Principle of Non-Specificity

[C. Coppock]

Principle of Non-Specificity [1]

In cases of recognition and in detailed formalized forensic comparisons such as with the ACE-V methodology, the exact same information is never used twice to effect individualization. Holistically derived inferential information cannot be run through a standardized formula, as it is unique in itself. This is a principle of: non-specificity within the cognitive comparative process. The use of specific (exact) information is not necessary, but rather the information must be relevant and sufficient for the purpose. The reason that the same exact information cannot be duplicated is due to the infinite (holistic) variability in where the relevant information can be found, and how those bits, portions, and combinations of information are used. It is the end result that matters. The sufficient information needed to sustain recognition (or formalized individualization) can possibly be found anywhere in the supporting information data set and cannot, due to the complex and unique cognitive process, be derived from specifically defined data. Therefore all specified data, must be generalized in nature.

Ref:
[1] Coppock, Craig: Complexity Of Recognition Inductive And Inferential Processes In Forensic Science.
2004, clpex.com
Recent update 08-24-2008 Available at Google Groups- [Forensics: Forensic Student Info]

Note: When we speak of a minutiae or Galton points, we are in fact being very general. There are many aspects, details, and relative relationships in a single Galton point we do not specify. It is also understood that each examiner will have a different understanding and experience base to apply to this single Galton point information. Therefore, we could consider a single point as a collection of information. It is the sum of its parts. Multiply this with the ACE-V methodology and we have a very complex and understandably unique cognitive process that repeatably yields the correct results with its proper application.

[C. Coppock]

It may help us understand our Forensic Comparison process with research in Measurement Theory (MT). MT deals with needed axioms and error to make imprecise measurements practical.

[Kasey Wertheim]

A very high-level onine reference exist at: http://www.capgo.com/Resources/Measurem ... heory.html

Introduction to measurement theory
Measurement is the process of associating numbers with physical quantities and phenomena. The process is accomplished through the comparison of a measured value with some known quantity (standard) of the same kind. The subject has become of vital importance in sciences, engineering and to much everyday activity.

While measurement theory began with the Greeks in the 4th century BC, the first useful work appeared in the 18th century by English mathematician Thomas Simpson on observation error - perhaps the most important single aspect of measurement theory.

Measurement error
Practically all measurement of continuums involve errors. Understanding the nature and source of these errors can help in reducing their impact and in may instances prevent the drawing of incorrect conclusions.

In earlier times it was thought that errors in measurement could be eliminated by improvements in technique and equipment, however most scientists now accept this is not the case. Today, nearly all scientific and engineering results are routinely reported with likely error bounds. The types of errors that must be understood include instrumental errors, systematic errors, random errors, sampling errors and indirect errors.

Systematic error
An error that can be predicted and hence eventually removed from data is a systematic error. Systematic errors may change with time, so it is important that sufficient reference data be collected with the data set to allow the systematic errors to be quantified and subtracted from the data set: [Instrumental errors, Sensor placement errors, Indirect errors]

Instrumental errors
Examples of measurement equipment systematic errors include calibration errors, input zero drift and gain drift. Measuring equipment can also induce nonsystematic errors. Instrument errors are considered in more detail in the Measurement Methods pages.

Sensor placement errors
An often overlooked systematic error source is associated with the location of the sensor. Errors can be caused by measured parameter gradients or the impact of other parameters on the sensor. For example, in precision air temperature measurement, it is likely that temperature gradients exist or radiant energy be heating the sensor - so just what is being measured? Also radiant heat may heat the sensor directly giving an erroneous reading.

Indirect errors
These are associated with calibration and conversions. Generally these errors are small, but can become significant with some types of measurement for example light intensity.

Nonsystematic errors
A nonsystematic error is one that cannot be predicted due to a randomness in its nature. Nonsystematic errors limit the ultimate accuracy of a measurement process by a masking effect that leads to information loss. They include: [Quantizing error, Rounding and truncation errors, Sampling errors, Random or noise errors, Sensor cross sensitivity errors]

Quantizing error
All measuring equipment has a resolution limit, input variations below which can not be detected or measured, leading to unrecoverable information loss. In systems with evenly spaced quantization boundaries, quantization errors can be reduced by adding noise to the input and averaging many samples.

