Learning Simulator: A simulation software for animal and human learning

Jonsson, Markus; Ghirlanda, Stefano; Lind, Johan; Vinken, Vera; Enquist, Magnus

doi:10.21105/joss.02891

Cited by 3 publications

(2 citation statements)

References 22 publications

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“…For example, they might increase the precision and communication of hypotheses, make clear predictions, and offer the ability to simulate effects (see [ 70 – 76 ] for discussion). In animal cognition research, evolutionary theory [ 77 ], and learning theory [ 78 , 79 ], are two possible sources of strong theory to ground research programmes in, and tools and tutorials for using theories like this in study design and analysis are increasingly available [ 80 , 81 ]. However, it is unclear the extent to which formal models can effectively be generated for all research lines of interest.…”

Section: Discussionmentioning

confidence: 99%

The hidden side of animal cognition research: Scientists’ attitudes toward bias, replicability and scientific practice

2021

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Animal cognition research aims to understand animal minds by using a diverse range of methods across an equally diverse range of species. Throughout its history, the field has sought to mitigate various biases that occur when studying animal minds, from experimenter effects to anthropomorphism. Recently, there has also been a focus on how common scientific practices might affect the reliability and validity of published research. Usually, these issues are discussed in the literature by a small group of scholars with a specific interest in the topics. This study aimed to survey a wider range of animal cognition researchers to ask about their attitudes towards classic and contemporary issues facing the field. Two-hundred and ten active animal cognition researchers completed our survey, and provided answers on questions relating to bias, replicability, statistics, publication, and belief in animal cognition. Collectively, researchers were wary of bias in the research field, but less so in their own work. Over 70% of researchers endorsed Morgan’s canon as a useful principle but many caveated this in their free-text responses. Researchers self-reported that most of their studies had been published, however they often reported that studies went unpublished because they had negative or inconclusive results, or results that questioned “preferred” theories. Researchers rarely reported having performed questionable research practices themselves—however they thought that other researchers sometimes (52.7% of responses) or often (27.9% of responses) perform them. Researchers near unanimously agreed that replication studies are important but too infrequently performed in animal cognition research, 73.0% of respondents suggested areas of animal cognition research could experience a ‘replication crisis’ if replication studies were performed. Consistently, participants’ free-text responses provided a nuanced picture of the challenges animal cognition research faces, which are available as part of an open dataset. However, many researchers appeared concerned with how to interpret negative results, publication bias, theoretical bias and reliability in areas of animal cognition research. Collectively, these data provide a candid overview of barriers to progress in animal cognition and can inform debates on how individual researchers, as well as organizations and journals, can facilitate robust scientific research in animal cognition.

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Section: Discussionmentioning

confidence: 99%

The hidden side of animal cognition research: Scientists’ attitudes toward bias, replicability and scientific practice

2021

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“…To perform the simulations we used the newly developed Learning Simulator [36], an open source software designed for simulating learning in animals and humans. The simulator is publicly available at https://www.learningsimulator.org/ where documentation and some example scripts can be found.…”

Section: Learning Simulatormentioning

confidence: 99%

Can associative learning be the general process for intelligent behavior in non-human animals?

Lind

Vinken

2021

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The general process- and adaptive specialization hypotheses represent two contrasting explanations for understanding intelligence in non-human animals. The general process hypothesis proposes that associative learning underlies all learning, whereas the adaptive specialization hypothesis suggests additional distinct learning processes required for intelligent behavior. Here, we use a selection of experimental paradigms commonly used in comparative cognition to explore these hypotheses. We tested if a novel computational model of associative learning — A-learning — could solve the problems presented in these tests. Results show that this formulation of associative learning suffices as a mechanism for general animal intelligence, without the need for adaptive specialization, as long as adequate motor- and perceptual systems are there to support learning. In one of the tests, however, the addition of a short-term trace memory was required for A-learning to solve that particular task. We further provide a case study showcasing the flexibility, and lack thereof, of associative learning, when looking into potential learning of self-control and the development of behavior sequences. From these simulations we conclude that the challenges do not so much involve the complexity of a learning mechanism, but instead lie in the development of motor- and perceptual systems, and internal factors that motivate agents to explore environments with some precision, characteristics of animals that have been fine-tuned by evolution for million of years.Author summaryWhat causes animal intelligence? One hypothesis is that, among vertebrates, intelligence relies upon the same general processes for both memory and learning. A contrasting hypothesis states that important aspects of animal intelligence come from species- and problem specific cognitive adaptations. Here, we use a recently formulated model of associative learning and subject it, through computer simulations, to a battery of tests designed to probe cognitive abilities in animals. Our computer simulations show that this associative learning model can account well for how animals learn these various tasks. We conclude that a major challenge in understanding animal and machine intelligence lies in describing behavior systems. Specifically, how motor flexibility and perceptual systems together with internal factors allow animals and machines to navigate the world. As a consequence of our results, together with current progress in both animal- and machine learning, we cannot reject the idea that associative learning provides a general process for animal intelligence.

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