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.

This study aimed to assess the role of reinforced behavioral variability in the learning of a 6-digit target sequence (211212) with 3 groups of human participants (n = 39). For the first group (Control), only the target sequence was reinforced. For the second group (Any), the target sequence was reinforced, and any sequence other than the target sequence could be reinforced on a variable interval 60-s schedule. For the third group (Variable), the target sequence was reinforced, and any sequence other than the target sequence could be reinforced on a variable interval 60-s schedule, if it met a variability criterion. The Control group produced the target sequence significantly more often than the Variable group by the end of the experimental sessions. These findings contradict previous studies with rats that have shown that reinforcement of behavioral variability facilitates the learning of difficult response sequences but are consistent with results from previous studies with humans. Potential reasons for this disparity are discussed.

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.

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.

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.

The performance of hens under multiple fixed-ratio fixed-ratio schedules was examined when the consequences were two different durations of reinforcement. The components of the multiple schedule were arranged so that the four possible transitions between reinforcer durations occurred (short to long, long to short, short to short and long to long). The response requirement in effect was the same in both components and varied over conditions. The between-ratio pauses increased with response requirement increases for all transitions. The pauses were consistently longest when the previous reinforcer duration was long and the upcoming duration was short. The next longest between-ratio pauses occurred when both the previous and the upcoming reinforcer durations were short. The between-ratio pauses were short when the upcoming reinforcer duration was long, tending to be shortest when the previous duration was short rather than when it was long. These data showed that the upcoming reinforcer had the greatest affect, although the previous reinforcer also had some effect on pause length. Longer between-ratio pauses were not the result of having just received access to a longer reinforcer. When the discriminative stimuli were removed and the response requirement was varied, between-ratio pauses again increased with FR increases. They were now similar and longer when the previous reinforcer duration was long and similar and shorter when the previous duration was short, regardless of the upcoming reinforcer duration. Thus pauses following a longer reinforcer were longer than those following a shorter reinforcer, as is seen with the magnitude of reinforcer effect. The range of pause lengths was reduced when compared to that with the multiple schedule, with the shorter pauses being longer and longer pauses being shorter.

The independence of dimensions of operant responses by humans was investigated in two experiments using a computerized rectangle drawing task from Ross and Neuringer (2002). Variability on the dimensions of area, shape and location was required for reinforcement for one group (VAR); and variability was not required for the other (YOKE). For all three dimensions, U-values, a measure of variability, were higher for the VAR group than for YOKE group; and the number of trials that met the criteria for reinforcement was higher for the VAR group than for the YOKE group. In Experiment 2, reinforcement was contingent on variability on two dimensions regardless of variability on the third. Participants were divided into three groups; each group had one dimension that was not required to vary. U-values were higher for dimensions when reinforcement was contingent on varying shape and location, or area and location. However, U-values did not differ significantly across dimensions when reinforcement was contingent on varying just area and shape. The results of Experiment 1 and 2 are broadly consistent with those of Ross and Neuringer (2002). The importance of orthogonality of dimensions on this task will be discussed.

Two experiments with college students were carried out to examine whether learned variability on two dimensions of a behaviour would generalise to a third dimension that occurred simultaneously using Ross and Neuringer's (2002) rectangle drawing task. The dimensions being measured were the sizes, shapes and the locations on the screen of the rectangles. Performances of a group receiving reinforcement independent of the variability of all three dimensions and another group receiving reinforcement contingent on the variability of two of the three dimensions were compared. Results showed that overall, the variability in the shapes and locations of the rectangles was higher when these two dimensions occurred with other two dimensions that were required to vary; however, no difference was found for the variability in sizes between the two groups. The results suggested it was likely there was generalization from reinforcing variability on sizes and locations to shape and from reinforcing variability on sizes and shapes to locations. U-value as a measure of variability was also examined, with simulated data and data collected from one of the experiments. Limitations of the measure were identified. The attentions needed to report U-values would be discussed. Cautions needed when interpreting U-values as a measure of variability would be highlighted.

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 behavioural 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 added reinforcement of variable responding will facilitate the learning of a difficult target sequence in rats, however, the added requirement of variability has been shown to impede difficult sequence learning in humans. The present study aimed to explore the notion of sequence difficulty in humans by manipulating sequence length (6-12 items). Eighty participants were randomly allocated to one of two groups: Control and Variable. 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. For the six-item sequence (122121) the Control group were most likely to produce the target sequence, while for the twelve digit sequence (221112211121) there was no difference between the two groups. The Variable group were most likely to produce the target sequence for the intermediate nine-digit sequence (112212121).The use of sequence length as a definition of sequence difficulty in both the current and previous studies are discussed.