Rounding and truncation errors
In processing the measuring system's readings, the precision of calculation (number of significant digits) can compromise results.

Sampling errors
The frequency and sample window time can impact accuracy, especially for changing or noisy quantity.

Random or noise errors
Random noise is always present in measurement, and sometimes is the dominant source of error. Depending on the noise spectrum, the noise error can generally be reduced by averaging many readings.

Sensor cross sensitivity errors
Few measuring systems respond only to the parameter being measured. All sensors have a degree of sensitivity to other parameters. For example a temperature sensor's output may change with pressure, humidity and/or ionizing radiation.

[Gerald Clough]

With reference to Kasey's enumeration of errors:

I seems to me that the source of many errors made in good faith is the error of misinterpreting any part of an image. This subjective interpretation must be made if that feature is to become useful in forming an objective conclusion. The image feature may represent one of three realities, a plain continuous ridge segment, an artifact, and a genuine diversion from a plain continuous ridge. If a genuine diversion, it may be (1) a diversion or discontinuity, the precise nature of which cannot reasonably be guessed, (2) a diversion or discontinuity that can be described as exhibiting a certain behavior of ridge flow and continuity that cannot, however, be described with certainty at every point and cannot therefore be depicted by a system of schematic lines, (3) a diversion or discontinuity that can be described with considerable certainty at every point and depicted by a system of continuous and discontinuous schematic lines. There is, of course, another subjective judgment, that of whether or not some area of the image is so poorly represented that contains no data and can be recognized only as a lack of data at that point. This includes the areas where recorded impression ends in a blank or black area.

Leaving aside any argument about the value or weight of some general kind of genuine feature that cannot be precisely described, and assuming that the goal at this point is only to subjectively establish each part of the image according to the breakdown above, how do we classify the types of error that can appear at this stage? Aside from plain bad eyesight, simply a poor sensor requiring frank badly founded guesses, I seem to keep thinking of errors as (1) an incorrect guess that assigns characteristics to a portion of the image so poorly represented that it contains no data, (2) an incorrect guess that inaccurately describes the precise nature of a feature when the quality of the impression can support only a general description, and (3) an incorrect guess that precisely but inaccurately describes a portion of the image that is represented sufficiently well that it could have been accurately represented. A gross example of (1) might be an interpretation of an ending ridge at the termination of a ridge impression at a blank area. An example of (2) might be assigning a description as a bifurcation diverting from the ridge to lying to the right when the image was only sufficient to determine that a ridge lying between two on either side either ended or diverted into the ridge to the left or right. An example of (3) might be assigning a bifurcation to one ridge when the image quality was sufficient to accurately describe the diverting ridge to be formed to the adjacent ridge on the other side. And I think we can most reasonably consider these errors to be often the result of unconscious biases, rather than conscious mental coin tosses.

To avoid confusion over the sort of error we are talking about, I rightly or wrongly omitted the case where we correctly guess the precise nature of a feature when the image quality cannot reasonably convey anything but the general nature.

Again, we are not concerned at this moment about whether or not some feature of general description is used in comparison, merely how it is interpreted during analysis. My question for Kasey and Craig is: Are these errors meaningful in terms of Measurement Theory, or are they best described in a less fundamental way in terms of recognition, since I think what we most often mean when we talk about biased interpretation is some untoward unconscious factor?

Or has my description become so convoluted as to confound the experts? (A worthy result in itself.)

[Kasey Wertheim]

I'm definitely no expert in Measurement Theory - I merely posted some additional relevant info for the benefit of those who might read on, but not specifically seek it out themselves. But there are several aspects of measurement theory that apply to LPE's.

For example, Dad and I have always mentioned in our training courses that the eye and subsequent human vision system is the capture mechanism through which the human brain is first engaged in the comparison process. If we consider this the "sensor" in MT, we could relate latent print examiner vision problems such as inadequately corrected eyesight or grayscale perception deficiencies to the MT model.

