Rats exhibit similar biases in foraging and intertemporal choice tasks
Abstract
Animals, including humans, consistently exhibit myopia in two different contexts: foraging, in which they harvest locally beyond what is predicted by optimal foraging theory, and intertemporal choice, in which they exhibit a preference for immediate vs. delayed rewards beyond what is predicted by rational (exponential) discounting. Despite the similarity in behavior between these two contexts, previous efforts to reconcile these observations in terms of a consistent pattern of time preferences have failed. Here, via extensive behavioral testing and quantitative modeling, we show that rats exhibit similar time preferences in both contexts: they prefer immediate vs. delayed rewards and they are sensitive to opportunity costs of delays to future decisions. Further, a quasi-hyperbolic discounting model, a form of hyperbolic discounting with separate components for short- and long-term rewards, explains individual rats' time preferences across both contexts, providing evidence for a common mechanism for myopic behavior in foraging and intertemporal choice.
Data availability
All data generated or analysed during this study are included in the manuscript and supporting files.
Article and author information
Author details
Funding
National Institute of Mental Health (F31MH109286)
- Gary A Kane
- Jonathan D Cohen
The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.
Reviewing Editor
- Geoffrey Schoenbaum, National Institute on Drug Abuse, National Institutes of Health, United States
Ethics
Animal experimentation: This study was performed in strict accordance with the recommendations in the Guide for the Care and Use of Laboratory Animals of the National Institutes of Health. All procedures were approved by the Princeton University (Protocol 1969) and Rutgers University (Protocol 14-075) Institutional Animal Care and Use Committees.
Version history
- Received: May 14, 2019
- Accepted: September 17, 2019
- Accepted Manuscript published: September 18, 2019 (version 1)
- Version of Record published: October 15, 2019 (version 2)
Copyright
© 2019, Kane et al.
This article is distributed under the terms of the Creative Commons Attribution License permitting unrestricted use and redistribution provided that the original author and source are credited.
Metrics
-
- 2,470
- views
-
- 335
- downloads
-
- 19
- citations
Views, downloads and citations are aggregated across all versions of this paper published by eLife.
Download links
Downloads (link to download the article as PDF)
Open citations (links to open the citations from this article in various online reference manager services)
Cite this article (links to download the citations from this article in formats compatible with various reference manager tools)
Further reading
-
- Computational and Systems Biology
- Genetics and Genomics
We propose a new framework for human genetic association studies: at each locus, a deep learning model (in this study, Sei) is used to calculate the functional genomic activity score for two haplotypes per individual. This score, defined as the Haplotype Function Score (HFS), replaces the original genotype in association studies. Applying the HFS framework to 14 complex traits in the UK Biobank, we identified 3619 independent HFS–trait associations with a significance of p < 5 × 10−8. Fine-mapping revealed 2699 causal associations, corresponding to a median increase of 63 causal findings per trait compared with single-nucleotide polymorphism (SNP)-based analysis. HFS-based enrichment analysis uncovered 727 pathway–trait associations and 153 tissue–trait associations with strong biological interpretability, including ‘circadian pathway-chronotype’ and ‘arachidonic acid-intelligence’. Lastly, we applied least absolute shrinkage and selection operator (LASSO) regression to integrate HFS prediction score with SNP-based polygenic risk scores, which showed an improvement of 16.1–39.8% in cross-ancestry polygenic prediction. We concluded that HFS is a promising strategy for understanding the genetic basis of human complex traits.
-
- Computational and Systems Biology
Revealing protein binding sites with other molecules, such as nucleic acids, peptides, or small ligands, sheds light on disease mechanism elucidation and novel drug design. With the explosive growth of proteins in sequence databases, how to accurately and efficiently identify these binding sites from sequences becomes essential. However, current methods mostly rely on expensive multiple sequence alignments or experimental protein structures, limiting their genome-scale applications. Besides, these methods haven’t fully explored the geometry of the protein structures. Here, we propose GPSite, a multi-task network for simultaneously predicting binding residues of DNA, RNA, peptide, protein, ATP, HEM, and metal ions on proteins. GPSite was trained on informative sequence embeddings and predicted structures from protein language models, while comprehensively extracting residual and relational geometric contexts in an end-to-end manner. Experiments demonstrate that GPSite substantially surpasses state-of-the-art sequence-based and structure-based approaches on various benchmark datasets, even when the structures are not well-predicted. The low computational cost of GPSite enables rapid genome-scale binding residue annotations for over 568,000 sequences, providing opportunities to unveil unexplored associations of binding sites with molecular functions, biological processes, and genetic variants. The GPSite webserver and annotation database can be freely accessed at https://bio-web1.nscc-gz.cn/app/GPSite.