Evaluating the Bottom-Up Method for Functional Decomposition in Product Dissection Tasks

2014 ◽  
Author(s):  
Joran W. Booth ◽  
Abihnav K. Bhasin ◽  
Tahira Reid ◽  
Karthik Ramani
Keyword(s):  
Prior Work ◽  
Top Down ◽  
Specific Design ◽  
Bottom Up ◽  
Functional Decomposition ◽  
Identification Methods ◽  
Future Studies ◽  
Statistical Support ◽  
Prior Literature ◽  
Function Identification

The purpose of this study is to continue to explore which function identification methods work best for specific design tasks. Prior literature describes the top-down and bottom-up approaches as equivalent methods for functional decomposition. Building on our prior work, this study tests the bottom-up method against the top-down and enumeration methods. We used a 3-factor within-subject study (n=136). While most of our diagram-oriented metrics were not statistically different, we found statistical support that: 1.) students reported that the dissection activity was more useful when using bottom-up, and 2.) that student engineers committed many more syntax errors when using the bottom-up method (by listing parts instead of functions). We believe that both these results are due to the increased focus on individual parts. We do not know if an increased attention to the parts would increase novelty or fixation, and recommend future studies to find out.

scholarly journals Comparing Functional Analysis Methods for Product Dissection Tasks

2015 ◽  
Vol 137 (8) ◽  
Author(s):  
Joran W. Booth ◽  
Tahira N. Reid ◽  
Claudia Eckert ◽  
Karthik Ramani
Keyword(s):  
Functional Analysis ◽  
Energy Flow ◽  
Prior Experience ◽  
Qualitative Difference ◽  
Top Down ◽  
Identification Methods ◽  
Future Studies ◽  
Core Concepts ◽  
Power Drill ◽  
Function Identification

The purpose of this study is to begin to explore which function identification methods work best for specific tasks. We use a three-level within-subject study (n = 78) to compare three strategies for identifying functions: energy-flow, top-down, and enumeration. These are tested in a product dissection task with student engineers who have minimal prior experience. Participants were asked to dissect a hair dryer, power drill, and toy dart gun and generate function trees to describe how these work. The function trees were evaluated with several metrics including the total number of functions generated, the number of syntactical errors, and the number of unique (relevant and nonredundant) functions. We found no statistical, practical, or qualitative difference between the trees produced by each method. This suggests that the cognitive load for this task for novices is high enough to obscure any real differences between methods. We also found some generalized findings through surveys that the most difficult aspects of using functional decomposition include identifying functions, choosing function verbs, and drawing the diagram. Together, this may also mean that for novice engineers, the method does not matter as much as core concepts such as identifying functions and structuring function diagrams. This also indicates that any function identification method may be used as a baseline for comparison between novices in future studies.

scholarly journals Statistical mechanics of neocortical interactions: EEG eigenfunctions of short-term memory

2000 ◽  
Vol 23 (3) ◽  
pp. 403-405 ◽  
Author(s):  
Lester Ingber
Keyword(s):  
Statistical Mechanics ◽  
Short Term Memory ◽  
Probability Distributions ◽  
Regional Scale ◽  
Top Down ◽  
Short Term ◽  
Bottom Up ◽  
Term Memory ◽  
Future Studies ◽  
Scalp Eeg

This commentary focuses on how bottom-up neocortical models can be developed into eigenfunction expansions of probability distributions appropriate to describe short-term memory in the context of scalp EEG. The mathematics of eigenfunctions are similar to the top-down eigenfunctions developed by Nunez, despite different physical manifestations. The bottom-up eigenfunctions are at the local mesocolumnar scale, whereas the top-down eigenfunctions are at the global regional scale. Our respective approaches have regions of substantial overlap, and future studies may expand top-down eigenfunctions into the bottom-up eigenfunctions, yielding a model of scalp EEG expressed in terms of columnar states of neocortical processing of attention and short-term memory.

scholarly journals Calculation Methods for Construction Material Stocks: A Systematic Review

2021 ◽  
Vol 11 (14) ◽  
pp. 6612
Author(s):  
Usman Nasir ◽  
Ruidong Chang ◽  
Hossein Omrany
Keyword(s):  
Statistical Data ◽  
Construction Material ◽  
Research Area ◽  
Embodied Energy ◽  
Small Scale ◽  
Calculation Methods ◽  
Top Down ◽  
Bottom Up ◽  
Future Studies ◽  
Socio Economic Factors

