Skip to main content Accessibility help

We use cookies to distinguish you from other users and to provide you with a better experience on our websites. Close this message to accept cookies or find out how to manage your cookie settings.

Close cookie message
×
Hostname: page-component-745bb68f8f-cphqk Total loading time: 0 Render date: 2025-01-12T08:00:39.007Z Has data issue: false hasContentIssue false

8 - Attention, Information-Seeking, and Active Sampling

Empirical Evidence and Applications for Learning

from Part II - Rewards, Incentives, and Choice

Published online by Cambridge University Press:  15 February 2019

K. Ann Renninger  and
Suzanne E. Hidi
By
Avanti Dey  and
Jacqueline Gottlieb
K. Ann Renninger
Affiliation:
Swarthmore College, Pennsylvania
Suzanne E. Hidi
Affiliation:
University of Toronto
Get access

Summary

In this chapter, we present an overview of the literature addressing the neuroscience of attention, information-seeking, and active sampling, and we discuss its potential significance for learning and learning progress. First, we review the emerging hypothesis that attention is an active mechanism for information sampling and exploration in the environment. We then turn to a discussion of how reward motivates attention and how attention can be employed to reduce uncertainty about knowledge of one's current state. We further consider the way rewards interact with other factors (including novelty, surprise, and task relevance). Throughout the review, we particularly focus on the distinction between extrinsic and intrinsic motivation, highlighting curiosity as a key example of the latter in motivating the search for intrinsically desirable information that benefits learning on both long and short timescales. Finally, we discuss the role of cognitive control in directing attention during learning, as well as the way neural systems underlying cognition and motivation have implications for informing techniques for teaching and learning in wider educational contexts.

Type
Chapter
Information
The Cambridge Handbook of Motivation and Learning , pp. 183 - 208
Publisher: Cambridge University Press
Print publication year: 2019

