↓ Skip to main content

‘Proactive’ use of cue-context congruence for building reinforcement learning’s reward function

Overview of attention for article published in BMC Neuroscience, October 2016
Altmetric Badge

About this Attention Score

  • Good Attention Score compared to outputs of the same age (66th percentile)
  • Good Attention Score compared to outputs of the same age and source (66th percentile)

Mentioned by

twitter
7 tweeters

Readers on

mendeley
22 Mendeley
You are seeing a free-to-access but limited selection of the activity Altmetric has collected about this research output. Click here to find out more.
Title
‘Proactive’ use of cue-context congruence for building reinforcement learning’s reward function
Published in
BMC Neuroscience, October 2016
DOI 10.1186/s12868-016-0302-7
Pubmed ID
Authors

Judit Zsuga, Klara Biro, Gabor Tajti, Magdolna Emma Szilasi, Csaba Papp, Bela Juhasz, Rudolf Gesztelyi

Abstract

Reinforcement learning is a fundamental form of learning that may be formalized using the Bellman equation. Accordingly an agent determines the state value as the sum of immediate reward and of the discounted value of future states. Thus the value of state is determined by agent related attributes (action set, policy, discount factor) and the agent's knowledge of the environment embodied by the reward function and hidden environmental factors given by the transition probability. The central objective of reinforcement learning is to solve these two functions outside the agent's control either using, or not using a model. In the present paper, using the proactive model of reinforcement learning we offer insight on how the brain creates simplified representations of the environment, and how these representations are organized to support the identification of relevant stimuli and action. Furthermore, we identify neurobiological correlates of our model by suggesting that the reward and policy functions, attributes of the Bellman equitation, are built by the orbitofrontal cortex (OFC) and the anterior cingulate cortex (ACC), respectively. Based on this we propose that the OFC assesses cue-context congruence to activate the most context frame. Furthermore given the bidirectional neuroanatomical link between the OFC and model-free structures, we suggest that model-based input is incorporated into the reward prediction error (RPE) signal, and conversely RPE signal may be used to update the reward-related information of context frames and the policy underlying action selection in the OFC and ACC, respectively. Furthermore clinical implications for cognitive behavioral interventions are discussed.

Twitter Demographics

The data shown below were collected from the profiles of 7 tweeters who shared this research output. Click here to find out more about how the information was compiled.

Mendeley readers

The data shown below were compiled from readership statistics for 22 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 22 100%

Demographic breakdown

Readers by professional status Count As %
Researcher 6 27%
Student > Bachelor 6 27%
Student > Ph. D. Student 2 9%
Student > Master 2 9%
Lecturer > Senior Lecturer 1 5%
Other 2 9%
Unknown 3 14%
Readers by discipline Count As %
Medicine and Dentistry 4 18%
Neuroscience 4 18%
Psychology 3 14%
Nursing and Health Professions 2 9%
Economics, Econometrics and Finance 2 9%
Other 3 14%
Unknown 4 18%

Attention Score in Context

This research output has an Altmetric Attention Score of 4. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 01 December 2016.
All research outputs
#3,889,851
of 13,847,550 outputs
Outputs from BMC Neuroscience
#248
of 1,054 outputs
Outputs of similar age
#95,591
of 290,010 outputs
Outputs of similar age from BMC Neuroscience
#27
of 81 outputs
Altmetric has tracked 13,847,550 research outputs across all sources so far. This one has received more attention than most of these and is in the 71st percentile.
So far Altmetric has tracked 1,054 research outputs from this source. They receive a mean Attention Score of 3.7. This one has done well, scoring higher than 75% of its peers.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 290,010 tracked outputs that were published within six weeks on either side of this one in any source. This one has gotten more attention than average, scoring higher than 66% of its contemporaries.
We're also able to compare this research output to 81 others from the same source and published within six weeks on either side of this one. This one has gotten more attention than average, scoring higher than 66% of its contemporaries.