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21 results for “Choice prediction”
Raw data for CPC2015: A Choice Prediction Competition for decisions under risk, under Ambiguity, and from experience
<p>This is the raw data collected as part of a choice prediction competition organized by the three authors for decisions under risk, under ambiguity, and from experience. It includes over 330,000 consequential choices of human participants between two risky and/or uncertain prospects. More details about the competition and the data can be found in http://departments.agri.huji.ac.il/cpc2015.</p> <p>If you use this data, please cite the paper describing the competition: Erev, I., Ert, E., Plonsky, O., Cohen, D., & Cohen, O. (2017). From anomalies to forecasts: Toward a descriptive model of decision under risk, under ambiguity, and from experience. <em>Psychological Review,</em> 124(4), 369-409.<em> </em>DOI: 10.1037/rev0000062</p>
Calibration Data for Choice Prediction Competition 2018 (CPC18)
<p>These are the complete raw data for the Calibration Set of a choice prediction competition for decisions under risk, under ambiguity, and from experience (titled CPC18), organized by the authors and their colleagues. The data includes 510,750 consequential choices of human decision makers choosing between two risky and/or uncertain prospects with up to 10 possible outcomes each.</p> <p>Part of these data (published herein at: https://zenodo.org/record/321652#.WZlniSgjE2z) were previously used in CPC15 (a previous choice prediction competition), and are described in the paper: Erev, I., Ert, E., Plonsky, O., Cohen, D., & Cohen, O. (2017). From anomalies to forecasts: Toward a descriptive model of decision under risk, under ambiguity, and from experience. <em>Psychological Review,</em> 124(4), 369-409.<em> </em>DOI: 10.1037/rev0000062 </p> <p>More details about the new competition (CPC18) can be found at https://cpc18.wordpress.com/</p> <p>Keywords: Choice between gambles; Decision making; Prospect theory; Decisions from experience; Description-experience gap Decisions under uncertainty</p>
All raw data for CPC18: Choice prediction competition 2018
<p>These are the complete raw data for CPC18: a choice prediction competition for decisions under risk, under ambiguity, and from experience, organized by the authors and their colleagues. The data includes 694,500 consequential choices of human decision makers choosing between two risky and/or uncertain prospects with up to 10 possible outcomes each.</p> <p>This dataset includes both the training and the testing sets of the competition. The training (calibration) set of this data is published at https://zenodo.org/record/845873#.XGqnvegzaUk.</p> <p>Part of these data (published herein at: https://zenodo.org/record/321652#.WZlniSgjE2z) were previously used in CPC15 (a previous choice prediction competition), and are described in the paper: Erev, I., Ert, E., Plonsky, O., Cohen, D., & Cohen, O. (2017). From anomalies to forecasts: Toward a descriptive model of decision under risk, under ambiguity, and from experience. <em>Psychological Review,</em> 124(4), 369-409.<em> </em>DOI: 10.1037/rev0000062 </p> <p>More details about CPC18 can be found at https://cpc-18.com/</p>
Data and code from: A timid choice: Risk-taking behavior predicts individualized niche in a varying landscape of safety
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Data from: Odour information enables patch choice by mammalian herbivores from afar, leading to predictable plant associational effects
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Model Predictive Control (MPC) Artificial Pancreas vs. Sensor Augmented Pump (SAP)/Predictive Low Glucose Suspend (PLGS) With Different Food Choices in the Outpatient Setting
ClinicalTrials.gov study NCT03767790. IPD Sharing: NO. Countries: 1. Publications: 1.
PRESENCE 2: Predicting Sedentary Entertainment Choices and Effects
ClinicalTrials.gov study NCT01523795. IPD Sharing: Not stated. Countries: 1. Publications: 1.
