Noisy Memory Research Articles

Optimal Strategy of Mean-Field FBSDE Games with Delay and Noisy Memory Based on Malliavin Calculus

Optimal Strategy of Mean-Field FBSDE Games with Delay and Noisy Memory Based on Malliavin Calculus

Pareto optimality of stochastic cooperative differential game with general delays in finite horizon

The Pareto optimality of stochastic cooperative differential game with a discrete delay, a moving-average delay and a noisy memory process is studied. We establish two sets of equivalent necessary and sufficient conditions for Pareto efficient strategies. The first set comes from reduction to a discrete delayed Pareto optimality, while the second set given by Malliavin derivative is derived by the decomposition of the adjoint equation and the relationship between two Hamiltonian functions. As applications, we use the theoretical results to an indefinite linear quadratic (LQ) Pareto game with general delays.

Risk-based optimal portfolio of an insurance firm with regime switching and noisy memory

Risk-based optimal portfolio of an insurance firm with regime switching and noisy memory

A sufficient maximum principle for backward stochastic systems with mixed delays.

In this paper, we study the problem of optimal control of backward stochastic differential equations with three delays (discrete delay, moving-average delay and noisy memory). We establish the sufficient optimality condition for the stochastic system. We introduce two kinds of time-advanced stochastic differential equations as the adjoint equations, which involve the partial derivatives of the function $ f $ and its Malliavin derivatives. We also show that these two kinds of adjoint equations are equivalent. Finally, as applications, we discuss a linear-quadratic backward stochastic system and give an explicit optimal control. In particular, the stochastic differential equations with time delay are simulated by means of discretization techniques, and the effect of time delay on the optimal control result is explained.

Mixtur: An R package for designing, analysing, and modelling continuous report visual short-term memory studies

Visual short-term memory (vSTM) is often measured via continuous-report tasks whereby participants are presented with stimuli that vary along a continuous dimension (e.g., colour) with the goal of memorising the stimulus features. At test, participants are probed to recall the feature value of one of the memoranda in a continuous manner (e.g., by clicking on a colour wheel). The angular deviation between the participant response and the true feature value provides an estimate of recall precision. Two prominent models of performance on such tasks are the two- and three-component mixture models (Bays et al., Journal of Vision, 9(10), Article 7, 2009; Zhang and Luck, Nature, 453(7192), 233–235, 2008). Both models decompose participant responses into probabilistic mixtures of: (1) responses to the true target value based on a noisy memory representation; (2) random guessing when memory fails. In addition, the three-component model proposes (3) responses to a non-target feature value (i.e., binding errors). Here we report the development of mixtur, an open-source package written for the statistical programming language R that facilitates the fitting of the two- and three-component mixture models to continuous report data. We also conduct simulations to develop recommendations for researchers on trial numbers, set sizes, and memoranda similarity, as well as parameter recovery and model recovery. In the Discussion, we discuss how mixtur can be used to fit the slots and the slots-plus-averaging models, as well as how mixtur can be extended to fit explanatory models of visual short-term memory. It is our hope that mixtur will lower the barrier of entry for utilising mixture modelling.

Application of an artificial intelligence algorithm model of memory retrieval and roaming in sorting Chinese medicinal materials

In order to capture autobiographical memory, inspired by the development of human intelligence, a computational AM model for autobiographical memory is proposed in this paper, which is a three-layer network structure, in which the bottom layer encodes the event-specific knowledge comprising 5W1H, and provides retrieval clues to the middle layer, encodes the related events, and the top layer encodes the event set. According to the bottom-up memory search process, the corresponding events and event sets can be identified in the middle layer and the top layer respectively; At the same time, AM model can simulate human memory roaming through the process of rule-based memory retrieval. The computational AM model proposed in this paper not only has robust and flexible memory retrieval, but also has better response performance to noisy memory retrieval cues than the commonly used memory retrieval model based on keyword query method, and can also imitate the roaming phenomenon in memory.

