Optimal use of reminders: Metacognition, effort, and cognitive offloading.

Individuals frequently choose between accomplishing goals using unaided cognitive abilities or offloading cognitive demands onto external tools and resources. For example, in order to remember an upcoming appointment one might rely on unaided memory or create a reminder by setting a smartphone alert. Setting a reminder incurs both a cost (the time/effort to set it up) and a benefit (increased likelihood of remembering). Here we investigate whether individuals weigh such costs/benefits optimally or show systematic biases. In 3 experiments, participants performed a memory task where they could choose between (a) earning a maximum reward for each remembered item, using unaided memory; or (b) earning a lesser amount per item, using external reminders to increase the number remembered. Participants were significantly biased toward using external reminders, even when they had a financial incentive to choose optimally. Individual differences in this bias were stable over time, and predicted by participants' erroneous metacognitive underconfidence in their memory abilities. Bias was eliminated, however, when participants received metacognitive advice about which strategy was likely to maximize performance. Furthermore, we found that metacognitive interventions (manipulation of feedback valence and practice-trial difficulty) yielded shifts in participants' reminder bias that were mediated by shifts in confidence. However, the bias could not be fully attributed to metacognitive error. We conclude that individuals have stable biases toward using external versus internal cognitive resources, which result at least in part from inaccurate metacognitive evaluations. Finding interventions to mitigate these biases can improve individuals' adaptive use of cognitive tools. (PsycINFO Database Record (c) 2020 APA, all rights reserved).

Prior expectation of objects in space is dependent on the direction of gaze.

Many studies of multisensory spatial localization have shown that observers' responses are well-characterized by Bayesian inference, as localization judgments are influenced not only by the reliability of sensory encoding, but expectations about where things occur in space. Here, we investigate the frame of reference for the prior expectation of objects in space. Using an audiovisual localization task, we systematically manipulate fixation position and evaluate whether this prior is encoded in an eye-centered, head-centered, or hybrid frame of reference. Results show that in a majority of participants, this prior is encoded in an eye-centered frame of reference.

Domain-general enhancements of metacognitive ability through adaptive training.

The metacognitive ability to introspect about self-performance varies substantially across individuals. Given that effective monitoring of performance is deemed important for effective behavioral control, intervening to improve metacognition may have widespread benefits, for example in educational and clinical settings. However, it is unknown whether and how metacognition can be systematically improved through training independently of task performance, or whether metacognitive improvements generalize across different task domains. Across 8 sessions, here we provided feedback to two groups of participants in a perceptual discrimination task: an experimental group (n = 29) received feedback on their metacognitive judgments, while an active control group (n = 32) received feedback on their decision performance only. Relative to the control group, adaptive training led to increases in metacognitive calibration (as assessed by Brier scores), which generalized both to untrained stimuli and an untrained task (recognition memory). Leveraging signal detection modeling we found that metacognitive improvements were driven both by changes in metacognitive efficiency (meta-d'/d') and confidence level, and that later increases in metacognitive efficiency were positively mediated by earlier shifts in confidence. Our results reveal a striking malleability of introspection and indicate the potential for a domain-general enhancement of metacognitive abilities. (PsycINFO Database Record (c) 2018 APA, all rights reserved).

Achieving Rapid Blood Pressure Control With Digital Therapeutics: Retrospective Cohort and Machine Learning Study.

BACKGROUND: Behavioral therapies, such as electronic counseling and self-monitoring dispensed through mobile apps, have been shown to improve blood pressure, but the results vary and long-term engagement is a challenge. Machine learning is a rapidly advancing discipline that can be used to generate predictive and responsive models for the management and treatment of chronic conditions and shows potential for meaningfully improving outcomes. OBJECTIVE: The objectives of this retrospective analysis were to examine the effect of a novel digital therapeutic on blood pressure in adults with hypertension and to explore the ability of machine learning to predict participant completion of the intervention. METHODS: Participants with hypertension, who engaged with the digital intervention for at least 2 weeks and had paired blood pressure values, were identified from the intervention database. Participants were required to be ≥18 years old, reside in the United States, and own a smartphone. The digital intervention offers personalized behavior therapy, including goal setting, skill building, and self-monitoring. Participants reported blood pressure values at will, and changes were calculated using averages of baseline and final values for each participant. Machine learning was used to generate a model of participants who would complete the intervention. Random forest models were trained at days 1, 3, and 7 of the intervention, and the generalizability of the models was assessed using leave-one-out cross-validation. RESULTS: The primary cohort comprised 172 participants with hypertension, having paired blood pressure values, who were engaged with the intervention. Of the total, 86.1% participants were women, the mean age was 55.0 years (95% CI 53.7-56.2), baseline systolic blood pressure was 138.9 mmHg (95% CI 136.6-141.3), and diastolic was 86.2 mmHg (95% CI 84.8-87.7). Mean change was -11.5 mmHg for systolic blood pressure and -5.9 mmHg for diastolic blood pressure over a mean of 62.6 days (P<.001). Among participants with stage 2 hypertension, mean change was -17.6 mmHg for systolic blood pressure and -8.8 mmHg for diastolic blood pressure. Changes in blood pressure remained significant in a mixed-effects model accounting for the baseline systolic blood pressure, age, gender, and body mass index (P<.001). A total of 43% of the participants tracking their blood pressure at 12 weeks achieved the 2017 American College of Cardiology/American Heart Association definition of blood pressure control. The 7-day predictive model for intervention completion was trained on 427 participants, and the area under the receiver operating characteristic curve was .78. CONCLUSIONS: Reductions in blood pressure were observed in adults with hypertension who used the digital therapeutic. The degree of blood pressure reduction was clinically meaningful and achieved rapidly by a majority of the studied participants. Greater improvement was observed in participants with more severe hypertension at baseline. A successful proof of concept for using machine learning to predict intervention completion was presented.

Emergence of digital biomarkers to predict and modify treatment efficacy: machine learning study.

OBJECTIVES: Development of digital biomarkers to predict treatment response to a digital behavioural intervention. DESIGN: Machine learning using random forest classifiers on data generated through the use of a digital therapeutic which delivers behavioural therapy to treat cardiometabolic disease. Data from 13 explanatory variables (biometric and engagement in nature) generated in the first 28 days of a 12-week intervention were used to train models. Two levels of response to treatment were predicted: (1) systolic change ≥10 mm Hg (SC model), and (2) shift down to a blood pressure category of elevated or better (ER model). Models were validated using leave-one-out cross validation and evaluated using area under the curve receiver operating characteristics (AUROC) and specificity- sensitivity. Ability to predict treatment response with a subset of nine variables, including app use and baseline blood pressure, was also tested (models SC-APP and ER-APP). SETTING: Data generated through ad libitum use of a digital therapeutic in the USA. PARTICIPANTS: Deidentified data from 135 adults with a starting blood pressure ≥130/80, who tracked blood pressure for at least 7 weeks using the digital therapeutic. RESULTS: The SC model had an AUROC of 0.82 and a sensitivity of 58% at a specificity of 90%. The ER model had an AUROC of 0.69 and a sensitivity of 32% at a specificity at 91%. Dropping explanatory variables related to blood pressure resulted in an AUROC of 0.72 with a sensitivity of 42% at a specificity of 90% for the SC-APP model and an AUROC of 0.53 for the ER-APP model. CONCLUSIONS: Machine learning was used to transform data from a digital therapeutic into digital biomarkers that predicted treatment response in individual participants. Digital biomarkers have potential to improve treatment outcomes in a digital behavioural intervention.