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Victoria L. M. Herrera - One of the best experts on this subject based on the ideXlab platform.
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Identification of a Novel V1-type AVP Receptor Based on the Molecular Recognition Theory
Molecular Medicine, 2001Co-Authors: Victoria L. M. Herrera, Nelson Ruiz-opazoAbstract:Background The molecular Recognition Theory predicts that binding domains of peptide hormones and their corresponding receptor binding domains evolved from complementary strands of genomic DNA, and that a process of selective evolutionary mutational events within these primordial domains gave rise to the high affinity and high specificity of peptide hormone-receptor interactions observed today in different peptide hormone-receptor systems. Moreover, this Theory has been broadened as a general hypothesis that could explain the evolution of intermolecular protein-protein and intramolecular peptide interactions. Materials and Methods Applying a molecular cloning strategy based on the molecular Recognition Theory, we screened a rat kidney cDNA library with a vasopressin (AVP) antisense oligonucleotide probe, expecting to isolate potential AVP receptors. Results We isolated a rat kidney cDNA encoding a functional V1-type vasopressin receptor. Structural analysis identified a 135 amino acid-long polypeptide with a single transmembrane domain, quite distinct from the rhodopsin-based G protein-coupled receptor superfamily. Functional analysis of the expressed V1-type receptor in Cos-1 cells revealed AVP-specific binding, AVP-specific coupling to Ca^2+ mobilizing transduction system, and characteristic V1-type antagonist inhibition. Conclusions This is the second AVP receptor cDNA isolated using AVP antipeptide-based oligonucleotide screening, thus providing compelling evidence in support of the molecular Recognition Theory as the basis of the evolution of this peptide hormone-receptor system, as well as adds molecular complexity and diversity to AVP receptor systems.
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identification of a novel v1 type avp receptor based on the molecular Recognition Theory
Molecular Medicine, 2001Co-Authors: Victoria L. M. Herrera, Nelson RuizopazoAbstract:Background The molecular Recognition Theory predicts that binding domains of peptide hormones and their corresponding receptor binding domains evolved from complementary strands of genomic DNA, and that a process of selective evolutionary mutational events within these primordial domains gave rise to the high affinity and high specificity of peptide hormone-receptor interactions observed today in different peptide hormone-receptor systems. Moreover, this Theory has been broadened as a general hypothesis that could explain the evolution of intermolecular protein-protein and intramolecular peptide interactions.
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identification of a novel dual angiotensin ii vasopressin receptor on the basis of molecular Recognition Theory
Nature Medicine, 1995Co-Authors: Nelson Ruizopazo, Kaoru Akimoto, Victoria L. M. HerreraAbstract:The molecular Recognition Theory suggests that binding sites of interacting proteins, for example, peptide hormone and its receptor binding site, were originally encoded by and evolved from complementary strands of genomic DNA. To test this Theory, we screened a rat kidney complementary DNA library twice: first with the angiotensin II (All) followed by the vasopressin (AVP) antisense oligonucleotide probe, expecting to isolate cDNA clones of the respective receptors. Surprisingly, the identical cDNA clone was isolated twice independently. Structural analysis revealed a single receptor polypeptide with seven predicted transmembrane regions, distinct All and AVP putative binding domains, a Gs, protein–activation motif, and an internalization Recognition sequence. Functional analysis revealed specific binding to both All and AVP as well as All– and AVP–induced coupling to the adenylate cyclase second messenger system. Site–directed mutagenesis of the predicted All binding domain obliterates All binding but preserves AVP binding. This corroborates the dual nature of the receptor and provides direct molecular genetic evidence for the molecular Recognition Theory.
Nelson Ruiz-opazo - One of the best experts on this subject based on the ideXlab platform.