Maximum principle for infinite horizon optimal control of mean-field backward stochastic systems with delay and noisy memory

In this paper, we consider a problem of optimal control of an infinite horizon mean-field backward stochastic differential equation with delay and noisy memory under partial information. We derive necessary and sufficient maximum principles using Malliavin calculus technique for such a system. A class of mean-field time-advanced stochastic differential equations is introduced as the adjoint process which involves partial derivatives of the Hamiltonian functions and their Malliavin derivatives. To illustrate our theoretical results, we give an example for a linear-quadratic mean-field backward delay stochastic system with noisy memory on infinite horizon to obtain the optimal control. Also, we apply our results to pension fund problems with delay and noisy memory which are arising from the financial market.

A Neural Network Prefetcher for Arbitrary Memory Access Patterns

Memory prefetchers are designed to identify and prefetch specific access patterns, including spatiotemporal locality (e.g., strides, streams), recurring patterns (e.g., varying strides, temporal correlation), and specific irregular patterns (e.g., pointer chasing, index dereferencing). However, existing prefetchers can only target premeditated patterns and relations they were designed to handle and are unable to capture access patterns in which they do not specialize. In this article, we propose a context-based neural network (NN) prefetcher that dynamically adapts to arbitrary memory access patterns. Leveraging recent advances in machine learning, the proposed NN prefetcher correlates program and machine contextual information with memory accesses patterns, using online-training to identify and dynamically adapt to unique access patterns exhibited by the code. By targeting semantic locality in this manner, the prefetcher can discern the useful context attributes and learn to predict previously undetected access patterns, even within noisy memory access streams. We further present an architectural implementation of our NN prefetcher, explore its power, energy, and area limitations, and propose several optimizations. We evaluate the neural network prefetcher over SPEC2006, Graph500, and several microbenchmarks and show that the prefetcher can deliver an average speedup of 21.3% for SPEC2006 (up to 2.3×) and up to 4.4× on kernels over a baseline of PC-based stride prefetcher and 30% for SPEC2006 over a baseline with no prefetching.

Two minds are not always better than one: Modeling evidence for a single sentence analyzer

A challenge for grammatical theories and models of language processing alike is to explain conflicting online and offline judgments about the acceptability of sentences. A prominent example of the online/offline mismatch involves “agreement attraction” in sentences like *The key to the cabinets were rusty, which are often erroneously treated as acceptable in time-restricted “online” measures, but judged as less acceptable in untimed “offline” tasks. The prevailing assumption is that online/offline mismatches are the product of two linguistic analyzers: one analyzer for rapid communication (the “parser”) and another, slower analyzer that classifies grammaticality (the “grammar”). A competing hypothesis states that online/offline mismatches reflect a single linguistic analyzer implemented in a noisy memory architecture that creates the opportunity for errors and conflicting judgments at different points in time. A challenge for the single-analyzer account is to explain why online and offline tasks sometimes yield conflicting responses if they are mediated by the same analyzer. The current study addresses this challenge by showing how agreement attraction effects might come and go over time in a single-analyzer architecture. Experiments 1 and 2 use an agreement attraction paradigm to directly compare online and offline judgments, and confirm that the online/offline contrast reflects the time restriction in online tasks. Experiment 3 then uses computational modeling to capture the mapping from online to offline responses as a process of sequential memory sampling in a single-analyzer framework. This demonstration provides some proof-of-concept for the single-analyzer account and offers an explicit process model for the mapping between online and offline responses.

Noisy Memory and Over-Reaction to News

We propose a model of optimal decision-making subject to a memory constraint. The constraint is a limit on the complexity of memory measured using Shannon's mutual information, as in models of rational inattention. We show that the model implies that both forecasts and actions will exhibit idiosyncratic random variation; that beliefs will fluctuate forever around the rational-expectations (perfect-memory) beliefs with a variance that does not fall to zero; and that more recent news will be given disproportionate weight. The model provides a simple explanation for a number of features of expectations in laboratory and field settings.

