An extension of the rating scale model with an application to the measurement of change

The polytomous unidimensional Rasch model with equidistant scoring, also known as the rating scale model, is extended in such a way that the item parameters are linearly decomposed into certain basic parameters. The extended model is denoted as the linear rating scale model (LRSM). A conditional maximum likelihood estimation procedure and a likelihood-ratio test of hypotheses within the framework of the LRSM are presented. Since the LRSM is a generalization of both the dichotomous Rasch model and the rating scale model, the present algorithm is suited for conditional maximum likelihood estimation in these submodels as well. The practicality of the conditional method is demonstrated by means of a dichotomous Rasch example with 100 items, of a rating scale example with 30 items and 5 categories, and in the light of an empirical application to the measurement of treatment effects in a clinical study.Work supported in part by the Fonds zur Förderung der Wissenschaftlichen Forschung under Grant No. P6414.     

An expanded model for perceptual visual single object recognition system using expectation priming following neuroscientific evidence

Under numerous circumstances, humans recognize visual objects in their environment with remarkable response times and accuracy. Existing artificial visual object recognition systems have not yet surpassed human vision, especially in its universality of application. We argue that modeling the recognition process in an exclusive feedforward manner hinders those systems’ performance. To bridge that performance gap between them and human vision, we present a brief review of neuroscientific data, which suggests that considering an agent’s internal influences (from cognitive systems that peripherally interact with visual-perceptual processes) recognition can be improved. Then, we propose a model for visual object recognition which uses these systems’ information, such as affection, for generating expectation to prime the object recognition system, thus reducing its execution times. Later, an implementation of the model is described. Finally, we present and discuss an experiment and its results.     

An integrated model of eye movements and visual encoding

Recent computational models of cognition have made good progress in accounting for the visual processes needed to encode external stimuli. However, these models typically incorporate simplified models of visual processing that assume a constant encoding time for all visual objects and do not distinguish between eye movements and shifts of attention. This paper presents a domain-independent computational model, EMMA, that provides a more rigorous account of eye movements and visual encoding and their interaction with a cognitive processor. The visual-encoding component of the model describes the effects of frequency and foveal eccentricity when encoding visual objects as internal representations. The eye-movement component describes the temporal and spatial characteristics of eye movements as they arise from shifts of visual attention. When integrated with a cognitive model, EMMA generates quantitative predictions concerning when and where the eyes move, thus serving to relate higher-level cognitive processes and attention shifts with lower-level eye-movement behavior. The paper evaluates EMMA in three illustrative domains — equation solving, reading, and visual search — and demonstrates how the model accounts for aspects of behavior that simpler models of cognitive and visual processing fail to explain.     

Placing inspection time, reaction time, and perceptual speed in the broader context of cognitive ability: The VPR model in the Lothian Birth Cohort 1936

The idea that information processing speed is related to cognitive ability has a long history. Much evidence has been amassed in its support, with respect to both individual differences in general intelligence and developmental trajectories. Two so-called elementary cognitive tasks, reaction time and inspection time, have been used to compile this evidence, but most studies have used either one or the other. Relations between speed and fluid intelligence have tended to be stronger than those between speed and crystallized intelligence, but studies testing this have confounded verbal abilities with crystallized intelligence and spatial/perceptual abilities with fluid intelligence. Questions have also been raised regarding whether speed contributes directly to general intelligence or to more specific cognitive abilities to which general intelligence also contributes. We used 18 ability and speed measures in the Lothian Birth Cohort 1936, assessed at approximately age 70, to construct alternative versions of the Verbal-Perceptual-Image Rotation (Johnson & Bouchard, 2005a) model of cognitive ability to test different hypotheses regarding these issues. Though differences in the extents to which our models fit the data were relatively small, they suggested that reaction and inspection time tasks were comparable indicators of information processing speed with respect to general intelligence, that verbal and spatial abilities were similarly related to information processing speed, and that spatial, verbal, and perceptual speed abilities were more directly related to information processing speed than was general intelligence. We discuss the theoretical implications of these results.     

The simplest complete model of choice response time: linear ballistic accumulation

We propose a linear ballistic accumulator (LBA) model of decision making and reaction time. The LBA is simpler than other models of choice response time, with independent accumulators that race towards a common response threshold. Activity in the accumulators increases in a linear and deterministic manner. The simplicity of the model allows complete analytic solutions for choices between any number of alternatives. These solutions (and freely-available computer code) make the model easy to apply to both binary and multiple choice situations. Using data from five previously published experiments, we demonstrate that the LBA model successfully accommodates empirical phenomena from binary and multiple choice tasks that have proven difficult for other theoretical accounts. Our results are encouraging in a field beset by the tradeoff between complexity and completeness.     