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

Abuhamdeh, S. & Csikszentmihalyi, M. (2012). The importance of challenge for the enjoyment of intrinsically motivated, goal-directed activities. Personality and Social Psychology Bulletin, 38(3), 317–30. doi: 10.1177/0146167211427147.CrossRefGoogle ScholarPubMed
Adcock, R. A., Thangavel, A., Whitfield-Gabrieli, S., Knutson, B., & Gabrieli, J. D. (2006). Reward-motivated learning: Mesolimbic activation precedes memory formation. Neuron, 50(3), 507–17. doi: 10.1016/j.neuron.2006.03.036.Google Scholar
Anderson, B. A., Laurent, P. A., & Yantis, S. (2011). Value-driven attentional capture. Proceedings of the National Academy of Sciences of the United States of America, 108(25), 10367–71. doi: 10.1073/pnas.1104047108.Google ScholarPubMed
Baddeley, A. D. (1986). Working memory. Oxford: Oxford University Press.Google Scholar
Baldassarre, G. & Mirolli, M. (2013). Intrinsically motivated learning systems: An overview. In Intrinsically motivated learning in natural and artificial systems (pp. 1–14). Berlin Heidelberg: Springer. doi: 10.1007/978-3-642-32375-1_1.Google Scholar
Baldi, P. & Itti, L. (2010). Of bits and wows: A Bayesian theory of surprise with applications to attention. Neural Networks, 23(5), 649–66. doi: 10.1016/j.neunet.2009.12.007.Google Scholar
Baranes, A. & Oudeyer, P. Y. (2013). Active learning of inverse models with intrinsically motivated goal exploration in robots. Robotics and Autonomous Systems, 61(1), 49–73. doi: 10.1016/j.robot.2012.05.00.Google Scholar
Baranes, A., Oudeyer, P. Y., & Gottlieb, J. (2015). Eye movements reveal epistemic curiosity in human observers. Vision Research, 117, 81–90. doi: 10.1016/j.visres.2015.10.009.Google Scholar
Barto, A. G. (2013). Intrinsic motivation and reinforcement learning. In Intrinsically motivated learning in natural and artificial systems (pp. 17–47). Berlin Heidelberg: Springer. doi: 10.1007/978-3-642-32375-1_2.CrossRefGoogle Scholar
Barto, A., Mirolli, M., & Baldassarre, G. (2013). Novelty or surprise? Frontiers in Psychology, 4. doi: 10.3389/fpsyg.2013.00907.Google Scholar
Beck, J. M., Ma, W. J., Kiani, R., Hanks, T., Churchland, A. K., Roitman, J., ... Pouget, A. (2008). Probabilistic population codes for Bayesian decision making. Neuron, 60(6), 1142–52. doi: 10.1016/j.neuron.2008.09.021.Google Scholar
Berlyne, D. E. (1960). Conflict, arousal, and curiosity. McGraw-Hill.Google Scholar
Berridge, K. C. & Kringelbach, M. L. (2008). Affective neuroscience of pleasure: Reward in humans and animals. Psychopharmacology, 199(3), 457–80. doi: 10.1007/s00213-008-1099-6.CrossRefGoogle ScholarPubMed
Bialek, W., Nemenman, I., & Tishby, N. (2001). Predictability, complexity, and learning. Neural Computation, 13(11), 2409–63. doi: 10.1162/089976601753195969.Google Scholar
Blake, A. & Yuille, A. (1992). Active vision. Cambridge, MA: MIT Press.Google Scholar
Blanchard, T. C., Hayden, B. Y., & Bromberg-Martin, E. S. (2015). Orbitofrontal cortex uses distinct codes for different choice attributes in decisions motivated by curiosity. Neuron, 85(3), 602–14. doi: 10.1016/j.neuron.2014.12.050.Google Scholar
Botvinick, M. M., Braver, T. S., Barch, D. M., Carter, C. S., & Cohen, J. D. (2001). Conflict monitoring and cognitive control. Psychological Review, 108(3), 624. doi: 10.1037/0033-295X.108.3.624.Google Scholar
Britten, K. H., Shadlen, M. N., Newsome, W. T., & Movshon, J. A. (1992). The analysis of visual motion: A comparison of neuronal and psychophysical performance. Journal of Neuroscience, 12(12), 4745–65.Google Scholar
Brockmole, J. R. & Henderson, J. M. (2005a). Object appearance, disappearance, and attention prioritization in real-world scenes. Psychonomic Bulletin & Review, 12(6), 1061–7. doi: 10.3758/BF03206444.Google Scholar
Brockmole, J. R. & Henderson, J. M. (2005b). Prioritization of new objects in real-world scenes: Evidence from eye movements. Journal of Experimental Psychology–Human Perception and Performance, 31(5), 857–68. doi: 10.1037/0096-1523.31.5.857.Google Scholar
