A Computational Role for Dopamine Delivery in Human Decision-Making

1998 ◽  
Vol 10 (5) ◽  
pp. 623-630 ◽  
Author(s):  
David M. Egelman ◽  
Christophe Person ◽  
P. Read Montague
Keyword(s):  
Decision Making ◽  
Human Subjects ◽  
Neural Mechanisms ◽  
Behavioral Strategies ◽  
Pharmacological Interventions ◽  
Decision Strategies ◽  
Human Decision ◽  
Future Events ◽  
New Interpretation ◽  
Mesencephalic Dopamine

Recent work suggests that fluctuations in dopamine delivery at target structures represent an evaluation of future events that can be used to direct learning and decision-making. To examine the behavioral consequences of this interpretation, we gave simple decision-making tasks to 66 human subjects and to a network based on a predictive model of mesencephalic dopamine systems. The human subjects displayed behavior similar to the network behavior in terms of choice allocation and the character of deliberation times. The agreement between human and model performances suggests a direct relationship between biases in human decision strategies and fluctuating dopamine delivery. We also show that the model offers a new interpretation of deficits that result when dopamine levels are increased or decreased through disease or pharmacological interventions. The bottom-up approach presented here also suggests that a variety of behavioral strategies may result from the expression of relatively simple neural mechanisms in different behavioral contexts.

scholarly journals Predicting human decision making in psychological tasks with recurrent neural networks

2021 ◽  
Author(s):  
Baihan Lin ◽  
Djallel Bouneffouf ◽  
Guillermo Cecchi
Keyword(s):  
Decision Making ◽  
Time Series ◽  
Prisoner's Dilemma ◽  
Iowa Gambling Task ◽  
Human Subjects ◽  
Prisoner’S Dilemma ◽  
Gambling Task ◽  
Iterated Prisoner's Dilemma ◽  
Human Decision ◽  
Iterated Prisoner’S Dilemma

Unlike traditional time series, the action sequences of human decision making usually involve many cognitive processes such as beliefs, desires, intentions and theory of mind, i.e. what others are thinking. This makes predicting human decision making challenging to be treated agnostically to the underlying psychological mechanisms. We propose to use a recurrent neural network architecture based on long short-term memory networks (LSTM) to predict the time series of the actions taken by the human subjects at each step of their decision making, the first application of such methods in this research domain. In this study, we collate the human data from 8 published literature of the Iterated Prisoner's Dilemma comprising 168,386 individual decisions and postprocess them into 8,257 behavioral trajectories of 9 actions each for both players. Similarly, we collate 617 trajectories of 95 actions from 10 different published studies of Iowa Gambling Task experiments with healthy human subjects. We train our prediction networks on the behavioral data from these published psychological experiments of human decision making, and demonstrate a clear advantage over the state-of-the-art methods in predicting human decision making trajectories in both single-agent scenarios such as the Iowa Gambling Task and multi-agent scenarios such as the Iterated Prisoner's Dilemma. In the prediction, we observe that the weights of the top performers tends to have a wider distribution, and a bigger bias in the LSTM networks, which suggests possible interpretations for the distribution of strategies adopted by each group.

scholarly journals The roles of online and offline replay in planning

2020 ◽  
Author(s):  
Eran Eldar ◽  
Gaëlle Lièvre ◽  
Peter Dayan ◽  
Raymond J. Dolan
Keyword(s):  
Decision Making ◽  
Task Performance ◽  
Human Subjects ◽  
Reward Structure ◽  
Decision Strategies ◽  
Rest Periods ◽  
Decision Algorithms

AbstractAnimals and humans replay neural patterns encoding trajectories through their environment, both whilst they solve decision-making tasks and during rest. Both on-task and off-task replay are believed to contribute to flexible decision making, though how their relative contributions differ remains unclear. We investigated this question by using magnetoencephalography to study human subjects while they performed a decision-making task that was designed to reveal the decision algorithms employed. We characterized subjects in terms of how flexibly each adjusted their choices to changes in temporal, spatial and reward structure. The more flexible a subject, the more they replayed trajectories during task performance, and this replay was coupled with re-planning of the encoded trajectories. The less flexible a subject, the more they replayed previously and subsequently preferred trajectories during rest periods between task epochs. The data suggest that online and offline replay both participate in planning but support distinct decision strategies.