This paper aims to critically review the current body of literature relating to the calculation methods of construction material stock. To this end, this study adopts a systematic literature review technique in order to identify the relevant studies. The findings revealed that the bottom-up and top-down methodologies were commonly employed by the reviewed studies. Based on the findings, it is recommended that the bottom-up approach should be utilized when dealing with small-scale areas or where more accurate results are required. The top-down method should be used wherein the research area is large, and the results could be estimated based upon assumptions and statistical data. Similarly, the demand-driven methodology should be used to find the material stock accumulation due to socio-economic factors. The study also found that the material stock results can be used as data for other research, such as waste management and embodied energy. Further, this paper proposes a conceptual framework to ease the process of calculating construction material stocks in different projects. The outcomes of this research shall be beneficial for future studies that explore the literature connected to the construction material stock and recommend methods and techniques that should be used to quantify the material stock.

scholarly journals What shall we call God? An exploration of metaphors coded from descriptions of God from a large U.S. undergraduate sample

PLoS ONE ◽  
2021 ◽  
Vol 16 (7) ◽  
pp. e0254626
Author(s):  
Adam K. Fetterman ◽  
Nicholas D. Evans ◽  
Julie J. Exline ◽  
Brian P. Meier
Keyword(s):  
Behavioral Outcomes ◽  
Current Investigation ◽  
Top Down ◽  
Bottom Up ◽  
Future Studies ◽  
Language Analysis ◽  
The Common

People use numerous metaphors to describe God. God is seen as a bearded man, light, and love. Based on metaphor theories, the metaphors people use to refer to God reflect how people think about God and could, in turn, reflect their worldview. However, little work has explored the common metaphors for God. This was the purpose of the current investigation. Four trained raters coded open-ended responses from predominantly Christian U.S. undergraduates (N = 2,923) describing God for the presence or absence of numerous metaphoric categories. We then assessed the frequency of each of the metaphor categories. We identified 16 metaphor categories that were present in more than 1% of the responses. The top categories were “GOD IS POWER,” “GOD IS HUMAN,” and “GOD IS MALE.” These findings were similar across religious affiliations. We attempted to support our coding analysis using top-down and bottom-up automated language analysis. Results from these analyses provided added confidence to our conclusions. We discuss the implications of our findings and the potential for future studies investigating important psychological and behavioral outcomes of using different metaphors for God.

scholarly journals Bottom-up synthesis of recursive functional programs using angelic execution

2022 ◽  
Vol 6 (POPL) ◽  
pp. 1-29
Author(s):  
Anders Miltner ◽  
Adrian Trejo Nuñez ◽  
Ana Brendel ◽  
Swarat Chaudhuri ◽  
Isil Dillig
Keyword(s):  
Prior Work ◽  
Top Down ◽  
Bottom Up ◽  
Synthesis Algorithm ◽  
Version Space ◽  
Main Challenge ◽  
Recursive Programs ◽  
Benchmark Suite ◽  
Logical Specifications ◽  
Better Than

We present a novel bottom-up method for the synthesis of functional recursive programs. While bottom-up synthesis techniques can work better than top-down methods in certain settings, there is no prior technique for synthesizing recursive programs from logical specifications in a purely bottom-up fashion. The main challenge is that effective bottom-up methods need to execute sub-expressions of the code being synthesized, but it is impossible to execute a recursive subexpression of a program that has not been fully constructed yet. In this paper, we address this challenge using the concept of angelic semantics. Specifically, our method finds a program that satisfies the specification under angelic semantics (we refer to this as angelic synthesis), analyzes the assumptions made during its angelic execution, uses this analysis to strengthen the specification, and finally reattempts synthesis with the strengthened specification. Our proposed angelic synthesis algorithm is based on version space learning and therefore deals effectively with many incremental synthesis calls made during the overall algorithm. We have implemented this approach in a prototype called Burst and evaluate it on synthesis problems from prior work. Our experiments show that Burst is able to synthesize a solution to 94% of the benchmarks in our benchmark suite, outperforming prior work.

Psychology and Culture: Top Down Versus Bottom Up?

PsycCRITIQUES ◽  
2005 ◽  
Vol 50 (19) ◽  
Author(s):  
Michael Cole
Keyword(s):  
Top Down ◽  
Bottom Up
Sign in / Sign up

Export Citation Format

Share Document