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An important feature of human cognition is the ability to flexibly and efficiently adapt behavior in response to continuously changing contextual demands. We leverage a large-scale dataset from Lumosity, an online cognitive-training platform, to investigate how cognitive processes involved in cued switching between tasks are affected by level of task practice across the adult lifespan. We develop a computational account of task switching that specifies the temporal dynamics of activating task-relevant representations and inhibiting task-irrelevant representations and how they vary with extended task practice across a number of age groups. Practice modulates the level of activation of the task-relevant representation and improves the rate at which this information becomes available, but has little effect on the task-irrelevant representation. While long-term practice improves performance across all age groups, it has a greater effect on older adults. Indeed, extensive task practice can make older individuals functionally similar to less-practiced younger individuals, especially for cognitive measures that focus on the rate at which task-relevant information becomes available.

An important feature of human cognition is the ability to flexibly and efficiently adapt behavior in response to continuously changing contextual demands. We leverage a large-scale dataset from Lumosity, an online cognitive-training platform, to investigate how cognitive processes involved in cued switching between tasks are affected by level of task practice across the adult lifespan. We develop a computational account of task switching that specifies the temporal dynamics of activating task-relevant representations and inhibiting task-irrelevant representations and how they vary with extended task practice across a number of age groups. Practice modulates the level of activation of the task-relevant representation and improves the rate at which this information becomes available, but has little effect on the task-irrelevant representation. While long-term practice improves performance across all age groups, it has a greater effect on older adults. Indeed, extensive task practice can make older individuals functionally similar to less-practiced younger individuals, especially for cognitive measures that focus on the rate at which task-relevant information becomes available.

(Immunity 48, 812–830.e1–e14; April 17, 2018) In the originally published version of this article, the authors neglected to include Younes Mokrab and Aaron M. Newman as co-authors and misspelled the names of authors Charles S. Rabkin and Ilya Shmulevich. The author names have been corrected here and online. In addition, the concluding sentence of the subsection “Immune Signature Compilation” in the Method Details in the original published article was deemed unclear because it did not specify differences among the gene set scoring methods. The concluding sentences now reads “Gene sets from Bindea et al., Senbabaoglu et al., and the MSigDB C7 collection were scored using single-sample gene set enrichment (ssGSEA) analysis (Barbie et al., 2009), as implemented in the GSVA R package (Hänzelmann et al., 2013). All other signatures were scored using methods found in the associated citations.”

Most data analyses rely on models. To complement statistical models, psychologists have developed cognitive models, which translate observed variables into psychologically interesting constructs. Response time models, in particular, assume that response time and accuracy are the observed expression of latent variables including 1) ease of processing, 2) response caution, 3) response bias, and 4) non-decision time. Inferences about these psychological factors, hinge upon the validity of the models' parameters. Here, we use a blinded, collaborative approach to assess the validity of such model-based inferences. Seventeen teams of researchers analyzed the same 14 data sets. In each of these two-condition data sets, we manipulated properties of participants' behavior in a two-alternative forced choice task. The contributing teams were blind to the manipulations, and had to infer what aspect of behavior was changed using their method of choice. The contributors chose to employ a variety of models, estimation methods, and inference procedures. Our results show that, although conclusions were similar across different methods, these "modeler's degrees of freedom" did affect their inferences. Interestingly, many of the simpler approaches yielded as robust and accurate inferences as the more complex methods. We recommend that, in general, cognitive models become a typical analysis tool for response time data. In particular, we argue that the simpler models and procedures are sufficient for standard experimental designs. We finish by outlining situations in which more complicated models and methods may be necessary, and discuss potential pitfalls when interpreting the output from response time models.

Most data analyses rely on models. To complement statistical models, psychologists have developed cognitive models, which translate observed variables into psychologically interesting constructs. Response time models, in particular, assume that response time and accuracy are the observed expression of latent variables including 1) ease of processing, 2) response caution, 3) response bias, and 4) non-decision time. Inferences about these psychological factors, hinge upon the validity of the models' parameters. Here, we use a blinded, collaborative approach to assess the validity of such model-based inferences. Seventeen teams of researchers analyzed the same 14 data sets. In each of these two-condition data sets, we manipulated properties of participants' behavior in a two-alternative forced choice task. The contributing teams were blind to the manipulations, and had to infer what aspect of behavior was changed using their method of choice. The contributors chose to employ a variety of models, estimation methods, and inference procedures. Our results show that, although conclusions were similar across different methods, these "modeler's degrees of freedom" did affect their inferences. Interestingly, many of the simpler approaches yielded as robust and accurate inferences as the more complex methods. We recommend that, in general, cognitive models become a typical analysis tool for response time data. In particular, we argue that the simpler models and procedures are sufficient for standard experimental designs. We finish by outlining situations in which more complicated models and methods may be necessary, and discuss potential pitfalls when interpreting the output from response time models.

