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Cognitive science has a rich history of developing theories of processing that characterize the mental steps involved in performance of many tasks. Recent work in neuroimaging and machine learning has greatly improved our ability to link cognitive processes with what is happening in the brain. This article analyzes a hidden semi‐Markov model‐multivoxel pattern‐analysis (HSMM‐MVPA) methodology that we have developed for inferring the sequence of brain states one traverses in the performance of a cognitive task. The method is applied to a functional magnetic resonance imaging (fMRI) experiment where task boundaries are known that should separate states. The method is able to accurately identify those boundaries. Then, applying the method to synthetic data, we explore more fully those factors that influence performance of the method: signal‐to‐noise ratio, numbers of states, state sojourn times, and numbers of underlying experimental conditions. The results indicate the types of experimental tasks where applications of the HSMM‐MVPA method are likely to yield accurate and insightful results.

Hematophagous arthropod bloodmeal identification has remained a challenge in the field of vector biology, but these studies are important to understand blood feeding patterns of arthropods, spatial, and temporal patterns in arbovirus transmission cycles, and risk of human and veterinary disease. We investigated the use of an existing vertebrate primer set for use on the droplet digital polymerase chain reaction (ddPCR) platform, to explore the use of this technology in the identification and quantification of vertebrate DNA in mosquito blood meals. Host DNA was detectable 48-h post-engorgement in some mosquitoes by ddPCR, compared with 24-h post-engorgement using traditional PCR. The capability of ddPCR for absolute quantification of template DNA offers unique potential applications of this new technology to field studies on the ecology of vector-borne diseases, but currently with limited scope.

Reducing recidivism is a central goal of treatment programs for offenders. Preliminary evidence suggests that cognitive-behavioral group interventions based on the National Institute of Corrections curriculum (Bush, Glick, & Taymans, 1997) may be effective in reducing recidivism rates among adult probationers. We evaluated the effectiveness of a program based on this curriculum among 167 high- and medium-risk probationers assigned to this program and a comparison group of 120 high- and medium-risk probationers matched on age and number of prior criminal charges. Improvements over prior studies included use of survival analytic methods and propensity score matching, a longer follow-up interval, and examination of treatment effectiveness within ethnic groups. Relative to the comparison group, treatment group probationers were more likely to complete probation satisfactorily and survive longer before rearrest. Moreover, supplementary analyses suggested that ethnicity was associated with differences in intervention effectiveness. Treatment was predictive of lower recidivism rates among European Americans and African Americans but was less effective among Latino American probationers.

Serological cross-reactivity among flaviviruses makes determining the prior arbovirus exposure of animals challenging in areas where multiple flavivirus strains are circulating. We hypothesized that prior infection with ZIKV could be confirmed through the presence of subgenomic flavivirus RNA (sfRNA) of the 3′ untranslated region (UTR), which persists in tissues due to XRN-1 stalling during RNA decay. We amplified ZIKV sfRNA but not NS5 from three experimentally-infected Jamaican fruit bats, supporting the hypothesis of sfRNA tissue persistence. Applying this approach to 198 field samples from Uganda, we confirmed presence of ZIKV sfRNA, but not NS5, in four bats representing three species: Eidolon helvum (n = 2), Epomophorus labiatus (n = 1), and Rousettus aegyptiacus (n = 1). Amplified sequence was most closely related to Asian lineage ZIKV. Our results support the use of sfRNA as a means of identifying previous flavivirus infection and describe the first detection of ZIKV RNA in East African bats.

