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Now showing results 1-7 of 7.
1. Stimulus-Dependent Dopamine Release in Attention-Deficit/Hyperactivity Disorder (EJ776716)
Author(s):
Sikstrom, Sverker; Soderlund, Goran
Source:
Psychological Review, v114 n4 p1047-1075 Oct 2007
Pub Date:
2007-10-00
Pub Type(s):
Journal Articles; Reports - Descriptive
Peer-Reviewed:
Yes
Descriptors: Stimuli; Hyperactivity; Cognitive Processes; Attention Deficit Disorders; Brain; Neurology; Environmental Influences
Abstract: Attention-deficit/hyperactivity disorder (ADHD) is related to an attenuated and dysfunctional dopamine system. Normally, a high extracellular dopamine level yields a tonic dopaminergic input that down-regulates stimuli-evoked phasic dopamine responses through autoreceptors. Abnormally low tonic extracellular dopamine in ADHD up-regulates the autoreceptors so that stimuli-evoked phasic dopamine is boosted. The authors propose that these boosted phasic responses yield hypersensitivity to environmental stimuli in ADHD. Stimuli evoking moderate brain arousal lead to well-functioning performance, whereas either too little or too much stimuli attenuate cognitive performance. Strong, salient stimuli may easily disrupt attention, whereas an environment with impoverished stimuli causes low arousal, which is typically compensated for by hyperactivity. Stochastic resonance is the phenomenon that makes a moderate noise facilitate stimulus discrimination and cognitive performance. Computational modeling shows that more noise is required for stochastic resonance to occur in dopamine-deprived neural systems in ADHD. This prediction is supported by empirical data. Note:The following two links are not-applicable for text-based browsers or screen-reading software. Show Hide Full Abstract
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2. Listen to the Noise: Noise Is Beneficial for Cognitive Performance in ADHD (EJ813274)
Soderlund, Goran; Sikstrom, Sverker; Smart, Andrew
Journal of Child Psychology and Psychiatry, v48 n8 p840-847 Aug 2007
2007-08-00
Journal Articles; Reports - Research
Descriptors: Control Groups; Hyperactivity; Attention Deficit Disorders; Memory; Memorization; Cognitive Ability; Brain; Models; Acoustics; Task Analysis
Abstract: Background: Noise is typically conceived of as being detrimental to cognitive performance. However, given the mechanism of stochastic resonance, a certain amount of noise can benefit performance. We investigate cognitive performance in noisy environments in relation to a neurocomputational model of attention deficit hyperactivity disorder (ADHD) and dopamine. The Moderate Brain Arousal model (MBA; Sikstrom & Soderlund, 2007) suggests that dopamine levels modulate how much noise is required for optimal cognitive performance. We experimentally examine how ADHD and control children respond to different encoding conditions, providing different levels of environmental stimulation. Methods: Participants carried out self-performed mini tasks (SPT), as a high memory performance task, and a verbal task (VT), as a low memory task. These tasks were performed in the presence, or absence, of auditory white noise. Results: Noise exerted a positive effect on cognitive performance for the ADHD group and deteriorated performance for the control group, indicating that ADHD subjects need more noise than controls for optimal cognitive performance. Conclusions: The positive effect of white noise is explained by the phenomenon of stochastic resonance (SR), i.e., the phenomenon that "moderate" noise facilitates cognitive performance. The MBA model suggests that noise in the environment, introduces internal noise into the neural system through the perceptual system. This noise induces SR in the neurotransmitter systems and makes this noise beneficial for cognitive performance. In particular, the peak of the SR curve depends on the dopamine level, so that participants with low dopamine levels (ADHD) require more noise for optimal cognitive performance compared to controls. Note:The following two links are not-applicable for text-based browsers or screen-reading software. Show Hide Full Abstract
3. Correlating Trainee Attributes to Performance in 3D CAD Training (EJ801485)
Hamade, Ramsey F.; Artail, Hassan A.; Sikstrom, Sverker
Journal of European Industrial Training, v31 n2 p112-126 2007
2007-00-00
Descriptors: Computer Assisted Design; Correlation; Methods; Trainees; Cognitive Development; Student Attitudes; Student Characteristics; Competence; Skill Development; Prior Learning; Computer Software; Questionnaires