Likewise, even our critics say that a latent print examiner cannot be removed from the ACE-V method to assess error rate separately because we are "the instrument" that applies it. As an instrument, we have unique calibration that directly contributes to error. Our ability (training, experience, talent, motivation, daily variables) defines who we are and how we conduct our work. We accept that some instruments are more accurate than others. Some are faster than others. Hopefully the increase in speed is because of an increase in proficiency, not a tradeoff with accuracy... but we (hopefully) get better with experience.

Part of instrument calibration is testing borderline samples in an accurate fashion. MT relates the impact of "noisy" samples, and a lot of that could apply to latent prints. Think of the skin as the ground truth, and any impression of the 3-D surface as an opportunity to mask the true nature of the sample with the "noise" introduced during that particular rendering. Multiple samples can sometimes allow you to get a better understanding of the source and therefore the ground truth. When an extremely noisy sample (a latent print) is evaluated by an instrument (the examiner), the accuracy of the instrument becomes critical.

When the instrument isn't calibrated correctly under the MT model, this can lead to error. Training is what allows us to be more finely calibrated to deal with "noisy" borderline latent prints. If an examiner places incorrect weight on a feature in a "noisy" image as distortion when in fact it is different, it sets up the potential for an erroneous identification. If an examiner incorrectly classifies a distortion artifact as a difference, it sets up the potential for an erroneous exclusion. With training, an examiner is more able to accurately determine distortion from dissimilarity and becomes a better instrument for measuring similarity or dissimilarity in latent prints.

I haven't looked into enough detail on each of these MT topics to really do justice on how they could exactly help out our discipline. I read Craig's post, and having background and insight into human factors and error, I thought the relation was good enough to bring up some general principles to the board. I would encourage any reader who is interested (you included, Gerald) to pursue a deep-dive into one or two of these, use them to push the latent print discipline forward, and write it up for publication and peer review.

-Kasey

[Gerald Clough]

Thanks. I wonder, though, if it's just a mistake to chase a model of the human process as a technological system. It's attractive, because we take shots on account of "science" issues and because we feel a symbiosis with automation. I'll have to think on that. I sometimes sits and thinks - when I don't just sits.

[Pat A. Wertheim]

to chase a model of the human process as a technological system

I sits and I thinks maybe it's an analogy. There ain't no perfect analogy, but if it helps us understand a thing, it ain't all bad, either.

[Les Bush]

Firstly I think Craig can step into the mantle of being authoritative about the process of individualisation since the level of material he consistently produces is well researched/relevant and his interpretations well balanced.
[quote="C. Coppock"]Principle of Non-Specificity [1]

In cases of recognition and in detailed formalized forensic comparisons such as with the ACE-V methodology, the exact same information is never used twice to effect individualization. Holistically derived inferential information cannot be run through a standardized formula, as it is unique in itself. This is a principle of: non-specificity within the cognitive comparative process. The use of specific (exact) information is not necessary, but rather the information must be relevant and sufficient for the purpose. The reason that the same exact information cannot be duplicated is due to the infinite (holistic) variability in where the relevant information can be found, and how those bits, portions, and combinations of information are used. It is the end result that matters. The sufficient information needed to sustain recognition (or formalized individualization) can possibly be found anywhere in the supporting information data set and cannot, due to the complex and unique cognitive process, be derived from specifically defined data. Therefore all specified data, must generalized in nature.[/quote]
This information is very important to me as a fingerprint expert. We establish individualisation be a combination of similarity, sequence, spatial arrangement and sufficiency. By doing so we identify the souce of both the exemplar and latent prints. And our conclusions are definitive in that we form the belief that no other person/s have a duplication of the same pattern of fingerprint detail. We have arrived at this point by following a very pragmatic path based on personal experience and collective knowledge that no other expert has found two prints to be identical from two different persons. This thread appears to be about how we can associate the variables used in individualisation with some more meaningful scientific outcome of measurement.

