The power of feedback.

Historically, the locus coeruleus-norepinephrine (LC-NE) system has been implicated in arousal, but recent findings suggest that this system plays a more complex and specific role in the control of behavior than investigators previously thought. We review neurophysiological and modeling studies in monkey that support a new theory of LC-NE function. LC neurons exhibit two modes of activity, phasic and tonic. Phasic LC activation is driven by the outcome of task-related decision processes and is proposed to facilitate ensuing behaviors and to help optimize task performance (exploitation). When utility in the task wanes, LC neurons exhibit a tonic activity mode, associated with disengagement from the current task and a search for alternative behaviors (exploration). Monkey LC receives prominent, direct inputs from the anterior cingulate (ACC) and orbitofrontal cortices (OFC), both of which are thought to monitor task-related utility. We propose that these frontal areas produce the above patterns of LC activity to optimize utility on both short and long timescales.

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An Integrative Theory of Locus Coeruleus-Norepinephrine Function: Adaptive Gain and Optimal Performance.

The Akaike information criterion (AIC; Akaike, 1973) is a popular method for comparing the adequacy of multiple, possibly nonnested models. Current practice in cognitive psychology is to accept a single model on the basis of only the “raw” AIC values, making it difficult to unambiguously interpret the observed AIC differences in terms of a continuous measure such as probability. Here we demonstrate that AIC values can be easily transformed to so-called Akaike weights (e.g., Akaike, 1978, 1979; Bozdogan, 1987; Burnham & Anderson, 2002), which can be directly interpreted as conditional probabilities for each model. We show by example how these Akaike weights can greatly facilitate the interpretation of the results of AIC model comparison procedures.

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AIC model selection using Akaike weights

In this article, the authors consider optimal decision making in two-alternative forced-choice (TAFC) tasks. They begin by analyzing 6 models of TAFC decision making and show that all but one can be reduced to the drift diffusion model, implementing the statistically optimal algorithm (most accurate for a given speed or fastest for a given accuracy). They prove further that there is always an optimal trade-off between speed and accuracy that maximizes various reward functions, including reward rate (percentage of correct responses per unit time), as well as several other objective functions, including ones weighted for accuracy. They use these findings to address empirical data and make novel predictions about performance under optimality.

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The physics of optimal decision making: a formal analysis of models of performance in two-alternative forced-choice tasks.

What does it feel like to own, to control, and to be inside a body? The multidimensional nature of this experience together with the continuous presence of one's biological body, render both theoretical and experimental approaches problematic. Nevertheless, exploitation of immersive virtual reality has allowed a reframing of this question to whether it is possible to experience the same sensations towards a virtual body inside an immersive virtual environment as toward the biological body, and if so, to what extent. The current paper addresses these issues by referring to the Sense of Embodiment (SoE). Due to the conceptual confusion around this sense, we provide a working definition which states that SoE consists of three subcomponents: the sense of self-location, the sense of agency, and the sense of body ownership. Under this proposed structure, measures and experimental manipulations reported in the literature are reviewed and related challenges are outlined. Finally, future experimental studies are proposed to overcome those challenges, toward deepening the concept of SoE and enhancing it in virtual applications.

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The sense of embodiment in virtual reality

Virtual reality (VR) environments are increasingly being used by neuroscientists to simulate natural events and social interactions. VR creates interactive, multimodal sensory stimuli that offer unique advantages over other approaches to neuroscientific research and applications. VR's compatibility with imaging technologies such as functional MRI allows researchers to present multimodal stimuli with a high degree of ecological validity and control while recording changes in brain activity. Therapists, too, stand to gain from progress in VR technology, which provides a high degree of control over the therapeutic experience. Here we review the latest advances in VR technology and its applications in neuroscience research.

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Virtual reality in neuroscience research and therapy

The effect of immediate versus delayed feedback on rule-based and information-integration category learning was investigated. Accuracy rates were examined to isolate global performance deficits, and model-based analyses were performed to identify the types of response strategies used by observers. Feedback delay had no effect on the accuracy of responding or on the distribution of best fitting models in the rule-based category-learning task. However, delayed feedback led to less accurate responding in the information-integration category-learning task. Model-based analyses indicated that the decline in accuracy with delayed feedback was due to an increase in the use of rule-based strategies to solve the information-integration task. These results provide support for a multiple-systems approach to category learning and argue against the validity of single-system approaches.

Delayed feedback effects on rule-based and information-integration category learning.

The effects of two different kinds of categorization training were investigated In observational training, observers are presented with a category label and then shown an exemplar from that category In feedback training, they are shown an exemplar, asked to assign it to a category, and then given feedback about the accuracy of their response These two types of training were compared as observers learned two types of category structures—those in which optimal accuracy could be achieved via some explicit rule-based strategy, and those in which optimal accuracy required integrating information from separate perceptual dimensions at some predecisional stage There was an overall advantage for feedback training over observational training, but most importantly, type of training interacted strongly with type of category structure With rule-based structures, the effects of training type were small, but with information-integration structures, accuracy was substantially higher with feedback training, and people were less likely to use suboptimal rule-based strategies The implications of these results for current theories of category learning are discussed

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Observational versus feedback training in rule-based and information-integration category learning.

The consistency of the mapping from category to response location was investigated to test the hypothesis that abstract category labels are learned by the hypothesis testing system to solve rule-based tasks, whereas response position is learned by the procedural-learning system to solve informationintegration tasks. Accuracy rates were examined to isolate global performance deficits, and modelbased analyses were performed to identify the types of response strategies used by observers. A-B training (consistent mapping) led to more accurate responding relative to yes-no training (variable mapping) in the information-integration category learning task. Model-based analyses indicated that the yes-no accuracy decline was due to an increase in the use of rule-based strategies to solve the information-integration task. Yes-no training had no effect on the accuracy of responding or distribution of best-fitting models relative to A-B training in the rule-based category learning tasks. These results both provide support for a multiple-systems approach to category learning in which one system is procedural-learning-based and argue against the validity of single-system approaches.

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Evidence for a procedural-learning-based system in perceptual category learning.

The optimality of multidimensional perceptual categorization performance with unequal base rates and payoffs was examined. In Experiment 1, observers learned simultaneously the category structures and base rates or payoffs. Observers showed conservative cutoff placement when payoffs were unequal and extreme cutoff placement when base rates were unequal. In Experiment 2, observers were trained on the category structures before the base-rate or payoff manipulation. Simultaneous base-rate and payoff manipulations tested the hypothesis that base-rate information and payoff information are combined independently. Observers showed (a) small suboptimalities in base-rate and payoff estimation, (b) no qualitative differences across base-rate and payoff conditions, and (c) support for the hypothesis that base-rate and payoff information is combined independently. Implications for current theories of base-rate and payoff learning are discussed.

https://psycnet.apa.org/journals/xlm/24/6/1459.pdf

Corey J. Bohil

Papers

Virtual reality in neuroscience research and therapy

Delayed feedback effects on rule-based and information-integration category learning.

Observational versus feedback training in rule-based and information-integration category learning.

Evidence for a procedural-learning-based system in perceptual category learning.

Base-rate and payoff effects in multidimensional perceptual categorization.