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A primer on the use of computational modelling to investigate affective states, affective disorders and animal welfare in non-human animals.

Neville, V ; Mendl, M ; et al.
In: Cognitive, affective & behavioral neuroscience, Jg. 24 (2024-04-01), Heft 2, S. 370-383
Online academicJournal

Titel:
A primer on the use of computational modelling to investigate affective states, affective disorders and animal welfare in non-human animals.
Autor/in / Beteiligte Person: Neville, V ; Mendl, M ; Paul, ES ; Seriès, P ; Dayan, P
Link:
Zeitschrift: Cognitive, affective & behavioral neuroscience, Jg. 24 (2024-04-01), Heft 2, S. 370-383
Veröffentlichung: 2011- : New York : Springer ; <i>Original Publication</i>: Austin, TX : Psychonomic Society, c2001-, 2024
Medientyp: academicJournal
ISSN: 1531-135X (electronic)
DOI: 10.3758/s13415-023-01137-w
Schlagwort:
  • Animals
  • Humans
  • Affect physiology
  • Disease Models, Animal
  • Animal Welfare
  • Computer Simulation
  • Mood Disorders physiopathology
Sonstiges:
  • Nachgewiesen in: MEDLINE
  • Sprachen: English
  • Publication Type: Journal Article; Research Support, Non-U.S. Gov't
  • Language: English
  • [Cogn Affect Behav Neurosci] 2024 Apr; Vol. 24 (2), pp. 370-383. <i>Date of Electronic Publication: </i>2023 Nov 30.
  • MeSH Terms: Animal Welfare* ; Computer Simulation* ; Mood Disorders* / physiopathology ; Animals ; Humans ; Affect / physiology ; Disease Models, Animal
  • References: Akam, T., Lustig, A., Rowland, J. M., Kapanaiah, S. K., Esteve-Agraz, J., Panniello, M., Márquez, C., Kohl, M. M., Kätzel, D., Costa, R. M., et al. (2022). Open-source, python-based, hardware and software for controlling behavioural neuroscience experiments. Elife, 11, e67846. (PMID: 350437828769647) ; American Psychiatric Association (2013). Diagnostic and statistical manual of mental disorders: DSM-5. American Psychiatric Association Arlington, VA, 5th edition. ; Aston-Jones, G., & Cohen, J. D. (2005). Adaptive gain and the role of the locus coeruleus-norepinephrine system in optimal performance. Journal of Comparative Neurology, 493(1), 99–110. (PMID: 16254995) ; Barrett, L. F., & Finlay, B. L. (2018). Concepts, goals and the control of survival-related behaviors. Current Opinion in Behavioral Sciences, 24, 172–179. (PMID: 311572896541420) ; Barrett, L. F., Lindquist, K. A., Bliss-Moreau, E., Duncan, S., Gendron, M., Mize, J., & Brennan, L. (2007). Of mice and men: Natural kinds of emotions in the mammalian brain? a response to panksepp and izard. Perspectives on Psychological Science, 2(3), 297–312. (PMID: 190795522597798) ; Bathellier, B., Tee, S. P., Hrovat, C., & Rumpel, S. (2013). A multiplicative reinforcement learning model capturing learning dynamics and interindividual variability in mice. Proceedings of the National Academy of Sciences, 110(49), 19950–19955. ; Baum, W. M. (1974). On two types of deviation from the matching law: Bias and undermatching 1. Journal of the Experimental Analysis of Behavior, 22(1), 231–242. (PMID: 168117821333261) ; Beck, A. T., Ward, C. H., Mendelson, M., Mock, J., & Erbaugh, J. (1961). An inventory for measuring depression. Archives of General Psychiatry, 4(6), 561–571. (PMID: 13688369) ; Bellman, R. (1952). On the theory of dynamic programming. Proceedings of the National Academy of Sciences of the United States of America, 38(8), 716–719. (PMID: 165891661063639) ; Bishop, S. J., & Gagne, C. (2018). Anxiety, depression, and decision making: a computational perspective. Annual Review of Neuroscience, 41, 371–388. (PMID: 29709209) ; Blanco, N. J., Otto, A. R., Maddox, W. T., Beevers, C. G., & Love, B. C. (2013). The