Philosophy and Memory Traces defends two theories of autobiographical memory. One is a bewildering historical view of memories as dynamic patterns in fleeting animal spirits, nervous fluids which rummaged through the pores of brain and body. The other is new connectionism, in which memories are 'stored' only superpositionally, and reconstructed rather than reproduced. Both models, argues John Sutton, depart from static archival metaphors by employing distributed representation, which brings interference and confusion between memory traces. Both raise urgent issues (...) about control of the personal past, and about relations between self and body. Sutton demonstrates the role of bizarre body fluids in moral physiology, as philosophers from Descartes and Locke to Coleridge struggled to control their own innards and impose cognitive discipline on 'the phantasmal chaos of association'. Going on to defend connectionism against Fodor and critics of passive mental representations, he shows how problems of the self are implicated in cognitive science. (shrink)

Philosophy and Memory Traces, the book to which this is the preface, defends two theories of autobiographical memory. One is a bewildering historical view of memories as dynamic patterns in fleeting animal spirits, nervous fluids which rummaged through the pores of brain and body. The other is new connectionism, in which memories are ‘stored’ only superpositionally, and are reconstructed rather than reproduced. Both models depart from static archival metaphors by employing distributed representation, which brings interference and confusion between (...) memory traces. Both raise urgent issues about control of the personal past, and about relations between self and body. The book’s historical argument is anchored by a reinterpretation of Descartes’ dynamic physiology of memory and strange philosophy of the body. English critics of Descartes’ view of memories as motions complained that mechanistic neurophilosophy could not guarantee order in memory, and instead sought techniques for controlling the brain. In a new account of 18th-century philosophers’ fears of confusion in remembering, the author demonstrates the role of bizarre body fluids in moral physiology, as philosophers from Locke to Reid and Coleridge struggled to control their own innards and impose cognitive discipline on ‘the phantasmal chaos of association’. Finally, in a defence of connectionism against Jerry Fodor and against phenomenological and Wittgensteinian critics of passive mental representations, the author shows how problems of the self are implicated in contemporary sciences of mind. The book is an experiment in historical cognitive science, based on a belief that the interdisciplinary study of memory can exemplify the simultaneous attention to brain, body, and culture towards which psychological sciences must aim. (shrink)

In this Master's dissertation, I try to show that the causal theory of memory, which is the only theory developed so far that at first view seems more plausible and that could be integrated with psychological explanations and investigations of memory, shows some conceptual and ontological problems that go beyond the internal inconsistencies that each version can present. On one hand, the memory phenomenon analyzed is very limited: in general it is reduced to the conscious act of remembering expressed in (...) a propositional format: the idea of an agent who can control his memory reports, in contrast to a passive subject who merely retrieve his encoded memories, is not even considered. Furthermore, the representational function of memory is the only function taken into account; and all the diachronic changes that could entail a dynamic conception of memory are ignored, as is the consideration of context in a broad sense of the term. On the other hand, causal theories make some implicit assumptions that are obviously questionable (e.g., the difference between facts and events) and leave some of their key concepts unexplained, like the nature of the causality, the isomorphism between the memory trace and the memory event, etc. Nonetheless, the principal point that we criticized is the idea of a correspondence between a past event or representation of it, the correspondent memory trace and the memory representation of it. Psychological models of memory, with the exception of the spatial analogy, question some of these assumptions and suggest that memory information is organized in a way other than that implied by the causal theory. But it is distributed connectionism that actually challenges the causal theory of memory, not only through some of its basic postulates, like the superpositional conception of memory traces, their mutual influence and constant state of change, but also because it is compatible with a more situated approach to memory phenomena as well as with the idea that memory in reality accomplishes a variety of functions that go beyond the representational one. The explicit and implicit ideas provided by distributed connectionism, together with the previous criticisms that we made to of causal theory, clearly suggest the possibility of developing another kind of philosophical theory of memory, different from existing theories. This task, nonetheless, implies: (a) A much deeper study of connectionism principles and memory models, as well as of the rival candidate, symbolism, plus the evaluation of these models in the light of psychological and neurophysiological empirical research, which unfortunately I have not dealt with in this work. (b) An analysis of the metaphysical nature of events and facts, in order to see if we should decide in favor of a realist or a non-realist account. It is also clear that this decision can’t be taken without an examination of perception studies (and not exclusively philosophical ones) which would be crucial for determining what is encoded in memory, and studies devoted to language, which, I suspect, plays an important role in the segmentation of reality. (c) A clarification of the notion of causality, because we wouldn’t like to deny any kind entail the complete disappearance of boundaries between memory and imagination. And even if I suspect that my future work will question the existence of absolute boundaries between these two capacities, the notion of causality can’t be completely erase from the theory of memory without erasing the memory phenomenon itself. But this causality, as we’ve already showed, can’t consist in the naïve conception adopted by the causal accounts of memory, nor can it be explained in counterfactual terms. If we think of the reconsolidation concept, of the interdependence between all the memory system, and of the deciding influence of context, it’s almost sure that more than one notion of causality will need to be invoked in order to explain memory. (d) Because I do not want to reduce the study of memory exclusively to cases of remembering, it’s essential to analyze how memory intervenes not only in imagination, but also in perception, reasoning and future projection, for example. In this respect, we could quote the interesting articles of Atance & O’Neill (2001) and Bucker & Carroll (2006), the first concerning the relationship between episodic memory and episodic future thinking, the second concerning the relationship between episodic memory and all kinds of self-projection, such as navigation and theory of mind, relations that would also have to be explored from a philosophical perspective, in particular to overcome the tendency to suppose that representation is the only function of memory. I will also mention the necessity of combining the notion of metamemory with that of memory to some extent; as I have already remarked, it is implausible to suppose that a subject who consciously remembers something is unable to exert any kind of control over his memory reports. (e) Finally, in order to integrate a situated conception of memory into a philosophical account, it will also be indispensable to take into consideration what is known as “everyday memory research” as well as studies in social and collective memory. (shrink)

