The Influence of Feedback on Task-Switching Performance:A Drift Diffusion Modeling Account

Task-switching is an important cognitive skill that facilitates our ability to choose appropriate behavior in a varied and changing environment. Task-switching training studies have sought to improve this ability by practicing switching between multiple tasks. However, an efficacious training paradigm has been difficult to develop in part due to findings that small differences in task parameters influence switching behavior in a non-trivial manner. Here, for the first time we employ the Drift Diffusion Model (DDM) to understand the influence of feedback on task-switching and investigate how drift diffusion parameters change over the course of task switch training. We trained 316 participants on a simple task where they alternated sorting stimuli by color or by shape. Feedback differed in six different ways between subjects groups, ranging from No Feedback (NFB) to a variety of manipulations addressing trial-wise vs. Block Feedback (BFB), rewards vs. punishments, payment bonuses and different payouts depending upon the trial type (switch/non-switch). While overall performance was found to be affected by feedback, no effect of feedback was found on task-switching learning. Drift Diffusion Modeling revealed that the reductions in reaction time (RT) switch cost over the course of training were driven by a continually decreasing decision boundary. Furthermore, feedback effects on RT switch cost were also driven by differences in decision boundary, but not in drift rate. These results reveal that participants systematically modified their task-switching performance without yielding an overall gain in performance

Motivated Learning: The Influence of Reinforcers

Extant research suggests a number of systems, including reinforcement and attentional systems, contribute to learning. The overall goal of this dissertation is to expand our understanding of how reinforcement systems contribute to learning. Chapters 1 and 2 use a task-irrelevant learning paradigm, which has been used to study the role of reinforcement systems in learning. To first understand how reinforcement systems influence learning, Chapter 1 tests the hypothesis that task-irrelevant learning is mediated by the norepinephrine reinforcement system, by using pupillometry as an indirect measure of norepinephrine system activity. Consistent with this hypothesis results indicate an increased change in pupil size accompanying learning. Chapter 2 investigates how emotion stimuli, which are thought to activate distinct reinforcement systems than the norepinephrine system, influence learning. Consistent with this hypothesis, results indicate that learning, found to be influenced by the norepinephrine system, is moderated by emotion stimuli. Chapter 3 used a task-switching training task manipulating explicit feedback (i.e. points), to investigate how reinforcement systems influence learning in the executive function domain. Consistent with the hypothesis that reinforcement systems ‘tag’ task-relevant brain states, results indicate that feedback schedules which favored speeded responses, biased response strategies to sacrifice accuracy for speed. In conclusion, this dissertation furthers our understanding of the role of reinforcement systems in learning by providing a method of measuring norepinephrine reinforcement system activity during learning as well as provides a novel framework to understand how multiple reinforcement systems contribute to learning

Motivated Learning: The Influence of Reinforcers

Extant research suggests a number of systems, including reinforcement and attentional systems, contribute to learning. The overall goal of this dissertation is to expand our understanding of how reinforcement systems contribute to learning. Chapters 1 and 2 use a task-irrelevant learning paradigm, which has been used to study the role of reinforcement systems in learning. To first understand how reinforcement systems influence learning, Chapter 1 tests the hypothesis that task-irrelevant learning is mediated by the norepinephrine reinforcement system, by using pupillometry as an indirect measure of norepinephrine system activity. Consistent with this hypothesis results indicate an increased change in pupil size accompanying learning. Chapter 2 investigates how emotion stimuli, which are thought to activate distinct reinforcement systems than the norepinephrine system, influence learning. Consistent with this hypothesis, results indicate that learning, found to be influenced by the norepinephrine system, is moderated by emotion stimuli. Chapter 3 used a task-switching training task manipulating explicit feedback (i.e. points), to investigate how reinforcement systems influence learning in the executive function domain. Consistent with the hypothesis that reinforcement systems ‘tag’ task-relevant brain states, results indicate that feedback schedules which favored speeded responses, biased response strategies to sacrifice accuracy for speed. In conclusion, this dissertation furthers our understanding of the role of reinforcement systems in learning by providing a method of measuring norepinephrine reinforcement system activity during learning as well as provides a novel framework to understand how multiple reinforcement systems contribute to learning

