Application of a Prediction Error Theory to Pavlovian Conditioning in an Insect

Abstract

Elucidation of the conditions in which associative learning occurs is a critical issue in neuroscience and comparative psychology. In Pavlovian conditioning in mammals, it is thought that the discrepancy, or error, between the actual reward and the predicted reward determines whether learning occurs. This theory stems from the finding of Kamin’s blocking effect, in which after pairing of a stimulus with an unconditioned stimulus (US), conditioning of a second stimulus is blocked when the two stimuli are presented in compound and paired with the same US. Whether this theory is applicable to any species of invertebrates, however, has remained unknown. We first showed blocking and one-trial blocking of Pavlovian conditioning in the cricket Gryllus bimaculatus, which supported the Rescorla–Wagner model but not attentional theories, the major competitive error-correction learning theories to account for blocking. To match the prediction error theory, a neural circuit model was proposed, and prediction from the model was tested: the results were consistent with the Rescorla–Wagner model but not with the retrieval theory, another competitive theory to account for blocking. The findings suggest that the Rescorla–Wagner model best accounts for Pavlovian conditioning in crickets and that the basic computation rule underlying Pavlovian conditioning in crickets is the same to those suggested in mammals. Moreover, results of pharmacological studies in crickets suggested that octopamine and dopamine mediate prediction error signals in appetitive and aversive conditioning, respectively. This was in contrast to the notion that dopamine mediates appetitive prediction error signals in mammals. The functional significance and evolutionary implications of these findings are discussed.

Development of behavioural automaticity by extended Pavlovian training in an insect

“…In this study, we investigated whether extended Pavlovian training leads to a change of sensitivity of CR to devaluation of US in the cricket, Gryllus bimaculatus. Crickets are newly emerging animal models for the study of basic mechanisms of learning and memory, because they have excellent learning capabilities and it is relatively easy to apply various experimental manipulations such as pharmacological intervention, RNA interference, and genome editing [14][15][16][17][18][19]. We previously showed that two to four pairing trials to associate an odour with water, with inter-trial intervals (ITIs) of 5 min, are sufficient to lead to protein synthesis-dependent long-term memory that lasts for at least 4 days [20,21].…”

What Is Learned in Pavlovian Conditioning in Crickets? Revisiting the S-S and S-R Learning Theories

“…Terao et al (2015) observed blocking and a specific case of blocking, "one-trial blocking, " and suggested that Pavlovian conditioning in crickets is best accounted for by the Rescorla and Wagner (1972) model, one of most influential models among error-correction learning theories that are proposed to account for Pavlovian conditioning. Moreover, our pharmacological studies suggested that OA neurons mediate prediction error signals for appetitive conditioning (Terao et al, 2015), whereas DA neurons mediate prediction error signals for aversive conditioning (Terao and Mizunami, 2017;Mizunami et al, 2018), although evidence for the latter is incomplete. These suggestions are comparable to findings in mammals that different types of DA neurons in the midbrain mediate appetitive and aversive prediction error signals, respectively, in Pavlovian conditioning as well as in instrumental conditioning (Schultz, 2013(Schultz, , 2015Engelhard et al, 2019;Gershman and Uchida, 2019).…”

The evolution of cultural gadgets

“…What we know about the processes occurring during goal-directed learning in non-human animals supports Dickinson's suggestion. The great evolutionary conservation that we so clearly see in the morphological development of animals is also seen with respect to learning: Associative learning remained grounded in the same basic molecular factors and cybernetic logic we see in slugs, planarians and crickets, which enable prediction-error driven blocking and other behavioral feats (reviewed in Terao, Matsumoto & Mizunami, 2015;Mizunami, Terao & Alvarez, 2018). Going beyond these behaviors requires the evolution of additional dedicated hierarchical structures in the brain: Top layers that integrate and control the information arriving from lowers levels of the hierarchy, and back and forth interactions among layers that enable the formation of associations that involve processes of reentry among groups of neurons in addition to associations between single neurons (Edelman & Gally, 2013;Märtens, Meier, Hillebrand, Tewarie & Van Mieghem, 2017); dedicated memory structures such as the hippocampus and cerebellum in vertebrates that enable the storage of compound representations, as well as flexible, integrating, reward systems.…”