A case study in historical cognitive science, this paper addresses two claims made by radical proponents of new dynamical approaches. It queries their historical narrative, which sees embodied, situated cognition as correcting an individualist, atemporal framework originating in Descartes. In fact, new Descartes scholarship shows that 17th-century animal spirits neurophysiology realized a recognizably distributed model of memory; explicit representations are patterns of spirit flow, and memory traces are changes left by experience in connections between brain pores. This historical sketch (...) supports the second dynamicist claim, that connectionists' stress on the cognitive importance of pattern-recreation needs supplementing by dynamicists' real-time focus and attention to the active roles of body and environment. Animal spirits theory exhibits just the 'continuous reciprocal causation' between brain, body, and environment which Andy Clark sees as dynamicism's central contribution, and allows for the embedding of brains in culture as well as the physical world. (shrink)

According to the Causal Theory of Memory, remembering a particular past event requires a causal connection between that event and its subsequent representation in memory, specifically, a connection sustained by a memory trace. The CTM is the default view of memory in contemporary philosophy, but debates persist over what the involved memory traces must be like. Martin and Deutscher argued that the CTM required memory traces to be structural analogues of past events. Bernecker and Michaelian, contemporary CTM proponents, reject (...) structural analogues in favor of memory traces as distributed patterns of event features. The proposals are understood as distinct accounts of how memory traces represent past events. But there are two distinct questions one could ask about a trace’s representational features. One might ask how memory traces, qua mental representations, have their semantic properties. Or, what makes memory traces, qua mental representations of memories, distinct from other mental representations. Proponents of the CTM, both past and present, have failed to keep these two questions distinct. The result is a serious but unnoticed problem for the CTM in its current form. Distributed memory traces are incompatible with the CTM. Such traces do not provide a way to track the causal history of individual memories, as the CTM requires. If memory traces are distributed patterns of event features, as Bernecker and Michaelian each claim, then the CTM cannot be right. (shrink)

There has been much interest in the possibility of connectionist models whose representations can be endowed with compositional structure, and a variety of such models have been proposed. These models typically use distributed representations that arise from the functional composition of constituent parts. Functional composition and decomposition alone, however, yield only an implementation of classical symbolic theories. This paper explores the possibility of moving beyond implementation by exploiting holistic structure-sensitive operations on distributed representations. An experiment is performed using (...) Pollack’s Recursive Auto-Associative Memory. RAAM is used to construct distributed representations of syntactically structured sentences. A feed-forward network is then trained to operate directly on these representations, modeling syn- tactic transformations of the represented sentences. Successful training and generalization is obtained, demonstrating that the implicit structure present in these representations can be used for a kind of structure-sensitive processing unique to the connectionist domain. (shrink)

Memory exhibits episodic superposition, an analog of the quantum superposition of physical states: Before a cue for a presented or unpresented item is administered on a memory test, the item has the simultaneous potential to occupy all members of a mutually exclusive set of episodic states, though it occupies only one of those states after the cue is administered. This phenomenon can be modeled with a nonadditive probability model called overdistribution (OD), which implements fuzzy-trace theory's distinction between verbatim and gist (...) representations. We show that it can also be modeled based on quantum probability theory. A quantum episodic memory (QEM) model is developed, which is derived from quantum probability theory but also implements the process conceptions of global matching memory models. OD and QEM have different strengths, and the current challenge is to identify contrasting empirical predictions that can be used to pit them against each other. (shrink)

