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Janne V. Kujala - One of the best experts on this subject based on the ideXlab platform.
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Testing Selective Influence directly using trackball movement tasks
Journal of Mathematical Psychology, 2019Co-Authors: Ru Zhang, Cheng-ta Yang, Janne V. KujalaAbstract:Abstract Systems factorial technology (SFT; Townsend & Nozawa, 1995) is regarded as a useful tool to diagnose if features (or dimensions) of the investigated stimulus are processed in a parallel or serial fashion. In order to use SFT, one has to assume the speed to process each feature is Influenced by that feature only, termed as Selective Influence (Sternberg, 1969). This assumption is usually untestable as the processing time for a stimulus feature is not observable. Stochastic dominance is traditionally used as an indirect evidence for Selective Influence (e.g., Townsend & Fific, 2004). However, one should keep in mind that Selective Influence may be violated even when stochastic dominance holds. The current study proposes a trackball movement paradigm for a direct test of Selective Influence. The participants were shown a reference stimulus and a test stimulus simultaneously on a computer screen. They were asked to use the trackball to adjust the test stimulus until it appeared to match the position or shape of the reference stimulus. We recorded the reaction time, the parameters that defined the reference stimulus (denoted as α and β ), and the parameters that defined the test stimulus (denoted as A and B ). It was expected that the participants implemented the serial AND, parallel AND, or coactive manner to adjust A and B , and serial OR and parallel OR strategies were prohibited. We tested Selective Influence of α and β on the amount of time to adjust A and B through testing Selective Influence of α and β on the values of A and B using the linear feasibility test (LFT; Dzhafarov & Kujala, 2010). We found that when LFT was passed and stochastic dominance held, the inferred architecture was as expected, which was further confirmed by the trajectory of A and B observed in each trial. However, if stochastic dominance was satisfied but Selective Influence of α and β on the values of A and B was violated, then SFT could erroneously lead one to infer a prohibited architecture. Our results indicate the proposed method is more reliable for testing Selective Influence on the processing speed than examining stochastic dominance only.
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Testing Selective Influence Directly Using Trackball Movement Tasks
arXiv: Applications, 2018Co-Authors: Ru Zhang, Cheng-ta Yang, Janne V. KujalaAbstract:Systems factorial technology (SFT; Townsend & Nozawa, 1995) is regarded as a useful tool to diagnose if features (or dimensions) of the investigated stimulus are processed in a parallel or serial fashion. In order to use SFT, one has to assume the speed to process each feature is Influenced by that feature only, termed as Selective Influence (Sternberg, 1969). This assumption is usually untestable as the processing time for a stimulus feature is not observable. Stochastic dominance is traditionally used as an indirect evidence for Selective Influence (e.g., Townsend & Fific, 2004). However, one should keep in mind that Selective Influence may be violated even when stochastic dominance holds. The current study proposes a trackball movement paradigm for a direct test of Selective Influence. The participants were shown a reference stimulus and a test stimulus simultaneously on a computer screen. They were asked to use the trackball to adjust the test stimulus until it appeared to match the position or shape of the reference stimulus. We recorded the reaction time, the parameters defined the reference stimulus (denoted as \alpha and \beta ), and the parameters defined the test stimulus (denoted as A and B). We tested Selective Influence of \alpha and \beta on the amount of time to adjust A and B through testing Selective Influence of \alpha and \beta on the values of A and B using the linear feasibility test (Dzhafarov & Kujala, 2010). We found that when the test was passed and stochastic dominance held, the inferred architecture was as expected, which was further confirmed by the trajectory of A and B observed in each trial. However, with stochastic dominance only SFT can suggest a prohibited architecture. Our results indicate the proposed method is more reliable for testing Selective Influence on the processing speed than examining stochastic dominance only.
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the joint distribution criterion and the distance tests for Selective probabilistic causality
Frontiers in Psychology, 2010Co-Authors: Ehtibar N. Dzhafarov, Janne V. KujalaAbstract:A general definition and a criterion (a necessary and sufficient condition) are formulated for an arbitrary set of external factors to Selectively Influence a corresponding set of random entities (generalized random variables, with values in arbitrary observation spaces), jointly distributed at every treatment (a set of factor values containing precisely one value of each factor). The random entities are Selectively Influenced by the corresponding factors if and only if the following condition, called the joint distribution criterion, is satisfied : there is a jointly distributed set of random entities, one entity for every value of every factor, such that every subset of this set that corresponds to a treatment is distributed as the original variables at this treatment. The distance tests (necessary conditions) for Selective Influence previously formulated for two random variables in a two-by-two factorial design (Kujala & Dzhafarov, 2008, J. Math. Psychol., 52, 128–144) are extended to arbitrary sets of factors and random variables. The generalization turns out to be the simplest possible one: the distance tests should be applied to all two-by-two designs extractable from a given set of factors.
