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)

Koriat & Goldsmith propose that the correspondence metaphor captures the essence of everyday memory research. We suggest that correspondence is often not at issue because objective assessments of everyday events are frequently lacking. In these cases, other questions arise, such as how individuals evaluate the validity of memories and the significance they attach to those evaluations.

Koriat & Goldsmith's abstract correspondence metaphor is unlikely to prove useful to memory science. It aims to motivate and inform the investigation of everyday memory, but that movement has prospered without it. The irrelevance of its competitor – the more concrete storehouse metaphor – as a guiding force in memory research presages a similar fate for the correspondence perspective.

Koriat and Goldsmith clearly show the need for an alternative to the storehouse metaphor; however, the alternative metaphor they choose – the correspondence metaphor – is problematic. A more suitable one is the capacity metaphor.

The target article interprets current psychological research on everyday memory in terms of a correspondence metaphor. This metaphor is based on a reduction of everyday memory to autobiographical and eyewitness memory. This commentary focuses on everyday memory as it functions in activity. Viewed from this perspective, the joining of everyday memory to a correspondence metaphor is problematic. A more natural way to frame the processes of everyday memory is in terms of context, practice, and pragmatics.

Recent evidence suggests that experienced events are often mapped to too many episodic states, including those that are logically or experimentally incompatible with one another. For example, episodic over-distribution patterns show that the probability of accepting an item under different mutually exclusive conditions violates the disjunction rule. A related example, called subadditivity, occurs when the probability of accepting an item under mutually exclusive and exhaustive instruction conditions sums to a number >1. Both the over-distribution effect and subadditivity have been widely (...) observed in item and source-memory paradigms. These phenomena are difficult to explain using standard memory frameworks, such as signal-detection theory. A dual-trace model called the over-distribution model can explain the episodic over-distribution effect, but not subadditivity. Our goal is to develop a model that can explain both effects. In this paper, we propose the Generalized Quantum Episodic Memory model, which extends the Quantum Episodic Memory model developed by Brainerd, Wang, and Reyna. We test GQEM by comparing it to the OD model using data from a novel item-memory experiment and a previously published source-memory experiment examining the over-distribution effect. Using the best-fit parameters from the over-distribution experiments, we conclude by showing that the GQEM model can also account for subadditivity. Overall these results add to a growing body of evidence suggesting that quantum probability theory is a valuable tool in modeling recognition memory. (shrink)

In their exhaustive study of the cognitive operation of analogy, Hofstadter and Sander arrive at a paradox: the creative and inexhaustible production of analogies in our thought must derive from a “reminding” operation based upon the availability of the detailed totality of our experience. Yet the authors see no way that our experience can be stored in the brain in such detail nor do they see how such detail could be accessed or retrieved such that the innumerable analogical remindings we (...) experience can occur. Analogy creation, then, should not be possible. The intent here is to sharpen and deepen our understanding of the paradox, emphasizing its criticality. It will be shown that the retrieval problem has its origins in the failure of memory theory to recognize the actual dynamic structure of events. This structure is comprised of invariance laws as per J. J. Gibson, and this event “invariance structure” is exactly what supports Hofstadter and Sander’s missing mechanism for analogical reminding. Yet these structures of invariants, existing only over optical flows, auditory flows, haptic flows, etc., are equally difficult to imagine being stored in a static memory, and thus only exacerbate the problem of the storage of experience in the brain. A possible route to the solution of this dilemma, based in the radical model of Bergson, is also sketched. (shrink)

This commentary deals with criteria for assigning truth values to memory contents. A parallel with perception shows how truth values can be assigned by considering subjects' beliefs about the truth state of the memory content. This topic is also relevant to the study of processes of control over retrieval.

Classical antiquity provides not just the storehouse metaphor, which postdates Plato, but also parts of the correspondence metaphor. In the fifth century B.C., Thucydides (1.22) considered the role of gist and accuracy in writing history, and Aristotle (Poetics1451b, 1460b 8–11) offered an explanation. Finally, the Greek for truth (alêtheia) means “that which is not forgotten.”.

This paper critically examines the claim that parallel distributed processing (PDP) networks are autonomous learning systems. A PDP model of a simple distributed associative memory is considered. It is shown that the 'generic' PDP architecture cannot implement the computations required by this memory system without the aid of external control. In other words, the model is not autonomous. Two specific problems are highlighted: (i) simultaneous learning and recall are not permitted to occur as would be required of an autonomous system; (...) (ii) connections between processing units cannot simultaneously represent current and previous network activation as would be required if learning is to occur. Similar problems exist for more sophisticated networks constructed from the generic PDP architecture. We argue that this is because these models are not adequately constrained by the properties of the functional architecture assumed by PDP modelers. It is also argued that without such constraints, PDP researchers cannot claim to have developed an architecture radically different from that proposed by the Classical approach in cognitive science. (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)