Aedes aegypti is a major vector of Zika, dengue, and other arboviruses. Permethrin adulticidal spraying, which targets the voltage-gated sodium channel (VGSC), is commonly done to reduce local mosquito populations and protect humans from exposure to arbovirus pathogens transmitted by this dangerous pest. Permethrin resistance, however, is a growing problem and understanding its underlying molecular basis may identify avenues to combat it. We identified a single G:C polymorphism in pre-miR-33 that was genetically associated with permethrin resistance; resulting isoforms had structural differences that may affect DICER-1/pre-miRNA processing rates. We then assessed the effects of overexpression of pre-miR-33 isoforms on permethrin toxicological phenotypes, VGSC transcript abundance and protein levels for two genetically related mosquito strains. One strain had its naturally high permethrin resistance levels maintained by periodic treatment, and the other was released from selection. VGSC protein levels were lower in the permethrin resistant strain than in the related permethrin-susceptible strain. Overexpression of the G-pre-miR-33 isoform reduced VGSC expression levels in both strains. To further elucidate changes in gene expression associated with permethrin resistance, exome-capture gDNA deep sequencing, genetic association mapping and subsequent gene set enrichment analysis revealed that transport genes, in particular, were selected in resistant versus susceptible mosquitoes. Collectively, these data indicate that miR-33 regulates VGSC expression as part of a nuanced system of neuronal regulation that contributes to a network of heritable features determining permethrin resistance.

We examined the detailed behavioral characteristics of transfer of skill and the ability of the adaptive control of thought rational (ACT-R) architecture to account for this with its new Controller module. We employed a simple action video game called Auto Orbit and investigated the control tuning of timing skills across speed perturbations of the environment. In Auto Orbit, players needed to learn to alternate turn and shot actions to blow and burst balloons under time constraints imposed by balloon resets and deflations. Cognitive and motor skill transfer was assessed both in terms of game performance and in terms of the details of their motor actions. We found that skill transfer across speeds necessitated the recalibration of action timing skills. In addition, we found that acquiring skill in Auto Orbit involved a progressive decrease in variability of behavior. Finally, we found that players with higher skill levels tended to be less variable in terms of action chunking and action timing. These findings further shed light on the complex cognitive and motor mechanisms of skill transfer across speeds in complex task environments.

Researchers (see Siegler, 1987; Newell, 1973) have demonstrated the dangers of aggregating data over strategies. In this paper, we provide a current demonstration of this point using our recent work in the study of cognitive skill acquisition as a case study. Moreover, we call particular attention to the relation between cognitive modeling and microanalysis as driving forces toward a more thorough understanding of the role of strategies in cognitive skill acquisition.

Memorializes Gordon H. Bower (1932-2020). One of the founders and leaders of cognitive science, Gordon was widely recognized for his contributions to science. He was elected to the National Academy of Sciences, the American Academy of Arts and Sciences, the Society of Experimental Psychologists, and the American Philosophical Society. Among his many awards were the Distinguished Scientific Contributions Award from the American Psychological Association in 1979 and the National Medal of Science in 2005 "for his unparalleled contributions to cognitive and mathematical psychology, for his lucid analysis of memory and learning, and for his important service to psychology and American science." Gordon was active both at Stanford University, serving as department chair and associate dean of humanities and sciences, and on the national stage, where he served as chief scientific adviser to the National Institute of Mental Health and was president of the Cognitive Science Society, the Psychonomic Society, and the Association for Psychological Science. (PsycInfo Database Record (c) 2020 APA, all rights reserved).

