A handful of studies have examined the relationship between schedules of reinforcement and behavioural variability, with mixed results. The present study compares a greater range of schedules, examining the effects on location variability. Hens worked in an operant chamber containing five, horizontally arranged and active response keys. Experiment 1 compared eight schedules: FR 40, FR 10, FI (yoked FR 40), FI (yoked FR 10), VR 40, VR 10, VI (yoked VR 40) and VI (yoked VI 10). Experiment 2 compared a series of DRL schedules: 0.5 s to DRL 19.2 s, incrementing in step sizes of 20%. Location variability was measured as the percentage of switching across keys from within trials, between trials (switching from the reinforced response location to the first response location of the following trial), and the number of keys used. Results from Experiment 1 suggested no relation between schedules and location variability. However a relation between response rate and location variability was found; faster response rates resulted in less variability. Experiment 2, in attempt to control for response rate, found no relation between response rate and location variability. Switches within trials occurred more frequently in Experiment 2 than in Experiment 1, an aspect that needs further examination.

Food and sounds were used to condition 32 chicks to one side of a 2-compartment apparatus. This process was repeated in the alternate compartment with no food or sound present. Post-conditioning test sessions showed a conditioned place preference towards the area associated with food and away from the area associated with white noise but no significant effects were found for a food call or the sounds of other chicks. Overall, these results showed it was possible to use the conditioned place preference procedure to assess the effects of sounds and that the procedure has potential use for assessing other environmental stimuli.

Hursh and Silberberg (2008) proposed an exponential function to describe the curvilinear demand functions obtained in much animal research. An advantage of this was that it gave a single measure of the value of the reinforcer, alpha, which they called essential value. This measure has scalar invariance and should not be affected by dose size, amount, or duration of the reinforcer. This paper examines the essential value measure obtained from studies with hens. In each study fixed-ratio schedules were used to generate demand functions. The properties of the reinforcer differed both within and across studies. Foster et al. (2009) and Lim (2010) varied food quality using 40-min sessions. Both found that the essential value was larger for the less preferred reinforcer when consumption was measured by number of reinforcers. Jackson (2011), using sessions terminated after 40 reinforcers and with body weight strictly controlled, found essential value (based on reinforcer rate) was the same for these same two foods. Lim (2010) found the preferred food had the greater essential value when the consumption was measured as weight of food consumed. Grant's (2005) data showed longer reinforcer durations were associated with lower essential values when consumption was measured as numbers of reinforcers. For these data the weight of food consumed generally resulted in the longest durations having the highest essential value. Harris (2011) varied delay to the reinforcer and found longer delays normally gave lower essential values. Stuart (2013) compared delays to the reinforcer and inter-trial-intervals (ITIs). She found essential was lower with the longer intervals for all hens with ITI and, for some hens, with delay and was lower with delays than with ITIs. Thus the measure of essential value has been found to vary in circumstances where this would not be predicted, to be the reverse of what might be expected in some cases, and to be affected by the procedure used. The present data show that essential value does not provide an easily interpretable measure.

Manipulation of unconditioned Motivating Operations (MOs) such as pain, food and water deprivation and the lowering of body weight can be potentially harmful and distressing to animals. Past research that has manipulated such MOs in order to motivate animals to respond for reinforcers is reviewed and directions for future research are discussed. The focus is on the extent that potentially distressing MO's need to be altered in order to change motivation for reinforcers in animals. In addition research directions for investigating alternative MOs that can motivate animals to respond for reinforcers are discussed.

This Special Issue is titled 'Quantitative Analysis of Behavior'. The 37th Annual Meeting of the Society for the Quantitative Analyses of Behavior (SQAB) took place at the McCormick Place Convention Center (Chicago, IL) May 22-May 24, 2014. Presentations from that conference are reported in this special issue as reviews, theoretical papers or reports of original research. Quantitative models of behavior can to some look like an impenetrable forest of equations, assumptions, and parameters. To others those trees provide the wood to build frameworks for assisting our understanding of forces that control behaviour. In this special issue, quantitative models of behaviour are examined from philosophical, theoretical, experimental and applied perspectives. The variety and breadth of approaches and areas of study covered by contributors highlight the continued importance and value of quantitative analyses to our understanding of behavior.

