Bayesian Cue Integration as a Developmental Outcome of Reward Mediated Learning

Weisswange, Thomas H.; Rothkopf, Constantin A.; Rodemann, Tobias; Triesch, Jochen

doi:10.1371/journal.pone.0021575

Cited by 24 publications

(34 citation statements)

References 76 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The simulation setup is based partly on [10]. At each time step, a stimulus is generated randomly in one of the 30 discrete positions and each sensor observes a noisy representation of it.…”

Section: Experiments and Resultsmentioning

confidence: 99%

Reward Maximization Justifies the Transition from Sensory Selection at Childhood to Sensory Integration at Adulthood

2014

View full text Add to dashboard Cite

In a multisensory task, human adults integrate information from different sensory modalities -behaviorally in an optimal Bayesian fashion- while children mostly rely on a single sensor modality for decision making. The reason behind this change of behavior over age and the process behind learning the required statistics for optimal integration are still unclear and have not been justified by the conventional Bayesian modeling. We propose an interactive multisensory learning framework without making any prior assumptions about the sensory models. In this framework, learning in every modality and in their joint space is done in parallel using a single-step reinforcement learning method. A simple statistical test on confidence intervals on the mean of reward distributions is used to select the most informative source of information among the individual modalities and the joint space. Analyses of the method and the simulation results on a multimodal localization task show that the learning system autonomously starts with sensory selection and gradually switches to sensory integration. This is because, relying more on modalities -i.e. selection- at early learning steps (childhood) is more rewarding than favoring decisions learned in the joint space since, smaller state-space in modalities results in faster learning in every individual modality. In contrast, after gaining sufficient experiences (adulthood), the quality of learning in the joint space matures while learning in modalities suffers from insufficient accuracy due to perceptual aliasing. It results in tighter confidence interval for the joint space and consequently causes a smooth shift from selection to integration. It suggests that sensory selection and integration are emergent behavior and both are outputs of a single reward maximization process; i.e. the transition is not a preprogrammed phenomenon.

show abstract

Section: Experiments and Resultsmentioning

confidence: 99%

Reward Maximization Justifies the Transition from Sensory Selection at Childhood to Sensory Integration at Adulthood

2014

View full text Add to dashboard Cite

show abstract

“…For example, artificial neural networks can be trained to perform Bayesian cue integration and causal inference using reinforcement learning [53]. Specifically, the network learns to optimally combine sensory information by predicting the reward that an action will produce for a given set of sensory information.…”

Section: Development and Calibration Of Multisensory Integrationmentioning

confidence: 99%

Models and processes of multisensory cue combination

Seilheimer

Rosenberg

Angelaki

2014

Current Opinion in Neurobiology

View full text Add to dashboard Cite

Fundamental to our perception of a unified and stable environment is the capacity to combine information across the senses. Although this process appears seamless as an adult, the brain’s ability to successfully perform multisensory cue combination takes years to develop and relies on a number of complex processes including cue integration, cue calibration, causal inference, and reference frame transformations. Further complexities exist because multisensory cue combination is implemented by populations of noisy neurons. In this review, we discuss recent behavioral studies exploring how the brain combines information from different sensory systems, neurophysiological studies relating behavior to neuronal activity, and a theory of neural sensory encoding that can account for many of these experimental findings.

show abstract

“…Psychophysical studies have shown that animals and humans can behave as optimal Bayesian observers—they integrate noisy sensory cues, their own predictions and prior beliefs in order to maximize the expected outcome of their actions (Kording and Wolpert, 2004; Ernst, 2007; Navalpakkam et al, 2010; Rao, 2010; Weisswange et al, 2011; Karim et al, 2012). A key to maximizing the outcome is to have knowledge of the certainty of sensory evidence, for higher degrees of uncertainty encourage the nervous system to weaken its belief in the ongoing model and to explore alternative stimulus-outcome associations (Karlsson et al, 2012).…”

Section: Discussionmentioning

confidence: 99%

Learning not to feel: reshaping the resolution of tactile perception

Omrani¹,

Lak²,

Diamond³

2013

Front. Syst. Neurosci.

View full text Add to dashboard Cite

We asked whether biased feedback during training could cause human subjects to lose perceptual acuity in a vibrotactile frequency discrimination task. Prior to training, we determined each subject's vibration frequency discrimination capacity on one fingertip, the Just Noticeable Difference (JND). Subjects then received 850 trials in which they performed a same/different judgment on two vibrations presented to that fingertip. They gained points whenever their judgment matched the computer-generated feedback on that trial. Feedback, however, was biased: the probability per trial of “same” feedback was drawn from a normal distribution with standard deviation twice as wide as the subject's JND. After training, the JND was significantly widened: stimulus pairs previously perceived as different were now perceived as the same. The widening of the JND extended to the untrained hand, indicating that the decrease in resolution originated in non-topographic brain regions. In sum, the acuity of subjects' sensory-perceptual systems shifted in order to match the feedback received during training.

show abstract

Bayesian Cue Integration as a Developmental Outcome of Reward Mediated Learning

Cited by 24 publications

References 76 publications

Reward Maximization Justifies the Transition from Sensory Selection at Childhood to Sensory Integration at Adulthood

Reward Maximization Justifies the Transition from Sensory Selection at Childhood to Sensory Integration at Adulthood

Models and processes of multisensory cue combination

Learning not to feel: reshaping the resolution of tactile perception

Contact Info

Product

Resources

About