Abstract
The frame problem refers to the fact that organisms must be able to zero in on relevant aspects of the world and intelligently ignore the vast majority of the world that is irrelevant to their goals. In this paper we aim to point out the connection between two leading frameworks for thinking about how organisms achieve this. Predictive processing is a rapidly growing framework within cognitive science which suggests that organisms assign a high ‘weight’ to relevant aspects of the world, effectively treating relevant aspects of the world as if they are more precise (in a Bayesian sense). This assignment of weight is called precision-weighting and is the predictive processing account of how organisms allocate their attention. Relevance Realization is a framework that conceptualizes an organism’s ability to realize relevance as resulting from a dynamical system in which a cognitive agent makes use of opponent processing relationships to zero in on relevant aspects of the world. In this paper we use recent work on the diametric model of autism and psychosis to demonstrate that the tradeoffs inherent to precision-weighting are also inherent to relevance realization. This connection will demonstrate that although these frameworks have a different intellectual background and use a different set of concepts and vocabulary, they are both pointing to the same underlying process. The fact that these different frameworks have converged on such a similar solution to the problem of how organisms realize relevance serves to demonstrate the plausibility of that solution.
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This is not to say that people with ASD are never curious, but rather that their curiosity tends to be within more highly structured (predictable) domains. The result is that they may tend to be more comfortable exploring within particular domains rather than moving between domains (e.g., the focus on special interests that occurs in people with ASD). Thanks to Sander Van de Cruys for highlighting this nuance.
References
Abu-Akel, A., Apperly, I., Spaniol, M. M., Geng, J. J., & Mevorach, C. (2018). Diametric effects of autism tendencies and psychosis proneness on attention control irrespective of task demands. Scientific Reports, 8(1), 1–11. https://doi.org/10.1038/s41598-018-26821-7
Abu-Akel, A., Webb, M. E., de Montpellier, E., Von Bentivegni, S., Luechinger, L., Ishii, A., & Mohr, C. (2020). Autistic and positive schizotypal traits respectively predict better convergent and divergent thinking performance. Thinking Skills and Creativity, 36, 100656. https://doi.org/10.1016/j.tsc.2020.100656
Abu-Akel, A., Wood, S. J., Hansen, P. C., & Apperly, I. A. (2015). Perspective-taking abilities in the balance between autism tendencies and psychosis proneness. Proceedings of the Royal Society B: Biological Sciences, 282(1808), 20150563. https://doi.org/10.1098/rspb.2015.0563
Alcántara, J. I., Weisblatt, E. J. L., Moore, B. C. J., & Bolton, P. F. (2004). Speech-in-noise perception in high-functioning individuals with autism or Asperger’s syndrome. Journal of Child Psychology and Psychiatry, 45(6), 1107–1114. https://doi.org/10.1111/j.1469-7610.2004.t01-1-00303.x
Andersen, B. P. (2022). Autistic-like traits and positive schizotypy as diametric specializations of the predictive mind. Perspectives on Psychological Science. https://doi.org/10.1177/17456916221075252
Andersen, B. P., Al-Shawaf, L., & Bearden, R. (2021). Positive schizotypy predicts migration intentions and desires. Personality and Individual Differences, 182, 111096. https://doi.org/10.1016/j.paid.2021.111096
Annus, A. (2018). Asceticism in Ancient Mesopotamia and Syria: A Study of Autism in Antiquity (pp. 521–539)
Baron-Cohen, S. (2006). The hyper-systemizing, assortative mating theory of autism. Progress in Neuro-Psychopharmacology & Biological Psychiatry, 30(5), 865–872. https://doi.org/10.1016/j.pnpbp.2006.01.010
