10 papers · ranked by Valyu relevance
Philipe M. Bujold, Simone Ferrari-Toniolo, Wolfram Schultz
This study investigated the influence of experienced reward distributions on the shape of utility functions inferred from economic choice. Utility is the hypothetical variable that appears to be maximized by the choice. Despite the generally accepted notion that utility functions are not insensitive to external…
Jarrett E. K. Byrnes, Fabian Roger, Robert Bagchi
In ecology, multifunctionality measures the simultaneous provision of multiple ecosystem functions. If species diversity describes the variety of species that together build the ecosystem, multifunctionality attempts to describe the variety of functions these species perform. A range of methods have been proposed to…
Angela Jones, Eric Schulz, Björn Meder, Azzurra Ruggeri
How do people actively explore to learn about functional rules, that is, how continuous inputs map onto continuous outputs? We introduce a novel paradigm to investigate information search in continuous, multi-feature function learning scenarios. Participants either actively selected or passively observed information to…
Keith D Farnsworth, Larissa Albantakis, Tancredi Caruso
The concept of function arises at all levels of biological study and is often loosely and variously defined, especially within ecology. This has led to ambiguity, obscuring the common structure that unites levels of biological organisation, from molecules to ecosystems. Here we build on already successful ideas from…
Charley M. Wu, Eric Schulz, Samuel J Gershman
How do people learn functions on structured spaces? And how do they use this knowledge to guide their search for rewards in situations where the number of options is large? We study human behavior on structures with graph-correlated values and propose a Bayesian model of function learning to describe and predict their…
Stefano Mammola, Pedro Cardoso
The use of kernel density n-dimensional hypervolumes [Global Ecol. Biogeogr. 23(5):595–609] in trait-based ecology is rapidly increasing. By representing the functional space of a species or community as a Hutchinsonian niche space, this relatively new approach is showing great potential for the advance of functional…
Charley M. Wu, Eric Schulz, Samuel J. Gershman
From social networks to public transportation, graph structures are a ubiquitous feature of life. Yet little is known about how humans learn functions on graphs, where relationships are defined by the connectivity structure. We adapt a Bayesian framework for function learning to graph structures, and propose that…
Ceri Webster, Joanna Barker, David Curnick, Matthew Gollock + 12 more
Robust species-level methods for quantifying ecological differences have yet to be incorporated into conservation strategies. Here, we present a conservation prioritisation approach that integrates species trait data and extinction risk to quantify the contribution of individual species to overall functional diversity.…
Eric Schulz, Joshua B. Tenenbaum, David Duvenaud, Maarten Speekenbrink + 1 more
How do people recognize and learn about complex functional structure? Taking inspiration from other areas of cognitive science, we propose that this is achieved by harnessing compositionality: complex structure is decomposed into simpler building blocks. We formalize this idea within the framework of Bayesian…
Carlos P. Carmona, Nicola Pavanetto, Giacomo Puglielli
Functional trait space analyses are pivotal to define species’ ecological strategies across the tree of life. Yet, there is no single application that streamlines the many sometimes-troublesome steps needed to build and analyze functional trait spaces. To fill this gap, we propose funspace, an R package to easily…