Neuroscience

Does Self-Reassurance Reduce Neural and Self-Report Reactivity to Negative Life Events?

1. Jeffrey J. Kim
2. James R. Doty
3. Ross Cunnington
4. James N. Kirby

• Curated by eLife

  Summary: Kim and colleagues present a secondary analysis of an already published imaging dataset in 40 participants going through a two-week compassion training paradigm. They show participants standardized statements that are emotional or neutral and further have participants either engage in "self-criticism" or "self-reassurance" while considering the statements. The authors report on differences in brain regions (what they refer to as "neural pain") depending on criticism or reassurance condition. Concerns with the conceptual framework, approach, and interpretation substantially dampened our enthusiasm.

Reviewed by eLife

Reinstatement of Cortical Outcome Representations during Higher-Order Learning

1. Lennart Luettgau
2. Emanuele Porcu
3. Claus Tempelmann
4. Gerhard Jocham

• Curated by eLife

  Summary: All reviewers agreed that the neural mechanisms by which value is conferred to stimuli that were never directly paired with reinforcement is an important topic. However, individual reviewers raised questions regarding the study design and data analysis. In particular, reviewers agreed it was not clear how you could distinguish BOLD responses to CS1 and CS2 given the temporal proximity of their presentation. They also wondered whether the current results would provide enough advance beyond previous work.

Reviewed by eLife

Frontal cortical regions associated with attention connect more strongly to central than peripheral V1

1. Sara A. Sims
2. Pinar Demirayak
3. Simone Cedotal
4. Kristina M. Visscher

• Curated by eLife

  Summary: The manuscript is a replication of findings from Griffis et al., 2017, and it seeks to validate those findings using a different modality (diffusion-weighted imaging; DWI). While the questions asked in this manuscript are of considerable interest to the field, the findings' focus and implications are relatively narrow. Further, the study does not reveal new conclusions about brain function or organization. Authors may be cautious about interpreting the findings as representing direct structural connections between the occipital and frontal cortex -- as the reported structural and functional connectivity values may not be strong enough to support such a strong interpretation. The reviewers also agree that the methods are not presented clearly, in a manner that is straightforward to follow and critique.

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Starburst amacrine cells amplify optogenetic visual restoration through gap junctions

1. Yusaku Katada
2. Hiromitsu Kunimi
3. Naho Serizawa
4. Deokho Lee
5. Kenta Kobayashi
6. Kazuno Negishi
7. Hideyuki Okano
8. Kenji F. Tanaka
9. Kazuo Tsubota
10. Toshihide Kurihara

Reviewed by eLife

DRAXIN regulates interhemispheric fissure remodelling to influence the extent of corpus callosum formation

1. Laura Morcom
2. Timothy J Edwards
3. Eric Rider
4. Dorothy Jones-Davis
5. Jonathan WC Lim
6. Kok-Siong Chen
7. Ryan J Dean
8. Jens Bunt
9. Yunan Ye
10. Ilan Gobius
11. Rodrigo Suárez
12. Simone Mandelstam
13. Elliott H Sherr
14. Linda J Richards

• Curated by eLife

  Summary: Your manuscript is an excellent account of the cellular and genetic mechanisms involved in the diversity of corpus callosum dysgenesis (CCD) phenotypes in humans and in a mouse model. Your work over the years has revealed that interhemispheric fissure (IHF) fusion is critical for proper formation of the callosum and its failure is the main cause of complete CCD. Here you nicely show that the extent of aberrant interhemispheric fissure (IHF) remodeling does in fact correlate with commissure dysgenesis severity, in inbred and outcrossed BTBR mouse strains, as well as in humans with partial CCD. The phenotypes in the mouse are very similar to what is found in humans, and also variable, perhaps related to stochasticity on the mechanisms involved, or to the dependency on other allelic variants.

  You also identify an eight base pair deletion in Draxin and misregulated astroglial and leptomeningeal proliferation as genetic and cellular factors for variable IHF remodelling and CCD in BTBR acallosal strains. The Draxin mutations interrupt the normal remodeling (closing) of interhemispheric fissure necessary for callosal axons to cross. Your study thus places the focus on midline cellular populations and away from axonal navigation as the main source of corpus callosum dysgenesis. The findings are important to understand what mutations cause CCD in humans and how, mechanistically, it occurs.

