User modeling via stereotypes

This paper addresses the problems that must be considered if computers are going to treat their users as individuals with distinct personalities, goals, and so forth. It first outlines the issues, and then proposes stereotypes as a useful mechanism for building models of individual users on the basis of a small amount of information about them. In order to build user models quickly, a large amount of uncertain knowledge must be incorporated into the models. The issue of how to resolve the conflicts; hot will arise along such inferences is discussed. A system, Grundy, is described that builds models of its users, with the aid of stereotypes, and then exploits those models to guide it in its task, suggesting novels that people may find interesting. If stereotypes are to be useful to Grundy, they must accurately characterize the users of the system. Some techniques to modify stereotypes on the basis of experience are discussed. An analysis of Grundy's performance shows that its user models are effective in guiding its performance.     

A hierarchical state space approach to affective dynamics

Linear dynamical system theory is a broad theoretical framework that has been applied in various research areas such as engineering, econometrics and recently in psychology. It quantifies the relations between observed inputs and outputs that are connected through a set of latent state variables. State space models are used to investigate the dynamical properties of these latent quantities. These models are especially of interest in the study of emotion dynamics, with the system representing the evolving emotion components of an individual. However, for simultaneous modeling of individual and population differences, a hierarchical extension of the basic state space model is necessary. Therefore, we introduce a Bayesian hierarchical model with random effects for the system parameters. Further, we apply our model to data that were collected using the Oregon adolescent interaction task: 66 normal and 67 depressed adolescents engaged in a conflict-oriented interaction with their parents and second-to-second physiological and behavioral measures were obtained. System parameters in normal and depressed adolescents were compared, which led to interesting discussions in the light of findings in recent literature on the links between cardiovascular processes, emotion dynamics and depression. We illustrate that our approach is flexible and general: The model can be applied to any time series for multiple systems (where a system can represent any entity) and moreover, one is free to focus on various components of this versatile model.     

Looking, language, and memory: Bridging research from the visual world and visual search paradigms

In the visual world paradigm as used in psycholinguistics, eye gaze (i.e. visual orienting) is measured in order to draw conclusions about linguistic processing. However, current theories are underspecified with respect to how visual attention is guided on the basis of linguistic representations. In the visual search paradigm as used within the area of visual attention research, investigators have become more and more interested in how visual orienting is affected by higher order representations, such as those involved in memory and language. Within this area more specific models of orienting on the basis of visual information exist, but they need to be extended with mechanisms that allow for language-mediated orienting. In the present paper we review the evidence from these two different – but highly related – research areas. We arrive at a model in which working memory serves as the nexus in which long-term visual as well as linguistic representations (i.e. types) are bound to specific locations (i.e. tokens or indices). The model predicts that the interaction between language and visual attention is subject to a number of conditions, such as the presence of the guiding representation in working memory, capacity limitations, and cognitive control mechanisms.     

Joint effects of semantic priming and repetition in a lexical decision task: Implications for a model of lexical access

An experiment is reported which showed that in a lexical decision task semantic priming by a related preceding word and repetition of target words produce additive effects on decision latency. Previous models of lexical access and modifications of them are discussed, and it is argued that some such models predict an interaction of priming and repetition, while others are insufficiently precise to make a prediction. It is suggested that the generality of effects across tasks requiring lexical access must be established and the components of complex effects must be separated before an adequate model can be devised to account for the data.     

Saliency on a natural scene background: Effects of color and luminance contrast add linearly

In natural vision, shifts in spatial attention are associated with shifts of gaze. Computational models of such overt attention typically use the concept of a saliency map: Normalized maps of center-surround differences are computed for individual stimulus features and added linearly to obtain the saliency map. Although the predictions of such models correlate with fixated locations better than chance, their mechanistic assumptions are less well investigated. Here, we tested one key assumption: Do the effects of different features add linearly or according to a max-type of interaction? We measured the eye position of observers viewing natural stimuli whose luminance contrast and/or color contrast (saturation) increased gradually toward one side. We found that these feature gradients biased fixations toward regions of high contrasts. When two contrast gradients (color and luminance) were superimposed, linear summation of their individual effects predicted their combined effect. This demonstrated that the interaction of color and luminance contrast with respect to human overt attention is—irrespective of the precise model—consistent with the assumption of linearity, but not with a max-type interaction of these features.     

Is computer simulation changing the face of experimentation?

