Equivalence relations and the reinforcement contingency

Where do equivalence relations come from? One possible answer is that they arise directly from the reinforcement contingency. That is to say, a reinforcement contingency produces two types of outcome: (a) 2‐, 3‐, 4‐, 5‐, or n‐term units of analysis that are known, respectively, as operant reinforcement, simple discrimination, conditional discrimination, second‐order conditional discrimination, and so on; and (b) equivalence relations that consist of ordered pairs of all positive elements that participate in the contingency. This conception of the origin of equivalence relations leads to a number of new and verifiable ways of conceptualizing equivalence relations and, more generally, the stimulus control of operant behavior. The theory is also capable of experimental disproof.     

From case management to prevention of scientific dishonesty in Denmark

In 1992, The Danish Medical Research Council established a national committee on scientific dishonesty with the twofold task of handling cases of scientific misconduct and taking preventive initiatives. Scientific dishonesty was proven in only five cases, but in another nine cases lesser degrees of deviations from good scientific practice were found. The experiences from a total of 24 treated cases indicated that three key areas were at the basis of most of the accusations and the deviations from good practice: uncertainty about 1) authorship, about 2) rights and duties to use scientific data and about 3) agreements at the initiation of joint studies. As a consequence guidelines on good practice have been issued on these key subjects. An earlier version of this paper was presented at a symposium, Scientific Misconduct. An International Perspective, organised by The Medical University of Warsaw, 16 November, 1998.     

Rate of conditioned reinforcement affects observing rate but not resistance to change

The effects of rate of conditioned reinforcement on the resistance to change of operant behavior have not been examined. In addition, the effects of rate of conditioned reinforcement on the rate of observing have not been adequately examined. In two experiments, a multiple schedule of observing-response procedures was used to examine the effects of rate of conditioned reinforcement on observing rates and resistance to change. In a rich component, observing responses produced a higher frequency of stimuli correlated with alternating periods of random-interval schedule primary reinforcement or extinction. In a lean component, observing responses produced similar schedule-correlated stimuli but at a lower frequency. The rate of primary reinforcement in both components was the same. In Experiment 1, a 4:1 ratio of stimulus production was arranged by the rich and lean components. In Experiment 2, the ratio of stimulus production rates was increased to 6:1. In both experiments, observing rates were higher in the rich component than in the lean component. Disruptions in observing produced by presession feeding, extinction of observing responses, and response-independent food deliveries during intercomponent intervals usually were similar in the rich and lean components. When differences in resistance to change did occur, observing tended to be more resistant to change in the lean component. If resistance to change is accepted as a more appropriate measure of response strength than absolute response rates, then the present results provide no evidence that higher rates of stimuli generally considered to function as conditioned reinforcers engender greater response strength.     

Neural correlates of a default response in a delayed go/no-go task

Working memory, the ability to temporarily retain task-relevant information across a delay, is frequently investigated using delayed matching-to-sample (DMTS) or delayed Go/No-Go tasks (DGNG). In DMTS tasks, sample cues instruct the animal which type of response has to be executed at the end of a delay. Typically, performance decreases with increasing delay duration, indicating that working memory fades across a delay. However, no such performance decrease has been found when the sample cues exist of present vs. absent stimuli, suggesting that pigeons do not rely on working memory, but seem to respond by default in those trials. We trained 3 pigeons in a DGNG task and found a similar default response pattern: The diverging slopes of the retention functions on correct Go and No-Go trials suggested that pigeons by default omitted their response following No-Go stimuli, but actively retained task-relevant information across the delay for successful responses on Go trials. We conducted single-cell recordings in the avian nidopallium caudolaterale, a structure comparable to the mammalian prefrontal cortex. On Go trials, many neurons displayed sustained elevated activity during the delay preceding the response, replicating previous findings and suggesting that task-relevant information was neurally represented and maintained across the delay. However, the same units did not show enhanced delay activity preceding correct response suppressions in No-Go trials. This activation-inactivation pattern presumably constitutes a neural correlate of the default response strategy observed in the DGNG task.     

On the effects of signaling reinforcer probability and magnitude in delayed matching to sample

Two experiments examined whether postsample signals of reinforcer probability or magnitude affected the accuracy of delayed matching to sample in pigeons. On each trial, red or green choice responses that matched red or green stimuli seen shortly before a variable retention interval were reinforced with wheat access. In Experiment 1, the reinforcer probability was either 0.2 or 1.0 for both red and green responses. Reinforcer probability was signaled by line or cross symbols that appeared after the sample had been presented. In Experiment 2, all correct responses were reinforced, and the signaled reinforcer durations were 1.0 s and 4.5 s. Matching was more accurate when larger or more probable reinforcers were signaled, independently of retention interval duration. Because signals were presented postsample, the effects were not the result of differential attention to the sample.     

"结构":解读古典经济学哲学构架的逻辑中轴--瓦尔特·本雅明:作品、生活、时代和历史的交叠

马克思的"古典经济学"概念具有明确内涵、外延并与一定的"知识型"相关,这与西方学者在一般的、含糊的意义使用这个概念具有根本的异质性.它界划了一种分析的范式,说明了马克思与资产阶级经济学之间存在着"知识型"的断裂,这种分析范式就是从配第和布阿吉尔贝尔开始的探讨社会结构及由这种结构所决定的运动规律,这是"古典经济学"概念之独特内涵所在.福柯等人忽视了古典经济学的"结构"视角而从"劳动"范畴出发,认为马克思对李嘉图的修正在西方知识的最深层中并未产生真正的间断性,这是没有读懂马克思"古典政治经济学"的真正内涵.     

Explicit Instruction With or Without High-<Emphasis Type="Italic">p</Emphasis> Sequences: Which is More Effective to Teach Multiplication Facts?

Basic fact acquisition is an important component for developing higher-order math skills. However, getting students with a history of academic noncompliance to engage in activities related to skills acquisition can be difficult. Prior research demonstrates that engagement increases when nonpreferred activities are preceded by a series of brief activities with a high probability of completion. This technique, called high-p task/request sequences, was not fully explored within the context of skill acquisition. The purpose of this study was to examine the effects of adding high-p sequences to explicit instruction on the math fact acquisition of three elementary-age students in a learning support classroom. Results showed no differences in fact acquisition between explicit instruction and explicit instruction with an added high-p component. However, the high-p sessions took nearly twice as long to complete when compared to explicit instruction alone. Implications for instructional efficiency and limitations of the high-p procedures for acquisition tasks are discussed.     

Academic Self-Schemas and their Self-Congruent Learning Patterns: Findings Verified with Culturally Different Samples

This paper reported two studies investigating high school students’ academic self-schemas in learning mathematics and their self-congruent learning engagement patterns. Using cluster analyses, Study 1 located two contrasting groups of students holding positive and negative self-schemas in learning mathematics among Chinese participants. MANOVA analyses showed that these two groups of Chinese schematic students differed from each other in the use of achievement goals, approaches to learning, and expected levels of performance. These findings were validated and extended in Study 2 using a culturally different sample, Australian students. Again, cluster analyses successfully classified Australian participants into positive and negative schematic clusters. It was also found that these two groups of schematic students approached learning mathematics in a self-congruent manner similar to those found in Study 1. The converging results in both studies lent empirical support to the theoretical formulation of positive and negative schematic students and the validity of using the self-schema concept to investigate motivation and learning. The differences in their learning engagement patterns were discussed in terms of students’ different academic self-schemas in learning mathematics.