Opponent Identity Influences Value Learning in Simple Games.

نویسندگان

  • Timothy J Vickery
  • Matthew R Kleinman
  • Marvin M Chun
  • Daeyeol Lee
چکیده

UNLABELLED Context plays a pivotal role in many decision-making scenarios, including social interactions wherein the identities and strategies of other decision makers often shape our behaviors. However, the neural mechanisms for tracking such contextual information are poorly understood. Here, we investigated how opponent identity affects human reinforcement learning during a simulated competitive game against two independent computerized opponents. We found that strategies of participants were affected preferentially by the outcomes of the previous interactions with the same opponent. In addition, reinforcement signals from the previous trial were less discriminable throughout the brain after the opponent changed, compared with when the same opponent was repeated. These opponent-selective reinforcement signals were particularly robust in right rostral anterior cingulate and right lingual regions, where opponent-selective reinforcement signals correlated with a behavioral measure of opponent-selective reinforcement learning. Therefore, when choices involve multiple contextual frames, such as different opponents in a game, decision making and its neural correlates are influenced by multithreaded histories of reinforcement. Overall, our findings are consistent with the availability of temporally overlapping, context-specific reinforcement signals. SIGNIFICANCE STATEMENT In real-world decision making, context plays a strong role in determining the value of an action. Similar choices take on different values depending on setting. We examined the contextual dependence of reward-based learning and reinforcement signals using a simple two-choice matching-pennies game played by humans against two independent computer opponents that were randomly interleaved. We found that human subjects' strategies were highly dependent on opponent context in this game, a fact that was reflected in select brain regions' activity (rostral anterior cingulate and lingual cortex). These results indicate that human reinforcement histories are highly dependent on contextual factors, a fact that is reflected in neural correlates of reinforcement signals.

برای دانلود رایگان متن کامل این مقاله و بیش از 32 میلیون مقاله دیگر ابتدا ثبت نام کنید

ثبت نام

اگر عضو سایت هستید لطفا وارد حساب کاربری خود شوید

منابع مشابه

Robust Opponent Modeling in Real-Time Strategy Games using Bayesian Networks

Opponent modeling is a key challenge in Real-Time Strategy (RTS) games as the environment is adversarial in these games, and the player cannot predict the future actions of her opponent. Additionally, the environment is partially observable due to the fog of war. In this paper, we propose an opponent model which is robust to the observation noise existing due to the fog of war. In order to cope...

متن کامل

Learning against sequential opponents in repeated stochastic games

This article considers multiagent algorithms that aim to find the best response in strategic interactions by learning about the game and their opponents from observations. In contrast to many state-of-the-art algorithms that assume repeated interaction with a fixed set of opponents (or even self-play), a learner in the real world is more likely to encounter the same strategic situation with cha...

متن کامل

Evolving Adaptive Play for the Game of Spoof

For game playing in general it is important for players to be adaptive, this is particularly true for games where no optimal fixed strategy is known to exist. Adaptive artificial opponents capable of learning and opponent modelling are highly desirable in computer games. Typically, a great deal of a game’s ability to maintain the interest of human players is provided by multiplayer functionalit...

متن کامل

Yuichi Yamamoto “ Individual Learning and Cooperation in Noisy Repeated Games ” PIER Working Paper 12 - 044

We investigate whether two players in a long-run relationship can maintain cooperation when the details of the underlying game are unknown. Specifically, we consider a new class of repeated games with private monitoring, where an unobservable state of the world influences the payoff functions and/or the monitoring structure. Each player privately learns the state over time, but cannot observe w...

متن کامل

Enhancing Artificial Intelligence on a Real Mobile Game

Mobile games represent a killer application that is attracting millions of subscribers worldwide. One of the aspects crucial to the commercial success of a game is ensuring an appropriately challenging artificial intelligence (AI) algorithm against which to play. However, creating this component is particularly complex as classic search AI algorithms cannot be employed by limited devices such a...

متن کامل

ذخیره در منابع من


  با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید

عنوان ژورنال:
  • The Journal of neuroscience : the official journal of the Society for Neuroscience

دوره 35 31  شماره 

صفحات  -

تاریخ انتشار 2015