Ehtibar N. Dzhafarov - One of the best experts on this subject based on the ideXlab platform.
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unfalsifiability and mutual translatability of major modeling schemes for choice reaction time
Psychological Review, 2014Co-Authors: Matt Jones, Ehtibar N. DzhafarovAbstract:: [Correction Notice: An Erratum for this article was reported in Vol 121(1) of Psychological Review (see record 2014-03591-005). The link to supplemental material was missing. All versions of this article have been corrected.] Much current research on speeded choice utilizes models in which the response is triggered by a stochastic process crossing a deterministic threshold. This article focuses on 2 such model classes, 1 based on continuous-time diffusion and the other on linear ballistic accumulation (LBA). Both models assume random variability in growth rates and in other model components across trials. We show that if the form of this variability is unconstrained, the models can exactly match any possible pattern of response probabilities and response time distributions. Thus, the explanatory or predictive content of these models is determined not by their structural assumptions but, rather, by distributional assumptions (e.g., Gaussian distributions) that are traditionally regarded as implementation details. Selective Influence assumptions (i.e., which experimental manipulations affect which model parameters) are shown to have no restrictive effect, except for the theoretically questionable assumption that speed-accuracy instructions do not affect growth rates. The 2nd contribution of this article concerns translation of falsifiable models between universal modeling languages. Specifically, we translate the predictions of the diffusion and LBA models (with their parametric and Selective Influence assumptions intact) into the Grice modeling framework, in which accumulation processes are deterministic and thresholds are random variables. The Grice framework is also known to reproduce any possible pattern of response probabilities and times, and hence it can be used as a common language for comparing models. It is found that only a few simple properties of empirical data are necessary predictions of the diffusion and LBA models.
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the joint distribution criterion and the distance tests for Selective probabilistic causality
Frontiers in Psychology, 2010Co-Authors: Ehtibar N. Dzhafarov, Janne V. KujalaAbstract:A general definition and a criterion (a necessary and sufficient condition) are formulated for an arbitrary set of external factors to Selectively Influence a corresponding set of random entities (generalized random variables, with values in arbitrary observation spaces), jointly distributed at every treatment (a set of factor values containing precisely one value of each factor). The random entities are Selectively Influenced by the corresponding factors if and only if the following condition, called the joint distribution criterion, is satisfied : there is a jointly distributed set of random entities, one entity for every value of every factor, such that every subset of this set that corresponds to a treatment is distributed as the original variables at this treatment. The distance tests (necessary conditions) for Selective Influence previously formulated for two random variables in a two-by-two factorial design (Kujala & Dzhafarov, 2008, J. Math. Psychol., 52, 128–144) are extended to arbitrary sets of factors and random variables. The generalization turns out to be the simplest possible one: the distance tests should be applied to all two-by-two designs extractable from a given set of factors.
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Notes on Selective Influence, probabilistic causality, and probabilistic dimensionality
Journal of Mathematical Psychology, 2006Co-Authors: Ehtibar N. Dzhafarov, Ilya GluhovskyAbstract:The paper provides conceptual clarifications for the issues related to the dependence of jointly distributed systems of random entities on external factors. This includes the theory of Selective Influence as proposed in Dzhafarov [(2003a). Selective Influence through conditional independence. Psychometrika, 68, 7–26] and generalized versions of the notions of probabilistic causality [Suppes, P., & Zanotti, M. (1981). When are probabilistic explanations possible? Synthese, 48, 191–199] and dimensionality in the latent variable models [Levine, M. V. (2003). Dimension in latent variable models. Journal of Mathematical Psychology, 47, 450–466]. One of the basic observations is that any system of random entities whose joint distribution depends on a factor set can be represented by functions of two arguments: a single factor-independent source of randomness and the factor set itself. In the case of random variables (i.e., real-valued random entities endowed with Borel sigma-algebras) the single source of randomness can be chosen to be any random variable with a continuous distribution (e.g., uniformly distributed between 0 and 1).
