In fact, computational explanation was initially imported from computability theory into the science of mind by neuroscientists, who justified this move on neurophysiological grounds. Establishing the legitimacy and importance of computational explanation in neuroscience is one thing; shedding light on it is another. I raise some philosophical questions pertaining to computational explanation and outline some promising answers that (...) are being developed by a number of authors. (shrink)

Each of our theories of mental representation provides some insight into how the mind works. However, these insights often seem incompatible, as the debates between symbolic, dynamical, emergentist, sub-symbolic, and grounded approaches to cognition attest. Mental representations—whatever they are—must share many features with each of our theories of representation, and yet there are few hypotheses about how a synthesis could be possible. Here, I develop a theory of the underpinnings of symbolic cognition that shows how sub-symbolic dynamics may give rise (...) to higher-level cognitive representations of structures, systems of knowledge, and algorithmic processes. This theory implements a version of conceptual role semantics by positing an internal universal representation language in which learners may create mental models to capture dynamics they observe in the world. The theory formalizes one account of how truly novel conceptual content may arise, allowing us to explain how even elementary logical and computational operations may be learned from a more primitive basis. I provide an implementation that learns to represent a variety of structures, including logic, number, kinship trees, regular languages, context-free languages, domains of theories like magnetism, dominance hierarchies, list structures, quantification, and computational primitives like repetition, reversal, and recursion. This account is based on simple discrete dynamical processes that could be implemented in a variety of different physical or biological systems. In particular, I describe how the required dynamics can be directly implemented in a connectionist framework. The resulting theory provides an “assembly language” for cognition, where high-level theories of symbolic computation can be implemented in simple dynamics that themselves could be encoded in biologically plausible systems. (shrink)

Humans and other animals are able not only to coordinate their actions with their current sensorimotor state, but also to imagine, plan and act in view of the future, and to realize distal goals. In this paper we discuss whether or not their future-oriented conducts imply (future-oriented) representations. We illustrate the role played by anticipatory mechanisms in natural and artificial agents, and we propose a notion of representation that is grounded in the agent’s predictive capabilities. Therefore, we argue that the (...) ability that characterizes and defines a true cognitive mind, as opposed to a merely adaptive system, is that of building representations of the non-existent, of what is not currently (yet) true or perceivable, of what is desired. A real mental activity begins when the organism is able to endogenously (i.e. not as the consequence of current perceptual stimuli) produce an internal representation of the world in order to select and guide its conduct goal-directed: the mind serves to coordinate with the future. (shrink)

We propose a view of embodied representations that is alternative to both symbolic/linguistic approaches and purely sensorimotor views of cognition, and can account for procedural and declarative knowledge manipulation. In accordance with recent evidence in cognitive neuroscience and psychology, we argue that anticipatory and simulative mechanisms, which arose during evolution for action control and not for cognition, determined the first form of representational content and were exapted for increasingly sophisticated cognitive uses. In particular, procedural and declarative forms of knowledge can (...) be explained, respectively, in terms of on-line sensorimotor anticipation and off-line simulations of potential actions, which can give access to tacit knowledge and make it explicit. That is, mechanisms that evolved for the on-line prediction of the consequences of one's own actions (i.e. forward models) determine a (procedural) form of representation, and became exapted for off-line use. They can therefore be used to produce (declarative) knowledge of the world, by running a simulation of the action that would produce the relevant information. We conclude by discussing how embodied representations afford a form of internal manipulation that can be described as internalized situated action. (shrink)