influence of depression symptoms on exploratory decision-making. Cognition, 129(3), 563–568. (PMID: 24055832) ; Borsini, F., Podhorna, J., & Marazziti, D. (2002). Do animal models of anxiety predict anxiolytic-like effects of antidepressants? Psychopharmacology, 163(2), 121–141. (PMID: 12202959) ; Brenes, J. C., Padilla, M., & Fornaguera, J. (2009). A detailed analysis of open-field habituation and behavioral and neurochemical antidepressant-like effects in postweaning enriched rats. Behavioural brain research, 197(1), 125–137. (PMID: 18786573) ; Brielmann, A. A., & Dayan, P. (2022). A computational model of aesthetic value. Psychological review, 129(6), 1319–1337. (PMID: 35786988) ; Browning, M., Behrens, T. E., Jocham, G., O’reilly, J. X., & Bishop, S. J. (2015). Anxious individuals have difficulty learning the causal statistics of aversive environments. Nature Neuroscience, 18(4), 590. (PMID: 257306694644067) ; Carli, M., Prontera, C., & Samanin, R. (1989). Effect of 5-ht1a agonists on stress-induced deficit in open field locomotor activity of rats: evidence that this model identifies anxiolytic-like activity. Neuropharmacology, 28(5), 471–476. (PMID: 2566948) ; Churchland, P. S. & Sejnowski, T. J. (2016). The computational brain. MIT press. ; Clark, J. E., Watson, S., & Friston, K. J. (2018). What is mood? a computational perspective. Psychological Medicine, 48(14), 2277–2284. (PMID: 294784316340107) ; Daw, N. D. et al. (2011). Trial-by-trial data analysis using computational models. Decision making, affect, and learning: Attention and performance XXIII, 23(1). ; Dayan, P. (1994). Computational modelling. Current Opinion in Neurobiology, 4(2), 212–217. (PMID: 8038579) ; Dayan, P., Niv, Y., Seymour, B., & Daw, N. D. (2006). The misbehavior of value and the discipline of the will. Neural Networks, 19(8), 1153–1160. (PMID: 16938432) ; De Waal, F. B. (1999). Anthropomorphism and anthropodenial: Consistency in our thinking about humans and other animals. Philosophical Topics, 27(1), 255–280. ; Dolensek, N., Gehrlach, D. A., Klein, A. S., & Gogolla, N. (2020). Facial expressions of emotion states and their neuronal correlates in mice. Science, 368(6486), 89–94. (PMID: 32241948) ; Doya, K. (2002). Metalearning and neuromodulation. Neural Networks, 15(4–6), 495–506. (PMID: 12371507) ; Eldar, E., Rutledge, R. B., Dolan, R. J., & Niv, Y. (2016). Mood as representation of momentum. Trends in Cognitive Sciences, 20(1), 15–24. (PMID: 265458534703769) ; Forbes, N. F., Stewart, C. A., Matthews, K., & Reid, I. C. (1996). Chronic mild stress and sucrose consumption: Validity as a model of depression. Physiology & Behavior, 60(6), 1481–1484. ; Fradkin, I., Adams, R. A., Parr, T., Roiser, J. P., & Huppert, J. D. (2020). Searching for an anchor in an unpredictable world: A computational model of obsessive compulsive disorder. Psychological Review, 127(5), 672. (PMID: 32105115) ; Friston, K. J., Redish, A. D., & Gordon, J. A. (2017). Computational nosology and precision psychiatry. Computational Psychiatry (Cambridge, Mass.), 1, 2. ; Ging-Jehli, N. R., Ratcliff, R., & Arnold, L. E. (2021). Improving neurocognitive testing using computational psychiatry-a systematic review for adhd. Psychological Bulletin, 147(2), 169. (PMID: 33370129) ; Glimcher, P. W. (2011). Understanding dopamine and reinforcement learning: the dopamine reward prediction error hypothesis. Proceedings of the National Academy of Sciences, 108(Supplement 3), 15647–15654. ; Goldway, N., Eldar, E., Shoval, G., & Hartley, C. A. (2023). Computational mechanisms of addiction and anxiety: A developmental perspective. Biological Psychiatry, 93(8), 739–750. (PMID: 