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Identification of a Novel V1-type AVP Receptor Based on the Molecular Recognition Theory
Molecular Medicine, 2001Co-Authors: Victoria L. M. Herrera, Nelson Ruiz-opazoAbstract:Background The molecular Recognition Theory predicts that binding domains of peptide hormones and their corresponding receptor binding domains evolved from complementary strands of genomic DNA, and that a process of selective evolutionary mutational events within these primordial domains gave rise to the high affinity and high specificity of peptide hormone-receptor interactions observed today in different peptide hormone-receptor systems. Moreover, this Theory has been broadened as a general hypothesis that could explain the evolution of intermolecular protein-protein and intramolecular peptide interactions. Materials and Methods Applying a molecular cloning strategy based on the molecular Recognition Theory, we screened a rat kidney cDNA library with a vasopressin (AVP) antisense oligonucleotide probe, expecting to isolate potential AVP receptors. Results We isolated a rat kidney cDNA encoding a functional V1-type vasopressin receptor. Structural analysis identified a 135 amino acid-long polypeptide with a single transmembrane domain, quite distinct from the rhodopsin-based G protein-coupled receptor superfamily. Functional analysis of the expressed V1-type receptor in Cos-1 cells revealed AVP-specific binding, AVP-specific coupling to Ca^2+ mobilizing transduction system, and characteristic V1-type antagonist inhibition. Conclusions This is the second AVP receptor cDNA isolated using AVP antipeptide-based oligonucleotide screening, thus providing compelling evidence in support of the molecular Recognition Theory as the basis of the evolution of this peptide hormone-receptor system, as well as adds molecular complexity and diversity to AVP receptor systems.
Nelson Ruizopazo - One of the best experts on this subject based on the ideXlab platform.
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identification of a novel v1 type avp receptor based on the molecular Recognition Theory
Molecular Medicine, 2001Co-Authors: Victoria L. M. Herrera, Nelson RuizopazoAbstract:Background The molecular Recognition Theory predicts that binding domains of peptide hormones and their corresponding receptor binding domains evolved from complementary strands of genomic DNA, and that a process of selective evolutionary mutational events within these primordial domains gave rise to the high affinity and high specificity of peptide hormone-receptor interactions observed today in different peptide hormone-receptor systems. Moreover, this Theory has been broadened as a general hypothesis that could explain the evolution of intermolecular protein-protein and intramolecular peptide interactions.
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identification of a novel dual angiotensin ii vasopressin receptor on the basis of molecular Recognition Theory
Nature Medicine, 1995Co-Authors: Nelson Ruizopazo, Kaoru Akimoto, Victoria L. M. HerreraAbstract:The molecular Recognition Theory suggests that binding sites of interacting proteins, for example, peptide hormone and its receptor binding site, were originally encoded by and evolved from complementary strands of genomic DNA. To test this Theory, we screened a rat kidney complementary DNA library twice: first with the angiotensin II (All) followed by the vasopressin (AVP) antisense oligonucleotide probe, expecting to isolate cDNA clones of the respective receptors. Surprisingly, the identical cDNA clone was isolated twice independently. Structural analysis revealed a single receptor polypeptide with seven predicted transmembrane regions, distinct All and AVP putative binding domains, a Gs, protein–activation motif, and an internalization Recognition sequence. Functional analysis revealed specific binding to both All and AVP as well as All– and AVP–induced coupling to the adenylate cyclase second messenger system. Site–directed mutagenesis of the predicted All binding domain obliterates All binding but preserves AVP binding. This corroborates the dual nature of the receptor and provides direct molecular genetic evidence for the molecular Recognition Theory.
F. Gregory Ashby - One of the best experts on this subject based on the ideXlab platform.
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Testing Separability and Independence of Perceptual Dimensions with General Recognition Theory: A Tutorial and New R Package (grtools).
Frontiers in psychology, 2017Co-Authors: Fabian A. Soto, Emily Zheng, Johnny Fonseca, F. Gregory AshbyAbstract:Determining whether perceptual properties are processed independently is an important goal in perceptual science, and tools to test independence should be widely available to experimental researchers. The best analytical tools to test for perceptual independence are provided by General Recognition Theory (GRT), a multidimensional extension of signal detection Theory. Unfortunately, there is currently a lack of software implementing GRT analyses that is ready-to-use by experimental psychologists and neuroscientists with little training in computational modeling. This paper presents grtools, an R package developed with the explicit aim of providing experimentalists with the ability to perform full GRT analyses using only a couple of command lines. We describe the software and provide a practical tutorial on how to perform each of the analyses available in grtools. We also provide advice to researchers on best practices for experimental design and interpretation of results when applying GRT and grtools.
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Testing separability and independence of perceptual dimensions with general Recognition Theory: A tutorial and new R package (grtools)
arXiv: Neurons and Cognition, 2016Co-Authors: Fabian A. Soto, Emily Zheng, Johnny Fonseca, F. Gregory AshbyAbstract:Determining whether perceptual properties are processed independently is an important goal in perceptual science, and tools to test independence should be widely available to experimental researchers. The best analytical tools to test for perceptual independence are provided by General Recognition Theory (GRT), a multidimensional extension of signal detection Theory. Unfortunately, there is currently a lack of software implementing GRT analyses that is ready-to-use by experimental psychologists and neuroscientists with little training in computational modeling. This paper presents grtools, an R package developed with the explicit aim of providing experimentalists with the ability to perform full GRT analyses using only a couple of command lines. We describe the software and provide a practical tutorial on how to perform each of the analyses available in grtools. We also provide advice to researchers on best practices for experimental design and interpretation of results.