Baron-Cohen, S., Ashwin, E., Ashwin, C., Tavassoli, T., & Chakrabarti, B. (2009). Talent in autism: Hyper-systemizing, hyper-attention to detail and sensory hypersensitivity. Philosophical Transactions of the Royal Society B: Biological Sciences, 364(1522), 1377–1383. https://doi.org/10.1098/rstb.2008.0337
Boulter, C., Freeston, M., South, M., & Rodgers, J. (2014). Intolerance of Uncertainty as a Framework for Understanding Anxiety in Children and Adolescents with Autism Spectrum Disorders. Journal of Autism and Developmental Disorders, 44(6), 1391–1402. https://doi.org/10.1007/s10803-013-2001-x
Bubic, A., von Cramon, D. Y., & Schubotz, R. I. (2010). Prediction, Cognition and the Brain. Frontiers in Human Neuroscience. https://doi.org/10.3389/fnhum.2010.00025. 4
Carson, S. H. (2010). Latent inhibition and creativity. Latent inhibition: Cognition, neuroscience and applications to schizophrenia (pp. 183–198). Cambridge University Press. https://doi.org/10.1017/CBO9780511730184.010
Carson, S. H. (2018). Creativity and psychopathology: A relationship of shared neurocognitive vulnerabilities. The Cambridge handbook of the neuroscience of creativity (pp. 136–157). Cambridge University Press. https://doi.org/10.1017/9781316556238.009
Carter, C., Hass, R. W., Charfadi, M., & Dinzeo, T. J. (2019). Probing Linear and Nonlinear Relations Among Schizotypy, Hypomania, Cognitive Inhibition, and Creativity. Creativity Research Journal, 31(1), 83–92. https://doi.org/10.1080/10400419.2019.1580091
Carver, C. S., & Scheier, M. F. (1998). On the Self-Regulation of Behavior (1st ed.). Cambridge University Press
Chen, A. G., Benrimoh, D., Parr, T., & Friston, K. J. (2020). A Bayesian Account of Generalist and Specialist Formation Under the Active Inference Framework. Frontiers in Artificial Intelligence, 3, https://doi.org/10.3389/frai.2020.00069
Clark, A. (2013). Whatever next? Predictive brains, situated agents, and the future of cognitive science. The Behavioral and Brain Sciences, 36(3), 181–204. https://doi.org/10.1017/S0140525X12000477
Clark, A. (2015). Surfing Uncertainty: Prediction, Action, and the Embodied Mind (Reprint edition). Oxford University Press
Clark, A. (2017). Predictions, precision, and agentive attention. Consciousness and Cognition, 56, 115–119. https://doi.org/10.1016/j.concog.2017.06.013
Crespi, B., & Badcock, C. (2008). Psychosis and autism as diametrical disorders of the social brain. Behavioral and Brain Sciences, 31(3), 241–261. https://doi.org/10.1017/s0140525x08004214
Crespi, B., & Dinsdale, N. (2019). Autism and psychosis as diametrical disorders of embodiment. Evolution Medicine and Public Health, 2019(1), 121–138. https://doi.org/10.1093/emph/eoz021
Crespi, B., Leach, E., Dinsdale, N., Mokkonen, M., & Hurd, P. (2016). Imagination in human social cognition, autism, and psychotic-affective conditions. Cognition, 150, 181–199. https://doi.org/10.1016/j.cognition.2016.02.001
Del Giudice, M. (2018). Evolutionary Psychopathology: A Unified Approach. Oxford University Press
Del Giudice, M., Angeleri, R., Brizio, A., & Elena, M. R. (2010). The Evolution of Autistic-Like and Schizotypal Traits: A Sexual Selection Hypothesis. Frontiers in Psychology, 1, 41. https://doi.org/10.3389/fpsyg.2010.00041
Del Giudice, M., & Crespi, B. J. (2018). Basic functional trade-offs in cognition: An integrative framework. Cognition, 179, 56–70. https://doi.org/10.1016/j.cognition.2018.06.008
Del Giudice, M., Gangestad, S. W., & Kaplan, H. S. (2015). Life History Theory and Evolutionary Psychology. In The Handbook of Evolutionary Psychology (pp. 1–27). American Cancer Society. https://doi.org/10.1002/9781119125563.evpsych102
Del Giudice, M., Klimczuk, A. C. E., Traficonte, D. M., & Maestripieri, D. (2014). Autistic-like and schizotypal traits in a life history perspective: Diametrical associations with impulsivity, sensation seeking, and sociosexual behavior. Evolution and Human Behavior, 35(5), 415–424. https://doi.org/10.1016/j.evolhumbehav.2014.05.007
