Changing state disruption of lip-reading by irrelevant sound in perceptual and memory tasks

Three experiments investigated irrelevant sound interference of lip-read lists. In Experiment 1, an acoustically changing sequence of nine irrelevant utterances was more disruptive to spoken immediate identification of lists of nine lip-read digits than nine repetitions of the same utterances (the changing-state effect; Jones, Madden, & Miles, 1992). Experiment 2 replicated this finding when lip-read items were sampled with replacement from the nine digits to form the lip-read lists. In Experiment 3, when the irrelevant sound was confined to the retention interval of a delayed recall task, a changing-state pattern of disruption also occurred. Results confirm a changing-state effect in memory for lip-read items but also point to the possibility that, for lip-reading, changing-state effects may occur at an earlier, perceptual stage.

Global exponential periodicity of a class of bidirectional associative memory networks with finite distributed delays

In this paper, we study the periodic oscillatory behavior of a class of bidirectional associative memory (BAM) networks with finite distributed delays. A set of criteria are proposed for determining global exponential periodicity of the proposed BAM networks, which assume neither differentiability nor monotonicity of the activation function of each neuron. In addition, our criteria are easily checkable. (c) 2005 Elsevier Inc. All rights reserved.

Auto-associative memory using n-tuple techniques

The use of n-tuple or weightless neural networks as pattern recognition devices has been well documented. They have a significant advantages over more common networks paradigms, such as the multilayer perceptron in that they can be easily implemented in digital hardware using standard random access memories. To date, n-tuple networks have predominantly been used as fast pattern classification devices. The paper describes how n-tuple techniques can be used in the hardware implementation of a general auto-associative network.

I owe you: age-related similarities and differences in associative memory for gains and losses

Older adults often experience associative memory impairments but can sometimes remember important information. The current experiments investigate potential age-related similarities and differences associate memory for gains and losses. Younger and older participants were presented with faces and associated dollar amounts, which indicated how much money the person “owed” the participant, and were later given a cued recall test for the dollar amount. Experiment 1 examined face-dollar amount pairs while Experiment 2 included negative dollar amounts to examine both gains and losses. While younger adults recalled more information relative to older adults, both groups were more accurate in recalling the correct value associated with high value faces compared to lower value faces and remembered gist-information about the values. However, negative values (losses) did not have a strong impact on recall among older adults versus younger adults, illustrating important associative memory differences between younger and older adults.

Self-organising neural networks for adaptive control

Self-organizing neural networks have been implemented in a wide range of application areas such as speech processing, image processing, optimization and robotics. Recent variations to the basic model proposed by the authors enable it to order state space using a subset of the input vector and to apply a local adaptation procedure that does not rely on a predefined test duration limit. Both these variations have been incorporated into a new feature map architecture that forms an integral part of an Hybrid Learning System (HLS) based on a genetic-based classifier system. Problems are represented within HLS as objects characterized by environmental features. Objects controlled by the system have preset targets set against a subset of their features. The system's objective is to achieve these targets by evolving a behavioural repertoire that efficiently explores and exploits the problem environment. Feature maps encode two types of knowledge within HLS — long-term memory traces of useful regularities within the environment and the classifier performance data calibrated against an object's feature states and targets. Self-organization of these networks constitutes non-genetic-based (experience-driven) learning within HLS. This paper presents a description of the HLS architecture and an analysis of the modified feature map implementing associative memory. Initial results are presented that demonstrate the behaviour of the system on a simple control task.

Neurofuzzy mixture of experts network parallel learning and model construction algorithms

A connection between a fuzzy neural network model with the mixture of experts network (MEN) modelling approach is established. Based on this linkage, two new neuro-fuzzy MEN construction algorithms are proposed to overcome the curse of dimensionality that is inherent in the majority of associative memory networks and/or other rule based systems. The first construction algorithm employs a function selection manager module in an MEN system. The second construction algorithm is based on a new parallel learning algorithm in which each model rule is trained independently, for which the parameter convergence property of the new learning method is established. As with the first approach, an expert selection criterion is utilised in this algorithm. These two construction methods are equivalent in their effectiveness in overcoming the curse of dimensionality by reducing the dimensionality of the regression vector, but the latter has the additional computational advantage of parallel processing. The proposed algorithms are analysed for effectiveness followed by numerical examples to illustrate their efficacy for some difficult data based modelling problems.

Neurofuzzy design and model construction of nonlinear dynamical processes from data

A common problem in many data based modelling algorithms such as associative memory networks is the problem of the curse of dimensionality. In this paper, a new two-stage neurofuzzy system design and construction algorithm (NeuDeC) for nonlinear dynamical processes is introduced to effectively tackle this problem. A new simple preprocessing method is initially derived and applied to reduce the rule base, followed by a fine model detection process based on the reduced rule set by using forward orthogonal least squares model structure detection. In both stages, new A-optimality experimental design-based criteria we used. In the preprocessing stage, a lower bound of the A-optimality design criterion is derived and applied as a subset selection metric, but in the later stage, the A-optimality design criterion is incorporated into a new composite cost function that minimises model prediction error as well as penalises the model parameter variance. The utilisation of NeuDeC leads to unbiased model parameters with low parameter variance and the additional benefit of a parsimonious model structure. Numerical examples are included to demonstrate the effectiveness of this new modelling approach for high dimensional inputs.