Bromberg-Martin, E. S. & Hikosaka, O. (2009). Midbrain dopamine neurons signal preference for advance information about upcoming rewards. Neuron, 63(1), 119–26. doi: 10.1016/j.neuron.2009.06.009.Google Scholar
Buschman, T. J. & Miller, E. K. (2007). Top-down versus bottom-up control of attention in the prefrontal and posterior parietal cortices. Science, 315(5820), 1860–2.CrossRefGoogle ScholarPubMed
Castro, D. C. & Berridge, K. C. (2014). Advances in the neurobiological bases for food “liking” versus “wanting”. Physiology & Behavior, 136, 22–30. doi: 10.1126/science.1138071.Google Scholar
Chiba, A. A., Bucci, D. J., Holland, P. C., & Gallagher, M. (1995). Basal forebrain cholinergic lesions disrupt increments but not decrements in conditioned stimulus processing. Journal of Neuroscience, 15(11), 7315–22.Google Scholar
Csikszentmihalyi, M. (1997). Flow and the psychology of discovery and invention. New York, NY: Harper Perennial.Google Scholar
Daddaoua, N., Lopes, M., & Gottlieb, J. (2016). Intrinsically motivated oculomotor exploration guided by uncertainty reduction and conditioned reinforcement in non-human primates. Scientific Reports, 6. doi: 10.1038/srep20202.Google Scholar
Dalley, J. W., McGaughy, J., O'Connell, M. T., Cardinal, R. N., Levita, L., & Robbins, T. W. (2001). Distinct changes in cortical acetylcholine and noradrenaline efflux during contingent and noncontingent performance of a visual attentional task. Journal of Neuroscience, 21(13), 4908–14.Google Scholar
Dayan, P. & Daw, N. D. (2008). Decision theory, reinforcement learning, and the brain. Cognitive, Affective, & Behavioral Neuroscience, 8(4), 429–53. doi: 10.3758/cabn.8.4.429.Google Scholar
Dayan, P., Niv, Y., Seymour, B., & Daw, N. D. (2006). The misbehavior of value and the discipline of the will. Neural Networks, 19(8), 1153–60. doi: 10.1016/j.neunet.2006.03.002.Google Scholar
Della Libera, C. & Chelazzi, L. (2009). Learning to attend and to ignore is a matter of gains and losses. Psychological Science, 20(6), 778–84. doi: 10.1111/j.1467-9280.2009.02360.x.Google Scholar
Dosenbach, N. U., Fair, D. A., Cohen, A. L., Schlaggar, B. L., & Petersen, S. E. (2008). A dual-networks architecture of top-down control. Trends in Cognitive Sciences, 12(3), 99–105. doi: 10.1016/j.tics.2008.01.001.Google Scholar
Everitt, B. J. & Robbins, T. W. (1997). Central cholinergic systems and cognition. Annual Review of Psychology, 48(1), 649–84. doi: 10.1146/annurev.psych.48.1.649.Google Scholar
Falkenstein, M., Koshlykova, N. A., Kiroj, V. N., Hoormann, J., & Hohnsbein, J. (1995). Late ERP components in visual and auditory Go/Nogo tasks. Electroencephalography and Clinical Neurophysiology/Evoked Potentials Section, 96(1), 36–43. doi: 10.1016/0013-4694(94)00182-k.Google Scholar
Flagel, S. B., Clark, J. J., Robinson, T. E., Mayo, L., Czuj, A., Willuhn, I., ... Akil, H., (2011). A selective role for dopamine in reward learning. Nature, 469(7328), 53. doi: 10.1038/nature09588.Google Scholar
Foley, N. C., Jangraw, D. C., Peck, C., & Gottlieb, J. (2014). Novelty enhances visual salience independently of reward in the parietal lobe. Journal of Neuroscience, 34(23), 7947–57. doi: 10.1523/jneurosci.4171-13.2014.Google Scholar
Foley, N. C., Kelly, S. P., Mhatre, H., Lopes, M., & Gottlieb, J. (2017). Parietal neurons encode expected gains in instrumental information. Proceedings of the National Academy of Sciences of the United States of America, 114(16), E3315-E3323.Google Scholar
Forestier, S. & Oudeyer, P. Y. (2016). Curiosity-driven development of tool use precursors: A computational model. In 38th Annual Conference of the Cognitive Science Society (CogSci 2016) (pp. 1859–64).Google Scholar
Friedman-Hill, S. R., Robertson, L. C., Desimone, R., & Ungerleider, L. G. (2003). Posterior parietal cortex and the filtering of distractors. Proceedings of the National Academy of Sciences of the United States of America, 100(7), 4263–8. doi: 10.1073/pnas.0730772100.Google Scholar
