Discovering skill.

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.

Zika virus noncoding sfRNAs sequester multiple host-derived RNA-binding proteins and modulate mRNA decay and splicing during infection.

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.

Reconstructing fine-grained cognition from brain activity.

We describe the Sketch-and-Stitch method for bringing together a cognitive model and EEG to reconstruct the cognition of a subject. The method was tested in the context of a video game where the actions are highly interdependent and variable: simply changing whether a key was pressed or not for a 30th of a second can lead to a very different outcome. The Sketch level identifies the critical events in the game and the Stitch level fills in the detailed actions between these events. The critical events tend to produce robust EEG signals and the cognitive model provides probabilities of various transitions between critical events and the distribution of intervals between these events. This information can be combined in a hidden semi-Markov model that identifies the most probable sequence of critical events and when they happened. The Stitch level selects detailed actions from an extensive library of model games to produce these critical events. The decision about which sequence of actions to select from the library is made on the basis of how well they would produce weaker aspects of the EEG signal. The resulting approach can produce quite compelling replays of actual games from the EEG of a subject.

Spatiotemporal analysis of event-related fMRI to reveal cognitive states.

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.

Evaluation of a cognitive-behavioral intervention for high- and medium-risk probationers.

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.

Mapping working memory retrieval in space and in time: A combined electroencephalography and electrocorticography approach.

In this study, we investigated the time course and neural correlates of the retrieval process underlying visual working memory. We made use of a rare dataset in which the same task was recorded using both scalp electroencephalography (EEG) and Electrocorticography (ECoG), respectively. This allowed us to examine with great spatial and temporal detail how the retrieval process works, and in particular how the medial temporal lobe (MTL) is involved. In each trial, participants judged whether a probe face had been among a set of recently studied faces. With a method that combines hidden semi-Markov models and multivariate pattern analysis, the neural signal was decomposed into a sequence of latent cognitive stages with information about their durations on a trial-by-trial basis. Analyzed separately, EEG and ECoG data yielded converging results on discovered stages and their interpretation, which reflected 1) a brief pre-attention stage, 2) encoding the stimulus, 3) retrieving the studied set, and 4) making a decision. Combining these stages with the high spatial resolution of ECoG suggested that activity in the temporal cortex reflected item familiarity in the retrieval stage; and that once retrieval is complete, there is active maintenance of the studied face set in the decision stage in the MTL. During this same period, the frontal cortex guides the decision by means of theta coupling with the MTL. These observations generalize previous findings on the role of MTL theta from long-term memory tasks to short-term memory tasks.

The Common Time Course of Memory Processes Revealed.

Magnetoencephalography (MEG) was used to compare memory processes in two experiments, one involving recognition of word pairs and the other involving recall of newly learned arithmetic facts. A combination of hidden semi-Markov models and multivariate pattern analysis was used to locate brief "bumps" in the sensor data that marked the onset of different stages of cognitive processing. These bumps identified a separation between a retrieval stage that identified relevant information in memory and a decision stage that determined what response was implied by that information. The encoding, retrieval, decision, and response stages displayed striking similarities across the two experiments in their duration and brain activation patterns. Retrieval and decision processes involve distinct brain activation patterns. We conclude that memory processes for two different tasks, associative recognition versus arithmetic retrieval, follow a common spatiotemporal neural pattern and that both tasks have distinct retrieval and decision stages.

Psychopathy, self-identified race/ethnicity, and nonviolent recidivism: A longitudinal study.

Psychopathy has long been noted to play an important role in the prediction of criminal behavior and offending. Although many studies have demonstrated that psychopathic traits are predictive of violent recidivism among offenders, relatively few studies have examined the predictive validity of psychopathic traits for nonviolent recidivism and very few have examined this issue in a sample of offenders in the United States. To address this issue, we examined the predictive validity of psychopathy for both nonviolent and general recidivism using the Psychopathy Checklist-Revised (PCL-R) in a sample of 422 county jail inmates. We also examined whether ratings on Factors 1 and 2 differentially predicted nonviolent and general recidivism and whether predictive validity varied among European American, African American, and Latino American male offenders. Psychopathic traits were modestly predictive of nonviolent and general (total) recidivism. Factor 2 ratings were not more predictive of nonviolent recidivism than Factor 1 ratings in this sample, but the two factor scores also predicted nonviolent recidivism interactively. Psychopathic traits were also predictive of both outcomes in subsamples of European American and African American offenders, but not among Latino American offenders. Findings are consistent in magnitude and pattern with prior studies addressing the prediction of violence, and they show that the relationship between psychopathy and criminal conduct generalizes to the prediction of nonviolent crime in a United States offender sample. Results suggest potential differences between the predictive validity of psychopathy among Latino American offenders and other racial/ethnic groups, which suggest the need for additional research. (PsycINFO Database Record (c) 2018 APA, all rights reserved).