Data and code archive for 'Time perception and patience: Individual differences in interval timing precision predict choice impulsivity in European starlings, Sturnus vulgaris'
<p>Data and code for Andrews et al. '<strong>Time perception and patience: Individual differences in interval timing precision predict choice impulsivity in European starlings, <em>Sturnus vulgaris'</em></strong></p> <p>Version of November 02 2020</p> <p>The data are presented here with different degrees of processing (i.e. from every trial on every day by every bird separately in 'timing data.Rdata', to one summary row per bird in 'timing data by bird.csv'). Separate scripts do the processing, fit polynomials, reproduce the by-bird analyses in the paper, and run the numerical model. Please see the document 'Description of R scripts and files' for details of the different versions of the data and what each script does.</p> <p> </p>
Data from: MHC genotype predicts mate choice in the ring-necked pheasant Phasianus colchicus
Females of several vertebrate species selectively mate with males on the basis of the major histocompatibility complex (MHC) genes. As androgen-mediated maternal effects have long-lasting consequences for the adult phenotype, both mating and reproductive success may depend on the combined effect of MHC genotype and exposure to androgens during early ontogeny. We studied how MHC-based mate choice in ring-necked pheasants (Phasianus colchicus) was influenced by an experimental in ovo testosterone (T) increase. There was no conclusive evidence of in ovo T treatment differentially affecting mate choice in relation to MHC genotype. However, females avoided mating with males with a wholly different MHC genotype compared with males sharing at least one MHC allele. Females also tended to avoid mating with MHC-identical males, though not significantly so. These findings suggest that female pheasants preferred males with intermediate MHC dissimilarity. Male MHC heterozygosity or diversity did not predict the expression of ornaments or male dominance rank. Thus, MHC-based mating preferences in the ring-necked pheasant do not seem to be mediated by ornaments' expression and may have evolved mainly to reduce the costs of high heterozygosity at MHC loci for the progeny, such as increased risk of autoimmune diseases or disruption of coadapted gene pools.
Data from: Predictions of response to temperature are contingent on model choice and data quality
The equations used to account for the temperature dependence of biological processes, including growth and metabolic rates are the foundations of our predictions of how global biogeochemistry and biogeography change in response to global climate change. We review and test the use of 12 equations used to model the temperature dependence of biological processes across the full range of their temperature response, including supra- and sub-optimal temperatures. We focus on fitting these equations to thermal response curves for phytoplankton growth, but also tested the equations on a variety of traits across a wide diversity of organisms. We found that many of the surveyed equations have comparable abilities to fit data and equally high requirements for data quality (number of test temperatures and range of response captured), but lead to different estimates of cardinal temperatures and of the biological rates at these temperatures. When these rate estimates are used for biogeographic predictions, differences between the estimates of even the best fitting models can exceed the global biological change predicted for a decade of global warming. As a result, studies of the biological response to global changes in temperature must make careful consideration of model selection and of the quality of the data used for parametrizing these models.
Data from: Autocorrelation structure at rest predicts value correlates of single neurons during reward-guided choice
Correlates of value are routinely observed in the prefrontal cortex (PFC) during reward-guided decision making. In previous work (Hunt et al., 2015), we argued that PFC correlates of chosen value are a consequence of varying rates of a dynamical evidence accumulation process. Yet within PFC, there is substantial variability in chosen value correlates across individual neurons. Here we show that this variability is explained by neurons having different temporal receptive fields of integration, indexed by examining neuronal spike rate autocorrelation structure whilst at rest. We find that neurons with protracted resting temporal receptive fields exhibit stronger chosen value correlates during choice. Within orbitofrontal cortex, these neurons also sustain coding of chosen value from choice through the delivery of reward, providing a potential neural mechanism for maintaining predictions and updating stored values during learning. These findings reveal that within PFC, variability in temporal specialisation across neurons predicts involvement in specific decision-making computations.
Data from: Cultural flies: conformist social learning in fruit flies predicts long-lasting mate-choice traditions
Despite theoretical justification for the evolution of animal culture, there is still scant empirical evidence for it beyond mammals and birds, and we still know little about the process of cultural inheritance. Here, we propose a mechanism-driven definition of animal culture and test it in the fruit fly. We found that fruit flies have five cognitive capacities that enable them to transmit mating preferences culturally across generations, potentially fostering persistent traditions (the main marker of culture) in mating preference. A transmission chain experiment validates a model of the emergence of local traditions indicating that such social transmission may lead initially neutral traits to become adaptive, hence strongly selecting for copying and conformity, a situation that, although suggested decades ago, still had little empirical support.