[quote="Kasey Wertheim"]Introduction to measurement theory
Measurement is the process of associating numbers with physical quantities and phenomena. The process is accomplished through the comparison of a measured value with some known quantity (standard) of the same kind. The subject has become of vital importance in sciences, engineering and to much everyday activity.[/quote]

So where does that leave us with fingerprints? The scale of fingerprint detail is macroscopic but no-one in science has ever said measurements have to be on the large size, just that they are relevant and reproducible. So how can our individualisations produce quantitative data that can be validated with a known association to the source skin? If we look closer at the detail in much the same way that AFIS does there is an abundance of measurements to be gleaned by any pattern of ridge detail. The opportunity exists for every latent and exemplar prints to be surveyed/analysed for their geometric relationships that define the uniqueness of their pattern of detail. These same patterns will have correspondence to the source skin arrangement while variations in their (exemplar/latent) appearance/results can be interpreted/explained by the transfer conditions. To grasp this concept will require acceptance that the permanence and uniqueness of the source skin is exclusive to one person. To the courts our tabled results of measurements are an extension and a presentation of the detail used to conclude individualisation. To the science community the measurements of latent/exemplar patterns are our way of proving the analysis to a degree of relevance with a prediction that if the source skin was available it would validate the results. Without access to the source skin the results could be strongly inferred by using other exemplars (repeat recidivists) as verifications. Regards from oz and thanks Craig for an interesting thread. Les

[C. Coppock]

There are several points of our science that I would could focus some additional attention. Regarding proof of a hypothesis, we must realized that most scientific theories, hypothesis and even some mathematical statements are not provable beyond a practice nature. This ties us in with uncertainty, measurement theory, and its attention to error [and] axioms. Much needed axioms! We also have axioms, in that we don't need to compare all friction skin in the world each time we want to offer a hypothesis of Individualization or exclusion. I attempted to cover some of this information in the paper: Complexity of Recognition. I think we are on the right track, we just need to have a stronger tie-in with general science. After a brief discussion on probability theory and its implications, a CLPE stated that there is no need to understand probability theory within our Science of Fingerprints. Perhaps our our science gap is bigger than we would like.

Sincerely,

[C. Coppock]

Yes Gerald you're correct,

Our biggest challenge is to understand our methodology, it's limits (errors), and its needed axioms, utilizing such things as Measurment Theory as a relevant guide rather than a hard rule. Most of the issues are indeed relevant, just clouded within the complex human cognitive process. Non-Specificity is a core, yet we need to refine our axioms, and organize the errors within sets. Of course, many of our process errors are simply different versions of each other.

Interestingly, I have been doing some research in Artificial Intelligence. These AI folks have already been breaking down human cogative tasks. I see some areas that are relevant to our issues and our attempts to break the process down into understandable bits. ...some of which has been rolled up into a short paper on Uncertainty and the Development of a Inference Process Theory. I will post an draft version on my Google Group [Forensics: Forensic Student Info] as soon as I can get file transfer ability.

[C. Coppock]

Just below the surface logic and debate... Can we describe both the "comparison set" and/or our "comparison process" as fractals?

[C. Coppock]

Fractals. We normally think of them as complex geometric descriptions, yet there is more... Here is a mental primer.