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Mental architectures with Selectively Influenced but stochastically interdependent components
Journal of Mathematical Psychology, 2004Co-Authors: Ehtibar N. Dzhafarov, Richard Schweickert, Kyongje SungAbstract:Abstract The way external factors Influence distribution functions for the overall time required to perform a mental task (such as responding to a stimulus, or solving a problem) may be informative as to the underlying mental architecture, the hypothetical network of interconnected processes some of which are Selectively Influenced by some of the external factors. Under the assumption that all processes contributing to the overall performance time are stochastically independent, several basic results have been previously established. These results relate patterns of response time distribution functions produced by manipulating external factors to such questions as whether the hypothetical constituent processes in the mental architecture enter AND gates or OR gates, and whether pairs of processes are sequential or concurrent. The present study shows that all these results are also valid for stochastically interdependent component times, provided the Selective dependence of these components upon external factors is understood within the framework of a recently proposed theory of Selective Influence. According to this theory each component is representable as a function of three arguments: the factor set Selectively influencing it, a component-specific source of randomness, and a source of randomness shared by all the components.
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Selective Influence through conditional independence
Psychometrika, 2003Co-Authors: Ehtibar N. DzhafarovAbstract:Let each of several (generally interdependent) random vectors, taken separately, be Influenced by a particular set of external factors. Under what kind of the joint dependence of these vectors on the union of these factor sets can one say that each vector is Selectively Influenced by “its own” factor set? The answer proposed and elaborated in this paper is: One can say this if and only if one can find a factor-independent random vector given whose value the vectors in question are conditionally independent, with their conditional distributions Selectively Influenced by the corresponding factor sets. Equivalently, the random vectors should be representable as deterministic functions of “their” factor sets and of some mutually independent and factor-independent random variables, some of which may be shared by several of the functions.
Jeffrey D. Schall - One of the best experts on this subject based on the ideXlab platform.
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Selective Influence and Sequential Operations: A Research Strategy for Visual Search.
Visual cognition, 2019Co-Authors: Kaleb A. Lowe, Thomas R. Reppert, Jeffrey D. SchallAbstract:We discuss the problem of elucidating mechanisms of visual search. We begin by considering the history, logic, and methods of relating behavioural or cognitive processes with neural processes. We t...
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Selective Influence and Sequential Operations: A Research Strategy for Visual Search
2019Co-Authors: Kaleb A. Lowe, Thomas R. Reppert, Jeffrey D. SchallAbstract:We introduce conceptually and empirically a powerful but underutilized experimental approach to dissect the cognitive processes supporting performance of a visual search task with factorial manipulations of singleton-distractor identifiability and stimulus-response cue discriminability. We show that systems factorial technology can distinguish processing architectures from the performance of macaque monkeys. This demonstration offers new opportunities to distinguish neural mechanisms through Selective manipulation of visual encoding, search selection, rule encoding, and stimulus-response mapping.
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neural chronometry and coherency across speed accuracy demands reveal lack of homomorphism between computational and neural mechanisms of evidence accumulation
Philosophical Transactions of the Royal Society B, 2013Co-Authors: Richard P Heitz, Jeffrey D. SchallAbstract:The stochastic accumulation framework provides a mechanistic, quantitative account of perceptual decision-making and how task performance changes with experimental manipulations. Importantly, it provides an elegant account of the speed–accuracy trade-off (SAT), which has long been the litmus test for decision models, and also mimics the activity of single neurons in several key respects. Recently, we developed a paradigm whereby macaque monkeys trade speed for accuracy on cue during visual search task. Single-unit activity in frontal eye field (FEF) was not homomorphic with the architecture of models, demonstrating that stochastic accumulators are an incomplete description of neural activity under SAT. This paper summarizes and extends this work, further demonstrating that the SAT leads to extensive, widespread changes in brain activity never before predicted. We will begin by reviewing our recently published work that establishes how spiking activity in FEF accomplishes SAT. Next, we provide two important extensions of this work. First, we report a new chronometric analysis suggesting that increases in perceptual gain with speed stress are evident in FEF synaptic input, implicating afferent sensory-processing sources. Second, we report a new analysis demonstrating Selective Influence of SAT on frequency coupling between FEF neurons and local field potentials. None of these observations correspond to the mechanics of current accumulator models.
Ru Zhang - One of the best experts on this subject based on the ideXlab platform.