3677505010038924) ; Gueguen, M. C., Schweitzer, E. M., & Konova, A. B. (2021). Computational theory-driven studies of reinforcement learning and decision-making in addiction: What have we learned? Current Opinion in Behavioral Sciences, 38, 40–48. (PMID: 34423103) ; Hales, C. A., Houghton, C. J., & Robinson, E. S. (2017). Behavioural and computational methods reveal differential effects for how delayed and rapid onset antidepressants effect decision making in rats. European Neuropsychopharmacology, 27(12), 1268–1280. (PMID: 291008195720479) ; Hales, C. A., Robinson, E. S., & Houghton, C. J. (2016). Diffusion modelling reveals the decision making processes underlying negative judgement bias in rats. PloS One, 11(3), e0152592. (PMID: 270234424811525) ; Harding, E. J., Paul, E. S., & Mendl, M. (2004). Animal behaviour: Cognitive bias and affective state. Nature, 427(6972), 312. (PMID: 14737158) ; Herrnstein, R. J. (1961). Relative and absolute strength of response as a function of frequency of reinforcement. Journal of the Experimental Analysis of Behavior, 4(3), 267. (PMID: 137137751404074) ; Hisey, E. E., Fritsch, E. L., Newman, E. L., Ressler, K. J., Kangas, B. D., & Carlezon Jr, W. A, (2023). Early life stress in male mice blunts responsiveness in a translationally-relevant reward task. Neuropsychopharmacology. ; Hodgkin, A. L., & Huxley, A. F. (1952). A quantitative description of membrane current and its application to conduction and excitation in nerve. The Journal of Physiology, 117(4), 500–544. (PMID: 129912371392413) ; Huys, Q. J., Eshel, N., O’Nions, E., Sheridan, L., Dayan, P., & Roiser, J. P. (2012). Bonsai trees in your head: How the pavlovian system sculpts goal-directed choices by pruning decision trees. PLoS Computational Biology, 8(3), e1002410. (PMID: 224123603297555) ; Huys, Q. J., Guitart-Masip, M., Dolan, R. J., & Dayan, P. (2015). Decision-theoretic psychiatry. Clinical. Psychological Science, 3(3), 400–421. ; Huys, Q. J., Maia, T. V., & Frank, M. J. (2016). Computational psychiatry as a bridge from neuroscience to clinical applications. Nature Neuroscience, 19(3), 404–413. (PMID: 269065075443409) ; Huys, Q. J., Pizzagalli, D. A., Bogdan, R., & Dayan, P. (2013). Mapping anhedonia onto reinforcement learning: A behavioural meta-analysis. Biology of Mood & Anxiety Disorders, 3(1), 12. ; Iigaya, K., Jolivald, A., Jitkrittum, W., Gilchrist, I. D., Dayan, P., Paul, E., & Mendl, M. (2016). Cognitive bias in ambiguity judgements: Using computational models to dissect the effects of mild mood manipulation in humans. PloS One, 11(11), e0165840. (PMID: 278290415102472) ; Insel, T., Cuthbert, B., Garvey, M., Heinssen, R., Pine, D. S., Quinn, K., Sanislow, C., & Wang, P. (2010). Research domain criteria (rdoc): Toward a new classification framework for research on mental disorders. American Journal of Psychiatry, 167(7), 748–751. (PMID: 20595427) ; Ironside, M., Kumar, P., Kang, M.-S., & Pizzagalli, D. A. (2018). Brain mechanisms mediating effects of stress on reward sensitivity. Current Opinion in Behavioral Sciences, 22, 106–113. (PMID: 303498726195323) ; Jolles, J. W. (2021). Broad-scale applications of the raspberry pi: A review and guide for biologists. Methods in Ecology and Evolution, 12(9), 1562–1579. ; Jones, S., Neville, V., Higgs, L., Paul, E. S., Dayan, P., Robinson, E. S., & Mendl, M. (2018). Assessing animal affect: An automated and self-initiated judgement bias task based on natural investigative behaviour. Scientific Reports, 8(1), 12400. (PMID: 301203156098098) ; Kangas, B. D., Wooldridge, L. M., Luc, O. T., Bergman, J., & Pizzagalli, D. A. (2020). Empirical validation of a touchscreen probabilistic reward