The Experts below are selected from a list of 360 Experts worldwide ranked by ideXlab platform
Emery N Brown - One of the best experts on this subject based on the ideXlab platform.
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estimating a state space model from point process observations
Neural Computation, 2003Co-Authors: Anne C Smith, Emery N BrownAbstract:A widely used signal Processing Paradigm is the state-space model. The state-space model is defined by two equations: an observation equation that describes how the hidden state or latent process is observed and a state equation that defines the evolution of the process through time. Inspired by neurophysiology experiments in which neural spiking activity is induced by an implicit (latent) stimulus, we develop an algorithm to estimate a state-space model observed through point process measurements. We represent the latent process modulating the neural spiking activity as a gaussian autoregressive model driven by an external stimulus. Given the latent process, neural spiking activity is characterized as a general point process defined by its conditional intensity function. We develop an approximate expectation-maximization (EM) algorithm to estimate the unobservable state-space process, its parameters, and the parameters of the point process. The EM algorithm combines a point process recursive nonlinear filter algorithm, the fixed interval smoothing algorithm, and the state-space covariance algorithm to compute the complete data log likelihood efficiently. We use a Kolmogorov-Smirnov test based on the time-rescaling theorem to evaluate agreement between the model and point process data. We illustrate the model with two simulated data examples: an ensemble of Poisson neurons driven by a common stimulus and a single neuron whose conditional intensity function is approximated as a local Bernoulli process.
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estimating a state space model from point process observations
Neural Computation, 2003Co-Authors: Anne C Smith, Emery N BrownAbstract:A widely used signal Processing Paradigm is the state-space model. The state-space model is defined by two equations: an observation equation that describes how the hidden state or latent process is observed and a state equation that defines the evolution of the process through time. Inspired by neurophysiology experiments in which neural spiking activity is induced by an implicit (latent) stimulus, we develop an algorithm to estimate a state-space model observed through point process measurements. We represent the latent process modulating the neural spiking activity as a gaussian autoregressive model driven by an external stimulus. Given the latent process, neural spiking activity is characterized as a general point process defined by its conditional intensity function. We develop an approximate expectation-maximization (EM) algorithm to estimate the unobservable state-space process, its parameters, and the parameters of the point process. The EM algorithm combines a point process recursive nonlinear filter algorithm, the fixed interval smoothing algorithm, and the state-space covariance algorithm to compute the complete data log likelihood efficiently. We use a Kolmogorov-Smirnov test based on the time-rescaling theorem to evaluate agreement between the model and point process data. We illustrate the model with two simulated data examples: an ensemble of Poisson neurons driven by a common stimulus and a single neuron whose conditional intensity function is approximated as a local Bernoulli process.
Henry L Roediger - One of the best experts on this subject based on the ideXlab platform.
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reactivity of judgments of learning in a levels of Processing Paradigm
Zeitschrift für Psychologie, 2020Co-Authors: Eylul Tekin, Henry L RoedigerAbstract:Abstract. Recent studies have shown that judgments of learning (JOLs) are reactive measures in paired-associate learning Paradigms. However, evidence is scarce concerning whether JOLs are reactive ...
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congruity effects between materials and Processing tasks in the survival Processing Paradigm
Journal of Experimental Psychology: Learning Memory and Cognition, 2009Co-Authors: Andrew C Butler, Sean H K Kang, Henry L RoedigerAbstract:Nairne, Thompson, and Pandeirada (2007) reported a series of experiments in which Processing unrelated words in terms of their relevance to a grasslands survival scenario led to better retention relative to other semantic Processing tasks. The impetus for their study was the premise that human memory systems evolved under the selection pressures of our ancestral past. In 3 experiments, we extended this functional approach to investigate the congruity effect-the common finding that people remember items better if those items are congruent with the way in which they are processed. Experiment 1 was a replication of Nairne et al.'s (2007) experiment and showed congruity effects in the survival Processing Paradigm. To avoid potential item-selection artifacts from randomly selected words, we manipulated congruence between words and Processing condition in Experiments 2 and 3. As expected, final recall was highest when the type of Processing and the materials were congruent, indicating that people remember stimuli better if the stimuli are congruent with the goals associated with their Processing. However, contrary to our predictions, no survival Processing advantage emerged between the 2 congruent conditions or for a list of irrelevant words. When congruity was controlled in a mixed list design, the survival Processing advantage disappeared.
Anne C Smith - One of the best experts on this subject based on the ideXlab platform.
