Effects of Value of Reinforcement upon Expectancy Statements in “Gambling” and Achievement Tasks

1965 ◽  
Vol 20 (1) ◽  
pp. 27-32 ◽  
Author(s):  
Robert W. Bell ◽  
Nancy Jamison
Keyword(s):  
College Students ◽  
Success Rate ◽  
Learning Task ◽  
Random Pattern ◽  
Reward Value ◽  
Systematic Pattern

The effects of probability of reinforcement and reward value on expectancy of success were investigated in both learning and gambling tasks. Forty-eight college students were asked to predict their success rate in correctly anticipating which of four lights would next be turned on. For some Ss the pattern of lights was random. For others a systematic pattern was repeated, permitting learning. Different probabilities of reinforcement effectively varied the level of expectancy, as did the gradual learning of the pattern in the learning task. Amount of reward, using poker chips with cash value, did not significantly alter expectancies of success in either the learning or gambling (random pattern) task. The implications of the results for expectancy theories were discussed.

Overlearning Reversal Effect with a Number-Classification Task

1985 ◽  
Vol 56 (1) ◽  
pp. 167-170 ◽  
Author(s):  
John Paul Szalai
Keyword(s):  
College Students ◽  
Learning Task ◽  
Classification Task ◽  
Verbal Feedback ◽  
Group Design ◽  
Reversal Effect ◽  
Dimensional Complexity

A significant overlearning reversal effect was found in an experiment using number classification by oddness-evenness as the learning task, 19 college students and graduates as subjects, and both positive and negative verbal feedback as the reinforcer. A randomized two-group design was used. The importance of the dimensional complexity of the learning task in experiments producing overlearning reversal effect is discussed.

Leveling-Sharpening and Representational Learning

1970 ◽  
Vol 30 (1) ◽  
pp. 211-214
Author(s):  
Donald Fitzgerald ◽  
Heungim O. Hong
Keyword(s):  
College Students ◽  
Associative Learning ◽  
Learning Task ◽  
Delayed Recognition ◽  
Assimilation Model

Performance of college students on the Squares Test was related to immediate and delayed recognition of unfamiliar concept names in a representational learning task. Although previous research has not substantiated predictions from the assimilation model when the task involved associative learning, the present study clearly indicated ( p < .01) the superiority of “sharpeners” to “levelers” in representational learning with the stimulus phrases embedded in prose material and low-meaning response terms.

scholarly journals Evaluation Model of College Students’ Entrepreneurship Success Rate Based on Grey Relational Theory

2021 ◽  
Vol 235 ◽  
pp. 03019
Author(s):  
Xiaonan Yan ◽  
Jian Tian
Keyword(s):  
College Students ◽  
Data Analysis ◽  
Success Rate ◽  
Comprehensive Evaluation ◽  
Evaluation Model ◽  
Relational Theory ◽  
Experimental Simulation ◽  
Practical Significance ◽  
Using Data ◽  
Rate Evaluation

In order to improve the success rate of entrepreneurship of college students, it is necessary to establish a success rate evaluation model to analyze the success rate of entrepreneurship and make the optimal discrimination. However the current models failed to reach high accuracy due to the lack of factors set. To this end, this paper proposes a college students’ entrepreneurial success evaluation model based on big data analysis, which is based on ambiguity comprehensive evaluation theory. Through building the evaluation factors set, calculating the relative impact of indicators, giving relative weights ratio of factors, and using data analysis. The experimental simulation results show that the model has high evaluation accuracy, and therefore has practical significance in improving the success rate of college students’ entrepreneurship.

scholarly journals Dopamine neurons learn relative chosen value from probabilistic rewards

eLife ◽  
2016 ◽  
Vol 5 ◽  
Author(s):  
Armin Lak ◽  
William R Stauffer ◽  
Wolfram Schultz
Keyword(s):  
Neuronal Response ◽  
Learning Task ◽  
Dopamine Neurons ◽  
Decision Variable ◽  
Response Acquisition ◽  
Economic Theories ◽  
Reward Value ◽  
Reward Probability ◽  
Pavlovian Learning ◽  
Decision Mechanisms

Economic theories posit reward probability as one of the factors defining reward value. Individuals learn the value of cues that predict probabilistic rewards from experienced reward frequencies. Building on the notion that responses of dopamine neurons increase with reward probability and expected value, we asked how dopamine neurons in monkeys acquire this value signal that may represent an economic decision variable. We found in a Pavlovian learning task that reward probability-dependent value signals arose from experienced reward frequencies. We then assessed neuronal response acquisition during choices among probabilistic rewards. Here, dopamine responses became sensitive to the value of both chosen and unchosen options. Both experiments showed also the novelty responses of dopamine neurones that decreased as learning advanced. These results show that dopamine neurons acquire predictive value signals from the frequency of experienced rewards. This flexible and fast signal reflects a specific decision variable and could update neuronal decision mechanisms.

