This volume began as a remembrance of Alonzo Church while he was still with us and is now finally complete. It contains papers by many well-known scholars, most of whom have been directly influenced by Church's own work. Often the emphasis is on foundational issues in logic, mathematics, computation, and philosophy - as was the case with Church's contributions, now universally recognized as having been of profound fundamental significance in those areas. The volume will be of interest to logicians, (...) class='Hi'>computer scientists, philosophers, and linguists. The contributions concern classical first-order logic, higher-order logic, non-classical theories of implication, set theories with universal sets, the logical and semantical paradoxes, the lambda-calculus, especially as it is used in computation, philosophical issues about meaning and ontology in the abstract sciences and in natural language, and much else. The material will be accessible to specialists in these areas and to advanced graduate students in the respective fields. (shrink)

A symbolic computer model, employing the perceptual strategy, is presented for solving Tower of Hanoi problems. The model is calibrated—in terms of the number of problems solved, time taken, and number of moves made—to the performance of 20 normal subjects. It is then “lesioned” by increasing the decay rate of elements in working memory to model the performance of 20 patients with lesions to the prefrontal cortex. The model captures both the main effects of subject groups (patients and (...) normal controls) performance, and the subject groups (patients and normal controls) by problem difficulty interactions. This leads us to support the working memory hypothesis of frontal lobe functions, but for a narrow range of problems. (shrink)

Computational methods and perspectives can transform the history of science by enabling the pursuit of novel types of questions, dramatically expanding the scale of analysis , and offering novel forms of publication that greatly enhance access and transparency. This essay presents a brief summary of a computational research system for the history of science, discussing its implications for research, education, and publication practices and its connections to the open-access movement and similar transformations in the natural and social sciences that emphasize (...) big data. It also argues that computational approaches help to reconnect the history of science to individual scientific disciplines. (shrink)

This study presents a process model of very long‐term episodic memory. The process presented is a reconstructive process. The process involves application of three kinds of reconstructive strategies—component‐to‐context instantiation strategies, component‐instantiation strategies, and context‐to‐context instantiation strategies. The first is used to direct search to appropriate conceptual categories in memory. The other two are used to direct search within the chosen conceptual category. A fourth type of strategy, called executive search strategies, guide search for concepts related to the one (...) targeted for retrieval.A conceptual memory organization implied by human reconstructive memory is presented along with examples which motivate it. A basic retrieval algorithm is presented for traversing that stucture. Retrieval strategies arise from failures in that algorithm. The memory organization and retrieval processes are implemented in a computer program called CYRUS which stores events in the lives of former Secretaries of State Cyrus Vance and Edmund Muskie and answers questions posed in English concerning that information. Examples which motivate the process model are drawn from protocols of human memory search. Examples of CYRUS's behavior demonstrate the implemented process model. Conclusions are drawn concerning retrieval failures and the relationship of episodic and semantic memory. (shrink)

Increasing working memory (WM) capacity is often cited as a major influence on children's development and yet WM capacity is difficult to examine independently of long‐term knowledge. A computational model of children's nonword repetition (NWR) performance is presented that independently manipulates long‐term knowledge and WM capacity to determine the relative contributions of each in explaining the developmental data. The simulations show that (a) both mechanisms independently cause the same overall developmental changes in NWR performance, (b) increase in long‐term knowledge (...) provides the better fit to the child data, and (c) varying both long‐term knowledge and WM capacity adds no significant gains over varying long‐term knowledge alone. Given that increases in long‐term knowledge must occur during development, the results indicate that increases in WM capacity may not be required to explain developmental differences. An increase in WM capacity should only be cited as a mechanism of developmental change when there are clear empirical reasons for doing so. (shrink)

Memory, attention, and decision-making are three major areas of cognitive neuroscience. They are however frequently studied in isolation, using a range of models to understand them. This book brings a unified approach to understanding these three processes, showing how these fundamental functions can be understood in a common and unifying framework.