Zika virus (ZIKV) can establish infection in immune privileged sites such as the testes, eye, and placenta. Whether ZIKV infection of white blood cells is required for dissemination of the virus to immune privileged sites has not been definitively shown. To assess whether initial ZIKV replication in myeloid cell populations is critical for dissemination during acute infection, recombinant ZIKVs were generated that could not replicate in these specific cells. ZIKV was cell restricted by insertion of a complementary sequence to a myeloid-specific microRNA in the 3′ untranslated region. Following inoculation of a highly sensitive immunodeficient mouse model, crucial immune parameters, such as quantification of leukocyte cell subsets, cytokine and chemokine secretion, and viremia, were assessed. Decreased neutrophil numbers in the spleen were observed during acute infection with myeloid-restricted ZIKV that precluded the generation of viremia and viral dissemination to peripheral organs. Mice inoculated with a nontarget microRNA control ZIKV demonstrated increased expression of key cytokines and chemokines critical for neutrophil and monocyte recruitment and increased neutrophil influx in the spleen. In addition, ZIKV-infected Ly6C(hi) monocytes were identified in vivo in the spleen. Mice inoculated with myeloid-restricted ZIKV had a decrease in Ly6C(hi) ZIKV RNA-positive monocytes and a lack of inflammatory cytokine production compared to mice inoculated with control ZIKV. IMPORTANCE Myeloid cells, including monocytes, play a crucial role in immune responses to pathogens. Monocytes have also been implicated as “Trojan horses” during viral infections, carrying infectious virus particles to immune privileged sites and/or to sites protected by physical blood-tissue barriers, such as the blood-testis barrier and the blood-brain barrier. In this study, we found that myeloid cells are crucial to Zika virus (ZIKV) pathogenesis. By engineering ZIKV clones to encode myeloid-specific microRNA target sequences, viral replication was inhibited in myeloid cells by harnessing the RNA interference pathway. Severely immunodeficient mice inoculated with myeloid-restricted ZIKV did not demonstrate clinical signs of disease and survived infection. Furthermore, viral dissemination to peripheral organs was not observed in these mice. Lastly, we identified Ly6C(mid/hi) murine monocytes as the major myeloid cell population that disseminates ZIKV.

The performance of contamination sensitive components—such as optical components—can be degraded by particulate matter depositing on the surfaces. Particles can accumulate during manufacturing, handling and operation. For a space-based system, particles can shed from the fairing and redistribute onto sensitive surfaces during launch. An engineering modeling approach has been developed for modeling particle migration during launch. The approach involves particle detachment from the fairing, particle transport through the venting atmosphere inside the fairing, and attachment to the receiving surface. Particle size and amounts on the fairing surface can be modeled using distributions from standards, such as IEST-STDCC1246E, as well as from empirical data obtained from tape lifts. Surface interactions are modeled using theoretical as well as empirical data. Commercial computational fluid dynamics codes are used to calculate the gas flow in the fairing during depressurization during launch. This approach not only provides insight into particle redistribution during launch but also can be used to establish fairing cleanliness requirements.

Coronaviruses, including SARS‐Cov‐2, are RNA‐based pathogens that interface with a large variety of RNA‐related cellular processes during infection. These processes include capping, polyadenylation, localization, RNA stability, translation, and regulation by RNA binding proteins or noncoding RNA effectors. The goal of this article is to provide an in‐depth perspective on the current state of knowledge of how various coronaviruses interact with, usurp, and/or avoid aspects of these cellular RNA biology machineries. A thorough understanding of how coronaviruses interact with RNA‐related posttranscriptional processes in the cell should allow for new insights into aspects of viral pathogenesis as well as identify new potential avenues for the development of anti‐coronaviral therapeutics. This article is categorized under:RNA in Disease and Development > RNA in Disease, Coronavirus interplay with mechanisms associated with the RNA biology of the host cell play a key role in numerous aspects of virus replication and pathogenesis.

This report presents the first known documented case of Takotsubo cardiomyopathy, characterized by transient left ventricular systolic dysfunction after a transjugular intrahepatic portosystemic shunt procedure. A 59-year-old woman with decompensated cirrhosis underwent transjugular intrahepatic portosystemic shunt procedure with subsequent new-onset heart failure without previous diagnostic evidence of underlying cardiovascular disease, including cirrhotic cardiomyopathy. After 2 weeks of medical management with β-blockade and diuretics, the patient had recovery of a left ventricular ejection fraction.

