Biology subjects

Watson, M. R.

Publications and source records attributed to Watson, M. R..

5 recordsLinked to original sources

bioRxiv2023

A Multi-task Platform for Profiling Cognitive and Motivational Constructs in Humans and Nonhuman Primates

BackgroundUnderstanding the neurobiological substrates of psychiatric disorders requires comprehensive evaluations of cognitive and motivational functions in preclinical research settings. The translational validity of such evaluations will be supported by (1) tasks with high construct validity that are engaging and easy to teach to human and nonhuman participants, (2) software that enables efficient switching between multiple tasks in single sessions, (3) software that supports tasks across a broad range of physical experimental setups, and (4) by platform architectures that are easily extendable and customizable to encourage future optimization and development. New MethodWe describe the Multi-task Universal Suite for Experiments (M-USE), a software platform designed to meet these requirements. It leverages the Unity video game engine and C# programming language to (1) support immersive and engaging tasks for humans and nonhuman primates, (2) allow experimenters or participants to switch between multiple tasks within-session, (3) generate builds that function across computers, tablets, and websites, and (4) is freely available online with documentation and tutorials for users and developers. M-USE includes a task library with seven pre-existing tasks assessing cognitive and motivational constructs of perception, attention, working memory, cognitive flexibility, motivational and affective self-control, relational long-term memory, and visuo-spatial problem solving. ResultsM-USE was used to test NHPs on up to six tasks per session, all available as part of the Task Library, and to extract performance metrics for all major cognitive and motivational constructs spanning the Research Domain Criteria (RDoC) of the National Institutes of Mental Health. Comparison with Existing MethodsOther experiment design and control systems exist, but do not provide the full range of features available in M-USE, including a pre-existing task library for cross-species assessments; the ability to switch seamlessly between tasks in individual sessions; cross-platform build capabilities; license-free availability; and its leveraging of video-engine capabilities used to gamify tasks. ConclusionsThe new multi-task platform facilitates cross-species translational research for understanding the neurobiological substrates of higher cognitive and motivational functions.

animal behavior and cognition↗

bioRxiv2020

Learning at variable attentional load requires cooperation between working memory, meta-learning and attention-augmented reinforcement learning

Flexible learning of changing reward contingencies can be realized with different strategies. A fast learning strategy involves using working memory of recently rewarded objects to guide choices. A slower learning strategy uses prediction errors to gradually update value expectations to improve choices. How the fast and slow strategies work together in scenarios with real-world stimulus complexity is not well known. Here, we disentangle their relative contributions in rhesus monkeys while they learned the relevance of object features at variable attentional load. We found that learning behavior across six subjects is consistently best predicted with a model combining (i) fast working memory (ii) slower reinforcement learning from differently weighted positive and negative prediction errors, as well as (iii) selective suppression of non-chosen feature values and (iv) a meta-learning mechanism adjusting exploration rates based on a memory trace of recent errors. These mechanisms cooperate differently at low and high attentional loads. While working memory was essential for efficient learning at lower attentional loads, enhanced weighting of negative prediction errors and meta-learning were essential for efficient learning at higher attentional loads. Together, these findings pinpoint a canonical set of learning mechanisms and demonstrate how they cooperate when subjects flexibly adjust to environments with variable real-world attentional demands. Significance statementLearning which visual features are relevant for achieving our goals is challenging in real-world scenarios with multiple distracting features and feature dimensions. It is known that in such scenarios learning benefits significantly from attentional prioritization. Here we show that beyond attention, flexible learning uses a working memory system, a separate learning gain for avoiding negative outcomes, and a meta-learning process that adaptively increases exploration rates whenever errors accumulate. These subcomponent processes of cognitive flexibility depend on distinct learning signals that operate at varying timescales, including the most recent reward outcome (for working memory), memories of recent outcomes (for adjusting exploration), and reward prediction errors (for attention augmented reinforcement learning). These results illustrate the specific mechanisms that cooperate during cognitive flexibility.

animal behavior and cognition↗

bioRxiv2020

Gains and Losses Differentially Regulate Attentional Efficacy and Learning at Low and High Attentional Load

Prospective gains and losses modulate cognitive processing, but it is unresolved whether gains and losses can facilitate flexible learning in changing environments. The prospect of gains might enhance flexible learning through prioritized processing of reward-predicting stimuli but is unclear how far this learning benefit extends when task demands increase. Similarly, experiencing losses might facilitate learning when they trigger attentional re-orienting away from loss-inducing stimuli, but losses may also impair learning by reducing the precise encoding of loss-inducing stimuli. To clarify these divergent views, we tested how varying magnitudes of gains and losses affect the flexible learning of object values in environments that varied attentional load by increasing the number of interfering object features during learning. With this task design we found that larger prospective gains improved learning efficacy and learning speed, but only when attentional load was low. In contrast, expecting losses generally impaired learning efficacy and this impairment was larger at higher attentional load. These findings functionally dissociate the contributions of prospective gains and losses on flexible learning, suggesting they operate via separate control mechanisms. One process is triggered by experiencing loss and seems to disrupt the encoding of specific loss-inducing features which leads to less efficient exploration during learning. The second process is triggered by experiencing gains which enhances learning through a more efficient prioritizing of reward-predicting stimulus features as long as the interference of distracting information is limited. These results demonstrate strengths and limitations of motivational regulation of learning efficacy in multidimensional environments having variable attentional loads. Significance statementIncreasing the prospective gains is assumed to enhance flexible learning, but there is no consensus on whether imposing losses enhances or impairs flexible learning. We show that anticipating loss of already attained assets generally reduced learning changes in the relevance of visual objects and that this learning impediment is more pronounced when learning demands higher attentional control of interference from distracting object features. Moreover, we show that increasing the prospective gains indeed facilitates learning, but only when the learning problem has intermediate or low attentional demands. These findings document that the beneficial effects of gains hit a limit when task demands increase, and that prospective losses reduce cognitive flexibility already at low task demands which is exacerbated when task demands increase. These findings provide novel insight into the strengths and limitations of gains and of losses to support flexible learning in multidimensional environments imposing variable attentional loads.