A fractal is an object (or quantity) that displays self-similarity, in a somewhat technical sense, on all scales. The object need not exhibit exactly the same structure at all scales, but the same "type" of structures must appear on all scales. A plot of the quantity on a log-log graph versus scale then gives a straight line, whose slope is said to be the fractal dimension. http://mathworld.wolfram.com/Fractal.html
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A mathematical fractal is based on an equation that undergoes iteration, a form of feedback based on recursion. “”“The term has a variety of meanings specific to a variety of disciplines ranging from linguistics to logic.” http://www.absoluteastronomy.com/topics/Recursion””
(Note: There is discussion on recursion elsewhere CLPEX)
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The most common application of recursion is in mathematics and computer science, in which it refers to a method of defining functions in which the function being defined is applied within its own definition.
A fractal often has the following features:
•It has a fine structure at arbitrarily small scales.
•It is too irregular to be easily described in traditional Euclidean geometric language.
•It is self-similar (at least approximately or stochastically).
•It has a simple and recursive definition.
•It has a Hausdorff dimension which is greater than its topological dimension…
http://www.crystalinks.com/fractals.html
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Thus, fractals are used again to try to find a pattern in visible chaos. Using a process called "correlated percolation", very accurate representations of city growth can be achieved. The best successes with the fractal city researchers have been Berlin and London, where a very exact mathematical relationship that included exponential equations was able to closely model the actual city growth. The end theory is that central planning has only a limited effect on cities - that people will continue to live where they want to, as if drawn there naturally - fractally.
Fractal Geometry https://www.dreamessays.com/customessay ... /11469.htm
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Fractals appear in the world both as objects and as (time records of processes). Practically every example observed involves what appears to be some element of randomness, perhaps due to the interactions of very many small parts of the process.
Random Fractals and the Stock Market http://classes.yale.edu/fractals/RandFrac/welcome.html
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What does this mean to us? How does it relate to Uniqueness, Randomness, Complexity, Uncertainty, and our semi-repetitive process of comparison and infinite process variability as in non-specificity?

[C. Coppock]

I would like to offer that “Edgeoscopy” the edge geometry of the ridges can be described as a fractal.

Here is another interesting Quote:
“The concept of a fractal structure, which lacks a characteristic length scale, can be extended to the analysis of complex “temporal processes”. However, a challenge in detecting and quantifying self-similar scaling in complex time series is the following: Although time series are usually plotted on a 2-dimensional surface, a time series actually involves two different physical variables. For example, in Figure 1, the horizontal axis represents ``time,'' while the vertical axis represents the value of the variable that changes over time (in this case, heart rate). These two axes have independent physical units, minutes and beats/minute, respectively. (Even in cases where the two axes of a time series have the same units, their intrinsic physical meaning is still different.) This situation is different from that of geometrical curves (such as coastlines and mountain ranges [and Edgeoscopy]) embedded in a 2-dimensional plane, where both axes represent the same physical variable. To determine if a 2-dimensional curve is self-similar, we can do the following test: (i) take a subset of the object and rescale it to the same size of the original object, using the same magnification factor for both its width and height; and then (ii) compare the statistical properties of the rescaled object with the original object. In contrast, to properly compare a subset of a time series with the original data set, we need two magnification factors (along the horizontal and vertical axes), since these two axes represent different physical variables.”
Fractal Objects and Self-Similar Processes http://www.physionet.org/tutorials/fmnc/node3.html

[C. Coppock]

In reference to “The concept of a fractal structure, in the analysis of complex temporal processes;” I suggest that we investigate the gross analytical processes rather than specific applications within forensic comparison. Essentially, detecting and quantifying any self-similar scaling in complex time series of analytical, and perhaps documentation, processes from the Macro to the Micro with consideration for expected phase transitions.

This may assist us in our quest to better understand and describe the cognitive comparison processes we enroll in our scientific analysis, specifically ACE-V.

[C. Coppock]

An information set or problem to be analyzed can represent a lot of data, yet how much of that potential information must we know or acquire to solve the problem? How can we solve the problem if we will not know all the information? Our biological (cognitive) neural networks can store incredible amounts of experience based information and correlations of that information, yet it won’t contain or be able to acquire all the relevant information in a complex problem set. Unique relationships of points can represent a set of possibilities: In our case consider level 2 characteristics.
For example, 40 characteristics will equal 780 unique level 2 relationships within such a set.
Add inter-related sets, non-specificity and scaling effects, the information’s potential inter-relatedness quickly reaches extremely large numbers.

When solving a nonlinear problem, such as with forensic comparisons, I see there are several baseline factors to consider.
a. Not all relationships need to be analyzed. Only a fraction may be sufficient for comprehension of the problem set.
b. What is the minimum requirement to solve the problem? We can call this “Minimum sufficiency” as I do not know an official term. This is initially accomplished using applied probability based Intuition as we can’t run hard numbers on a soft set of data with unknown variables.
c. We will never be aware of all possibilities for complex sets. There will always be a level of Uncertainty.
d. All our judgments and actions must occur within this framework of uncertainty. We problem solve within this shadow.
e. Different types of logic may be utilized to successfully solve a problem.