Spacecraft orbiting Jupiter and performing flybys or landing on Jovian moons, such as Europa, experience an environment with high radiation levels. A Radiation Induced Outgassing Test (RIOT) campaign was initiated to study of radiation effects on contamination products evolving from spacecraft materials. Materials were irradiated with a flux of 2.6×1010 electrons/cm2/s at an energy of 1.5 MeV. Preliminary results from the ongoing test campaign include determining species produced by radiation scissioning of silicones, as well as measuring outgassing rates from the material. A physics based outgassing model has been developed to explain the observed experimental results. Material outgassing rates measured during, and subsequent to radiation exposure, can be modeled using a simple diffusion model. Use of Fick's law with a source term during irradiation, and without a source term when the radiation is turned off, can explain some of the experimental results. However, not all of the experimental results can be explained by this model. The observed discrepancies can be due to a combination of radiation interference with the test instrumentation and also due to radiation interaction with the test sample surface. Although additional work is needed, the experimental results can provide a basis for extrapolating experimentally measured outgassing rates to predict contamination outgassing as a function of the radiation levels encountered during a mission at Jupiter.

Pure insertion describes a scenario where a mental process is inserted within a sequence of other processes without altering the other processes. Under the assumption of pure insertion, the duration of the inserted process can be identified by calculating the difference in overall response times when the process is present versus absent (i.e., Donder’s subtraction method). Additionally, under the assumption of pure insertion, brain regions associated with the inserted process can be identified in fMRI studies by contrasting activation when the process is present versus absent. However, the assumption of pure insertion does not hold in many situations. In this study, we adopted a novel approach for identifying the impact of insertion by decomposing the EEG signal into a sequence of latent stages, each with a distinct topographical distribution and duration. Based on these latent stages, it is possible to identify when, and for how long, a process occurred. We crossed two factors in the experiment: whether the trial required substituting a letter with a number from memory and whether the trial required calculating the product of two numbers. By crossing these factors, we could examine whether inserting substitution and calculation processes affected the durations of other mental processing stages. Behavioral data in the form of response latencies, and averaged EEG signal in the form of event-related potentials (ERPs), provided no evidence of violations of pure insertion. However, our analysis of single-trial EEG signal allowed us both to show that inserting substitution or calculation did affect other stages and to understand why.

This paper shows how identical skills can emerge either from instruction or discovery when both result in an understanding of the causal structure of the task domain. The paper focuses on the discovery process, extending the skill acquisition model of Anderson et al. (2019) to address learning by discovery. The discovery process involves exploring the environment and developing associations between discontinuities in the task and events that precede them. The growth of associative strength in ACT-R serves to identify potential causal connections. The model can derive operators from these discovered causal relations just as does with the instructed causal information. Subjects were given a task of learning to play a video game either with a description of the game's causal structure (Instruction) or not (Discovery). The Instruction subjects learned faster, but successful Discovery subjects caught up. After 20 3-minute games the behavior of the successful subjects in the two groups was largely indistinguishable. The play of these Discovery subjects jumped in the same discrete way as did the behavior of simulated subjects in the model. These results show how implicit processes (associative learning, control tuning) and explicit processes (causal inference, planning) can combine to produce human learning in complex environments.

How does processing differ during purely symbolic problem solving versus when mathematical operations can be mentally associated with meaningful (here, visuospatial) referents? Learners were trained on novel math operations (↓, ↑), that were defined strictly symbolically or in terms of a visuospatial interpretation (operands mapped to dimensions of shaded areas, answer = total area). During testing (scanner session), no visuospatial representations were displayed. However, we expected visuospatially-trained learners to form mental visuospatial representations for problems, and exhibit distinct activations. Since some solution intervals were long (~10s) and visuospatial representations might only be instantiated in some stages during solving, group differences were difficult to detect when treating the solving interval as a whole. However, an HSMM-MVPA process (Anderson and Fincham, 2014a) to parse fMRI data identified four distinct problem-solving stages in each group, dubbed: 1) encode; 2) plan; 3) compute; and 4) respond. We assessed stage-specific differences across groups. During encoding, several regions implicated in general semantic processing and/or mental imagery were more active in visuospatially-trained learners, including: bilateral supramarginal, precuneus, cuneus, parahippocampus, and left middle temporal regions. Four of these regions again emerged in the computation stage: precuneus, right supramarginal/angular, left supramarginal/inferior parietal, and left parahippocampal gyrus. Thus, mental visuospatial representations may not just inform initial problem interpretation (followed by symbolic computation), but may scaffold on-going computation. In the second stage, higher activations were found among symbolically-trained solvers in frontal regions (R. medial and inferior and L. superior) and the right angular and middle temporal gyrus. Activations in contrasting regions may shed light on solvers' degree of use of symbolic versus mental visuospatial strategies, even in absence of behavioral differences.

