scholarly journals Reward associations do not explain transitive inference performance in monkeys

2019 ◽  
Vol 5 (7) ◽  
pp. eaaw2089 ◽  
Author(s):  
Greg Jensen ◽  
Yelda Alkan ◽  
Vincent P. Ferrera ◽  
Herbert S. Terrace
Keyword(s):  
Choice Behavior ◽  
Decision Rules ◽  
Transitive Inference ◽  
Abstract Model ◽  
Rule Based ◽  
Model Free ◽  
Extensive Training ◽  
Correct Rule ◽  
Reward Value ◽  
List Order

Most accounts of behavior in nonhuman animals assume that they make choices to maximize expected reward value. However, model-free reinforcement learning based on reward associations cannot account for choice behavior in transitive inference paradigms. We manipulated the amount of reward associated with each item of an ordered list, so that maximizing expected reward value was always in conflict with decision rules based on the implicit list order. Under such a schedule, model-free reinforcement algorithms cannot achieve high levels of accuracy, even after extensive training. Monkeys nevertheless learned to make correct rule-based choices. These results show that monkeys’ performance in transitive inference paradigms is not driven solely by expected reward and that appropriate inferences are made despite discordant reward incentives. We show that their choices can be explained by an abstract, model-based representation of list order, and we provide a method for inferring the contents of such representations from observed data.

scholarly journals Reward associations do not explain transitive inference performance in monkeys

2018 ◽  
Author(s):  
Greg Jensen ◽  
Yelda Alkan ◽  
Vincent P Ferrera ◽  
Herbert S Terrace
Keyword(s):  
Reward Magnitude ◽  
Mental Representations ◽  
Transitive Inference ◽  
Rule Based ◽  
Correct Rule ◽  
Reward Value ◽  
The Face ◽  
Countervailing Incentives

The observation that monkeys appear to make transitive inferences has been taken as evidence of their ability to form and manipulate mental representations. However, alternative explanations have been proposed arguing that transitive inference performance based on expected or experienced reward value. To test the contribution of reward value to monkeys’ behavior in TI paradigms, we performed two experiments in which we manipulated the amount of reward associated with each item in an ordered list. In these experiments, monkeys were presented with pairs of items drawn from the list, and delivered rewards if subjects selected the item with the earlier list rank. When reward magnitude was biased to favor later list items, correct responding was reduced. However, monkeys eventually learned to make correct rule-based choices despite countervailing incentives. The results demonstrate that monkeys’ performance in TI paradigms is not driven solely by expected reward, but that they are able to make appropriate inferences in the face of discordant reward associations.

scholarly journals Reward associations do not explain transitive inference performance in monkeys

2018 ◽  
Author(s):  
Greg Jensen ◽  
Yelda Alkan ◽  
Vincent P Ferrera ◽  
Herbert S Terrace
Keyword(s):  
Reward Magnitude ◽  
Mental Representations ◽  
Transitive Inference ◽  
Rule Based ◽  
Correct Rule ◽  
Reward Value ◽  
The Face ◽  
Countervailing Incentives

The observation that monkeys appear to make transitive inferences has been taken as evidence of their ability to form and manipulate mental representations. However, alternative explanations have been proposed arguing that transitive inference performance based on expected or experienced reward value. To test the contribution of reward value to monkeys’ behavior in TI paradigms, we performed two experiments in which we manipulated the amount of reward associated with each item in an ordered list. In these experiments, monkeys were presented with pairs of items drawn from the list, and delivered rewards if subjects selected the item with the earlier list rank. When reward magnitude was biased to favor later list items, correct responding was reduced. However, monkeys eventually learned to make correct rule-based choices despite countervailing incentives. The results demonstrate that monkeys’ performance in TI paradigms is not driven solely by expected reward, but that they are able to make appropriate inferences in the face of discordant reward associations.

scholarly journals Reward associations do not explain transitive inference performance in monkeys

2018 ◽  
Author(s):  
Greg Jensen ◽  
Yelda Alkan ◽  
Vincent P Ferrera ◽  
Herbert S Terrace
Keyword(s):  
Reward Magnitude ◽  
Mental Representations ◽  
Transitive Inference ◽  
Rule Based ◽  
Correct Rule ◽  
Reward Value ◽  
The Face ◽  
Countervailing Incentives

The observation that monkeys appear to make transitive inferences has been taken as evidence of their ability to form and manipulate mental representations. However, alternative explanations have been proposed arguing that transitive inference performance based on expected or experienced reward value. To test the contribution of reward value to monkeys’ behavior in TI paradigms, we performed two experiments in which we manipulated the amount of reward associated with each item in an ordered list. In these experiments, monkeys were presented with pairs of items drawn from the list, and delivered rewards if subjects selected the item with the earlier list rank. When reward magnitude was biased to favor later list items, correct responding was reduced. However, monkeys eventually learned to make correct rule-based choices despite countervailing incentives. The results demonstrate that monkeys’ performance in TI paradigms is not driven solely by expected reward, but that they are able to make appropriate inferences in the face of discordant reward associations.

scholarly journals Reward associations do not explain transitive inference performance in monkeys

2018 ◽  
Author(s):  
Greg Jensen ◽  
Yelda Alkan ◽  
Vincent P Ferrera ◽  
Herbert S Terrace
Keyword(s):  
Reward Magnitude ◽  
Mental Representations ◽  
Transitive Inference ◽  
Rule Based ◽  
Correct Rule ◽  
Reward Value ◽  
The Face ◽  
Countervailing Incentives

The observation that monkeys appear to make transitive inferences has been taken as evidence of their ability to form and manipulate mental representations. However, alternative explanations have been proposed arguing that transitive inference performance based on expected or experienced reward value. To test the contribution of reward value to monkeys’ behavior in TI paradigms, we performed two experiments in which we manipulated the amount of reward associated with each item in an ordered list. In these experiments, monkeys were presented with pairs of items drawn from the list, and delivered rewards if subjects selected the item with the earlier list rank. When reward magnitude was biased to favor later list items, correct responding was reduced. However, monkeys eventually learned to make correct rule-based choices despite countervailing incentives. The results demonstrate that monkeys’ performance in TI paradigms is not driven solely by expected reward, but that they are able to make appropriate inferences in the face of discordant reward associations.

