The effect of the amount of blocking cue training on blocking of appetitive conditioning in mice

Evidence of associative blocking during foraging in the social wasp Vespula germanica

While foraging, Vespula germanica usually return to abundant food sites. During this relocation behavior, these wasps learn to identify contextual cues associated with food position. We analyzed associative blocking in this species, that is, how an association with a conditioned stimulus (CS1) blocks subsequent learning when a novel stimulus (CS2) is added on a second foraging visit. Three groups of wasps (A, B, and C; total 74 individual wasps) were observed while collecting meat during one or two consecutive visits. In group A, an environmental cue (CS1) was paired with food placed at a specific site, and on the second visit, a second cue (CS2) was added while food remained in the same position. In a subsequent testing phase, CS1 was removed and the food source displaced nearby. We then recorded the number of hovers performed over the empty dish (previously baited). Group A wasps appeared to ignore the addition of CS2 on their second visit because they performed fewer hovers over the learned site. For group A, the duration of the decision-making process to finally fly toward the baited dish was shorter than when CS1 and CS2 were presented together on their first visit (group B). This is the first study to demonstrate the occurrence of associative blocking in vespids, confirming that a prior foraging experience influences subsequent food relocation in V. germanica. Our findings reveal that first learning episodes block further associations with novel contextual cues, contributing to understanding of complex cognitive processes involved in V. germanica´s foraging behavior.

The effects of spatial stability and cue type on spatial learning: Implications for theories of parallel memory systems

Some theories of spatial learning predict that associative rules apply under only limited circumstances. For example, learning based on a boundary has been claimed to be immune to cue competition effects because boundary information is the basis for the formation of a cognitive map, whilst landmark learning does not involve cognitive mapping. This is referred to as the cue type hypothesis. However, it has also been claimed that cue stability is a prerequisite for the formation of a cognitive map, meaning that whichever cue type was perceived as stable would enter a cognitive map and thus be immune to cue competition, while unstable cues will be subject to cue competition, regardless of cue type. In experiments 1 and 2 we manipulated the stability of boundary and landmark cues when learning the location of two hidden goals. One goal location was constant with respect to the boundary, and the other constant with respect to the landmark cues. For both cue types, the presence of distal orientation cues provided directional information. For half the participants the landmark cues were unstable relative to the boundary and orientation cues, whereas for the remainder of the participants the boundary was unstable relative to landmarks and orientation cues. In a second stage of training, all cues remained stable so that both goal locations could be learned with respect to both landmark and boundary information. According to the cue type hypothesis, boundary information should block learning about landmarks regardless of cue stability. According to the cue stability hypothesis, however, landmarks should block learning about the boundary when the landmarks appear stable relative to the boundary. Regardless of cue type or stability the results showed reciprocal blocking, contrary to both formulations of incidental cognitive mapping. Experiment 3 established that the results of Experiments 1 and 2 could not be explained in terms of difficulty in learning certain locations with respect to different cue types. In a final experiment, following training in which both landmarks and boundary cues signalled two goal locations, a new goal location was established with respect to the landmark cues, before testing with the boundary, which had never been used to define the new goal location. The results of this novel test of the interaction between boundary and landmark cues indicated that new learning with respect to the landmark had a profound effect on navigation with respect to the boundary, counter to the predictions of incidental cognitive mapping of boundaries.

Cue duration determines response rate but not rate of acquisition of Pavlovian conditioning in mice

The duration of a conditioned stimulus (CS) is a key determinant of Pavlovian conditioning. Rate estimation theory (RET) proposes that reinforcement rate is calculated over cumulative exposure to a cue and the reinforcement rate of a cue, relative to the background reinforcement rate, determines the speed of acquisition of conditioned responding. Consequently, RET predicts that shorter-duration cues require fewer trials to acquisition than longer-duration cues due to the difference in reinforcement rates. We tested this prediction by reanalysing the results of a previously published experiment. Mice received appetitive Pavlovian conditioning of magazine approach behaviour with a 10-s CS and a 40-s CS. Cue duration did not affect the rate at which responding emerged or the rate at which it peaked. The 10-s CS did elicit higher levels of responding than the 40-s CS. These results are not consistent with rate estimation theory. Instead, they are consistent with an associative analysis that assumes that asymptotic levels of responding reflect the balance between increments and decrements in associative strength across cumulative exposure to a cue.

Cortico-Hippocampal Computational Modeling Using Quantum Neural Networks to Simulate Classical Conditioning Paradigms

Most existing cortico-hippocampal computational models use different artificial neural network topologies. These conventional approaches, which simulate various biological paradigms, can get slow training and inadequate conditioned responses for two reasons: increases in the number of conditioned stimuli and in the complexity of the simulated biological paradigms in different phases. In this paper, a cortico-hippocampal computational quantum (CHCQ) model is proposed for modeling intact and lesioned systems. The CHCQ model is the first computational model that uses the quantum neural networks for simulating the biological paradigms. The model consists of two entangled quantum neural networks: an adaptive single-layer feedforward quantum neural network and an autoencoder quantum neural network. The CHCQ model adaptively updates all the weights of its quantum neural networks using quantum instar, outstar, and Widrow–Hoff learning algorithms. Our model successfully simulated several biological processes and maintained the output-conditioned responses quickly and efficiently. Moreover, the results were consistent with prior biological studies.

Green model to adapt classical conditioning learning in the hippocampus

Compared with the biological paradigms of classical conditioning, non-adaptive computational models are not capable of realistically simulating the biological behavioural functions of the hippocampal regions, because of their implausible requirement for a large number of learning trials, which can be on the order of hundreds. Additionally, these models did not attain a unified, final stable state even after hundreds of learning trials. Conversely, the output response has a different threshold for similar tasks in various models with prolonged transient response of unspecified status via the training or even testing phases. Accordingly, a green model is a combination of adaptive neuro-computational hippocampal and cortical models that is proposed by adaptively updating the whole weights in all layers for both intact networks and lesion networks using instar and outstar learning rules with adaptive resonance theory (ART). The green model sustains and expands the classical conditioning biological paradigms of the non-adaptive models. The model also overcomes the irregular output response behaviour by using the proposed feature of adaptivity. Further, the model successfully simulates the hippocampal regions without passing the final output response back to the whole network, which is considered to be biologically implausible. The results of the Green model showed a significant improvement confirmed by empirical studies of different tasks. In addition, the results indicated that the model outperforms the previously published models. All the obtained results successfully and quickly attained a stable, desired final state (with a unified concluding state of either "1" or "0") with a significantly shorter transient duration.

Delay of reinforcement versus rate of reinforcement in Pavlovian conditioning

Conditioned stimulus (CS) duration is a determinant of conditioned responding, with increases in duration leading to reductions in response rates. The CS duration effect has been proposed to reflect sensitivity to the reinforcement rate across cumulative exposure to the CS, suggesting that the delay of reinforcement from the onset of the cue is not crucial. Here, we compared the effects of delay and rate of reinforcement on Pavlovian appetitive conditioning in mice. In Experiment 1, the influence of reinforcement delay on the timing of responding was removed by making the duration of cues variable across trials. Mice trained with variable duration cues were sensitive to differences in the rate of reinforcement to a similar extent as mice trained with fixed duration cues. Experiments 2 and 3 tested the independent effects of delay and reinforcement rate. In Experiment 2, food was presented at either the termination of the CS or during the CS. In Experiment 3, food occurred during the CS for all cues. The latter experiment demonstrated an effect of delay, but not reinforcement rate. Experiment 4 ruled out the possibility that the lack of effect of reinforcement rate in Experiment 3 was due to mice failing to learn about the nonreinforced CS exposure after the presentation of food within a trial. These results demonstrate that although the CS duration effect is not simply a consequence of timing of conditioned responses, it is dependent on the delay of reinforcement. The results provide a challenge to current associative and nonassociative, time-accumulation models of learning. (PsycINFO Database Record (c) 2019 APA, all rights reserved).

Failures to replicate blocking are surprising and informative-Reply to Soto (2018)

The blocking effect has inspired numerous associative learning theories and is widely cited in the literature. We recently reported a series of 15 experiments that failed to obtain a blocking effect in rodents. On the basis of those consistent failures, we claimed that there is a lack of insight into the boundary conditions for blocking. In his commentary, Soto (2018) argued that contemporary associative learning theory does provide a specific boundary condition for the occurrence of blocking, namely the use of same- versus different-modality stimuli. Given that in 10 of our 15 experiments same-modality stimuli were used, he claims that our failure to observe a blocking effect is unsurprising. We disagree with that claim, because of theoretical, empirical, and statistical problems with his analysis. We also address 2 other possible reasons for a lack of blocking that are referred to in Soto's (2018) analysis, related to generalization and salience, and dissect the potential importance of both. Although Soto's (2018) analyses raise a number of interesting points, we see more merit in an empirically guided analysis and call for empirical testing of boundary conditions on blocking. (PsycINFO Database Record