Starting in the 1950s, computer programs for simulating cognitive processes and intelligent behaviour were the hallmark of Good Old-Fashioned Artificial Intelligence and ‘cognitivist’ cognitive science. This article examines a somewhat neglected case of simulation pursued by one of the founding fathers of simulation methodology, Herbert A. Simon. In the 1970s and 1980s, Simon had repeated contacts with Marxist countries and scientists, in the context of which he advanced the idea that cognitivism could be used as a framework for simulating dialectical (...) materialism. Simon's idea was, in particular, to represent dialectical processes through a ‘symbolic’ version of dialectical logic. This article explores the context of Simon's interaction with Marxist countries—China and the USSR—and also assesses the outcome of the simulation. The difficulty with simulating distinctive features of dialectical materialism is read in light of the underlying assumptions of cognitivism and, ultimately, in light of the attempt to tame a rival world view. (shrink)

A computational modeling approach was used to test one possible explanation for the limited capacity of the subitizing phenomenon. Most existing models of this phenomenon associate the subitizing span with an assumed structural limitation of the human information processing system. In contrast, we show how this limit might emerge as the combinatorics of the space of enumeration problems interacts with the human cognitive architecture in the context of an enumeration task. Subitizing‐like behavior was generated in two different models of enumeration, (...) one based on the ACT‐R cognitive architecture and the other based on the principles of parallel distributed processing (PDP). Our results provide good qualitative fits to results obtained in a variety of empirical studies. (shrink)

This paper considers the impact of the AI R&D programme on human society and the individual human being on the assumption that a full realisation of the engineering objective of AI, namely, construction of human-level, domain-independent intelligent entities, is possible. Our assumption is essentially identical tothe maximum progress scenario of the Office of Technology Assessment, US Congress.Specifically, the first section introduces some of the significant issues on the relational nexus among work, education and the human-machine boundary. In particular, based on (...) a Russellian conception of rationality I briefly argue that we need to change our related conceptions of work, employment and free time, through a new human-centred education. On the human-machine boundary problem, I make a couple of tentative suggestions and put forward some crucial open questions.Section two discusses the impact of the emerging machine intelligence on human nature both as modification of its self-image, keeping human nature itself unchanged, and its potential for altering human nature itself. I briefly argue that: (i) in a certain context, the question of the supremacy or uniqueness of human intelligence loses much, if not all, of its ‘weight’; and (ii) appearance of Robot-X species would immortalise the human spirit. (shrink)

In this paper we review some problems with traditional approaches for acquiring and representing knowledge in the context of developing user interfaces. Methodological implications for knowledge engineering and for human-computer interaction are studied. It turns out that in order to achieve the goal of developing human-oriented (in contrast to technology-oriented) human-computer interfaces developers have to develop sound knowledge of the structure and the representational dynamics of the cognitive system which is interacting with the computer.We show that in a first step (...) it is necessary to study and investigate the different levels and forms of representation that are involved in the interaction processes between computers and human cognitive systems. Only if designers have achieved some understanding about these representational mechanisms, user interfaces enabling individual experiences and skill development can be designed. In this paper we review mechanisms and processes for knowledge representation on a conceptual, epistemological, and methodologieal level, and sketch some ways out of the identified dilemmas for cognitive modeling in the domain of human-computer interaction. (shrink)

In our target article, we argued that there is a profound functional discontinuity between the cognitive abilities of modern humans and those of all other extant species. Unsurprisingly, our hypothesis elicited a wide range of responses from commentators. After responding to the commentaries, we conclude that our hypothesis lies closer to Darwin's views on the matter than to those of many of our contemporaries.

Over the last quarter century, the dominant tendency in comparative cognitive psychology has been to emphasize the similarities between human and nonhuman minds and to downplay the differences as (Darwin 1871). In the present target article, we argue that Darwin was mistaken: the profound biological continuity between human and nonhuman animals masks an equally profound discontinuity between human and nonhuman minds. To wit, there is a significant discontinuity in the degree to which human and nonhuman animals are able to approximate (...) the higher-order, systematic, relational capabilities of a physical symbol system (PSS) (Newell 1980). We show that this symbolic-relational discontinuity pervades nearly every domain of cognition and runs much deeper than even the spectacular scaffolding provided by language or culture alone can explain. We propose a representational-level specification as to where human and nonhuman animals' abilities to approximate a PSS are similar and where they differ. We conclude by suggesting that recent symbolic-connectionist models of cognition shed new light on the mechanisms that underlie the gap between human and nonhuman minds. (shrink)