Drug cue induced overshadowing: selective disruption of natural reward processing by cigarette cues amongst abstinent but not satiated smokers

2011 ◽  
Vol 42 (1) ◽  
pp. 161-171 ◽  
Author(s):  
T. P. Freeman ◽  
C. J. A. Morgan ◽  
T. Beesley ◽  
H. V. Curran
Keyword(s):  
Associative Learning ◽  
Attentional Bias ◽  
Prediction Error ◽  
Learning Task ◽  
Reward Processing ◽  
Financial Reward ◽  
Drug Cues ◽  
Reward Value ◽  
Drug Dependent ◽  
Drug Cue

BackgroundAddicts show both reward processing deficits and increased salience attribution to drug cues. However, no study to date has demonstrated that salience attribution to drug cues can directly modulate inferences of reward value to non-drug cues. Associative learning depends on salience: a more salient predictor of an outcome will ‘overshadow’ a less salient predictor of the same outcome. Similarly, blocking, a demonstration that learning depends on prediction error, can be influenced by the salience of the cues employed.MethodThis study investigated whether salient drug cues might interact with neutral cues predicting financial reward in an associative learning task indexing blocking and overshadowing in satiated smokers (n=24), abstaining smokers (n=24) and non-smoking controls (n=24). Attentional bias towards drug cues, craving and expired CO were also indexed.ResultsAbstaining smokers showed drug cue induced overshadowing, attributing higher reward value to drug cues than to neutral cues that were equally predictive of reward. Overshadowing was positively correlated with expired CO levels, which, in turn, were correlated with craving in abstainers. An automatic attentional bias towards cigarette cues was found in abstainers only.ConclusionsThese findings provide the first evidence that drug cues interact with reward processing in a drug dependent population.

scholarly journals An Examination of Strategy Implementation During Abstract Nonlinguistic Category Learning in Aphasia

2015 ◽  
Vol 58 (4) ◽  
pp. 1195-1209 ◽  
Author(s):  
Sofia Vallila-Rohter ◽  
Swathi Kiran
Keyword(s):  
Category Learning ◽  
Cognitive Abilities ◽  
Learning Task ◽  
Strategy Use ◽  
Learning Ability ◽  
Strategy Development ◽  
Random Pattern ◽  
Feedback Processing ◽  
Learning Tasks ◽  
Single Feature

Purpose Our purpose was to study strategy use during nonlinguistic category learning in aphasia. Method Twelve control participants without aphasia and 53 participants with aphasia (PWA) completed a computerized feedback-based category learning task consisting of training and testing phases. Accuracy rates of categorization in testing phases were calculated. To evaluate strategy use, strategy analyses were conducted over training and testing phases. Participant data were compared with model data that simulated complex multi-cue, single feature, and random pattern strategies. Learning success and strategy use were evaluated within the context of standardized cognitive–linguistic assessments. Results Categorization accuracy was higher among control participants than among PWA. The majority of control participants implemented suboptimal or optimal multi-cue and single-feature strategies by testing phases of the experiment. In contrast, a large subgroup of PWA implemented random patterns, or no strategy, during both training and testing phases of the experiment. Conclusions Person-to-person variability arises not only in category learning ability but also in the strategies implemented to complete category learning tasks. PWA less frequently developed effective strategies during category learning tasks than control participants. Certain PWA may have impairments of strategy development or feedback processing not captured by language and currently probed cognitive abilities.