The cognitive architecture ACT-R is at the same time a psychological theory and a modeling framework for constructing cognitive models that adhere to the principles of the theory. ACT-R can be used in combination with fMRI data in two different ways: (1) fMRI data can be used to evaluate and constrain models in ACT-R by means of predefined Region-of-Interest (ROI) analysis, and (2) predictions from ACT-R models can be used to locate neural correlates of model processes and representations by means of model based fMRI analysis. In this paper we provide a step-by-step tutorial on both approaches. Note that this tutorial neither teaches the ACT-R theory in any detail, nor fMRI analysis, but explains how ACT-R can be used in combination with fMRI data. To this end, we provide all data and computer code necessary to run the ACT-R model, carry out the analyses, and recreate the figures in the paper. As an example dataset we use a relatively simple algebra task. In the first section, we develop an ACT-R model of this task and fit it to behavioral data. In the second section, we apply a predefined ROI-analysis to evaluate the model using fMRI data. In the third section, we use model-based fMRI analysis to locate the following processes in the brain: retrieval of mathematical facts from memory, working memory updates, motor responses, and visually encoding the problems. After working through this tutorial, the reader will have learned what can be achieved with the two different analysis methods and how they are conducted; the example code can then be adapted to a new dataset. (C) 2016 Elsevier Inc. All rights reserved.

The P300 is one of the most widely studied components of the human event-related potential. According to a longstanding view, the P300, and particularly its posterior subcomponent (i.e., the P3b), is driven by stimulus categorization. Whether the P3b relates to tactical processes involved in immediate responding or strategic processes that affect future behavior remains controversial, however. It is difficult to determine whether variability in P3b latencies relates to variability in response times because of limitations in the methods currently available to quantify the latency of the P3b during single trials. In this paper, we report results from the Psychomotor Vigilance Task (PVT), the Hitchcock Radar Task, and a 3-Stimulus Oddball Task. These represent variants of the one-, two-, and three-stimulus oddball paradigms commonly used to study the P3b. The PVT requires simple detection, whereas the Hitchcock Radar Task and the 3-Stimulus Task require detection and categorization. We apply a novel technique that combines hidden semi-Markov models and multi-voxel pattern analysis (HSMM-MVPA) to data from the three experiments. HSMM-MVPA revealed a processing stage in each task corresponding to the P3b. Trial-by-trial variability in the latency of the processing stage correlated with response times in the Hitchcock Radar Task and the 3-Stimulus Task, but not the PVT. These results indicate that the P3b reflects a stimulus categorization process, and that its latency is strongly associated with response times when the stimulus must be categorized before responding. In addition to those theoretical insights, the ability to detect the onset of the P3b and other components on a single-trial basis using HSMM-MVPA opens the door for new uses of mental chronometry in cognitive neuroscience.

