Quantitative multisensory integration modeling (Sciuridae)

Knowledge graph centered on Quantitative multisensory integration modeling (Sciuridae) with 23 nodes and 93 connections. Top connected: Marmota, Yellow-bellied Marmot, Spiders, focal animal sampling (Sciuridae), Canis lupus.

Method synopsis

Development of a mathematical framework to predict when animals should integrate multisensory stimuli based on uncertainty, predator probability, and cost-benefit ratios. Uses signal detection theory and Bayesian updating.

Synthesized from method descriptions across 1 paper using this protocol.

Procedure as described in the canonical source

Steps below were extracted from the paper that introduces this protocol — Optimal multisensory integration (2020), Behavioral Ecology. Implementations in other papers (listed below) may differ.

1. Theoretical model development
   Developed a quantitative model examining when prey receives two sequential stimuli in different sensory modalities with different uncertainties for each stimulus. Model assumes prey can forage (F) or hide (H), world is in predator present (PRED) or nonthreat present (NONE) state, and prey makes antipredator decisions following signal detection theory.
   Quantities: Model includes parameters PPRED (prior probability of predator), BNONE and BPRED (benefits), costs K1 and K2, uncertainties U1 and U2Duration: Not specifiedConditions: Theoretical framework conditions
   Equipment: Mathematical modeling
2. Sensitivity analysis using Latin Hypercube Sampling
   Conducted sensitivity analysis by calculating Spearman partial rank correlation coefficients (PRCC) between input parameters and V2. Varied U1 and U2 from 0 to 1, S1 from −1 to 1, PPRED from 0 to 1, BNONE and BPRED from 0 to 5, and K2 from 0 to 5. Used uniform priors for all parameters and constrained V1 ≥ 0 for all parameter combinations.
   Quantities: 500 simulations with parameters sampled using Latin Hypercube Sampling (LHS)Duration: Not specifiedConditions: Computational analysis conditions
   Equipment: R version 3.0.2, R Core Team 2016

Typical Equipment

• Mathematical modeling
• R version 3.0.2

Output Measurements

• favorability of integration
• value of information
• optimal behavioral decisions

Papers Using This Protocol (1)

Species Studied With This Protocol

Places Where Used