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Testing Selective Influence directly using trackball movement tasks
Journal of Mathematical Psychology, 2019Co-Authors: Ru Zhang, Cheng-ta Yang, Janne V. KujalaAbstract:Abstract Systems factorial technology (SFT; Townsend & Nozawa, 1995) is regarded as a useful tool to diagnose if features (or dimensions) of the investigated stimulus are processed in a parallel or serial fashion. In order to use SFT, one has to assume the speed to process each feature is Influenced by that feature only, termed as Selective Influence (Sternberg, 1969). This assumption is usually untestable as the processing time for a stimulus feature is not observable. Stochastic dominance is traditionally used as an indirect evidence for Selective Influence (e.g., Townsend & Fific, 2004). However, one should keep in mind that Selective Influence may be violated even when stochastic dominance holds. The current study proposes a trackball movement paradigm for a direct test of Selective Influence. The participants were shown a reference stimulus and a test stimulus simultaneously on a computer screen. They were asked to use the trackball to adjust the test stimulus until it appeared to match the position or shape of the reference stimulus. We recorded the reaction time, the parameters that defined the reference stimulus (denoted as α and β ), and the parameters that defined the test stimulus (denoted as A and B ). It was expected that the participants implemented the serial AND, parallel AND, or coactive manner to adjust A and B , and serial OR and parallel OR strategies were prohibited. We tested Selective Influence of α and β on the amount of time to adjust A and B through testing Selective Influence of α and β on the values of A and B using the linear feasibility test (LFT; Dzhafarov & Kujala, 2010). We found that when LFT was passed and stochastic dominance held, the inferred architecture was as expected, which was further confirmed by the trajectory of A and B observed in each trial. However, if stochastic dominance was satisfied but Selective Influence of α and β on the values of A and B was violated, then SFT could erroneously lead one to infer a prohibited architecture. Our results indicate the proposed method is more reliable for testing Selective Influence on the processing speed than examining stochastic dominance only.
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Testing Selective Influence Directly Using Trackball Movement Tasks
arXiv: Applications, 2018Co-Authors: Ru Zhang, Cheng-ta Yang, Janne V. KujalaAbstract:Systems factorial technology (SFT; Townsend & Nozawa, 1995) is regarded as a useful tool to diagnose if features (or dimensions) of the investigated stimulus are processed in a parallel or serial fashion. In order to use SFT, one has to assume the speed to process each feature is Influenced by that feature only, termed as Selective Influence (Sternberg, 1969). This assumption is usually untestable as the processing time for a stimulus feature is not observable. Stochastic dominance is traditionally used as an indirect evidence for Selective Influence (e.g., Townsend & Fific, 2004). However, one should keep in mind that Selective Influence may be violated even when stochastic dominance holds. The current study proposes a trackball movement paradigm for a direct test of Selective Influence. The participants were shown a reference stimulus and a test stimulus simultaneously on a computer screen. They were asked to use the trackball to adjust the test stimulus until it appeared to match the position or shape of the reference stimulus. We recorded the reaction time, the parameters defined the reference stimulus (denoted as \alpha and \beta ), and the parameters defined the test stimulus (denoted as A and B). We tested Selective Influence of \alpha and \beta on the amount of time to adjust A and B through testing Selective Influence of \alpha and \beta on the values of A and B using the linear feasibility test (Dzhafarov & Kujala, 2010). We found that when the test was passed and stochastic dominance held, the inferred architecture was as expected, which was further confirmed by the trajectory of A and B observed in each trial. However, with stochastic dominance only SFT can suggest a prohibited architecture. Our results indicate the proposed method is more reliable for testing Selective Influence on the processing speed than examining stochastic dominance only.
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Selective Influence and Classificatory Separability (Perceptual Separability) in Perception and Cognition: Similarities, Distinctions, and Synthesis
Systems Factorial Technology, 2017Co-Authors: James T. Townsend, Yanjun Liu, Ru ZhangAbstract:Abstract The question of whether and how two or more dimensions, features, or other psychological objects interact precedes the origins of scientific psychology. Using experimental factors to uncover vital aspects of psychological systems is perhaps not quite so hoary, but can be traced to at least the late 19th century. Two essential, but apparently distinct, avenues these take are, for item one, perceptual separability and, for item two, Selective Influence . This study examines their individual mathematical and logical foundations as well as their separate goals, philosophical underpinnings, and experimental applications. It is determined that deep structural correspondences exist between them, but that there are also profound distinctions, particularly in their respective goals and usages.
Richard Schweickert - One of the best experts on this subject based on the ideXlab platform.