Morrison points out many similarities between the roles of simulation models and other sorts of models in science. On the basis of these similarities she claims that running a simulation is epistemologically on a par with doing a traditional experiment and that the output of a simulation therefore counts as a measurement. I agree with her premises but reject the inference. The epistemological payoff of a traditional experiment is greater (or less) confidence in the fit between a model and a target system. The source of this payoff is the existence of a causal interaction with the target system. A computer experiment, which does not go beyond the simulation system itself, lacks any such interaction. So computer experiments cannot confer any additional confidence in the fit (or lack thereof) between the simulation model and the target system.     

A Monte Carlo study of the effects of correlated method variance in moderated multiple regression analysis

It has been suggested that hierarchical regression analysis provides an unambiguous conclusion with regard to the existence of moderator effects (Arnold & Evans, 1979). This paper examines the impact of correlated error among the dependent and independent variables in order to explore whether or not artificial interaction terms can be generated. A Monte Carlo study was performed to investigate the effects of correlated error on noninteraction and interaction models. The results are clear-cut. Artifactual interaction cannot be created; true interactions can be attentuated. Some practical suggestions are provided for drawing inferences from hierarchical regression analysis.     

Human-computer interaction: A preliminary social psychological analysis

Considering an interactive computer as a social stimulus suggests that contemporary social psychological theories can contribute to the prediction of user attitude and performance. In order to assist in the systematic exploration of this possibility, we developed DIALOGUE, an on-line system to investigate the effects of varying the computer’s responses to the user. This system involves a presentation program that displays the computer’s responses, performs the pacing of video information, and collects a variety of measurements, including the user’s response time and the number of correct/incorrect user responses. DIALOGUE also includes a data manager that allows the experimenter to examine or modify the information collected by the presentation program. Utilizing DIALOGUE, we conducted a preliminary investigation of one aspect of human-computer interaction, the effects of varying the degree of human-like responses exhibited by the computer. Results suggest that (1) there are underlying dimensions of judgment involving perception of interactive computers, (2) a manipulation of human-like computer responses is reflected primarily in certain of these dimensions, and (3) such a manipulation influences user performance and feelings of responsibility. Factors related to the implementation of DIALOGUE are considered, and its potential for investigations of a variety of human-computer interactions is discussed.     

Predictive and diagnostic learning within causal models: asymmetries in cue competition.

Several researchers have recently claimed that higher order types of learning, such as categorization and causal induction, can be reduced to lower order associative learning. These claims are based in part on reports of cue competition in higher order learning, apparently analogous to blocking in classical conditioning. Three experiments are reported in which subjects had to learn to respond on the basis of cues that were defined either as possible causes of a common effect (predictive learning) or as possible effects of a common cause (diagnostic learning). The results indicate that diagnostic and predictive reasoning, far from being identical as predicted by associationistic models, are not even symmetrical. Although cue competition occurs among multiple possible causes during predictive learning, multiple possible effects need not compete during diagnostic learning. The results favor a causal-model theory.     

Investigating the Interaction Between Sleep Symptoms of Arousal and Acquired Capability in Predicting Suicidality

Heightened arousal significantly interacts with acquired capability to predict suicidality. We explore this interaction with insomnia and nightmares independently of waking state arousal symptoms, and test predictions of the Interpersonal Theory of Suicide (IPTS) and Escape Theory in relation to these sleep arousal symptoms. Findings from our e‐survey (n = 540) supported the IPTS over models of Suicide as Escape. Sleep‐specific measurements of arousal (insomnia and nightmares) showed no main effect, yet interacted with acquired capability to predict increased suicidality. The explained variance in suicidality by the interaction (1%–2%) using sleep‐specific measures was comparable to variance explained by interactions previously reported in the literature using measurements composed of a mix of waking and sleep state arousal symptoms. Similarly, when entrapment (inability to escape) was included in models, main effects of sleep symptoms arousal were not detected yet interacted with entrapment to predict suicidality. We discuss findings in relation to treatment options suggesting that sleep‐specific interventions be considered for the long‐term management of at‐risk individuals.     

