Citizens Versus the Internet: Confronting Digital Challenges With Cognitive Tools

The Internet has evolved into a ubiquitous and indispensable digital environment in which people communicate, seek information, and make decisions. Despite offering various benefits, online environments are also replete with smart, highly adaptive choice architectures designed primarily to maximize commercial interests, capture and sustain users' attention, monetize user data, and predict and influence future behavior. This online landscape holds multiple negative consequences for society, such as a decline in human autonomy, rising incivility in online conversation, the facilitation of political extremism, and the spread of disinformation. Benevolent choice architects working with regulators may curb the worst excesses of manipulative choice architectures, yet the strategic advantages, resources, and data remain with commercial players. One way to address some of this imbalance is with interventions that empower Internet users to gain some control over their digital environments, in part by boosting their information literacy and their cognitive resistance to manipulation. Our goal is to present a conceptual map of interventions that are based on insights from psychological science. We begin by systematically outlining how online and offline environments differ despite being increasingly inextricable. We then identify four major types of challenges that users encounter in online environments: persuasive and manipulative choice architectures, AI-assisted information architectures, false and misleading information, and distracting environments. Next, we turn to how psychological science can inform interventions to counteract these challenges of the digital world. After distinguishing among three types of behavioral and cognitive interventions-nudges, technocognition, and boosts-we focus on boosts, of which we identify two main groups: (a) those aimed at enhancing people's agency in their digital environments (e.g., self-nudging, deliberate ignorance) and (b) those aimed at boosting competencies of reasoning and resilience to manipulation (e.g., simple decision aids, inoculation). These cognitive tools are designed to foster the civility of online discourse and protect reason and human autonomy against manipulative choice architectures, attention-grabbing techniques, and the spread of false information.

Assessment of visual misperceptions in patients with Parkinson's disease using single and bistable percepts as testing tools

BACKGROUND

Visual misperceptions (VMs) and hallucinations (VHs) often go unreported in patients with Parkinson's disease (PD). We assessed the utility of single and bistable visual percepts as testing tools for visual perceptual abnormalities in PD.

AIM

To assess VM in patients with PD using single and bistable percepts as testing tools.

SETTINGS AND DESIGN

This was a case-control study conducted at a movement disorders clinic.

MATERIALS AND METHODS

Thirty patients with PD and 30 age and sex-matched controls were assessed for motor severity and stage using Unified Parkinson's Disease Rating Scale-III (UPDRS-III) and modified Hoehn and Yahr scale. Higher mental functions were assessed by Mini-Mental State Examination (MMSE), Frontal Assessment Battery (FAB), and Trail making tests (TMT-A and B) scores. The participants were presented with monochromatic images representing either "single" or "bistable percepts" and the misperceptions were recorded. VM scores of patients and controls were compared. The correlation between disease duration, treatment period, motor severity, frontal executive functions, and VMs were determined.

RESULTS

Twenty-six patients had mild-to-moderate PD. Patients with PD had higher mean VM scores (P < 0.0005). None of the patients reported VHs. TMT-A, TMT-B, TMT-B - A scores were significantly lower in the control group (P < 0.0005). Cases showed significant positive correlation of VM with disease duration, treatment duration, UPDRS-III score, H and Y stage, and TMT A and B and an inverse correlation with MMSE and FAB scores. The patients with VM score greater than the upper limit of normal (Mean + 1.5 standard deviation [SD]), calculated from the control group, showed similar correlation of VM with motor and cognitive parameters.

CONCLUSIONS

VMs are frequent in patients with PD when assessed using single and bistable visual percepts. VM correlates with frontal executive dysfunction, disease duration, and severity.

Cognitive Machine-Learning Algorithm for Cardiac Imaging: A Pilot Study for Differentiating Constrictive Pericarditis From Restrictive Cardiomyopathy

BACKGROUND

Associating a patient's profile with the memories of prototypical patients built through previous repeat clinical experience is a key process in clinical judgment. We hypothesized that a similar process using a cognitive computing tool would be well suited for learning and recalling multidimensional attributes of speckle tracking echocardiography data sets derived from patients with known constrictive pericarditis and restrictive cardiomyopathy.

METHODS AND RESULTS

Clinical and echocardiographic data of 50 patients with constrictive pericarditis and 44 with restrictive cardiomyopathy were used for developing an associative memory classifier-based machine-learning algorithm. The speckle tracking echocardiography data were normalized in reference to 47 controls with no structural heart disease, and the diagnostic area under the receiver operating characteristic curve of the associative memory classifier was evaluated for differentiating constrictive pericarditis from restrictive cardiomyopathy. Using only speckle tracking echocardiography variables, associative memory classifier achieved a diagnostic area under the curve of 89.2%, which improved to 96.2% with addition of 4 echocardiographic variables. In comparison, the area under the curve of early diastolic mitral annular velocity and left ventricular longitudinal strain were 82.1% and 63.7%, respectively. Furthermore, the associative memory classifier demonstrated greater accuracy and shorter learning curves than other machine-learning approaches, with accuracy asymptotically approaching 90% after a training fraction of 0.3 and remaining flat at higher training fractions.

CONCLUSIONS

This study demonstrates feasibility of a cognitive machine-learning approach for learning and recalling patterns observed during echocardiographic evaluations. Incorporation of machine-learning algorithms in cardiac imaging may aid standardized assessments and support the quality of interpretations, particularly for novice readers with limited experience.

Mangarevan invention of binary steps for easier calculation

When Leibniz demonstrated the advantages of the binary system for computations as early as 1703, he laid the foundation for computing machines. However, is a binary system also suitable for human cognition? One of two number systems traditionally used on Mangareva, a small island in French Polynesia, had three binary steps superposed onto a decimal structure. Here, we show how this system functions, how it facilitated arithmetic, and why it is unique. The Mangarevan invention of binary steps, centuries before their formal description by Leibniz, attests to the advancements possible in numeracy even in the absence of notation and thereby highlights the role of culture for the evolution of and diversity in numerical cognition.