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11 results for “task solving”
Review of Recent Trends in Measuring the Computing Systems Intelligence-igure 2. Intelligence of different living creature (accessed 01.11.2017). 2.1. A crow solving a complex task (https://www.disclose.tv/spooky-genius-crow-had-to-be-removed-from-scientific-experiment- 314886). 2.2. A group of dolphins with a social behaviour (http://www.sciencemag.org/news/2012/04/teamwork-builds-big-brains); 2.3. An orangutan that use a spear to fish (https://primatology.net/2008/04/29/orangutan-photographed-using-tool-as-spear-to-fish)
<p>Some species of birds have been shown capable of using different tools. Many studies consider the crows as very intelligent. Smirnova, Lazareva, and Zorina (2000) suggested that crows have some kind of numerical ability. Figure 2.1 presents a crow that uses a tool, a small stone in order to catch a worm from a glass of water.The dolphins in many studies are considered intelligent at the individual level. An advanced ability of dolphins is the self-awareness. Marten and Psarakos (1995) presented an interesting study based on self-view television to distinguish between self-examination and social behavior in the Bottlenose dolphin. The most well-known abilities of dolphins are to teach, learn and cooperate. Dolphins have a complex communication and social behaviour. Figure 2.2 presents the image of a common group of dolphins. Some studies prove that primates are one of the most intelligent in the class of animals (Reader, Hager, & Laland, 2011). Orangutans are one of the most intelligent primates. The ability of orangutans to use different types of tools in order to perform tasks is well-known. Figure 2.3 presents an orangutan that uses a spear to catch fish. The orangutans can be considered intelligent at individual level.</p>
Review of Recent Trends in Measuring the Computing Systems Intelligence-Figure 1. Intelligence of different simple living creature (accessed 01.11.2017). 1.1. A carnivorous plants catching an insect (https://phys.org/news/2016-05-colombia-peace-reveal-jungle-species.html); 1.2. A colony of ants solving a very complex task (https://mappingignorance.org/2016/05/27/rafting-ants); 1.3. The collective behaviour of a school of fish (https://simple.wikipedia.org/wiki/Shoaling_and_schooling)
<p>The biological intelligence of different life forms, ranging from very simple (such as plants) to very complex (such as humans) is the subject of many studies and a large amount of research. Frequent studies related to different kind of biological intelligence include: the intelligence of horses (Krueger, & Heinze, 2008; Krueger, Farmer, & Heinze, 2014; Schuetz, Farmer, & Krueger, 2016), intelligence of pigs (Broom, Sena, & Moynihan, 2009), intelligence of dogs (Coren, 1995), intelligence of primates (Reader, Hager, & Laland, 2011) and so one. Figures 1, 2, and 3 present some biological life forms that are frequently considered intelligent. Trewavas (2002; 2005) considered that plants intelligence should be based on principles such as their ability to adjust their morphology, and phenotype accordingly to ensure self- preservation and reproduction. Figure 1.1 presents an intelligent plant (carnivorous) that uses a strategy for catching very fast flying insects. In order to eat the insect, it makes a movement. Figure 1.1 presents the catching of an insect by a carnivorous plant. The intelligence of colonies of ants, termites and other insects that live in large colonies is considered at the colony level (Brady, Fisher, Schultz, & Ward, 2014; Johnson, Borowiec, Chiu, Lee, Atallah, & Ward, 2013). Figure 1.2 presents the coherent intelligent surviving behaviour of a colony of a species of ants. The ants make a structural reorganization in order to move on the surface of the water. Figure 1.3 presents a very large school of fish with an intelligent coherent collective feeding and self-protecting behaviour. Each individual fish has a very simple behavior. Based on this it cannot be considered intelligent. The intelligence in large schools of fish emerges at the collective level (Shaw, 1978; Parrish, Viscedo, & Grunbaum, 2002).</p>
Reinforcement learning theory reveals the cognitive requirements for solving the cleaner fish market task
<p>Learning is an adaptation that allows individuals to respond to environmental stimuli in ways that improve their reproductive outcomes. The degree of sophistication in learning mechanisms potentially explains variation in behavioural responses. Here, we present a model of learning that is inspired by documented intra- and interspecific variation in the performance in a simultaneous two-choice task, the 'biological market task'. The task presents a problem that cleaner fish often face in nature: the decision of choosing between two client types; one that is willing to wait for inspection and one that may leave if ignored. The cleaners' choice hence influences the future availability of clients, i.e. it influences food availability. We show that learning the preference that maximizes food intake requires subjects to represent in their memory different combinations of pairs of client types rather than just individual client types. In addition, subjects need to account for future consequences of actions, either by estimating expected long-term reward or by experiencing a client leaving as a penalty (negative reward). Finally, learning is influenced by the absolute and relative abundance of client types. Thus, cognitive mechanisms and ecological conditions jointly explain intra and interspecific variation in the ability to learn the adaptive response.</p>
