Cited by 1Embodied and Extended CognitionCognitive Science and Education ResearchPhilosophy and Theoretical Science

Affective value in the predictive mind

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Van de Cruys, Sander·2017·MIND Group

Abstract

Although affective value is fundamental in explanations of behavior, it is still a somewhat alien concept in cognitive science. It implies a normativity or directionality that mere information processing models cannot seem to provide. In this paper we trace how affective value can emerge from information processing in the brain, as described by predictive processing. We explain the grounding of predictive processing in homeostasis, and articulate the implications this has for the concept of reward and motivation. However, at first sight, this new conceptualization creates a strong tension with conventional ideas on reward and affective experience. We propose this tension can be resolved by realizing that valence, a core component of all emotions, might be the reflection of a specific aspect of predictive information processing, namely the dynamics in prediction errors across time and the expectations we, in turn, form about these dynamics. Specifically, positive affect seems to be caused by positive rates of prediction error reduction, while negative affect is induced by a shift in a state with lower prediction errors to one with higher prediction errors (i.e., a negative rate of error reduction). We also consider how intense emotional episodes might be related to unexpected changes in prediction errors, suggesting that we also build (meta)predictions on error reduction rates. Hence in this account emotions appear as the continuous non-conceptual feedback on evolving —increasing or decreasing—uncertainties relative to our predictions. The upshot of this view is that the various emotions, from “basic” ones to the non-typical ones such as humor, curiosity and aesthetic affects, can be shown to follow a single underlying logic. Our analysis takes several cues from existing emotion theories but deviates from them in revealing ways. The account on offer does not just specify the interactions between emotion and cognition, rather it entails a deep integration of the two.

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  1. Alter, A. L. & Oppenheimer, D. M. (2009). Uniting the tribes of fluency to form a metacognitive nation. Pers. Soc. Psychol. Rev., 13 (3), 219-235.
  2. Anselme, P. (2010). The uncertainty processing theory of motivation. Behav. Brain Res., 208 (2), 291-310.
  3. Bach, D. R. & Dolan, R. J. (2012). Knowing how much you don't know: A neural organization of uncertainty esti- mates. Nat. Rev. Neurosci., 13 (8), 572-586.
  4. Barrett, L. F. (2014). The conceptual act theory: A précis. Emot. Rev., 6 (4), 292-297.
  5. Barrett, L. F. & Simmons, W. K. (2015). Interoceptive pre- dictions in the brain. Nat. Rev. Neurosci., 16 (7), 419-429.
  6. Bastos, A. M., Usrey, W. M., Adams, R. A., Mangun, G. R., Fries, P. & Friston, K. J. (2012). Canonical microcircuits for predictive coding. Neuron, 76 (4), 695-711.
  7. Berlyne, D. E. (1970). Novelty, complexity, and hedonic val- ue. Percept. Psychophys., 8 (5), 279-286.
  8. Bless, H. (2000). The interplay of affect and cognition: The mediating role of general knowledge structures. Feeling and thinking: The role of affect in social cognition (pp. 201-222). New York, NY: Cambridge University Press.
  9. Bodenhausen, G. V., Kramer, G. P. & Süsser, K. (1994). Happiness and stereotypic thinking in social judgment. J. Pers. Soc. Psychol., 66 (4), 621-632.
  10. Bromberg-Martin, E. S. & Hikosaka, O. (2011). Lateral habenula neurons signal errors in the prediction of re- ward information. Nat. Neurosci., 14 (9), 1209-1216.
  11. Cacioppo, J. T. & Petty, R. E. (1982). The need for cognition. J. Pers. Soc. Psychol., 42 (1), 116-131.
  12. Carver, C. S. & Scheier, M. F. (1990). Origins and functions of positive and negative affect: A control-process view. Psychol. Rev., 97 (1), 19-35.
  13. Chater, N. & Vlaev, I. (2011). The instability of value. In M. Delgado, E. A. Phelps & T. W. Robbins (Eds.) Decision making: Attention and performance XXIII (pp. 81-100).