Descartes argued that productivity, namely our ability to generate an unlimited number of new thoughts or ideas from previous ones, derives from a single undividable source in the human soul. Cognitive scientists, in contrast, have viewed productivity as a modular phenomenon. According to this latter view, syntactic, semantic, musical or visual productivity emerges each from their own generative engines in the human brain. Recent evidence has, however, led some authors to revitalize the Cartesian theory. According to this view, a single (...) source or a single mechanism in the human brain produces productivity in every cognitive domain, whether in the domain of music, semantics or syntax. In this article, we will address recent evidence concerning the single source hypothesis from brain-imaging studies, linguistics, cognitive theories of music perception, biology of cognition and cognitive development, along with several objections that have been presented against this hypothesis. We formulate two versions of the Cartesian theory which combine the more recent computational theory of cognition with Descartes' view on productivity. (shrink)

This article presents a sobering view of the discipline of cognitive neuropsychology as practiced over the last three or four decades. Our judgment is that, although the study of abnormal cognition resulting from brain injury or disease in previously normal adults has produced a catalogue of fascinating and highly selective deficits, it has yielded relatively little advance in understanding how the brain accomplishes its cognitive business. We question the wisdom of the following three “choices” in mainstream cognitive neuropsychology: (a) single‐case (...) methodology, (b) dissociation between functions as the most important source of evidence, and (c) a central goal of diagramming the functional architecture of cognition rather than specifying how its components work. These choices may all stem from an excessive commitment to strict and fine‐grained modularity. Although different brain regions are undoubtedly specialized for different functions, we argue that parallel and interactive processing is a better assumption about cognitive processing. The essential goal of specifying representations and processes can, we claim, be significantly assisted by computational modeling which, by its very nature, requires such specification. (shrink)

Brain damage may doubly dissociate cognitive modules, but the practice of revealing dissociations is predicated on modularity being true (T. Shallice, 1988). This article questions the utility of assuming modularity, as it examines a paradigmatic double dissociation of reading modules. Reading modules illustrate two general problems. First, modularity fails to converge on a fixed set of exclusionary criteria that define pure cases. As a consequence, competing modular theories force perennial quests for purer cases, which simply perpetuates growth in the list (...) of exclusionary criteria. The first problem leads, in part, to the second problem. Modularity fails to converge on a fixed set of pure cases. The second failure perpetuates unending fractionation into more modules. (shrink)

The communicative theory of emotions postulates that emotions are communications both within the brain and between individuals. Basic emotions owe their evolutionary origins to social mammals, and they enable human beings to use repertoires of mental resources appropriate to recurring and distinctive kinds of events. These emotions also enable them to cooperate with other individuals, to compete with them, and to disengage from them. The human system of emotions has also grafted onto basic emotions propositional contents about the cause of (...) the emotion, the self, and other matters. Complex emotions always contain such contents, whereas basic emotions can be experienced without them. This article explains the role of basic emotions in social relationships, their effects on reasoning, and their pathology in psychological illness, such as depression and obsessive-compulsive disorder. (shrink)

A central goal of cognitive science is to develop a general theory of transfer to explain how people use and apply their prior knowledge to solve new problems. Previous work has identified multiple mechanisms of transfer including (but not limited to) analogy, knowledge compilation, and constraint violation. The central hypothesis investigated in the current work is that the particular profile of transfer processes activated for a given situation depends on both (a) the type of knowledge to be transferred and how (...) it is represented, and (b) the processing demands of the transfer task. This hypothesis was investigated in two laboratory training studies. The results from Experiment 1 show that each mechanism predicts specific behavioural patterns of performance across a common set of transfer tasks. The results from Experiment 2 show that people can adaptively shift between transfer mechanisms depending on their prior knowledge and the characteristics of the task environment. A framework for the development of a general theory of transfer based on multiple mechanisms is proposed and implications of the theory are discussed for measuring and understanding knowledge transfer. (shrink)