An INAR(1) negative multinomial regression model for longitudinal count data

This paper discusses a regression model for the analysis of longitudinal count data observed in a panel study. An integer-valued first-order autoregressive [INAR(1)] Poisson process is adapted to represent time-dependent correlations among the counts. By combining the INAR(1)-representation with a random effects approach, a new negative multinomial distribution is derived that includes the bivariate negative binomial distribution proposed by Edwards and Gurland (1961) and Subrahmaniam (1966) as a special case. A detailed analysis of the relationship between personality factors and daily emotion experiences illustrates the approach.This research was partially supported by NSF grant SBR-9409531. The author is grateful to Ulrich Schimmack and Ed Diener for providing the data set used in the application section and for helpful comments on this research.     

A mixture model for responses and response times with a higher-order ability structure to detect rapid guessing behaviour

Many educational and psychological assessments focus on multidimensional latent traits that often have a hierarchical structure to provide both overall-level information and fine-grained diagnostic information. A test will usually have either separate time limits for each subtest or an overall time limit for administrative convenience and test fairness. In order to complete the items within the allocated time, examinees frequently adopt different test-taking behaviours during the test, such as solution behaviour and rapid guessing behaviour. In this paper we propose a new mixture model for responses and response times with a hierarchical ability structure, which incorporates auxiliary information from other subtests and the correlation structure of the abilities to detect rapid guessing behaviour. A Markov chain Monte Carlo method is proposed for model estimation. Simulation studies reveal that all model parameters could be recovered well, and the parameter estimates had smaller absolute bias and mean squared error than the mixture unidimensional item response theory (UIRT) model. Moreover, the true positive rate of detecting rapid guessing behaviour is also higher than when using the mixture UIRT model separately for each subscale, whereas the false detection rate is much lower than the mixture UIRT model. The deviance information criterion and the logarithm of the pseudo-marginal likelihood are employed to evaluate the model fit. Finally, a real data analysis is presented to demonstrate the practical value of the proposed model.     

An analogue study of intrusions

According to cognitive theorists, intrusive trauma memories have their origin in how information during the event is processed. Two studies investigated functional cognitive strategies during medical crises that might protect against intrusions. In Study 1, interviews with health-care professionals were used to identify cognitive strategies judged to be effective in controlling emotions and dealing with medical crises. Study 2 systematically manipulated the use of those strategies in a trauma analogue film paradigm. Experimental participants reported fewer intrusions, and less fear and avoidance of film-related stimuli during the subsequent week than controls. The manipulation did not affect anxiety during the film or memory disorganization. Implications for cognitive theories of intrusion development are discussed.     

The relationship between social anxiety and the perception of depth-ambiguous biological motion stimuli is mediated by inhibitory ability

Orthographically projected biological motion stimuli are depth-ambiguous. Consequently, their projection when oriented towards the viewer is the same as when oriented away. Despite this, observers tend to interpret such stimuli as facing the viewer more often. Some have speculated that this facing-the-viewer bias may exist for sociobiological reasons: Mistaking another human as retreating when they are actually approaching could have more severe consequences than the opposite error. An implication of this theory is that the facing-towards percept may be perceived as more threatening than the facing-away percept. Given this, as well as the finding that anxious individuals have been found to display an attentional bias towards threatening stimuli, we reasoned that more anxious individuals might have stronger facing-the-viewer biases. Furthermore, since anxious individuals have been found to perform poorer on inhibition tasks, we hypothesized that inhibitory ability would mediate the relationship between anxiety and the facing-the-viewer bias (i.e., difficulty inhibiting the threatening percept). Exploring individual differences, we asked participants to complete anxiety questionnaires, to perform a Go/No-Go task, and then to complete a perceptual task that allowed us to assess their facing-the-viewer biases. As hypothesized, we found that both greater anxiety and weaker inhibitory ability were associated with greater facing-the-viewer biases. In addition, we found that inhibitory ability significantly mediated the relationship between anxiety and facing-the-viewer biases. Our results provide further support that the facing-the-viewer bias is sensitive to the sociobiological relevance of biological motion stimuli, and that the threat bias for ambiguous visual stimuli is mediated by inhibitory ability.     