  14. Clark, A. (2013). Whatever next? Predictive brains, situated agents, and the future of cognitive science. Behav. Brain Sci., 36 (03), 181-204.
  15. Dayan, P. (2012). How to set the switches on this thing. Curr. Opin. Neurobiol., 22 (6), 1068-1074.
  16. de Lafuente, V. & Romo, R. (2011). Dopamine neurons code subjective sensory experience and uncertainty of perceptual decisions. Proc. Natl. Acad. Sci. U. S. A., 108 (49), 19767-19771.
  17. Dember, W. N., Earl, R. W. & Paradise, N. (1957). Response by rats to differential stimulus complexity. J. Comp. Physiol. Psychol., 50 (5), 514-518.
  18. Diuk, C., Tsai, K., Wallis, J., Botvinick, M. & Niv, Y. (2013). Hierarchical learning induces two simultaneous, but separable, prediction errors in human basal ganglia. J. Neurosci., 33 (13), 5797-5805.
  19. Dreisbach, G. & Fischer, R. (2012). Conflicts as aversive signals. Brain Cogn., 78 (2), 94-98.
  20. Duncan, S. & Barrett, L. F. (2007). The role of the amygdala in visual awareness. Trends in Cognitive Science, 11 (5), 190-192.
  21. Duncker, K. (1941). On pleasure, emotion, and striving. Philosophy and Phenomenological Research, 1 (4), 391- 430.
  22. Egbert, M. & Canamero, L. (2014). In H. Sayama, J. Rieffel, S. Risi, R. Doursat & H. Lipson (Eds.) Habit-based regu- lation of essential variables. Cambridge, MA: MIT Press.
  23. Egbert, M., Virgo, N., Egbert, M. D., Froese, T., Kampis, G., Karsai, I. & Szathmáry, E. (2013). For biological systems, maintaining essential variables within viability limits is not passive. Constructivist Foundations, 9 (1), 109-111.
  24. Festinger, L. (1962). Cognitive dissonance. Sci. Am., 207 (4), 93-107.
  25. ---(2006). The laws of emotion. Mahwah, N.J: Psychol- ogy Press.
  26. Friston, K. J. (2003). Learning and inference in the brain. Neural Netw., 16 (9), 1325-1352.
  27. ---(2009). The free-energy principle: A rough guide to the brain? Trends Cogn. Sci., 13 (7), 293-301.
  28. ---(2010). The free-energy principle: A unified brain theory? Nat. Rev. Neurosci., 11 (2), 127-138.
  29. Friston, K. J., Daunizeau, J. & Kiebel, S. J. (2009). Rein- forcement learning or active inference? PLoS One, 4 (7), e6421.
  30. Friston, K. J., Daunizeau, J., Kilner, J. & Kiebel, S. J. (2010). Action and behavior: A free-energy formulation. Biol. Cybern., 102 (3), 227-260.
  31. Friston, K. J., Shiner, T., FitzGerald, T., Galea, J. M. A., Brown, H., Dolan, R. J., Moran, R. A. & Bestmann, S. (2012b). Dopamine, affordance and active inference. PLoS Comput. Biol., 8 (1), e1002327.
  32. Friston, K. J., Rigoli, F., Ognibene, D. M., Fitzgerald, T. & Pezzulo, G. (2015). Active inference and epistemic value. Cogn. Neurosci., 6 (4), 187-214.
  33. Froese, T. & Ikegami, T. (2013). The brain is not an isolated ''black box, '' nor is its goal to become one. Behav. Brain Sci., 36 (03), 213-214.
  34. Gerken, L., Balcomb, F. K. & Minton, J. L. (2011). Infants avoid 'labouring in vain' by attending more to learnable than unlearnable linguistic patterns. Dev. Sci., 14 (5), 972-979.
  35. Gu, X., Hof, P. R., Friston, K. J. & Fan, J. (2013). Anterior in- sular cortex and emotional awareness. J. Comp. Neurol., 521 (15), 3371-3388.
