An accumulator model of semantic interference

To explain latency effects in picture-word interference tasks, cognitive models need to account for both interference and stimulus onset asynchrony (SOA) effects. As opposed to most models of picture-word interference, which model the time course during the task in a ballistic manner, the RACE model (retrieval by accumulating evidence) presented in this paper accounts for semantic interference during the interval between the retrieval onset and the actual retrieval. RACE is implemented as an extension to the ACT-R architecture of cognition. By modeling the retrieval process, RACE offers a more precise account of semantic memory retrieval latencies in different interference and SOA conditions than other ACT-R models. In this paper, we discuss the architectural assumptions underlying RACE and simulations of a picture-word interference experiment [Glaser, W. R., & Düngelhoff, F. J. (1984). The time course of picture-word interference. Journal of Experimental Psychology: Human Perception and Performance, 10(5), pp. 640–654.].     

Modeling adaptive cooperative and competitive metaphors as mental models for joint decision making

In this paper, joint decision making processes are studied and the role of cognitive metaphors as mental models in them. A second-order self-modeling network model is introduced based on mechanisms known from cognitive and social neuroscience and cognitive metaphor and mental model literature. The cognitive metaphors were modeled as specific forms of mental models providing a form of modulation within the joint decision making process. The model addresses not only the use of these mental models in the decision making, but also their hebbian learning and the control over the learning. The obtained self-modeling network model was applied to two types of metaphors that affect joint decision making in different manners: a cooperative metaphor and a competitive metaphor. By a number of scenarios it was shown how the obtained self-modeling network model can be used to simulate and analyze joint decision processes and how they are influenced by such cognitive metaphors.     

A sequential exploratory diagnostic model using a Pólya-gamma data augmentation strategy

Cognitive diagnostic models provide a framework for classifying individuals into latent proficiency classes, also known as attribute profiles. Recent research has examined the implementation of a Pólya-gamma data augmentation strategy binary response model using logistic item response functions within a Bayesian Gibbs sampling procedure. In this paper, we propose a sequential exploratory diagnostic model for ordinal response data using a logit-link parameterization at the category level and extend the Pólya-gamma data augmentation strategy to ordinal response processes. A Gibbs sampling procedure is presented for efficient Markov chain Monte Carlo (MCMC) estimation methods. We provide results from a Monte Carlo study for model performance and present an application of the model.     

The dynamics of deferred decision

Decision makers are often unable to choose between the options that they are offered. In these settings they typically defer their decision, that is, delay the decision to a later point in time or avoid the decision altogether. In this paper, we outline eight behavioral findings regarding the causes and consequences of choice deferral that cognitive theories of decision making should be able to capture. We show that these findings can be accounted for by a deferral-based time limit applied to existing sequential sampling models of preferential choice. Our approach to modeling deferral as a time limit in a sequential sampling model also makes a number of novel predictions regarding the interactions between choice probabilities, deferral probabilities, and decision times, and we confirm these predictions in an experiment. Choice deferral is a key feature of everyday decision making, and our paper illustrates how established theoretical approaches can be used to understand the cognitive underpinnings of this important behavioral phenomenon.     

Neurological basis of language and sequential cognition: evidence from simulation,aphasia, and ERP studies

The current research addresses the hypothesis that certain aspects of sequential cognition have made substantial contributions to the human language processing capability, from a functional neurophysiology perspective. We first describe a cognitive sequence processing model that was developed based on the functional neuroanatomy of primate cortex and basal ganglia. We demonstrate how this model is capable of simulating the behavior of human infants in extracting serial, temporal and abstract structure from language-like sound sequences as revealed in recent psycholinguistic experiments. We then demonstrate how, through training, this model can perform adult level syntactic comprehension, based on dissociated processing streams for open vs. closed class words. The model subsequently predicts: (1) that impaired syntactic processing (as in agrammatic aphasia) will be associated with impairments in corresponding non-linguistic cognitive sequencing tasks, and (2) that neurophysiological processes (as revealed by ERPs) involved in syntactic processing should also be involved in the corresponding non-linguistic cognitive sequencing tasks. Data confirming these predictions are reviewed. We conclude that the study of sequential cognition will provide a new paradigm for the investigation of the neurophysiological bases of language.     