Effects of practice on task architecture: combined evidence from interference experiments and random-walk models of decision making

Does extensive practice reduce or eliminate central interference in dual-task processing? We explored the reorganization of task architecture with practice by combining interference analysis (delays in dual-task experiment) and random-walk models of decision making (measuring the decision and non-decision contributions to RT). The main delay observed in the Psychologically Refractory Period at short stimulus onset asynchronies (SOA) values was largely unaffected by training. However, the range of SOAs over which this interference regime held diminished with learning. This was consistent with an overall shift observed in single-task performance from a highly variable decision time to a reliable (non-decision time) contribution to response time. Executive components involved in coordinating dual-task performance decreased (and became more stable) after extensive practice. The results suggest that extensive practice reduces the duration of central decision stages, but that the qualitative property of central seriality remains a structural invariant.     

On the mean and variance of response times under the diffusion model with an application to parameter estimation

We give closed form expressions for the mean and variance of RTs for Ratcliff’s diffusion model [Ratcliff, R. (1978). A theory of memory retrieval. Psychological Review, 85, 59-108] under the simplifying assumption that there is no variability across trials in the parameters. These expressions are more general than those currently available. As an application, we demonstrate their use in a method-of-moments estimation procedure that addresses some of the weaknesses of the EZ method [Wagenmakers, E.-J., van der Maas, H. L. J., & Grasman, R. P. P. P. (2007). An EZ-diffusion model for response time and accuracy. Psychonomic Bulletin & Review, 14, 3-22], and illustrate this with lexical decision data. We discuss further possible applications.     

Absent minds and absent agents: Attention-lapse induced alienation of agency

We report a novel task designed to elicit transient attention-lapse induced alienation (ALIA) of agency experiences in normal participants. When attention-related action slips occur during the task, participants reported substantially decreased self control as well as a high degree of perceived agency attributed to the errant hand. In addition, participants reported being surprised by, and annoyed with, the actions of the errant hand. We argue that ALIA experiences occur because of constraints imposed by the close and precise temporal relations between intention formation and a contrary action employed in this paradigm. We note similarities between ALIA experiences and anarchic hand sign (AHS) and argue that, despite important differences, both ALIA experiences and AHS phenomenology reflect failures of executive control to intervene and cancel contrary affordance-driven habitual motor plans.     

Testing the efficiency of Markov chain Monte Carlo with People using facial affect categories

Exploring how people represent natural categories is a key step toward developing a better understanding of how people learn, form memories, and make decisions. Much research on categorization has focused on artificial categories that are created in the laboratory, since studying natural categories defined on high-dimensional stimuli such as images is methodologically challenging. Recent work has produced methods for identifying these representations from observed behavior, such as reverse correlation (RC). We compare RC against an alternative method for inferring the structure of natural categories called Markov chain Monte Carlo with People (MCMCP). Based on an algorithm used in computer science and statistics, MCMCP provides a way to sample from the set of stimuli associated with a natural category. We apply MCMCP and RC to the problem of recovering natural categories that correspond to two kinds of facial affect (happy and sad) from realistic images of faces. Our results show that MCMCP requires fewer trials to obtain a higher quality estimate of people's mental representations of these two categories.     

Effects of cognitive load presence and duration on driver eye movements and event detection performance

This study examined the effects of cognitive load on driving performance for interactions with an in-vehicle information system (IVIS) that varied in duration from 1 to 4 min. Twelve participants drove in a simulator while intermittently performing the IVIS task. There were three IVIS conditions: interacting with the IVIS, non-IVIS periods between IVIS interactions, and baseline driving without the IVIS task. Contrary to our hypothesis, driver response to lead vehicle braking was surprisingly uniform across IVIS conditions. IVIS interaction did undermine driver ability to detect the bicyclist along the side of the road, and some of these performance decrements persisted after the IVIS interaction had ended. Reaction time for bicyclist detection increased from the first to the subsequent minutes of the interaction. Eye movements were influenced by the IVIS conditions but not by task duration. Both ANOVA and factor analyses revealed that some of the changes in eye movements were concurrent with IVIS interaction while others persisted after the driver completed the IVIS interaction. Overall, the findings suggest that two mechanisms might account for the distraction-related performance decrements in this study: competition for processing resources and interference due to activation of competing goals.     