In this study, we investigated the information processing stages underlying associative recognition. We recorded EEG data while participants performed a task that involved deciding whether a probe word triple matched any previously studied triple. We varied the similarity between probes and studied triples. According to a model of associative recognition developed in the Adaptive Control of Thought-Rational cognitive architecture, probe similarity affects the duration of the retrieval stage: Retrieval is fastest when the probe is similar to a studied triple. This effect may be obscured, however, by the duration of the comparison stage, which is fastest when the probe is not similar to the retrieved triple. Owing to the opposing effects of probe similarity on retrieval and comparison, overall RTs provide little information about each stage's duration. As such, we evaluated the model using a novel approach that decomposes the EEG signal into a sequence of latent states and provides information about the durations of the underlying information processing stages. The approach uses a hidden semi-Markov model to identify brief sinusoidal peaks (called bumps) that mark the onsets of distinct cognitive stages. The analysis confirmed that probe type has opposite effects on retrieval and comparison stages.

This article presents a new research area called interactive task learning (ITL), in which an agent actively tries to learn not just how to perform a task better but the actual definition of a task through natural interaction with a human instructor while attempting to perform the task. The authors provide an analysis of desiderata for ITL systems, a review of related work, and a discussion of possible application areas for ITL systems.

We describe how to extend the ACT-R production system to model human errors in the performance of a high-level cognitive task: to solve simple linear algebra problems while memorizing a digit span. Errors of omission are produced by introducing a cutoff on the latency of memory retrievals. If a memory chunk cannot gather enough activation to be retrieved before the threshold i s reached, retrieval fails. Adding Gaussian noise to chunk activation produces a pattern quantitatively similar to subject errors. Errors of commission are introduced by allowing imperfect matching in the condition side of productions. The wrong memory chunk can be retrieved if its activation is large enough to allow it to overcome the mismatch penalty. This mechanism provides a qualitative and quantitative fit to subject errors. In conclusion, this paper demonstrates that human-like errors, sometimes thought of as the exclusive domain of connectionist models, can be successfully duplicated in production system models.

Insect-borne flaviviruses produce a 300–500–base long noncoding RNA, termed subgenomic flavivirus RNA (sfRNA), by stalling the cellular 5′–3′-exoribonuclease 1 (XRN1) via structures located in their 3′ UTRs. In this study, we demonstrate that sfRNA production by Zika virus represses XRN1 analogous to what we have previously shown for other flaviviruses. Using protein–RNA reconstitution and a stringent RNA pulldown assay with human choriocarcinoma (JAR) cells, we demonstrate that the sfRNAs from both dengue type 2 and Zika viruses interact with a common set of 21 RNA-binding proteins that contribute to the regulation of post-transcriptional processes in the cell, including splicing, RNA stability, and translation. We found that four of these sfRNA-interacting host proteins, DEAD-box helicase 6 (DDX6) and enhancer of mRNA decapping 3 (EDC3) (two RNA decay factors), phosphorylated adaptor for RNA export (a regulator of the biogenesis of the splicing machinery), and apolipoprotein B mRNA-editing enzyme catalytic subunit 3C (APOBEC3C, a nucleic acid–editing deaminase), inherently restrict Zika virus infection. Furthermore, we demonstrate that the regulations of cellular mRNA decay and RNA splicing are compromised by Zika virus infection as well as by sfRNA alone. Collectively, these results reveal the large extent to which Zika virus–derived sfRNAs interact with cellular RNA-binding proteins and highlight the potential for widespread dysregulation of post-transcriptional control that likely limits the effective response of these cells to viral infection.

A theory is presented about how instruction and experience combine to produce human fluency in a complex skill. The theory depends critically on 4 aspects of the ACT-R architecture. The first is the timing of various modules, particularly motor timing, which results in behavior that closely matches human behavior. The second is the ability to interpret declarative representations of instruction so that they lead to action. The third aspect concerns how practice converts this declarative knowledge into a procedural form so that appropriate actions can be quickly executed. The fourth component, newly added to the architecture, is a Controller module that learns the setting of control variables for actions. The overall theory is implemented in a computational model that is capable of simulating human learning. Its predictions are confirmed in a first experiment involving 2 games derived from the experimental video game Space Fortress. The second experiment tests predictions from the Controller module about lack of transfer between video games. Across the 2 experiments a single model, with the same parameter settings, is shown to simulate human learning of 3 video games. (PsycINFO Database Record (c) 2019 APA, all rights reserved).

