Knowledge of transitive relationships between items can contribute to learning the order of a set of stimuli from pairwise comparisons. However, cognitive mechanisms of transitive inferences based on rank order remain unclear, as are relative contributions of reward associations and rule-based inference. To explore these issues, we created a conflict between rule- and reward-based learning during a serial ordering task. Rhesus macaques learned two lists, each containing five stimuli that were trained exclusively with adjacent pairs. Selection of the higher-ranked item resulted in rewards. "Small reward" lists yielded two drops of fluid reward, whereas "large reward" lists yielded five drops. Following training of adjacent pairs, monkeys were tested on novels pairs. One item was selected from each list, such that a ranking rule could conflict with preferences for large rewards. Differences between the corresponding reward magnitudes had a strong influence on accuracy, but we also observed a symbolic distance effect. That provided evidence of a rule-based influence on decisions. RT comparisons suggested a conflict between rule- and reward-based processes. We conclude that performance reflects the contributions of two strategies and that a model-based strategy is employed in the face of a strong countervailing reward incentive.
Download full-text PDF |
Source |
---|---|
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8939389 | PMC |
http://dx.doi.org/10.1162/jocn_a_01823 | DOI Listing |
Cogn Neurodyn
December 2024
School of Systems Science, Beijing Normal University, Beijing, 100875 China.
Adaptive mechanisms of learning models play critical roles in interpreting adaptive behavior of humans and animals. Different learning models, varying from Bayesian models, deep learning or regression models to reward-based reinforcement learning models, adopt similar update rules. These update rules can be reduced to the same generalized mathematical form: the Rescorla-Wagner equation.
View Article and Find Full Text PDFNat Commun
August 2023
Institute for Science and Technology of Brain-inspired Intelligence (ISTBI), Fudan University, Shanghai, China.
Smoking of cigarettes among young adolescents is a pressing public health issue. However, the neural mechanisms underlying smoking initiation and sustenance during adolescence, especially the potential causal interactions between altered brain development and smoking behaviour, remain elusive. Here, using large longitudinal adolescence imaging genetic cohorts, we identify associations between left ventromedial prefrontal cortex (vmPFC) gray matter volume (GMV) and subsequent self-reported smoking initiation, and between right vmPFC GMV and the maintenance of smoking behaviour.
View Article and Find Full Text PDFLearn Mem
July 2023
Center for Interdisciplinary Research in Biology (CIRB), College de France, Centre National de la Recherche Scientifique (CNRS), Institut National de la Santé et de la Recherche Médicale (INSERM), Université Paris Sciences et Lettres (PSL), Paris 75005, France
Prefrontal cortical and striatal areas have been identified by inactivation or lesion studies to be required for behavioral flexibility, including selecting and processing of different types of information. In order to identify these networks activated selectively during the acquisition of new reward contingency rules, rats were trained to discriminate orientations of bars presented in pseudorandom sequence on two video monitors positioned behind the goal sites on a T-maze with return arms. A second group already trained in the visual discrimination task learned to alternate left and right goal arm visits in the same maze while ignoring the visual cues still being presented.
View Article and Find Full Text PDFFront Integr Neurosci
June 2023
Institute of Neuroscience and Medicine (INM-6) and Institute for Advanced Simulation (IAS-6) and JARA-BRAIN Institute I, Jülich Research Centre, Jülich, Germany.
To acquire statistical regularities from the world, the brain must reliably process, and learn from, spatio-temporally structured information. Although an increasing number of computational models have attempted to explain how such sequence learning may be implemented in the neural hardware, many remain limited in functionality or lack biophysical plausibility. If we are to harvest the knowledge within these models and arrive at a deeper mechanistic understanding of sequential processing in cortical circuits, it is critical that the models and their findings are accessible, reproducible, and quantitatively comparable.
View Article and Find Full Text PDFEnter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!