Biology subjects

Wilson, R. C.

Publications and source records attributed to Wilson, R. C..

4 recordsLinked to original sources

bioRxiv2018

Regulation of evidence accumulation by pupil-linked arousal processes

Integrating evidence over time is crucial for effective decision making. For simple perceptual decisions, a large body of work suggests that humans and animals are capable of integrating evidence over time fairly well, but that their performance is far from optimal. This suboptimality is thought to arise from a number of different sources including: (1) noise in sensory and motor systems, (2) unequal weighting of evidence over time, (3) order effects from previous trials and (4) irrational side biases for one choice over another. In this work we investigated these di.erent sources of suboptimality and how they are related to pupil dilation, a putative correlate of norepinephrine tone. In particular, we measured pupil response in humans making a series of decisions based on rapidly-presented auditory information in an evidence accumulation task. We found that people exhibited all four types of suboptimality, and that some of these suboptimalities covaried with each other across participants. Pupillometry showed that only noise and the uneven weighting of evidence over time, the integration kernel, were related to the change in pupil response during the stimulus. Moreover, these two different suboptimalities were related to different aspects of the pupil signal, with the individual differences in pupil response associated with individual differences in integration kernel, while trial-by-trial fluctuations in pupil response were associated with trial-by-trial fluctuations in noise. These results suggest that di.erent sources of suboptimality in human perceptual decision making are related to distinct pupil-linked processes possibly related to tonic and phasic norepinephrine activity.

neuroscience↗

bioRxiv2018

The Eighty Five Percent Rule for Optimal Learning

Researchers and educators have long wrestled with the question of how best to teach their clients be they human, animal or machine. Here we focus on the role of a single variable, the difficulty of training, and examine its effect on the rate of learning. In many situations we find that there is a sweet spot in which training is neither too easy nor too hard, and where learning progresses most quickly. We derive conditions for this sweet spot for a broad class of learning algorithms in the context of binary classification tasks, in which ambiguous stimuli must be sorted into one of two classes. For all of these gradient-descent based learning algorithms we find that the optimal error rate for training is around 15.87% or, conversely, that the optimal training accuracy is about 85%. We demonstrate the efficacy of this Eighty Five Percent Rule for artificial neural networks used in AI and biologically plausible neural networks thought to describe human and animal learning.

animal behavior and cognition↗

bioRxiv2017

A state representation for reinforcement learning and decision-making in the orbitofrontal cortex

Despite decades of research, the exact ways in which the orbitofrontal cortex (OFC) influences cognitive function have remained mysterious. Anatomically, the OFC is characterized by remarkably broad connectivity to sensory, limbic and subcortical areas, and functional studies have implicated the OFC in a plethora of functions ranging from facial processing to value-guided choice. Notwithstanding such diversity of findings, much research suggests that one important function of the OFC is to support decision making and reinforcement learning. Here, we describe a novel theory that posits that OFCs specific role in decision-making is to provide an up-to-date representation of task-related information, called a state representation. This representation reflects a mapping between distinct task states and sensory as well as unobservable information. We summarize evidence supporting the existence of such state representations in rodent and human OFC and argue that forming these state representations provides a crucial scaffold that allows animals to efficiently perform decision making and reinforcement learning in high-dimensional and partially observable environments. Finally, we argue that our theory offers an integrating framework for linking the diversity of functions ascribed to OFC and is in line with its wide ranging connectivity.

neuroscience↗

bioRxiv2017

A causal role for right frontopolar cortex in directed, but not random, exploration

The explore-exploit dilemma occurs anytime we must choose between exploring unknown options for information and exploiting known resources for reward. Previous work suggests that people use two different strategies to solve the explore-exploit dilemma: directed exploration, driven by information seeking, and random exploration, driven by decision noise. Here, we show that these two strategies rely on different neural systems. Using transcranial magnetic stimulation to inhibit the right frontopolar cortex, we were able to selectively inhibit directed exploration while leaving random exploration intact. This suggests a causal role for right frontopolar cortex in directed, but not random, exploration and that directed and random exploration rely on (at least partially) dissociable neural systems.

neuroscience↗