The molarity of molecular theory and the molecularity of molar theory.

Dinsmoor (2001) rejects shock-frequency reduction as a reinforcer for avoidance behavior, and considers this to be an invalidation of so-called molar avoidance theory. This is a narrow view of operant avoidance theory, for which shock-frequency reduction is by no means the only reinforcer.     

The Latent Structure of Substance Use Among American Indian Adolescents: An Example Using Categorical Variables

Researchers frequently have only categorical data to analyze and cannot, for theoretical or methodological reasons, assume that the observed variables are discrete representations of an underlying continuous variable. We present latent class analysis as an alternative method of measuring latent variables in these circumstances. Latent class analysis does not require the assumptions of factor analyses about the nature of manifest and latent variables, but does allow the use of more precise model selection than techniques such as cluster analysis. We modeled the lifetime substance use of American Indian youth. The latent class model of American Indian teenagers' substance use had four classes: Abstaining, Predominantly Alcohol, Predominantly Alcohol and Marijuana, and Plural Substance. We then demonstrated the usefulness of this latent variable by using it to differentiate levels of several variables in a manner consistent with Social Cognitive Theory.     

Cross-Cultural Generalizability of CBCL Syndromes Across Three Continents: From the USA and Holland to Australia

The study asked how well Achenbach's 8-factor cross-informant model for the Child Behavior Checklist (Achenbach, 1991a, 1991b, 1991c) fits clinic data in the USA, Holland, and Australia. DeGroot et al.'s Dutch 8-factor model (DeGroot, Koot, & Verhulst 1994) was also tested for its cross-cultural generalizability. Achenbach's matched clinical sample data (N = 2110) were analyzed and contrasted with the previously reported Dutch findings (N = 2335), as well as a new data set collected on clinic referred children and adolescents in Australia (N = 2237). Confirmatory factor analyses supported the Dutch as much as the American model in the USA, Holland, and Australia. Although about 90% of items showed convergent validity across models and countries, the attention and especially the social problems factor found least support. Most double loadings in the current models were not upheld. Instead, additional analyses discovered a number of unmodelled loadings including many cross-loadings. This led to the redefinition of the social problems factor as a mean aggression factor (with associated social problems) whereas the original aggression factor focuses on emotional acting out and the delinquent factor describes an evasive, covert type of antisocial behavior. Overall most support was obtained for the withdrawn, somatic, anxious/depressed, thought problems, and aggressive factors.     

Effects of response-allocation constraints on multiple-schedule performance

Four pigeons were trained on multiple variable-interval schedules in which components alternated after a fixed number of responses had been emitted. In Part 1, each component change occurred after 20 responses; in Part 2, the number was 40; and in Part 3, the number of responses before change was 10. Component reinforcer rates were varied over five experimental conditions in each of Parts 1 to 3. Component response rates decreased as the specified number of responses per component was increased. However, the relation between component response-rate ratios and component reinforcer-rate ratios was independent of the specified number of responses per component, and was similar to that found when components alternate after fixed time periods. In the fourth part of the experiment, the results from Parts 1 to 3 were systematically replicated by keeping the component reinforcer rates constant, but different, while the number of responses that produced component alternation was varied from 5 to 60 responses. The results showed that multiple-schedule performance under component-response-number constraint is similar to that under conventional component-duration constraint. They further suggest that multiple-schedule response rates are controlled by component reinforcer rates and not by principles of maximizing overall reinforcer rates or meliorating component reinforcer rates.     

In search of the feedback function for variable-interval schedules

Finding a theoretically sound feedback function for variable-interval schedules remains an important unsolved problem. It is important because interval schedules model a significant feature of the world: the dependence of reinforcement on factors beyond the organism's control. The problem remains unsolved because no feedback function yet proposed satisfies all the theoretical and empirical requirements. Previous suggestions that succeed in fitting data fail theoretically because they violate a newly recognized theoretical requirement: The slope of the function must approach or equal 1.0 at the origin. A function is presented that satisfies all requirements but lacks any theoretical justification. This function and two suggested by Prelec and Herrnstein (1978) and Nevin and Baum (1980) are evaluated against several sets of data. All three fitted the data well. The success of the two theoretically incorrect functions raises an empirical puzzle: Low rates of reinforcement are coupled with response rates that seem anomalously high. It remains to be discovered what this reflects about the temporal patterning of operant behavior at low reinforcement rates. A theoretically and empirically correct function derived from basic assumptions about operant behavior also remains to be discovered.     

Multilevel covariance structure analysis of sibling substance use and intrafamily conflict

Conventional covariance structure analysis, such as factor analysis, is often applied to data that are obtained in a hierarchical fashion, such as siblings observed within families. A more appropriate specification is demonstrated which explicitly models the within-level and between-level covariance matrices of sibling substance use and intrafamily conflict. Participants were 267 target adolescents (mean age=13.11 years) and 318 siblings (mean age=15.03 years). The level of homogeneity within sibling clusters, and heterogeneity among families, was sufficient to conduct a multilevel covariance structure analysis (MCA). Parental and family-level variables consisting of marital status, socioeconomic status, marital discord, parent use, and modeling of substances were used to explain heterogeneity among families. Marital discord predicted intrafamily conflict, and parent marital status and modeling of substances predicted sibling substance use. Advantages and uses of hierarchical designs and conventional covariance structure software for multilevel data are discussed.     