A tutorial introduction to Bayesian models of cognitive development

We present an introduction to Bayesian inference as it is used in probabilistic models of cognitive development. Our goal is to provide an intuitive and accessible guide to the what, the how, and the why of the Bayesian approach: what sorts of problems and data the framework is most relevant for, and how and why it may be useful for developmentalists. We emphasize a qualitative understanding of Bayesian inference, but also include information about additional resources for those interested in the cognitive science applications, mathematical foundations, or machine learning details in more depth. In addition, we discuss some important interpretation issues that often arise when evaluating Bayesian models in cognitive science.     

The extension of component analysis to four-mode matrices

A model for four-mode component analysis is developed and presented. The developed model, which is an extension of Tucker's three-mode factor analytic model, allows for the simultaneous analysis of all modes of a four-mode data matrix and the consideration of relationships among the modes. An empirical example based upon viewer perceptions of repetitive advertising shows the four-mode model applicable to real data.This research was supported by the University of Kansas School of Business Research Fund provided by the Fourth National Bank & Trust Company, Wichita. The ideas and opinions expressed herein are solely those of the author.     

Parallel averaging of size is possible but range-limited: A reply to Marchant,Simons, and De Fockert

In their recent paper, Marchant, Simons, and De Fockert (2013) claimed that the ability to average between multiple items of different sizes is limited by small samples of arbitrarily attended members of a set. This claim is based on a finding that observers are good at representing the average when an ensemble includes only two sizes distributed among all items (regular sets), but their performance gets worse when the number of sizes increases with the number of items (irregular sets). We argue that an important factor not considered by Marchant et al. (2013) is the range of size variation that was much bigger in their irregular sets. We manipulated this factor across our experiments and found almost the same efficiency of averaging for both regular and irregular sets when the range was stabilized. Moreover, highly regular sets consisting only of small and large items (two-peaks distributions) were averaged with greater error than sets with small, large, and intermediate items, suggesting a segmentation threshold determining whether all variable items are perceived as a single ensemble or distinct subsets. Our results demonstrate that averaging can actually be parallel but the visual system has some difficulties with it when some items differ too much from others.     

Modeling the Covariance Structure of Complex Datasets Using Cognitive Models: An Application to Individual Differences and the Heritability of Cognitive Ability

Understanding individual differences in cognitive performance is an important part of understanding how variations in underlying cognitive processes can result in variations in task performance. However, the exploration of individual differences in the components of the decision process—such as cognitive processing speed, response caution, and motor execution speed—in previous research has been limited. Here, we assess the heritability of the components of the decision process, with heritability having been a common aspect of individual differences research within other areas of cognition. Importantly, a limitation of previous work on cognitive heritability is the underlying assumption that variability in response times solely reflects variability in the speed of cognitive processing. This assumption has been problematic in other domains, due to the confounding effects of caution and motor execution speed on observed response times. We extend a cognitive model of decision‐making to account for relatedness structure in a twin study paradigm. This approach can separately quantify different contributions to the heritability of response time. Using data from the Human Connectome Project, we find strong evidence for the heritability of response caution, and more ambiguous evidence for the heritability of cognitive processing speed and motor execution speed. Our study suggests that the assumption made in previous studies—that the heritability of cognitive ability is based on cognitive processing speed—may be incorrect. More generally, our methodology provides a useful avenue for future research in complex data that aims to analyze cognitive traits across different sources of related data, whether the relation is between people, tasks, experimental phases, or methods of measurement.     

Escape from serial stimuli leading to food

If the functional relations governing the strength of a conditioned reinforcer correspond to those obtained with other Pavlovian procedures (e.g., Kaplan, 1984), the termination of stimuli appearing early in the interval between successive food deliveries should be reinforcing. During initial training we presented four key colors, followed by food, in a recurrent sequence to each of 6 pigeons. This established a baseline level of autoshaped pecking. In later sessions, we terminated each of these colors or only the first color for a brief period following each peck, replacing the original color with a standard substitute to avoid darkening the key. Pecking decreased in the presence of the last color in the sequence but increased in the presence of the first. In accord with contemporary models of Pavlovian conditioning, these and other data suggest that the behavioral effects of stimuli in a chain may be better understood in terms of what each stimulus predicts, as measured by relative time to the terminal reinforcer, than in the exclusively positive terms of the traditional formulation (Skinner, 1938). The same model may also account for the initial pause under fixed-interval and fixed-ratio schedules of reinforcement.