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. But we assume that the brain has no direct access to this posterior, but can only sample hypotheses according to their posterior probabilities. Accordingly, we argue that the primary problem of normative concern in decision-making is integrating stochastic hypotheses, rather than stochastic sensory information, to make categorical decisions. This implies that human response variability arises mainly from posterior sampling rather than sensory noise. Because human hypothesis generation is serially correlated, hypothesis samples will be autocorrelated. Guided by this new problem formulation, we develop a new process, the Autocorrelated Bayesian Sampler (ABS), which grounds autocorrelated hypothesis generation in a sophisticated sampling algorithm. The ABS provides a single mechanism that qualitatively explains many empirical effects of probability judgments, estimates, confidence intervals, choice, confidence judgments, response times, and their relationships. Our analysis demonstrates the unifying power of a perspective shift in the exploration of normative models. It also exemplifies the proposal that the "Bayesian brain" operates using samples not probabilities, and that variability in human behavior may primarily reflect computational rather than sensory noise. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

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. This study met its primary endpoint with 15% of patients having durable responses exceeding 1 year. Therefore, there is a need to evaluate PARP inhibitors in relapsed mesothelioma patients, where options are limited. Niraparib is the PARP inhibitor used in NERO. METHODS: NERO is a multicentre, two-arm, open-label UK randomised phase II trial designed to evaluate the efficacy of PARP inhibition in relapsed mesothelioma. 84 patients are being recruited. NERO is not restricted by line of therapy; however, eligible participants must have been treated with an approved platinum based systemic therapy. Participants will be randomised 2:1, stratified according to histology and response to prior platinum-based chemotherapy, to receive either active symptom control (ASC) and niraparib or ASC alone, for up to 24 weeks. Participants will be treated until disease progression, withdrawal, death or development of significant treatment limiting toxicity. Participants randomised to niraparib will receive 200 or 300 mg daily in a 3-weekly cycle. The primary endpoint is progression-free survival, where progression is determined by modified Response Evaluation Criteria in Solid Tumors (mRECIST) or RECIST 1.1; investigator reported progression; or death from any cause, whichever comes first. Secondary endpoints include overall survival, best overall response, 12-week and 24 week disease control, duration of response, treatment compliance and safety/tolerability. If NERO shows niraparib to be safe and biologically effective, it may lead to future late phase randomised controlled trials in relapsed mesothelioma. ETHICS AND DISSEMINATION: The study received ethical approval from London-Hampstead Research Ethics Committee on 06-May-2022 (22/LO/0281). Data from all centres will be analysed together and published as soon as possible. TRIAL REGISTRATION NUMBER: ISCRTN16171129; NCT05455424.

Perceptual and Cognitive Judgments Show Both Anchoring and Repulsion.

One of the most robust effects in cognitive psychology is anchoring, 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 repulsion. Here, we united these two literatures, testing in two experiments with adults (total N = 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. We found that in both perceptual and cognitive tasks, anchoring and repulsion co-occur. Further, the direction of the bias depends on the comparison value: Distant values attract judgments, whereas nearby values repulse judgments. Because none of the leading theories for either effect account for both biases, theoretical integration is needed. As a starting point, we describe one such joint theory based on sampling models of cognition.

Using Occam's razor and Bayesian modelling to compare discrete and continuous representations in numerosity judgements.

Previous research has established that numeric estimates are based not just on perceptual data but also past experience, and so may be influenced by the form of this stored information. It remains unclear, however, how such experience is represented: numerical data can be processed by either a continuous analogue number system or a discrete symbolic number system, with each predicting different generalisation effects. The present paper therefore contrasts discrete and continuous prior formats within the domain of numerical estimation using both direct comparisons of computational models of this process using these representations, as well as empirical contrasts exploiting different predicted reactions of these formats to uncertainty via Occam's razor. Both computational and empirical results indicate that numeric estimates commonly rely on a continuous prior format, mirroring the analogue approximate number system, or 'number sense'. This implies a general preference for the use of continuous numerical representations even where both stimuli and responses are discrete, with learners seemingly relying on innate number systems rather than the symbolic forms acquired in later life. There is however remaining uncertainty in these results regarding individual differences in the use of these systems, which we address in recommendations for future work.

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. This will, however, also require careful consideration of appropriate algorithmic constraints alongside these methods in order to find a combination which captures both the strengths and weaknesses of human cognition.