DCPS is a connectionist production system interpreter that uses distributed representations. As a connectionist model it consists of many simple, richly interconnected neuron‐like computing units that cooperate to solve problems in parallel. One motivation for constructing DCPS was to demonstrate that connectionist models are capable of representing and using explicit rules. A second motivation was to show how “coarse coding” or “distributed representations” can be used to construct a working memory that requires far fewer units than the number (...) of different facts that can potentially be stored. The simulation we present is intended as a detailed demonstration of the feasibility of certain ideas and should not be viewed as a full implementation of production systems. Our current model only has a few of the many interesting emergent properties that we eventually hope to demonstrate: It is damage‐resistant, it performs matching and variable binding by massively parallel constraint satisfaction, and the capacity of its working memory is dependent on the similarity of the items being stored. (shrink)

Marinov''s critique I argue, is vitiated by its failure to recognize the distinctive role of superposition within the distributed connectionist paradigm. The use of so-called subsymbolic distributed encodings alone is not, I agree, enough to justify treating distributed connectionism as a distinctive approach. It has always been clear that microfeatural decomposition is both possible and actual within the confines of recognizably classical approaches. When such approaches also involve statistically-driven learning algorithms — as in the case of ID3 (...) — the fundamental differences become even harder to spot. To see them, it is necessary to consider not just the nature of an acquired input-output function but the nature of the representational scheme underlying it. Differences between such schemes make themselves best felt outside the domain of immediate problem solving. It is in the more extended contexts of performance DURING learning and cognitive change as a result of SUBSEQUENT training on new tasks (or simultaneous training on several tasks) that the effects of superpositional storage techniques come to the fore. I conclude that subsymbols, distribution and statistically driven learning alone are indeed not of the essence. But connectionism is not just about subsymbols and distribution. It is about the generation of whole subsymbol SYSTEMS in which multiple distributed representations are created and superposed. (shrink)

This paper offers both a theoretical and an experimental perspective on the relationship between connectionist and Classical (symbol-processing) models. Firstly, a serious flaw in Fodor and Pylyshyn’s argument against connectionism is pointed out: if, in fact, a part of their argument is valid, then it establishes a conclusion quite different from that which they intend, a conclusion which is demonstrably false. The source of this flaw is traced to an underestimation of the differences between localist and distributed representation. (...) It has been claimed that distributed representations cannot support systematic operations, or that if they can, then they will be mere implementations of traditional ideas. This paper presents experimental evidence against this conclusion: distributed representations can be used to support direct structure-sensitive operations, in a man- ner quite unlike the Classical approach. Finally, it is argued that even if Fodor and Pylyshyn’s argument that connectionist models of compositionality must be mere implementations were correct, then this would still not be a serious argument against connectionism as a theory of mind. (shrink)

Connectionism and phenomenology can mutually inform and mutually constrain each other. In this manifesto I outline an approach to consciousness based on distinctions developed by connectionists. Two core identities are central to a connectionist theory of consciouness: conscious states of mind are identical to occurrent activation patterns of processing units; and the variable dispositional strengths on connections between units store latent and unconscious information. Within this broad framework, a connectionist model of consciousness succeeds according to the degree of correspondence between (...) the content of human consciousness (the world as it is experienced) and the interpreted content of the network. Constitutive self-awareness and reflective self-awareness can be captured in a model through its ability to respond to self-reflexive information, identify self-referential categories, and process information in the absence of simultaneous input. The qualitative feel of sensation appears in a model as states of activation that are not fully discriminated by later processing. Connectionism also uniquely explains several specific features of experience. The most important of these is the superposition of information in consciousness — our ability to perceive more than meets the eye, and to apprehend complex categorical and temporal information in a single highly-cognized glance. This superposition in experience matches a superposition of representational content in distributed representations. (shrink)

Traces of many past events are often layered or superposed, in brain, body, and world alike. This often poses challenges for individuals and groups, both in accessing specific past events and in regulating or managing coexisting emotions or attitudes. We sometimes struggle, for example, to find appropriate modes of engagement with places with complex and difficult pasts. More generally, there can appear to be a tension between what we know about the highly constructive nature of remembering, whether it is drawing (...) on neural or worldly resources or both, and the ways that we need and use memory to make claims on the past, and to maintain some appropriate causal connections to past events. I assess the current state of work on situated affect and distributed memory, and the recent criticisms of the ‘dogma of harmony’ in these fields. I then deploy these frameworks to examine some affective dimensions of place memory, sketching a strongly distributed conception of places as sometimes partly constituting the processes and activities of feeling and remembering. These approaches also offer useful perspectives on the problems of how to engage – politically and aesthetically – with difficult pasts and historically burdened heritage. In assessing artistic interventions in troubled places, we can seek responsibly to do justice to the past while fully embracing the dynamic and contested constructedness of our present emotions, memories, and activities. (shrink)

