Book contents
- The Drive for Knowledge
- The Drive for Knowledge
- Copyright page
- Contents
- Figures
- Tables
- Notes on Contributors
- Preface
- Part I What Drives Humans to Seek Information?
- Part II How Do Humans Search for Information?
- Part III Which Machinery Supports the Drive for Knowledge?
- Chapter 9 Information-Seeking in the Brain
- Chapter 10 Attention as Rational Choice
- Chapter 11 Seeking Inner Knowledge
- Chapter 12 Edgework
- Future Challenges
- Index
- References
Chapter 10 - Attention as Rational Choice
from Part III - Which Machinery Supports the Drive for Knowledge?
Published online by Cambridge University Press: 19 May 2022
Book contents
- The Drive for Knowledge
- The Drive for Knowledge
- Copyright page
- Contents
- Figures
- Tables
- Notes on Contributors
- Preface
- Part I What Drives Humans to Seek Information?
- Part II How Do Humans Search for Information?
- Part III Which Machinery Supports the Drive for Knowledge?
- Chapter 9 Information-Seeking in the Brain
- Chapter 10 Attention as Rational Choice
- Chapter 11 Seeking Inner Knowledge
- Chapter 12 Edgework
- Future Challenges
- Index
- References
Summary
I argue for a new operationalization of attention as a process of information selection that is endogenous, rather than exogenous, to decision-makers’ goals and constraints. Traditional accounts postulate that attention is captured in a “bottom-up” fashion by external sensory stimuli or in a “top-down” fashion by external experimental instructions. In contrast, recent studies of information-demand provide a powerful alternative view whereby attention is allocated endogenously to serve a decision-maker’s goals, and is subject to the decision-maker’s knowledge, biases, and constraints. I review neurophysiological evidence supporting this view, with a focus on optimal and potentially suboptimal forms of attention allocation aimed to reduce uncertainty and enhance reward gains.
- Type
- Chapter
- Information
- The Drive for KnowledgeThe Science of Human Information Seeking, pp. 217 - 236Publisher: Cambridge University PressPrint publication year: 2022
References
Awh, E., Belopolsky, A. V., & Theeuwes, J. (2012). Top-down versus bottom-up attentional control: A failed theoretical dichotomy. Trends in Cognitive Science, 16(8), 437–443.Google Scholar
Bisley, J. W., & Goldberg, M. E. (2010). Attention, intention, and priority in the parietal lobe. Annual Review of Neuroscience, 33, 1–21.Google Scholar
Callaway, F., Rangel, A., & Griffiths, T. L. (2021). Fixation patterns in simple choice reflect optimal information sampling. PLoS Comp Biol. https://doi.org/10.1371/journal.pcbi.1008863.Google Scholar
Carrasco, M., Eckstein, M., Verghese, P., Boynton, G., & Treue, S. (2009). Visual attention: Neurophysiology, psychophysics and cognitive neuroscience. Vision Research, 49(10), 1033–1036. https://doi.org/10.1016/j.visres.2009.04.022.Google Scholar
Charpentier, C. J., Bromberg-Martin, E. S., & Sharot, , T., S. (2018). Valuation of knowledge and ignorance in mesolimbic reward circuitry. Proceedings of the National Academy of Sciences of the United States of America., 115(31), E7255-E7264.Google Scholar
Coenen, A., Nelson, J. D., & Gureckis, T. M. (2018). Asking the right questions about the psychology of human inquiry: Nine open challenges. Psychonomic Bulletin & Review, Jun. 4. https://doi.org/10.3758/s13423-018–1470–5. [Epub ahead of print]Google Scholar
Daddaoua, N., Lopes, M., & Gottlieb, J. (2016). Intrinsically motivated oculomotor exploration guided by uncertainty reduction and conditioned reinforcement in non-human primates. Sci Rep, 6(20202). https://doi.org/10.1038/srep20202.Google Scholar
Foley, N. C., Kelley, S. P., Mhatre, H., Lopes, M., & Gottlieb, J. (2017). Parietal neurons encode expected gains in instrumental information. Proceedings of the National Academy of Science, 114(16), E3315–E3323.Google Scholar
Gottlieb, J., Kusunoki, M., & Goldberg, M. E. (1998). The representation of visual salience in monkey parietal cortex. Nature, 391, 481–484.Google Scholar
Gottlieb, J., & Oudeyer, P. Y. (2018). Toward a neuroscience of active sampling and curiosity. Nature Reviews Neuroscience, 19(12), 758–770.Google Scholar
Horan, M., Daddaoua, N., & Gottlieb, J. (2019). Parietal neurons encode information sampling based on decision uncertainty. Nature Neuroscience, 22(8), 1327–1335. https://doi.org/10.1038/s41593-019-0440-1.Google Scholar
Hunt, L. T., Rutledge, R. B., Malalasekera, W. M., Kennerley, S. W., & Dolan, R. J. (2016). Approach-induced biases in human information sampling. PLoS Biol., 14(11), e2000638. https://doi.org/10.1371/journal.pbio.2000638.CrossRefGoogle ScholarPubMed