Abstract: Purpose: The purpose of this exploratory study is to identify trainee attributes relevant for development of skills in 3D computer-aided design (CAD). Design/methodology/approach: Participants were trained to perform cognitive tasks of comparable complexity over time. Performance data were collected on the time needed to construct test models, and the number of features used to construct them. A written questionnaire survey profiled the trainees' technical capabilities, motivation, and dedication. Findings: Correlation analysis between the trainees' attributes/capabilities and performance showed that a mix of the trainees' psychological and technical attributes contributed to CAD competence development. Prior technical knowledge influenced initial performance whereas dedication had a strong influence on the rate of improvement. Practical implications: The methodology serves as basis for developing specific guidelines for constructing questionnaires for trainee profiling and for customizing the training of mechanical 3D CAD. Originality/value: This original research proposes a framework for classifying CAD-training candidates based on their technical and personality profiles, which may lead to more effective training. (Contains 5 figures, 3 tables and 2 notes.) Note:The following two links are not-applicable for text-based browsers or screen-reading software. Show Hide Full Abstract
4. The Isolation, Primacy, and Recency Effects Predicted by an Adaptive LTD/LTP Threshold in Postsynaptic Cells (EJ747077)
Sikstrom, Sverker
Cognitive Science, v30 n2 p243-275 2006
2006-00-00
Descriptors: Cognitive Processes; Primacy Effect; Short Term Memory; Depression (Psychology); Learning Processes; Serial Ordering; Learning Theories; Context Effect; Brain
Abstract: An item that stands out (is isolated) from its context is better remembered than an item consistent with the context. This isolation effect cannot be accounted for by increased attention, because it occurs when the isolated item is presented as the first item, or by impoverished memory of nonisolated items, because the isolated item is better remembered than a control list consisting of equally different items. The isolation effect is seldom experimentally or theoretically related to the primacy or the recency effects--that is, the improved performance on the first few and last items, respectively, on the serial position curve. The primacy effect cannot easily be accounted for by rehearsal in short-term memory because it occurs when rehearsal is eliminated. This article suggests that the primacy, the recency, and the isolation effects can be accounted for by experience-dependent synaptic plasticity in neural cells. Neurological empirical data suggest that the threshold that determines whether cells will show long-term potentiation (LTP) or long-term depression (LTD) varies as a function of recent postsynaptic activity and that synaptic plasticity is bounded. By implementing an adaptive LTP-LTD threshold in an artificial neural network, the various aspects of the isolation, the primacy, and the recency effects are accounted for, whereas none of these phenomena are accounted for if the threshold is constant. This theory suggests a possible link between the cognitive and the neurological levels. Note:The following two links are not-applicable for text-based browsers or screen-reading software. Show Hide Full Abstract
5. A Model for Stochastic Drift in Memory Strength to Account for Judgments of Learning (EJ735379)
Sikstrom, Sverker; Jonsson, Fredrik
Psychological Review, v112 n4 p932-950 Oct 2005
2005-10-00
No
Descriptors: Retention (Psychology); Cues; Recall (Psychology); Memory; Learning; Models; Predictor Variables; Statistical Analysis
Abstract: Previous research has shown that judgments of learning (JOLs) made immediately after encoding have a low correlation with actual cued-recall performance, whereas the correlation is high for delayed judgments. In this article, the authors propose a formal theory describing the stochastic drift of memory strength over the retention interval to account for the delayed-JOL effect. This is done by first decomposing the aggregated memory strength into exponential functions with slow and fast memory traces. The mean aggregated memory strength shows power-function forgetting curves. The drift of the memory strength is large for immediate JOLs (causing a low predictability) and weak for delayed JOLs (causing a high predictability). Consistent with empirical data, the model makes a novel prediction of JOL asymmetry, or that immediate weak JOLs are more predictive of future performance than are immediate strong JOLs. The JOL distributions for immediate and delayed JOLs are also accounted for. Note:The following two links are not-applicable for text-based browsers or screen-reading software. Show Hide Full Abstract