task in rats Translational. Psychiatry, 10(1), 285. ; Kangas, B. D., Der-Avakian, A., & Pizzagalli, D. A. (2022). Probabilistic reinforcement learning and anhedonia Curr Top. Behav Neurosci, 58, 355–377. ; Kremer, L., Holkenborg, S. K., Reimert, I., Bolhuis, J., & Webb, L. (2020). The nuts and bolts of animal emotion. Neuroscience & Biobehavioral Reviews, 113, 273–286. ; Kumar, V., Bhat, Z. A., & Kumar, D. (2013). Animal models of anxiety: A comprehensive review. Journal of Pharmacological and Toxicological Methods, 68(2), 175–183. (PMID: 23684951) ; Lagisz, M., Zidar, J., Nakagawa, S., Neville, V., Sorato, E., Paul, E. S., Bateson, M., Mendl, M., & Løvlie, H. (2020). Optimism, pessimism and judgement bias in animals: A systematic review and meta-analysis. Neuroscience & Biobehavioral Reviews. ; LeDoux, J. (2012). Rethinking the emotional brain. Neuron, 73(4), 653–676. (PMID: 223655423625946) ; Loewenstein, G. (2000). Emotions in economic theory and economic behavior. American economic review, 90(2), 426–432. ; Loosen, A. M., & Hauser, T. U. (2020). Towards a computational psychiatry of juvenile obsessive-compulsive disorder. Neuroscience & Biobehavioral Reviews, 118, 631–642. ; Ma, W. J., & Jazayeri, M. (2014). Neural coding of uncertainty and probability. Annual Review of Neuroscience, 37, 205–220. (PMID: 25032495) ; Mendl, M., Burman, O. H., Parker, R. M., & Paul, E. S. (2009). Cognitive bias as an indicator of animal emotion and welfare: Emerging evidence and underlying mechanisms. Applied Animal Behaviour Science, 118(3–4), 161–181. ; Mendl, M., Burman, O. H., & Paul, E. S. (2010). An integrative and functional framework for the study of animal emotion and mood. Proceedings of the Royal Society B: Biological Sciences, 277(1696), 2895–2904. (PMID: 2982018) ; Mendl, M., Neville, V., & Paul, E. S. (2022). Bridging the gap: Human emotions and animal emotions. Affective Science, 3(4), 703–712. (PMID: 365191489743877) ; Mendl, M., & Paul, E. S. (2020). Animal affect and decision-making. Neuroscience and Biobehavioral Reviews, 112, 144–163. (PMID: 31991192) ; Meyniel, F., Goodwin, G. M., Deakin, J. W., Klinge, C., MacFadyen, C., Milligan, H., Mullings, E., Pessiglione, M., & Gaillard, R. (2016). A specific role for serotonin in overcoming effort cost. Elife, 5, e17282. (PMID: 278245545100997) ; Millner, A. J., den Ouden, H. E., Gershman, S. J., Glenn, C. R., Kearns, J. C., Bornstein, A. M., Marx, B. P., Keane, T. M., & Nock, M. K. (2019). Suicidal thoughts and behaviors are associated with an increased decision-making bias for active responses to escape aversive states. Journal of Abnormal Psychology, 128(2), 106. ; Mobbs, D., Adolphs, R., Fanselow, M. S., Barrett, L. F., LeDoux, J. E., Ressler, K., & Tye, K. M. (2019). Viewpoints: Approaches to defining and investigating fear. Nature Neuroscience, 22(8), 1205–1216. (PMID: 313323746943931) ; Montague, P. R., Dayan, P., & Sejnowski, T. J. (1996). A framework for mesencephalic dopamine systems based on predictive hebbian learning. Journal of Neuroscience, 16(5), 1936–1947. (PMID: 8774460) ; Montague, P. R., Dolan, R. J., Friston, K. J., & Dayan, P. (2012). Computational psychiatry. Trends in Cognitive Sciences, 16(1), 72–80. (PMID: 22177032) ; Moutoussis, M., Story, G., & Dolan, R. J. (2015). The computational psychiatry of reward: Broken brains or misguided minds? Frontiers in Psychology, 6, 1445. (PMID: 264837134586432) ; Nesse, R. M. (2000). Is depression an adaptation? Archives of general psychiatry, 57(1), 14–20. (PMID: 10632228) ; Nettle, D., & Bateson, M. (2012). The evolutionary origins of mood and its disorders. Current Biology, 22(17), R712–R721. (PMID: 