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      Monetary, Food, and Social Rewards Induce Similar Pavlovian-to-Instrumental Transfer Effects

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          Abstract

          Multiple types of reward, such as money, food or social approval, are capable of driving behavior. However, most previous investigations have only focused on one of these reward classes in isolation, as such it is not clear whether different reward classes have a unique influence on instrumental responding or whether the subjective value of the reward, rather than the reward type per se, is most important in driving behavior. Here, we investigate behavior using a well-established reward paradigm, Pavlovian-to-instrumental transfer (PIT), and three different reward types: monetary, food and social rewards. The subjective value of each reward type was matched using a modified Becker-DeGroot-Marschak (BDM) auction where subjective reward value was expressed through physical effort using a bimanual grip force task. We measured the influence of reward-associated stimuli on how participants distributed forces between hands when reaching a target effort range on the screen bimanually and on how much time participants spent in this target range. Participants spent significantly more time in the target range (15% ± 2% maximal voluntary contraction) when a stimulus was presented that was associated with a reward used during instrumental conditioning or Pavlovian conditioning compared to a stimulus associated with a neutral outcome (i.e., general PIT). The strength of the PIT effect was modulated by subjective value (i.e., individuals who showed a stronger PIT effect rated the value of rewards more highly), but not by reward type, demonstrating that stimuli of all reward types were able to act as appetitive reinforcers and influenced instrumental responding, when matched to the same subjective reward value. This is the first demonstration that individually matched monetary, food and social rewards are equally effective as appetitive reinforcers in PIT. These findings strengthen the hypotheses that the subjective value is crucial for how much reward-associated stimuli influence behavior.

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          Measuring utility by a single-response sequential method.

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            Move over ANOVA: progress in analyzing repeated-measures data and its reflection in papers published in the Archives of General Psychiatry.

            The analysis of repeated-measures data presents challenges to investigators and is a topic for ongoing discussion in the Archives of General Psychiatry. Traditional methods of statistical analysis (end-point analysis and univariate and multivariate repeated-measures analysis of variance [rANOVA and rMANOVA, respectively]) have known disadvantages. More sophisticated mixed-effects models provide flexibility, and recently developed software makes them available to researchers. To review methods for repeated-measures analysis and discuss advantages and potential misuses of mixed-effects models. Also, to assess the extent of the shift from traditional to mixed-effects approaches in published reports in the Archives of General Psychiatry. The Archives of General Psychiatry from 1989 through 2001, and the Department of Veterans Affairs Cooperative Study 425. Studies with a repeated-measures design, at least 2 groups, and a continuous response variable. The first author ranked the studies according to the most advanced statistical method used in the following order: mixed-effects model, rMANOVA, rANOVA, and end-point analysis. The use of mixed-effects models has substantially increased during the last 10 years. In 2001, 30% of clinical trials reported in the Archives of General Psychiatry used mixed-effects analysis. Repeated-measures ANOVAs continue to be used widely for the analysis of repeated-measures data, despite risks to interpretation. Mixed-effects models use all available data, can properly account for correlation between repeated measurements on the same subject, have greater flexibility to model time effects, and can handle missing data more appropriately. Their flexibility makes them the preferred choice for the analysis of repeated-measures data.
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              Reward representations and reward-related learning in the human brain: insights from neuroimaging.

              This review outlines recent findings from human neuroimaging concerning the role of a highly interconnected network of brain areas including orbital and medial prefrontal cortex, amygdala, striatum and dopaminergic mid-brain in reward processing. Distinct reward-related functions can be attributed to different components of this network. Orbitofrontal cortex is involved in coding stimulus reward value and in concert with the amygdala and ventral striatum is implicated in representing predicted future reward. Such representations can be used to guide action selection for reward, a process that depends, at least in part, on orbital and medial prefrontal cortex as well as dorsal striatum.
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                Author and article information

                Contributors
                Journal
                Front Behav Neurosci
                Front Behav Neurosci
                Front. Behav. Neurosci.
                Frontiers in Behavioral Neuroscience
                Frontiers Media S.A.
                1662-5153
                04 January 2017
                2016
                : 10
                : 247
                Affiliations
                [1] 1Neural Control of Movement Lab, Department of Health Science and Technology, Federal Institute of Technology Zurich Zurich, Switzerland
                [2] 2Neuroscience Center Zurich, Federal Institute of Technology Zurich, University and Balgrist Hospital Zurich, University of Zurich Zurich, Switzerland
                [3] 3Laboratory for Social and Neural Systems Research, Department of Economics, University of Zurich Zurich, Switzerland
                [4] 4Movement Control and Neuroplasticity Research Group, Department of Kinesiology, Biomedical Sciences Group, KU Leuven Leuven, Belgium
                Author notes

                Edited by: Giuseppe Di Pellegrino, University of Bologna, Italy

                Reviewed by: Manuela Sellitto, University of Giessen, Germany; Sara Garofalo, University of Cambridge, UK

                *Correspondence: Rea Lehner rea.lehner@ 123456hest.ethz.ch
                Article
                10.3389/fnbeh.2016.00247
                5209382
                28101010
                648b4bfe-1e87-410d-9032-b1e4152d70a3
                Copyright © 2017 Lehner, Balsters, Herger, Hare and Wenderoth.

                This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution and reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

                History
                : 19 October 2016
                : 19 December 2016
                Page count
                Figures: 3, Tables: 0, Equations: 0, References: 69, Pages: 12, Words: 9629
                Categories
                Neuroscience
                Original Research

                Neurosciences
                pavlovian-to-instrumental transfer,cue-controlled behavior,becker-degroot-marschak auction,reward type,subjective reward value,effort

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