Auditory distractor processing in sequential selection tasks

In this review, we analyze the cognitive processes contributing to selection in audition. In particular, we focus on the processing of auditory distractors in sequential selection paradigms in which target stimuli are accompanied by distractors. We review the evidence from two established tasks, namely the auditory negative priming and the auditory distractor–response binding task, and discuss the cognitive mechanisms contributing to the results typically observed in these tasks. In fact, several processes have been suggested as to explain how distractors are processed and handled in audition; that is, auditory distractors can be inhibited, encoded with a do-not-respond-tag, integrated into a stimulus–response episode containing the response to the target, or upheld in working memory and matched/mismatched with the following distractor. In addition, variables possibly modulating these cognitive processes are discussed. Finally, auditory distractor processing is compared with distractor processing in vision.     

A computational model of fractionated conflict-control mechanisms in task-switching

A feature of human cognition is the ability to monitor and adjust one's own behavior under changing circumstances. A dynamic balance between controlled and rapid responding is needed to adapt to a fluctuating environment. We suggest that cognitive control may include, among other things, two distinct processes. Incongruent stimuli may drive top-down facilitation of task-relevant responses to bias performance toward exploitation vs. exploration. Task or response switches may generally slow responses to bias toward accuracy vs. speed and exploration vs. exploitation. Behavioral results from a task switching study demonstrate these two distinct processes as revealed by higher-order sequential effects. A computational model implements the two conflict-control mechanisms, which allow it to capture many complex and novel sequential effects. Lesion studies with the model demonstrate that the model is unable to capture these effects without the conflict-control loops and show how each monitoring component modulates cognitive control. The results suggest numerous testable predictions regarding the neural substrates of cognitive control.     

A dynamic context model of interactive behavior

A dynamic context model of interactive behavior was developed to explain results from two experiments that tested the effects of interaction costs on encoding strategies, cognitive representations, and response selection processes in a decision-making and a judgment task. The model assumes that the dynamic context defined by the mixes of internal and external representations and processes are sensitive to the interaction cost imposed by the task environment. The model predicts that changes in the dynamic context may lead to systematic biases in cognitive representations and processes that eventually influence decision-making and judgment outcomes. Consistent with the predictions by the model, results from the experiments showed that as interaction costs increased, encoding strategies and cognitive representations shifted from perception-based to memory-based. Memory-based comparisons of the stimuli enhanced the similarity and dominance effects, and led to stronger systematic biases in response outcomes in a choice task. However, in a judgment task, memory-based representations enhanced only the dominance effects. Results suggested that systematic response biases in the dominance context were caused by biases in the cognitive representations of the stimuli, but response biases in the similarity context were caused by biases in the comparison process induced by the choice task. Results suggest that changes in interaction costs not only change whether information was assessed from the external world or from memory but also introduce systematic biases in the cognitive representation of the information, which act as biased inputs to the subsequent decision-making and judgment processes. Results are consistent with the idea of interactive cognition, which proposes that representations and processes are contingent on the dynamic context defined by the information flow between the external task environment and internal cognition.     

A sequential cognitive diagnosis model for polytomous responses

This paper proposes a general polytomous cognitive diagnosis model for a special type of graded responses, where item categories are attained in a sequential manner, and associated with some attributes explicitly. To relate categories to attributes, a category‐level Q‐matrix is used. When the attribute and category association is specified a priori, the proposed model has the flexibility to allow different cognitive processes (e.g., conjunctive, disjunctive) to be modelled at different categories within a single item. This model can be extended for items where categories cannot be explicitly linked to attributes, and for items with unordered categories. The feasibility of the proposed model is examined using simulated data. The proposed model is illustrated using the data from the Trends in International Mathematics and Science Study 2007 assessment.     