This book will be valuable predominantly to specialists who are already familiar with the literature on the subject. It bristles with scholarly references, and includes a 43 page bibliography. Sutton’s approach is an interdisciplinary one, drawing on cognitive science, medicine, and neurophysiology as well as literature, psychology, and philosophy. The purpose of the book is to describe and defend a set of theories of autobiographical memory, both historical and contemporary, which view memories as dynamic patterns rather than static archives, fragmentary (...) traces to be reconstructed rather than coherent things to be reproduced. (shrink)

In “Connectionism and the fats of folk psychology”, Forster and Saidel argue that the central claim of Ramsey, Stich and Garon (1991)—that distributed connectionist models are incompatible with the causal discreteness of folk psychology—is mistaken. To establish their claim, they offer an intriguing model which allegedly shows how distributed representations can function in a causally discrete manner. They also challenge our position regarding projectibility of folk psychology. In this essay, I offer a response to their account and show (...) how their model fails to demonstrate that our original argument was mistaken. While I will discuss several difficulties with their model, my primary criticism will be that the features of their model that are causally discrete are not truly distributed, while the features that are distributed are not really discrete. Concerning the issue of projectibility, I am more inclined to agree with Forster and Saidel and I offer a revised account of what we should have said originally. (shrink)

Cognitive function certainly poses the biggest challenge for computational neuroscience. As we argue, past efforts to build neural models of cognition (the target article included) had too narrow a focus on implementing rule-based language processing. The problem with these models is that they sacrifice the advantages of connectionism rather than building on them. Recent and more promising approaches for modeling cognition build on the mathematical properties of distributed neural representations. These approaches truly exploit the key advantages of connectionism, that (...) is, the high representational power of distributed neural codes and similarity-based pattern recognition. The architectures for cognitive computing that emerge from these approaches are neural associative memories endowed with additional mapping operations to handle invariances and to form reduced representations of combinatorial structures. (shrink)

There are many conflicting views concerning the nature of distributed representation, its compatibility or otherwise with symbolic representation, and its importance in characterizing the nature of connectionist models and their relationship to more traditional symbolic approaches to understanding cognition. Many have simply assumed that distribution is merely an implementation issue, and that symbolic mechanisms can be designed to take advantage of the virtues of distribution if so desired. Others, meanwhile, see the use of distributed representation (...) as marking a fundamental difference between the two approaches. One reason for this diversity of opinion is the fact that the relevant notions - especially that of. (shrink)

In this paper I defend the propriety of explaining the behavior of distributed connectionist networks by appeal to selected data stored therein. In particular, I argue that if there is a problem with such explanations, it is a consequence of the fact that information storage in networks is superpositional, and not because it is distributed. I then develop a ``proto-account'''' of causation for networks, based on an account of Andy Clark''s, that shows even superpositionality does not undermine information-based (...) explanation. Finally, I argue that the resulting explanations are genuinely informative and not vacuous. (shrink)

We focus on two components of Page's argument in favour of localist representations in connectionist networks: First, we take issue with the claim that localist representations can give rise to generalisation and show that whenever generalisation occurs, distributed representations are involved. Second, we counter the alleged shortcomings of distributed representations and show that their properties are preferable to those of localist approaches.

This paper is a tentative step towards a historical cognitive science, in the domain of memory and personal identity. I treat theoretical models of memory in history as specimens of the way cultural norms and artifacts can permeate ('proto')scientific views of inner processes. I apply this analysis to the topic of psychological control over one's own body, brain, and mind. Some metaphors and models for memory and mental representation signal the projection inside of external aids. Overtly at least, medieval (...) and Renaissance theorists agreed that such models had to allow for, or even guarantee, some conception of cognitive order and discipline. Individual memory traces had to be independent, not mixed up or interfering with others. The long tradition of improving or bypassing 'natural memory' by deliberately internalizing artefactual models was part of an arduous process of self-fashioning. Moral panic about confusion and mixture features centrally in the imposition of cognitive discipline in local memory traditions. (shrink)

Recent studies of semantic memory have investigated two theories of optimal search adopted from the animal foraging literature: Lévy flights and marginal value theorem. Each theory makes different simplifying assumptions and addresses different findings in search behaviors. In this study, an experiment is conducted to test whether clustering in semantic memory may play a role in evidence for both theories. Labeled magnets and a whiteboard were used to elicit spatial representations of semantic knowledge about animals. Category recall sequences from a (...) separate experiment were used to trace search paths over the spatial representations of animal knowledge. Results showed that spatial distances between animal names arranged on the whiteboard were correlated with inter-response intervals during category recall, and distributions of both dependent measures approximated inverse power laws associated with Lévy flights. In addition, IRIs were relatively shorter when paths first entered animal clusters, and longer when they exited clusters, which is consistent with marginal value theorem. In conclusion, area-restricted searches over clustered semantic spaces may account for two different patterns of results interpreted as supporting two different theories of optimal memory foraging. (shrink)

