Search Results for « Learning rates »

Computational phenotyping is a powerful approach for characterizing individual variability across cognitive domains, with significant applications in computational psychiatry. Within the reinforcement learning framework, the learning rate is widely used to classify individuals based on learning patterns and deviations from optimal behavior. However, its interpretation depends on psychometric properties, which are rarely examined. To assess construct validity, we collected single-trial learning data (N = 126) from an uncertainty-augmented reward learning task to obtain participants' performance and learning rates. We additionally collected psychometric data across a battery of executive functioning tasks to obtain measures of cognitive constructs with high relevance to decision-making and reward learning. Individual differences in learning rates were not systematically explained by cognitive abilities, suggesting limited construct validity, despite moderate validity in learning performance. We identify low-reliability scores and lack of generalizability for the learning rate as a possible explanation for its low validity. These findings highlight the need to improve the psychometric properties of learning rates for studying individual differences, despite their success in capturing group-level learning behavior.

People often have to switch back and forth between different environments that come with different problems and volatilities. While volatile environments require fast learning (i.e., high learning rates), stable environments call for lower learning rates. Previous studies have shown that people adapt their learning rates, but it remains unclear whether they can also learn about environment-specific learning rates, and instantaneously retrieve them when revisiting environments. Here, using optimality simulations and hierarchical Bayesian analyses across three experiments, we show that people can learn to use different learning rates when switching back and forth between two different environments. We even observe a signature of these environment-specific learning rates when the volatility of both environments is suddenly the same. We conclude that humans can flexibly adapt and learn to associate different learning rates to different environments, offering important insights for developing theories of meta-learning and context-specific control.

Research on individual decision-making often finds a positivity bias, where people weight positive outcomes more strongly than negative ones during learning. This can be beneficial when rewards are rare, by amplifying relative value differences. Yet, we know very little about learning rate biases in social settings, where a key advantage is being able to vicariously learn from the negative experiences of others. This would imply a benefit for focusing on negative outcomes when learning socially, but is at odds with the seemingly inflexible positivity bias found in individual learning. Here, we examine learning rate biases across both individual and social settings, testing for adaptivity versus generally stable biases. Overall, participants appear more flexible in their learning rate biases when learning socially than when learning individually. This implies that human social learning may be more flexible and closer to normatively optimal behavior than individual learning.

Research on individual decision-making often finds a positivity bias, where people weight positive outcomes more strongly than negative ones during learning. This can be beneficial when rewards are rare, by amplifying relative value differences. Yet, we know very little about learning rate biases in social settings, where a key advantage is being able to vicariously learn from the negative experiences of others. This would imply a benefit for focusing on negative outcomes when learning socially, but is at odds with the seemingly inflexible positivity bias found in individual learning. Here, we examine learning rate biases across both individual and social settings, testing for adaptivity versus generally stable biases. Overall, participants appear more flexible in their learning rate biases when learning socially than when learning individually. This implies that human social learning may be more flexible and closer to normatively optimal behavior than individual learning.

Research on individual decision-making often finds a positivity bias, where people weight positive outcomes more strongly than negative ones during learning. This can be beneficial when rewards are rare, by amplifying relative value differences. Yet, we know very little about learning rate biases in social settings, where a key advantage is being able to vicariously learn from the negative experiences of others. This would imply a benefit for focusing on negative outcomes when learning socially, but is at odds with the seemingly inflexible positivity bias found in individual learning. Here, we examine learning rate biases across both individual and social settings, testing for adaptivity versus generally stable biases. Overall, participants appear more flexible in their learning rate biases when learning socially than when learning individually. This implies that human social learning may be more flexible and closer to normatively optimal behavior than individual learning.

The comprehensive analysis of 221 Massive Open Online Courses (MOOCs) by Katy Jordan provides crucial insights for health professionals navigating the rapidly evolving landscape of digital learning. Her study, published in the International Review of Research in Open and Distributed Learning, examined completion rates across diverse platforms including Coursera, Open2Study, and others from 78 institutions.

The comprehensive analysis of 221 Massive Open Online Courses (MOOCs) by Katy Jordan provides crucial insights for health professionals navigating the rapidly evolving landscape of digital learning. Her study, published in the International Review of Research in Open and Distributed Learning, examined completion rates across diverse platforms including Coursera, Open2Study, and others from 78 institutions.

Abstract 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 between the agent’s expectation and the actual outcome) and learning rates (LRs; a coefficient used by agents to update their beliefs about the environment) has not been studied in detail. Here, across (i) simulations, (ii) reanalyses of readily available datasets and (iii) a novel experiment, we demonstrate that the relationship between PEs and LRs is (i) nonlinear over the PE/LR space, and (ii) it can be better accounted for by an exponential-logarithmic function that can transform the magnitude of PEs instantaneously to LRs. In line with the temporal predictions of this model, we show that physiological correlates of LRs accumulate while learners observe the outcome of their choices and update their beliefs about the environment.