The influence of strain on demand functions for water in rats (Rattus Norvegicus)
Bibliographic record
Abstract
Demand functions generated by operant conditioning techniques may be used to assess animal priorities (Dawkins 1990, Matthews & Ladewig 1994, Sherwin & Nicol 1997, Fraser & Matthews 1997, Matthews 1998) . The animal needs to perform a number of simple responses (e.g. bar-presses), to obtain one unit of a reinforcer (Lea 1978, Dawkins 1990, Matthews & Ladewig 1994). This reinforcer enables the animal to perform a certain behaviour. The relationship between the workload (traditionally set by a fixed ratio (FR) reinforcement schedule) and the amount obtained of the reinforcer is described by a curve with FR-value on the horizontal axis as the independent variable and the amount consumed as the dependent variable on the vertical axis (Hursh 1980) . The resulting slope of the demand function will then provide a measure for the demand of the reinforcer. If the animal is highly motivated to obtain the reinforcer the animal will work at an increasing rate as the workload increases and thus keep its intake close to constant. The slope of the demand function will be close to zero, which indicates a high demand for the reinforcer. The steeper the demand function, the less important the reinforcer (Lea 1978, Hursh 1984). The slope of the demand function is nearly always negative and it is not influenced by e.g. the weight of the animal (Hursh 1984, Ladewig 1997). The size of an animal will influence the intercept of the demand function, but not the slope of the demand function. Using this method it should be possible to measure quantitatively to which extent an animal is motivated to obtain a given reinforcer and to compare demands for different reinforcers among different animals. It is important to note that the demand function reflects the demand for the reinforcer. The demand depends on both the internal preferences of the animal but also on the decisions made by the animal of how much to obtain of the reinforcer considering the workload imposed on the animal and the availability of alternatives. Many factors influence the slope of the demand curve; some are varied by the experimenter (e.g. FR-values and test-time), some factors might depend on the animal’s gender and genetics (e.g. sensory capacities and basic level of anxiety), and some are physiological factors influencing the motivational state (e.g. thirst, hunger, aggression, or phase in oestrous cycle). Furthermore, it is well known that, at least in rats, differences exist in performance in cognitive and operant tasks between different rat strains (Andrews et al. 1995). The purpose of this experiment was to determine if the method was able to detect differences in demand for water between two different strains. For the selection of the two strains of rats, three criteria were used. First, we wanted inbred strains in order to minimize the variation between rats in the two test groups. Second, in order to avoid any confounding influence of rats having difficulties in performing the operant response-task, we needed two strains, which were known to perform well in operant systems. Third, the strains should neither be transgenic nor spontaneous animal models of human disorders (Svendsen & Hau 1994). The two strains chosen were pigmented inbred Long Evans rats (LE/Mol) and albino inbred Wistar-Kyoto rats (WKY/Mol).
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".