Humans and organizations have limitations of computational capacity and information management. Such constraints are synonymous with bounded rationality. Therefore, in order to extend the human and organizational boundaries to more advanced models of cognition, this research proposes concepts of cognitive machines in organizations. From a micro point of view, what makes this research distinct is that, beyond people, it includes in the list of participants of the organization the cognitive machines. From a macro point of view, this paper relies on (...) the premise that cognitive machines can improve the cognitive abilities of the organization. From such perspectives, it presents rationale and principles of a class of cognitive machines with capabilities to carry out complex cognitive tasks in organizations. It also introduces analyses of the cognitive machines in organizations through theories of bounded rationality, economic decision-making, and conflict resolution. The analyses indicate that these machines can solve or reduce intra-individual and group dysfunctional conflicts which arise from decision-making processes in the organization, and thus they can improve the degree of organizational cognition. From all these backgrounds, this research outlines implications of cognitive machines for organizations. (shrink)

Cognitive architectures are theories of cognition that try to capture the essential representations and mechanisms that underlie cognition. Research in cognitive architectures has gradually moved from a focus on the functional capabilities of architectures to the ability to model the details of human behavior, and, more recently, brain activity. Although there are many different architectures, they share many identical or similar mechanisms, permitting possible future convergence. In judging the quality of a particular cognitive model, it is pertinent to not just (...) judge its fit to the experimental data but also its simplicity and ability to make predictions. (shrink)

In this article, I use a mental models computational account of representation to illustrate some details of my previously presented inferential model of scientific understanding. The hope is to shed some light on possible mechanisms behind the notion of scientific understanding. I argue that if mental models are a plausible approach to modelling cognition, then understanding can best be seen as the coupling of specific rules. I present our beliefs as ?ordinary? conditional rules, and the coupling process as one where (...) the consequent of one ordinary rule (OR) matches and activates the antecedent of the rule to which it is coupled in virtue of the activation of an intermediate ?inference? rule. I argue that on this approach knowledge of an explanation is the activation of ORs in a cognitive hierarchy, while understanding is achieved when those activated ORs are also coupled via correct inference rules. I do not directly address issues regarding the plausibility of mental models themselves. This article should therefore be seen as an exercise in refining the inferential model within an already presupposed computational setting, not one of arguing for the psychological adequacy of computational approaches. (shrink)

In this article I argue that two current accounts of scientific understanding are incorrect and I propose an alternative theory. My new account draws on recent research in cognitive psychology which reveals the importance of making causal and logical inferences on the basis of incoming information. To understand a phenomenon we need to make particular kinds of inferences concerning the explanations we are given. Specifically, we come to understand a phenomenon scientifically by developing mental models that incorporate the correct causal (...) and logical properties responsible for the causes or logical properties of the phenomenon being explained. (shrink)

How can we study bounded rationality? We answer this question by proposing rational task analysis —a systematic approach that prevents experimental researchers from drawing premature conclusions regarding the rationality of agents. RTA is a methodology and perspective that is anchored in the notion of bounded rationality and aids in the unbiased interpretation of results and the design of more conclusive experimental paradigms. RTA focuses on concrete tasks as the primary interface between agents and environments and requires explicating essential task elements, (...) specifying rational norms, and bracketing the range of possible performance, before contrasting various benchmarks with actual performance. After describing RTA’s core components we illustrate its use in three case studies that examine human memory updating, multitasking behavior, and melioration. We discuss RTA’s characteristic elements and limitations by comparing it to related approaches. We conclude that RTA provides a useful tool to render the study of bounded rationality more transparent and less prone to theoretical confusion. (shrink)