A model of memory retrieval is described. The model embodies 4 main claims: (a) temporal memory— traces of items are represented in memory partly in terms of their temporal distance from the present; (b) scale-similarity—similar mechanisms govern retrieval from memory over many different timescales; (c) local distinctiveness—performance on a range of memory tasks is determined by interference from near psychological neighbors; and (d) interference-based forgetting—all memory loss is due to interference and not trace decay. The model is applied to data on free recall and serial recall. The account emphasizes qualitative similarity in the retrieval principles involved in memory performance at all timescales, contrary to models that emphasize distinctions between short-term and long-term memory.

Despite a century of research, the mechanisms underlying short-term or working memory for serial order remain uncertain. Recent theoretical models have converged on a particular account, based on transient associations between independent item and context representations. In the present article, the authors present an alternative model, according to which sequence information is encoded through sustained patterns of activation within a recurrent neural network architecture. As demonstrated through a series of computer simulations, the model provides a parsimonious account for numerous benchmark characteristics of immediate serial recall, including data that have been considered to preclude the application of recurrent neural networks in this domain. Unlike most competing accounts, the model deals naturally with findings concerning the role of background knowledge in serial recall and makes contact with relevant neuroscientific data. Furthermore, the model gives rise to numerous testable predictions that differentiate it from competing theories. Taken together, the results presented indicate that recurrent neural networks may offer a useful framework for understanding short-term memory for serial order.

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Draft: Do not quote Association and context constitute two of the central ideas in the history of episodic memory research. Following a brief discussion of the history of these ideas, we review data that demonstrate the complementary roles of temporal contiguity and semantic relatedness in determining the order in which subjects recall lists of items and the timing of their successive recalls. These analyses reveal that temporal contiguity effects persist over very long time scales, a result that challenges traditional psychological and neuroscientific models of association. The form of the temporal contiguity effect is conserved across all of the major recall tasks and even appears in item recognition when subjects respond with high confidence. The nearuniversal form of the contiguity effect and its appearance at diverse time scales is shown to place tight constraints on the major theories of association. [Y]ou are wrong to say that we cannot move about in Time. For instance, if I am recalling an incident very vividly I go back to the instant of its occurrence: I become absent-minded, as you say. I jump back for a moment. H. G. Wells, The Time Machine, 1898 In the above quote from Wells ’ classic science-fiction novel, the protagonist compares his actual travels through time to the mental time travel one experiences through the act of

The remarkable successes of the physical sciences have been built on highly general quantitative laws, which serve as the basis for understanding an enormous variety of specific physical systems. How far is it possible to construct universal principles in the cognitive sciences, in terms of which specific aspects of perception, memory, or decision making might be modelled? Following Shepard (e.g., 1987), it is argued that some universal principles may be attainable in cognitive science. Here we propose two examples: The simplicity principle (which states that the cognitive system prefers patterns that provide simpler explanations of available data); and the scale-invariance principle, which states that many cognitive phenomena are independent of the scale of relevant underlying physical variables, such as time, space, luminance, or sound pressure. We illustrate how principles may be combined to explain specific cognitive processes by using these principles to derive SIMPLE, a formal model of memory for serial order (Brown, Neath & Chater, in press), and briefly mention some extensions to models of identification and categorization. We also consider the scope and limitations of universal laws in cognitive science.

There is a large range of models of working memory, each with different scopes and emphases. Current interest focuses strongly on the interaction of working memory with long-term memory, as it has become clear that models of working memory alone are incapable of capturing some of our complex cognitive abilities. Most models have contrasting views on how this interaction is implemented. In this thesis, three classes of models are defined, each proposing a different type of interaction. The first model proposes that working memory acts as a gateway for perceptual input on its way to long-term memory. In the unitary model, working memory is seen as comprising the activated portion of long-term memory. The workspace model views working memory as a workspace that is separate from, and deals with the activated contents of long-term memory. The main aim of this thesis was to address the differences between these three models experimentally. Experiments 1 – 7 employed a dual-task paradigm to investigate the effects of irrelevant visual input on visuo-spatial working memory tasks. Two main findings emerged: (1) maintenance of images in working memory was largely insensitive to the effects of concurrent perceptual input; (2) mental imagery was susceptible to interference from irrelevant visual input. This interference effect was selective, as demonstrated by a lack of disruption of imagery by other secondary tasks. Experiment 8 further tested the three models by investigating implicit processing of visual information by neglect patients. It was found that implicit processing is mediated by the activation of long-term memory, in the absence of a conscious representation in working memory. These results together converge to support the workspace model, and suggest a view in which perceptual input activates the contents of long-term memory, prior to these activated representations being made available in a functionally separate working memory system for further processing. The gateway model and unitary model are unable to accommodate all findings. The implications of these results for existing theories about working memory are discussed.

The conceptual focus of this dissertation is the ability of humans to target episodic memories, i.e., to re-access past states of the world encoded by the memory system. I describe a framework for understanding this process that relies on the interaction of three cognitive systems in the brain: semantic memory, episodic memory, and a context maintenance system that acts to probe the episodic memory system. I build upon previous studies of these systems, in which each of these cognitive systems is mapped onto a particular anatomical region of the brain. Respectively, these areas are posterior cortex, medial temporal lobe, and prefrontal cortex. This framework is investigated in a series of studies of the free recall paradigm. First, I describe a neuroimaging experiment in which I use pattern classification methods to track the second-by-second fluctuations of patterns of brain activity during free recall. The results of this experiment provide evidence of contextual reinstatement processes during the recall period. These results highlight the idea that context effects on memory organization can be carried out by any brain area that has both a relatively stable pattern of activity during the encoding process, and connections with medial temporal lobe brain regions. Second, a behavioral study manipulates the accessibility of memories by changing encoding

The top-down guidance of visual attention is one of the main factors allowing humans to effectively process vast amounts of incoming visual information. Nevertheless we still lack a full understanding of the visual, semantic, and memory processes governing visual attention. In this paper, we present a computational model of visual search capable of predicting the most likely positions of target objects. The model does not require a separate training phase, but learns likely target positions in an incremental fashion based on a memory of previous fixations. We evaluate the model on two search tasks and show that it outperforms saliency alone and comes close to the maximal performance the Contextual Guidance Model can achieve (CGM, Torralba et al. 2006; Ehinger et al. 2009), even though our model does not perform scene recognition or compute global image statistics.

continuous-distracter free recall, sensory-motor imitation, chunking, sequence learning, prefrontal cortex, parietal cortex, position coding, rank order cells, cerebral cortex, laminar computing * Authors are listed in alphabetical order. 1