Flying in the face of nature

The active time model of concurrent choice

The following paper describes a steady-state model of concurrent choice, termed the active time model (ATM). ATM is derived from maximization principles and is characterized by a semi-Markov process. The model proposes that the controlling stimulus in concurrent variable-interval (VI) VI schedules of reinforcement is the time interval since the most recent response, termed here "the active interresponse time" or simply "active time." In the model after a response is generated, it is categorized by a function that relates active times to switch/stay probabilities. In the paper the output of ATM is compared with predictions made by three other models of operant conditioning: melioration, a version of scalar expectancy theory (SET), and momentary maximization. Data sets considered include preferences in multiple-concurrent VI VI schedules, molecular choice patterns, correlations between switching and perseveration, and molar choice proportions. It is shown that ATM can account for all of these data sets, while the other models produce more limited fits. However, rather than argue that ATM is the singular model for concurrent VI VI choice, a consideration of its concept space leads to the conclusion that operant choice is multiply-determined, and that an adaptive viewpoint-one that considers experimental procedures both as selecting mechanisms for animal choice as well as tests of the controlling variables of that choice-is warranted.

Mechanistic models must link the field and the lab

In the theory outlined in the target article, an animal forages continuously, making sequential decisions in a world where the amount of food and its uncertainty are fixed, but delays are variable. These assumptions contrast with the risk-sensitive foraging theory and create a problem for comparing the predictions of this model with many laboratory experiments that do not make these assumptions.

Decision-making under uncertainty: biases and Bayesians

Animals (including humans) often face circumstances in which the best choice of action is not certain. Environmental cues may be ambiguous, and choices may be risky. This paper reviews the theoretical side of decision-making under uncertainty, particularly with regard to unknown risk (ambiguity). We use simple models to show that, irrespective of pay-offs, whether it is optimal to bias probability estimates depends upon how those estimates have been generated. In particular, if estimates have been calculated in a Bayesian framework with a sensible prior, it is best to use unbiased estimates. We review the extent of evidence for and against viewing animals (including humans) as Bayesian decision-makers. We pay particular attention to the Ellsberg Paradox, a classic result from experimental economics, in which human subjects appear to deviate from optimal decision-making by demonstrating an apparent aversion to ambiguity in a choice between two options with equal expected rewards. The paradox initially seems to be an example where decision-making estimates are biased relative to the Bayesian optimum. We discuss the extent to which the Bayesian paradigm might be applied to the evolution of decision-makers and how the Ellsberg Paradox may, with a deeper understanding, be resolved.

Evolutionary models of metabolism, behaviour and personality

I explore the relationship between metabolism and personality by establishing how selection acts on metabolic rate and risk-taking in the context of a trade-off between energy and predation. Using a simple time budget model, I show that a high resting metabolic rate is not necessarily associated with a high daily energy expenditure. The metabolic rate that minimizes the time spent foraging does not maximize the net gain rate while foraging, and it is not always advantageous for animals to have a higher metabolic rate when food availability is high. A model based on minimizing the ratio of mortality rate to net gain rate is used to determine how a willingness to take risks should be correlated with metabolic rate. My results establish that it is not always advantageous for animals to take greater risks when metabolic rate is high. When foraging intensity and metabolic rate coevolve, I show that in a particular case different combinations of foraging intensity and metabolic rate can have equal fitness.

An effect of inter-trial duration on the gambler's fallacy choice bias

The gambler's fallacy is defined as the avoidance of a winning outcome in a stochastic environment with a constant probability. We tested the possibility that the gambler's fallacy in humans is responsive to the amount of time between choice allocations. Two groups of subjects were placed in a six-choice betting game in which the choices were clustered into two "patches." Groups were defined by the length of time - 2s or 6s - between trials. On any given trial subjects allocated six points among the alternatives, and retained any points that were bet on the winning alternative. Both groups showed evidence of the gambler's fallacy bias. However, the bias was stronger in the 6-s ITI group than in the 2-s ITI group. This difference was found primarily to be due to differences in the number of subjects showing an opposing bias to the gambler's fallacy, namely a preference for the most recent winning alternative. This choice bias is termed the hot hand fallacy. Our findings contradict predictions derived from a foraging heuristic and from traditional accounts of the gambler's fallacy.

From foraging to operant conditioning: a new computer-controlled Skinner box to study free-flying nectar gathering behavior in bees

The experimental study of nectar foraging behavior in free-flying bees requires the use of automated devices to control solution delivery and measure dependent variables associated with nectar gathering. We describe a new computer-controlled artificial flower and provide calibration data to measure the precision of the apparatus. Our device is similar to a "Skinner box" and we present data of an experiment where various amounts of a 50% sugar solution are presented randomly to individual bees. These data show large individual variations among subjects across several dependent variables. Finally, we discuss possible applications of our device to problems in behavioral sciences.