Memory, attention, and decision-making are three major areas of psychology. They are frequently studied in isolation, and using a range of models to understand them. This book brings a unified approach to understanding these three processes. It shows how these fundamental functions for cognitive neuroscience can be understood in a common and unifying computational neuroscience framework. This framework links empirical research on brain function from neurophysiology, functional neuroimaging, and the effects of brain damage, to a description of how neural (...) networks in the brain implement these functions using a set of common principles. The book describes the principles of operation of these networks, and how they could implement such important functions as memory, attention, and decision-making.The topics covered includeThe hippocampus and memoryReward and punishment related learning: emotion and motivationVisual object recognition learningShort term memoryAttention, short term memory, and biased competitionProbabilistic decision-makingAction selectionDecision-makingAlso included are tutorial appendices onNeural networks in the brainNeural encoding in the brain'Memory, Attention and Decision-Making' will be valuable for those in the fields of neuroscience, psychology, and cognitive neuroscience from advanced undergraduate level upwards. It will also be of interest to those interested in neuroeconomics, animal behaviour, zoology, evolutionary biology, psychiatry, medicine, and philosophy. The book has been written with modular chapters and sections, making it possible to select particular Chapters for course work. (shrink)

As part of the development of the Symbolic and Sub-symbolic Robotics Intelligence Control System, we have implemented a memory store to allow a robot to retain knowledge from previous exp...

In the field of economic analysis, computability in the formation of economic hypotheses is seen as the way forward. In this book, Professor Velupillai implements a theoretical research program along these lines. Choice theory, learning rational expectations equlibria, the persistence of adaptive behaviour, arithmetical games, aspects of production theory, and economic dynamics are given recursion theoretic interpretations. These interpretations lead to new kinds of questions being posed by the economic theorist. In particular, recurison theoretic decision problems replace standard optimisation paradigms (...) in economic analysis. Economic theoretic questions, posed recursion-theoretically, lead to answers that are ambiguous: undecidable choices, uncomputable learning processes, and algorithmically unplayable games become standard answers. Professor Velupillai argues that a recursion theoretic formalisation of economic analysisComputable Economicsmakes the subject intrinsically inductive and computational. (shrink)

It is well known that working memory performance changes with age. Two recent computational models of working memory, TBRS* and SOB-CS, developed from young adults WM performances are opposed regarding the postulated causes of forgetting, namely time-based decay and interference for TBRS* and SOB-CS, respectively. In the present study, these models are applied on a set of complex span data produced by young and older adults. As expected, these models are unable to account for the older adult data. (...) An investigation on the effect of the main parameters of these models showed that the poorer performance of older adults does not come from a weaker encoding of item but rather from difficulties during the free time that immediately follows each distractor, as well as from a higher level of confusion between items. These results are discussed with respect to the current theories of working memory and aging. (shrink)

Recent advances in philosophical thinking about consciousness, such as cognitive phenomenology and mereological analysis, provide a framework that facilitates using computational models to explore issues surrounding the nature of consciousness. Here we suggest that, in particular, studying the computational mechanisms of working memory and its cognitive control is highly likely to identify computational correlates of consciousness and thereby lead to a deeper understanding of the nature of consciousness. We describe our recent computational models of human working memory and (...) propose that three computational correlates of consciousness follow from the results of this work: itinerant attractor sequences, top-down gating, and very fast weight changes. Our current investigation is focused on evaluating whether these three correlates are sufficient to create more complex working memory models that encompass compositionality and basic causal inference. We conclude that computational models of working memory are likely to be a fruitful approach to advancing our understanding of consciousness in general and in determining the long-term potential for development of an artificial consciousness specifically. (shrink)

One challenge to creating realistic cognitive models of memory is the inability to account for the vast common–sense knowledge of human participants. Large computational knowledge bases such as WordNet and DBpedia may offer a solution to this problem but may pose other challenges. This paper explores some of these difficulties through a semantic network spreading activation model of the Deese–Roediger–McDermott false memory task. In three experiments, we show that these knowledge bases only capture a subset of human associations, (...) while irrelevant information introduces noise and makes efficient modeling difficult. We conclude that the contents of these knowledge bases must be augmented and, more important, that the algorithms must be refined and optimized, before large knowledge bases can be widely used for cognitive modeling. (shrink)

