Easy-to-hard effects in perceptual learning depend upon the degree to which initial trials are “easy”

Wisniewski, Matthew G.; Church, Barbara A.; Mercado, Eduardo; Radell, Milen L.; Zakrzewski, Alexandria C.

doi:10.3758/s13423-019-01627-4

Cited by 13 publications

(7 citation statements)

References 25 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Moreover, for this task, Figure 21 compares the speed and efficiency of the control and experimental results of the CHCQ model with those of the Green model. Generally, the CHCQ model’s results for this task are confirmed by the experimental studies of [ 51 , 52 , 53 , 54 , 55 , 56 , 57 ].…”

Section: Resultsmentioning

confidence: 52%

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

Khalid

Ali

et al. 2020

Brain Sciences

View full text Add to dashboard Cite

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.

show abstract

Section: Resultsmentioning

confidence: 52%

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

Khalid

Ali

et al. 2020

Brain Sciences

View full text Add to dashboard Cite

show abstract

“…In this design, performance of mice trained on the Single + Compound phase first and on the Single phase second (as done here), would be compared against performance of mice with training phases reversed. To the extent that learning a more complex stimulus discrimination depends on learning simpler stimulus discriminations first (Liu et al, 2008; Wisniewski et al, 2019), mice that undergo the Single Training phase first are likely to perform above chance level in the subsequent Single + Compound Training phase. Correspondingly, that is the effect we detected when comparing performance between Initial and Terminal Single + Compound training phases.…”

Section: Methodsmentioning

confidence: 99%

Assessing complex odor discrimination in mice using a novel instrumental patterning task.

Gupta¹,

Daniels²,

Espinoza³

et al. 2022

Journal of Experimental Psychology: Animal Learning and Cogniti

View full text Add to dashboard Cite

Negative patterning tasks are a key tool to unveil the mechanisms by which stimulus representations are acquired—a central concern in Robert Rescorla’s research. In these tasks, target stimuli are reinforced when presented individually (A+/B+) but not when presented in compound (AB−). The discrimination of single stimuli from their compound presentation is a challenge for theories of associative learning, because it cannot be explained by the simple accrual of associative strength. The present study examined the conditions under which mice learn this part–whole discrimination in olfactory stimuli using a novel instrumental methodology. In two experiments, reinforcement was contingent on distinct responses depending on whether a set of odor mixtures were presented in isolation or as a compound. Using C57BL/6 mice, Experiment 1 showed a mutual interference between learning a response to individual odors and learning a different response to those odors presented in compound. Using wild-type APP/PS1 mice (a control strain for a murine model of Alzheimer’s disease), Experiment 2 replicated this interference and showed that it is stimulus-specific. These experiments show that the instrumental patterning task may not only complement Pavlovian negative patterning tasks but may also motivate its own questions on the representation of complex stimuli.

show abstract

“…All videos were neutral valence (e.g., nature-, recreation-, or transportation-related; cf. Wisniewski et al, 2019 ).…”

Section: Methodsmentioning

confidence: 99%

Familiarization with meaningless sound patterns facilitates learning to detect those patterns among distracters

Wisniewski

2022

Front. Psychol.

Self Cite

View full text Add to dashboard Cite

Initially “meaningless” and randomly generated sounds can be learned over exposure. This is demonstrated by studies where repetitions of randomly determined sound patterns are detected better if they are the same sounds presented on previous trials than if they are novel. This experiment posed two novel questions about this learning. First, does familiarization with a sound outside of the repetition detection context facilitate later performance? Second, does familiarization enhance performance when repeats are interleaved with distracters? Listeners were first trained to categorize a unique pattern of synchronous complex tone trains (210 ms in duration) from other tone trains with similar qualities (familiarization phase). They were then tasked to detect repeated pattern presentations interleaved with similar distracters in 4.2 s long excerpts (repetition detection phase). The familiarized pattern (Familiar Fixed – FF), an unfamiliar pattern that remained fixed throughout (Unfamiliar Fixed – UF), or patterns that were uniquely determined on each trial (Unfamiliar Unfixed – UU) could be presented as repeats. FF patterns were learned at a faster rate and achieved higher repetition detection sensitivity than UF and UU patterns. Similarly, FF patterns also showed steeper learning slopes in their response times (RTs) than UF patterns. The data show that familiarity with a “meaningless” sound pattern on its own (i.e., without repetition) can facilitate repetition detection even in the presence of distracters. Familiarity effects become most apparent in the potential for learning.

show abstract

Easy-to-hard effects in perceptual learning depend upon the degree to which initial trials are “easy”

Cited by 13 publications

References 25 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

Assessing complex odor discrimination in mice using a novel instrumental patterning task.

Familiarization with meaningless sound patterns facilitates learning to detect those patterns among distracters

Contact Info

Product

Resources

About