Percentage reward in an operant analogue to foraging

doi:10.1016/S0003-3472(85)80168-8

Animal Behaviour

Volume 33, Issue 4, November 1985, Pages 1096-1101

https://doi.org/10.1016/S0003-3472(85)80168-8 Get rights and content

Abstract

Pigeons (Columba livia) responding on an operant, conditioning analogue to foraging could either accept or reject a variable-interval 20-s schedule which always led to food or a variable-interval 5-s schedule which led to food on only a percentage of trials. The probability of accepting the certain alternative increased as the percentage of food trials for the uncertain alternative decreased. The probability of accepting the uncertain alternative increased with the percentage of food trials for this alternative. Subjects receiving all of their food in the experiment (‘closed economy’) and those requiring supplementation (‘open economy’) preferred whichever alternative provided the higher overall mean rate of reinforcement.

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Cited by (19)

An embodied biologically constrained model of foraging: From classical and operant conditioning to adaptive real-world behavior in DAC-X
2015, Neural Networks
Citation Excerpt :
It is exactly for this reason that a computational exploration can assist in advancing hypotheses on the system level properties of the whole brain as it engages with the world through the body. Several aspects of complex animal behavior have been previously described within the context of the learning paradigms of classical and operant conditioning (Abarca, Fantino, & Ito, 1985; Agetsuma, 1998; Domjan, 2004; Fantino & Abarca, 2010; Hull, 1951; Skinner, 1948; Thorndike, 1927). In classical conditioning, an animal learns to relate an initially neutral stimulus with an innate reflex that comprises a predefined stimulus–response relation.
Animals successfully forage within new environments by learning, simulating and adapting to their surroundings. The functions behind such goal-oriented behavior can be decomposed into 5 top-level objectives: ‘how’, ‘why’, ‘what’, ‘where’, ‘when’ (H4W). The paradigms of classical and operant conditioning describe some of the behavioral aspects found in foraging. However, it remains unclear how the organization of their underlying neural principles account for these complex behaviors. We address this problem from the perspective of the Distributed Adaptive Control theory of mind and brain (DAC) that interprets these two paradigms as expressing properties of core functional subsystems of a layered architecture. In particular, we propose DAC-X, a novel cognitive architecture that unifies the theoretical principles of DAC with biologically constrained computational models of several areas of the mammalian brain. DAC-X supports complex foraging strategies through the progressive acquisition, retention and expression of task-dependent information and associated shaping of action, from exploration to goal-oriented deliberation. We benchmark DAC-X using a robot-based hoarding task including the main perceptual and cognitive aspects of animal foraging. We show that efficient goal-oriented behavior results from the interaction of parallel learning mechanisms accounting for motor adaptation, spatial encoding and decision-making. Together, our results suggest that the H4W problem can be solved by DAC-X building on the insights from the study of classical and operant conditioning. Finally, we discuss the advantages and limitations of the proposed biologically constrained and embodied approach towards the study of cognition and the relation of DAC-X to other cognitive architectures.
Energy budget versus temporal discounting as determinants of preference in risky choice
2004, Behavioural Processes
Four pigeons and three ringneck doves responded on an operant simulation of natural foraging. After satisfying a schedule of reinforcement associated with search time, subjects could “accept” or “reject” another schedule of reinforcement associated with handling time. Two schedules of reinforcement were available, a variable interval, and a fixed interval with the same mean value. Food available in the session (a variable related to the energy budget) was manipulated in the different conditions either by increases of the value of the search state schedule of reinforcement, or by increases in the mean value of the handling state schedules. The results indicate that the amount of food available in the session did not affect the preference for variable schedules of reinforcement, as would be predicted by an influential theory of risk sensitive foraging. Instead, the preference for variability depended on the relationship between the time spent in the search and the handling states, as is predicted by a family of models of choice that are based on the temporal proximity to the reinforcer.
Pricing effects on foraging in a simulated Internet shopping mall
2003, Journal of Economic Psychology
Consumers shopped for compact disks in a simulated Internet mall in a successive-choice procedure over 10 sessions. The procedure extended that of two previous consumer behavior experiments [DiClemente & Hantula, Psychol. Market. (in press); Rajala & Hantula, Managerial Decis. Econom. 21 (2000) 145] by assessing the influence of pricing on consumer preferences. Across five virtual stores, mean CD prices were $9.99, $11.99, $13.99, $15.99, and $17.99. Within-store prices were generated randomly from a $4 range around the mean. Preference was measured as the total proportion of purchases made within each store. Consistent with previous studies of delay manipulations, a hyperbolic function provided the best fit to the store preference data. The results offer evidence of congruence between the effects of price and delay on consumer preferences, and support the contention that the price of goods may be interpretable as contributing to total delay time to primary reinforcement. Integrating evolutionary theory and the procedures of operant psychology, this research contributes to the development of a behavioral ecology of consumption and advances the experimental analysis of consumer behavior.
Behavioral Ecology
1991, Handbook of Behavioral Neuroscience
This chapter provides an overview of work investigating the roles of memory, perception, and species differences in foraging and the areas of behavioral ecology other than foraging. Behavioral ecology studies the organism's fitness as a function of the variables in its environment. Some of the areas addressed by behavioral ecologists include foraging , territoriality, mate selection, kin recognition, weaning decisions, and thermoregulation. The area that has received by far the most attention from researchers with a behavior-analytic background is foraging. Behavioral ecologists are attracted to behaviour–analytic methodology because this methodology promises to provide the ecologists with sharper tools to test more precisely their predictions, especially about decisions of foragers. To better test some of their theoretical predictions, however, behavioral ecologists have increasingly conducted laboratory simulations of the natural environment. Behavior analysts and behavioral ecologists have participated in many interdisciplinary efforts. Many of the interactions between behavioral ecologists and behavior analysts have concerned the economic and temporal factors that influence foraging.
Foraging as Operant Behavior and Operant Behavior as Foraging: What Have We Learned?
1988, Psychology of Learning and Motivation - Advances in Research and Theory
This chapter highlights the way psychology can supply mechanistic analyses of foraging and examines the role of particular behavioral mechanisms in particular aspects of foraging. New foraging models have been developed, and psychological data and theory are integral to test models dealing with risk and information. Standard models of prey and patch choice have been refined to take into account constraints on a forager such as imperfect counting, timing, and discrimination. At the same time, new theory and data on timing and choice have encouraged the analysis of foraging behavior at a finer level of detail. The analysis of any specific foraging problem leaves a number of unanswered questions. Apparently, conflicting functional and mechanistic predictions have been tested in the areas of sampling behavior, response to time horizon, and the effects of energy budget on response to risk. A standard model of diet selection prescribes rejecting an unprofitable prey item whenever a forager cannot expect to finish handling it before the next profitable item arrives.
Do pigeons (Columba livia) use information about the absence of food appropriately? A further look into suboptimal choice
2017, Journal of Comparative Psychology