Friston, K., Schwartenbeck, P., FitzGerald, T., Moutoussis, M., Behrens, T., & Dolan, R. (2013). The anatomy of choice: active inference and agency. Frontiers in Human Neuroscience, 7. doi: 10.3389/fnhum.2013.00598.Google Scholar
Gehring, W. J., Goss, B., Coles, M. G., Meyer, D. E., & Donchin, E. (1993). A neural system for error detection and compensation. Psychological Science, 4(6), 385–90. doi: 10.1111/j.1467-9280.1993.tb00586.x.Google Scholar
Gehring, W. J. & Knight, R. T. (2002). Lateral prefrontal damage affects processing selection but not attention switching. Cognitive Brain Research, 13(2), 267–79. doi: 10.1016/s0926-6410(01)00132-x.Google Scholar
Germain, C. M. & Hess, T. M. (2007). Motivational influences on controlled processing: Moderating distractibility in older adults. Aging, Neuropsychology, and Cognition, 14(5), 462–86. doi: 10.1080/13825580600611302.Google Scholar
Gold, J. I. & Shadlen, M. N. (2007). The neural basis of decision making. Annual Review of Neuroscience, 30, 535–74. doi: 10.1146/annurev.neuro.29.051605.113038.Google Scholar
Goldberg, M. E., Bisley, J. W., Powell, K. D., & Gottlieb, J. (2006). Saccades, salience and attention: the role of the lateral intraparietal area in visual behavior. Progress in Brain Research, 155, 157–75. doi: 10.1016/S0079-6123(06)55010-1.Google Scholar
Gottlieb, J. (2012). Attention, learning, and the value of information. Neuron, 76(2), 281–95. doi: 10.1016/j.neuron.2012.09.034.CrossRefGoogle ScholarPubMed
Gottlieb, J. (2018). Understanding active sampling strategies: empirical approaches and implications for attention and decision research. Cortex, 102, 150–60. doi: 10.1016/j.cortex.2017.08.019.Google Scholar
Gottlieb, J. & Balan, P. (2010). Attention as a decision in information space. Trends in Cognitive Sciences, 14(6), 240–8. doi: 10.1016/j.tics.2010.03.001.Google Scholar
Gottlieb, J. & Goldberg, M. E. (1999). Activity of neurons in the lateral intraparietal area of the monkey during an antisaccade task. Nature Neuroscience, 2(10), 906–13. doi: 10.1038/13209.Google Scholar
Gottlieb, J., Hayhoe, M., Hikosaka, O., & Rangel, A. (2014). Attention, reward, and information seeking. Journal of Neuroscience, 34(46), 15497–504. doi: 10.1523/JNEUROSCI.3270-14.2014.Google Scholar
Gottlieb, J., Lopes, M., & Oudeyer, P. Y. (2016). Motivated cognition: Neural and computational mechanisms of curiosity, attention, and intrinsic motivation. In Kim, S., Reeve, J. & Bong, M. (Eds.), Advances in motivation and achievement: Recent developments in neuroscience research on human motivation (pp. 149–72). Bingley: Emerald Group Publishing.Google Scholar
Gottlieb, J., Oudeyer, P. Y., Lopes, M., & Baranes, A. (2013). Information-seeking, curiosity, and attention: computational and neural mechanisms. Trends in Cognitive Sciences, 17(11), 585–93. doi: 10.1016/j.tics.2013.09.001.Google Scholar
Grossnickle, E. M. (2015). The expression and enactment of interest and curiosity in a multiple source use task (Doctoral dissertation). University of Maryland, College Park.Google Scholar
Gruber, M. J., Gelman, B. D., & Ranganath, C. (2014). States of curiosity modulate hippocampus-dependent learning via the dopaminergic circuit. Neuron, 84(2), 486–96. doi: 10.1016/j.neuron.2014.08.060.Google Scholar
Hickey, C., Chelazzi, L., & Theeuwes, J. (2010). Reward changes salience in human vision via the anterior cingulate. Journal of Neuroscience. 30, 11096–103. doi: 10.1523/JNEUROSCI.1026-10.2010.Google Scholar
Hidi, S. (1990). Interest and its contribution as a mental resource for learning. Review of Educational Research, 60(4), 549–71. doi: 10.3102/00346543060004549.Google Scholar
Hidi, S. (2016). Revisiting the role of rewards in motivation and learning: Implications of neuroscientific research. Educational Psychology Review, 28(1), 61–93. doi: 10.1007/s10648-015-9307-5.CrossRefGoogle Scholar