Instance‐based Learning: A General Model of Repeated Binary Choice

A common practice in cognitive modeling is to develop new models specific to each particular task. We question this approach and draw on an existing theory, instance‐based learning theory (IBLT), to explain learning behavior in three different choice tasks. The same instance‐based learning model generalizes accurately to choices in a repeated binary choice task, in a probability learning task, and in a repeated binary choice task within a changing environment. We assert that, although the three tasks are different, the source of learning is equivalent and therefore, the cognitive process elicited should be captured by one single model. This evidence supports previous findings that instance‐based learning is a robust learning process that is triggered in a wide range of tasks from the simple repeated choice tasks to the most dynamic decision making tasks. Copyright © 2010 John Wiley & Sons, Ltd.     

A phase transition model for the speed-accuracy trade-off in response time experiments

Most models of response time (RT) in elementary cognitive tasks implicitly assume that the speed-accuracy trade-off is continuous: When payoffs or instructions gradually increase the level of speed stress, people are assumed to gradually sacrifice response accuracy in exchange for gradual increases in response speed. This trade-off presumably operates over the entire range from accurate but slow responding to fast but chance-level responding (i.e., guessing). In this article, we challenge the assumption of continuity and propose a phase transition model for RTs and accuracy. Analogous to the fast guess model (Ollman, 1966), our model postulates two modes of processing: a guess mode and a stimulus-controlled mode. From catastrophe theory, we derive two important predictions that allow us to test our model against the fast guess model and against the popular class of sequential sampling models. The first prediction--hysteresis in the transitions between guessing and stimulus-controlled behavior--was confirmed in an experiment that gradually changed the reward for speed versus accuracy. The second prediction--bimodal RT distributions--was confirmed in an experiment that required participants to respond in a way that is intermediate between guessing and accurate responding.     

Can cognitive psychological research on reasoning enhance the discussion around moral judgments?

In this article we will demonstrate how cognitive psychological research on reasoning and decision making could enhance discussions and theories of moral judgments. In the first part, we will present recent dual-process models of moral judgments and describe selected studies which support these approaches. However, we will also present data that contradict the model predictions, suggesting that approaches to moral judgment might be more complex. In the second part, we will show how cognitive psychological research on reasoning might be helpful in understanding moral judgments. Specifically, we will highlight approaches addressing the interaction between intuition and reflection. Our data suggest that a sequential model of engaging in deliberation might have to be revised. Therefore, we will present an approach based on Signal Detection Theory and on intuitive conflict detection. We predict that individuals arrive at the moral decisions by comparing potential action outcomes (e.g., harm caused and utilitarian gain) simultaneously. The response criterion can be influenced by intuitive processes, such as heuristic moral value processing, or considerations of harm caused.     

Individual differences in 10‐month‐olds' performance on the A‐not‐B task

This study used the classical A‐not‐B task (Piaget, 1954 ) to explore individual differences in cognitive flexibility in 10‐month‐old infants by: (1) examining how differences in search performance during A trials relate to search performance during B trials; (2) studying the relation between temperamental dimensions and A‐not‐B performance; and (3) investigating differences in search performance between looking and reaching responses within the same task. Forty infants were tested on a fixed‐design‐version of the A‐not‐B task, not allowing for training or individual adjustment, but instead eliciting additional search behaviors than the common correct responses in A trials and perseverative errors in B trials. Infants were also rated by their parents on the temperamental scales Activity level and Attention span. The main findings were: (1) performance on A trials affected B trial performance, with infants being more correct on A trials having more incorrect and less ‘no search’ responses on B trials; (2) activity level, but not attention span, was related to performance on the A‐not‐B task, with infants performing better on A trials having a lower activity level; and (3) there were a few differences in performance with regard to modality, indicating that responding correctly by looking may be less cognitively demanding than doing so by reaching. This study demonstrated that 10‐month‐olds show a wide variation of search behaviors on this A‐not‐B task, resulting in individual differences in performance. These differences are suggested to reflect variation in temperamental activity level as well as maturity of short term/working memory, inhibition and cognitive flexibility.     