Motivating Operations (MOs) are frequently manipulated (by altering access to commodities and manipulating other variables such as body weight) in order to change responding. This study had two aims, firstly to investigate the effect of altering body weight on concurrent schedule performance of hens, secondly to investigate the effect of altering body weight on the time duration of each component of hens' pecks under these schedules when analysed from high speed videos filmed at 240 fps. Six hens (at 85% 5%) were shaped (three via the method of successive approximations and three via autoshaping) to respond for food reinforcers on an infra-red screen. Hens then responded under a range of concurrent VI VI schedules, with body weight held at 85% 5%, 95 5% and 100 5% over conditions. It was found that applying the Generalised Matching Law to the data did not result in any consistent differences in responding with the three body weights. However, response rates, inter-response times and video analysis of the individual components of the hens pecking responses did show consistent differences between responding at the three weights.

Previous research shows that reinforcement of variable responding will facilitate sequence learning in rats (Neuringer, Deiss & Olson, 2000) but may interfere with sequence learning in humans (Maes & van der Goot, 2006). The present study aimed to replicate and extend previous research by assessing the role of behavioral variability in the learning of difficult target sequences across 3 species: humans (n = 60), hens (n = 18) and possums (n = 6). Participants were randomly allocated to one of three experimental conditions (Control, Variable, Any). In the Control conditions sequences were only reinforced if they were the target sequence, in the Variability conditions sequences were concurrently reinforced on a Variable Interval 60-s schedule if the just entered sequence met a variability criterion, and in the Any condition sequences were concurrently reinforced on a Variable Interval 60-s schedule for any sequence entered. The results support previous findings with animals and humans; hens and possums were more likely to learn the target sequence in the Variability condition, and human participants were more likely to learn the target sequence in the Control condition. Possible explanations for differences between the performance of humans and animals on this task will be discussed.

The demand for six different foods was assessed using a concurrent available Fixed and Progressive Ratios for the Common Brushtail Possum. Responses to the left lever were reinforced with timed access to one food type on a FR 30, and responses to the right lever were reinforced according a geometric PR schedule with timed access to another food type. The food pairs were based on the results of single and paired stimulus preference assessments combinging pairs of mushroom, egg, foliage, blackberry, raw chicken, egg and locust. Cross-point demand functions showed the shift from the PR schedule to the constant FR 30 schedule occurred at about 30 responses for most food pairs. Cross-points that occurred before the equivalence point suggested a preference for food available on the FR schedule as the possum responded more for this food over responding on the PR schedule which initially required fewer responses. There was no significant difference in overall elasticity between the foods. This means that as the response requirement increased, responding decreased. Pooled consumption data from within each pair of foods showed inelastic demand for several preferred foods when paired with non-preferred foods. These data show that the context for assessing demand affects the conclusions of food preference.

Research shows that reinforcement of variable responding facilitates sequence learning in rats but may interfere with sequence learning in humans. Experiment 1 examined sequence difficulty in humans by manipulating sequence length and task instruction. Experiment 2 investigated the effect of removing or adding a variability contingency within the experimental session for a 6-item sequence. Participants were allocated to either a Control or Variable group. The Control group only received reinforcement for production of the target sequences. The Variability group received reinforcers on a Variable Interval 60-s schedule if the sequence met a variability criterion and for production of the target sequence. In Experiment 2 after 10 reinforcer deliveries the variability contingency was either removed or added. In Experiment 1, the Control group produced more target sequences for the 6-digit conditions, the Variable group produced more target sequences for the 9-digit condition and there was no difference between groups for the 12-digit condition. Task instructions had little impact on the results. In Experiment 2 the Control performed better than the Variability group - addition or removal of the variability contingency had little effect on performance. Results will be discussed in relation to previously published research on sequence learning with animals and humans.