Connectionist models provide a promising alternative to the traditional computational approach that has for several decades dominated cognitive science and artificial intelligence, although the nature of connectionist models and their relation to symbol processing remains controversial. Connectionist models can be characterized by three general computational features: distinct layers of interconnected units, recursive rules for updating the strengths of the connections during learning, and “simple” homogeneous computing elements. Using just these three features one can construct surprisingly elegant and powerful models of (...) memory, perception, motor control, categorization, and reasoning. What makes the connectionist approach unique is not its variety of representational possibilities or its departure from explicit rule-based models, or even its preoccupation with the brain metaphor. Rather, it is that connectionist models can be used to explore systematically the complex interaction between learning and representation, as we try to demonstrate through the analysis of several large networks. (shrink)

Bifurcation analysis of a real-time implementation of an ART network, which is functionally similar to the generalized localist model discussed in Page's manifesto shows that it yields a phase transition from local to distributed representation owing to continuous variation of the range of inhibitory connections. Hence there appears to be a qualitative dichotomy between local and distributed representations at the level of connectionistic networks conceived of as instances of nonlinear dynamical systems.

The introduction of connectionist or parallel distributed processing (PDP) systems to model cognitive functions has raised the question of the possible relations between these models and traditional information processing models which employ rules to manipulate representations. After presenting a brief account of PDP models and two ways in which they are commonly interpreted by those seeking to use them to explain cognitive functions, I present two ways one might relate these models to traditional information processing models and so not (...) totally repudiate the tradition of modelling cognition through systems of rules and representations. The proposal that seems most promising is that PDP-type structures might provide the underlying framework in which a rule and representation model might be implemented. To show how one might pursue such a strategy, I discuss recent research by Barsalou on the instability of concepts and show how that might be accounted for in a system whose microstructure had a PDP architecture. I also outline how adopting a multi-leveled view of the mind, where on one level the mind employed a PDP-type system and at another level constituted a rule processing system, would allow researchers to relocate some problems which seemed difficult to explain at one level, such as the capacity for concept learning, to another level where it could be handled in a straightforward manner. (shrink)

There is disagreement over the notion of representation in cognitive science. Many investigators equate representations with symbols, that is, with syntactically defined elements in an internal symbol system. In recent years there have been two challenges to this orthodoxy. First, a number of philosophers, including many outside the symbolist orthodoxy, have argued that "representation" should be understood in its classical sense, as denoting a "stands for" relation between representation and represented. Second, there has been a growing challenge (...) to orthodoxy under the banner of connectionism. Although this connectionist challenge has evoked emotionally-charged rebukes from the symbolist camp, connectionists as a group have not articulated a conception of representation to replace the symbolist view. Nonetheless, most agree on the need for a nonsymbolic notion of representation. This paper advances a "stands for" sense of representation as primary by incorporating it into a general approach to cognitive science consonant with the connectionism. The idea is to marry connectionism to a particular version of functionalism, viz., one that builds its notion of "function" on the similarity between functional analysis in biology and in psychology. It builds on my earlier work adapting and revising Marr's tri-level approach to cognition. My proposal frees Marr's analysis from its moorings in the orthodox symbolic view of representation and allies it with a notion of functional analysis akin to that proposed by Cummins, developed further by Haugeland, and invoked by Millikan and Dretske. Unlike these authors, however, I do not wed representation to a general belief-desire analysis of behavior. Rather, I follow what I take to be the lesson of psychological practice, according to which the investigator does not seek to explain behavior in general but seeks to analyze the cognitive capacities that underlie behavior, such as vision, memory, learning, and linguistic capacities. Accordingly, ascriptions of representational content are made not by working back from belief-desire ascriptions, but in the context of forming psychological models to account for specific cognitive capacities. Because conceptions of representation in cognitive science typically are embedded in a general approach to the study of cognition, arguments for the comparative plausibility of a particular conception of representation must scout these larger frameworks. I therefore begin by characterizing the interlocking set of assumptions that gave life to the orthodox symbolist approach, paying special attention to the complementarity between representation and process that naturally arises from those assumptions. I then consider two versions of an alternative approach to representation: Dretske's and my own. Finally, I urge the merits of the "cognitive capacities" over the "belief-desire" approach to the subject-matter of cognitive science. (shrink)