Phonology competes with syntax: experimental evidence for the interaction of word order and accent placement in the realization of Information Structure

In this paper, we investigate the interaction of phonological and syntactic constraints on the realization of Information Structure in Greek, a free word order language. We use magnitude estimation as our experimental paradigm, which allows us to quantify the influence of a given linguistic constraint on the acceptability of a sentence. We present results from two experiments. In the first experiment, we focus on the interaction of word order and context. In the second experiment, we investigate the additional effect of accent placement and clitic doubling. The results show that word order, in contrast to standard assumptions in the theoretical literature, plays only a secondary role in marking the Information Structure of a sentence. Order preferences are relatively weak and can be overridden by constraints on accent placement and clitic doubling. Our experiments also demonstrate that a null context shows the same preference pattern as an all focus context, indicating that 'default' word order and accent placement (in the absence of context) can be explained in terms of Information Structure. In the theoretical part of this paper, we formalize the interaction of syntactic and phonological constraints on Information Structure. We argue that this interaction is best captured using a notion of grammatical competition, such as the one developed by Optimality Theory (Prince & Smolensky, 1993, Optimality theory: constraint interaction in generative grammar (Technical Report No. 2). Center for Cognitive Science, Rutgers University, Piscataway, NJ; Science 275 (1997) 1604). In particular, we exploit the optimality theoretic concept of constraint ranking to account for the fact that some constraint violations are more serious than others. We extend standard Optimality Theory to obtain a grammar model that predicts not only the optimal (i.e. grammatical) realization of a given input, but also makes predictions about the relative grammaticality of suboptimal structures. This allows us to derive a constraint hierarchy that accounts for the interaction of phonological and syntactic constraints on Information Structure and models the acceptability patterns found in the experimental data.     

The induced asynchrony effect: Its role in visual judgments of temporal order and its relation to other dynamic perceptual phenomena

A context-induced “illusion” in visual judgments of temporal order, termed the induced asynchrony effect (IAE), is reported. It consists of an apparent ordering in time of two simultaneous light onsets, produced by the preceding, asynchronous offsets of two other lights. The joint effect of a real stimulus onset asynchrony and a preceding stimulus offset asynchrony bn judgments of onset order appears to be additive, given a Gaussian transformation of response probability. This result is shown to be consistent with a simple statistical decision model, which provides a conceptual framework for drawing inferences from temporal order judgment data. However, it is emphasized that certain interpretations of such models are not empirically testable on the basis of temporal order data alone. An attempt is made to relate the IAE to three other dynamic perceptual phenomena; all four effects may reflect a tendency of observers to perceive the velocity of apparent motion as being constant. Questions raised by the demonstration of the IAE are discussed, and directions for further research are suggested.     

Hierarchical modeling of sequential behavioral data: examining complex association patterns in mediation models

This article presents new methods for modeling the strength of association between multiple behaviors in a behavioral sequence, particularly those involving substantively important interaction patterns. Modeling and identifying such interaction patterns becomes more complex when behaviors are assigned to more than two categories, as is the case for most observational research. The authors propose multilevel empirical Bayes methods to overcome the challenges inherent in such data. Furthermore, these methods allow the study of how variation in interaction patterns can mediate the effects of antecedents or intervention on distal outcomes. New procedures are developed to compare alternative mediation models and pinpoint which random effects operate as mediators. These models are then applied to observational data taken from a study of the behavioral interactions of 254 couples.     

An Ecological Approach to Modeling Vision: Quantifying Form Perception Using the Circle Map Equation

Abstract

Object perception occurs within a dynamic world, where the environment and the observer (both body and eyes) are continually moving, shifting and changing. We seek to characterize and quantify this process from a perspective accounting for the interconnected system of motion in the environment, the perceiver and the eye, unfolding through time. Specifically, we build a mathematical representation for object perception based off the circle map equation. We describe an interaction between the eyes’ movement and the movement in the world, in order to better understand how those work together to result in perception. Across three experiments, we show that the stability of the relationship between object perception and complex eye movements can be perturbed and will have a predictable response to said perturbations. In so doing, we provide a different context – a dynamical systems framework – under which we can begin to consider the ecological validity of visual perception models, while recognizing the degree to which the visuo-spatial world is continuously being perturbed and disrupted. In fact, we postulate that such perturbations are capitalized on by the perceptual system, contributing to accurate object and motion identification.     