Dennett, D. (1987). Cognitive Wheels: The Frame Problem of AI. In K. M. Ford & Z. W. Pylyshyn (Eds.), The Robots Dilemma Revisited: The Frame Problem in Artificial Intelligence (2nd edition). Praeger
DeYoung, C. G. (2013). The neuromodulator of exploration: A unifying theory of the role of dopamine in personality. Frontiers in Human Neuroscience, 7. https://doi.org/10.3389/fnhum.2013.00762
DeYoung, C. G. (2015). Cybernetic Big Five Theory. Journal of Research in Personality, 56, 33–58. https://doi.org/10.1016/j.jrp.2014.07.004
DeYoung, C. G., Peterson, J. B., & Higgins, D. M. (2002). Higher-order factors of the Big Five predict conformity: Are there neuroses of health? Personality and Individual Differences, 33(4), 533–552. https://doi.org/10.1016/S0191-8869(01)00171-4
Digman, J. M. (1997). Higher-order factors of the Big Five. Journal of Personality and Social Psychology, 73(6), 1246–1256
Dinsdale, N. L., Hurd, P. L., Wakabayashi, A., Elliot, M., & Crespi, B. J. (2013). How Are Autism and Schizotypy Related? Evidence from a Non-Clinical Population. PLOS ONE, 8(5), e63316. https://doi.org/10.1371/journal.pone.0063316
Feldman, H., & Friston, K. J. (2010). Attention, Uncertainty, and Free-Energy. Frontiers in Human Neuroscience. https://doi.org/10.3389/fnhum.2010.00215. 4
Friston, K., Breakspear, M., & Deco, G. (2012). Perception and self-organized instability. Frontiers in Computational Neuroscience, 6. https://doi.org/10.3389/fncom.2012.00044
Friston, K. J. (2009). The free-energy principle: A rough guide to the brain? Trends in Cognitive Sciences, 13(7), 293–301. https://doi.org/10.1016/j.tics.2009.04.005
Friston, K. J. (2010). The free-energy principle: A unified brain theory? Nature Reviews Neuroscience, 11(2), 127–138. https://doi.org/10.1038/nrn2787
Grandin, T., & Sacks, O. (2006). Thinking in Pictures, Expanded Edition: My Life with Autism (Expanded ed. edition). Vintage
Gray, N. S., Fernandez, M., Williams, J., Ruddle, R. A., & Snowden, R. J. (2002). Which schizotypal dimensions abolish latent inhibition? The British Journal of Clinical Psychology, 41(Pt 3), 271–284. https://doi.org/10.1348/014466502760379136
Happé, F. (1999). Autism: Cognitive deficit or cognitive style? Trends in Cognitive Sciences, 3(6), 216–222. https://doi.org/10.1016/S1364-6613(99)01318-2
Hesp, C., Smith, R., Parr, T., Allen, M., Friston, K. J., & Ramstead, M. J. D. (2021). Deeply Felt Affect: The Emergence of Valence in Deep Active Inference. Neural Computation, 33(2), 398–446. https://doi.org/10.1162/neco_a_01341
Hohwy, J. (2013). The Predictive Mind (1 edition). Oxford University Press
Holt, N. J. (2019). The expression of schizotypy in the daily lives of artists. Psychology of Aesthetics Creativity and the Arts, 13(3), 359–371. https://doi.org/10.1037/aca0000176
Hutchinson, J. B., & Barrett, L. F. (2019). The Power of Predictions: An Emerging Paradigm for Psychological Research. Current Directions in Psychological Science, 28(3), 280–291. https://doi.org/10.1177/0963721419831992
James, W. (1890). The stream of thought. In W. James (Ed.), The principles of psychology, Vol I (pp. 224–290). Henry Holt and Co. https://doi.org/10.1037/10538-009
Kidd, C., Piantadosi, S. T., & Aslin, R. N. (2012). The Goldilocks Effect: Human Infants Allocate Attention to Visual Sequences That Are Neither Too Simple Nor Too Complex. PLOS ONE, 7(5), e36399. https://doi.org/10.1371/journal.pone.0036399
Kiverstein, J., Miller, M., & Rietveld, E. (2019). The feeling of grip: Novelty, error dynamics, and the predictive brain. Synthese, 196(7), 2847–2869. https://doi.org/10.1007/s11229-017-1583-9
Kumari, V., & Ettinger, U. (2010). Latent inhibition in schizophrenia and schizotypy: A review of the empirical literature. In I. Weiner & R. Lubow (Eds.), Latent Inhibition: Cognition, Neuroscience and Applications to Schizophrenia (pp. 417–447). Cambridge University Press. https://doi.org/10.1017/CBO9780511730184.018