  36. Hebb, D. O. (1946). On the nature of fear. Psychol. Rev., 53 (5), 259-276.
  37. Hirsh, J. B., Mar, R. A. & Peterson, J. B. (2012). Psychologi- cal entropy: A framework for understanding uncertain- ty-related anxiety. Psychol. Rev., 119 (2), 304-320.
  38. Hoffmann, J. (2003). Anticipatory behavioral control. In M. V. Butz, O. Sigaud & P. Gérard (Eds.) Anticipatory be- havior in adaptive learning systems (pp. 44-65). Springer Berlin Heidelberg.
  39. Hohwy, J. (2011). Phenomenal variability and introspective reliability. Mind Lang., 26 (3), 261-286.
  40. Horn, B. K. P. (1980). Derivation of invariant scene charac- teristics from images (pp. 371-376). New York, NY, USA: ACM.
  41. Hsee, C. K. & Ruan, B. (2016). The pandora effect: The pow- er and peril of curiosity. Psychol. Sci., 27 (5), 659-666.
  42. Iglesias, S., Mathys, C., Brodersen, K. H. A., Piccirelli, M. & den Ouden, H. E. M. A. (2013). Hierarchical prediction errors in midbrain and basal forebrain during sensory learning. Neuron, 80 (2), 519-530.
  43. Inzlicht, M., Tullett, A. M. & Good, M. (2011). The need to believe: A neuroscience account of religion as a motivat- ed process. Religion Brain Behav., 1 (3), 192-212.
  44. Izard, C. E. (2007). Basic emotions, natural kinds, emotion schemas, and a new paradigm. Perspect. Psychol. Sci., 2 (3), 260-280.
  45. James, W. (1890). The principles of psychology. Harvard UP, Cambridge, MA.
  46. Joffily, M. & Coricelli, G. (2013). Emotional valence and the Free-energy principle. PLoS Comput. Biol., 9 (6), e1003094.
  47. Kagan, J. (1972). Motives and development. J. Pers. Soc. Psychol., 22 (1), 51-66.
  48. Kaplan, H. (1987). Lottery winners: The myth and reality. J. Gambl. Stud., 3 (3), 168-178.
  49. Kaplan, F. & Oudeyer, P.-Y. (2007). In search of the neural circuits of intrinsic motivation. Front. Neurosci., 1 (1), 225-236.
  50. Keramati, M. & Gutkin, B. S. (2011). A reinforcement learning theory for homeostatic regulation. Advances in neural information processing systems (pp. 82-90).
  51. Kidd, C., Piantadosi, S. T. & Aslin, R. N. (2012). The gold- ilocks effect: Human infants allocate attention to visual sequences that are neither too simple nor too complex. PLoS One, 7 (5), e36399.
  52. Klein, C. (2014). The penumbral theory of masochistic pleasure. Rev.Phil.Psych., 5 (1), 41-55.
  53. Klein, D. C. & Seligman, M. E. (1976). Reversal of perfor- mance deficits and perceptual deficits in learned help- lessness and depression. J. Abnorm. Psychol., 85 (1), 11-26.
  54. Knight, E. J., Klepac, K. M. & Kralik, J. D. (2013). Too good to be true: Rhesus monkeys react negatively to bet- ter-than-expected offers. PLoS One, 8 (10), e75768.
  55. Lauwereyns, J. (2010). The anatomy of bias: How neural cir- cuits weigh the options. Cambridge, MA: MIT Press.
  56. Little, D. Y.-J. & Sommer, F. T. (2013). Maximal mutual in- formation, not minimal entropy, for escaping the '' dark room'' . Behav. Brain Sci., 36 (03), 220-221.
  57. Mandler, G. (2003). Emotion. In D. K. Freedheim & I. B. Weiner (Eds.) History of psychology. John Wiley and Sons.
  58. Millikan, R. G. (2004). Varieties of meaning: The 2002 Jean Nicod lectures. Cambridge, MA: Mit Press.
  59. Mineka, S. & Hendersen, R. W. (1985). Controllability and predictability in acquired motivation. Annu. Rev. Psy- chol., 36 (1), 495-529.
