Venue: ICLR 2023 TinyPapers

Reviews:

final_score: rating: Needs Clarification (NC): a submission which does not meet the reviewing criteria and needs clarification for its described problem or solution
review_content: summary_of_contributions: This paper explores how quickly humans tend to change context (e.g., by swiping) when interacting with media on a smartphone, which is substantially faster than context switches on, for example, television. Based on this, the paper argues that visual stimuli (such as ads) should be (re)designed in ways that account for this difference.
strengths_and_weaknesses: ### Strengths
- The paper is well written and the general message is clear.
- While I'm not familiar with the economics- and neuroscience-based literature largely cited in this paper, it appears to me that the paper likely has a good overview of the related and relevant literature.

### Weaknesses
- I see no Machine Learning content in this paper. While I don't see an explicit list of topics that are considered in or out of scope for Tiny Papers, I assume that roughly the same topics would be considered relevant as for the main ICLR conference. If this is the case, I unfortunately think the topic of the paper is simply not within the scope for ICLR, and ICLR is not a suitable venue for this work.
suggested_changes: To be considered on-topic and relevant for ICLR, there should be at least some Machine Learning aspect in the paper.
review_title: null

final_score: rating: Needs Clarification (NC): a submission which does not meet the reviewing criteria and needs clarification for its described problem or solution
review_content: summary_of_contributions: This paper proposes a final second decision making framework (FSDM). This FSDM framework aims to understand how the brain understands and processes visual information in short time horizons such as in the context of smartphone use and makes actions from that.
strengths_and_weaknesses: Strengths:

- There is clear motivation detailed in the introduction regarding the need to tackle settings where visual stimulus time horizons are short and there are fast context shifts.

Weaknesses
- General paper's writing quality could be improved, some sentences are difficult to understand (see some examples in suggested changes)
- The introduction and discussion mention the utilization of the neuroscience of decision-making as a lens for approaching human machine interaction, however the paper only applies it in theory by citing a few papers and hypothesizing some clams in the secondary research section. It is unclear how neuroscience is really being applied to the core claims of this paper, and whether the secondary research section adds any value to those claims. Moreover, as a neuroscience paper the secondary research section should be revised to back up the claims behind the proposed components of the FSDM, these seem to be mostly postulations.
- In the introduction it is mentioned that the paper is inspired by past studies and so the approach is to find the minimum and maximum time (perhaps a better description would be the distribution of time) a visual stimulus on a smartphone is there for. However the section 2.2 results don't seem to say much about this distribution (or min/max times) other then mentioning the average. While the average is a useful metric, it would be useful to include a chart of the distribution of the measured times. Moreover, it is unclear how this is related to the FSDM other than clarifying that a decision making framework in a short time horizon context should focus on the final second. More clarification here would be great.
- It is unclear what the data is collected by this paper and how it contributes to any of the results/discussions. It seems data is pulled from another paper (Howarth, 2023) and the 2.7s average is from this paper. If this is the case, it would great to clarify what section 2.2's purpose is and the need for an experiment.
- The discussion section claims to investigate the relevant processes leading to an action (which as I understand is backed by section 3 on secondary research) but 
- The content and contributions of the paper does not seem suitable for ICLR as it appears to synthesize data from another paper (Howarth, 2023) and discuss some potential implications of it. However, since this is the new Tiny paper's track I may misunderstand the requirements so if relevance to the ICLR community is not critical this comment can be ignored.
suggested_changes: Major:
- It would be important to include the distributions of data (averaged across all participants if needed) as a single average metric says little about the data and is difficult to make claims around
- The experiment results can be better utilized and explained how they support a claim
- I would recommend heavily revising the secondary research section by clarifying how it relates to the core claims


Minor:
- Section 1: "To begin understanding the time required to decide in the brain we take learnings from sports, where the batsman decides which shot to play in 200ms." Sentence could be reorganized e.g. "To understand the decision making time of the brain, we take learnings from sports such as Cricket where batsmen take 200ms to make a decision (McLeod, 1987)."
- Section 1: "Looking further we found Facebook (fac) and Netflix studies quoting that the time any user spends considering watching a
piece of content is just about 1.8 seconds (abo) in a browsing session." Seems to be incorrect citation / what does fac and abo mean?
- Section 3: "We propose that an Emotional Category-Need Trigger, e.g., thirst triggered by looking at visuals of a sandy desert, heat
waves, and condensation of water droplets on a chilled bottle of a soft drink." seems to be unfinished, what does this sentence want to say about proposing this?
review_title: null

Meta-review: This paper and topic falls within scope, and the authors are asked to revise according to the reviewers' feedback.
Decision: Invite to revise