18 research outputs found
Reinforcement and inference in cross-situational word learning
Cross-situational word learning is based on the notion that a learner can
determine the referent of a word by finding something in common across many
observed uses of that word. Here we propose an adaptive learning algorithm that
contains a parameter that controls the strength of the reinforcement applied to
associations between concurrent words and referents, and a parameter that
regulates inference, which includes built-in biases, such as mutual
exclusivity, and information of past learning events. By adjusting these
parameters so that the model predictions agree with data from representative
experiments on cross-situational word learning, we were able to explain the
learning strategies adopted by the participants of those experiments in terms
of a trade-off between reinforcement and inference. These strategies can vary
wildly depending on the conditions of the experiments. For instance, for fast
mapping experiments (i.e., the correct referent could, in principle, be
inferred in a single observation) inference is prevalent, whereas for
segregated contextual diversity experiments (i.e., the referents are separated
in groups and are exhibited with members of their groups only) reinforcement is
predominant. Other experiments are explained with more balanced doses of
reinforcement and inference
Local attractors, degeneracy and analyticity: symmetry effects on the locally coupled Kuramoto model
In this work we study the local coupled Kuramoto model with periodic boundary
conditions. Our main objective is to show how analytical solutions may be
obtained from symmetry assumptions, and while we proceed on our endeavor we
show apart from the existence of local attractors, some unexpected features
resulting from the symmetry properties, such as intermittent and chaotic period
phase slips, degeneracy of stable solutions and double bifurcation composition.
As a result of our analysis, we show that stable fixed points in the
synchronized region may be obtained with just a small amount of the existent
solutions, and for a class of natural frequencies configuration we show
analytical expressions for the critical synchronization coupling as a function
of the number of oscillators, both exact and asymptotic.Comment: 15 pages, 12 figure
Multistable behavior above synchronization in a locally coupled Kuramoto model
A system of nearest neighbors Kuramoto-like coupled oscillators placed in a
ring is studied above the critical synchronization transition. We find a
richness of solutions when the coupling increases, which exists only within a
solvability region (SR). We also find that they posses different
characteristics, depending on the section of the boundary of the SR where the
solutions appear. We study the birth of these solutions and how they evolve
when {K} increases, and determine the diagram of solutions in phase space.Comment: 8 pages, 10 figure
Mean-field analysis of the majority-vote model broken-ergodicity steady state
We study analytically a variant of the one-dimensional majority-vote model in
which the individual retains its opinion in case there is a tie among the
neighbors' opinions. The individuals are fixed in the sites of a ring of size
and can interact with their nearest neighbors only. The interesting feature
of this model is that it exhibits an infinity of spatially heterogeneous
absorbing configurations for whose statistical properties we
probe analytically using a mean-field framework based on the decomposition of
the -site joint probability distribution into the -contiguous-site joint
distributions, the so-called -site approximation. To describe the
broken-ergodicity steady state of the model we solve analytically the
mean-field dynamic equations for arbitrary time in the cases n=3 and 4. The
asymptotic limit reveals the mapping between the statistical
properties of the random initial configurations and those of the final
absorbing configurations. For the pair approximation () we derive that
mapping using a trick that avoids solving the full dynamics. Most remarkably,
we find that the predictions of the 4-site approximation reduce to those of the
3-site in the case of expectations involving three contiguous sites. In
addition, those expectations fit the Monte Carlo data perfectly and so we
conjecture that they are in fact the exact expectations for the one-dimensional
majority-vote model
How animals move along? Exactly solvable model of superdiffusive spread resulting from animal’s decision making
Statistical mechanics of animal movement: Animals's decision-making can result in superdiffusive spread
Peculiarities of individual animal movement and dispersal have been a major focus of recent research as they are thought to hold the key to the understanding of many phenomena in spatial ecology. Superdiffusive spread and long-distance dispersal have been observed in different species but the underlying biological mechanisms often remain obscure. In particular, the effect of relevant animal behavior has been largely unaddressed. In this paper, we show that a superdiffusive spread can arise naturally as a result of animal behavioral response to small-scale environmental stochasticity. Surprisingly, the emerging fast spread does not require the standard assumption about the fat tail of the dispersal kernel