Applied Rough Set Logics for Multi-Criteria Decision Analysis in Stock Index Volatility Projection

2011 ◽  
Vol 14 (04) ◽  
pp. 715-735
Author(s):  
Wen-Rong Jerry Ho
Keyword(s):  
Decision Making ◽  
Set Theory ◽  
Rough Set ◽  
Rough Set Theory ◽  
Decision Rules ◽  
Multiple Criteria Decision Making ◽  
Stock Index ◽  
Rule Based ◽  
Set Up ◽  
Decision Making Model

The main purpose of this paper is to advocate a rule-based forecasting technique for anticipating stock index volatility. This paper intends to set up a stock index indicators projection prototype by using a multiple criteria decision making model consisting of the cluster analysis (CA) technique and Rough Set Theory (RST) to select the important attributes and forecast TSEC Capitalization Weighted Stock Index. The projection prototype was then released to forecast the stock index in the first half of 2009 with an accuracy of 66.67%. The results point out that the decision rules were authenticated to employ in forecasting the stock index volatility appropriately.

scholarly journals Deep Neural Networks Constrained by Decision Rules

2019 ◽  
Vol 33 ◽  
pp. 2496-2505
Author(s):  
Yuzuru Okajima ◽  
Kunihiko Sadamasa
Keyword(s):  
Neural Networks ◽  
Deep Neural Networks ◽  
Predictive Accuracy ◽  
Decision Rules ◽  
Hybrid Technique ◽  
Complex Data ◽  
Rule Based ◽  
Prior Probabilities ◽  
The Neural Network ◽  
Latent Representations

Deep neural networks achieve high predictive accuracy by learning latent representations of complex data. However, the reasoning behind their decisions is difficult for humans to understand. On the other hand, rule-based approaches are able to justify the decisions by showing the decision rules leading to them, but they have relatively low accuracy. To improve the interpretability of neural networks, several techniques provide post-hoc explanations of decisions made by neural networks, but they cannot guarantee that the decisions are always explained in a simple form like decision rules because their explanations are generated after the decisions are made by neural networks.In this paper, to balance the accuracy of neural networks and the interpretability of decision rules, we propose a hybrid technique called rule-constrained networks, namely, neural networks that make decisions by selecting decision rules from a given ruleset. Because the networks are forced to make decisions based on decision rules, it is guaranteed that every decision is supported by a decision rule. Furthermore, we propose a technique to jointly optimize the neural network and the ruleset from which the network select rules. The log likelihood of correct classifications is maximized under a model with hyper parameters about the ruleset size and the prior probabilities of rules being selected. This feature makes it possible to limit the ruleset size or prioritize human-made rules over automatically acquired rules for promoting the interpretability of the output. Experiments on datasets of time-series and sentiment classification showed rule-constrained networks achieved accuracy as high as that achieved by original neural networks and significantly higher than that achieved by existing rule-based models, while presenting decision rules supporting the decisions.

Comparison of Tobacco and Electronic Cigarette Reward Value Measured During a Cue-Reactivity Task: An Extension of the Choice Behavior Under Cued Conditions Procedure

2018 ◽  
Vol 21 (10) ◽  
pp. 1394-1400 ◽  
Author(s):  
Ashley N Dowd ◽  
Stephen T Tiffany
Keyword(s):  
Choice Behavior ◽  
Cue Reactivity ◽  
Test Procedure ◽  
Electronic Cigarette ◽  
Cigarette Use ◽  
Tobacco Cigarette ◽  
Reward Value ◽  
Multiple Trials ◽  
Primary Focus ◽  
Specific Reactions

Abstract Introduction Up to 24% of electronic cigarette (e-cigarette) users are concurrent users of both tobacco and e-cigarettes (dual users). Dual users provide an opportunity to assess key motivational processes supporting e-cigarette use, such as the reward value of e-cigarettes. This study used the Choice Behavior Under Cued Conditions procedure to examine cue-specific reactions to tobacco and e-cigarettes with a primary focus on evaluating the relative reward value of both forms of cigarettes. Methods Fifty-four dual users were exposed to a lit tobacco cigarette, their own e-cigarette, or a cup of water across multiple trials. On each trial, participants rated their craving for both tobacco and e-cigarettes and indicated the amount of money they would spend to access the cue. Key measures included craving, amount of money spent to access the cue, latency to access the cue, spending choice time, and consumption. Results Participants reported significantly higher craving and spent significantly more money on tobacco and e-cigarette trials than on water trials. The magnitude of cue-specific craving was comparable across tobacco and e-cigarettes, but participants spent significantly more to access tobacco cigarettes than e-cigarettes. Conclusions This is the first study to demonstrate cue-specific reactivity to e-cigarettes utilizing a neutral comparison condition and to examine the reward value of e-cigarettes relative to tobacco cigarettes. Overall, the data suggest that e-cigarette puffs are less valued and generate less craving than tobacco cigarette puffs for dual users. The data provide clear support for the utility of Choice Behavior Under Cued Conditions for examining a range of motivational processes supporting e-cigarette use. Implications The test procedure used in this research generates multiple indices of nicotine and tobacco motivation and allows for an explicit comparison of those variables in people who use both e-cigarettes and tobacco cigarettes.

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