The effect of training procedures on the overlearning reversal effect in young children

The performance of 80 preschool children on a reversal problem was studied as a function of amount of training and type of training procedure used during acquisition and reversal. In the extinction phase of reversal learning, subjects given a correction procedure during the reversal problem made fewer perseverative errors than subjects given noncorrection. In the reversal midplateau phase of reversal learning, overtraining facilitated reversal learning for subjects receiving noncorrection during the acquistion problem, but not for subjects receiving correction. A shift in training procedure between acquisition and reversal increased the number of subjects who reached criterion immediately after perseveration. Since these results are difficult to explain in terms of traditional learning theories, an alternative response-switching strategy explanation was proposed.     

Avoidance of overlearning characterises the spacing effect

The spacing of a fixed amount of study time across multiple sessions usually increases subsequent test performance—a finding known as the spacing effect. In the spacing experiment reported here, subjects completed multiple learning trials, and each included a study phase and a test. Once a subject achieved a perfect test, the remaining learning trials within that session comprised what is known as overlearning. The number of these overlearning trials was reduced when learning trials were spaced across multiple sessions rather than massed in a single session. In addition, the degree to which spacing reduced overlearning predicted the size of the spacing effect, which is consistent with the possibility that spacing increases subsequent recall by reducing the occurrence of overlearning. By this account, overlearning is an inefficient use of study time, and the efficacy of spacing depends at least partly on the degree to which it reduces the occurrence of overlearning.     

The effect of overlearning on transfer of training

The effect of overlearning on transfer of training on the A-B:A-B_r paradigm was studied in paired-associate learning with meaningful material (adjective pairs). One group of subjects was trained to criterion on list A-B, and two additional groups were given 100 per cent and 200 per cent overlearning on list A-B. Rate of learning list A-B_r was found to be directly related to amount of overlearning. Negative transfer on list A-B_r was found for errors with the criterion group, while positive transfer was found for the 100 per cent and 200 per cent overlearning groups. The results were consistent with previous paired-associate experiments, and with results of some maze, reversal learning experiments with infrahuman subjects.

The notation “A-B:A-B_r” will be used for the transfer paradigm in which the same stimuli and responses are used in both the original acquisition phase of paired associate learning and in the subsequent transfer of training phase. In the transfer phase, however, the responses (i.e. B) are rearranged so that they are paired with different stimuli (i.e. A). The notation “A-B:A-C” will be used for the transfer paradigm in which a new set of responses (i.e. C) are paired with the old set of stimuli in the transfer of training phase.     

The effect of overlearning on long‐term retention

Once material has been learned to a criterion of one perfect trial, further study within the same session constitutes overlearning. Although overlearning is a popular learning strategy, its effect on long‐term retention is unclear. In two experiments presented here, 218 college students learned geography facts (Experiment 1) or word definitions (Experiment 2). The degree of learning was manipulated and measured via multiple test‐with‐feedback trials, and participants returned for a final cued recall test between 1 and 9 weeks later. The overlearners recalled far more than the low learners at the 1‐week test, but this difference decreased dramatically thereafter. These data suggest that overlearning (and its concomitant demand for additional study time) is an inefficient strategy for learning material for meaningfully long periods of time. Copyright © 2004 John Wiley & Sons, Ltd.     

Some Variables in the Blue (Red) Phenomenon

The relation between spontaneous color choices and ratings of the prototypicality, conspicuousness, and pleasantness of colors was studied in a sample of Dutch college students. Pleasantness, not prototypicality or conspicuousness, determined the four most frequent spontaneous choices—red, blue, green, and purple. Subjects considered blue the most pleasant of the 12 colors studied, which suggests a pleasantness explanation for the “blue phenomenon” found in some countries. The predominance of red as a spontaneous choice replicated the results of previous studies in the Netherlands. Red also was rated highest in prototypicality but was not rated higher by red choosers than by choosers of other colors, so that a simple prototypicality explanation was rejected. Further analysis showed that the predominance of red as the spontaneous choice of Dutch subjects can be explained by an interaction between prototypicality and pleasantness: Subjects tended to choose red when they rated it highly on both variables.     

Thermosensory reversal effect quantified

At room temperature, some materials feel colder than others due to differences in thermal conductivity, heat capacity and geometry. When the ambient temperature is well above skin temperature, the roles of ‘cold’ and ‘warm’ materials are reversed. In this paper, this effect is quantified by measuring discrimination thresholds for subjective coldness at different ambient temperatures using stimuli of different thicknesses. The reversal point was found to be at 34 °C, somewhat above skin temperature. At this reversal point, discrimination is quite impossible. At room temperature, subjects were able to discriminate between stimuli of different thickness based on subjective coldness, showing that the sense of touch, unlike vision, can penetrate solid objects. Furthermore, somewhat surprisingly, at ambient temperatures well below normal room temperature, discrimination is worse than at room temperature.     