One challenge to creating realistic cognitive models of memory is the inability to account for the vast common–sense knowledge of human participants. Large computational knowledge bases such as WordNet and DBpedia may offer a solution to this problem but may pose other challenges. This paper explores some of these difficulties through a semantic network spreading activation model of the Deese–Roediger–McDermott false memory task. In three experiments, we show that these knowledge bases only capture a subset of human associations, (...) while irrelevant information introduces noise and makes efficient modeling difficult. We conclude that the contents of these knowledge bases must be augmented and, more important, that the algorithms must be refined and optimized, before large knowledge bases can be widely used for cognitive modeling. (shrink)

In this paper, I present an overview of personal and intimate technologies within a pedagogical context. I describe two courses that I have developed for Computation Arts at Concordia University: “Tangible Media and Physical Computing” and “Second Skin and Soft Wear.” Each course deals with different aspects of physical computing and tangible media in a Fine Arts context. In both courses, I introduce concepts of soft computation and intimate reactive artifacts as artworks. I emphasize the concept of memory (contrasting (...) computer memory and personal, interpretive memory), and explore how responsive or interactive objects can create a new medium for annotating architectural space and objects, for leaving traces of presence, and for recording personal histories. At the core of this pedagogical practice is a strong emphasis on engaging a vulnerable, personal approach to working with electronics and physical computation. To contextualize the teaching practice, I begin by presenting some of my own research projects developed at Extra Soft Labs (also known as XS Labs), then segue into a detailed discussion of these two classes, and conclude with a discussion of some student work. (shrink)

Computational models of memory presented in this issue reflect varied empirical data and levels of representation. From mathematical models to neural and cognitive architectures, all aim to converge on a unified theory of the mind.

Human biological memory systems have adapted to use technological artifacts to overcome some of the limitations of these systems. For example, when performing a difficult calculation, we use pen and paper to create and store external number symbols; when remembering our appointments, we use a calendar; when remembering what to buy, we use a shopping list. This chapter looks at the history of memory artifacts, describing the evolution from cave paintings to virtual reality. It first characterizes memory (...) artifacts, memory systems, and the two main functions such artifacts have, which are to aid individual users in completing memory tasks and as a cultural inheritance channel (section 2). It then outlines some of our first symbolic practices such as making cave paintings and figurines, and then moves on to outline several key developments in external representational systems and the artifacts that support these such as written language, numeral systems and counting devices, diagrams and maps, measuring devices, libraries and archives, photographs, analogue and digital computational artifacts, the World Wide Web, virtual reality, and smartphones (section 3). After that, it makes some brief points about the cumulative nature of the cultural evolution of memory artifacts and speculates about the possible future of memory artifacts, arguing that it is very difficult to look beyond an epistemological horizon of more than five years (section 4). (shrink)

During each school semester, students face an onslaught of material to be learned. Students work hard to achieve initial mastery of the material, but when they move on, the newly learned facts, concepts, and skills degrade in memory. Although both students and educators appreciate that review can help stabilize learning, time constraints result in a trade-off between acquiring new knowledge and preserving old knowledge. To use time efficiently, when should review take place? Experimental studies have shown benefits to long-term (...) retention with spaced study, but little practical advice is available to students and educators about the optimal spacing of study. The dearth of advice is due to the challenge of conducting experimental studies of learning in educational settings, especially where material is introduced in blocks over the time frame of a semester. In this study, we turn to two established models of memory—ACT-R and MCM—to conduct simulation studies exploring the impact of study schedule on long-term retention. Based on the premise of a fixed time each week to review, converging evidence from the two models suggests that an optimal review schedule obtains significant benefits over haphazard (suboptimal) review schedules. Furthermore, we identify two scheduling heuristics that obtain near optimal review performance: (a) review the material from μ-weeks back, and (b) review material whose predicted memory strength is closest to a particular threshold. The former has implications for classroom instruction and the latter for the design of digital tutors. (shrink)

