scholarly journals Rational After All: Changes in Probability Matching Behaviour Across Time in Humans and Monkeys

2021 ◽  
Author(s):  
Carmen Saldana ◽  
Nicolas Claidière ◽  
Joel Fagot ◽  
Kenny Smith

Probability matching—where subjects given probabilistic in-put respond in a way that is proportional to those input probabilities—has long been thought to be characteristic of primate performance in probability learning tasks in a variety of contexts, from decision making to the learning of linguistic variation in humans. However, such behaviour is puzzling because it is not optimal in a decision theoretic sense; the optimal strategy is to always select the alternative with the highest positive-outcome probability, known as maximising(in decision making) or regularising (in linguistic tasks). While the tendency to probability match seems to depend somewhat on the participants and the task (i.e., infants are less likely to probability match than adults, monkeys probability matchless than humans, and probability matching is less likely in linguistic tasks), existing studies suffer from a range of deficiencies which make it difficult to robustly assess these differences. In this project we present a series of experiments which systematically test the development of probability matching behaviour over time in simple decision making tasks, across species (humans and Guinea baboons), task complexity, and task domain (linguistic vs non-linguistic).

Author(s):  
Cleston Alexandre dos Santos ◽  
Paulo Roberto da Cunha

ABSTRACT Objective: the study aimed to assess the moderating effect of confidence in the joint influence of time pressure and complexity in judgment and decision-making (JDM) in auditing. The behavioral decision theory (BDT) was used from the perspective of the anchoring heuristic. Methods: as a method, the 2x2x2 experiment was used with a final sample of 126 independent auditors. For analysis, the t-test and multiple linear regressions were used. Results: the findings allow us to infer that factors such as trust, time pressure, and complexity, individually and jointly, influence JDM. The study showed that trust moderates the joint influence of time pressure and complexity on JDM. Time pressure and task complexity negatively influence JDM, but when including trust as a moderating factor, the effect of time pressure and complexity is mitigated, reducing the auditor’s difficulties and uncertainties in JDM. Conclusion: the study contributes to BDT, moving academic research toward understanding the interrelationships between personal, environmental, and task factors. It also contributes by presenting evidence that there is a need for considering and observing the effects generated by the factors altogether, in order to contribute to improving the quality of the audit.


2019 ◽  
Author(s):  
Samuel McDougle ◽  
Anne Collins

What determines the speed of our decisions? Various models of decision-making have focused on perceptual evidence, past experience, and task complexity as important factors determining the degree of deliberation needed for a decision. Here, we build on a sequential sampling decision-making framework to develop a new model that captures a range of reaction time (RT) effects by accounting for both working memory and instrumental learning processes. The model captures choices and RTs at various stages of learning, and in learning environments with varying complexity. Moreover, the model generalizes from tasks with deterministic reward contingencies to probabilistic ones. The model succeeds in part by incorporating prior uncertainty over actions when modeling RT. This straightforward process model provides a parsimonious account of decision dynamics during instrumental learning and makes unique predictions about internal representations of action values.


2010 ◽  
Vol 22 (7) ◽  
pp. 1698-1717 ◽  
Author(s):  
Johannes Friedrich ◽  
Robert Urbanczik ◽  
Walter Senn

We investigate a recently proposed model for decision learning in a population of spiking neurons where synaptic plasticity is modulated by a population signal in addition to reward feedback. For the basic model, binary population decision making based on spike/no-spike coding, a detailed computational analysis is given about how learning performance depends on population size and task complexity. Next, we extend the basic model to [Formula: see text]-ary decision making and show that it can also be used in conjunction with other population codes such as rate or even latency coding.


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