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Cognitive models aim to explain complex human behavior in terms of hypothesized mechanisms of the mind. These mechanisms can be formalized in terms of mathematical structures containing parameters that are theoretically meaningful. For example, in the case of perceptual decision making, model parameters might correspond to theoretical constructs like response bias, evidence quality, response caution, and the like. Formal cognitive models go beyond verbal models in that cognitive mechanisms are instantiated in terms of mathematics and they go beyond statistical models in that cognitive model parameters are psychologically interpretable. We explore three key elements used to formally evaluate cognitive models: parameter estimation, model prediction, and model selection. We compare and contrast traditional approaches with Bayesian statistical approaches to performing each of these three elements. Traditional approaches rely on an array of seemingly ad hoc techniques, whereas Bayesian statistical approaches rely on a single, principled, internally consistent system. We illustrate the Bayesian statistical approach to evaluating cognitive models using a running example of the Linear Ballistic Accumulator model of decision making (Brown SD, Heathcote A. The simplest complete model of choice response time: linear ballistic accumulation. Cogn Psychol 2008, 57:153-178). WIREs Cogn Sci 2018, 9:e1458. doi: 10.1002/wcs.1458 This article is categorized under: Neuroscience > Computation Psychology > Reasoning and Decision Making Psychology > Theory and Methods.
© 2017 Wiley Periodicals, Inc.
We survey models of response inhibition having different degrees of mathematical, computational and neurobiological specificity and generality. The independent race model accounts for performance of the stop-signal or countermanding task in terms of a race between GO and STOP processes with stochastic finishing times. This model affords insights into neurophysiological mechanisms that are reviewed by other authors in this volume. The formal link between the abstract GO and STOP processes and instantiating neural processes is articulated through interactive race models consisting of stochastic accumulator GO and STOP units. This class of model provides quantitative accounts of countermanding performance and replicates the dynamics of neural activity producing that performance. The interactive race can be instantiated in a network of biophysically plausible spiking excitatory and inhibitory units. Other models seek to account for interactions between units in frontal cortex, basal ganglia and superior colliculus. The strengths, weaknesses and relationships of the different models will be considered. We will conclude with a brief survey of alternative modelling approaches and a summary of problems to be addressed including accounting for differences across effectors, species, individuals, task conditions and clinical deficits.This article is part of the themed issue 'Movement suppression: brain mechanisms for stopping and stillness'.
© 2017 The Author(s).
We propose a taxonomy of psychopathology based on patterns of shared causal influences identified in a review of multivariate behavior genetic studies that distinguish genetic and environmental influences that are either common to multiple dimensions of psychopathology or unique to each dimension. At the phenotypic level, first-order dimensions are defined by correlations among symptoms; correlations among first-order dimensions similarly define higher-order domains (e.g., internalizing or externalizing psychopathology). We hypothesize that the robust phenotypic correlations among first-order dimensions reflect a . Some nonspecific etiologic factors increase risk for all first-order dimensions of psychopathology to varying degrees through a general factor of psychopathology. Other nonspecific etiologic factors increase risk only for all first-order dimensions within a more specific higher-order domain. Furthermore, each first-order dimension has its own unique causal influences. Genetic and environmental influences common to family members tend to be nonspecific, whereas environmental influences unique to each individual are more dimension-specific. We posit that these causal influences on psychopathology are moderated by sex and developmental processes. This causal taxonomy also provides a novel framework for understanding the of each first-order dimension: Different persons exhibiting similar symptoms may be influenced by different combinations of etiologic influences from each of the 3 levels of the etiologic hierarchy. Furthermore, we relate the proposed causal taxonomy to transdimensional psychobiological processes, which also impact the heterogeneity of each psychopathology dimension. This causal taxonomy implies the need for changes in strategies for studying the etiology, psychobiology, prevention, and treatment of psychopathology. (PsycINFO Database Record
(c) 2017 APA, all rights reserved).
