scholarly journals Equal evidence perceptual tasks suggest key role for interactive competition in decision-making

2019 ◽  
Author(s):  
Ryan Kirkpatrick ◽  
Brandon Turner ◽  
Per B. Sederberg
Keyword(s):  
Decision Making ◽  
Response Times ◽  
Sequential Sampling ◽  
The Past ◽  
Human Decision ◽  
Complex Decision Making ◽  
Complex Decision ◽  
The Difference ◽  
Best Fit ◽  
Made In

The dynamics of decision-making have been widely studied over the past several decades through the lens of an overarching theory called sequential sampling theory (SST). Within SST, choices are represented as accumulators, each of which races toward a decision boundary by drawing stochastic samples of evidence through time. Although progress has been made in understanding how decisionsare made within the SST framework, considerable debate centers on whether the accumulators exhibit dependency during the evidence accumulation process; namely whether accumulators are independent, fully dependent, or partially dependent. To evaluate which type of dependency is the most plausible representation of human decision-making, we applied a novel twist on two classic perceptual tasks; namely, in addition to the classic paradigm (i.e., the unequal-evidence conditions), we used stimuli that provided different magnitudes of equal-evidence (i.e., the equal-evidence conditions). In equal-evidence conditions, response times systematically decreased with increases in the magnitude of evidence, whereas in unequal evidence conditions, response times systematically increased as the difference in evidence between the two alternatives decreased. We designed a spectrum of models that ranged from independent accumulation to fully dependent accumulation, while also examining the effects of within-trial and between-trial variability. We then fit the set of models to our two experiments and found that models instantiating the principles of partial dependency provided the best fit to the data. Our results further suggest that mechanisms inducing partial dependency, such as lateral inhibition, are beneficial for understanding complex decision-making dynamics, even when the task is relatively simple.

Classroom Teaching, Implicit Learning and the Deleterious Effects of Inappropriate Explication

1987 ◽  
Vol 31 (3) ◽  
pp. 289-292 ◽  
Author(s):  
David B. Porter
Keyword(s):  
Decision Making ◽  
Explicit Knowledge ◽  
Decision Accuracy ◽  
Natural Context ◽  
Rate Information ◽  
Human Decision ◽  
Complex Decision Making ◽  
Complex Decision ◽  
Implicit And Explicit ◽  
Experimental Group

Eighty-five senior cadets participated in a class exercise involving complex decision-making in a natural context. One experimental group was induced to employ explicit decisional processing and another was allowed to simply guess appropriate responses. Decision accuracy was measured at three levels of information availability. Both groups performed significantly above the level of chance when no reliable, objective information was provided. However, neither accurate base rate information nor conditional probabilities increased the decision accuracy of either experimental group. The group allowed to simply guess made significantly more accurate responses than did the group induced to explicate their decisional choices. These results provide convergent support for the dissociation of implicit and explicit knowledge. The exercise itself was a useful combination of research and experiential learning and encouraged classroom discussions of many issues related to human decision making.

Anterior Prefrontal Function and the Limits of Human Decision-Making

Science ◽  
2007 ◽  
Vol 318 (5850) ◽  
pp. 594-598 ◽  
Author(s):  
Etienne Koechlin ◽  
Alexandre Hyafil
Keyword(s):  
Decision Making ◽  
Anterior Part ◽  
Task Execution ◽  
Prefrontal Function ◽  
Frontopolar Cortex ◽  
Human Decision ◽  
Complex Decision Making ◽  
Complex Decision ◽  
Executive System

The frontopolar cortex (FPC), the most anterior part of the frontal lobes, forms the apex of the executive system underlying decision-making. Here, we review empirical evidence showing that the FPC function enables contingent interposition of two concurrent behavioral plans or mental tasks according to respective reward expectations, overcoming the serial constraint that bears upon the control of task execution in the prefrontal cortex. This function is mechanistically explained by interactions between FPC and neighboring prefrontal regions. However, its capacity appears highly limited, which suggests that the FPC is efficient for protecting the execution of long-term mental plans from immediate environmental demands and for generating new, possibly more rewarding, behavioral or cognitive sequences, rather than for complex decision-making and reasoning.

