Dataset Information

Asymmetric coupling of action and outcome valence in active and observational feedback learning.

ABSTRACT: Learning to execute a response to obtain a reward or to inhibit a response to avoid punishment is much easier than learning the reverse, which has been referred to as "Pavlovian" biases. Despite a growing body of research into similarities and differences between active and observational learning, it is as yet unclear if Pavlovian learning biases are specific for active task performance, i.e., learning from feedback provided for one's own actions, or if they persist also when learning by observing another person's actions and subsequent outcomes. The present study, therefore, investigated the influence of action and outcome valence in active and observational feedback learning. Healthy adult volunteers completed a go/nogo task that decoupled outcome valence (win/loss) and action (execution/inhibition) either actively or by observing a virtual co-player's responses and subsequent feedback. Moreover, in a more naturalistic follow-up experiment, pairs of subjects were tested with the same task, with one subject as active learner and the other as observational learner. The results revealed Pavlovian learning biases both in active and in observational learning, with learning of go responses facilitated in the context of reward obtainment, and learning of nogo responses facilitated in the context of loss avoidance. Although the neural correlates of active and observational feedback learning have been shown to differ to some extent, these findings suggest similar mechanisms to underlie both types of learning with respect to the influence of Pavlovian biases. Moreover, performance levels and result patterns were similar in those observational learners who had observed a virtual co-player and those who had completed the task together with an active learner, suggesting that inclusion of a virtual co-player in a computerized task provides an effective manipulation of agency.

SUBMITTER: Peterburs J

PROVIDER: S-EPMC8211594 | biostudies-literature |

REPOSITORIES: biostudies-literature

ACCESS DATA

Similar Datasets

Project description:Converging theories suggest that organisms learn and exploit probabilistic models of their environment. However, it remains unclear how such models can be learned in practice. The open-ended complexity of natural environments means that it is generally infeasible for organisms to model their environment comprehensively. Alternatively, action-oriented models attempt to encode a parsimonious representation of adaptive agent-environment interactions. One approach to learning action-oriented models is to learn online in the presence of goal-directed behaviours. This constrains an agent to behaviourally relevant trajectories, reducing the diversity of the data a model need account for. Unfortunately, this approach can cause models to prematurely converge to sub-optimal solutions, through a process we refer to as a bad-bootstrap. Here, we exploit the normative framework of active inference to show that efficient action-oriented models can be learned by balancing goal-oriented and epistemic (information-seeking) behaviours in a principled manner. We illustrate our approach using a simple agent-based model of bacterial chemotaxis. We first demonstrate that learning via goal-directed behaviour indeed constrains models to behaviorally relevant aspects of the environment, but that this approach is prone to sub-optimal convergence. We then demonstrate that epistemic behaviours facilitate the construction of accurate and comprehensive models, but that these models are not tailored to any specific behavioural niche and are therefore less efficient in their use of data. Finally, we show that active inference agents learn models that are parsimonious, tailored to action, and which avoid bad bootstraps and sub-optimal convergence. Critically, our results indicate that models learned through active inference can support adaptive behaviour in spite of, and indeed because of, their departure from veridical representations of the environment. Our approach provides a principled method for learning adaptive models from limited interactions with an environment, highlighting a route to sample efficient learning algorithms.

Dataset Information

Asymmetric coupling of action and outcome valence in active and observational feedback learning.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets