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

Sanderson, David J.; Jones, William S.; Austen, Joseph M.

doi:10.1016/j.beproc.2015.11.007

Cited by 13 publications

(13 citation statements)

References 30 publications

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“…Figure 11 shows the CR of the final phase of learning in the intact system; which presented the blocking effect due to the prior conditioning in the preceding phases, as confirmed experimentally by [ 38 , 39 , 40 ]. The blocking effect can be eliminated by extending the conditioning of the second phase, which made it longer, as shown in Figure 12 and confirmed experimentally by [ 38 , 41 , 42 ]. Although lesioning the CHCQ model did not affect the output CR, as shown in Figure 13 , this is supported by the experimental findings of [ 43 , 44 , 45 ].…”

Section: Resultssupporting

confidence: 54%

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

Khalid

Ali

et al. 2020

Brain Sciences

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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.

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Section: Resultssupporting

confidence: 54%

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

Khalid

Ali

et al. 2020

Brain Sciences

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“…Within the field of associative learning more generally, a recent response by Soto (2018) to a study investigating the replicability of blocking in Pavlovian conditioning in rats (Maes et al, 2016) argued that a number of existing models of associative learning anticipate the parameters that are likely to lead to a failure of blocking (Kinder & Lachnit, 2003;Soto, Gershman, & Niv, 2014;Wagner, 2003), and are able to predict the failures of blocking reported by Maes et al (2016). Sanderson, Jones, & Austen (2016) recently examined the parameters that promote the detection of blocking in mice, such as cue salience and the extent of phase 1 conditioning, but more systematic studies of this kind are necessary. In relation to studies on incidental spatial learning, there is a clear need for experiments that do not rely on an absence of evidence to support the existence of distinct learning mechanisms, and in particular any claims of incidental learning of cues.…”

Section: Discussionmentioning

confidence: 99%

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

Buckley¹,

Austen²,

Myles³

et al. 2021

Preprint

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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.

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“…This indirect way of assessing learning may be less prone to ceiling and floor effects that may occur. Indeed, we have previously found that cues that elicit similar levels of conditioned responding may result in different levels of blocking ( Sanderson et al, 2016 ). A potential disadvantage of this method, however, is that cue competition effects may be mediated by the strength of within-compound associations between the competing cues ( Blaisdell et al, 1999 ).…”

Section: Discussionmentioning

confidence: 99%

“…The modality of the 10-s cue was also included as a nuisance factor. We have previously found that mice respond more to auditory cues than visual cues ( Sanderson et al, 2016 ). Therefore, the variance caused by this counterbalancing factor was controlled by including it in the ANOVA, but the main effect of modality and any interactions that included modality were ignored.…”

Section: Methodsmentioning

confidence: 99%

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

Austen

Sanderson

2020

Quarterly Journal of Experimental Psychology

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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.

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

Cited by 13 publications

References 30 publications

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

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

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

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

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