There is disagreement over the notion of representation in cognitive science. Many investigators equate representations with symbols, that is, with syntactically defined elements in an internal symbol system. In recent years there have been two challenges to this orthodoxy. First, a number of philosophers, including many outside the symbolist orthodoxy, have argued that "representation" should be understood in its classical sense, as denoting a "stands for" relation between representation and represented. Second, there has been a growing challenge (...) to orthodoxy under the banner of connectionism. Although this connectionist challenge has evoked emotionally-charged rebukes from the symbolist camp, connectionists as a group have not articulated a conception of representation to replace the symbolist view. Nonetheless, most agree on the need for a nonsymbolic notion of representation. This paper advances a "stands for" sense of representation as primary by incorporating it into a general approach to cognitive science consonant with the connectionism. The idea is to marry connectionism to a particular version of functionalism, viz., one that builds its notion of "function" on the similarity between functional analysis in biology and in psychology. It builds on my earlier work adapting and revising Marr's tri-level approach to cognition. My proposal frees Marr's analysis from its moorings in the orthodox symbolic view of representation and allies it with a notion of functional analysis akin to that proposed by Cummins, developed further by Haugeland, and invoked by Millikan and Dretske. Unlike these authors, however, I do not wed representation to a general belief-desire analysis of behavior. Rather, I follow what I take to be the lesson of psychological practice, according to which the investigator does not seek to explain behavior in general but seeks to analyze the cognitive capacities that underlie behavior, such as vision, memory, learning, and linguistic capacities. Accordingly, ascriptions of representational content are made not by working back from belief-desire ascriptions, but in the context of forming psychological models to account for specific cognitive capacities. Because conceptions of representation in cognitive science typically are embedded in a general approach to the study of cognition, arguments for the comparative plausibility of a particular conception of representation must scout these larger frameworks. I therefore begin by characterizing the interlocking set of assumptions that gave life to the orthodox symbolist approach, paying special attention to the complementarity between representation and process that naturally arises from those assumptions. I then consider two versions of an alternative approach to representation: Dretske's and my own. Finally, I urge the merits of the "cognitive capacities" over the "belief-desire" approach to the subject-matter of cognitive science. (shrink)

According to the standard view, the causal process connecting a past representation and its subsequent recall involves intermediary memory traces. Yet Bertrand Russell and Ludwig Wittgenstein held that since the physiological evidence for memory traces isn't quite conclusive, it is prudent to come up with an account of memory causation-referred to as nmemic causation—that manages without the stipulation of memory traces. Given mnemic causation, a past representation is directly causally active over a temporal distance. I argue that the (...) stipulation of memory traces is indeed indispensable for analyzing mernory causation. (shrink)

Considerable work during the past two decades has focused on modeling the structure of semantic memory, although the performance of these models in complex and unconstrained semantic tasks remains relatively understudied. We introduce a two‐player cooperative word game, Connector (based on the boardgame Codenames), and investigate whether similarity metrics derived from two large databases of human free association norms, the University of South Florida norms and the Small World of Words norms, and two distributional semantic models based on large language (...) corpora (word2vec and GloVe) predict performance in this game. Participant dyads were presented with 20‐item word boards with word pairs of varying relatedness. The speaker received a word pair from the board (e.g., exam‐algebra) and generated a one‐word semantic clue (e.g., math), which was used by the guesser to identify the word pair on the board across three attempts. Response times to generate the clue, as well as accuracy and latencies for the guessed word pair, were strongly predicted by the cosine similarity between word pairs and clues in random walk‐based associative models, and to a lesser degree by the distributional models, suggesting that conceptual representations activated during free association were better able to capture search and retrieval processes in the game. Further, the speaker adjusted subsequent clues based on the first attempt by the guesser, who in turn benefited from the adjustment in clues, suggesting a cooperative influence in the game that was effectively captured by both associative and distributional models. These results indicate that both associative and distributional models can capture relatively unconstrained search processes in a cooperative game setting, and Connector is particularly suited to examine communication and semantic search processes. (shrink)

This paper investigates connectionism's potential to solve the frame problem. The frame problem arises in the context of modelling the human ability to see the relevant consequences of events in a situation. It has been claimed to be unsolvable for classical cognitive science, but easily manageable for connectionism. We will focus on a representational approach to the frame problem which advocates the use of intrinsic representations. We argue that although connectionism's distributed representations may look promising from this perspective, doubts (...) can be raised about the potential of distributed representations to allow large amounts of complexly structured information to be adequately encoded and processed. It is questionable whether connectionist models that are claimed to effectively represent structured information can be scaled up to a realistic extent. We conclude that the frame problem provides a difficulty to connectionism that is no less serious than the obstacle it constitutes for classical cognitive science. (shrink)

