Repetitive negative thinking as a transdiagnostic cognitive process
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Mapping computational cognitive profiles of aging to dissociable brain and sociodemographic factors
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Boredom signals deviation from a cognitive homeostatic set point
Boredom is the feeling of wanting but failing to engage the mind and can be conceived as one among many signals of suboptimal utilization of cognitive and neural resources. Using homeostasis as an analogy, this perspective argues that boredom represents a signal indicating deviation from optimal engagement—that is, deviation from a cognitive homeostatic set point. Within this model, allostasis accounts for chronic boredom (i.e., trait boredom proneness), according to which faulty internal models are responsible for why the highly boredom prone may set unrealistic expectations for engagement. In other words, the model characterizes boredom as a dynamic response to both internal and external exigencies, leading to testable hypotheses for both the nature of the state and the trait disposition. Furthermore, this perspective presents the broader notion that humans strive to optimally engage with their environs to maintain a kind of cognitive homeostatic set-point.
Smartwatch- and smartphone-based remote assessment of brain health and detection of mild cognitive impairment
Consumer-grade mobile devices are used by billions worldwide. Their ubiquity provides opportunities to robustly capture everyday cognition. ‘Intuition’ was a remote observational study that enrolled 23,004 US adults, collecting 24 months of longitudinal multimodal data via their iPhones and Apple Watches using a custom research application that captured routine device use, self-reported health information and cognitive assessments. The study objectives were to classify mild cognitive impairment (MCI), characterize cognitive trajectories and develop tools to detect and track cognitive health at scale. The study addresses sources of bias in current cognitive health research, including limited representativeness (for example, racial/ethnic, geographic) and accuracy of cognitive measurement tools. We describe study design and provide baseline cohort characteristics. Next, we present foundational proof-of-concept MCI classification modeling results using interactive cognitive assessment data. Initial findings support the reliability and validity of remote MCI detection and the usefulness of such data in describing at-risk cognitive health trajectories in demographically diverse aging populations. ClinicalTrials.gov identifier: NCT05058950.
Cognitive reserve is associated with education, social determinants, and cognitive outcomes among older American Indians in the Strong Heart Study
Cognitive reserve, a component of resilience, may be conceptualized as the ability to overcome accumulating neuropathology and maintain healthy aging and function. However, research measuring and evaluating it in American Indians is needed. We recruited American Indians from 3 regional centers for longitudinal examinations (2010-13, n = 818; 2017-19, n = 403) including MRI, cognitive, clinical, and questionnaire data. We defined cognitive reserve by measuring the residual from individual regressions of cognitive tests over imaged brain volumes, adjusted for age and sex. Analyses examined three different metrics of cognitive reserve against sociodemographic, clinical, and longitudinal cognitive data in causal mediation models. Better cognitive reserve was significantly associated with more education, higher income, lower prevalence of depression, lower prevalence of diabetes, and lower prevalence of kidney disease, but we found no statistically significant evidence for an association with plasma biomarkers for Alzheimer’s disease and related dementias, APOE e4 carrier status, alcohol use, body mass, or hypertension. Better cognitive reserve was associated with better cognitive function over mean 6.7 years follow-up (range 4-9 years); and the association for education with cognition over time was mediated in part (15-24%) by cognitive reserve. Cognitive reserve, although challenging to measure, appears important for understanding the range of cognitive aging in American Indians.
Spontaneous thought separates into clusters of negative, positive, and flexible thinking
The nature and frequency of spontaneous thoughts play a critical role in cognitive processes like perception, decision-making, attention, and memory. Deficits in these processes are also greatly associated with the development and maintenance of psychopathology. However, the underlying cognitive dynamics of free and stuck spontaneous thought remain unclear, as these often occur in the absence of measurable behaviors. Here, we analyze free word-association data using attractor-state dynamic modeling, which conceptualizes stuck spontaneous thought as navigating a multidimensional semantic space while in the presence of strong attractor locations. Word-association data was collected from an exploratory sample (N1 = 65), a first replication sample (N2 = 79), and, following pre-registration, a second replication sample (N3 = 222). After the data was embedded into a 3-dimensional semantic space and fit by our dynamic model, unsupervised learning consistently grouped data into four clusters across all independent samples. These clusters were characterized by two distinct patterns of stuck negative thinking, a pattern of protective positive thinking, and a pattern of flexible mind-wandering. Our results support a method for modeling spontaneous thought and isolate distinct sub-types that may not be accessible using retrospective self-report methods. We discuss implications for clinical and cognitive science.
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