Psychological freedom, rationality, and the naive theory of reasoning.

To make sense of the social world, people reason about others' mental states, including whether and in what ways others can form new mental states. We propose that people's judgments concerning the dynamics of mental state change invoke a "naive theory of reasoning." On this theory, people conceptualize reasoning as a rational, semi-autonomous process that individuals can leverage, but not override, to form new rational mental states. Across six experiments, we show that this account of people's naive theory of reasoning predicts judgments about others' ability to form rational and irrational beliefs, desires, and intentions, as well as others' ability to act rationally and irrationally. This account predicts when, and explains why, people judge others as psychologically constrained by coercion and other forms of situational pressure. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Causal inference in cognitive neuroscience.

Causal inference is a key step in many research endeavors in cognitive science and neuroscience, and particularly cognitive neuroscience. Statistical knowledge is sufficient for prediction and diagnosis, but causal knowledge is required for action and intervention. Most statistics courses and textbooks emphasize the difficulty of causal inference, focusing on the maxim that "correlation does not mean causation": there can be multiple causal possibilities, often many of them, consistent with given observed statistics. This paper focuses instead on the conceptual issues and assumptions that confront causal and other kinds of inference, primarily focusing on cognitive neuroscience. We connect inference methods with goals and challenges, and provide concrete guidance about how to select appropriate tools for the scientific task. This article is categorized under: Psychology > Theory and Methods Philosophy > Foundations of Cognitive Science.

Preoperative Identification of Patient-Dependent Blood Pressure Targets Associated With Low Risk of Intraoperative Hypotension During Noncardiac Surgery.

BACKGROUND: Intraoperative hypotension (IOH) is strongly linked to organ system injuries and postoperative death. Blood pressure itself is a powerful predictor of IOH; however, it is unclear which pressures carry the lowest risk and may be leveraged to prevent subsequent hypotension. Our objective was to develop a model that predicts, before surgery and according to a patient's unique characteristics, which intraoperative mean arterial pressures (MAPs) between 65 and 100 mm Hg have a low risk of IOH, defined as an MAP <65 mm Hg, and may serve as testable hemodynamic targets to prevent IOH. METHODS: Adult, noncardiac surgeries under general anesthesia at 2 tertiary care hospitals of the University of Pittsburgh Medical Center were divided into training and validation cohorts, then assigned into smaller subgroups according to preoperative risk factors. Primary outcome was hypotension risk, defined for each intraoperative MAP value from 65 to 100 mm Hg as the proportion of a value's total measurements followed by at least 1 MAP <65 mm Hg within 5 or 10 minutes, and calculated for all values in each subgroup. Five models depicting MAP-associated IOH risk were compared according to best fit across subgroups with proportions whose confidence interval was <0.05. For the best fitting model, (1) performance was validated, (2) low-risk MAP targets were identified according to applied benchmarks, and (3) preoperative risk factors were evaluated as predictors of model parameters. RESULTS: A total of 166,091 surgeries were included, with 121,032 and 45,059 surgeries containing 5.4 million and 1.9 million MAP measurements included in the training and validation sets, respectively. Thirty-six subgroups with at least 21 eligible proportions (confidence interval <0.05) were identified, representing 92% and 94% of available MAP measurements, respectively. The exponential with theta constant model demonstrated the best fit (weighted sum of squared error 0.0005), and the mean squared error of hypotension risk per MAP did not exceed 0.01% in validation testing. MAP targets ranged between 69 and 90 mm Hg depending on the subgroup and benchmark used. Increased age, higher American Society of Anesthesiologists physical status, and female sexindependently predicted ( P < .05) hypotension risk curves with less rapid decay and higher plateaus. CONCLUSIONS: We demonstrate that IOH risk specific to a given MAP is patient-dependent, but predictable before surgery. Our model can identify intraoperative MAP targets before surgery predicted to reduce a patient's exposure to IOH, potentially allowing clinicians to develop more personalized approaches for managing hemodynamics.

Causal Learning through Deliberate Undersampling.