22975002) ; Neville, V., Dayan, P., Gilchrist, I. D., Paul, E. S., & Mendl, M. (2021). Dissecting the links between reward and loss, decision-making, and self-reported affect using a computational approach. PLOS Computational Biology, 17(1), e1008555. (PMID: 334175957819615) ; Neville, V., Dayan, P., Gilchrist, I. D., Paul, E. S., & Mendl, M. (2021). Dissecting the links between reward and loss, decision-making, and self-reported affect using a computational approach. PLOS Computational Biology, 17(1), e1008555. (PMID: 334175957819615) ; Neville, V., Dayan, P., Gilchrist, I. D., Paul, E. S., & Mendl, M. (2021). Using primary reinforcement to enhance translatability of a human affect and decision-making judgment bias task. Journal of Cognitive Neuroscience, 33(12), 2523–2535. (PMID: 34477879) ; Neville, V., King, J., Gilchrist, I. D., Dayan, P., Paul, E. S., & Mendl, M. (2020). Reward and punisher experience alter rodent decision-making in a judgement bias task. Scientific Reports, 10(1), 1– 14. ; Neville, V., Nakagawa, S., Zidar, J., Paul, E. S., Lagisz, M., Bateson, M., Løvlie, H., & Mendl, M. (2020). Pharmacological manipulations of judgement bias: A systematic review and meta-analysis. Neuroscience and Biobehavioral Reviews, 108, 269–286. (PMID: 317475526966323) ; Noworyta-Sokolowska, K., Kozub, A., Jablonska, J., Rodriguez Parkitna, J., Drozd, R., & Rygula, R. (2019). Sensitivity to negative and positive feedback as a stable and enduring behavioural trait in rats. Psychopharmacology, 236, 2389–2403. (PMID: 313758496695373) ; Otto, A. R., & Eichstaedt, J. C. (2018). Real-world unexpected outcomes predict city-level mood states and risk-taking behavior. PloS one, 13(11), e0206923. (PMID: 304853046261541) ; Ousdal, O. T., Huys, Q., Mildë, A. M., Craven, A. R., Ersland, L., Endestad, T., Melinder, A., Hugdahl, K., & Dolan, R. J. (2018). The impact of traumatic stress on pavlovian biases. Psychological medicine, 48(2), 327–336. (PMID: 28641601) ; Overstreet, D. H., Friedman, E., Mathé, A. A., & Yadid, G. (2005). The flinders sensitive line rat: A selectively bred putative animal model of depression. Neuroscience & Biobehavioral Reviews, 29(4–5), 739–759. ; Panksepp, J. (2005). Affective consciousness: Core emotional feelings in animals and humans. Consciousness and Cognition, 14(1), 30–80. (PMID: 15766890) ; Panksepp, J. (2011). The basic emotional circuits of mammalian brains: do animals have affective lives? Neuroscience & Biobehavioral Reviews, 35(9), 1791–1804. ; Paul, E. S., Harding, E. J., & Mendl, M. (2005). Measuring emotional processes in animals: The utility of a cognitive approach. Neuroscience & Biobehavioral Reviews, 29(3), 469–491. ; Paul, E. S., Sher, S., Tamietto, M., Winkielman, P., & Mendl, M. T. (2020). Towards a comparative science of emotion: Affect and consciousness in humans and animals. Neuroscience & Biobehavioral Reviews, 108, 749–770. ; Pike, A. C. & Robinson, O. J. (2022). Reinforcement learning in patients with mood and anxiety disorders vs control individuals: A systematic review and meta-analysis. JAMA psychiatry. ; Piray, P., Dezfouli, A., Heskes, T., Frank, M. J., & Daw, N. D. (2019). Hierarchical bayesian inference for concurrent model fitting and comparison for group studies. PLoS Computational Biology, 15(6). ; Poirier, C., Bateson, M., Gualtieri, F., Armstrong, E. A., Laws, G. C., Boswell, T., & Smulders, T. V. (2019). Validation of hippocampal biomarkers of cumulative affective experience. Neuroscience & Biobehavioral Reviews, 101, 113–121. ; Rae, C. L., Critchley, H. D., & Seth, A. K. (2019). A bayesian account of the sensory-motor interactions underlying symptoms of tourette syndrome. Frontiers in