Horvitz, J. C. (2000). Mesolimbocortical and nigrostriatal dopamine responses to salient non-reward events. Neuroscience, 96(4), 651–6. doi: 10.1016/S0306-4522(00)00019-1.Google Scholar
Isaacowitz, D. M., Wadlinger, H. A., Goren, D., & Wilson, H. R. (2006). Selective preference in visual fixation away from negative images in old age? An eye-tracking study. Psychology and Aging, 21(1), 40. doi: 10.1037/0882-7974.21.1.40.Google Scholar
Itti, L. & Baldi, P. (2009). Bayesian surprise attracts human attention. Vision Research, 49(10), 1295–306. doi: 10.1016/j.visres.2008.09.007.Google Scholar
Johnson, L., Sullivan, B., Hayhoe, M., & Ballard, D. (2014). Predicting human visuomotor behavior in a driving task. Philosophical Transactions of the Royal Society B: Biological Sciences, 369(1636), 20130044. doi: 10.1098/rstb.2013.0044.Google Scholar
Kable, J. W. & Glimcher, P. W. (2009). The neurobiology of decision: consensus and controversy. Neuron, 63(6), 733–45. doi: 10.1016/j.neuron.2009.09.003.Google Scholar
Kaelbling, L. P., Littman, M. L., & Cassandra, A. R. (1998). Planning and acting in partially observable stochastic domains. Artificial Intelligence, 101(1), 99–134. doi: 10.1016/s0004-3702(98)00023-x.Google Scholar
Kakade, S. & Dayan, P. (2002). Dopamine: Generalization and bonuses. Neural Networks, 15(4), 549–59. doi: 10.1016/s0893-6080(02)00048-5.Google Scholar
Kang, M. J., Hsu, M., Krajbich, I. M., Loewenstein, G., McClure, S. M., Wang, J. T. Y., & Camerer, C. F. (2009). The wick in the candle of learning: Epistemic curiosity activates reward circuitry and enhances memory. Psychological Science, 20(8), 963–73. doi: 10.2139/ssrn.1308286.Google Scholar
Kaplan, F. & Oudeyer, P.-Y. (2007). In search of the neural circuits of intrinsic motivation. Frontiers in Neuroscience, 1, 17Google Scholar
Kaplan, F. & Oudeyer, P.-Y. (2011). From hardware and software to kernels and envelopes: A concept shift for robotics, developmental psychology, and brain sciences. In Krichmar, J. L. & Wagatsuma, H. (Eds.), Neuromorphic and brain-based robots (pp. 217–50). Cambridge: Cambridge University Press. doi: 10.1017/cbo9780511994838.011.Google Scholar
Keller, J. & Bless, H. (2008). Flow and regulatory compatibility: An experimental approach to the flow model of intrinsic motivation. Personality and Social Psychology Bulletin, 34(2), 196–209. doi: 10.1177/0146167207310026.Google Scholar
Laurent, P. A. (2008). The emergence of saliency and novelty responses from reinforcement learning principles. Neural Networks, 21(10), 1493–9. doi: 10.1016/j.neunet.2008.09.004.Google Scholar
Litman, J. A. (2005). Curiosity and the pleasures of learning: Wanting and liking new information. Cognition & Emotion, 19(6), 793–814. doi: 10.1080/026999 30541000101.Google Scholar
Litman, J. A. (2007). Curiosity as a feeling of interest and feeling of deprivation: The I/D model of curiosity. Issues in the Psychology of Motivation, 149–56. doi: 10.1037/t27877-000.Google Scholar
Litman, J. A. (2008). Interest and deprivation factors of epistemic curiosity. Personality and Individual Differences, 44(7), 1585–95. doi: 10.1016/j.paid.2008.01.014.Google Scholar
Loewenstein, G. (1994). The psychology of curiosity: A review and reinterpretation. Psychological Bulletin, 116(1), 75. doi: 10.1037//0033-2909.116.1.75.Google Scholar
Lopes, M. & Oudeyer, P. Y. (2012). The strategic student approach for life-long exploration and learning. In 2012 IEEE International Conference on Development and Learning and Epigenetic Robotics (ICDL) (pp. 1–8). IEEE. doi: 10.1109/devlrn.2012.6400807.Google Scholar
Mather, M. & Carstensen, L. L. (2003). Aging and attentional biases for emotional faces. Psychological Science, 14(5), 409–15. doi: 10.1111/1467-9280.01455.Google Scholar
Maunsell, J. H. (2004). Neuronal representations of cognitive state: reward or attention? Trends in Cognitive Sciences, 8(6), 261–5. doi: 10.1016/j.tics.2004.04.003.Google Scholar