Optimal control of mean-field jump-diffusion systems with noisy memory

ABSTRACTThis paper is concerned with an optimal control problem under mean-field jump-diffusion systems with delay and noisy memory. First, we derive necessary and sufficient maximum principles using Malliavin calculus technique. Meanwhile, we introduce a new mean-field backward stochastic differential equation as the adjoint equation which involves not only partial derivatives of the Hamiltonian function but also their Malliavin derivatives. Then, applying a reduction of the noisy memory dynamics to a two-dimensional discrete delay optimal control problem, we establish the second set of necessary and sufficient maximum principles under partial information. Moreover, a natural link between the above two approaches is established via the adjoint equations. Finally, we apply our theoretical results to study a mean-field linear-quadratic optimal control problem.

Monkeys and humans take local uncertainty into account when localizing a change

Since sensory measurements are noisy, an observer is rarely certain about the identity of a stimulus. In visual perception tasks, observers generally take their uncertainty about a stimulus into account when doing so helps task performance. Whether the same holds in visual working memory tasks is largely unknown. Ten human and two monkey subjects localized a single change in orientation between a sample display containing three ellipses and a test display containing two ellipses. To manipulate uncertainty, we varied the reliability of orientation information by making each ellipse more or less elongated (two levels); reliability was independent across the stimuli. In both species, a variable-precision encoding model equipped with an “uncertainty–indifferent” decision rule, which uses only the noisy memories, fitted the data poorly. In both species, a much better fit was provided by a model in which the observer also takes the levels of reliability-driven uncertainty associated with the memories into account. In particular, a measured change in a low-reliability stimulus was given lower weight than the same change in a high-reliability stimulus. We did not find strong evidence that observers took reliability-independent variations in uncertainty into account. Our results illustrate the importance of studying the decision stage in comparison tasks and provide further evidence for evolutionary continuity of working memory systems between monkeys and humans.

Spatial working memory capacity predicts bias in estimates of location.

Spatial memory research has attributed systematic bias in location estimates to a combination of a noisy memory trace with a prior structure that people impose on the space. Little is known about intraindividual stability and interindividual variation in these patterns of bias. In the current work, we align recent empirical and theoretical work on working memory capacity limits and spatial memory bias to generate the prediction that those with lower working memory capacity will show greater bias in memory of the location of a single item. Reanalyzing data from a large study of cognitive aging, we find support for this prediction. Fitting separate models to individuals' data revealed a surprising variety of strategies. Some were consistent with Bayesian models of spatial category use, however roughly half of participants biased estimates outward in a way not predicted by current models and others seemed to combine these strategies. These analyses highlight the importance of studying individuals when developing general models of cognition. (PsycINFO Database Record

The dynamics of fidelity over the time course of long-term memory

Bayesian models of cognition assume that prior knowledge about the world influences judgments. Recent approaches have suggested that the loss of fidelity from working to long-term (LT) memory is simply due to an increased rate of guessing (e.g. Brady, Konkle, Gill, Oliva, & Alvarez, 2013). That is, recall is the result of either remembering (with some noise) or guessing. This stands in contrast to Bayesian models of cognition while assume that prior knowledge about the world influences judgments, and that recall is a combination of expectations learned from the environment and noisy memory representations. Here, we evaluate the time course of fidelity in LT episodic memory, and the relative contribution of prior category knowledge and guessing, using a continuous recall paradigm. At an aggregate level, performance reflects a high rate of guessing. However, when aggregate data is partitioned by lag (i.e., the number of presentations from study to test), or is un-aggregated, performance appears to be more complex than just remembering with some noise and guessing. We implemented three models: the standard remember-guess model, a three-component remember-guess model, and a Bayesian mixture model and evaluated these models against the data. The results emphasize the importance of taking into account the influence of prior category knowledge on memory.

Optimal control of systems with noisy memory and BSDEs with Malliavin derivatives

In this article we consider a stochastic optimal control problem where the dynamics of the state process, X(t), is a controlled stochastic differential equation with jumps, delay and noisy memory. The term noisy memory is, to the best of our knowledge, new. By this we mean that the dynamics of X(t) depend on ∫t−δtX(s)dB(s) (where B(t) is a Brownian motion). Hence, the dependence is noisy because of the Brownian motion, and it involves memory due to the influence from the previous values of the state process.We derive necessary and sufficient maximum principles for this stochastic control problem in two different ways, resulting in two sets of maximum principles. The first set of maximum principles is derived using Malliavin calculus techniques, while the second set comes from reduction to a discrete delay optimal control problem, and application of previously known results by Øksendal, Sulem and Zhang. The maximum principles also apply to the case where the controller has only partial information, in the sense that the admissible controls are adapted to a sub-σ-algebra of the natural filtration.