Domain scientists interested in causal mechanisms are usually limited by the frequency at which they can collect the measurements of social, physical, or biological systems. A common and plausible assumption is that higher measurement frequencies are the only way to gain more informative data about the underlying dynamical causal structure. This assumption is a strong driver for designing new, faster instruments, but such instruments might not be feasible or even possible. In this paper, we show that this assumption is incorrect: there are situations in which we can gain additional information about the causal structure by measuring more slowly than our current instruments. We present an algorithm that uses graphs at multiple measurement timescales to infer underlying causal structure, and show that inclusion of structures at slower timescales can nonetheless reduce the size of the equivalence class of possible causal structures. We provide simulation data about the probability of cases in which deliberate undersampling yields a gain, as well as the size of this gain.

The case for information fiduciaries: The implementation of a data ethics checklist at Seattle Children's Hospital.

There is little debate about the importance of ethics in health care, and clearly defined rules, regulations, and oaths help ensure patients' trust in the care they receive. However, standards are not as well established for the data professions within health care, even though the responsibility to treat patients in an ethical way extends to the data collected about them. Increasingly, data scientists, analysts, and engineers are becoming fiduciarily responsible for patient safety, treatment, and outcomes, and will require training and tools to meet this responsibility. We developed a data ethics checklist that enables users to consider the possible ethical issues that arise from the development and use of data products. The combination of ethics training for data professionals, a data ethics checklist as part of project management, and a data ethics committee holds potential for providing a framework to initiate dialogues about data ethics and can serve as an ethical touchstone for rapid use within typical analytic workflows, and we recommend the use of this or equivalent tools in deploying new data products in hospitals.

Amalgamating evidence of dynamics.

Many approaches to evidence amalgamation focus on relatively static information or evidence: the data to be amalgamated involve different variables, contexts, or experiments, but not measurements over extended periods of time. However, much of scientific inquiry focuses on dynamical systems; the system's behavior over time is critical. Moreover, novel problems of evidence amalgamation arise in these contexts. First, data can be collected at different measurement timescales, where potentially none of them correspond to the underlying system's causal timescale. Second, missing variables have a significantly different impact on time series measurements than they do in the traditional static setting; in particular, they make causal and structural inference much more difficult. In this paper, we argue that amalgamation should proceed by integrating causal knowledge, rather than at the level of "raw" evidence. We defend this claim by first outlining both of these problems, and then showing that they can be solved only if we operate on causal structures. We therefore must use causal discovery methods that are reliable given these problems. Such methods do exist, but their successful application requires careful consideration of the problems that we highlight.

The triple variable index combines information generated over time from common monitoring variables to identify patients expressing distinct patterns of intraoperative physiology.

BACKGROUND: Mean arterial pressure (MAP), bispectral index (BIS), and minimum alveolar concentration (MAC) represent valuable, yet dynamic intraoperative monitoring variables. They provide information related to poor outcomes when considered together, however their collective behavior across time has not been characterized. METHODS: We have developed the Triple Variable Index (TVI), a composite variable representing the sum of z-scores from MAP, BIS, and MAC values that occur together during surgery. We generated a TVI expression profile, defined as the sequential TVI values expressed across time, for each surgery where concurrent MAP, BIS, and MAC monitoring occurred in an adult patient (≥18 years) at the University of Pittsburgh Medical Center between January and July 2014 (n = 5296). Patterns of TVI expression were identified using k-means clustering and compared across numerous patient, procedure, and outcome characteristics. TVI and the triple low state were compared as prediction models for 30-day postoperative mortality. RESULTS: The median frequency MAP, BIS, and MAC were recorded was one measurement every 3, 5, and 5 min. Three expression patterns were identified: elevated, mixed, and depressed. The elevated pattern displayed the highest average MAP, BIS, and MAC values (86.5 mmHg, 45.3, and 0.98, respectively), while the depressed pattern displayed the lowest values (76.6 mmHg, 38.0, 0.66). Patterns (elevated, mixed, depressed) were distinct across the following characteristics: average patient age (52, 53, 54 years), American Society of Anesthesiologists Physical Status 4 (6.7, 16.1, 27.3%) and 5 (0.1, 0.6, 1.6%) categories, cardiac (2.2, 6.5, 16.1%) and emergent (5.8, 10.5, 12.8%) surgery, cardiopulmonary bypass use (0.3, 2.6, 9.8%), intraoperative medication administration including etomidate (3.0, 7.3, 12.6%), hydromorphone (47.6, 26.3, 25.2%), ketamine (11.2, 4.6, 3.0%), dexmedetomidine (18.4, 16.6, 13.6%), phenylephrine (74.0, 74.8, 83.0), epinephrine (2.0, 6.0, 18.0%), norepinephrine (2.4, 7.5, 21.2%), vasopressin (3.4, 7.6, 21.0%), succinylcholine (74.0, 69.0, 61.9%), intraoperative hypotension (28.8, 33.0, 52.3%) and the triple low state (9.4, 30.3, 80.0%) exposure, and 30-day postoperative mortality (0.8, 2.7, 5.6%). TVI was a better predictor of patients that died or survived in the 30 days following surgery compared to cumulative triple low state exposure (AUC 0.68 versus 0.62, p < 0.05). CONCLUSIONS: Surgeries that share similar patterns of TVI expression display distinct patient, procedure, and outcome characteristics.