Neuroökonomie – Grundlagen und Grenzen

2007 ◽  
Vol 29 (1) ◽  
Author(s):  
Michael Pauen
Keyword(s):  
Decision Making ◽  
Basic Research ◽  
Neural Mechanisms ◽  
Long Run ◽  
Human Decision ◽  
The Mind ◽  
Widespread View

AbstractAccording to a widespread view, neuroscientific basic research tells us more about the essence of the mind than psychology and may, in the long run, even replace those higher level approaches. Contrary to this view, it is demonstrated that many features can only be observed and explained on a certain level of complexity. This is particularly obvious in the case of neuromarketing and neuroeconomics. In both cases, neuroscientific methods depend on behavioral paradigms. Still, neuroscientific research in these fields may enhance our understanding of the underlying neural mechanisms. In addition, neuroeconomics provide excellent conditions for the study of human decision making.

scholarly journals Neural mechanisms of human decision-making

2018 ◽  
Author(s):  
Kai Krueger ◽  
Seth Herd ◽  
Ananta Nair ◽  
Jessica Mollick ◽  
Randall O'Reilly
Keyword(s):  
Decision Making ◽  
Neural Mechanisms ◽  
Human Decision

scholarly journals Influence of expected reward on perceptual decision making

2018 ◽  
Author(s):  
Mohsen Rakhshan ◽  
Vivian Lee ◽  
Emily Chu ◽  
Lauren Harris ◽  
Lillian Laiks ◽  
...  
Keyword(s):  
Decision Making ◽  
Sensory Processing ◽  
Human Subjects ◽  
Target Selection ◽  
Judgment Task ◽  
Neural Mechanisms ◽  
Perceptual Bias ◽  
Perceptual Decision Making ◽  
Perceptual Decision ◽  
Reward Effects

AbstractPerceptual decision making is influenced by reward expected from alternative options or actions, but the underlying neural mechanisms are currently unknown. More specifically, it is debated whether reward effects are mediated through changes in sensory processing and/or later stages of decision making. To address this question, we conducted two experiments in which human subjects made saccades to what they perceived to be the first or second of two visually identical but asynchronously presented targets, while we manipulated expected reward from correct and incorrect responses on each trial. We found that unequal reward caused similar shifts in target selection (reward bias) between the two experiments. Moreover, observed reward biases were independent of the individual’s sensitivity to sensory signals. These findings suggest that the observed reward effects were determined heuristically via modulation of decision-making processes instead of sensory processing and thus, are more compatible with response bias rather than perceptual bias. To further explain our findings and uncover plausible neural mechanisms, we simulated our experiments with a cortical network model and tested alternative mechanisms for how reward could exert its influence. We found that our observations are more compatible with reward-dependent input to the output layer of the decision circuit. Together, our results suggest that during a temporal judgment task, the influence of reward information on perceptual choice is more compatible with changing later stages of decision making rather than early sensory processing.

scholarly journals Achieving Closed-Loop Control Simulation of Human-Artefact Interaction: A Comparative Review

2011 ◽  
Vol 2011 ◽  
pp. 1-16 ◽  
Author(s):  
Wilhelm Frederik van der Vegte ◽  
Imre Horváth
Keyword(s):  
Decision Making ◽  
Human Subjects ◽  
Low Cost ◽  
Simulation Models ◽  
Level Control ◽  
User Interactions ◽  
Loop Control ◽  
Low Level ◽  
Comparative Review ◽  
Human Decision

To include user interactions in simulations of product use, the most common approach is to couple human subjects to simulation models, using hardware interfaces to close the simulation-control loop. Testing with virtual human models could offer a low-cost addition to evaluation with human subjects. This paper explores the possibilities for coupling human and artefact models to achieve fully software-based interaction simulations. We have critically reviewed existing partial solutions to simulate or execute control (both human control and product-embedded control) and compared solutions from literature with a proof-of-concept we have recently developed. Our concept closes all loops, but it does not rely on validated algorithms to predict human decision making and low-level human motor control. For low-level control, validated solutions are available from other approaches. For human decision making, however, validated algorithms exist only to predict the timing but not the reasoning behind it. To identify decision-making schemes beyond what designers can conjecture, testing with human subjects remains indispensable.

