Biology subjects

Maloney, L.

Publications and source records attributed to Maloney, L..

4 recordsLinked to original sources

bioRxiv2025

Given the birds, where is the flock? Visual estimation of the location of collections of points

Many visual tasks resemble statistical judgments, estimating the center of a cloud of identical items for example. In statistical form, the observer sees a sample drawn from an invisible probability density function (PDF) : the observer attempts to estimate the location of the PDF. He receives larger rewards for lower variance. Optimal minimum variance estimators are different for different distributions. We evaluated performance for three location families (Gaussian, Laplacian, and Uniform). How does human perceptual organization stack up against optimal perceptual estimation? We found that observers adopted distinct estimators for distinct distribution families, each approximately minimizing variance for its family. To explain observers ability to adapt to different distributional families, we propose that observers adopt a single estimator based on clusters in the sample. This Visual Cluster Model accounted for performance across distributions, suggesting that perceptual grouping first aggregates parts into clusters and only then into a whole. Significance statementImagine a flock of birds. We can predict where additional birds might appear by estimating the probability density function (PDF) of the birds. We compared performance in estimating the location of PDFs drawn from Gaussian, Laplacian or Uniform location families, rewarding lower variance. The optimal estimators for the three families are different. Observers approximately minimized variance for each condition and used different estimators for different families. How could they do so? We propose that observers use a single estimator based on sample clusters: perceptual grouping first aggregates parts into clusters and only then into a whole.

neuroscience↗

bioRxiv2023

Using influence measures to test normative use of probability density information derived from a sample

Bayesian decision theory (BDT) is frequently used to model normative performance in perceptual, motor, and cognitive decision tasks where the outcome of each trial is a reward or penalty that depends on the subjects actions. The resulting normative models specify how decision makers should encode and use information about uncertainty and value - step by step - in order to maximize their expected reward. When prior, likelihood, and posterior are probabilities, the Bayesian computation requires only simple arithmetic operations: addition, etc. We focus on visual cognitive tasks where Bayesian computations are carried out not on probabilities but on (1) probability density functions and (2) these probability density functions are derived from samples. We break the BDT model into a serries of computations and test human ability to carry out each of these computations in isolation. We test three necessary properties of normative use of pdf information derived from a sample - accuracy, additivity and influence. Influence measures allows us to assess how much weight each point in the sample is assigned in making decisions and allows us to compare normative use (weighting) of samples to actual, point by point. We find that human decision makers violate accuracy and additivity systematically but that the cost of failure in accuracy or additivity would be minor in common decision tasks. However, a comparison of measured influence for each sample point with normative influence measures demonstrates that the individuals use of sample information is markedly different from the predictions of BDT. We demonstrate that the normative BDT model takes into account the geometric symmetries of the pdf while the human decision maker does not. A heuristic model basing decisions on a single extreme sample point provided a better account for participants data than the normative BDT model. Author SummaryBayesian decision theory (BDT) is used to model human performance in tasks where the decision maker must compensate for uncertainty in order to to gain rewards and avoid losses. BDT prescribes how the decision maker can combine available data, prior knowledge, and value to reach a decision maximizing expected winnings. Do human decision makers actually use BDT in making decisions? Researchers typically compare overall human performance (total winnings) to the predictions of BDT but we cannot conclude that BDT is an adequate model for human performance based on just overall performance. We break BDT down into elementary operations and test human ability to execute such operations. In two of the tests human performance deviated only slightly (but systematically) from the predictions of BDT. In the third test we use a novel method to measure the influence of each sample point provided to the human decision maker and compare it to the influence predicted by BDT. When we look at what human decision makers do - in detail - we find that they use sensory information very differently from what the normative BDT observer does. We advance an alternative non-Bayesian model that better predicts human performance.

animal behavior and cognition↗

bioRxiv2021

Mutant-SETBP1 activates transcription of Myc programs to accelerate CSF3R-driven myeloproliferative neoplasms

Colony stimulating factor 3 receptor (CSF3R) mutations lead to JAK pathway activation and are the molecular hallmark of chronic neutrophilic leukemia (CNL). Approximately half of CNL patients also have mutations in SET binding protein 1 (SETBP1). In this study, we developed models of SETBP1-mutant leukemia to understand the role that SETBP1 plays in CNL. SETBP1 mutations promote self-renewal of CSF3R-mutant hematopoietic progenitors in vitro and prevent cells from undergoing terminal differentiation. In vivo, SETBP1 mutations accelerate leukemia progression, leading to the rapid development of hepatosplenomegaly and granulocytosis. Through transcriptomic and epigenomic profiling, we found that SETBP1 enhances progenitor-associated programs--most strongly upregulating Myc and Myc target genes. This upregulation of Myc can be reversed by epigenetic modulatory drugs. In summary, we find that SETBP1 mutations promote aggressive hematopoietic cell expansion when expressed with mutant CSF3R through the upregulation of Myc-associated gene expression programs. Statement of SignificanceSETBP1 is frequently mutated in chronic neutrophilic leukemia, but its role in the biology of this disease is unclear. We find that mutant SETBP1 enhances transcription of Myc and Myc target genes to promote aggressive disease biology, and that these oncogenic transcriptional programs can be reversed by epigenetic modulatory drugs.

cancer biology↗

bioRxiv2021

Mutant SETBP1 enhances NRAS-driven MAPK pathway activation to promote aggressive leukemia

Mutations in SET binding protein 1 (SETBP1) are associated with poor outcomes in myeloid leukemias. In the Ras-driven leukemia, juvenile myelomonocytic leukemia, SETBP1 mutations are enriched in relapsed disease. While some mechanisms for SETBP1-driven oncogenesis have been established, it remains unclear how SETBP1 specifically modulates the biology of Ras-driven leukemias. In this study, we found that when co-expressed with Ras pathway mutations, SETBP1 promoted oncogenic transformation of murine bone marrow in vitro and aggressive myeloid leukemia in vivo. We demonstrate that SETBP1 enhances the NRAS gene expression signature, driving upregulation of mitogen-activated protein kinase (MAPK) signaling and downregulation of differentiation pathways. SETBP1 also enhances NRAS-driven phosphorylation of MAPK proteins. Cells expressing NRAS and SETBP1 are sensitive to inhibitors of the MAPK pathway, and treatment with the MEK inhibitor trametinib conferred a survival benefit in a mouse model of NRAS/SETBP1-mutant disease. Our data demonstrate that despite driving enhanced MAPK signaling, SETBP1-mutant cells remain susceptible to trametinib in vitro and in vivo, providing encouraging pre-clinical data for the use of trametinib in SETBP1-mutant disease.

cancer biology↗