Psychiatry, 10, 29. (PMID: 308909656412155) ; Ratcliff, R. (1978). A theory of memory retrieval. Psychological review, 85(2), 59. ; Rescorla, R. A., Wagner, A. R., et al. (1972). A theory of pavlovian conditioning: Variations in the effectiveness of reinforcement and nonreinforcement. Classical conditioning II: Current research and Theory, 2, 64–99. ; Rivalan, M., Valton, V., Series, P., Marchand, A. R., & Dellu-Hagedorn, F. (2013). Elucidating poor decision-making in a rat gambling task. PLoS One, 8(12), e82052. (PMID: 243399883855331) ; Rolls, E. T. (2013). What are emotional states, and why do we have them? Emotion Review, 5(3), 241–247. ; Royce, J. R. (1977). On the construct validity of open-field measures. Psychological bulletin, 84(6), 1098. ; Ruhé, H. G., Mason, N. S., & Schene, A. H. (2007). Mood is indirectly related to serotonin, norepinephrine and dopamine levels in humans: a meta-analysis of monoamine depletion studies. Molecular psychiatry, 12(4), 331–359. (PMID: 17389902) ; Rupniak, N. (2003). Animal models of depression: Challenges from a drug development perspective. Behavioural Pharmacology, 14(5), 385–390. (PMID: 14501252) ; Rupprechter, S., Stankevicius, A., Huys, Q. J., Steele, J. D., & Seriès, P. (2018). Major depression impairs the use of reward values for decision-making. Scientific reports, 8(1), 1–8. ; Rutledge, R. B., Skandali, N., Dayan, P., & Dolan, R. J. (2014). A computational and neural model of momentary subjective well-being. Proceedings of the National Academy of Sciences, 111(33), 12252–12257. ; Saez, I., & Gu, X. (2023). Invasive computational psychiatry. Biological psychiatry, 93(8), 661–670. (PMID: 36641365) ; Schrijver, N. C., Bahr, N. I., Weiss, I. C., & Würbel, H. (2002). Dissociable effects of isolation rearing and environmental enrichment on exploration, spatial learning and hpa activity in adult rats. Pharmacology Biochemistry and Behavior, 73(1), 209–224. (PMID: 12076740) ; Schüller, T., Fischer, A. G., Gruendler, T. O., Baldermann, J. C., Huys, D., Ullsperger, M., & Kuhn, J. (2020). Decreased transfer of value to action in tourette syndrome. Cortex, 126, 39–48. (PMID: 32062469) ; Schultz, W., Apicella, P., & Ljungberg, T. (1993). Responses of monkey dopamine neurons to reward and conditioned stimuli during successive steps of learning a delayed response task. Journal of neuroscience, 13(3), 900–913. (PMID: 8441015) ; Schultz, W., Dayan, P., & Montague, P. R. (1997). A neural substrate of prediction and reward. Science, 275(5306), 1593–1599. (PMID: 9054347) ; Series, P. (2020). Computational psychiatry: A primer. MIT Press. ; Slattery, D. A., Markou, A., & Cryan, J. F. (2007). Evaluation of reward processes in an animal model of depression. Psychopharmacology, 190, 555–568. (PMID: 17177055) ; Spiegler, K. M., Palmieri, J., Pang, K. C., & Myers, C. E. (2020). A reinforcement-learning model of active avoidance behavior: Differences between sprague dawley and wistar-kyoto rats. Behavioural Brain Research, 393, 112784. (PMID: 325852997423762) ; Stephan, K. E., Bach, D. R., Fletcher, P. C., Flint, J., Frank, M. J., Friston, K. J., Heinz, A., Huys, Q. J., Owen, M. J., Binder, E. B., et al. (2016). Charting the landscape of priority problems in psychiatry, part 1: classification and diagnosis. The Lancet Psychiatry, 3(1), 77–83. (PMID: 26573970) ; Stephan, K. E., & Mathys, C. (2014). Computational approaches to psychiatry. Current Opinion in Neurobiology, 25, 85–92. (PMID: 24709605) ; Sutton, R. S., & Barto, A. G. (1981). Toward a modern theory of adaptive networks: Expectation and prediction. Psychological Review, 88(2), 135. (PMID: 7291377) ; Sutton, R. S. & Barto, A. G. (2018). Introduction to reinforcement learning. MIT press Cambridge, 2 edition. ; Swanson, K., Averbeck, B. B., & Laubach, M. (2022). Noradrenergic regulation of two-armed bandit performance. Behavioral Neuroscience, 136(1), 84. (PMID: 34647770) ; Theisen, M., Lerche, V., von Krause, M., & Voss, A. (2021). Age differences in diffusion model parameters: A meta-analysis. Psychological Research, 85(5), 2012–2021. (PMID: 32535699) ; Ulrichsen, K. M., Alnaes, D., Kolskar, K. K., Richard, G., Sanders, A.