In this study, we investigated the cognitive processing stages underlying associative recognition using MEG. Over the last four decades, a model of associative recognition has been developed in the ACT-R cognitive architecture. This model was first exclusively based on behavior, but was later evaluated and improved based on fMRI and EEG data. Unfortunately, the limited spatial resolution of EEG and the limited temporal resolution of fMRI have made it difficult to fully understand the spatiotemporal dynamics of associative recognition. We therefore conducted an associative recognition experiment with MEG, which combines excellent temporal resolution with reasonable spatial resolution. To analyze the data, we applied non-parametric cluster analyses and a multivariate classifier. This resulted in a detailed spatio-temporal model of associative recognition. After the visual encoding of the stimuli in occipital regions, three separable memory processes took place: a familiarity process (temporal cortex), a recollection process (temporal cortex and supramarginal gyrus), and a representational process (dorsolateral prefrontal cortex). A late decision process (superior parietal cortex) then acted upon the recollected information represented in the prefrontal cortex, culminating in a late response process (motor cortex). We conclude that existing theories of associative recognition, including the ACT-R model, should be adapted to include these processes.

Many functional neuroimaging-based studies involve repetitions of a task that may require several phases, or states, of mental activity. An appealing idea is to use relevant brain regions to identify the states. We developed a novel change-point methodology that adapts to the repeated trial structure of such experiments by assuming the number of states stays fixed across similar trials while allowing the timing of change-points to change across trials. Model fitting is based on reversible-jump MCMC. Simulation studies verified its ability to identify change-points successfully. We applied this technique to data collected via functional magnetic resonance imaging (fMRI) while each of 20 subjects solved unfamiliar arithmetic problems. Our methodology supplies both a summary of state dimensionality and uncertainty assessments about number of states and the timing of state transitions. Copyright © 2016 John Wiley & Sons, Ltd.

We introduce a method for measuring the number and durations of processing stages from the electroencephalographic signal and apply it to the study of associative recognition. Using an extension of past research that combines multivariate pattern analysis with hidden semi-Markov models, the approach identifies on a trial-by-trial basis where brief sinusoidal peaks (called bumps) are added to the ongoing electroencephalographic signal. We propose that these bumps mark the onset of critical cognitive stages in processing. The results of the analysis can be used to guide the development of detailed process models. Applied to the associative recognition task, the hidden semi-Markov models multivariate pattern analysis method indicates that the effects of associative strength and probe type are localized to a memory retrieval stage and a decision stage. This is in line with a previously developed the adaptive control of thought-rational process model, called ACT-R, of the task. As a test of the generalization of our method we also apply it to a data set on the Sternberg working memory task collected by Jacobs, Hwang, Curran, and Kahana (2006). The analysis generalizes robustly, and localizes the typical set size effect in a late comparison/decision stage. In addition to providing information about the number and durations of stages in associative recognition, our analysis sheds light on the event-related potential components implicated in the study of recognition memory. (PsycINFO Database Record

To advance cognitive theory, researchers must be able to parse the performance of a task into its significant mental stages. In this article, we describe a new method that uses functional MRI brain activation to identify when participants are engaged in different cognitive stages on individual trials. The method combines multivoxel pattern analysis to identify cognitive stages and hidden semi-Markov models to identify their durations. This method, applied to a problem-solving task, identified four distinct stages: encoding, planning, solving, and responding. We examined whether these stages corresponded to their ascribed functions by testing whether they are affected by appropriate factors. Planning-stage duration increased as the method for solving the problem became less obvious, whereas solving-stage duration increased as the number of calculations to produce the answer increased. Responding-stage duration increased with the difficulty of the motor actions required to produce the answer.