Mazurek, M. E., Roitman, J. D., Ditterich, J., & Shadlen, M. N. (2003). A role for neural integrators in perceptual decision making. Cerebral Cortex, 13(11), 1257–69. doi: 10.1093/cercor/bhg097.Google Scholar
Moulin-Frier, C. & Oudeyer, P. Y. (2012). Curiosity-driven phonetic learning. In 2012 IEEE International Conference on Development and Learning and Epigenetic Robotics (ICDL) (pp. 1–8). IEEE. doi: 10.1109/devlrn.2012.6400583.Google Scholar
Mullaney, K. M., Carpenter, S. K., Grotenhuis, C., & Burianek, S. (2014). Waiting for feedback helps if you want to know the answer: The role of curiosity in the delay-of-feedback benefit. Memory & Cognition, 42(8), 1273–84. doi: 10.3758/s13421-014-0441-y.Google Scholar
Najemnik, J. & Geisler, W. S. (2005). Optimal eye movement strategies in visual search. Nature, 434(7031), 387. doi: 10.1016/j.ajo.2005.04.009.Google Scholar
Navalpakkam, V. & Itti, L. (2005). Modeling the influence of task on attention. Vision Research, 45(2), 205–31. doi: 10.1016/j.visres.2004.07.042.Google Scholar
Ngo, H., Luciw, M., Forster, A., & Schmidhuber, J. (2012). Learning skills from play: artificial curiosity on a katana robot arm. In 2012 International Joint Conference on Neural Networks (IJCNN) (pp. 1–8). IEEE. doi: 10.1109/ijcnn.2012.6252824.Google Scholar
Nguyen, S.M. & Oudeyer, P. Y. (2013). Socially guided intrinsic motivation for robot learning of motor skills. Autonomous Robots, 36(3), 273–94. doi: 10.1007/s10514-013-9339-y.Google Scholar
Norman, D. A. & Shallice, T. (1986). Attention to action. In Consciousness and self-regulation (pp. 1–18). NYC, NY: Springer US. doi: 10.1007/978-1-4757-0629-1_1.Google Scholar
Oudeyer, P. Y., Baranes, A., & Kaplan, F. (2013). Intrinsically motivated learning of real-world sensorimotor skills with developmental constraints. In M. Mirolli & G. Baldassarre (Eds.), Intrinsically motivated learning in natural and artificial systems (pp. 303–65). Berlin Heidelberg: Springer. doi: 10.1007/978-3-642-32375-1_13.Google Scholar
Oudeyer, P. Y., Gottlieb, J., & Lopes, M. (2016). Intrinsic motivation, curiosity, and learning: Theory and applications in educational technologies. Progress in Brain Research, 229, 257–84. doi: 10.1016/bs.pbr.2016.05.005.Google Scholar
Oudeyer, P. Y. & Kaplan, F. (2006). Discovering communication. Connection Science, 18(2), 189–206. doi: 10.1080/09540090600768567.Google Scholar
Oudeyer, P. Y., Kaplan, F., & Hafner, V. V. (2007). Intrinsic motivation systems for autonomous mental development. IEEE Transactions on Evolutionary Computation, 11(2), 265–86. doi: 10.1109/TEVC.2006.890271.Google Scholar
Pape, L., Oddo, C. M., Controzzi, M., Cipriani, C., Förster, A., Carrozza, M. C. & Schmidhuber, J. (2012). Learning tactile skills through curious exploration. Frontiers in Neurorobotics, 6. doi: 10.3389/fnbot.2012.00006.Google Scholar
Payzan-LeNestour, E. & Bossaerts, P. (2011). Risk, unexpected uncertainty, and estimation uncertainty: Bayesian learning in unstable settings. PLoS Computational Biology, 7(1), e1001048. doi: 10.1371/journal.pcbi.1001048.Google Scholar
Peck, C. J., Jangraw, D. C., Suzuki, M., Efem, R., & Gottlieb, J. (2009). Reward modulates attention independently of action value in posterior parietal cortex. Journal of Neuroscience, 29(36), 11182–91. doi: 10.1523/jneurosci.1929-09.2009.Google Scholar
Pessoa, L., Kastner, S., & Ungerleider, L. G. (2003). Neuroimaging studies of attention: From modulation of sensory processing to top-down control. Journal of Neuroscience, 23(10), 3990–8. doi: 0270-6474/03/233990-09.00/0.Google Scholar
Platt, M. L. & Glimcher, P. W. (1999). Neural correlates of decision variables in parietal cortex. Nature, 400(6741), 233.Google Scholar
Posner, M. I. (1994). Attention: The mechanisms of consciousness. Proceedings of the National Academy of Sciences of the United States of America, 91(16), 7398–403. doi: 10.1073/pnas.91. 16.7398.Google Scholar
Posner, M. I. & Dehaene, S. (1994). Attentional networks. Trends in Neurosciences, 17(2), 75–9. doi: 10.1016/0166-2236(94)90078-7.Google Scholar