-M., Dorum, E. S., Ihle-Hansen, H., Pedersen, M. L., Tornas, S., Nordvik, J. E., & Westlye, L. T. (2020). Dissecting the cognitive phenotype of post-stroke fatigue using computerized assessment and computational modeling of sustained attention. Psychological research, 85(5), 2012–2021. ; Valletta, J. J., Torney, C., Kings, M., Thornton, A., & Madden, J. (2017). Applications of machine learning in animal behaviour studies. Animal Behaviour, 124, 203–220. ; Valton, V., Romaniuk, L., Steele, J. D., Lawrie, S., & Seriès, P. (2017). Comprehensive review: Computational modelling of schizophrenia. Neuroscience & Biobehavioral Reviews, 83, 631–646. ; van Ravenzwaaij, D., Dutilh, G., & Wagenmakers, E.-J. (2012). A diffusion model decomposition of the effects of alcohol on perceptual decision making. Psychopharmacology, 218, 1017–1025. ; Vinckier, F., Jaffre, C., Gauthier, C., Smajda, S., Abdel-Ahad, P., Le Bouc, R., Daunizeau, J., Fefeu, M., Borderies, N., Plaze, M., et al. (2022). Elevated effort cost identified by computational modeling as a distinctive feature explaining multiple behaviors in patients with depression. Biological Psychiatry: Cognitive Neuroscience and Neuroimaging, 7(11), 1158–1169. ; Vulkan, N. (2000). An economist’s perspective on probability matching. Journal of Economic Surveys, 14(1), 101–118. ; Wallace, J. (2000). Humane endpoints and cancer research Institute for Laboratory. Animal Research, 41(2), 87–93. ; Whitton, A. E., Treadway, M. T., & Pizzagalli, D. A. (2015). Reward processing dysfunction in major depression, bipolar disorder and schizophrenia. Current Opinion in Psychiatry, 28(1), 7. (PMID: 254154994277233) ; Widrow, B. & Hoff, M. E. (1960). Adaptive switching circuits. Technical report, Stanford Univ Ca Stanford Electronics Labs. ; Willner, P. (2017). The chronic mild stress (cms) model of depression: History, evaluation and usage. Neurobiology of Stress, 6, 78–93. (PMID: 28229111) ; Willner, P., Towell, A., Sampson, D., Sophokleous, S., & Muscat, R. (1987). Reduction of sucrose preference by chronic unpredictable mild stress, and its restoration by a tricyclic antidepressant. Psychopharmacology, 93(3), 358–364. (PMID: 3124165) ; Wilson, R. C. & Collins, A. G. (2019). Ten simple rules for the computational modeling of behavioral data. eLife, 8, e49547. ; Wooldridge, L. M., Bergman, J., Pizzagalli, D. A., & Kangas, B. D. (2021). Translational assessments of reward responsiveness in the marmoset. International Journal of Neuropsychopharmacology, 24(5), 409–418. (PMID: 33280005)
  • Grant Information: BB/T002654/1,BB/X009696/1 United Kingdom BB_ Biotechnology and Biological Sciences Research Council
  • Entry Date(s): Date Created: 20231130 Date Completed: 20240423 Latest Revision: 20240520
  • Update Code: 20240521
  • PubMed Central ID: PMC11039423

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Bitte prüfen Sie, ob die Zitation formal korrekt ist, bevor Sie sie in einer Arbeit verwenden. Benutzen Sie gegebenenfalls den "Exportieren"-Dialog, wenn Sie ein Literaturverwaltungsprogramm verwenden und die Zitat-Angaben selbst formatieren wollen.

xs 0 - 576
sm 576 - 768
md 768 - 992
lg 992 - 1200
xl 1200 - 1366
xxl 1366 -