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Animal Behaviour

Abstract

References (26)

An empirical demonstration of risk-sensitive foraging preferences

Anim. Behav.

A sequential approach to risk-taking

Anim. Behav.

Central place foraging in Rattus norvegicus

Anim. Behav.

Foraging and reinforcement schedules in the pigeon: optimal and non-optimal aspects of choice

Anim. Behav.

Optimal foraging in patches: a case for stochasticity

Theor. Pop. Biol.

Choice and foraging

J. exp. Anal. Behav.

Choice, changeover, and travel

J. exp. Anal. Behav.

On foraging time allocation in a stochastic environment

Ecology

Energy budgets, risk and foraging preferences in dark-eyed juncos (Junco hyemalis)

Behav. Ecol. Sociobiol.

White-crowned sparrows (Zonotrichia leucophrys) foraging preferences in a risky environment

Behav. Ecol. Sociobiol.

Optimal foraging: attack strategy of a mantid

Am. Nat.

The operant revisited

A comparative analysis of optimal foraging behavior: laboratory simulations

Cited by (19)

An embodied biologically constrained model of foraging: From classical and operant conditioning to adaptive real-world behavior in DAC-X

Energy budget versus temporal discounting as determinants of preference in risky choice

Pricing effects on foraging in a simulated Internet shopping mall

Behavioral Ecology

Foraging as Operant Behavior and Operant Behavior as Foraging: What Have We Learned?

Do pigeons (Columba livia) use information about the absence of food appropriately? A further look into suboptimal choice