The Hard Question of memory is the following: how are memory representations stored and organized so as to be made available for retrieval in the appropriate circumstances and format? In this essay, I argue that philosophical theories of memory should engage with the Hard Question directly and seriously. I propose that declarative memory is a faculty performing a kind of cognitive triage: management of information for a variety of uses under significant computational constraints. In such triage, (...) memory representations are preferentially selected and stabilized, but also systematically modified and integrated into generalized, model-like representational structures. Further, I propose a hybrid theory of remembering, which takes into account both the nature of the cognitive processes underlying remembering and the norms that govern representational success in relevant cognitive/epistemic contexts. (shrink)

One striking feature of the contemporary modeling practice is its interdisciplinarity: the same function forms and equations, and mathematical and computational methods are being transferred across disciplinary boundaries. Within philosophy of science this interdisciplinary dimension of modeling has been addressed by both analogy and template-based approaches that have proceeded separately from each other. We argue that a more fully-blown account of model transfer needs both perspectives. We examine analogical reasoning and template application through a detailed case study on the transfer (...) of the Ising model from physics into neuroscience. Our account combines the analogy and template-based approaches through the notion of a model template that highlights the conceptual side of model transfer. (shrink)

There is a popular hypothesis that performance on implicit and explicit memory tasks reflects 2 distinct memory systems. Explicit memory is said to store those experiences that can be consciously recollected, and implicit memory is said to store experiences and affect subsequent behavior but to be unavailable to conscious awareness. Although this division based on awareness is a useful taxonomy for memory tasks, the authors review the evidence that the unconscious character of implicit memory (...) does not necessitate that it be treated as a separate system of human memory. They also argue that some implicit and explicit memory tasks share the same memory representations and that the important distinction is whether the task (implicit or explicit) requires the formation of a new association. The authors review and critique dissociations from the behavioral, amnesia, and neuroimaging literatures that have been advanced in support of separate explicit and implicit memory systems by highlighting contradictory evidence and by illustrating how the data can be accounted for using a simple computational memory model that assumes the same memory representation for those disparate tasks. (shrink)

In Tulving’s initial characterization, episodic memory was one of multiple memory systems. It was postulated, in pursuit of explanatory depth, as displaying proprietary operations, representations, and substrates such as to explain a range of cognitive, behavioural, and experiential phenomena. Yet the subsequent development of this research program has, paradoxically, introduced surprising doubts about the nature, and indeed existence, of episodic memory. On dominant versions of the ‘common system’ view, on which a single simulation system underlies both remembering (...) and imagining, there are no processes unique to memory to support robust generalizations with inductive potential. Eliminativism about episodic memory seems to follow from the claim that it has no dedicated neurocognitive system of its own. After identifying this under-noticed threat, we push back against modern eliminativists by surveying recent evidence that still indicates specialized mechanisms, computations, and representations that are distinctly mnemic in character. We argue that contemporary realists about episodic memory can retain lessons of the common system approach while resisting the further move to eliminativism. (shrink)

We compared the processing of natural language quantifiers in a group of patients with schizophrenia and a healthy control group. In both groups, the difficulty of the quantifiers was consistent with computational predictions, and patients with schizophrenia took more time to solve the problems. However, they were significantly less accurate only with proportional quantifiers, like more than half. This can be explained by noting that, according to the complexity perspective, only proportional quantifiers require working memory engagement.

In this paper, I argue that computationalism is a progressive research tradition. Its metaphysical assumptions are that nervous systems are computational, and that information processing is necessary for cognition to occur. First, the primary reasons why information processing should explain cognition are reviewed. Then I argue that early formulations of these reasons are outdated. However, by relying on the mechanistic account of physical computation, they can be recast in a compelling way. Next, I contrast two computational models of working (...) class='Hi'>memory to show how modeling has progressed over the years. The methodological assumptions of new modeling work are best understood in the mechanistic framework, which is evidenced by the way in which models are empirically validated. Moreover, the methodological and theoretical progress in computational neuroscience vindicates the new mechanistic approach to explanation, which, at the same time, justifies the best practices of computational modeling. Overall, computational modeling is deservedly successful in cognitive science. Its successes are related to deep conceptual connections between cognition and computation. Computationalism is not only here to stay, it becomes stronger every year. (shrink)