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Multidimensional Signal Detection Theory
Oxford Handbooks Online, 2015Co-Authors: F. Gregory Ashby, Fabian A. SotoAbstract:Multidimensional signal detection Theory is a multivariate extension of signal detection Theory that makes two fundamental assumptions, namely that every mental state is noisy and that every action requires a decision. The most widely studied version is known as general Recognition Theory (GRT). General Recognition Theory assumes that the percept on each trial can be modeled as a random sample from a multivariate probability distribution defined over the perceptual space. Decision bounds divide this space into regions that are each associated with a response alternative. General Recognition Theory rigorously defines and tests a number of important perceptual and cognitive conditions, including perceptual and decisional separability and perceptual independence. General Recognition Theory has been used to analyze data from identification experiments in two ways: (1) fitting and comparing models that make different assumptions about perceptual and decisional processing, and (2) testing assumptions by computing summary statistics and checking whether these satisfy certain conditions. Much has been learned recently about the neural networks that mediate the perceptual and decisional processing modeled by GRT, and this knowledge can be used to improve the design of experiments where a GRT analysis is anticipated.
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General Recognition Theory with individual differences: a new method for examining perceptual and decisional interactions with an application to face perception
Psychonomic bulletin & review, 2014Co-Authors: Fabian A. Soto, Lauren E. Vucovich, Robert D. Musgrave, F. Gregory AshbyAbstract:A common question in perceptual science is to what extent different stimulus dimensions are processed independently. General Recognition Theory (GRT) offers a formal framework via which different notions of independence can be defined and tested rigorously, while also dissociating perceptual from decisional factors. This article presents a new GRT model that overcomes several shortcomings with previous approaches, including a clearer separation between perceptual and decisional processes and a more complete description of such processes. The model assumes that different individuals share similar perceptual representations, but vary in their attention to dimensions and in the decisional strategies they use. We apply the model to the analysis of interactions between identity and emotional expression during face Recognition. The results of previous research aimed at this problem have been disparate. Participants identified four faces, which resulted from the combination of two identities and two expressions. An analysis using the new GRT model showed a complex pattern of dimensional interactions. The perception of emotional expression was not affected by changes in identity, but the perception of identity was affected by changes in emotional expression. There were violations of decisional separability of expression from identity and of identity from expression, with the former being more consistent across participants than the latter. One explanation for the disparate results in the literature is that decisional strategies may have varied across studies and influenced the results of tests of perceptual interactions, as previous studies lacked the ability to dissociate between perceptual and decisional interactions.
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A Stochastic version of general Recognition Theory
Journal of mathematical psychology, 2000Co-Authors: F. Gregory AshbyAbstract:Abstract General Recognition Theory (GRT) is a multivariate generalization of signal detection Theory. Past versions of GRT were static and lacked a process interpretation. This article presents a stochastic version of GRT that models moment-by-moment fluctuations in the output of perceptual channels via a multivariate diffusion process. A decision stage then computes a linear or quadratic function of the outputs from the perceptual channels, which drives a univariate diffusion process that determines the subject's response. Conditions are established under which the stochastic and static versions of GRT make identical accuracy predictions. These equivalence relations show that traditional estimates of perceptual noise may often be corrupted by decisional influences.
Noah H. Silbert - One of the best experts on this subject based on the ideXlab platform.
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A tutorial on General Recognition Theory
Journal of Mathematical Psychology, 2016Co-Authors: Noah H. Silbert, Robert X. D. HawkinsAbstract:Abstract General Recognition Theory (GRT; e.g., Ashby and Townsend, 1986, inter alia) is a two-stage, multidimensional model of encoding and response selection. In this tutorial, we present the basic conceptual and mathematical structure of GRT and review the three notions of dimensional interaction defined in the GRT framework: perceptual independence, perceptual separability, and decisional separability. Experimental protocols and data closely linked to the GRT model are discussed, and two sets of empirical tests of dimensional interaction are presented. These test procedures are illustrated via functions the new R package mdsdt .