Postcriterion overlearning reduces the effectiveness of the method of adjusted learning

One goal of the method of adjusted learning (“dropout procedure”) is to equate the strength of different Items for subsequent retention. Some studies have yielded results in accord with this goal while others have not, with no known reason for the discrepancy. We manipulated the number of postcriterion overlearning trials and found that the adjusted learning procedure was effective in equating items for subsequent retention when learning terminated after one correct response per item but ineffective when postcriterion overlearning trials occurred. This finding is especially important for those versions of the adjusted learning procedure, such as the frequently utilized variant developed by Battig (1965), that incorporate overlearning of the to-be-equated items.     

Serial reversal learning in bumblebees (Bombus impatiens)

Bumblebees are capable of rapidly learning discriminations, but flexibility in bumblebee learning is less well understood. We tested bumblebees (Bombus impatiens) on a serial reversal learning task. A serial reversal task requires learning of an initial discrimination between two differentially rewarded stimuli, followed by multiple reversals of the reward contingency between stimuli. A reduction in errors with repeated reversals in a serial reversal task is an indicator of behavioural flexibility. Bees were housed in a large indoor environment and tested during foraging flights. Testing free-flying bees allowed for large numbers of trials and reversals. All bees were trained to perform a simultaneous discrimination between two colours for a nectar reward, followed by nine reversals of this discrimination. Results showed that bumblebees reduced errors and improved their performance across successive reversals. A reduction in perseverative errors was the major cause of the improvement in performance. Bees showed a slight increase in error rate in their final trials, perhaps as a consequence of increasing proactive interference, but proactive interference may also have contributed to the overall improvement in performance across reversals. Bumblebees are thus capable of behavioural flexibility comparable to that of other animals and may use proactive interference as a mechanism of behavioural flexibility in varying environments.     

Serial reversal learning in freshwater stingrays (Potamotrygon motoro)

Animal Cognition - Serial reversal learning is considered a reliable approach for the testing of behavioral flexibility, and animals that inhabit fluctuating habitats and different environments are...     

Repeated acquisition and discrimination reversal in the squirrel monkey (Saimiri sciureus)

Repeated acquisition and discrimination reversal tasks are often used to examine behavioral relations of, respectively, learning and cognitive flexibility. Surprisingly, despite their frequent use in cognitive neuroscience and behavioral pharmacology, variables that control performance under these two tasks have not been widely studied. The present studies were conducted to directly investigate the controlling variables in nonhuman primates. Squirrel monkeys were trained with a touchscreen variant of the repeated acquisition task in which a novel pair of S⁺/S^? stimuli was presented daily. Subjects learned to discriminate the two stimuli (acquisition) and, subsequently, with the contingencies switched (reversal). Results indicate that rates of both acquisition and reversal learning increased across successive sessions, but that rate of reversal learning remained slower than acquisition learning, i.e., more trials were needed for mastery. Subsequent experiments showed this difference between the rate of learning novel discriminations and reversal was reliable for at least 5 days between acquisition and reversal and notwithstanding the interpolation of additional discriminations. Experimental analysis of the S⁺/S^? elements of the tasks revealed that the difference in the rate of learning could not be attributed to a relatively aversive quality of the S^? or to a relatively appetitive quality of the S⁺, but, rather, to contextual control by the S⁺/S^? stimulus complex. Thus, if either element (S⁺ or S^?) of the stimulus complex was replaced by a novel stimulus, the rate of acquisition approximated that expected with a novel stimulus pair. These results improve our understanding of fundamental features of discrimination acquisition and reversal.     

The effects of overlearning and distributed practise on the retention of mathematics knowledge

In two experiments, 216 college students learned to solve one kind of mathematics problem before completing one of various practise schedules. In Experiment 1, students either massed 10 problems in a single session or distributed these 10 problems across two sessions separated by 1 week. The benefit of distributed practise was nil among students who were tested 1 week later but extremely large among students tested 4 weeks later. In Experiment 2, students completed three or nine practise problems in one session. The additional six problems constituted a strategy known as overlearning, but this extra effort had no effect on test scores 1 or 4 weeks later. Thus, long‐term retention was boosted by distributed practise and unaffected by overlearning. Unfortunately, most mathematics textbooks rely on a format that emphasises overlearning and minimises distributed practise. An easily adopted alternative format is advocated. Copyright © 2006 John Wiley & Sons, Ltd.     

Mastering the Use of Control Variables: the Hierarchical Iterative Control (HIC) Approach

There has been growing criticism of the established practice of automatically including control variables into analyses, especially with survey studies. Several authors have explained the pitfalls of improper use and have provided some best practice advice. I build upon this foundation in suggesting a programmatic approach to the use of control variables that can provide evidence to support or refute feasible explanations for why two or more variables are related. The hierarchical iterative control (HIC) approach begins by establishing a connection between two or more variables and then hierarchically adds control variables to rule in or out their possible influence. The HIC approach involves conducting a series of studies to iteratively test relationships among target variables, utilizing a variety of control variable strategies involving multiple methods. A 7-step programmatic approach is described beginning with development of the research question and background literature review and then conducting empirical tests in a hierarchical (within a study) and iterative (across studies) manner.