  60. Moors, A. (2010). Automatic constructive appraisal as a candidate cause of emotion. Emot. Rev., 2 (2), 139-156.
  61. Moutoussis, M., Fearon, P., El-Deredy, W., Dolan, R. J. & Friston, K. J. (2014). Bayesian inferences about the self (and others): A review. Conscious. Cogn., 25, 67-76.
  62. Niv, Y. & Chan, S. (2011). On the value of information and other rewards. Nat. Neurosci., 14 (9), 1095-1097.
  63. Noordewier, M. K. & Breugelmans, S. M. (2013). On the valence of surprise. Cogn. Emot., 27 (7), 1326-1334.
  64. Oudeyer, P.-Y., Kaplan, F. & Hafner, V. V. (2007). Intrinsic motivation systems for autonomous mental develop- ment. IEEE Trans. Evol. Comput., 11 (2), 265-286. www.predictive-mind.net
  65. Parrott, W. G. & Gleitman, H. (1989). Infants' expectations in play: The joy of peek-a-boo. Cogn. Emot., 3 (4), 291- 311.
  66. Picard, F. (2013). State of belief, subjective certainty and bliss as a product of cortical dysfunction. Cortex, 49 (9), 2494-2500.
  67. Polani, D. (2009). Information: Currency of life? HFSP J., 3 (5), 307-316.
  68. Proulx, T. & Heine, S. J. (2009). Connections from Kafka exposure to meaning threats improves implicit learning of an artificial grammar. Psychol. Sci., 20 (9), 1125-1131.
  69. Proulx, T., Heine, S. J. & Vohs, K. D. (2010). When is the unfamiliar the uncanny? Meaning affirmation after ex- posure to absurdist literature, humor, and art. Pers. Soc. Psychol. Bull., 36 (6), 817-829.
  70. Purves, D., Wojtach, W. T. & Lotto, R. B. (2011). Under- standing vision in wholly empirical terms. Proceedings of the National Academy of Sciences, 108 (Supplement\_3), 15588-15595.
  71. Reber, R., Schwarz, N. & Winkielman, P. (2004). Processing fluency and aesthetic pleasure: Is beauty in the perceiv- er's processing experience? Pers. Soc. Psychol. Rev., 8 (4), 364-382.
  72. Reisenzein, R. (2009). Emotional experience in the com- putational belief-desire theory of emotion. Emot. Rev., 1 (3), 214-222.
  73. Rozin, P. & Kennel, K. (1983). Acquired preferences for pi- quant foods by chimpanzees. Appetite, 4 (2), 69-77.
  74. Rozin, P., Rozin, A., Appel, B. & Wachtel, C. (2006). Doc- umenting and explaining the common AAB pattern in music and humor: Establishing and breaking expecta- tions. Emotion, 6 (3), 349-355.
  75. Russell, J. A. (2003). Core affect and the psychological con- struction of emotion. Psychol. Rev., 110 (1), 145-172.
  76. Schiffer, A.-M., Ahlheim, C. & Wurm, M. F. S. (2012). Surprised at all the entropy: Hippocampal, caudate and midbrain contributions to learning from prediction er- rors. PLoS One, 7 (5), e36445.
  77. Schmidhuber, J. (2010). Formal theory of creativity, fun, and intrinsic motivation (1990-2010). IEEE Trans. Au- ton. Ment. Dev., 2 (3), 230-247.
  78. Schrödinger, E. (1992). What is life? With Mind and Matter and Autobiographical Sketches. Cambridge University Press.
  79. Schultz, W. (2007). Reward. Scholarpedia J., 2 (3), 1652.
  80. Serences, J. T. (2008). Value-based modulations in human visual cortex. Neuron, 60 (6), 1169-1181.
  81. Seth, A. K. (2013). Interoceptive inference, emotion, and the embodied self. Trends Cogn. Sci., 17 (11), 565-573.
  82. Silvia, P. J. (2001). Interest and interests: The psychology of constructive capriciousness. Rev. Gen. Psychol., 5 (3), 270-290.
  83. ---(2008). Interest-The curious emotion. Curr. Dir. Psychol. Sci., 17 (1), 57-60.