  This manuscript was co-submitted with https://www.biorxiv.org/content/10.1101/2020.08.03.233593v1

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Trading mental effort for confidence in the metacognitive control of value-based decision-making

1. Douglas G Lee
2. Jean Daunizeau

• Curated by eLife

  Summary: This manuscript addresses a timely subject: the role of cognitive control (or mental effort) in value-based decision making. While there are plenty of models explaining value-based choice, and there is a growing number of computational accounts concerning effort-allocation, little theoretical work has been done to relate the two literatures. This manuscript contributes a novel and interesting step in this direction, by introducing a computational account of meta-control in value-based decision making. According to this account, meta-control can be described as a cost-benefit analysis that weighs the benefits of allocating mental effort against associated costs. The benefits of mental effort pertain to the integration of value-relevant information to form posterior beliefs about option values. Given a small set of parameters, as well as pre-choice value ratings and pre-choice uncertainty ratings as inputs to the model, it can predict relevant decision variables as outputs, such as choice accuracy, choice confidence, choice induced preference changes, response time and subjective effort ratings. The study fits the model to data from a behavioral experiment involving value-based decisions between food items. The resulting behavioral fits reproduce a number of predictions derived from the model. Finally, the article describes how the model relates to established accumulator models of decision-making.

  The (relatively simple) model is impressive in its apparent ability to reproduce qualitative patterns across diverse data including choices, RTs, choice confidence ratings, subjective effort, and choice-induced changes in relative preferences successfully. The model also appears well-motivated, well-reasoned, and well-formulated. While all reviewers agreed that the manuscript is of potential interest, they also all felt that a stronger case needs to be made for the explanatory power of the model, and that the model should be embedded more thoroughly in the existing literature on this topic.

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Phasic oxygen dynamics confounds fast choline-sensitive biosensor signals in the brain of behaving rodents

1. Ricardo M Santos
2. Anton Sirota

Reviewed by eLife

De novo learning versus adaptation of continuous control in a manual tracking task

1. Christopher S Yang
2. Noah J Cowan
3. Adrian M Haith

Reviewed by eLife

Value Certainty in Drift-Diffusion Models of Preferential Choice

1. Douglas Lee
2. Marius Usher

• Curated by eLife

  Summary: This study investigates how uncertainty about the values of choice alternatives affects decision-making from the perspective of drift-diffusion modeling. Both reviewers agree that this is an interesting question. The authors propose different candidate models for how uncertainty might affect the drift rate or the diffusion variance, and test these candidates on four food-preference datasets. The authors report that the best model is one in which the drift rate scales with the value of the options normalized by their respective uncertainties.

  Despite the relevance of the research question, both reviewers have found the contribution of the findings to existing knowledge to be not sufficiently strong and clear. Several empirical observations reported in the study are already well known, and several of the alternative models are known to be "strawmen" for researchers in value-based decision-making and drift-diffusion modeling. In particular, the reviewers have noted that is not surprising that a lower certainty alone cannot correspond to higher diffusion noise in a drift-diffusion model, and can thus be captured by a lower drift. They agreed, and further amplified in the consultation session amongst reviewers, that the precise computational way by which this drift modulation is implemented would need to be investigated much further. Furthermore, to increase the strength of the conclusions, the authors should explore in more detail the different classes of DDMs, and the ways in which value certainty could affect other parameters of the model than the ones considered in the manuscript.

Reviewed by eLife

A new model of decision processing in instrumental learning tasks

1. Steven Miletić
2. Russell J Boag
3. Anne C Trutti
4. Niek Stevenson
5. Birte U Forstmann
6. Andrew Heathcote

• Curated by eLife

  Summary: This cognitive modeling study on a timely topic investigates the combination of reinforcement learning and decision-making for modeling choice and reaction-time data in sequential reinforcement problems (e.g., bandit tasks). The central claim of the paper is that the often-used combination of reinforcement learning with the drift-diffusion model (which decides based on the difference between option values) does not provide an adequate model of instrumental learning. Instead, the authors propose an "advantage racing" model which provides better fits to choice and reaction-time data in different variants of two-alternative forced-choice tasks. Furthermore, the authors emphasize that their advantage racing model allows for fitting decision problems with more than two alternatives - something which the standard drift-diffusion model cannot do. These findings can be of interest for researchers investigating learning and decision-making.

  The study asks an important question for understanding the interaction between reinforcement learning and decision-making, the methods appear sound, and the manuscript is clearly written. The superiority of the advantage racing model is key to the novelty of the study, which otherwise relies on a canonical task studied in several recent papers on the same issue. However, the reviewers feel that the framing of the study and its conclusions would require additional analyses and experiments to transform the manuscript from a modest quantitative improvement into a qualitative theoretical advance. In particular, as described in the paragraphs below, the authors should test how their advantage racing model fares in reinforcement problems with more than two alternatives. This is, from their own account throughout the paper, a situation where their model could show most clearly its superiority over standard drift-diffusion models used in the recent literature.

Reviewed by eLife