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Tree inference: Selective Influence in multinomial processing trees with supplementary measures such as response time
Journal of Mathematical Psychology, 2018Co-Authors: Richard Schweickert, Xiaofang ZhengAbstract:Abstract Multinomial Processing Trees are successful models of response probabilities for many phenomena. Empirical validation is often based on manipulating an experimental factor intended to Selectively Influence a process represented in a Multinomial Processing Tree, to see whether the factor indeed has an effect on and only on a parameter associated with that process. Response times are rarely included, but have great potential for increasing resolution. We consider Multinomial Processing Trees in which outcomes of processes represented by vertices occur with probabilities (as usual), and also take time. For response time itself, the method of Selectively influencing processes is well developed. Established tests are based on response time means and distribution functions. We modify well established tests so they can be applied to Multinomial Processing Trees in which responses fall into two classes, say, correct and incorrect. The new tests are based on response time means and distribution functions, each multiplied by response probability. If two experimental factors Selectively Influence two different vertices in a two class Multinomial Processing Tree, the tree is equivalent to one of two simple trees. Patterns in response probabilities and times will indicate which of the two trees accounts for the data. In one of the two trees, the Selectively Influenced vertices are executed in order, in the other they are not. If there are more than two response classes, each class can be tested separately. If the patterns do not occur, no Multinomial Processing Tree exists in which the two experimental factors Selectively Influence two different vertices. We demonstrate the method with simulated data from a two factor experiment.
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Constructing a Binomial Processing Tree through Selective Influence with Application to Immediate Serial Recall
2004Co-Authors: Richard SchweickertAbstract:Title: Constructing a Binomial Processing Tree through Selective Influence with Application to Immediate Serial Recall A simple tree model gives a good account of immediate serial recall in several experiments, for effects of pairs of factors such as serial position, list length, and proactive interference. If we suppose a tree model underlies the data, is it plausible it would be simple? In a binomial tree, every vertex has two branches. An experimental factor is said to Selectively Influence a vertex if it changes the probabilities associated with the branches at that vertex, and no others. Necessary and sufficient conditions are given for probabilities to be generated by two experimental factors Selectively influencing two different vertices in a binomial tree. Two vertices are sequential if they are on a path together from the root to a terminal vertex. Patterns in the data distinguish whether the two vertices Selectively Influenced by two factors are sequential or not. If the Selectively Influenced vertices are sequential, their order on a path can sometimes be determined from patterns in the data. It is shown that if a suitable tree exists for two factors Selectively influencing two vertices, then an equivalent relatively simple tree exists.
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Mental architectures with Selectively Influenced but stochastically interdependent components
Journal of Mathematical Psychology, 2004Co-Authors: Ehtibar N. Dzhafarov, Richard Schweickert, Kyongje SungAbstract:Abstract The way external factors Influence distribution functions for the overall time required to perform a mental task (such as responding to a stimulus, or solving a problem) may be informative as to the underlying mental architecture, the hypothetical network of interconnected processes some of which are Selectively Influenced by some of the external factors. Under the assumption that all processes contributing to the overall performance time are stochastically independent, several basic results have been previously established. These results relate patterns of response time distribution functions produced by manipulating external factors to such questions as whether the hypothetical constituent processes in the mental architecture enter AND gates or OR gates, and whether pairs of processes are sequential or concurrent. The present study shows that all these results are also valid for stochastically interdependent component times, provided the Selective dependence of these components upon external factors is understood within the framework of a recently proposed theory of Selective Influence. According to this theory each component is representable as a function of three arguments: the factor set Selectively influencing it, a component-specific source of randomness, and a source of randomness shared by all the components.
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Selective Influence and response time cumulative distribution functions in serial-parallel task networks
Journal of mathematical psychology, 2000Co-Authors: Richard Schweickert, Mass Giorgini, Ehtibar N. DzhafarovAbstract:Abstract We analyze sets of mental processes, some of which are concurrent and some of which are sequential, under the assumption that the processes are partially ordered, that is, arranged in a directed acyclic network. Information about the process arrangement can be discovered by examining the effects on response time of Selectively influencing process durations. Previous work has mainly focused on analyses of mean response times. Here we consider analyses based on cumulative distribution functions, for one of the major classes of directed acyclic networks, serial-parallel networks. When two processes are Selectively Influenced, patterns in the cumulative distribution functions can be used to test whether the processes are sequential or concurrent and whether the task network has AND gates or OR gates. Cumulative distribution functions are potentially more informative than means, and some previous results for means are shown to follow from our results for cumulative distribution functions.