We performed an extensive immunogenomic analysis of more than 10,000 tumors comprising 33 diverse cancer types by utilizing data compiled by TCGA. Across cancer types, we identified six immune subtypes-wound healing, IFN-γ dominant, inflammatory, lymphocyte depleted, immunologically quiet, and TGF-β dominant-characterized by differences in macrophage or lymphocyte signatures, Th1:Th2 cell ratio, extent of intratumoral heterogeneity, aneuploidy, extent of neoantigen load, overall cell proliferation, expression of immunomodulatory genes, and prognosis. Specific driver mutations correlated with lower (CTNNB1, NRAS, or IDH1) or higher (BRAF, TP53, or CASP8) leukocyte levels across all cancers. Multiple control modalities of the intracellular and extracellular networks (transcription, microRNAs, copy number, and epigenetic processes) were involved in tumor-immune cell interactions, both across and within immune subtypes. Our immunogenomics pipeline to characterize these heterogeneous tumors and the resulting data are intended to serve as a resource for future targeted studies to further advance the field.

Context effects occur when a choice between 2 options is altered by adding a 3rd alternative. Three major context effects--similarity, compromise, and attraction--have wide-ranging implications across applied and theoretical domains, and have driven the development of new dynamic models of multiattribute and multialternative choice. We propose the multiattribute linear ballistic accumulator (MLBA), a new dynamic model that provides a quantitative account of all 3 context effects. Our account applies not only to traditional paradigms involving choices among hedonic stimuli, but also to recent demonstrations of context effects with nonhedonic stimuli. Because of its computational tractability, the MLBA model is more easily applied than previous dynamic models. We show that the model also accounts for a range of other phenomena in multiattribute, multialternative choice, including time pressure effects, and that it makes a new prediction about the relationship between deliberation time and the magnitude of the similarity effect, which we confirm experimentally.

Context effects occur when a choice between 2 options is altered by adding a 3rd alternative. Three major context effects--similarity, compromise, and attraction--have wide-ranging implications across applied and theoretical domains, and have driven the development of new dynamic models of multiattribute and multialternative choice. We propose the multiattribute linear ballistic accumulator (MLBA), a new dynamic model that provides a quantitative account of all 3 context effects. Our account applies not only to traditional paradigms involving choices among hedonic stimuli, but also to recent demonstrations of context effects with nonhedonic stimuli. Because of its computational tractability, the MLBA model is more easily applied than previous dynamic models. We show that the model also accounts for a range of other phenomena in multiattribute, multialternative choice, including time pressure effects, and that it makes a new prediction about the relationship between deliberation time and the magnitude of the similarity effect, which we confirm experimentally.

BackgroundThe subgenual anterior cingulate cortex (sgACC) and its connected circuitry have been heavily implicated in emotional functioning in adolescent-onset major depressive disorder (MDD). While several recent studies have examined sgACC functional connectivity (FC) in depressed youth at rest, no studies to date have investigated sgACC FC in adolescent depression during negative emotional processing.MethodsNineteen medication-naïve adolescents with MDD and 19 matched healthy controls (HCL) performed an implicit fear facial affect recognition task during functional magnetic resonance imaging (fMRI). We defined seeds in bilateral sgACC and assessed FC using the psychophysiological interaction method. We also applied cognitive behavioral modeling to estimate group differences in perceptual sensitivity in this task. Finally, we correlated connectivity strength with clinical data and perceptual sensitivity.ResultsDepressed adolescents showed increased sgACC-amygdala FC and decreased sgACC-fusiform gyrus, sgACC-precuneus, sgACC-insula, and sgACC-middle frontal gyrus FC compared to HCL (p

BackgroundThe subgenual anterior cingulate cortex (sgACC) and its connected circuitry have been heavily implicated in emotional functioning in adolescent-onset major depressive disorder (MDD). While several recent studies have examined sgACC functional connectivity (FC) in depressed youth at rest, no studies to date have investigated sgACC FC in adolescent depression during negative emotional processing.MethodsNineteen medication-naïve adolescents with MDD and 19 matched healthy controls (HCL) performed an implicit fear facial affect recognition task during functional magnetic resonance imaging (fMRI). We defined seeds in bilateral sgACC and assessed FC using the psychophysiological interaction method. We also applied cognitive behavioral modeling to estimate group differences in perceptual sensitivity in this task. Finally, we correlated connectivity strength with clinical data and perceptual sensitivity.ResultsDepressed adolescents showed increased sgACC-amygdala FC and decreased sgACC-fusiform gyrus, sgACC-precuneus, sgACC-insula, and sgACC-middle frontal gyrus FC compared to HCL (p