This target article draws together two groups of experimental studies on the control of human movement through peripheral feedback and centrally generated signals of motor commands. First, during natural movement, feedback from muscle, joint, and cutaneous afferents changes; in human subjects these changes have reflex and kinesthetic consequences. Recent psychophysical and microneurographic evidence suggests that joint and even cutaneous afferents may have a proprioceptive role. Second, the role of centrally generated motor commands in the control of normal movements and movements (...) following acute and chronic deafferentation is reviewed. There is increasing evidence that subjects can perceive their motor commands under various conditions, but that this is inadequate for normal movement; deficits in motor performance arise when the reliance on proprioceptive feedback is abolished either experimentally or because of pathology. During natural movement, the CNS appears to have access to functionally useful input from a range of peripheral receptors as well as from internally generated command signals. The unanswered questions that remain suggest a number of avenues for further research. (shrink)

The ArgumentScientific tools—measurement and calculation instruments, techniques of inference—straddle the line between the context of discovery and the context of justification. In discovery, new scientific tools suggest new theoretical metaphors and concepts; and in justification, these tool-derived theoretical metaphors and concepts are morelikely to be accepted by the scientific community if the tools are already entrenched in scientific practice.Techniques of statistical inference and hypothesis testing entered American psychology first as tools in the 1940s and 1950s and then as cognitive theories (...) in the 1960s and 1970s. Not only did psychologists resists statistical metaphors of mind prior to the institutionalization of inference techniques in their own practice; the cognitive theories they ultimately developed about “the mind as intuitive statistician” still bear the telltale marks of the practical laboratory context in which the tool was used. (shrink)

The value of accuracy and quantity as memory measures is assessed. It is argued that (1) accuracy does not measure correspondence (monitoring) because it ignores omissions and correct rejections, (2) quantity is confounded with monitoring in recall, and (3) in recognition, if targets and foils are unequal, both measures, even together, still ignore correct rejections.

Koriat & Goldsmith are right in their claim that the “ecological” and “traditional” approaches to memory rely on different metaphors. But the underlying ecological metaphor is notcorrespondence(which in any case is not a metaphorical notion): it isaction. Remembering is a kind of doing; like most other forms of action it is purposive, personal, and particular.

This study was aimed at explaining how and under what conditions surface similarity leads to the retrieval of an analogous base problem in LTM. Some elements of a theory of the organisation of knowledge in memory are proposed. Two levels of representation are distinguished. The first level represents directly accessible, local surface properties. The second level represents more abstract information pertaining to the category with which each analogous problem can be associated. Some results will be described showing that access to (...) an analogue in LTM based on surface properties is determined by the existence of a similarity at a higher level of abstraction. This is a search-area effect, in that categorical information seems to delimit a memory search space, within which the connection between local surface properties is more likely to be made. (shrink)

Engineers use neural networks to control systems too complex for conventional engineering solutions. To examine the behavior of individual hidden units would defeat the purpose of this approach because it would be largely uninterpretable. Yet neurophysiologists spend their careers doing just that! Hidden units contain bits and scraps of signals that yield only arcane hints about network function and no information about how its individual units process signals. Most literature on single-unit recordings attests to this grim fact. On the other (...) hand, knowing a system's function and describing it with elegant mathematics tell one very little about what to expect of interneuronal behavior. Examples of simple networks based on neurophysiology are taken from the oculomotor literature to suggest how single-unit interpretability might decrease with increasing task complexity. It is argued that trying to explain how any real neural network works on a cell-by-cell, reductionist basis is futile and we may have to be content with trying to understand the brain at higher levels of organization. (shrink)

The appropriate methodology for psychological research depends on whether one is studying mental algorithms or their implementation. Mental algorithms are abstract specifications of the steps taken by procedures that run in the mind. Implementational issues concern the speed and reliability of these procedures. The algorithmic level can be explored only by studying across-task variation. This contrasts with psychology's dominant methodology of looking for within-task generalities, which is appropriate only for studying implementational issues.The implementation-algorithm distinction is related to a number of (...) other “levels” considered in cognitive science. Its realization in Anderson's ACT theory of cognition is discussed. Research at the algorithmic level is more promising because it is hard to make further fundamental scientific progress at the implementational level with the methodologies available. Protocol data, which are appropriate only for algorithm-level theories, provide a richer source than data at the implementational level. Research at the algorithmic level will also yield more insight into fundamental properties of human knowledge because it is the level at which significant learning transitions are defined.The best way to study the algorithmic level is to look for differential learning outcomes in pedagogical experiments that manipulate instructional experience. This provides control and prediction in realistically complex learning situations. The intelligent tutoring paradigm provides a particularly fruitful way to implement such experiments.The implications of this analysis for the issue of modularity of mind, the status of language, research on human/computer interaction, and connectionist models are also examined. (shrink)