animal behavior and cognition↗

bioRxiv2018

USE: An integrative suite for temporally-precise psychophysical experiments in virtual environments

11.1 BackgroundThere is a growing interest in complex, active, and immersive behavioral neuroscience tasks. However, the development and control of such tasks present unique challenges.\n\n1.2 New MethodThe Unified Suite for Experiments (USE) is an integrated set of hardware and software tools for the design and control of behavioral neuroscience experiments. The software, developed using the Unity video game engine, supports both active tasks in immersive 3D environments and static 2D tasks used in more traditional visual experiments. The custom USE SyncBox hardware, based around an Arduino Mega2560 board, integrates and synchronizes multiple data streams from different pieces of experimental hardware. The suite addresses three key issues with developing cognitive neuroscience experiments in Unity: tight experimental control, accurate sub-ms timing, and accurate gaze target identification.\n\n1.3 ResultsUSE is a flexible framework to realize experiments, enabling (i) nested control over complex tasks, (ii) flexible use of 3D or 2D scenes and objects, (iii) touchscreen-, button-, joystick- and gaze-based interaction, and (v) complete offline reconstruction of experiments for post-processing and temporal alignment of data streams.\n\n1.4 Comparison with Existing MethodsMost existing experiment-creation tools are not designed to support the development of video-game-like tasks. Those that do use older or less popular video game engines as their base, and are not as feature-rich or enable as precise control over timing as USE.\n\n1.5 ConclusionsUSE provides an integrated, open source framework for a wide variety of active behavioral neuroscience experiments using human and nonhuman participants, and artificially-intelligent agents.\n\n2 GlossaryO_LIActive task: Experimental tasks which involve some combination of realistic, usually moving, stimuli, continuous opportunities for action, ecologically valid tasks, complex behaviours, etc. Here, they are contrasted with static tasks (see below)\nC_LIO_LIArduino: A multi-purpose generic micro-processor, here used to control inter-device communication and time synchronization.\nC_LIO_LIRaycast: A game-engine method that sends a vector between two points in a virtual three-dimensional environment, and returns the first object in that environment it hits. Often used to determine if a character in a game can see or shoot another character.\nC_LIO_LIState Machine (also Finite State Machine): A way of conceptualizing and implementing control in software, such that at any one moment the software is in one, and only one, state. In hierarchical state machines, as used in the present software suite, these can be organized into different levels, such that each level can only be in one state, but a state can pass control to a lower level.\nC_LIO_LIStatic task: Experimental tasks like those traditionally used in the cognitive neurosciences. Simple, usually stationary, stimuli, limited opportunities for action, simple behaviours, etc. Here, they are contrasted with active tasks (see above).\nC_LIO_LIUnity: One of the most popular video game engines. Freely available.\nC_LIO_LIVideo game engine: A software development kit designed to handle many of the common issues involved in creating video games, such as interfacing with controllers, simulating physical collisions and lighting, etc.\nC_LI

neuroscience↗

bioRxiv2017

Quaddles: A multidimensional 3D object set with parametrically-controlled and customizable features

Many studies of vision and cognition require novel three-dimensional object sets defined by a parametric feature space. Creating such sets and verifying that they are suitable for a given task, however, can be extremely time-consuming and effortful. Here we present a new set of multidimensional objects, Quaddles, designed for a study of feature-based learning and attention, but adaptable for many research purposes. Quaddles have features that are all equally-visible from any angle around the vertical axis, and which all have similar response biases on a feature detection task, thus removing one potential source of bias in object selection. They are available as two-dimensional images, rotating videos, and FBX object files suitable for use with any modern video game engine. We also provide examples and tutorials for modifying Quaddles or creating completely new object sets from scratch, hopefully greatly speeding up the development time of future novel object studies.\n\nAuthors noteThis work was supported by grant MOP 102482 from the Canadian Institutes of Health Research (TW) and by the Natural Sciences and Engineering Research Council of Canada Brain in Action CREATE-IRTG program (MRW, TW). The funders had no role in study design, data collection and analysis, the decision to publish, or the preparation of this manuscript. Authors would like to thank Hongying Wang for technical support, and Isabel Gauthier for comments on a draft version of the manuscript.\n\nThe study described herein was approved by the York University Office of Research Ethics (Certificate # 2016-214).\n\nA draft version of this manuscript is available online at BioR{chi}iv:

neuroscience↗