Decision Filed Theory

2019 ◽  
pp. 640-654
Author(s):  
Lan Shao ◽  
Jouni Markkula
Keyword(s):  
Decision Making ◽  
Intelligent Systems ◽  
Dft Theory ◽  
Practical Applications ◽  
Basic Model ◽  
Human Decision ◽  
Complex Decision Making ◽  
Ict System ◽  
Complex Decision ◽  
Decision Making Behavior

Human decision-making theories and formal models are increasingly used for developing advanced ICT-based intelligent systems and services. Decision filed theory (DFT) is one of the decision-making theories that has significant potential for practical applications in real-world decision-making situations. Successful empirical studied have shown that DFT theory is able to explain human decision-making behavior in real situations, and the model can be applied as a basis for ICT system and service design. In this chapter, the authors present the results of a systematic literature review conducted for analyzing and synthesizing the evidence of DFT development and its validated usage in different application areas. The results show that the interest in DFT and its applications has grown strongly during the last years. The basic model has been extended to cover more complex decision-making situations and its applications have been widening.

Decision Filed Theory

2018 ◽  
pp. 2108-2120
Author(s):  
Lan Shao ◽  
Jouni Markkula
Keyword(s):  
Decision Making ◽  
Literature Review ◽  
Intelligent Systems ◽  
Dft Theory ◽  
Practical Applications ◽  
Basic Model ◽  
Human Decision ◽  
Complex Decision Making ◽  
Ict System ◽  
Complex Decision

Human decision making theories and formal models are increasingly used for developing advanced ICT based intelligent systems and services. Decision Filed Theory (DFT) is one of the decision making theories that has significant potential for practical applications in real-world decision making situations. Successful empirical studied have shown that DFT theory is able to explain human decision making behaviour in real situations and the model can be applied as a basis for ICT system and service design. In this article, we present the results of a Systematic Literature Review that we conducted for analysing and synthesizing the evidence of DFT development and its validated usage in different application areas. The results show that the interest in DFT and its applications has grown strongly during the last years. The basic model has been extended to cover more complex decision making situations and its applications have been widening.

Evidence-based Assessment

2010 ◽  
pp. 76-98
Author(s):  
John Hunsley ◽  
Eric J. Mash
Keyword(s):  
Decision Making ◽  
Clinical Assessment ◽  
Assessment Process ◽  
Evidence Based ◽  
The Past ◽  
Specific Assessment ◽  
The Many ◽  
Selection Of ◽  
Made In

Evidence-based assessment relies on research and theory to inform the selection of constructs to be assessed for a specific assessment purpose, the methods and measures to be used in the assessment, and the manner in which the assessment process unfolds. An evidence-based approach to clinical assessment necessitates the recognition that, even when evidence-based instruments are used, the assessment process is a decision-making task in which hypotheses must be iteratively formulated and tested. In this chapter, we review (a) the progress that has been made in developing an evidence-based approach to clinical assessment in the past decade and (b) the many challenges that lie ahead if clinical assessment is to be truly evidence-based.

scholarly journals Goal-driven active learning

2021 ◽  
Vol 35 (2) ◽  
Author(s):  
Nicolas Bougie ◽  
Ryutaro Ichise
Keyword(s):  
Decision Making ◽  
Reinforcement Learning ◽  
Learning Process ◽  
Real World ◽  
Imitation Learning ◽  
Learning Approaches ◽  
Wide Range ◽  
Fixed Set ◽  
Complex Decision Making ◽  
Complex Decision

AbstractDeep reinforcement learning methods have achieved significant successes in complex decision-making problems. In fact, they traditionally rely on well-designed extrinsic rewards, which limits their applicability to many real-world tasks where rewards are naturally sparse. While cloning behaviors provided by an expert is a promising approach to the exploration problem, learning from a fixed set of demonstrations may be impracticable due to lack of state coverage or distribution mismatch—when the learner’s goal deviates from the demonstrated behaviors. Besides, we are interested in learning how to reach a wide range of goals from the same set of demonstrations. In this work we propose a novel goal-conditioned method that leverages very small sets of goal-driven demonstrations to massively accelerate the learning process. Crucially, we introduce the concept of active goal-driven demonstrations to query the demonstrator only in hard-to-learn and uncertain regions of the state space. We further present a strategy for prioritizing sampling of goals where the disagreement between the expert and the policy is maximized. We evaluate our method on a variety of benchmark environments from the Mujoco domain. Experimental results show that our method outperforms prior imitation learning approaches in most of the tasks in terms of exploration efficiency and average scores.

Sign in / Sign up

Export Citation Format

Share Document