Inhibitory sensory preconditioning

Espinet, Alfredo; González, Félix María Pedreira; Balleine, Bernard W.

doi:10.1080/02724990344000105

Cited by 23 publications

(24 citation statements)

References 11 publications

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“…If so, perhaps the reliance on reinforcement and unconditioned stimuli in standard prototypes of learning should be questioned. A more fitting prototype may be sensory preconditioning, where a sequence of contiguous elements is experienced first (A → B), before the later element is subsequently paired with an unconditioned stimulus (B + food), thereby allowing the learning of the pre-reinforced association to be assessed (test A; e.g., Blaisdell et al 2009; Sawa et al 2005; see Espinet et al 2004, for an example of inhibitory sensory preconditioning).…”

Section: Discussionmentioning

confidence: 99%

Implicit chaining in cotton-top tamarins (Saguinus oedipus) with elements equated for probability of reinforcement

et al. 2013

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Three experiments examined the implicit learning of sequences under conditions in which the elements comprising a sequence were equated in terms of reinforcement probability. In Experiment 1 cotton-top tamarins (Saguinus oedipus) experienced a five-element sequence displayed serially on a touch screen in which reinforcement probability was equated across elements at .16 per element. Tamarins demonstrated learning of this sequence with higher latencies during a random test as compared to baseline sequence training. In Experiments 2 and 3, manipulations of the procedure used in the first experiment were undertaken to rule out a confound owing to the fact that the elements in Experiment 1 bore different temporal relations to the intertrial interval (ITI), an inhibitory period. The results of Experiments 2 and 3 indicated that the implicit learning observed in Experiment 1 was not due to temporal proximity between some elements and the inhibitory ITI. The results taken together support two conclusion: First that tamarins engaged in sequence learning whether or not there was contingent reinforcement for learning the sequence, and second that this learning was not due to subtle differences in associative strength between the elements of the sequence.

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Section: Discussionmentioning

confidence: 99%

Implicit chaining in cotton-top tamarins (Saguinus oedipus) with elements equated for probability of reinforcement

et al. 2013

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“…Figure 27 shows the output CRs of all three phases, as confirmed by [ 62 , 63 , 64 , 65 , 66 , 67 , 68 , 69 ]. The effect of preconditioning on the CR value of the last trial in the third phase is clear.…”

Section: Resultsmentioning

confidence: 68%

“…In addition, as in the previous task, lesioning the CHCQ model had no effect on the compound preconditioning task. Generally, the results of the compound preconditioning task have been confirmed by the experimental research of [ 68 , 69 , 71 ].…”

Section: Resultsmentioning

confidence: 72%

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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“…SOME DATA Yin, Barnet, & Miller (1994) 11.4 Inhibitory sensory preconditioning is possible. SOME DATA Espinet, González, & Balleine (2004) 11.5 CS1-CS2 associations mediate conditioning and extinction of CS1 by manipulating CS2 and the US. Shevill & Hall (2004) Holland & Sherwood (2008) 12.…”

Section: Generalmentioning

confidence: 99%

Special issue on computational models of classical conditioning guest editors’ introduction

Alonso

Schmajuk

2012

Learn Behav

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Inhibitory sensory preconditioning

Cited by 23 publications

References 11 publications

Implicit chaining in cotton-top tamarins (Saguinus oedipus) with elements equated for probability of reinforcement

Implicit chaining in cotton-top tamarins (Saguinus oedipus) with elements equated for probability of reinforcement

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

Special issue on computational models of classical conditioning guest editors’ introduction

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