6. The Variance Reaction Time Model (EJ730297)
Cognitive Psychology, v48 n4 p371-421 Jun 2004
2004-06-00
Descriptors: Reaction Time; Models; Recognition (Psychology); Word Frequency
Abstract: The variance reaction time model (VRTM) is proposed to account for various recognition data on reaction time, the mirror effect, receiver-operating-characteristic (ROC) curves, etc. The model is based on simple and plausible assumptions within a neural network: VRTM is a two layer neural network where one layer represents items and one layer represents contexts. The recognition decision is based on a random walk of nodes activated at recognition. VRTM suggests theoretical constraints on the distributions of nodes activated at recognition and the noise in the random walk. The variability in the net inputs to nodes depends on the item frequency (the number of times that the item has been encoded) and the list length. The essential mechanism that accounts for the empirical data is a non-linear activation function. The mean activation threshold in the non-linear activation function is placed to achieve efficient discriminability between new and old items and there is variability in the activation threshold. VRTM predicts the mirror effect for low and high frequency words, a strength based mirror effect between conditions but not within one condition, appropriate ROC-curves for old/new and high/low frequency items, and list-length effects. Furthermore, it predicts appropriate means and distributions of reaction times for old/new, correct/incorrect, and high/low frequency items as well as speed/accuracy tradeoffs. VRTM has an explicit mathematical solution, it is simulated in a neural network, and it is fitted to a number of datasets. Note:The following two links are not-applicable for text-based browsers or screen-reading software. Show Hide Full Abstract
7. Forgetting Curves: Implications for Connectionist Models (EJ778713)
Cognitive Psychology, v45 n1 p95-152 Aug 2002
2002-08-00
Descriptors: Intervals; Recognition (Psychology); Long Term Memory; Knowledge Representation; Recall (Psychology); Primacy Effect; Models; Item Response Theory; Psychometrics; Learning Theories; Cognitive Psychology
Abstract: Forgetting in long-term memory, as measured in a recall or a recognition test, is faster for items encoded more recently than for items encoded earlier. Data on forgetting curves fit a power function well. In contrast, many connectionist models predict either exponential decay or completely flat forgetting curves. This paper suggests a connectionist model to account for power-function forgetting curves by using bounded weights and by generating the learning rates from a monotonically decreasing function. The bounded weights introduce exponential forgetting in each weight and a power-function forgetting results when weights with different learning rates are averaged. It is argued that these assumptions are biologically reasonable. Therefore power-function forgetting curves are a property that may be expected from biological networks. The model has an analytic solution, which is a good approximation of a power function displaced one lag in time. This function fits better than any of the 105 suggested two-parameter forgetting-curve functions when tested on the most precise recognition memory data set collected by Rubin, Hinton, and Wenzel (1999). Unlike the power-function normally used, the suggested function is defined at lag zero. Several functions for generating learning rates with a finite integral yield power-function forgetting curves; however, the type of function influences the rate of forgetting. It is shown that power-function forgetting curves cannot be accounted for by variability in performance between subjects because it requires a distribution of performance that is not found in empirical data. An extension of the model accounts for intersecting forgetting curves found in massed and spaced repetitions. The model can also be extended to account for a faster forgetting rate in item recognition (IR) compared to associative recognition in short but not long retention intervals. Note:The following two links are not-applicable for text-based browsers or screen-reading software. Show Hide Full Abstract
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