Evidence accumulation modelling in the wild: understanding safety-critical decisions

Evidence accumulation models (EAMs) are a class of computational cognitive model used to understand the latent cognitive processes that underlie human decisions and response times (RTs). They have seen widespread application in cognitive psychology and neuroscience. However, historically, the application of these models was limited to simple decision tasks. Recently, researchers have applied these models to gain insight into the cognitive processes that underlie observed behaviour in applied domains, such as air-traffic control (ATC), driving, forensic and medical image discrimination, and maritime surveillance. Here, we discuss how this modelling approach helps researchers understand how the cognitive system adapts to task demands and interventions, such as task automation. We also discuss future directions and argue for wider adoption of cognitive modelling in Human Factors research.     

Self‐Directed Learning Favors Local,Rather Than Global,Uncertainty

Collecting (or “sampling”) information that one expects to be useful is a powerful way to facilitate learning. However, relatively little is known about how people decide which information is worth sampling over the course of learning. We describe several alternative models of how people might decide to collect a piece of information inspired by “active learning” research in machine learning. We additionally provide a theoretical analysis demonstrating the situations under which these models are empirically distinguishable, and we report a novel empirical study that exploits these insights. Our model‐based analysis of participants' information gathering decisions reveals that people prefer to select items which resolve uncertainty between two possibilities at a time rather than items that have high uncertainty across all relevant possibilities simultaneously. Rather than adhering to strictly normative or confirmatory conceptions of information search, people appear to prefer a “local” sampling strategy, which may reflect cognitive constraints on the process of information gathering.     

Understanding the limits of self‐control: Positive affect moderates the impact of task switching on consecutive self‐control performance

Performing consecutive self‐control tasks typically leads to deterioration in self‐control performance. This effect can be explained within the strength model of self‐control or within a cognitive control perspective. Both theoretical frameworks differ in their predictions with regard to the impact of affect and task characteristics on self‐control deterioration within a two‐task paradigm. Whereas the strength model predicts decrements in self‐control performance whenever both tasks require a limited resource, under a cognitive control perspective, decrements should only occur when people switch to a different response conflict in the second task. Moreover, only the cognitive control model predicts an interaction between task switching and positive affect. In the present research, we investigated this interaction within a two‐task paradigm and found evidence that favored a cognitive control interpretation of the results. Positive affect only benefitted consecutive self‐control performance if response conflicts in the two tasks were different (resisting sweets followed by a Stroop task). If they were the same (two consecutive Stroop tasks), positive affect impaired self‐control performance. These effects were partially replicated in the second study that also examined negative affect, which did not affect self‐control performance. We conclude that drawing on cognitive control models could add substantially to research on self‐control. Copyright © 2013 John Wiley & Sons, Ltd.     

Modeling behavior in a clinically diagnostic sequential risk-taking task

This article models the cognitive processes underlying learning and sequential choice in a risk-taking task for the purposes of understanding how they occur in this moderately complex environment and how behavior in it relates to self-reported real-world risk taking. The best stochastic model assumes that participants incorrectly treat outcome probabilities as stationary, update probabilities in a Bayesian fashion, evaluate choice policies prior to rather than during responding, and maintain constant response sensitivity. The model parameter associated with subjective value of gains correlates well with external risk taking. Both the overall approach, which can be expanded as the basic paradigm is varied, and the specific results provide direction for theories of risky choice and for understanding risk taking as a public health problem.     

Dissociating controlled from automatic processing in temporal preparation

The aim of the present study was to investigate the controlled versus the automatic nature of temporal preparation. If temporal preparation involves controlled rather than automatic processing, it should be reduced by the addition of a concurrent demanding task. This hypothesis was tested by comparing participants' performance in a temporal preparation task that measured two main effects of temporal preparation (temporal orienting and sequential effects) between a single-task and a dual-task condition. In the single-task condition, participants responded to a visual target presented after symbolic cues that were highly predictive of the moment of target onset. In the dual-task condition, the temporal preparation task was performed concurrently with a working memory task. The results showed that sequential effects survived to dual-task interference, while temporal orienting was reduced as a function of the competition for executive resources required by both working memory and temporal preparation tasks. These findings provide direct behavioural evidence that temporal orienting and sequential effects involve dissociable cognitive processes.