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Optimal response selection and decisional separability in Gaussian general Recognition Theory
Journal of Mathematical Psychology, 2014Co-Authors: Noah H. Silbert, Robin D. ThomasAbstract:Abstract We provide the necessary and sufficient conditions for a Gaussian general Recognition Theory (GRT) model with an optimal response selection rule to be empirically indistinguishable from a model with linear decision bounds and decisional separability. General Recognition Theory assumes noisy, multidimensional perception and deterministic, multidimensional response selection; decisional separability holds if, and only if, the decision bounds that define response regions are parallel to the coordinate axes of the cognitive space of interest (e.g., perceptual space). The analysis of decisional separability is complicated by the fact that multiple response rules are possible in GRT. Recent work showed that failure of decisional separability is not identifiable in Gaussian GRT models with linear or piecewise linear decision bounds (Silbert & Thomas 2013). In the present work, we analyze the role of apparent decisional separability (and failures thereof) in an optimal GRT model. In addition to describing the necessary and sufficient conditions for optimal responding to mimic decisional separability, we show that invertible linear transformations of optimal models that explicitly mimic decisional separability produce models that implicitly mimic decisional separability. We end with a brief discussion of the effects of unequal prior stimulus probabilities and biased payoff schemes on the presence or absence of decisional separability.
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Decisional separability, model identification, and statistical inference in the general Recognition Theory framework
Psychonomic Bulletin & Review, 2013Co-Authors: Noah H. Silbert, Robin D. ThomasAbstract:Recent work in the general Recognition Theory (GRT) framework indicates that there are serious problems with some of the inferential machinery designed to detect perceptual and decisional interactions in multidimensional identification and categorization (Mack, Richler, Gauthier, & Palmeri, 2011 ). These problems are more extensive than previously recognized, as we show through new analytic and simulation-based results indicating that failure of decisional separability is not identifiable in the Gaussian GRT model with either of two common response selection models. We also describe previously unnoticed formal implicational relationships between seemingly distinct tests of perceptual and decisional interactions. Augmenting these formal results with further simulations, we show that tests based on marginal signal detection parameters produce unacceptably high rates of incorrect statistical significance. We conclude by discussing the scope of the implications of these results, and we offer a brief sketch of a new set of recommendations for testing relationships between dimensions in perception and response selection in the full-factorial identification paradigm.
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General Recognition Theory Extended to Include Response Times: Predictions for a Class of Parallel Systems
Journal of Mathematical Psychology, 2012Co-Authors: James T. Townsend, Joseph W. Houpt, Noah H. SilbertAbstract:General Recognition Theory (GRT; Ashby & Townsend, 1986) is a multidimensional Theory of classication. Originally developed to study various types of perceptual independence, it has also been widely employed in diverse cognitive venues, such as categorization. The initial Theory and applications have been static, that is, lacking a time variable and focusing on patterns of responses, such as confusion matrices. Ashby proposed a parallel, dynamic stochastic version of GRT with application to perceptual independence based on discrete linear systems Theory with imposed noise (Ashby, 1989). The current study again focuses on cognitive/perceptual independence within an identication classication paradigm. We extend stochastic GRT and its implicated methodology for cognitive/perceptual independence, to an entire class of parallel systems. This goal is met in a distribution-free manner and includes all linear and non-linear systems satisfying very general conditions. A number of theorems are proven concerning stochastic forms of independence. However, the theorems all assume the stochastic version of decisional separability. A vital task remains to investigate the consequences of failures of stochastic decisional separability.
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A GENERAL Recognition Theory STUDY OF RACE ADAPTATION
2011Co-Authors: Leslie M. Blaha, Noah H. Silbert, James T. TownsendAbstract:Studies of race aftereffects show that adaptation biases responses away from an adapting stimulus. However, it remains unclear if shifts in response frequencies result from changes in perceptual representations or in decisional mechanisms supporting race classification. General Recognition Theory (GRT) provides a modeling framework within which we investigated adaptation-induced changes on perceptual and decisional mechanisms. In a series of experiments, we replicated previous findings that adaptation shifts perceived features away from the adapting stimulus and showed similar effects for skin tone adaptation. GRT modeling was based on five complete-identification tasks. Baseline models derived from the no-adaptation condition found positive correlations between features and skin tone within and across stimuli. Adaptation-induced changes in representation from four adaptation conditions revealed shifts in perceptual representations away from adapting stimuli, variability in the within-stimulus correlations, and shifts in the decision bounds toward the adapting stimulus.