Human agents draw a variety of inferences effortlessly, spontaneously, and with remarkable efficiency – as though these inferences were a reflexive response of their cognitive apparatus. Furthermore, these inferences are drawn with reference to a large body of background knowledge. This remarkable human ability seems paradoxical given the complexity of reasoning reported by researchers in artificial intelligence. It also poses a challenge for cognitive science and computational neuroscience: How can a system of simple and slow neuronlike elements represent a large (...) body of systemic knowledge and perform a range of inferences with such speed? We describe a computational model that takes a step toward addressing the cognitive science challenge and resolving the artificial intelligence paradox. We show how a connectionist network can encode millions of facts and rules involving n-ary predicates and variables and perform a class of inferences in a few hundred milliseconds. Efficient reasoning requires the rapid representation and propagation of dynamic bindings. Our model (which we refer to as SHRUTI) achieves this by representing (1) dynamic bindings as the synchronous firing of appropriate nodes, (2) rules as interconnection patterns that direct the propagation of rhythmic activity, and (3) long-term facts as temporal pattern-matching subnetworks. The model is consistent with recent neurophysiological evidence that synchronous activity occurs in the brain and may play a representational role in neural information processing. The model also makes specific psychologically significant predictions about the nature of reflexive reasoning. It identifies constraints on the form of rules that may participate in such reasoning and relates the capacity of the working memory underlying reflexive reasoning to biological parameters such as the lowest frequency at which nodes can sustain synchronous oscillations and the coarseness of synchronization. (shrink)

Something remarkable is happening in the cognitive sciences. After a quarter of a century of cognitive models that were inspired by the metaphor of the digital computer, the newest cognitive models are inspired by the properties of the brain itself. Variously referred to as connectionist, parallel distributed processing, or neutral network models, they explore the idea that complex intellectual operations can be carried out by large networks of simple, neuron-like units. The units themselves are identical, very low-level and 'stupid'. (...) Intelligent performance is derived from the pattern of connection strengths between units, and the fundamental cognitive activity is pattern recognition and completion. Connectionism and the Mind provides an introduction to this newly emerging approach to understanding the mind. The first few chapters focus on network architecture, offering accessible treatment of the equations that describe learning and the propagation of activation. Furthermore, the reader is walked step-by-step through the activities of networks engaged in pattern recognition, learning, and cognitive tasks such as memory retrieval and prototype formation. The remainder of the book addresses the implications of connectionism for theories of the mind, both philosophical and psychological. Foe example: What Role is played by pattern recognition and completion as basic as cognitive functions? Connectionist models have particular strength in learning and pattern recognition; should they be limited to those functions, or can they provide an overall account of cognitive functioning? In particular, can connectionist models provide an adequate account of the ability to employ linguistic and other symbol systems, or must an adequate system incorporate symbol processing as a basic cognitive capacity? Finally, Connectionism and the Mind examines the relation of connectionist models to philosophical accounts of propositional attitudes, and to a variety of other inquiries in cognitive psychology, linguistics, developmental psychology, artificial intelligence and neuroscience. (shrink)

The current study addresses how information whose processing was not part of task requirement is represented in memory. Using a novel measure, recognition memory for unattended material was assessed twice, once when it appeared with the same attended study target and once with a new target. The data reveal memory for unattended study information only in the old target condition. Results suggest that the entire study event is encoded and represented in a memory trace, which contains both attended target information (...) along with unattended context information. In conclusion, manifestation of unattended memory may be dependent on the presence of old target information at test. (shrink)

Over the last decade, fully distributed models have become dominant in connectionist psychological modelling, whereas the virtues of localist models have been underestimated. This target article illustrates some of the benefits of localist modelling. Localist models are characterized by the presence of localist representations rather than the absence of distributed representations. A generalized localist model is proposed that exhibits many of the properties of fully distributed models. It can be applied to a number of problems that are (...) difficult for fully distributed models, and its applicability can be extended through comparisons with a number of classic mathematical models of behaviour. There are reasons why localist models have been underused, though these often misconstrue the localist position. In particular, many conclusions about connectionist representation, based on neuroscientific observation, can be called into question. There are still some problems inherent in the application of fully distributed systems and some inadequacies in proposed solutions to these problems. In the domain of psychological modelling, localist modelling is to be preferred. Key Words: choice; competition; connectionist modelling; consolidation; distributed; localist; neural networks; reaction-time. (shrink)

This essay analyzes the representations of time and memory in Holocaust literature through a comparative study of Charlotte Delbo’s memoir Days and Memory and Ida Fink’s three stories “A Scrap of Time,” “A Second Scrap of Time,” and “Traces.” Although both the writers make use of time and memory to represent the Holocaust, their ways of representation vary significantly. Memory and time are used in Delbo to show the timelessness in complex layers of memory and to recreate a reality (...) through inventive narrative style. Whereas, in Fink, they are used to delineate the scraps of time in the ruins of memory and to create a tragic domestic reality through conventional narrativity. Moreover, this essay cautions against the danger of misrepresentation of memory as “amnesia,” often represented in the canonical postmodernist views of memory. (shrink)