Parameters and predicted probabilities of mode choice and ride pass subscription for microtransit in Arlington, TX
<p>We provide parameters and predicted probabilities of mode choice (at trip level) and ride pass subscription (at individual level) for microtransit in Arlington, TX. The parameters are estimated by <a href="https://arxiv.org/abs/2408.12577">an agent-based, nested behavioral model</a> that is developed in the C2SMARTER project “Multi-modal Tripchain Planner for Disadvantaged Travelers to Incentivize Transit Usage” (Award #69A3551747124). We separate the choice related to microtransit into two parts: travel mode choice and ride pass subscription choice. Synthetic population data from <a href="https://www.replicahq.com/">Replica Inc.</a> and microtransit service data from <a href="https://www.arlingtontx.gov/">City of Arlington</a> are used for estimation. </p> <p>In the lower-branch travel mode choice, individuals decide on mode to use by considering factors such as travel time, cost, trip purpose, tour type, and mode-specific preferences. The mode choice set includes driving, walking, biking, carpool, and microtransit. Different from traditional mode choice models, we allow time and cost parameters to vary across individuals, and we allow mode specific constants to vary across trip OD pairs. This makes sense when we do not have sufficient data for socioeconomic attributes and built environment variables. The assumption we made here is that the impacts of these unobserved variables are included in the nonparametric distribution of individual and OD pair-level parameters.</p> <p>In the upper-branch ride pass subscription choice, individuals decide whether to purchase a weekly ride pass, a monthly ride pass, or no ride pass at all. By subscribing to a ride pass, travelers pay an amount of money in advance and enjoy free microtransit trips until the ride pass expires. The utility of purchasing a ride pass consists of four components: (1) the utility related to the prices of ride passes, (2) the utility related to the change in consumer surplus (or compensating variation) brought by free microtransit trips with a ride pass, (3) the utility specific to microtransit users, and (4) the alternative specific constant of the ride pass. Given the data availability, we consider the ride pass model as a simple MNL model with six parameters to calibrate. These ride pass parameters are calibrated using the Nelder-Mead Simplex Method. The cost function to minimize is the squared distance between the predicted ride pass market share and the observed one.</p> <p>Accordingly, this dataset consists of six .csv files:</p> <ul> <li>Mode_choice_parameters_weekday.csv</li> <li>Mode_choice_parameters_weekend.csv</li> <li>RidePass_subscription_parameters.csv</li> <li>Mode_choice_probability_weekday.csv</li> <li>Mode_choice_probability_weekend.csv</li> <li>Ridepass_subscription_probability.csv</li> </ul> <p> </p> <p><strong>Field definition in "Mode_choice_parameters_weekday.csv" and "Mode_choice_parameters_weekend.csv"</strong></p> <table> <tbody> <tr> <td><strong>Field Name</strong></td> <td><strong>Description</strong></td> </tr> <tr> <td>iid</td> <td>IDs of each synthetic individual</td> </tr> <tr> <td>trip_id</td> <td>IDs of each synthetic trip</td> </tr> <tr> <td>origin_bgrp</td> <td>FIPs code of the block group where the trip starts</td> </tr> <tr> <td>destination_bgrp</td> <td>FIPs code of the block group where the trip ends</td> </tr> <tr> <td>B_AUTO_TT</td> <td>The parameter of auto travel time (vary across individuals)</td> </tr> <tr> <td>B_MICRO_TT</td> <td>The parameter of microtransit waiting time (vary across individuals)</td> </tr> <tr> <td>B_NON_AUTO_TT</td> <td>The parameter of non-auto travel time (vary across individuals)</td> </tr> <tr> <td>B_COST</td> <td>The parameter of travel cost (vary across individuals)</td> </tr> <tr> <td>ASC_MIRCO</td> <td>The alternative specific constant of microtransit (vary across trip OD pairs)</td> </tr> <tr> <td>ASC_DRIVING</td> <td>The alternative specific constant of driving (vary across trip OD pairs)</td> </tr> <tr> <td>ASC_BIKING</td> <td>The alternative specific constant of biking (vary across trip OD pairs)</td> </tr> <tr> <td>ASC_WALKING</td> <td>The alternative specific constant of walking (vary across trip OD pairs)</td> </tr> <tr> <td>MICRO_P_SHOPPING</td> <td>The interaction effect between microtransit and shopping trip purpose (generic)</td> </tr> <tr> <td>MICRO_P_SCHOOL</td> <td>The interaction effect between microtransit and school trip purpose (generic)</td> </tr> <tr> <td>MICRO_P_OTHER</td> <td>The