Hungry pigeons prefer sooner rare food over later likely food or faster information.

Introduction: Making decisions and investing effort to obtain rewards may depend on various factors, such as the delay to reward, the probability of its occurrence, and the information that can be collected about it. As predicted by various theories, pigeons and other animals indeed mind these factors when deciding. Methods: We now implemented a task in which pigeons were allowed to choose among three options and to peck at the chosen key to improve the conditions of reward delivery. Pecking more at a first color reduced the 12-s delay before food was delivered with a 33.3% chance, pecking more at a second color increased the initial 33.3% chance of food delivery but did not reduce the 12-s delay, and pecking more at a third color reduced the delay before information was provided whether the trial will be rewarded with a 33.3% chance after 12 s. Results: Pigeons' preference (delay vs. probability, delay vs. information, and probability vs. information), as well as their pecking effort for the chosen option, were analyzed. Our results indicate that hungry pigeons preferred to peck for delay reduction but did not work more for that option than for probability increase, which was the most profitable alternative and did not induce more pecking effort. In this task, information was the least preferred and induced the lowest level of effort. Refed pigeons showed no preference for any option but did not drastically reduce the average amounts of effort invested. Discussion: These results are discussed in the context of species-specific ecological conditions that could constrain current foraging theories.

Supervised machine learning aided behavior classification in pigeons.

Manual behavioral observations have been applied in both environment and laboratory experiments in order to analyze and quantify animal movement and behavior. Although these observations contributed tremendously to ecological and neuroscientific disciplines, there have been challenges and disadvantages following in their footsteps. They are not only time-consuming, labor-intensive, and error-prone but they can also be subjective, which induces further difficulties in reproducing the results. Therefore, there is an ongoing endeavor towards automated behavioral analysis, which has also paved the way for open-source software approaches. Even though these approaches theoretically can be applied to different animal groups, the current applications are mostly focused on mammals, especially rodents. However, extending those applications to other vertebrates, such as birds, is advisable not only for extending species-specific knowledge but also for contributing to the larger evolutionary picture and the role of behavior within. Here we present an open-source software package as a possible initiation of bird behavior classification. It can analyze pose-estimation data generated by established deep-learning-based pose-estimation tools such as DeepLabCut for building supervised machine learning predictive classifiers for pigeon behaviors, which can be broadened to support other bird species as well. We show that by training different machine learning and deep learning architectures using multivariate time series data as input, an F1 score of 0.874 can be achieved for a set of seven distinct behaviors. In addition, an algorithm for further tuning the bias of the predictions towards either precision or recall is introduced, which allows tailoring the classifier to specific needs.

Overmatching under food uncertainty in foraging pigeons.

One major survival-related activity of organisms is to seek food in their environment. To this end, they exploit previously rewarding locations and attempt to approach the cues predictive of the edible items they detect or expect. But foraging is unlikely to be a matter of reinforcement only. If it was, however, foraging activity should follow principles of extinction learning: It should be abolished in a location without reinforcement and proportionally be reduced in a partially reinforced location relative to a fully reinforced one containing the same number of food items. We tested these two hypotheses using a foraging board, which allowed pigeons to find food items hidden in perforated holes. Our results showed that the overall time spent and the overall number of pecks given in one area was related to reinforcement density in that area. To a lesser extent, the same phenomenon occurred with respect to the number of visits per area. However, the time-per-visit and pecks-per-visit ratios were higher in the partially vs. fully reinforced area, suggesting that the pigeons foraged more than expected when food was uncertain. These results will be discussed in the context of the matching law and optimal foraging.

Unihemispheric evidence accumulation in pigeons.

Perceptual decision making involves choices between alternatives based on sensory information. Studies in primates and rodents revealed a stochastic perceptual evidence accumulation process that, after reaching threshold, results in action execution. Birds represent a cognitively highly successful vertebrate class that has been evolving independent from mammals for more than 300 million years. The present study investigated whether perceptual decision making in pigeons shows behavioral and computational dynamics comparable to those in mammals and rodents. Using a novel "pigeon helmet" with liquid shutter displays that controls visual input to individual eyes/hemispheres with precise timing, we indeed revealed highly similar dynamics of perceptual decision making. Thus, both mammals and birds seem to share this core cognitive process that possibly represents a fundamental constituent of decision making throughout vertebrates. Interestingly, in our experiments we additionally discovered that both avian hemispheres start independent sensory accumulation processes without any major interhemispheric exchange. Because birds lack a corpus callosum and have only a small anterior commissure, they seem to be forced to decide on motor responses based on unihemispheric decisions under conditions of time pressure. (PsycInfo Database Record (c) 2021 APA, all rights reserved).

Mirror Self-Recognition in Pigeons: Beyond the Pass-or-Fail Criterion.

Spontaneous mirror self-recognition is achieved by only a limited number of species, suggesting a sharp "cognitive Rubicon" that only few can pass. But is the demarcation line that sharp? In studies on monkeys, who do not recognize themselves in a mirror, animals can make a difference between their mirror image and an unknown conspecific. This evidence speaks for a gradualist view of mirror self-recognition. We hypothesize that such a gradual process possibly consists of at least two independent aptitudes, the ability to detect synchronicity between self- and foreign movement and the cognitive understanding that the mirror reflection is oneself. Pigeons are known to achieve the first but fail at the second aptitude. We therefore expected them to treat their mirror image differently from an unknown pigeon, without being able to understand that the mirror reflects their own image. We tested pigeons in a task where they either approached a mirror or a Plexiglas barrier to feed. Behind the Plexiglas an unknown pigeon walked at the same time toward the food bowl. Thus, we pitched a condition with a mirror-self and a foreign bird against each other, with both of them walking close toward the food bowl. By a detailed analysis of a whole suit of behavioral details, our results make it likely that the foreign pigeon was treated as a competitor while the mirror image caused hesitation as if being an uncanny conspecific. Our results are akin to those with monkeys and show that pigeons do not equal their mirror reflection with a conspecific, although being unable to recognize themselves in the mirror.