Optimal piecewise locally linear modeling

Associative memory networks such as Radial Basis Functions, Neurofuzzy and Fuzzy Logic used for modelling nonlinear processes suffer from the curse of dimensionality (COD), in that as the input dimension increases the parameterization, computation cost, training data requirements, etc. increase exponentially. Here a new algorithm is introduced for the construction of a Delaunay input space partitioned optimal piecewise locally linear models to overcome the COD as well as generate locally linear models directly amenable to linear control and estimation algorithms. The training of the model is configured as a new mixture of experts network with a new fast decision rule derived using convex set theory. A very fast simulated reannealing (VFSR) algorithm is utilized to search a global optimal solution of the Delaunay input space partition. A benchmark non-linear time series is used to demonstrate the new approach.

Data based constructive identification - overcoming the curse of dimensionality

The modelling of a nonlinear stochastic dynamical processes from data involves solving the problems of data gathering, preprocessing, model architecture selection, learning or adaptation, parametric evaluation and model validation. For a given model architecture such as associative memory networks, a common problem in non-linear modelling is the problem of "the curse of dimensionality". A series of complementary data based constructive identification schemes, mainly based on but not limited to an operating point dependent fuzzy models, are introduced in this paper with the aim to overcome the curse of dimensionality. These include (i) a mixture of experts algorithm based on a forward constrained regression algorithm; (ii) an inherent parsimonious delaunay input space partition based piecewise local lineal modelling concept; (iii) a neurofuzzy model constructive approach based on forward orthogonal least squares and optimal experimental design and finally (iv) the neurofuzzy model construction algorithm based on basis functions that are Bézier Bernstein polynomial functions and the additive decomposition. Illustrative examples demonstrate their applicability, showing that the final major hurdle in data based modelling has almost been removed.

Both younger and older adults have difficulty updating emotional memories

Objective. The main purpose of the study was to examine whether emotion impairs associative memory for previously seen items in older adults, as previously observed in younger adults. Method. Thirty-two younger adults and 32 older adults participated. The experiment consisted of 2 parts. In Part 1, participants learned picture–object associations for negative and neutral pictures. In Part 2, they learned picture–location associations for negative and neutral pictures; half of these pictures were seen in Part 1 whereas the other half were new. The dependent measure was how many locations of negative versus neutral items in the new versus old categories participants remembered in Part 2. Results. Both groups had more difficulty learning the locations of old negative pictures than of new negative pictures. However, this pattern was not observed for neutral items. Discussion. Despite the fact that older adults showed overall decline in associative memory, the impairing effect of emotion on updating associative memory was similar between younger and older adults.

Older adults encode-but do not always use-perceptual details: intentional versus unintentional effects of detail on memory judgments

Investigations of memory deficits in older individuals have concentrated on their increased likelihood of forgetting events or details of events that were actually encountered (errors of omission). However mounting evidence demonstrates that normal cognitive aging also is associated with an increased propensity for errors of commission-shown in false alarms or false recognition. The present study examined the origins of this age difference. Older and younger adults each performed three types of memory tasks in which details of encountered items might influence performance. Although older adults showed greater false recognition of related lures on a standard (identical) old/new episodic recognition task, older and younger adults showed parallel effects of detail on repetition priming and meaning-based episodic recognition (decreased priming and decreased meaning-based recognition for different relative to same exemplars). The results suggest that the older adults encoded details but used them less effectively than the younger adults in the recognition context requiring their deliberate, controlled use.

Implicit memory: intention and awareness revisited

One of the major developments in memory research in the past decade or so has been a growing interest in implicit memory – task performance that is not accompanied by conscious or intentional recollection. In this article, we examine evidence for perceptual and conceptual implicit memory, using the accepted definitions, and suggest that there is in fact a lack of strong evidence for implicit memory in normal subjects. If more convincing evidence cannot be obtained, one solution might be to modify the current terminology. We suggest that the term implicit memory might have outgrown its usefulness as an overall descriptor.

Transfer across form and modality in implicit and explicit memory

Three experiments examined transfer across form (words/pictures) and modality (visual/ auditory) in written word, auditory word, and pictorial implicit memory tests, as well as on a free recall task. Experiment 1 showed no significant transfer across form on any of the three implicit memory tests,and an asymmetric pattern of transfer across modality. In contrast, the free recall results revealed a very different picture. Experiment 2 further investigated the asymmetric modality effects obtained for the implicit memory measures by employing articulatory suppression and picture naming to control the generation of phonological codes. Finally, Experiment 3 examined the effects of overt word naming and covert picture labelling on transfer between study and test form. The results of the experiments are discussed in relation to Tulving and Schacter's (1990) Perceptual Representation Systems framework and Roediger's (1990) Transfer Appropriate Processing theory.

Conceptual and perceptual processing in prospective remembering

McDaniel, Robinson-Riegler, and Einstein (1998) recently reported findings in support of the proposal that prospective remembering is largely conceptually driven. In each of the three experiments they reported, however, the task in which the prospective memory target was encountered at test had a predominantly conceptual focus, thereby potentially facilitating retrieval of conceptually encoded features of the studied target event. We report two experiments in which we manipulated the dimension (perceptual or conceptual) along which a target event varied between study and test while using a processing task, at both study and test, compatible with the relevant dimension of target change. When the target was encountered in a sentence validity task at study and test, and the semantic context in which a target was encountered was changed between these two occasions, prospective remembering declined (Experiment 1). A similar decline occurred, using a readability rating task, when the perceptual context (font in which the word was printed) was altered (Experiment 2). These results indicate that both perceptual and conceptual processes can support prospective remembering.