Molar And Molecular Control In Variable-interval And Variable-ratio Schedules

Response rates are typically higher under variable-ratio than under variable-interval schedules of reinforcement, perhaps because of differences in the dependence of reinforcement rate on response rate or because of differences in the reinforcement of long interresponse times. A variable-interval-with-added-linear-feedback schedule is a variable-interval schedule that provides a response rate/reinforcement rate correlation by permitting the minimum interfood interval to decrease with rapid responding. Four rats were exposed to variable-ratio 15, 30, and 60 food reinforcement schedules, variable-interval 15-, 30-, and 60-s food reinforcement schedules, and two versions of variable-interval-with-added-linear-feedback 15-, 30-, and 60-s food reinforcement schedules. Response rates on the variable-interval-with-added-linear-feedback schedule were similar to those on the variable-interval schedule; all three schedules led to lower response rates than those on the variable-ratio schedules, especially when the schedule values were 30. Also, reinforced interresponse times on the variable-interval-with-added-linear-feedback schedule were similar to those on variable interval and much longer than those produced by variable ratio. The results were interpreted as supporting the hypothesis that response rates on variable-interval schedules in rats are lower than those on comparable variable-ratio schedules, primarily because the former schedules reinforce long interresponse times.     

Some Effects Of Intertrial Interval Duration On Discrete-trial Choice

Pigeons were trained in Experiment 1 on a discrete-trial concurrent variable-interval (VI) 1-min VI 3-min schedule, and in Experiment 2 on a discrete-trial concurrent VI 1.5-min VI 1.5-min schedule. In each experiment, the intertrial-interval durations (ITIs) were 0 s, 6 s, 22 s, and 120 s, and the schedules were both independent and interdependent. The purpose of the research was to determine whether lengthening the ITI would disrupt any local control that existed, measured with respect to relative response rate and changeover probability. In Experiment 1, with the independent schedules, both preference and obtained relative reinforcement rate approximated .75 at short ITIs, but then decreased toward .50 with longer ITIs. With interdependent schedules, both preference and obtained relative reinforcement rate approximated .75 at all ITIs. In both experiments, with both independent and interdependent schedules, changeover probabilities for each response in a sequence of up to five successive responses to a given schedule were variable for individual birds. The average changeover probabilities for all birds suggested perseveration rather than a systematic increase within a given ITI or a systematic trend toward chance responding as ITI duration increased. Finally, the changeover functions did not differ when a sequence of responses was calculated to begin anew after reinforcement rather than with the first response on a schedule. Taken together, the data were inconsistent with a theory holding that only local processes underlie choice in discrete-trial procedures.     

Response-rate differences in variable-interval and variable-ratio schedules: An old problem revisited

In Experiment 1, a variable-ratio 10 schedule became, successively, a variable-interval schedule with only the minimum interreinforcement intervals yoked to the variable ratio, or a variable-interval schedule with both interreinforcement intervals and reinforced interresponse times yoked to the variable ratio. Response rates in the variable-interval schedule with both interreinforcement interval and reinforced interresponse time yoking fell between the higher rates maintained by the variable-ratio schedule and the lower rates maintained by the variable-interval schedule with only interreinforcement interval yoking. In Experiment 2, a tandem variable-interval 15-s variable-ratio 5 schedule became a yoked tandem variable-ratio 5 variable-interval x-s schedule, and a tandem variable-interval 30-s variable-ratio 10 schedule became a yoked tandem variable-ratio 10 variable-interval x-s schedule. In the yoked tandem schedules, the minimum interreinforcement intervals in the variable-interval components were those that equated overall interreinforcement times in the two phases. Response rates did not decline in the yoked schedules even when the reinforced interresponse times became longer. Experiment 1 suggests that both reinforced interresponse times and response rate–reinforcement rate correlations determine response-rate differences in variable-ratio 10 and yoked variable-interval schedules in rats. Experiment 2 suggests a minimal role for the reinforced interresponse time in determining response rates on tandem variable-interval 30-s variable-ratio 10 and yoked tandem variable-ratio 10 variable-interval x-s schedules in rats.     

Response acquisition under targeted percentile schedules: a continuing quandary for molar models of operant behavior.

The number of responses rats made in a "run" of consecutive left-lever presses, prior to a trial-ending right-lever press, was differentiated using a targeted percentile procedure. Under the nondifferential baseline, reinforcement was provided with a probability of .33 at the end of a trial, irrespective of the run on that trial. Most of the 30 subjects made short runs under these conditions, with the mean for the group around three. A targeted percentile schedule was next used to differentiate run length around the target value of 12. The current run was reinforced if it was nearer the target than 67% of those runs in the last 24 trials that were on the same side of the target as the current run. Programming reinforcement in this way held overall reinforcement probability per trial constant at .33 while providing reinforcement differentially with respect to runs more closely approximating the target of 12. The mean run for the group under this procedure increased to approximately 10. Runs approaching the target length were acquired even though differentiated responding produced the same probability of reinforcement per trial, decreased the probability of reinforcement per response, did not increase overall reinforcement rate, and generally substantially reduced it (i.e., in only a few instances did response rate increase sufficiently to compensate for the increase in the number of responses per trial). Models of behavior predicated solely on molar reinforcement contingencies all predict that runs should remain short throughout this experiment, because such runs promote both the most frequent reinforcement and the greatest reinforcement per press. To the contrary, 29 of 30 subjects emitted runs in the vicinity of the target, driving down reinforcement rate while greatly increasing the number of presses per pellet. These results illustrate the powerful effects of local reinforcement contingencies in changing behavior, and in doing so underscore a need for more dynamic quantitative formulations of operant behavior to supplement or supplant the currently prevalent static ones.