Serial Consolidation of Orientation Information Into Visual Short-Term Memory

Previous research suggests that there is a limit to the rate at which items can be consolidated in visual short-term memory (VSTM). This limit could be due to either a serial or a limited-capacity parallel process. Historically, it has proven difficult to distinguish between these two types of processes. In the present experiment, we took a novel approach that allowed us to do so. Participants viewed two oriented gratings either sequentially or simultaneously and reported one of the gratings’ orientation via method of adjustment. Performance was worse for the simultaneous than for the sequential condition. We fit the data with a mixture model that assumes performance is limited by a noisy memory representation plus random guessing. Critically, the serial and limited-capacity parallel processes made distinct predictions regarding the model’s guessing and memory-precision parameters. We found strong support for a serial process, which implies that one can consolidate only a single orientation into VSTM at a time.

Visual Short-Term Memory Compared in Rhesus Monkeys and Humans

Change detection is a popular task to study visual short-term memory (STM) in humans [1-4]. Much of this work suggests that STM has a fixed capacity of 4 ± 1 items [1-6]. Here we report the first comparison of change-detection memory between humans and a species closely related to humans, the rhesus monkey. Monkeys and humans were tested in nearly identical procedures with overlapping display sizes. Although the monkeys' STM was well fit by a one-item fixed-capacity memory model, other monkey memory tests with four-item lists have shown performance impossible to obtain with a one-item capacity [7]. We suggest that this contradiction can be resolved using a continuous-resource approach more closely tied to the neural basis of memory [8, 9]. In this view, items have a noisy memory representation whose noise level depends on display size as a result of the distributed allocation of a continuous resource. In accord with this theory, we show that performance depends on the perceptual distance between items before and after the change, and d' depends on display size in an approximately power-law fashion. Our results open the door to combining the power of psychophysics, computation, and physiology to better understand the neural basis of STM.

A Bayesian account of reconstructive memory.

It is well established that prior knowledge influences reconstruction from memory, but the specific interactions of memory and knowledge are unclear. Extending work by Huttenlocher etal. (Psychological Review, 98 [1991] 352; Journal of Experimental Psychology: General, 129 [2000] 220), we propose a Bayesian model of reconstructive memory in which prior knowledge interacts with episodic memory at multiple levels of abstraction. The combination of prior knowledge and noisy memory representations is dependent on familiarity. We present empirical evidence of the influences of prior knowledge at multiple levels of abstraction, showing that the reconstruction of familiar objects is influenced toward the specific prior for that object, while unfamiliar objects are influenced toward the overall category.

Scheme for Reducing Decoherence in Quantum Computer Memory by Transformation to the Coherence-Preserving States

If quantum bits (qubits) couple to the same environment, it has been found the qubits decohere coherently. An interesting result from this phenomenon is that, for a kind of input states, i.e., the coherence-preserving states, coherence of the qubits can be preserved perfectly in quantum memory. In this paper, we propose a feasible scheme to transform an arbitrary unknown input state to the corresponding coherence-preserving state. The transformed state undergoes no decoherence in the noisy memory and, after that, it can be transformed back into the original state with decoherence much reduced. This scheme involves the use of an analogy of the ideas of quantum teleportation.

Detection of a Signal Specified Exactly with a Noisy Stored Reference Signal

This paper treats the optimization problem of detecting the presence of a signal in a background of white Gaussian noise, under the restriction that the signal is specified exactly but the receiver memory contains only a noisy version of the signal. The optimum receiver is specified. The performances of both the optimum receiver and the cross-correlation receiver with a noisy memory are calculated and compared for a special case.