In this brief review, we argue that attention operates along a hierarchy from peripheral through central mechanisms. We further argue that these mechanisms are distinguished not just by their functional roles in cognition, but also by a distinction between serial mechanisms (associated with central attention) and parallel mechanisms (associated with midlevel and peripheral attention). In particular, we suggest that peripheral attentional deployments in distinct representational systems may be maintained simultaneously with little or no interference, but that the serial nature of central attention means that even tasks that largely rely on distinct representational systems will come into conflict when central attention is demanded. We go on to review both the behavioral and neural evidence for this prediction. We conclude that even though the existing evidence mostly favors our account of serial central and parallel noncentral attention, we know of no experiment that has conclusively borne out these claims. As such, this article offers a framework of attentional mechanisms that will aid in guiding future research on this topic.
When evaluating cognitive models based on fits to observed data (or, really, any model that has free parameters), parameter estimation is critically important. Traditional techniques like hill climbing by minimizing or maximizing a fit statistic often result in point estimates. Bayesian approaches instead estimate parameters as posterior probability distributions, and thus naturally account for the uncertainty associated with parameter estimation; Bayesian approaches also offer powerful and principled methods for model comparison. Although software applications such as WinBUGS (Lunn, Thomas, Best, & Spiegelhalter, Statistics and Computing, 10, 325-337, 2000) and JAGS (Plummer, 2003) provide "turnkey"-style packages for Bayesian inference, they can be inefficient when dealing with models whose parameters are correlated, which is often the case for cognitive models, and they can impose significant technical barriers to adding custom distributions, which is often necessary when implementing cognitive models within a Bayesian framework. A recently developed software package called Stan (Stan Development Team, 2015) can solve both problems, as well as provide a turnkey solution to Bayesian inference. We present a tutorial on how to use Stan and how to add custom distributions to it, with an example using the linear ballistic accumulator model (Brown & Heathcote, Cognitive Psychology, 57, 153-178. doi: 10.1016/j.cogpsych.2007.12.002 , 2008).
In the real world, decision making processes must be able to integrate non-stationary information that changes systematically while the decision is in progress. Although theories of decision making have traditionally been applied to paradigms with stationary information, non-stationary stimuli are now of increasing theoretical interest. We use a random-dot motion paradigm along with cognitive modeling to investigate how the decision process is updated when a stimulus changes. Participants viewed a cloud of moving dots, where the motion switched directions midway through some trials, and were asked to determine the direction of motion. Behavioral results revealed a strong delay effect: after presentation of the initial motion direction there is a substantial time delay before the changed motion information is integrated into the decision process. To further investigate the underlying changes in the decision process, we developed a Piecewise Linear Ballistic Accumulator model (PLBA). The PLBA is efficient to simulate, enabling it to be fit to participant choice and response-time distribution data in a hierarchal modeling framework using a non-parametric approximate Bayesian algorithm. Consistent with behavioral results, PLBA fits confirmed the presence of a long delay between presentation and integration of new stimulus information, but did not support increased response caution in reaction to the change. We also found the decision process was not veridical, as symmetric stimulus change had an asymmetric effect on the rate of evidence accumulation. Thus, the perceptual decision process was slow to react to, and underestimated, new contrary motion information.
Copyright © 2015 Elsevier Inc. All rights reserved.
The human memory system is remarkable in its capacity to focus its search on items learned in a given context. This capacity can be so precise that many leading models of human memory assume that only those items learned in the context of a recently studied list compete for recall. We sought to extend the explanatory scope of these models to include not only intralist phenomena, such as primacy and recency effects, but also interlist phenomena such as proactive and retroactive interference. Building on retrieved temporal context models of memory search (e.g., Polyn, Norman, & Kahana, 2009), we present a substantially revised theory in which memory accumulates across multiple experimental lists, and temporal context is used both to focus retrieval on a target list, and to censor retrieved information when its match to the current context indicates that it was learned in a nontarget list. We show how the resulting model can simultaneously account for a wide range of intralist and interlist phenomena, including the pattern of prior-list intrusions observed in free recall, build-up of and release from proactive interference, and the ability to selectively target retrieval of items on specific prior lists (Jang & Huber, 2008; Shiffrin, 1970). In a new experiment, we verify that subjects' error monitoring processes are consistent with those predicted by the model.