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estimating a state space model from point process observations
Neural Computation, 2003Co-Authors: Anne C Smith, Emery N BrownAbstract:A widely used signal Processing Paradigm is the state-space model. The state-space model is defined by two equations: an observation equation that describes how the hidden state or latent process is observed and a state equation that defines the evolution of the process through time. Inspired by neurophysiology experiments in which neural spiking activity is induced by an implicit (latent) stimulus, we develop an algorithm to estimate a state-space model observed through point process measurements. We represent the latent process modulating the neural spiking activity as a gaussian autoregressive model driven by an external stimulus. Given the latent process, neural spiking activity is characterized as a general point process defined by its conditional intensity function. We develop an approximate expectation-maximization (EM) algorithm to estimate the unobservable state-space process, its parameters, and the parameters of the point process. The EM algorithm combines a point process recursive nonlinear filter algorithm, the fixed interval smoothing algorithm, and the state-space covariance algorithm to compute the complete data log likelihood efficiently. We use a Kolmogorov-Smirnov test based on the time-rescaling theorem to evaluate agreement between the model and point process data. We illustrate the model with two simulated data examples: an ensemble of Poisson neurons driven by a common stimulus and a single neuron whose conditional intensity function is approximated as a local Bernoulli process.
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estimating a state space model from point process observations
Neural Computation, 2003Co-Authors: Anne C Smith, Emery N BrownAbstract:A widely used signal Processing Paradigm is the state-space model. The state-space model is defined by two equations: an observation equation that describes how the hidden state or latent process is observed and a state equation that defines the evolution of the process through time. Inspired by neurophysiology experiments in which neural spiking activity is induced by an implicit (latent) stimulus, we develop an algorithm to estimate a state-space model observed through point process measurements. We represent the latent process modulating the neural spiking activity as a gaussian autoregressive model driven by an external stimulus. Given the latent process, neural spiking activity is characterized as a general point process defined by its conditional intensity function. We develop an approximate expectation-maximization (EM) algorithm to estimate the unobservable state-space process, its parameters, and the parameters of the point process. The EM algorithm combines a point process recursive nonlinear filter algorithm, the fixed interval smoothing algorithm, and the state-space covariance algorithm to compute the complete data log likelihood efficiently. We use a Kolmogorov-Smirnov test based on the time-rescaling theorem to evaluate agreement between the model and point process data. We illustrate the model with two simulated data examples: an ensemble of Poisson neurons driven by a common stimulus and a single neuron whose conditional intensity function is approximated as a local Bernoulli process.
Josefa N S Pandeirada - One of the best experts on this subject based on the ideXlab platform.
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congruity effects in the survival Processing Paradigm
Journal of Experimental Psychology: Learning Memory and Cognition, 2011Co-Authors: James S Nairne, Josefa N S PandeiradaAbstract:Five experiments were conducted to investigate a proposal by Butler, Kang, and Roediger (2009) that congruity (or fit) between target items and Processing tasks might contribute, at least partly, to the mnemonic advantages typically produced by survival Processing. In their research, no significant survival advantages were found when words were preselected to be highly congruent or incongruent with a survival and control (robbery) scenario. Experiments 1a and 1b of the present report show that survival advantages, in fact, generalize across a wide set of selected target words; each participant received a unique set of words, sampled without replacement from a large pool, yet significant survival advantages remained. In Experiment 2, we found a significant survival advantage using words that had been preselected by Butler et al. to be highly unrelated (or irrelevant) to both the survival and control scenarios. Experiment 3 showed a significant survival advantage using word sets that had been preselected to be highly congruent with both scenarios. Finally, Experiment 4 mixed congruent and incongruent words in the same list, more closely replicating the design used by Butler et al., and a highly reliable main effect of survival Processing was still obtained (although the survival advantage for the congruent words did not reach conventional levels of statistical significance). Our results suggest that the null effects of survival Processing obtained by Butler et al. may not generalize beyond their particular experimental design.
Edgar Erdfelder - One of the best experts on this subject based on the ideXlab platform.
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how can i use it the role of functional fixedness in the survival Processing Paradigm
Psychonomic Bulletin & Review, 2021Co-Authors: Meike Kroneisen, Michael Kriechbaumer, Siri-maria Kamp, Edgar ErdfelderAbstract:After imagining being stranded in the grasslands of a foreign land without any basic survival material and rating objects with respect to their relevance in this situation, participants show superior memory performance for these objects compared to a control scenario. A possible mechanism responsible for this memory advantage is the richness and distinctiveness with which information is encoded in the survival-scenario condition. When confronted with the unusual task of thinking about how an object can be used in a life-threatening context, participants will most likely consider both common and uncommon (i.e., novel) functions of this object. These ideas about potential functions may later serve as powerful retrieval cues that boost memory performance. We argue that objects differ in their potential to be used as novel, creative survival tools. Some objects may be low in functional fixedness, meaning that it is possible to use them in many different ways. Other objects, in contrast, may be high in functional fixedness, meaning that the possibilities to use them in non-standard ways is limited. We tested experimentally whether functional fixedness of objects moderates the strength of the survival-Processing advantage compared to a moving control scenario. As predicted, we observed an interaction of the functional fixedness level with scenario type: The survival-Processing memory advantage was more pronounced for objects low in functional fixedness compared to those high in functional fixedness. These results are in line with the richness-of-encoding explanation of the survival-Processing advantage.
