15 papers · ranked by Valyu relevance
Dorothée B. Hoppe, Petra Hendriks, Michael Ramscar, Jacolien van Rij
Error-driven learning algorithms, which iteratively adjust expectations based on prediction error, are the basis for a vast array of computational models in the brain and cognitive sciences that often differ widely in their precise form and application: they range from simple models in psychology and cybernetics to…
Yicong Zheng, Xiaonan L. Liu, Satoru Nishiyama, Charan Ranganath + 2 more
'Randall C. O’Reilly' 'Alireza Soltani'] The hippocampus plays a critical role in the rapid learning of new episodic memories. Many computational models propose that the hippocampus is an autoassociator that relies on Hebbian learning (i.e., “cells that fire together, wire together”). However, Hebbian learning is…
Shih-pi Ku, Eric L. Hargreaves, Sylvia Wirth, Wendy A. Suzuki
Computational models proposed that the medial temporal lobe (MTL) contributes importantly to error-driven learning, though little direct in-vivo evidence for this hypothesis exists. To test this, we recorded in the entorhinal cortex (EC) and hippocampus (HPC) as macaques performed an associative learning task using an…
Nicholas Ketz, Srinimisha G. Morkonda, Randall C. O'Reilly, Olaf Sporns
'Olaf Sporns'] The learning mechanism in the hippocampus has almost universally been assumed to be Hebbian in nature, where individual neurons in an engram join together with synaptic weight increases to support facilitated recall of memories later. However, it is also widely known that Hebbian learning mechanisms…
Rasmus Bruckner, Hauke R. Heekeren, Matthew R. Nassar
Learning allows humans and other animals to make predictions about the environment that facilitate adaptive behavior. Casting learning as predictive inference can shed light on normative cognitive mechanisms that improve predictions under uncertainty. Drawing on normative learning models, we illustrate how learning…
Yeray Mera, Nataliya Dianova, Eugenia Marin-Garcia
The pretesting effect suggests that attempting and failing to guess unknown information can improve memory compared to errorless study. A relevant question concerns the optimal timing for providing corrective feedback and administering the final test. This study explored two variables: (1) the timing of feedback after…
Charlène Truong, Célia Ruffino, Alexandre Crognier, Christos Paizis + 2 more
'Lionel Crognier' 'Charalambos Papaxanthis'] This study investigates the effects of error-based and reinforcement training on the acquisition and long-term retention of free throw accuracy in basketball. Sixty participants were divided into four groups (n = 15 per group): (i) the error-based group (sensory feedback)…
Steven Miletić, Russell J Boag, Anne C Trutti, Niek Stevenson + 4 more
Learning and decision-making are interactive processes, yet cognitive modeling of error-driven learning and decision-making have largely evolved separately. Recently, evidence accumulation models (EAMs) of decision-making and reinforcement learning (RL) models of error-driven learning have been combined into joint…
Taisei Sugiyama, Nicolas Schweighofer, Jun Izawa
Humans and animals develop learning-to-learn strategies throughout their lives to accelerate learning. One theory suggests that this is achieved by a metacognitive process of controlling and monitoring learning. Although such learning-to-learn is also observed in motor learning, the metacognitive aspect of learning…
Francesco Poli, Marlene Meyer, Rogier B. Mars, Sabine Hunnius
Exploration is curiosity-driven when it relies on the intrinsic motivation to know rather than on extrinsic rewards. Recent evidence shows that artificial agents perform better on a variety of tasks when their learning is curiosity-driven, and humans often engage in curiosity-driven learning when sampling information…
Noriyuki Matsuda, Hisashi Ogawa, Tsukasa Hirashima, Hirokazu Taki
Background Erroneous answers in multiple-answer problems not only make the correct answer harder to determine but also indicate why the correct choice is suitable and the erroneous one a mistake when compared to the correct answer. However, it is insufficient to simply create erroneous answers for this purpose…
Boluwatife Ikwunne, Jolie Parham, Erdem Pulcu, Camilla L Nord
Reinforcement-learning (RL) models have been pivotal to our understanding of how agents perform learning-based adaptions in dynamically changing environments. However, the exact nature of the relationship (e.g., linear, logarithmic etc.) between key components of RL models such as prediction errors (PEs; the difference…
Sámuel Varga, Joshua Kah Meng Khoo, Denis Cousineau, Jan Derrfuss + 2 more
Models of performance monitoring hold that following an error, the early postresponse period is devoted primarily to error detection rather than mitigation. However, recent evidence shows that erroneous actions can be terminated within ∼100 ms of their initiation. Prior demonstrations of such error cancellation are…
Tianhe Wang, Guy Avraham, Jonathan S. Tsay, Sabrina J. Abram + 2 more
'Richard B. Ivry' 'Barbara Webb'] Implicit adaptation has been regarded as a rigid process that automatically operates in response to movement errors to keep the sensorimotor system precisely calibrated. This hypothesis has been challenged by recent evidence suggesting flexibility in this learning process. One…
Matthias Deliano, Karsten Tabelow, Reinhard König, Jörg Polzehl + 1 more
'Lutz Jaencke'] Estimation of learning curves is ubiquitously based on proportions of correct responses within moving trial windows. Thereby, it is tacitly assumed that learning performance is constant within the moving windows, which, however, is often not the case. In the present study we demonstrate that violations…