Pupillometry as a Glimpse into the Neurochemical Basis of Human Memory Encoding

Neurochemical systems are well studied in animal learning; however, ethical issues limit methodologies to explore these systems in humans. Pupillometry provides a glimpse into the brain's neurochemical systems, where pupil dynamics in monkeys have been linked with locus coeruleus (LC) activity, which releases norepinephrine (NE) throughout the brain. Here, we use pupil dynamics as a surrogate measure of neurochemical activity to explore the hypothesis that NE is involved in modulating memory encoding. We examine this using a task-irrelevant learning paradigm in which learning is boosted for stimuli temporally paired with task targets. We show that participants better recognize images that are paired with task targets than distractors and, in correspondence, that pupil size changes more for target-paired than distractor-paired images. To further investigate the hypothesis that NE nonspecifically guides learning for stimuli that are present with its release, a second procedure was used that employed an unexpected sound to activate the LC-NE system and induce pupil-size changes; results indicated a corresponding increase in memorization of images paired with the unexpected sounds. Together, these results suggest a relationship between the LC-NE system, pupil-size changes, and human memory encoding

Uncertainty in fast task-irrelevant perceptual learning boosts learning of images in women but not men

International audienceA key tenet of models of reinforcement learning is that learning is most desirable in the times of maximum uncertainty. Here we examine the role of uncertainty in the paradigm of fast task-irrelevant perceptual learning (fast-TIPL), where stimuli that are consistently presented at relevant points in times (e.g., with task targets or rewards) are better encoded than when presented at other times. We manipulated two forms of uncertainty, expected uncertainty and unexpected uncertainty (Yu & Dayan, 2005), and compared fast-TIPL under uncertainty with fast-TIPL under no uncertainty. Results indicate a larger fast-TIPL effect under uncertainty than under no uncertainty without a difference between expected and unexpected uncertainty. However, interestingly, this effect of uncertainty on fast-TIPL was found in women but not in men. In men, equivalent fast-TIPL was observed under no uncertainty and uncertainty, whereas in women, confirming previous results (Leclercq & Seitz, 2012b), no fast-TIPL was observed in the no-uncertainty condition, but fast-TIPL was observed in the uncertainty conditions. We discuss how these results imply differences in attention or neuromodulatory processes between men and women

The Influence of Feedback on Task-Switching Performance: A Drift Diffusion Modeling Account.

Task-switching is an important cognitive skill that facilitates our ability to choose appropriate behavior in a varied and changing environment. Task-switching training studies have sought to improve this ability by practicing switching between multiple tasks. However, an efficacious training paradigm has been difficult to develop in part due to findings that small differences in task parameters influence switching behavior in a non-trivial manner. Here, for the first time we employ the Drift Diffusion Model (DDM) to understand the influence of feedback on task-switching and investigate how drift diffusion parameters change over the course of task switch training. We trained 316 participants on a simple task where they alternated sorting stimuli by color or by shape. Feedback differed in six different ways between subjects groups, ranging from No Feedback (NFB) to a variety of manipulations addressing trial-wise vs. Block Feedback (BFB), rewards vs. punishments, payment bonuses and different payouts depending upon the trial type (switch/non-switch). While overall performance was found to be affected by feedback, no effect of feedback was found on task-switching learning. Drift Diffusion Modeling revealed that the reductions in reaction time (RT) switch cost over the course of training were driven by a continually decreasing decision boundary. Furthermore, feedback effects on RT switch cost were also driven by differences in decision boundary, but not in drift rate. These results reveal that participants systematically modified their task-switching performance without yielding an overall gain in performance

Dissociable mappings of tonic and phasic pupillary features onto cognitive processes involved in mental arithmetic.

Pupil size modulations have been used for decades as a window into the mind, and several pupillary features have been implicated in a variety of cognitive processes. Thus, a general challenge facing the field of pupillometry has been understanding which pupil features should be most relevant for explaining behavior in a given task domain. In the present study, a longitudinal design was employed where participants completed 8 biweekly sessions of a classic mental arithmetic task for the purposes of teasing apart the relationships between tonic/phasic pupil features (baseline, peak amplitude, peak latency) and two task-related cognitive processes including mental processing load (indexed by math question difficulty) and decision making (indexed by response times). We used multi-level modeling to account for individual variation while identifying pupil-to-behavior relationships at the single-trial and between-session levels. We show a dissociation between phasic and tonic features with peak amplitude and latency (but not baseline) driven by ongoing task-related processing, whereas baseline was driven by state-level effects that changed over a longer time period (i.e. weeks). Finally, we report a dissociation between peak amplitude and latency whereby amplitude reflected surprise and processing load, and latency reflected decision making times