Lawson, R. P., Mathys, C., & Rees, G. (2017). Adults with autism over-estimate the volatility of the sensory environment. Nature Neuroscience, 20(9), 1293–1299. https://doi.org/10.1038/nn.4615
Litwin, P., & Miłkowski, M. (2020). Unification by Fiat: Arrested Development of Predictive Processing. Cognitive Science, 44(7), e12867. https://doi.org/10.1111/cogs.12867
Maes, J. H. R., Eling, P. A. T. M., Wezenberg, E., Vissers, C. T. W. M., & Kan, C. C. (2011). Attentional set shifting in autism spectrum disorder: Differentiating between the role of perseveration, learned irrelevance, and novelty processing. Journal of Clinical and Experimental Neuropsychology, 33(2), 210–217. https://doi.org/10.1080/13803395.2010.501327
Mahon, K., Perez-Rodriguez, M., Gunawardane, N., & Burdick, K. (2013). Dimensional endophenotypes in Bipolar Disorder: Affective Dysregulation and Psychosis Proneness. Journal of Affective Disorders, 151(2), https://doi.org/10.1016/j.jad.2013.08.003
McCarthy, J., & Hayes, P. J. (1969). Some Philosophical Problems from the Standpoint of Artificial Intelligence. In B. Meltzer, & D. Michie (Eds.), Machine Intelligence 4. Edinburgh University Press
Mehlhorn, K., Newell, B. R., Todd, P. M., Lee, M. D., Morgan, K., Braithwaite, V. A., Hausmann, D., Fiedler, K., & Gonzalez, C. (2015). Unpacking the exploration–exploitation tradeoff: A synthesis of human and animal literatures. Decision, 2(3), 191–215. https://doi.org/10.1037/dec0000033
Miller, M., & Clark, A. (2018). Happily entangled: Prediction, emotion, and the embodied mind. Synthese, 195(6), 2559–2575. https://doi.org/10.1007/s11229-017-1399-7
Miller, M., Rietveld, E., & Kiverstein, J. (2021). The Predictive Dynamics of Happiness and Well-Being. Emotion Review, 17540739211063852. https://doi.org/10.1177/17540739211063851
Nave, K., Deane, G., Miller, M., & Clark, A. (2020). Wilding the predictive brain. WIREs Cognitive Science, n/a), e1542. https://doi.org/10.1002/wcs.1542
Neil, L., Olsson, N. C., & Pellicano, E. (2016). The Relationship Between Intolerance of Uncertainty, Sensory Sensitivities, and Anxiety in Autistic and Typically Developing Children. Journal of Autism and Developmental Disorders, 46(6), 1962–1973. https://doi.org/10.1007/s10803-016-2721-9
Park, W. J., Schauder, K. B., Zhang, R., Bennetto, L., & Tadin, D. (2017). High internal noise and poor external noise filtering characterize perception in autism spectrum disorder. Scientific Reports, 7(1), 17584. https://doi.org/10.1038/s41598-017-17676-5
Parr, T., & Friston, K. J. (2019). Attention or salience? Current Opinion in Psychology, 29, 1–5. https://doi.org/10.1016/j.copsyc.2018.10.006
Peterson, J. B., & Carson, S. H. (2000). Latent Inhibition and Openness to Experience in a high-achieving student population. Personality and Individual Differences, 28(2), 323–332. https://doi.org/10.1016/S0191-8869(99)00101-4
Peterson, J. B., Smith, K. W., & Carson, S. H. (2002). Openness and extraversion are associated with reduced latent inhibition: Replication and commentary. Personality and Individual Differences, 33(7), 1137–1147. https://doi.org/10.1016/S0191-8869(02)00004-1
Plaisted, K. C. (2001). Reduced generalization in autism: An alternative to weak central coherence. The development of autism: Perspectives from theory and research (pp. 149–169). Lawrence Erlbaum Associates Publishers
Ransom, M., Fazelpour, S., Markovic, J., Kryklywy, J., Thompson, E. T., & Todd, R. M. (2020). Affect-biased attention and predictive processing. Cognition, 203, 104370. https://doi.org/10.1016/j.cognition.2020.104370
Robertson, C. E., Kravitz, D. J., Freyberg, J., Baron-Cohen, S., & Baker, C. I. (2013). Tunnel vision: Sharper gradient of spatial attention in autism. The Journal of Neuroscience, 33(16), 6776–6781. https://doi.org/10.1523/JNEUROSCI.5120-12.2013
