A Simple Artificial Life Model Explains Irrational Behavior in Human Decision-Making

Although praised for their rationality, humans often make poor decisions, even in simple situations. In the repeated binary choice experiment, an individual has to choose repeatedly between the same two alternatives, where a reward is assigned to one of them with fixed probability. The optimal strategy is to perseverate with choosing the alternative with the best expected return. Whereas many species perseverate, humans tend to match the frequencies of their choices to the frequencies of the alternatives, a sub-optimal strategy known as probability matching. Our goal was to find the primary cognitive constraints under which a set of simple evolutionary rules can lead to such contrasting behaviors. We simulated the evolution of artificial populations, wherein the fitness of each animat (artificial animal) depended on its ability to predict the next element of a sequence made up of a repeating binary string of varying size. When the string was short relative to the animats’ neural capacity, they could learn it and correctly predict the next element of the sequence. When it was long, they could not learn it, turning to the next best option: to perseverate. Animats from the last generation then performed the task of predicting the next element of a non-periodical binary sequence. We found that, whereas animats with smaller neural capacity kept perseverating with the best alternative as before, animats with larger neural capacity, which had previously been able to learn the pattern of repeating strings, adopted probability matching, being outperformed by the perseverating animats. Our results demonstrate how the ability to make predictions in an environment endowed with regular patterns may lead to probability matching under less structured conditions. They point to probability matching as a likely by-product of adaptive cognitive strategies that were crucial in human evolution, but may lead to sub-optimal performances in other environments

Monkey Steering Responses Reveal Rapid Visual-Motor Feedback

The neural mechanisms underlying primate locomotion are largely unknown. While behavioral and theoretical work has provided a number of ideas of how navigation is controlled, progress will require direct physiolgical tests of the underlying mechanisms. In turn, this will require development of appropriate animal models. We trained three monkeys to track a moving visual target in a simple virtual environment, using a joystick to control their direction. The monkeys learned to quickly and accurately turn to the target, and their steering behavior was quite stereotyped and reliable. Monkeys typically responded to abrupt steps of target direction with a biphasic steering movement, exhibiting modest but transient overshoot. Response latencies averaged approximately 300 ms, and monkeys were typically back on target after about 1 s. We also exploited the variability of responses about the mean to explore the time-course of correlation between target direction and steering response. This analysis revealed a broad peak of correlation spanning approximately 400 ms in the recent past, during which steering errors provoke a compensatory response. This suggests a continuous, visual-motor loop controls steering behavior, even during the epoch surrounding transient inputs. Many results from the human literature also suggest that steering is controlled by such a closed loop. The similarity of our results to those in humans suggests the monkey is a very good animal model for human visually guided steering

Avoiding moving obstacles

To successfully move our hand to a target, we must consider how to get there without hitting surrounding objects. In a dynamic environment this involves being able to respond quickly when our relationship with surrounding objects changes. People adjust their hand movements with a latency of about 120 ms when the visually perceived position of their hand or of the target suddenly changes. It is not known whether people can react as quickly when the position of an obstacle changes. Here we show that quick responses of the hand to changes in obstacle position are possible, but that these responses are direct reactions to the motion in the surrounding. True adjustments to the changed position of the obstacle appeared at much longer latencies (about 200 ms). This is even so when the possible change is predictable. Apparently, our brain uses certain information exceptionally quickly for guiding our movements, at the expense of not always responding adequately. For reaching a target that changes position, one must at some time move in the same direction as the target did. For avoiding obstacles that change position, moving in the same direction as the obstacle is not always an adequate response, not only because it may be easier to avoid the obstacle by moving the other way, but also because one wants to hit the target after passing the obstacle. Perhaps subjects nevertheless quickly respond in the direction of motion because this helps avoid collisions when pressed for time. © 2008 Springer-Verlag

Search for B → μ μ And B0 → μ+ μ- Decays

A search for the rare decays B → μ μ and B → μ μ is performed in pp collisions at ps = 7TeV, with a data sample corresponding to an integrated luminosity of 5 fb collected by the CMS experiment at the LHC. In both decays, the number of events observed after all selection requirements is consistent with the expectation from background plus standard model signal predictions. The resulting upper limits on the branching fractions are B(B → μ μ-) < 7:7 × 10 and B(B → μ μ ) < 1:8 × 10 at 95% confidence level

A learning-based approach to artificial sensory feedback leads to optimal integration

© 2014 Nature Publishing Group, a division of Macmillan Publishers Limited. All Rights Reserved. Proprioception—the sense of the body's position in space—is important to natural movement planning and execution and will likewise be necessary for successfu

Learning to Estimate Dynamical State with Probabilistic Population Codes

© 2015 Makin et al.Tracking moving objects, including one’s own body, is a fundamental ability of higher organisms, playing a central role in many perceptual and motor tasks. While it is unknown how the brain learns to follow and predict the dynamics of o

Recurrent Exponential-Family Harmoniums without Backprop-Through-Time

Exponential-family harmoniums (EFHs), which extend restricted Boltzmannmachines (RBMs) from Bernoulli random variables to other exponential families(Welling et al., 2005), are generative models that can be trained withunsupervised-learning techniques, lik

Learning Multisensory Integration and Coordinate Transformation via Density Estimation

Sensory processing in the brain includes three key operations: multisensory integration-the task of combining cues into a single estimate of a common underlying stimulus; coordinate transformations-the change of reference frame for a stimulus (e.g., retin

Recurrent Exponential-Family Harmoniums without Backprop-Through-Time

Exponential-family harmoniums (EFHs), which extend restricted Boltzmannmachines (RBMs) from Bernoulli random variables to other exponential families(Welling et al., 2005), are generative models that can be trained withunsupervised-learning techniques, lik