Exploring the effects of depression and treatment of depression in reinforcement learning

Castro-Rodrigues, Pedro; Oliveira‐Maia, Albino J.

doi:10.3389/fnint.2013.00072

Cited by 2 publications

(2 citation statements)

References 18 publications

(24 reference statements)

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“…In this collection, Herzallah et al ( 2013 ) dissociated among depressed patients with and without antidepressant medication, and healthy control subjects by observing the performance in learning from positive (reward) and negative (punishment) feedback. Castro-Rodrigues and Oliveira-Maia ( 2013 ) provided a useful commentary on this important original work. Finally, the comprehensive review by Asher et al ( 2013 ) proposed a closed-loop paradigm toward understanding serotonergic roles in decision making by involving behavioral experiments, game theory, computational modeling, and human–robotic interaction, a truly integrative neuroscience approach.…”

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confidence: 99%

Functions and computational principles of serotonergic and related systems at multiple scales

Nakamura

Wong‐Lin

2014

Front. Integr. Neurosci.

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confidence: 99%

Functions and computational principles of serotonergic and related systems at multiple scales

Nakamura

Wong‐Lin

2014

Front. Integr. Neurosci.

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“…For example, in Figure 1 D the choice of the learning rate parameter α affected both qualitative (compare 0.1 with 0.9) and quantitative (compare 0.1 with 0.3) behavior. Thirdly, model-based designs are often used in the clinical literature to examine neural correlate differences between patient and control populations (Castro-Rodrigues and Oliveira-Maia, 2013; Deserno et al, 2013). Without specificity, such comparisons are meaningless.…”

Section: Discussionmentioning

confidence: 99%

In model-based fMRI significant is less than specific

Peterson

Seger

2018

Preprint

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9By comparing computational model output to BOLD signal changes model-based fMRI has the potential to offer profound insight into what neural computations occur when. If this potential is to be fully realized, statistically significant outcomes must imply specific outcomes. That is, we must have a clear idea of how often a model not present in the BOLD signal but present in the predictor set will reach significance. We ran Monte Carlo simulations of reinforcement learning to examine this kind of specificity, focusing in on two aspects. One, to what degree can we tell related but theoretically distinct predictors apart. About 40% of the time the studied predictors were indistinguishable. Two, how well can we separate out different parameterizations of the same reinforcement learning terms. Nearly all parameter settings were indistinguishable. The lack of specificity between models and between parameters suggests a uncertain relation between significance and specificity. Follow up analyses suggest the temporally slow and prototyped nature of the haemodynamic response (HRF) can substantially increase correlations, ranging from -0.16 to 0.73 with an average of 0.27. Though we focused on a single case study, i.e., reinforcement learning, specificity concerns are potentially present in any design which does not account for the slow prototyped nature of the HRF. We suggest more specific conclusions can be reached by moving from null hypothesis testing approach to a model selection or model comparison framework. 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24onto each voxel's blood oxygen level dependent (BOLD) time course followed by a statistical contrast of 32 the two reward conditions, along with a multiple comparison correction. This is the standard statistical 33 parametric mapping (SPM) routine (Josephs et al., 1997) and it is relatively simple to understand and to 34 implement. It is also robust to noise and other natural (e.g. regional) HRF shape variation (Henson et al., 35 2001; Friston et al., 1998), as thousands of reports empirically demonstrate (Bandettini, 2007). 36 In reality however the neural response and the resulting HRF is not an all or none function. The 37HRF changes in size and shape as function of stimulus, e.g. as a function of visual contrast (Boynton 38 et al., 1996). In fact, stimulus and context dependent changes seem to be the norm. Reward valence and 39 magnitude (Delgado et al., 2003(Delgado et al., , 2000, motivation (Delgado et al., 2004), response accuracy (Seger and 40 Cincotta, 2005), strength of recall (Wais, 2008), degree of regret (Fujiwara et al., 2008), and many other 41 tasks and conditions all show distinct variations in HRF shape. 42Model-based fMRI tries to predict such shape variations by replacing impulse codes, where the HRF 43 shape representing each trial's response is identical, with varying trial-level estimates. We focus solely 44 here on estimates derived from computational modeling efforts. These model-based designs are therefore 45 specific hypotheses a...

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Exploring the effects of depression and treatment of depression in reinforcement learning

Cited by 2 publications

References 18 publications

Functions and computational principles of serotonergic and related systems at multiple scales

Functions and computational principles of serotonergic and related systems at multiple scales

In model-based fMRI significant is less than specific

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