Rybakowski, J. K., & Klonowska, P. (2011). Bipolar mood disorder, creativity and schizotypy: An experimental study. Psychopathology, 44(5), 296–302. https://doi.org/10.1159/000322814
Sajid, N., Parr, T., Hope, T. M., Price, C. J., & Friston, K. J. (2020). Degeneracy and Redundancy in Active Inference. Cerebral Cortex, 30(11), 5750–5766. https://doi.org/10.1093/cercor/bhaa148
Schaller, M., Kenrick, D. T., Neel, R., & Neuberg, S. L. (2017). Evolution and human motivation: A fundamental motives framework. Social and Personality Psychology Compass, 11(6), e12319. https://doi.org/10.1111/spc3.12319
Schelinski, S., & von Kriegstein, K. (2020). Brief Report: Speech-in-Noise Recognition and the Relation to Vocal Pitch Perception in Adults with Autism Spectrum Disorder and Typical Development. Journal of Autism and Developmental Disorders, 50(1), 356–363. https://doi.org/10.1007/s10803-019-04244-1
Song, Y., Hakoda, Y., Sanefuji, W., & Cheng, C. (2015). Can They See It? The Functional Field of View Is Narrower in Individuals with Autism Spectrum Disorder. Plos One, 10(7), https://doi.org/10.1371/journal.pone.0133237
Van De Cruys, S. (2017). Affective Value in the Predictive Mind. Philosophy and Predictive Processing. https://doi.org/10.15502/9783958573253
Van de Cruys, S., de-Wit, L., Evers, K., Boets, B., & Wagemans, J. (2013). Weak Priors versus Overfitting of Predictions in Autism: Reply to Pellicano and Burr (TICS, 2012). I-Perception, 4(2), 95–97. https://doi.org/10.1068/i0580ic
Van de Cruys, S., Evers, K., der Hallen, R. V., Eylen, L. V., Boets, B., de-Wit, L. H., & Wagemans, J. (2014). Precise minds in uncertain worlds: Predictive coding in autism. Psychological Review, 121(4), 649–675. https://doi.org/10.1037/a0037665
Van de Cruys, S., Van der Hallen, R., & Wagemans, J. (2017). Disentangling signal and noise in autism spectrum disorder. Brain and Cognition, 112, 78–83. https://doi.org/10.1016/j.bandc.2016.08.004
Vervaeke, J., & Ferraro, L. (2013). Relevance Realization and the Neurodynamics and Neuroconnectivity of General Intelligence. In I. Harvey, A. Cavoukian, G. Tomko, D. Borrett, H. Kwan, & D. Hatzinakos (Eds.), SmartData (pp. 57–68). Springer
Vervaeke, J., Lillicrap, T. P., & Richards, B. A. (2012). Relevance Realization and the Emerging Framework in Cognitive Science. Journal of Logic and Computation, 22(1), 79–99. https://doi.org/10.1093/logcom/exp067
Wastler, H. M., & Lenzenweger, M. F. (2019). Self-referential hypermentalization in schizotypy. Personality Disorders: Theory Research and Treatment, 10(6), 536–544. https://doi.org/10.1037/per0000344
Whitacre, J., & Bender, A. (2010). Degeneracy: A design principle for achieving robustness and evolvability. Journal of Theoretical Biology, 263(1), 143–153. https://doi.org/10.1016/j.jtbi.2009.11.008
Wilkinson, S., Deane, G., Nave, K., & Clark, A. (2019). Getting Warmer: Predictive Processing and the Nature of Emotion. In L. Candiotto (Ed.), The Value of Emotions for Knowledge (pp. 101–119). Springer Verlag
Zaidel, A., Goin-Kochel, R. P., & Angelaki, D. E. (2015). Self-motion perception in autism is compromised by visual noise but integrated optimally across multiple senses. Proceedings of the National Academy of Sciences, 112(20), 6461–6466. https://doi.org/10.1073/pnas.1506582112
Zhou, T., Carlson, J. M., & Doyle, J. (2005). Evolutionary dynamics and highly optimized tolerance. Journal of Theoretical Biology, 236(4), 438–447. https://doi.org/10.1016/j.jtbi.2005.03.023
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Andersen, B.P., Miller, M. & Vervaeke, J. Predictive processing and relevance realization: exploring convergent solutions to the frame problem. Phenom Cogn Sci (2022). https://doi.org/10.1007/s11097-022-09850-6
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DOI: https://doi.org/10.1007/s11097-022-09850-6