How do people predict a random walk? Lessons for models of human cognition.

Repeated forecasts of changing values are common in many everyday tasks, from predicting the weather to financial markets. A particularly simple and informative instance of such fluctuating values are : Sequences in which each point is a random movement from only its preceding value, unaffected by any previous points. Moreover, random walks often yield basic rational forecasting solutions in which predictions of new values should repeat the most recent value, and hence replicate the properties of the original series.

A gut microbiota rheostat forecasts responsiveness to PD-L1 and VEGF blockade in mesothelioma.

Malignant mesothelioma is a rare tumour caused by asbestos exposure that originates mainly from the pleural lining or the peritoneum. Treatment options are limited, and the prognosis is dismal. Although immune checkpoint blockade (ICB) can improve survival outcomes, the determinants of responsiveness remain elusive.

Targeting DNA Damage Response Deficiency in Thoracic Cancers.

Thoracic cancers comprise non-small cell lung cancers (NSCLCs), small cell lung cancers (SCLCs) and malignant pleural mesotheliomas (MPM). Collectively, they account for the highest rate of death from malignancy worldwide. Genomic instability is a universal feature of cancer, which fuels mutations and tumour evolution.

The statistics of cognitive variability: Explaining common patterns in individuals, groups and financial markets.

Psychological variability (i.e., "noise") displays interesting structure which is hidden by the common practice of averaging over trials.

Evaluating niraparib versus active symptom control in patients with previously treated mesothelioma (NERO): a study protocol for a multicentre, randomised, two-arm, open-label phase II trial in UK secondary care centres.

Background: Malignant mesothelioma is a rapidly lethal cancer that has been increasing at an epidemic rate over the last three decades. Targeted therapies for mesothelioma have been lacking. A previous study called MiST1 (NCT03654833), evaluated the efficacy of Poly (ADP-ribose) polymerase (PARP) inhibition in mesothelioma.

The autocorrelated Bayesian sampler: A rational process for probability judgments, estimates, confidence intervals, choices, confidence judgments, and response times.

Normative models of decision-making that optimally transform noisy (sensory) information into categorical decisions qualitatively mismatch human behavior. Indeed, leading computational models have only achieved high empirical corroboration by adding task-specific assumptions that deviate from normative principles. In response, we offer a Bayesian approach that implicitly produces a posterior distribution of possible answers (hypotheses) in response to sensory information.

Perceptual and Cognitive Judgments Show Both Anchoring and Repulsion.

One of the most robust effects in cognitive psychology is , in which judgments show a bias toward previously viewed values. However, in what is essentially the same task as used in anchoring research, a perceptual illusion demonstrates the opposite effect of . Here, we united these two literatures, testing in two experiments with adults (total = 200) whether prior comparative decisions bias cognitive and perceptual judgments in opposing directions or whether anchoring and repulsion are two domain-general biases whose co-occurrence has so far gone undetected.

Sampling as a resource-rational constraint.

Resource rationality is useful for choosing between models with the same cognitive constraints but cannot settle fundamental disagreements about what those constraints are. We argue that sampling is an especially compelling constraint, as optimizing accumulation of evidence or hypotheses minimizes the cost of time, and there are well-established models for doing so which have had tremendous success explaining human behavior.

What does the mind learn? A comparison of human and machine learning representations.

We present a brief review of modern machine learning techniques and their use in models of human mental representations, detailing three notable branches: spatial methods, logical methods and artificial neural networks. Each of these branches contains an extensive set of systems, and demonstrate accurate emulations of human learning of categories, concepts and language, despite substantial differences in operation. We suggest that continued applications will allow cognitive researchers the ability to model the complex real-world problems where machine learning has recently been successful, providing more complete behavioural descriptions.