Effort-motivated behavior resolves paradoxes in appetitive conditioning

Assessing reward preference using operant behavior in male and female mice

Many different solid food pellets are available as reinforcers for rodents in operant behavior tests. Different reward formulations have not been compared, so it is unclear whether mice show strong preferences for different rewards and whether such preferences are consistent within or across sex and background strain. Here we show that mice have strong preferences for two balanced diet food rewards over sucrose pellets, and preference for one balanced diet pellet formulation over another, in a simultaneous choice test using a low effort fixed ratio operant test. All mice, of both sexes and both CD1 and C57 background strains, showed the same strong preferences among these three types of reinforcers. In contrast, flavorings added to the reward pellets had relatively small and more variable effects on preference. The preference for balanced diet pellets over sucrose pellets was seen also in the total numbers of rewards consumed in low effort tests with food pellets or only sucrose pellets available. However, progressive ratio testing showed that mice worked harder for sucrose pellets than for the preferred balanced diet pellets. These findings indicate that reinforcers with similar and very different preference profiles are readily available and that testing with different rewards can produce different, and sometimes unexpected, results.

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.

Pavlovian learning and conditioned reinforcement

Conditioned reinforcers are widely used in applied behavior analysis. Basic research evidence reveals that Pavlovian learning plays an important role in the acquisition and efficacy of new conditioned-reinforcer functions. Thus, a better understanding of Pavlovian principles holds the promise of improving the efficacy of conditioned reinforcement in applied research and practice. This paper surveys how (and if) Pavlovian principles are presented in behavior-analytic textbooks; imprecisions and knowledge gaps within contemporary Pavlovian empirical findings are highlighted. Thereafter, six practical principles of Pavlovian conditioning are presented along with empirical support and knowledge gaps that should be filled by applied and translational behavior-analytic researchers. Innovative applications of these principles are outlined for research in language acquisition, token reinforcement, and self-control.

Response Flexibility: The Role of the Lateral Habenula

The ability to make appropriate decisions that result in an optimal outcome is critical for survival. This process involves assessing the environment as well as integrating prior knowledge about the environment with information about one's current internal state. There are many neural structures that play critical roles in mediating these processes, but it is not yet known how such information coalesces to influence behavioral output. The lateral habenula (LHb) has often been cited as a structure critical for adaptive and flexible responding when environmental contexts and internal state changes. A challenge, however, has been understanding how LHb promotes response flexibility. In this review, we hypothesize that the LHb enables flexible responding following the integration of context memory and internal state information by signaling downstream brainstem structures known to drive hippocampal theta. In this way, animals respond more flexibly in a task situation not because the LHb selects a particular action, but rather because LHb enhances a hippocampal neural state that is often associated with greater attention, arousal, and exploration. In freely navigating animals, these are essential conditions that are needed to discover and implement appropriate alternative choices and behaviors. As a corollary to our hypothesis, we describe short- and intermediate-term functions of the LHb. Finally, we discuss the effects on the behavior of LHb dysfunction in short- and intermediate-timescales, and then suggest that new therapies may act on the LHb to alleviate the behavioral impairments following long-term LHb disruption.