Renninger, K. A. & Hidi, S. (2011). Revisiting the conceptualization, measurement, and generation of interest. Educational Psychologist, 46(3), 168–84. doi: 10.1080/00461520.2011.587723.Google Scholar
Renninger, K. A. & Hidi, S. & Hidi, S. (2016). The power of interest for motivation and learning. New York, NY; Routledge.Google Scholar
Renninger, L. W., Verghese, P., & Coughlan, J. (2007). Where to look next? Eye movements reduce local uncertainty. Journal of Vision, 7(3), 6. doi: 10.1167/7.3.6.Google Scholar
Risko, E. F., Anderson, N. C., Lanthier, S., & Kingstone, A. (2012). Curious eyes: Individual differences in personality predict eye movement behavior in scene-viewing. Cognition, 122(1), 86–90. doi: 10.1016/j.cognition.2011.08.014.Google Scholar
Ryan, R. M. & Deci, E. L. (2000). Intrinsic and extrinsic motivations: Classic definitions and new directions. Contemporary Educational Psychology, 25(1), 54–67. doi: 10.1006/ceps.1999.1020.Google Scholar
Sarter, M., Gehring, W. J., & Kozak, R. (2006). More attention must be paid: The neurobiology of attentional effort. Brain Research Reviews, 51(2), 145–60. doi: 10.1016/j.brainresrev.2005.11.002.Google Scholar
Satterthwaite, T. D., Ruparel, K., Loughead, J., Elliot, M. A., Gerraty, R. T., Calkins, M. E., ... Wolf, D. H. (2012). Being right is its own reward: load and performance related ventral striatum activation to correct responses during a working memory task in youth. Neuroimage, 61(3), 723–29.Google Scholar
Schmidhuber, J. (2006). Developmental robotics, optimal artificial curiosity, creativity, music, and the fine arts. Connection Science, 18(2), 173–87. doi: 10.1080/09540090600768658.CrossRefGoogle Scholar
Schmidhuber, J. (2013). Maximizing fun by creating data with easily reducible subjective complexity. In Mirolli, M. & Baldassarre, G. (Eds.), Intrinsically motivated learning in natural and artificial systems (pp. 95–128). Berlin Heidelberg: Springer. doi: 10.1007/978-3-642-32375-1_5.Google Scholar
Singh, S., James, M. R., & Rudary, M. R. (2004). Predictive state representations: A new theory for modeling dynamical systems. In Proceedings of the 20th Conference on Uncertainty in Artificial Intelligence (pp. 512–19). AUAI Press.Google Scholar
Singh, S., Lewis, R. L., Barto, A. G., & Sorg, J. (2010). Intrinsically motivated reinforcement learning: An evolutionary perspective. IEEE Transactions on Autonomous Mental Development, 2(2), 70–82. doi: 10.1109/TAMD.2010.2051031.Google Scholar
Srivastava, R. K., Steunebrink, B. R., & Schmidhuber, J. (2013). First experiments with PowerPlay. Neural Networks, 41, 130–36. doi: 10.1016/j.neunet.2013.01.022.Google Scholar
Steels, L. (2004). The autotelic principle. Lecture Notes in Computer Science, (3139), 231–42. doi: 10.1007/978-3-540-27833-7_17.Google Scholar
Stuss, D. T., Shallice, T., Alexander, M. P., & Picton, T. W. (1995). A multidisciplinary approach to anterior attentional functions. Annals of the New York Academy of Sciences, 769(1), 191–212. doi: 10.1111/j.1749-6632.1995.tb38140.x.Google Scholar
Sugrue, L. P., Corrado, G. S., & Newsome, W. T. (2005). Choosing the greater of two goods: neural currencies for valuation and decision making. Nature Reviews. Neuroscience, 6(5), 363. doi: 10.1038/nrn1666.Google Scholar
Sullivan, B. T., Johnson, L., Rothkopf, C. A., Ballard, D., & Hayhoe, M. (2012). The role of uncertainty and reward on eye movements in a virtual driving task. Journal of Vision, 12(13), 19. doi: 10.1167/12.13.19.Google Scholar
Tatler, B. W., Hayhoe, M. M., Land, M. F., & Ballard, D. H. (2011). Eye guidance in natural vision: Reinterpreting salience. Journal of Vision, 11(5), 5. doi: 10.1167/11.5.5.Google Scholar
Thorndike, E. L. (1911). Animal intelligence. New York: Macmillan.Google Scholar
Tsotsos, J. K. (2011). A computational perspective on visual attention. Cambridge, MA: MIT Press. doi: 10.7551/mitpress/9780262015417.001.0001.Google Scholar
