A System Computational Model of Implicit Emotional Learning

Abstract

Nowadays, the experimental study of emotional learning is commonly based on classical conditioning paradigms and models, which have been thoroughly investigated in the last century. Unluckily, models based on classical conditioning are unable to explain or predict important psychophysiological phenomena, such as the failure of the extinction of emotional responses in certain circumstances (for instance, those observed in evaluative conditioning, in post-traumatic stress disorders and in panic attacks). In this manuscript, starting from the experimental results available from the literature, a computational model of implicit emotional learning based both on prediction errors computation and on statistical inference is developed. The model quantitatively predicts (a) the occurrence of evaluative conditioning, (b) the dynamics and the resistance-to-extinction of the traumatic emotional responses, (c) the mathematical relation between classical conditioning and unconditioned stimulus revaluation. Moreover, we discuss how the derived computational model can lead to the development of new animal models for resistant-to-extinction emotional reactions and novel methodologies of emotions modulation.

Wanting, liking and welfare: The role of affective states in proximate control of behaviour in vertebrates

“…The rewarding properties are considered innate for at least a few central stimuli such as food, high‐risk predators and potential mating partners (primary reinforcers: Georgiadis & Kringelbach, ; Kringelbach, Stein, & van Hartevelt, ; Rolls, ). The valences (negativity, positivity) of other stimuli need to be learnt based on some form of associative learning, for example novel food types, additional predators and other types of social relations (secondary reinforcers: Rolls, ; see also Marshall, ; Kobayashi & Schultz, ; Nelson, Lau, & Jarcho, ; Puviani & Rama, ). Operant conditioning and clicker training, for example, tap into the secondary reward system using novel stimuli as secondary reinforcers.…”

Computational Psychiatry and Psychometrics Based on Non-Conscious Stimuli Input and Pupil Response Output

“…In fact, provided that theoretical models for non-conscious (i.e., implicit) emotional processing are available [some developments at a preliminary stage can be found in Ref. (12, 52, 53; Puviani et al, under review 1 )], theoretically meaningful parameters can be extracted; such parameters can then be used as efficient, low-dimensional representations of the very high-dimensional data to which ML techniques for classification or regression can subsequently be applied. Moreover, the inherently reduced dimensionality improves the generalization of diagnostic (i.e., classification or regression) algorithms, partially prevents overfitting and reduces the number of patients needed in a training stage (50).…”

“…In processing pupillometry signals, the following phenomena should be taken into account: (1) the emotional arousal forward specific non-consciously processed stimuli (in order to assess, for instance, the implicit relevance of specific phobic, sexual, or abuse related stimuli); (2) the arousal triggered by non-consciously processed appetitive and aversive stimuli (e.g., fearful and happy faces); (3) implicit emotional contrast effects (52, 54) produced by the shift from positive (negative) to opposite stimulations in a continuous-like stimulation flux (i.e., a non-conscious perception of a series of temporally close discrete stimuli); (4) the tonic, phasic, and spontaneous fluctuations of neural gain; (5) the implicit learning of conditioned inhibitors; (6) the characteristic time constants and temporal reactions forward successive stimulations; and (7) specific combinations and relationships between the variables mentioned above.…”