scholarly journals The Neural Mechanisms Underlying the Influence of Pavlovian Cues on Human Decision Making

2008 ◽  
Vol 28 (22) ◽  
pp. 5861-5866 ◽  
Author(s):  
S. Bray ◽  
A. Rangel ◽  
S. Shimojo ◽  
B. Balleine ◽  
J. P O'Doherty
Keyword(s):  
Decision Making ◽  
Neural Mechanisms ◽  
Human Decision

scholarly journals Studying Collective Human Decision Making and Creativity with Evolutionary Computation

2015 ◽  
Vol 21 (3) ◽  
pp. 379-393 ◽  
Author(s):  
Hiroki Sayama ◽  
Shelley D. Dionne
Keyword(s):  
Decision Making ◽  
Evolutionary Computation ◽  
Interdisciplinary Research ◽  
Collaborative Design ◽  
Social Dynamics ◽  
Human Subjects ◽  
Computational Simulation ◽  
Idea Generation ◽  
Evolutionary Perspective ◽  
Human Decision

We report a summary of our interdisciplinary research project “Evolutionary Perspective on Collective Decision Making” that was conducted through close collaboration between computational, organizational, and social scientists at Binghamton University. We redefined collective human decision making and creativity as evolution of ecologies of ideas, where populations of ideas evolve via continual applications of evolutionary operators such as reproduction, recombination, mutation, selection, and migration of ideas, each conducted by participating humans. Based on this evolutionary perspective, we generated hypotheses about collective human decision making, using agent-based computer simulations. The hypotheses were then tested through several experiments with real human subjects. Throughout this project, we utilized evolutionary computation (EC) in non-traditional ways—(1) as a theoretical framework for reinterpreting the dynamics of idea generation and selection, (2) as a computational simulation model of collective human decision-making processes, and (3) as a research tool for collecting high-resolution experimental data on actual collaborative design and decision making from human subjects. We believe our work demonstrates untapped potential of EC for interdisciplinary research involving human and social dynamics.

scholarly journals Human-agent coordination in a group formation game

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Tuomas Takko ◽  
Kunal Bhattacharya ◽  
Daniel Monsivais ◽  
Kimmo Kaski
Keyword(s):  
Decision Making ◽  
Autonomous Agents ◽  
Human Subjects ◽  
Group Formation ◽  
Small World ◽  
Human Agent ◽  
Human Decision ◽  
Overall Performance ◽  
Behaviour Changes ◽  
Formation Game

AbstractCoordination and cooperation between humans and autonomous agents in cooperative games raise interesting questions on human decision making and behaviour changes. Here we report our findings from a group formation game in a small-world network of different mixes of human and agent players, aiming to achieve connected clusters of the same colour by swapping places with neighbouring players using non-overlapping information. In the experiments the human players are incentivized by rewarding to prioritize their own cluster while the model of agents’ decision making is derived from our previous experiment of purely cooperative game between human players. The experiments were performed by grouping the players in three different setups to investigate the overall effect of having cooperative autonomous agents within teams. We observe that the human subjects adjust to autonomous agents by being less risk averse, while keeping the overall performance efficient by splitting the behaviour into selfish and cooperative actions performed during the rounds of the game. Moreover, results from two hybrid human-agent setups suggest that the group composition affects the evolution of clusters. Our findings indicate that in purely or lesser cooperative settings, providing more control to humans could help in maximizing the overall performance of hybrid systems.

scholarly journals Neural Mechanisms of Human Decision-Making

2021 ◽  
Author(s):  
Seth Herd ◽  
Kai Krueger ◽  
Ananta Nair ◽  
Jessica Mollick ◽  
Randall O’Reilly
Keyword(s):  
Decision Making ◽  
Neural Mechanisms ◽  
Human Decision
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