Ullsperger, M. & von Cramon, D. Y. (2004). Neuroimaging of performance monitoring: Error detection and beyond. Cortex, 40(4), 593–604. doi: 10.1016/s0010-9452(08)70155-2.Google Scholar
van Duijvenvoorde, A. C., Peters, S., Braams, B. R., & Crone, E. A. (2016). What motivates adolescents? Neural responses to rewards and their influence on adolescents’ risk taking, learning, and cognitive control. Neuroscience & Biobehavioral Reviews, 70, 135–47. doi: 10.1016/j.neubiorev.2016.06.037.Google Scholar
Vossel, S., Mathys, C., Daunizeau, J., Bauer, M., Driver, J., & Friston, K. J., & Stephan, K. E. (2013). Spatial attention, precision, and Bayesian inference: A study of saccadic response speed. Cerebral Cortex, 24(6), 1436–450. doi: 10.1093/cercor/bhs418.Google Scholar
Vossel, S., Mathys, C., Stephan, K. E., & Friston, K. J. (2015). Cortical coupling reflects Bayesian belief updating in the deployment of spatial attention. Journal of Neuroscience, 35(33), 11532–42. doi: 10.1523/jneurosci.1382-15.2015.Google Scholar
Vossel, S., Thiel, C. M., & Fink, G. R. (2006). Cue validity modulates the neural correlates of covert endogenous orienting of attention in parietal and frontal cortex. Neuroimage, 32(3), 1257–64. doi: 10.1016/j.neuroimage.2006.05.019.Google Scholar
Wang, X. J. (2008). Decision making in recurrent neuronal circuits. Neuron, 60(2), 215–34. doi: 10.1016/j.neuron.2008.09.034.Google Scholar
Wise, R. A. (2004). Dopamine, learning and motivation. Nature Reviews Neuroscience, 5(6), 483.Google Scholar
Wittmann, B. C., Bunzeck, N., Dolan, R. J., & Düzel, E. (2007). Anticipation of novelty recruits reward system and hippocampus while promoting recollection. Neuroimage, 38(1), 194–202. doi: 10.1016/j.neuroimage.2007.06.038.Google Scholar
Wittmann, B. C., Daw, N. D., Seymour, B., & Dolan, R. J. (2008). Striatal activity underlies novelty-based choice in humans. Neuron, 58(6), 967–73. doi: 10.1016/j.neuron.2008.04.027.Google Scholar
Yang, H., Chen, X., & Zelinsky, G. J. (2009). A new look at novelty effects: Guiding search away from old distractors. Attention, Perception, & Psychophysics, 71(3), 554–64. doi: 10.3758/app.71.3.554.Google Scholar
Yang, S. C. H., Lengyel, M., & Wolpert, D. M. (2016). Active sensing in the categorization of visual patterns. eLife, 5, e12215. doi: 10.7554/elife.25660.CrossRefGoogle ScholarPubMed
Yang, T. & Shadlen, M. N. (2007). Probabilistic reasoning by neurons. Nature, 447(7148), 1075. doi: 10.1038/nature05852.Google Scholar
Yu, A.J. & Dayan, P. (2005). Uncertainty, neuromodulation, and attention. Neuron, 46(4), 681–92. doi: 10.1016/j.neuron.2005.04.026.Google Scholar

Save book to Kindle

To save this book to your Kindle, first ensure [email protected] is added to your Approved Personal Document E-mail List under your Personal Document Settings on the Manage Your Content and Devices page of your Amazon account. Then enter the ‘name’ part of your Kindle email address below. Find out more about saving to your Kindle.

Note you can select to save to either the @free.kindle.com or @kindle.com variations. ‘@free.kindle.com’ emails are free but can only be saved to your device when it is connected to wi-fi. ‘@kindle.com’ emails can be delivered even when you are not connected to wi-fi, but note that service fees apply.

Find out more about the Kindle Personal Document Service.

Available formats
×

Save book to Dropbox

To save content items to your account, please confirm that you agree to abide by our usage policies. If this is the first time you use this feature, you will be asked to authorise Cambridge Core to connect with your account. Find out more about saving content to Dropbox.

Available formats
×

Save book to Google Drive

To save content items to your account, please confirm that you agree to abide by our usage policies. If this is the first time you use this feature, you will be asked to authorise Cambridge Core to connect with your account. Find out more about saving content to Google Drive.

Available formats
×