(c) 2015 APA, all rights reserved).
BACKGROUND - Cognitive control impairments are linked to functional outcome in schizophrenia. The goal of the current study was to investigate precise abnormalities in two aspects of cognitive control: reactively changing a prepared response, and monitoring performance and adjusting behavior accordingly. We adapted an oculomotor task from neurophysiological studies of the cellular basis of cognitive control in nonhuman primates.
METHODS - 16 medicated outpatients with schizophrenia (SZ) and 18 demographically-matched healthy controls performed the modified double-step task. In this task, participants were required to make a saccade to a visual target. Infrequently, the target jumped to a new location and participants were instructed to rapidly inhibit and change their response. A race model provided an estimate of the time needed to cancel a planned movement. Response monitoring was assessed by measuring reaction time (RT) adjustments based on trial history.
RESULTS - SZ patients had normal visually-guided saccadic RTs but required more time to switch the response to the new target location. Additionally, the estimated latency of inhibition was longer in patients and related to employment. Finally, although both groups slowed down on trials that required inhibiting and changing a response, patients showed exaggerated performance-based adjustments in RTs, which was correlated with positive symptom severity.
CONCLUSIONS - SZ patients have impairments in rapidly inhibiting eye movements and show idiosyncratic response monitoring. These results are consistent with functional abnormalities in a network involving cortical oculomotor regions, the superior colliculus, and basal ganglia, as described in neurophysiological studies of non-human primates using an identical paradigm, and provide a translational bridge for understanding cognitive symptoms of SZ.
Copyright © 2015 Elsevier Inc. All rights reserved.
The interactive race model of saccadic countermanding assumes that response inhibition results from an interaction between a go unit, identified with gaze-shifting neurons, and a stop unit, identified with gaze-holding neurons, in which activation of the stop unit inhibits the growth of activation in the go unit to prevent it from reaching threshold. The interactive race model accounts for behavioral data and predicts physiological data in monkeys performing the stop-signal task. We propose an alternative model that assumes that response inhibition results from blocking the input to the go unit. We show that the blocked-input model accounts for behavioral data as accurately as the original interactive race model and predicts aspects of the physiological data more accurately. We extend the models to address the steady-state fixation period before the go stimulus is presented and find that the blocked-input model fits better than the interactive race model. We consider a model in which fixation activity is boosted when a stop signal occurs and find that it fits as well as the blocked input model but predicts very high steady-state fixation activity after the response is inhibited. We discuss the alternative linking propositions that connect computational models to neural mechanisms, the lessons to be learned from model mimicry, and generalization from countermanding saccades to countermanding other kinds of responses.
(c) 2015 APA, all rights reserved).
Perceptual expertise refers to learning that is specific to a domain, that transfers to new items within the trained domain, and that leads to automatic processing in the sense that expertise effects can be measured across a variety of tasks. It can be argued that most of us possess some degree of perceptual expertise in a least one, if not several domains, thereby giving the study of perceptual expertise broad application. Some object categories may in fact be objects of perceptual expertise to the majority of people: Faces appear to be one such example. Thus, the use of face stimuli, or the comparison of face and object perception, can be a powerful way to ask whether a given process is influenced by perceptual expertise. Here, we emphasize one characteristic way that face processing appears to differ from nonface processing: that is, the degree to which they recruit a "holistic" rather than a "featural" perceptual strategy. This review brings evidence that expertise influences perceptual processing together with recent findings that the capacity of visual short-term memory is greater in perceptual experts and explores the relationship between the two.
Copyright © 2009 Cognitive Science Society, Inc.