Data for: Representation of rewards differing in their hedonic valence in the caudate nucleus correlates with the performance in a problem-solving task in dogs (Canis familiaris)
<p>Abstract</p> <p>We have investigated dogs’ (<em>Canis familiaris</em>) abilities in associating different sounds with food rewards of different incentive value. The establishment of the association was tested in a problem-solving behavioural paradigm, as well as in an fMRI study on the same subjects (N=20). The aim was to show behavioural, as well as parallel neural effects of the association formation between the two sounds and two different associated food rewards.<br> The latency of solving the problem was considered as an indicator of motivational state. In our behaviour study we found that dogs were quicker in solving a problem upon hearing the sound associated with food higher in reward value, suggesting that they have successfully associated the sounds with the corresponding food value. In the fMRI study, the cerebral response to the two sounds was compared both before and after the associative training. Two bilateral regions of interest were explored: the caudate nucleus and the amygdala. After the associative training the response in the caudate nucleus was higher to the sound related to a higher reward value food than to the sound related to a lower reward value food, which difference was not present before the associative training. We found an increase in the amygdala response to both sounds after the training. In a whole-brain representational similarity analysis, we found that cerebral patterns in the caudate nucleus to the two sounds were different only after the training. Moreover, we found a positive correlation between the dissimilarity index in the caudate nucleus for activation responses to the two sounds and the difference in latencies to solve the behavioural task: the quicker the dog solved the behavioural task the greater the difference in the neural representation of the two sounds was. In summary, family dogs’ brain activation patterns reflected their expectations based on what they learned about the relationship between two sounds and their associated rewards.</p> <p>This dataset contains</p> <ul> <li>Raw data (four functional runs n = 20)</li> <li>Dog brain template</li> <li>ROI results (Caudate nucleus and amygdala percentage of BOLD signal change and caudate nucleus response to sounds during the two post-training runs adding or not the Inter-scan interval as a covariate in the GLM model n = 20,)</li> <li>RSA results ( Dissimilarity change between sounds (post-training > pre-training) and dissimilarity index per participant n = 20)</li> </ul>
Reinforcement learning theory reveals the cognitive requirements for solving the cleaner fish market task
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Data from: Female zebra finches prefer the songs of males who quickly solve a novel foraging task to the songs of males unable to solve the task
Correlative evidence suggests that high problem-solving and foraging abilities in a mate are associated with direct fitness advantages, so it would benefit females to prefer problem-solving males. Recent work has also shown that females of several bird species who directly observe males prefer those that can solve a novel foraging task over those that cannot. In addition to or instead of direct observation of cognitive skills, many species utilize assessment signals when choosing a mate. Here we test whether females can select a problem-solving male over a non-solving male when presented only with a signal known to be used in mate assessment: song. Using an operant conditioning assay, we compared female zebra finch (Taeniopygia guttata) preference for the songs of males that could quickly solve a novel foraging task to the songs of males that could not solve the task. Females were never housed with the test subject males whose song they heard, and the only information provided about the males was their song. We found that females elicited more songs of problem-solving males than of non-solvers, indicating that song may contain information about a male's ability to solve a novel foraging task and that naïve females prefer the songs of problem-solving males.
Data from: The effects of tradition on problem solving by two wild populations of bearded capuchin monkeys in a probing task
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Data from: Female zebra finches prefer the songs of males who quickly solve a novel foraging task to the songs of males unable to solve the task
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Evidence that bottlenose dolphins can communicate with vocal signals to solve a cooperative task
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Behaviour-Based Approach for Solving a Sparse Swarm Environment Exploration Task
<p>Videos to Behaviour-Based Approach for Solving a Sparse Swarm Environment Exploration Task.</p>
Data from: Goats display audience-dependent human-directed gazing behaviour in a problem-solving task
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Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.