LEADER 05117nam 2200565 450 001 9910137236003321 005 20230621135623.0 010 $a9782889193721 (ebook) 035 $a(CKB)3710000000506270 035 $a(WaSeSS)IndRDA00059216 035 $a(oapen)https://directory.doabooks.org/handle/20.500.12854/50581 035 $a(EXLCZ)993710000000506270 100 $a20160721d2015 uy | 101 0 $aeng 135 $aur||||||||||| 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 00$aIntrinsic motivations and open-ended development in animals, humans, and robots /$ftopic editors, Gianluca Baldassarre, Tom Stafford, Marco Mirolli, Peter Redgrave, Richard Michael Ryan and Andrew Barto 210 $cFrontiers Media SA$d2015 210 1$a[Lausanne, Switzerland] :$cFrontiers Media SA,$d2015. 215 $a1 online resource (350 pages) $cillustrations; digital, PDF file(s) 225 0 $aFrontiers Research Topics 320 $aIncludes bibliographical references. 330 $aThe aim of this Research Topic for Frontiers in Psychology under the section of Cognitive Science and Frontiers in Neurorobotics is to present state-of-the-art research, whether theoretical, empirical, or computational investigations, on open-ended development driven by intrinsic motivations. The topic will address questions such as: How do motivations drive learning? How are complex skills built up from a foundation of simpler competencies? What are the neural and computational bases for intrinsically motivated learning? What is the contribution of intrinsic motivations to wider cognition? Autonomous development and lifelong open-ended learning are hallmarks of intelligence. Higher mammals, and especially humans, engage in activities that do not appear to directly serve the goals of survival, reproduction, or material advantage. Rather, a large part of their activity is intrinsically motivated - behavior driven by curiosity, play, interest in novel stimuli and surprising events, autonomous goal-setting, and the pleasure of acquiring new competencies. This allows the cumulative acquisition of knowledge and skills that can later be used to accomplish fitness-enhancing goals. Intrinsic motivations continue during adulthood, and in humans artistic creativity, scientific discovery, and subjective well-being owe much to them. The study of intrinsically motivated behavior has a long history in psychological and ethological research, which is now being reinvigorated by perspectives from neuroscience, artificial intelligence and computer science. For example, recent neuroscientific research is discovering how neuromodulators like dopamine and noradrenaline relate not only to extrinsic rewards but also to novel and surprising events, how brain areas such as the superior colliculus and the hippocampus are involved in the perception and processing of events, novel stimuli, and novel associations of stimuli, and how violations of predictions and expectations influence learning and motivation. Computational approaches are characterizing the space of possible reinforcement learning algorithms and their augmentation by intrinsic reinforcements of different kinds. Research in robotics and machine learning is yielding systems with increasing autonomy and capacity for self-improvement: artificial systems with motivations that are similar to those of real organisms and support prolonged autonomous learning. Computational research on intrinsic motivation is being complemented by, and closely interacting with, research that aims to build hierarchical architectures capable of acquiring, storing, and exploiting the knowledge and skills acquired through intrinsically motivated learning. Now is an important moment in the study of intrinsically motivated open-ended development, requiring contributions and integration across a large number of fields within the cognitive sciences. This Research Topic aims to contribute to this effort by welcoming papers carried out with ethological, psychological, neuroscientific and computational approaches, as well as research that cuts across disciplines and approaches. 606 $aComputational neuroscience 606 $aAutonomous robots 610 $acomputational models 610 $aintrinsic motivations 610 $aautonomous robotics 610 $anovelty and surprise 610 $areview 610 $acumulative learning and development 610 $abrain and behavior 610 $areinforcement learning 615 0$aComputational neuroscience. 615 0$aAutonomous robots. 700 $aTom Stafford$4auth$01365345 702 $aDi Baldassarre$b Giuliano$f1978- 702 $aStafford$b Tom 702 $aMirolli$b Marco 702 $aRedgrave$b Peter 702 $aRyan$b Richard Michael 702 $aBarto$b Andrew 801 0$bWaSeSS 801 1$bWaSeSS 801 2$bUkMaJRU 912 $a9910137236003321 996 $aIntrinsic motivations and open-ended development in animals, humans, and robots$93387154 997 $aUNINA