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Many of our spoken languages are endangered and rapidly becoming extinct. Due to this, there are attempts to preserve as many of those languages as possible. One preservation approach is combining data collection and artificial intelligence-based language models. However, current data collection methods may only capture static data from a dynamic cognitive process. If data are not genuinely capturing the dynamic process, it raises questions about whether they capture all the essential knowledge about how a language functions. Here, we discuss the implications of this issue and its importance in preserving endangered languages.

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Procedural fidelity is defined as the extent to which the independent variable is implemented as prescribed. Research using computerized tasks has shown that fidelity errors involving consequences for behavior can hinder skill acquisition. However, studies examining the effects of these errors once skills have been mastered are lacking. Thus, this translational study investigated the effects of varying levels of fidelity following mastery of a computerized arbitrary matching-to-sample task. A group design (consisting of five groups) was used in which college students initially completed 250 trials during which no programmed errors (i.e., perfect fidelity) were arranged, followed by an additional 250 trials with consequences delivered across various levels of fidelity (i.e., 20, 40, 60, 80, and 100% of trials administered without errors). The results showed that participants assigned to higher fidelity conditions performed better (on average). These results extended the findings of previous studies by demonstrating how errors involving consequences affect behavior across various stages of learning.

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There have been several recent attempts at using Artificial Intelligence systems to model aspects of consciousness (Gamez, 2008; Reggia, 2013). Deep Neural Networks have been given additional functionality in the present attempt, allowing them to emulate phenological aspects of consciousness by self-generating information representing multi-modal inputs as either sounds or images. We added these functions to determine whether knowledge of the input's modality aids the networks' learning. In some cases, these representations caused the model to be more accurate after training and for less training to be required for the model to reach its highest accuracy scores.

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In concurrent schedules with a changeover delay (COD), choice often strongly favours the just-reinforced alternative immediately after a reinforcer delivery. These 'preference pulses' may be caused by a change in reinforcer availability created by the COD, and/or because the COD decreases the overall probability of switching. We investigated which explanation better accounts for preference pulses by arranging concurrent schedules that allowed us to separate the COD's effects on reinforcer availability from its effects on the probability of switching. When the reinforcer ratio was 1:1, pulses were inconsistently accompanied by changes in reinforcer availability, but consistently accompanied by longer visits. These pulses appeared to be related only to the decreased probability of switching caused by the COD, providing the first evidence of pulses after reinforcers caused by the probability of switching alone. When the reinforcer ratio was 1:5 or 5:1; preference pulses were accompanied by changes in reinforcer availability and by longer visits. These pulses appeared to be related to the COD's effects on reinforcer availability, although a small portion appeared to be related to low probability of switching. These findings suggest that the COD affects preference pulses by both decreasing the probability of switching and creating a change in reinforcer availability.

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Behavior reduced as a consequence of extinction or intervention can relapse. According to behavioral momentum theory, the extent to which behavior persists and relapses once it has been eliminated depends on the relative training reinforcement rate among discriminative stimuli. In addition, studies of context renewal reveal that relapse depends on the similarity between the training stimulus context and the test stimulus context following disruption by extinction. In the present experiments with pigeons, we arranged different reinforcement rates in the presence of distinct discriminative stimuli across components of a multiple schedule. Following extinction, we attempted to reinstate responding in the presence of those target components with response-independent food presentations. Importantly, we arranged the reinstating food presentations either within the target components or in separate components, either paired with extinction (Experiment 1) or reinforcement (Experiment 2) during baseline. Reinstatement increased with greater training reinforcement rates when the reinstating food presentations were arranged in the target components and the separate components paired with reinforcement during training. Reinstatement was smaller and was not systematically related to training reinforcement rates in the target components when reinstating food presentation occurred in separate components paired with extinction. These findings suggest that relapse depends on the history of reinforcement associated with the discriminative stimuli in which the relapse-inducing event occurs.

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We investigated why violations to the constant-ratio rule, an assumption of the generalized matching law, occur in procedures that arrange frequent changes to reinforcer ratios. Our investigation produced steady-state data and compared them with data from equivalent, frequently changing procedures. Six pigeons responded in a four-alternative concurrent-schedule experiment with an arranged reinforcer-rate ratio of 27:9:3:1. The same four variable-interval schedules were used in every condition, for 50 sessions, and the physical location of each schedule was changed across conditions. The experiment was a steady-state version of a frequently changing procedure in which the locations of four VI schedules were changed every 10 reinforcers. We found that subjects' responding was consistent with the constant-ratio rule in the steady-state procedure. Additionally, local analyses showed that preference after reinforcement was towards the alternative that was likely to produce the next reinforcer, instead of being towards the just-reinforced alternative as in frequently changing procedures. This suggests that the effect of a reinforcer on preference is fundamentally different in rapidly changing and steady-state environments. Comparing this finding to the existing literature suggests that choice is more influenced by reinforcer-generated signals when the reinforcement contingencies often change.

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Data from five experiments on choice between more than two variable-interval schedules were modeled with different equations for the Law of Effect. Navakatikyan's (2007) component-functions models with three, four and five free parameters were compared with Stevens' (1957), Herrnstein's (1970) and Davison and Hunter's (1976) equations. These latter models are consistent with the generalized-matching principle, whereas Navakatikyan's models are not. Navakatikyan's models performed better or on par with their competitors, especially in predicting residence-time data and generalized-matching sensitivities for time allocation. The models described well an observed decrease, in several of these data sets, in generalized-matching sensitivity between two alternatives when reinforcer rate increased on the other alternatives. Models built on the generalized-matching principle cannot do this. Navakatikyan's models also performed better, though to a lesser extent, than their competitors for data sets that are not obviously inconsistent with generalized matching.

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