The Effects of Probe Similarity on Retrieval and Comparison Processes in Associative Recognition.

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.

When math operations have visuospatial meanings versus purely symbolic definitions: Which solving stages and brain regions are affected?

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.

Inter-subject alignment of MEG datasets in a common representational space.

Pooling neural imaging data across subjects requires aligning recordings from different subjects. In magnetoencephalography (MEG) recordings, sensors across subjects are poorly correlated both because of differences in the exact location of the sensors, and structural and functional differences in the brains. It is possible to achieve alignment by assuming that the same regions of different brains correspond across subjects. However, this relies on both the assumption that brain anatomy and function are well correlated, and the strong assumptions that go into solving the under-determined inverse problem given the high-dimensional source space. In this article, we investigated an alternative method that bypasses source-localization. Instead, it analyzes the sensor recordings themselves and aligns their temporal signatures across subjects. We used a multivariate approach, multiset canonical correlation analysis (M-CCA), to transform individual subject data to a low-dimensional common representational space. We evaluated the robustness of this approach over a synthetic dataset, by examining the effect of different factors that add to the noise and individual differences in the data. On an MEG dataset, we demonstrated that M-CCA performs better than a method that assumes perfect sensor correspondence and a method that applies source localization. Last, we described how the standard M-CCA algorithm could be further improved with a regularization term that incorporates spatial sensor information. Hum Brain Mapp 38:4287-4301, 2017. © 2017 Wiley Periodicals, Inc.

XRN1 stalling in the 5' UTR of Hepatitis C virus and Bovine Viral Diarrhea virus is associated with dysregulated host mRNA stability.

We demonstrate that both Hepatitis C virus (HCV) and Bovine Viral Diarrhea virus (BVDV) contain regions in their 5' UTRs that stall and repress the enzymatic activity of the cellular 5'-3' exoribonuclease XRN1, resulting in dramatic changes in the stability of cellular mRNAs. We used biochemical assays, virus infections, and transfection of the HCV and BVDV 5' untranslated regions in the absence of other viral gene products to directly demonstrate the existence and mechanism of this novel host-virus interaction. In the context of HCV infection, we observed globally increased stability of mRNAs resulting in significant increases in abundance of normally short-lived mRNAs encoding a variety of relevant oncogenes and angiogenesis factors. These findings suggest that non-coding regions from multiple genera of the Flaviviridae interfere with XRN1 and impact post-transcriptional processes, causing global dysregulation of cellular gene expression which may promote cell growth and pathogenesis.

Change-point detection of cognitive states across multiple trials in functional neuroimaging.

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.

Tracking cognitive processing stages with MEG: A spatio-temporal model of associative recognition in the brain.

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.

Hidden Stages of Cognition Revealed in Patterns of Brain Activation.

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.

Phases of learning: How skill acquisition impacts cognitive processing.

This fMRI study examines the changes in participants' information processing as they repeatedly solve the same mathematical problem. We show that the majority of practice-related speedup is produced by discrete changes in cognitive processing. Because the points at which these changes take place vary from problem to problem, and the underlying information processing steps vary in duration, the existence of such discrete changes can be hard to detect. Using two converging approaches, we establish the existence of three learning phases. When solving a problem in one of these learning phases, participants can go through three cognitive stages: Encoding, Solving, and Responding. Each cognitive stage is associated with a unique brain signature. Using a bottom-up approach combining multi-voxel pattern analysis and hidden semi-Markov modeling, we identify the duration of that stage on any particular trial from participants brain activation patterns. For our top-down approach we developed an ACT-R model of these cognitive stages and simulated how they change over the course of learning. The Solving stage of the first learning phase is long and involves a sequence of arithmetic computations. Participants transition to the second learning phase when they can retrieve the answer, thereby drastically reducing the duration of the Solving stage. With continued practice, participants then transition to the third learning phase when they recognize the problem as a single unit and produce the answer as an automatic response. The duration of this third learning phase is dominated by the Responding stage.