interaction effect between microtransit and other trip purpose (generic)</td> </tr> <tr> <td>MICRO_T_COMMUTE</td> <td>The interaction effect between microtransit and commute tour type (generic)</td> </tr> <tr> <td>MICRO_T_HOME_BASED</td> <td>The interaction effect between microtransit and home-based tour type (generic)</td> </tr> </tbody> </table> <p> </p> <p><strong>Field definition in "RidePass_subscription_parameters.csv"</strong></p> <table> <tbody> <tr> <td><strong>Field Name</strong></td> <td><strong>Description</strong></td> </tr> <tr> <td>B_COST_RP</td> <td>A transfer factor from trip fare to ride pass price</td> </tr> <tr> <td>B_CS_WEEKDAY</td> <td>The parameter of increased consumer surplus (due to the ride pass) on weekday</td> </tr> <tr> <td>B_CS_WEEKEND</td> <td>The parameter of increased consumer surplus (due to the ride pass) on weekend</td> </tr> <tr> <td>B_MICRO_USER</td> <td>The parameter of a binary variable indicating former microtransit users</td> </tr> <tr> <td>ASC_WRP</td> <td>The alternative specific constant of subscribing weekly ride pass</td> </tr> <tr> <td>ASC_MRP</td> <td>The alternative specific constant of subscribing monthly ride pass</td> </tr> </tbody> </table> <p> </p> <p><strong>Field definition in "Mode_choice_probability_weekday.csv" and "Mode_choice_probability_weekend.csv"</strong></p> <table> <tbody> <tr> <td><strong>Field Name</strong></td> <td><strong>Description</strong></td> </tr> <tr> <td>iid</td> <td>IDs of each synthetic individual</td> </tr> <tr> <td>trip_id</td> <td>IDs of each synthetic trip</td> </tr> <tr> <td>origin_bgrp</td> <td>FIPs code of the block group where the trip starts</td> </tr> <tr> <td>destination_bgrp</td> <td>FIPs code of the block group where the trip ends</td> </tr> <tr> <td>P_biking</td> <td>The predicted probability of choosing biking</td> </tr> <tr> <td>P_carpool</td> <td>The predicted probability of choosing carpool</td> </tr> <tr> <td>P_microtransit</td> <td>The predicted probability of choosing microtransit</td> </tr> <tr> <td>P_driving</td> <td>The predicted probability of choosing driving</td> </tr> <tr> <td>P_walking</td> <td>The predicted probability of choosing walking</td> </tr> </tbody> </table> <p> </p> <p><strong>Field definition in "Ridepass_subscription_probability.csv"</strong></p> <table> <tbody> <tr> <td><strong>Field Name</strong></td> <td><strong>Description</strong></td> </tr> <tr> <td>iid</td> <td>IDs of each synthetic individual</td> </tr> <tr> <td>Micro_user</td> <td>A binary variable indicating whether the individual used microtransit before</td> </tr> <tr> <td>Population_seg</td> <td>Segment ID of the synthetic individual</td> </tr> <tr> <td>BLOCKGROUP</td> <td>FIPs code of the home block group</td> </tr> <tr> <td>P_weekly_pass</td> <td>The predicted probability of subscribing weekly ride pass</td> </tr> <tr> <td>P_monthly_pass</td> <td>The predicted probability of subscribing monthly ride pass</td> </tr> <tr> <td>P_None</td> <td>The predicted probability of subscribing no ride pass</td> </tr> </tbody> </table>
Investigation of Predictive Indices to Objectively Guide Choice of CPAP Interface
ClinicalTrials.gov study NCT03063567. IPD Sharing: UNDECIDED. Countries: 0. Publications: 1.
Data from: Predictions of response to temperature are contingent on model choice and data quality
Open the record for dataset details and reuse information.
Data from: Autocorrelation structure at rest predicts value correlates of single neurons during reward-guided choice
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Data from: MHC genotype predicts mate choice in the ring-necked pheasant Phasianus colchicus
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Data from: Cultural flies: conformist social learning in fruit flies predicts long-lasting mate-choice traditions
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Data from: Female mate choice predicts paternity success in the absence of additive genetic variance for other female paternity bias mechanisms in Drosophila serrata
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Histological Prediction and Choice of the Resection Strategy in Front of a Colorectal Lesion > 2 cm: Prospective Comparison of Endoscopic Characterization, Non-targeted and Targeted Biopsies
ClinicalTrials.gov study NCT04482491. IPD Sharing: NO. Countries: 1. Publications: 0.
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Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
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DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.