Privileged (Default) Causal Cognition: A Mathematical Analysis.

Causal cognition is a key part of human learning, reasoning, and decision-making. In particular, people are capable of learning causal relations from data, and then reasoning and planning using those cognitive representations. While there has been significant normative work on the causal structures that ought to be learned from evidence, there has been relatively little on the functional forms that should (normatively) be used or learned for those qualitative causal relations. Moreover, empirical research on causal inference-learning causal relations from observations and interventions-has found support for multiple different functional forms for causal connections. This paper argues that a combination of conceptual and mathematical constraints leads to a privileged (default) functional form for causal relations. This privileged function is shown to provide a theoretical unification of the widely-used noisy-OR/AND models and linear models, thereby showing how they are complementary rather than competing. This unification thus helps to explain the diverse empirical results, as these different functional forms are "merely" special cases of the more general, more privileged function.

LPCD framework: Analytical tool or psychological model?

The target article uses a mathematical framework derived from Bayesian decision making to demonstrate suboptimal decision making but then attributes psychological reality to the framework components. Rahnev & Denison's (R&D) positive proposal thus risks ignoring plausible psychological theories that could implement complex perceptual decision making. We must be careful not to slide from success with an analytical tool to the reality of the tool components.

Diagnostic Performance of Tuberculosis-Specific IgG Antibody Profiles in Patients with Presumptive Tuberculosis from Two Continents.

BACKGROUND: Development of rapid diagnostic tests for tuberculosis is a global priority. A whole proteome screen identified Mycobacterium tuberculosis antigens associated with serological responses in tuberculosis patients. We used World Health Organization (WHO) target product profile (TPP) criteria for a detection test and triage test to evaluate these antigens. METHODS: Consecutive patients presenting to microscopy centers and district hospitals in Peru and to outpatient clinics at a tuberculosis reference center in Vietnam were recruited. We tested blood samples from 755 HIV-uninfected adults with presumptive pulmonary tuberculosis to measure IgG antibody responses to 57 M. tuberculosis antigens using a field-based multiplexed serological assay and a 132-antigen bead-based reference assay. We evaluated single antigen performance and models of all possible 3-antigen combinations and multiantigen combinations. RESULTS: Three-antigen and multiantigen models performed similarly and were superior to single antigens. With specificity set at 90% for a detection test, the best sensitivity of a 3-antigen model was 35% (95% confidence interval [CI], 31-40). With sensitivity set at 85% for a triage test, the specificity of the best 3-antigen model was 34% (95% CI, 29-40). The reference assay also did not meet study targets. Antigen performance differed significantly between the study sites for 7/22 of the best-performing antigens. CONCLUSIONS: Although M. tuberculosis antigens were recognized by the IgG response during tuberculosis, no single antigen or multiantigen set performance approached WHO TPP criteria for clinical utility among HIV-uninfected adults with presumed tuberculosis in high-volume, urban settings in tuberculosis-endemic countries.

A Constraint Optimization Approach to Causal Discovery from Subsampled Time Series Data.