Knowledge Acquired from Learning: Procedural Cognition and its Declarative Cognizance

1992 ◽  
Vol 74 (1) ◽  
pp. 243-257 ◽  
Author(s):  
Alberto Montare
Keyword(s):  
College Students ◽  
Reversal Learning ◽  
Learning Task ◽  
Learning Processes ◽  
Rule Based ◽  
Abstract Rule ◽  
Level 3 ◽  
Cognitive Hierarchy ◽  
Level 1 ◽  
Level 2

62 college students articulated the procedural cognition acquired during successful learning of both original and reversal-shifts of the discrimination-reversal learning task. Articulations formed a four-level hierarchy of “declarative cognizance” (defined as correct articulation of reinforcement contingencies) as follows: Level 1 having no declarative cognizance, Level 2 of perceptually based cognizance, Level 3 of concrete-rule-based cognizance, and Level 4 of abstract-rule-based cognizance. The plausibility of this cognitive hierarchy is enhanced by observations that increasingly higher levels of declarative cognizance are associated with increasingly faster learning. Mon-tare's 1983 and 1988 concepts of primary and secondary signalization are invoked to account for the learning processes underlying these examples of procedural cognition and the hierarchy of declarative cognizance.

scholarly journals An Implementation of Interactive Healthy Eating Index and Healthcare System on Mobile Platform for College Students (Preprint)

2018 ◽  
Author(s):  
Tzer-Shyong Chen ◽  
Shyh-Wei Chen ◽  
Dai-Lun Chiang ◽  
Han-Yu Lin ◽  
Yufang Chung ◽  
...  
Keyword(s):  
College Students ◽  
Success Rate ◽  
Chronic Diseases ◽  
Healthcare System ◽  
Mobile Phones ◽  
Dietary Habits ◽  
Healthy Eating Index ◽  
The Past ◽  
Smart Healthcare ◽  
Diet And Exercise

BACKGROUND This research aims to conduct those college students who have not yet left their family before. At this phrase, students tend to form erroneous diet habits. These situations will lead to obesity and chronic diseases. The purpose of this research is to develop and design the smart Healthcare System for college students. Therefore, we hope to utilize the technology of information to make college students understand their dietary and whether they have enough physical activity or not. OBJECTIVE The objectives of this study is to develop an application. This application provide students a method to understand their habits both on the diet and the exercise. An interactive healthy diet evaluation and healthcare system is established in this research. With the convenience of mobile phones, the users can easily record the dietary contents, nutrient, and exercise process. According to the past dietary habits and exercise records, it also provides suggestions of nutrient allowance. The system, containing diet module and exercise module, can automatically offer suggestions according to the users’ basic information, including age, gender, favorite types of food, and amount of exercise. Students can inspect the nutrients they take to adjust their dietary and exercising habit. This can avoid the obesity which caused by the unbalanced long-term diet and the chronic diseases which might happen in the future. METHODS The mobile device application is applied at the system interface, the graphic interface, diagrams and images can effectively provide the users with various diets and exercise information. The users can use their own mobile devices whenever and wherever they need. They are not limited by the time and the space. Meanwhile, the system could record the amount of exercise by integrating with Google Map and rapidly inquire the past exercise records for the reference of self-inspection. In this research, we invite 80 students to conduct this experiment. We divide this experiment into two periods. For the first four weeks, students have to use hard paper to record the diet and exercise information. Students have to record at least three days within a week. We require two weekdays and one weekend and then we can assume the nutrients they have taken in a week. For the rest four weeks, every step is the same but the only difference is that students need to access the application of the mobile phones. RESULTS The diet module analysis system will give proper suggestions and calculate the required nutrients for users, so that the users can change their dietary habits. Moreover, the system will recommend the users suitable types of food in the criteria. It will automatically remind the users of excessive or insufficient nutrient so as to give the users a way to select a suitable food for individuals and not to cause the body overwhelmed by the unbalanced nutrients. The exercise module will analyze the dietary records, suggesting appropriate running distance for proper exercise, and store the running data and distance records into the database. We invite 80 college students to conduct in this experiment. We can view the success rate via recording the three meals on the mobile phones. 60 students can fulfill the record of breakfast, which is 75 percent; 72 students can fulfill the record of lunch, which is 90 percent; 74 students can fulfill the record of dinner, which is 75.4 percent; as for the other snacks, 56 students can fulfill it, which is 70 percent. Compared with the first stage of recording on the hard paper, we have inspected that the success rate of lunch and dinner achieve 70 percent and even more. CONCLUSIONS This system can store exercise data by integrating with Google Map and rapidly inquire the past exercise records for the reference of self-inspection. In this case, the users can understand whether the diet and exercise conform to the healthy demands of daily health records and further learn to select suitable food and improve the exercise habits. College students can bring the application conveniently and record the nutrients they take. This indeed can change the situation and the willingness. New generation needs to have a new tool and method to help them form good habits of dietary and exercise.

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