Estimation of nonlinear mixed-effects continuous-time models using the continuous-discrete extended Kalman filter

Many intensive longitudinal measurements are collected at irregularly spaced time intervals, and involve complex, possibly nonlinear and heterogeneous patterns of change. Effective modelling of such change processes requires continuous-time differential equation models that may be nonlinear and include mixed effects in the parameters. One approach of fitting such models is to define random effect variables as additional latent variables in a stochastic differential equation (SDE) model of choice, and use estimation algorithms designed for fitting SDE models, such as the continuous-discrete extended Kalman filter (CDEKF) approach implemented in the dynr R package, to estimate the random effect variables as latent variables. However, this approach's efficacy and identification constraints in handling mixed-effects SDE models have not been investigated. In the current study, we analytically inspect the identification constraints of using the CDEKF approach to fit nonlinear mixed-effects SDE models; extend a published model of emotions to a nonlinear mixed-effects SDE model as an example, and fit it to a set of irregularly spaced ecological momentary assessment data; and evaluate the feasibility of the proposed approach to fit the model through a Monte Carlo simulation study. Results show that the proposed approach produces reasonable parameter and standard error estimates when some identification constraint is met. We address the effects of sample size, process noise variance, and data spacing conditions on estimation results.     

Hierarchical diffusion models for two-choice response times

Two-choice response times are a common type of data, and much research has been devoted to the development of process models for such data. However, the practical application of these models is notoriously complicated, and flexible methods are largely nonexistent. We combine a popular model for choice response times-the Wiener diffusion process-with techniques from psychometrics in order to construct a hierarchical diffusion model. Chief among these techniques is the application of random effects, with which we allow for unexplained variability among participants, items, or other experimental units. These techniques lead to a modeling framework that is highly flexible and easy to work with. Among the many novel models this statistical framework provides are a multilevel diffusion model, regression diffusion models, and a large family of explanatory diffusion models. We provide examples and the necessary computer code.     

More Precise Estimation of Lower-Level Interaction Effects in Multilevel Models

In hierarchical data, the effect of a lower-level predictor on a lower-level outcome may often be confounded by an (un)measured upper-level factor. When such confounding is left unaddressed, the effect of the lower-level predictor is estimated with bias. Separating this effect into a within- and between-component removes such bias in a linear random intercept model under a specific set of assumptions for the confounder. When the effect of the lower-level predictor is additionally moderated by another lower-level predictor, an interaction between both lower-level predictors is included into the model. To address unmeasured upper-level confounding, this interaction term ought to be decomposed into a within- and between-component as well. This can be achieved by first multiplying both predictors and centering that product term next, or vice versa. We show that while both approaches, on average, yield the same estimates of the interaction effect in linear models, the former decomposition is much more precise and robust against misspecification of the effects of cross-level and upper-level terms, compared to the latter.     

Sometimes it does hurt to ask: The constructive role of articulating impressions

Decisions can sometimes have a constructive role, so that the act of, for example, choosing one option over another creates a preference for that option (e.g., , ,  and ). In this work we explore the constructive role of just articulating an impression, for a presented visual stimulus, as opposed to making a choice (specifically, the judgments we employ are affective evaluations). Using quantum probability theory, we outline a cognitive model formalizing such a constructive process. We predict a simple interaction, in relation to how a second image is evaluated, following the presentation of a first image, depending on whether there is a rating for the first image or not. The interaction predicted by the quantum model was confirmed across three experiments and a variety of control manipulations. The advantages of using quantum probability theory to model the present results, compared with existing models of sequence order effects in judgment (e.g., Hogarth & Einhorn, 1992) or other theories of constructive processes when a choice is made (e.g.,  and ) are discussed.     

Trial order affects cue interaction in contingency judgment.

Recent research on contingency judgment indicates that the judged predictiveness of a cue is dependent on the predictive strengths of other cues. Two classes of models correctly predict such cue interaction: associative models and statistical models. However, these models differ in their predictions about the effect of trial order on cue interaction. In five experiments reported here, college students viewed trial-by-trial data regarding several medical symptoms and a disease, judging the predictive strength of each symptom with respect to the disease. The results indicate that trial order influences the manner in which cues interact, but that neither the associative nor the statistical models can fully account for the data pattern. A possible variation of an associative account is discussed.     

Using aptitude measurements for the optimal assignment of subjects to treatments with and without mastery scores

For assigning subjects to treatments the point of intersection of within-group regression lines is ordinarily used as the critical point. This decision rule is critized and, for several utility functions and any number of treatments, replaced by optimal monotone, nonrandomized (Bayes) rules. Both treatments with and without mastery scores are considered. Moreover, the effect of unreliable criterion scores on the optimal decision rule is examined, and it is illustrated how qualitative information can be combined with aptitude measurements to improve treatment assignment decisions. Although the models in this paper are presented with special reference to the aptitude-treatment interaction problem in education, it is indicated that they apply to a variety of situations in which subjects are assigned to treatments on the basis of some predictor score, as long as there are no allocation quota considerations.