Iigaya, K., Story, G. W., Kurth-Nelson, Z., Dolan, R. J., & Dayan, P. (2016). The modulation of savouring by prediction error and its effects on choice. eLife, Apr. 21(5), e13747. https://doi.org/10.7554/eLife.13747.CrossRefGoogle Scholar
Kobayashi, K., Ravaioli, S., Baranès, A., Woodford, M., & Gottlieb, J. (2019). Diverse motives for human curiosity. Nature Human Behavior., 3(6), 587–595. https://doi.org/10.1038/s41562-019-0589-3.CrossRefGoogle ScholarPubMed
Krajbich, I., Armel, C., & Rangel, A. (2010). Visual fixations and the computation and comparison of value in simple choice. Nature Neuroscience, 13(10), 1292–1298. https://doi.org/nn.2635%5Bpii%5D10.1038/nn.2635.Google Scholar
Krauzlis, R. J., Lovejoy, L. P., & Zenon, A. (2013). Superior colliculus and visual spatial attention. Annual Review of Neuroscience, 36, 165–182. https://doi.org/10.1146/annurev-neuro-062012-170249.CrossRefGoogle ScholarPubMed
Leong, Y., Radulescu, A., Daniel, R., DeWoskin, V., & Niv, Y. (2017). Dynamic interaction between reinforcement learning and attention in multidimensional environments. Neuron, 93(2), 451–463.CrossRefGoogle ScholarPubMed
Loewenstein, G. (1987). Anticipation and the valuation of delayed consumption. The Economic Journal, 97(387), 666–684.Google Scholar
Maunsell, J. H. (2004). Neuronal representations of cognitive state: reward or attention? Trends in Cognitive Science, 8(6), 261–265.CrossRefGoogle ScholarPubMed
Morvan, C., & Maloney, L. (2012). Human visual search does not maximize the post-saccadic probability of identifying targets. PLoS Computational Biology, 8(2), e1002342. https://doi.org/10.1371/journal.pcbi.1002342.Google Scholar
Najemnik, J., & Geisler, W. S. (2008). Eye movement statistics in humans are consistent with an optimal search strategy. Journal of Vision, 8 (3), 1–14. https://doi.org/10.1167/8.3.4/8/3/4/ [pii].CrossRefGoogle ScholarPubMed
Nelson, J., McKenzie, C., Cottrell, G., & Sejnowski, T. (2010). Experience matters: Information acquisition optimizes probability gain. Psychological Science, 21(7), 960–969.CrossRefGoogle ScholarPubMed
Padmala, S., & Pessoa, L. (2011). Reward reduces conflict by enhancing attentional control and biasing visual cortical processing. Journal of Cognitive Neuroscience, 23(11), 3419–3432. https://doi.org/10.1162/jocn_a_00011.Google Scholar
Rothe, A., Lake, B., & Gureckis, T. M. (2016). Asking and evaluating natural language questions. In Papafragou, A, Grodner, D, Mirman, D, & Trueswell, J. C (Eds.), Proceedings of the 38th Annual Conference of the Cognitive Science Society (pp. 2051–2056). Austin, TX: Cognitive Science Society.Google Scholar
Sharot, T., & Sunstein, C. R. (2020). How people decide what they want to know. Nature Human Behavior, 4(1), 14–19. https://doi.org/10.1038/s41562-019-0793-1.Google Scholar
Shenhav, A., Botvinick, M., & Cohen, J. (2013). The expected value of control: an integrative theory of anterior cingulate cortex function. Neuron, 79(2), 217–240.Google Scholar
Shenhav, A., Musslick, S., Lieder, F., Kool, W., Griffiths, T. L., Cohen, J. D., & Botvinick, M. M. (2017). Toward a rational and mechanistic account of mental effort. Annual Review of Neuroscience, 40, 99–124. https://doi.org/10.1146/annurev-neuro-072116–031526.Google Scholar
Silvetti, M., Vassena, E., Abrahamse, E., & Verguts, T. (2018). Dorsal anterior cingulate-brainstem ensemble as a reinforcement meta-learner. PLoS Computational Biology, 14(8), e1006370. https://doi.org/10.1371/journal.pcbi.1006370.Google Scholar
Simons, D. J. (2000). Attentional capture and inattentional blindness. Trends in Cognitive Science, 4(4), 147–155. https://doi.org/10.1016/s1364-6613(00)01455-8.CrossRefGoogle ScholarPubMed
Sugrue, L. P., Corrado, G. S., & Newsome, W. T. (2004). Matching behavior and the representation of value in the parietal cortex. Science, 304(5678), 1782–1787.Google Scholar
Taghizadeh, B., Foley, N. C., Karimimehr, S., Cohanpour, M., Semework, M., Sheth, S. A., … Gottlieb, J. (2020). Reward uncertainty asymmetrically affects information transmission within the monkey fronto-parietal network. Communications Biology, 3(1), 594. https://doi.org/10.1038/s42003-020-01320-6.Google Scholar
Tatler, B. W., Hayhoe, M. N., Land, M. F., & Ballard, D. H. (2011). Eye guidance in natural vision: reinterpreting salience. Journal of Vision, 11(5), 5–25.Google Scholar
Thompson, K. G., & Bichot, N. P. (2005). A visual salience map in the primate frontal eye field. Progress in Brain Research, 147, 251–262.Google ScholarPubMed
Wei, X. X., & Stocker, A. A. (2015). A Bayesian observer model constrained by efficient coding can explain “anti-Bayesian” percepts. Nature Neuroscience 18(10), 1509–1517.Google Scholar
Yang, S. C., Lengyel, M., & Wolpert, D. M. (2016). Active sensing in the categorization of visual patterns. eLife. https://doi.org/10.7554/eLife.12215.Google Scholar