  84. Singer, T., Critchley, H. D. & Preuschoff, K. (2009). A com- mon role of insula in feelings, empathy and uncertainty. Trends Cogn. Sci., 13 (8), 334-340.
  85. Singh, S., Lewis, R. L. & Barto, A. G. (2009). Where do re- wards come from (pp. 2601-2606).
  86. Solway, A. & Botvinick, M. M. (2012). Goal-directed deci- sion making as probabilistic inference: A computational framework and potential neural correlates. Psychol. Rev., 119 (1), 120-154.
  87. Srivastava, N. & Schrater, P. (2015). Learning what to want: context-sensitive preference learning. PLoS One, 10 (10), e0141129.
  88. Sroufe, L. A. & Waters, E. (1976). The ontogenesis of smil- ing and laughter: A perspective on the organization of development in infancy. Psychol. Rev., 83 (3), 173-189.
  89. Stansbury, D. E., Naselaris, T. & Gallant, J. L. (2013). Nat- ural scene statistics account for the representation of scene categories in human visual cortex. Neuron, 79 (5), 1025-1034.
  90. Van de Cruys, S. & Wagemans, J. (2011). Putting reward in art: A tentative prediction error account of visual art. Iperception, 2 (9), 1035-1062.
  91. van Harreveld, F., Rutjens, B. T., Schneider, I., Nohlen, H. U. & Keskinis, K. (2014). In doubt and disorderly: Am- bivalence promotes compensatory perceptions of order. J. Exp. Psychol. Gen., 143 (4), 1666-1676.
  92. von Glasersfeld, E. (1995). Radical constructivism: A way of knowing and learning. studies in mathematics education series: 6. Bristol, PA: Falmer Press, Taylor & Francis Inc..
  93. Wacongne, C., Labyt, E., Wassenhove, V., Bekinschtein, T., Naccache, L. & Dehaene, S. (2011). Evidence for a hier- www.predictive-mind.net archy of predictions and prediction errors in human cor- tex. Proc. Natl. Acad. Sci. U. S. A., 108 (51), 20754-20759.
  94. Weber, A. & Varela, F. J. (2002). Life after Kant: Natural purposes and the autopoietic foundations of biological individuality. Phenomenol. Cognitive Sci., 1 (2), 97-125.
  95. Whalen, P. J. (2007). The uncertainty of it all. Trends Cogn. Sci., 11 (12), 499-500.
  96. Whitson, J. A. & Galinsky, A. D. (2008). Lacking control in- creases illusory pattern perception. Science, 322 (5898), 115-117.
  97. Wood, W. & Neal, D. T. (2007). A new look at habits and the habit-goal interface. Psychol. Rev., 114 (4), 843-863.
  98. Wänke, M. & Hansen, J. (2015). Relative processing fluen- cy. Curr. Dir. Psychol. Sci., 24 (3), 195-199.
  99. Wörgötter, F. & Porr, B. (2005). Temporal sequence learn- ing, prediction, and control: A review of different mod- els and their relation to biological mechanisms. Neural Comput., 17 (2), 245-319.
  100. Zajonc, R. B. (1980). Feeling and thinking: Preferences need no inferences. Am. Psychol., 35 (2), 151.

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Authors
Van de Cruys, S.
Venue
MIND Group
Publisher
MIND Group
Published
2017
OpenAlex
W6945073625
Cited by
1
References
0
Citations by year
2024: 1
FWCI
0.00
Type
book chapter
Language
en
Locations
  • Gutenberg Open Science (submittedVersion, cc-by-nd)
  • Gutenberg Open Science
Funders
  • Fonds Wetenschappelijk Onderzoek
  • Vlaamse regering (METH 08/02)
Topics
  • Life Sciences › Neuroscience › Cognitive Neuroscience › Embodied and Extended Cognition
  • Life Sciences › Neuroscience › Cognitive Neuroscience › Cognitive Science and Education Research
  • Social Sciences › Psychology › Experimental and Cognitive Psychology › Philosophy and Theoretical Science
Keywords
emotional value, predictive processing, valence, homeostasis, reward, motivation, emotions, emotion-cognition integration, humor, curiosity

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