“Natural Laboratory Complex” for novel primate neuroscience

We propose novel strategies for primate experimentation that are ethically valuable and pragmatically useful for cognitive neuroscience and neuropsychiatric research. Specifically, we propose Natural Laboratory Complex or Natural Labs, which are a combination of indoor-outdoor structures for studying free moving and socially housed primates in natural or naturalistic environment. We contend that Natural Labs are pivotal to improve primate welfare, and at the same time to implement longitudinal and socio-ecological studies of primate brain and behavior. Currently emerging advanced technologies and social systems (including recent COVID-19 induced “remote” infrastructures) can speed-up cognitive neuroscience approaches in freely behaving animals. Experimental approaches in natural(istic) settings are not in competition with conventional approaches of laboratory investigations, and could establish several benefits at the ethical, experimental, and economic levels.Introduction Animal models in neuroscience - The rodent model - The non-human primate model - Optimizing cognitive neuroscience research with animal models Novel strategies for primate experimentation - Natural laboratory complex -- In situ Lab-in-Nature -- Ex situ Nature-in-Lab Harmonization of cost and benefit trade-offs - Ethical balance - Socioeconomic balance - Legal balance Conclusio

A novel mind-set in primate experimentation: Implications for primate welfare

Abstract We emphasize the importance of studying the primate brain in cognitive neuroscience and suggest a new mind-set in primate experimentation within the boundaries of animal welfare regulations. Specifically, we list the advantages of investigating both genes and neural mechanisms and processes in the emergence of behavioral and cognitive functions, and propose the establishment of an open field of primate research. The latter may be conducted by implementing and harmonizing experimental practices with ethical guidelines that regulate (1) management of natural parks with free-moving populations of target nonhuman primates, (2) establishment of indoor-outdoor labs for both system genetics and neuroscience investigations, and (3) hotel space and technologies which remotely collect and dislocate information regarding primates geographically located elsewhere.1 Introduction 2 Animal models in Neurobiology 3 The research domain criteria approach to Neuropsychiatry 4 The open niche of primate experimentation 4.1 Primate natural parks 4.2 Hotel space and remote technologies 5 Cost and benefits trade-offs of primate experimentation 6 Concluding remark

Modulation of physical understanding by common marmosets (Callithrix jacchus)

The understanding of physical causality in common marmosets was tested using support problems in which a pair of sheets was presented to determine whether subjects would choose the sheet that had a food item on it (i.e., the sheet was supporting the food item). In two experiments, the conditions were manipulated in terms of the length of the sheet, the distance between the sheet and the food item, the presence of a gap separating the two sheets, and the size of the food item. In Experiment 1, the marmosets had difficulty rejecting an irretrievable food item when it was located closer to them than a retrievable item. Although their performance was strongly affected by the size of the irretrievable food item, they quickly learned to reject that alternative. In contrast, no improvement was found when one sheet was divided into two pieces such that the food item could not be retrieved when its near side was pulled. A similar response tendency was observed in Experiment 2, in which the effects of the large food item were examined in three different conditions. Thus, common marmosets were influenced by the perceptual features of the food in solving the support problems, as are other non-human primates. In addition, they consistently failed to appreciate the presence of a gap and, therefore, failed to reject the distracter alternative. However, all animals rapidly learned that the size of the food item was an irrelevant variable, and some showed an elementary conceptual understanding of support. These findings suggest that marmosets’ physical understanding may improve with experience

Sequential learning and rule abstraction in Bengalese finches

The Bengalese finch (Lonchura striata var. domestica) is a species of songbird. Males sing courtship songs with complex note-to-note transition rules, while females discriminate these songs when choosing their mate. The present study uses serial reaction time (RT) to examine the characteristics of the Bengalese finches’ sequential behaviours beyond song production. The birds were trained to produce the sequence with an “A–B–A” structure. After the RT to each key position was determined to be stable, we tested the acquisition of the trained sequential response by presenting novel and random three-term sequences (random test). We also examined whether they could abstract the embedded rule in the trained sequence and apply it to the novel test sequence (abstract test). Additionally, we examined rule abstraction through example training by increasing the number of examples in baseline training from 1 to 5. When considered as (gender) groups, training with 5 examples resulted in no statistically significant differences in the abstract tests, while statistically significant differences were observed in the random tests, suggesting that the male birds learned the trained sequences and transferred the abstract structure they had learned during the training trials. Individual data indicated that males, as opposed to females, were likely to learn the motor pattern of the sequence. The results are consistent with observations that males learn to produce songs with complex sequential rules, whereas females do not

Measuring Dynamical Uncertainty With Revealed Dynamics Markov Models

Concepts and measures of time series uncertainty and complexity have been applied across domains for behavior classification, risk assessments, and event detection/prediction. This paper contributes three new measures based on an encoding of the series' phase space into a descriptive Markov model. Here we describe constructing this kind of “Revealed Dynamics Markov Model” (RDMM) and using it to calculate the three uncertainty measures: entropy, uniformity, and effective edge density. We compare our approach to existing methods such as approximate entropy (ApEn) and permutation entropy using simulated and empirical time series with known uncertainty features. While previous measures capture local noise or the regularity of short patterns, our measures track holistic features of time series dynamics that also satisfy criteria as being approximate measures of information generation (Kolmogorov entropy). As such, we show that they can distinguish dynamical patterns inaccessible to previous measures and more accurately reflect their relative complexity. We also discuss the benefits and limitations of the Markov model encoding as well as requirements on the sample size