We consider causal structure estimation from time series data in which measurements are obtained at a coarser timescale than the causal timescale of the underlying system. Previous work has shown that such subsampling can lead to significant errors about the system's causal structure if not properly taken into account. In this paper, we first consider the search for system timescale causal structures that correspond to a given measurement timescale structure. We provide a constraint satisfaction procedure whose computational performance is several orders of magnitude better than previous approaches. We then consider finite-sample data as input, and propose the first constraint optimization approach for recovering system timescale causal structure. This algorithm optimally recovers from possible conflicts due to statistical errors. We then apply the method to real-world data, investigate the robustness and scalability of our method, consider further approaches to reduce underdetermination in the output, and perform an extensive comparison between different solvers on this inference problem. Overall, these advances build towards a full understanding of non-parametric estimation of system timescale causal structures from sub-sampled time series data.

Causal Discovery from Subsampled Time Series Data by Constraint Optimization.

This paper focuses on causal structure estimation from time series data in which measurements are obtained at a coarser timescale than the causal timescale of the underlying system. Previous work has shown that such subsampling can lead to significant errors about the system's causal structure if not properly taken into account. In this paper, we first consider the search for the system timescale causal structures that correspond to a given measurement timescale structure. We provide a constraint satisfaction procedure whose computational performance is several orders of magnitude better than previous approaches. We then consider finite-sample data as input, and propose the first constraint optimization approach for recovering the system timescale causal structure. This algorithm optimally recovers from possible conflicts due to statistical errors. More generally, these advances allow for a robust and non-parametric estimation of system timescale causal structures from subsampled time series data.

Mesochronal Structure Learning.

Standard time series structure learning algorithms assume that the measurement timescale is approximately the same as the timescale of the underlying (causal) system. In many scientific contexts, however, this assumption is violated: the measurement timescale can be substantially slower than the system timescale (so intermediate time series datapoints will be missing). This assumption violation can lead to significant learning errors. In this paper, we provide a novel learning algorithm to extract system-timescale structure from measurement data that undersample the underlying system. We employ multiple algorithmic optimizations that exploit the problem structure in order to achieve computational tractability. The resulting algorithm is highly reliable at extracting system-timescale structure from undersampled data.

Rate-Agnostic (Causal) Structure Learning.

Causal structure learning from time series data is a major scientific challenge. Extant algorithms assume that measurements occur sufficiently quickly; more precisely, they assume approximately equal system and measurement timescales. In many domains, however, measurements occur at a significantly slower rate than the underlying system changes, but the size of the timescale mismatch is often unknown. This paper develops three causal structure learning algorithms, each of which discovers all dynamic causal graphs that explain the observed measurement data, perhaps given undersampling. That is, these algorithms all learn causal structure in a "rate-agnostic" manner: they do not assume any particular relation between the measurement and system timescales. We apply these algorithms to data from simulations to gain insight into the challenge of undersampling.

Comorbid science?

We agree with Cramer et al.'s goal of the discovery of causal relationships, but we argue that the authors' characterization of latent variable models (as deployed for such purposes) overlooks a wealth of extant possibilities. We provide a preliminary analysis of their data, using existing algorithms for causal inference and for the specification of latent variable models.

Not different kinds, just special cases.

Machery's Heterogeneity Hypothesis depends on his argument that no theory of concepts can account for all the extant reliable categorization data. I argue that a single theoretical framework based on graphical models can explain all of the behavioral data to which this argument refers. These different theories of concepts thus (arguably) correspond to different special cases, not different kinds.

Diversity in representations; uniformity in learning.

Henrich et al.'s conclusion that psychologists ought not assume uniformity of psychological phenomena depends on their descriptive claim that there is no pattern to the great diversity in psychological phenomena. We argue that there is a pattern: uniformity of learning processes (broadly construed), and diversity of (some) mental contents (broadly construed).

A theory of causal learning in children: causal maps and Bayes nets.

The authors outline a cognitive and computational account of causal learning in children. They propose that children use specialized cognitive systems that allow them to recover an accurate "causal map" of the world: an abstract, coherent, learned representation of the causal relations among events. This kind of knowledge can be perspicuously understood in terms of the formalism of directed graphical causal models, or Bayes nets. Children's causal learning and inference may involve computations similar to those for learning causal Bayes nets and for predicting with them. Experimental results suggest that 2- to 4-year-old children construct new causal maps and that their learning is consistent with the Bayes net formalism.