[Edited from Conclusion section:] We have looked at various challenging issues to do with getting connectionism to cope with high-level cognitive activities such a reasoning and natural language understanding. The issues are to do with various facets of generalization that are not commonly noted. We have been concerned in particular with the special forms these issues take in the arena of propositional attitude processing. The main problems we have looked at are: (1) The need to construct explicit representations of generalizations, (...) not just generalize correctly to individual cases; (2) The need to be able to match two or more complex short-term information structures, to enable rapid generalization from recent examples rather than from long-term memories; (3) The need to represent and reason with anomalous combinations of concepts; (4) The need to perform embedded reasoning. This presents special problems for systems using non-concatenative representations (as in mainstream connectionist approaches). We also touched on vague quantification in attitude report complements. Neither this topic nor that of analogies between short-term structures (point 2) has been adequately addressed in the symbolic framework, let alone in connectionism. -/- The opportunities and problems covered are put forward as things worth being optimistic about or pessimistic about, respectively. They are not put forward as decisive arguments for or against connectionism. The hope is that this chapter contributes to a greater understanding of the connectionist/symbolist gap by presenting some unusual issues and by throwing new light on some well known ones. (shrink)

been to define various notions of distribution in terms of represented by one and the same distributed pattern (Mur- structures of correspondence between the represented items dock 1979). For example, it is standard in feedforward and the representational resources (e.g., van Gelder 1992). connectionist networks for one and the same set of synap- This approach may be misguided; the essence of this alter- tic weights to represent many associations between input native category of representation might be some other (...) prop- and output. erty entirely. For example, Haugeland (1991) has suggested • Equipotentiality In some cases, an item is represented by. (shrink)

The paper develops an account of minimal traces devoid of representational content and exploits an analogy to a predictive processing framework of perception. As perception can be regarded as a prediction of the present on the basis of sparse sensory inputs without any representational content, episodic memory can be conceived of as a “prediction of the past” on the basis of a minimal trace, i.e., an informationally sparse, merely causal link to a previous experience. The resulting notion of episodic memory (...) will be validated as a natural kind distinct from imagination. This trace minimalist view contrasts with two theory camps dominating the philosophical debate on memory. On one side, we face versions of the Causal Theory that hold on to the idea that episodic remembering requires a memory trace that causally links the event of remembering to the event of experience and carries over representational content from the content of experience to the content of remembering. The Causal Theory, however, fails to account for the epistemic generativity of episodic memory and is psychologically and information-theoretically implausible. On the other side, a new camp of simulationists is currently forming up. Motivated by empirical and conceptual deficits of the Causal Theory, they reject not only the necessity of preserving representational content, but also the necessity of a causal link between experience and memory. They argue that remembering is nothing but a peculiar form of imagination, peculiar only in that it has been reliably produced and is directed towards an episode of one’s personal past. Albeit sharing their criticism of the Causal Theory and, in particular, rejecting its demand for an intermediary carrier of representational content, the paper argues that a causal connection to experience is still necessary to fulfill even the minimal requirements of past-directedness and reliability. (shrink)

We demonstrate that the key components of cognitive architectures (declarative and procedural memory) and their key capabilities (learning, memory retrieval, probability judgment, and utility estimation) can be implemented as algebraic operations on vectors and tensors in a high‐dimensional space using a distributional semantics model. High‐dimensional vector spaces underlie the success of modern machine learning techniques based on deep learning. However, while neural networks have an impressive ability to process data to find patterns, they do not typically model high‐level cognition, and (...) it is often unclear how they work. Symbolic cognitive architectures can capture the complexities of high‐level cognition and provide human‐readable, explainable models, but scale poorly to naturalistic, non‐symbolic, or big data. Vector‐symbolic architectures, where symbols are represented as vectors, bridge the gap between the two approaches. We posit that cognitive architectures, if implemented in a vector‐space model, represent a useful, explanatory model of the internal representations of otherwise opaque neural architectures. Our proposed model, Holographic Declarative Memory (HDM), is a vector‐space model based on distributional semantics. HDM accounts for primacy and recency effects in free recall, the fan effect in recognition, probability judgments, and human performance on an iterated decision task. HDM provides a flexible, scalable alternative to symbolic cognitive architectures at a level of description that bridges symbolic, quantum, and neural models of cognition. (shrink)

Visual working memory is a construct hypothesized to store a small amount of accurate perceptual information that can be brought to bear on a task. Much research concerns the construct's capacity and the precision of the information stored. Two prominent theories of VWM representation have emerged: slot-based and continuous-resource mechanisms. Prior modeling work suggests that a continuous resource that varies over trials with variable capacity and a potential to make localization errors best accounts for the empirical data. Questions remain (...) regarding the variability in VWM capacity and precision. Using a novel eye-tracking paradigm, we demonstrate that VWM facilitates search and exhibits effects of fixation frequency and recency, particularly for prior targets. Whereas slot-based memory models cannot account for the human data, a novel continuous-resource model does capture the behavioral and eye tracking data, and identifies the relevant resource as item activation. (shrink)