Using model-based functional MRI to locate working memory updates and declarative memory retrievals in the fronto-parietal network.

In this study, we used model-based functional MRI (fMRI) to locate two functions of the fronto-parietal network: declarative memory retrievals and updating of working memory. Because regions in the fronto-parietal network are by definition coherently active, locating functions within this network is difficult. To overcome this problem, we applied model-based fMRI, an analysis method that uses predictions of a computational model to inform the analysis. We applied model-based fMRI to five previously published datasets with associated computational cognitive models, and subsequently integrated the results in a meta-analysis. The meta-analysis showed that declarative memory retrievals correlated with activity in the inferior frontal gyrus and the anterior cingulate, whereas updating of working memory corresponded to activation in the inferior parietal lobule, as well as to activation around the inferior frontal gyrus and the anterior cingulate.

The discovery of processing stages: analyzing EEG data with hidden semi-Markov models.

In this paper we propose a new method for identifying processing stages in human information processing. Since the 1860s scientists have used different methods to identify processing stages, usually based on reaction time (RT) differences between conditions. To overcome the limitations of RT-based methods we used hidden semi-Markov models (HSMMs) to analyze EEG data. This HSMM-EEG methodology can identify stages of processing and how they vary with experimental condition. By combining this information with the brain signatures of the identified stages one can infer their function, and deduce underlying cognitive processes. To demonstrate the method we applied it to an associative recognition task. The stage-discovery method indicated that three major processes play a role in associative recognition: a familiarity process, an associative retrieval process, and a decision process. We conclude that the new stage-discovery method can provide valuable insight into human information processing.

Visuospatial referents facilitate the learning and transfer of mathematical operations: extending the role of the angular gyrus.

Different external representations for learning and solving mathematical operations may affect learning and transfer. To explore the effects of learning representations, learners were each introduced to two new operations (b↑n and b↓n) via either formulas or graphical representations. Both groups became adept at solving regular (trained) problems. During transfer, no external formulas or graphs were present; however, graph learners' knowledge could allow them to mentally associate problem expressions with visuospatial referents. The angular gyrus (AG) has recently been hypothesized to map problems to mental referents (e.g., symbolic answers; Grabner, Ansari, Koschutnig, Reishofer, & Ebner Human Brain Mapping, 34, 1013-1024, 2013), and we sought to test this hypothesis for visuospatial referents. To determine whether the AG and other math (horizontal intraparietal sulcus) and visuospatial (fusiform and posterior superior parietal lobule [PSPL]) regions were implicated in processing visuospatial mental referents, we included two types of transfer problems, computational and relational, which differed in referential load (one graph vs. two). During solving, the activations in AG, PSPL, and fusiform reflected the referential load manipulation among graph but not formula learners. Furthermore, the AG was more active among graph learners overall, which is consistent with its hypothesized referential role. Behavioral performance was comparable across the groups on computational transfer problems, which could be solved in a way that incorporated learners' respective procedures for regular problems. However, graph learners were more successful on relational transfer problems, which assessed their understanding of the relations between pairs of similar problems within and across operations. On such problems, their behavioral performance correlated with activation in the AG, fusiform, and a relational processing region (BA 10).

End effects and cross-dimensional interference in identification of time and length: Evidence for a common memory mechanism.

Memory plays a critical role in time estimation, yet detailed mechanisms underlying temporal memory have not been fully understood. The current functional magnetic resonance imaging (fMRI) study investigated memory phenomena in absolute identification of time durations and line lengths. In both time and length identification, participants responded faster to end-of-range stimuli (e.g., the shortest or longest items of the stimulus set) than to middle stimuli. Participants performed worse in the incongruent condition (mismatch between time and length in the stimulus position) than in the congruent condition, indicating cross-dimensional interference between time and length. Both phenomena reflect increased difficulty of retrieving information relevant to the current context in the presence of context-irrelevant information. A region in the lateral inferior prefrontal cortex showed a greater response to the middle stimuli and in the incongruent condition suggesting greater demands for controlled memory retrieval. A cognitive model based on the ACT-R (Adaptive Control of Thought - Rational) declarative memory mechanisms accounted for the major behavioral and imaging results. The results suggest that contextual effects in temporal memory can be understood in terms of domain-general memory principles established outside the time estimation domain.