In a problem set with an estimated 100,000 points of reference, a subject may only be aware of 5k. Of these 5k relationships it may be possible to solve the problem to practical comprehension with only 2k points of reference and cross-reference. Perhaps a review of 598 relationships will get the job done for a more efficient and experienced problem solver. Each will have a different experience base and is expected to apply that experience differently. Thus, those particular references and cross-references are expected to be different in many aspects. There are many variables. Step away from pure numbers and you have interpretation issues such as distortion as a variable. Not just a single issue of distortion but many points of reference may need a separate level of evaluation.
Is there a repetitive pattern of problem solving at the most fundamental level, thus the process being similar at various Macro to Micro levels, within degrees of complexity eventually transitioning as a phase transition from a practical to a formal process? Is a there a repeating pattern of “minimum sufficiency” within the possibilities of large complex data relationships?

-Does an examiner need to compare all available latent data to make draw a hypothesis of individualization?
a. Each latent print is only a fraction of a whole and a conclusion would most likely be based on a fraction of that information when considering the scaling effects. The larger amount of information available, the less percentage is needed for comprehension of the solution. A latent with 7 level two characteristics has 21 unique relationships. One would expect a full detailed analysis. However, a latent print with 41 level two characteristics has 820 unique relationships. We don’t need to evaluation them all in great detail. As we don’t need to see the remaining friction skin characteristics that never made the impression in the first place.
b. Is any additional evaluation (above and beyond the stated conclusion) simply a verification means to increase probability support for the established hypothesis?
c. How do we know when we have “minimum sufficiency” within a framework that always includes uncertainty?
d. Does this cognitive pattern apply to other situations at different scales?

I see this pattern as being a ground level truth of how we apply our logic and draw conclusions. We can’t necessarily use all the data, nor can we have it. I see the application of ACE-V simply as a formal and detailed application of our common high speed approach to recognition. Practical vs. Formal is where we are acutely and scientifically aware of the need to minimize error. Error can be significantly reduced with a formal application of logic and applied scientific methodology. However, its probability of uncertainty cannot be eliminated. I ask these questions to help us better understand what we do and how we do it. We can then improve instruction with the potential of further increased accuracy.

Being a motorsport enthusiast, I frequently think of the analogy of a race car driver vs. new teenage driver making a single lap on race track. Should the teen make it around the track at some speed without running off the track or crashing he/she will have achieved “minimum sufficiency” in solving the problem set of; completing a circuit of the track. The job is done. From there you can improve the result details with better numbers and with improved accuracy. Essentially, it’s a ratio of effectiveness to failure. Eventually, you will go professional stepping up to the level of “trained to competency.” Here you may even win time trails at an increased risk of not completing a lap, which is failure. The best ratio may not be at the speeds needed to win races if your goal is accuracy and efficiency in lap completion. Your experience and application of skill is high quality yet, its proper application will depend on the problem to be solved. Formally, your skill may be consistently better than simply “sufficient”. Minimum sufficiency may be for passing the driver’s license test. It gets the job done, yet to a lower level of expertise combined with a higher level for potential error. The insurance companies remind us of this each time we insure our teenagers.
However, you did not need to be a racecar driver to circumnavigate the track with the vehicle. It was achieved with less than all the available information. There were unknown dynamics and physics at play in the car’s operation and its interface with the driver and track. The feat was accomplished in the face of uncertainty. Even at the level of the professional there was uncertainty. In addition, everyone that successfully circumnavigates the track will do so differently. They will utilize their unique experience-based perspective and have a different race line around the track, such as with the concept of non-specificity. Some drivers will be more efficient than others, some quicker. The quickest driver may post the best time, yet may not offer the most efficient lap. To crash, is an error that fails to solve the problem. The problem is the information set; the information set is a lap of the track.
As with all problem-solving, there is a learning curve in the application of logic to that information set. However, this curve is simply degrees of competence in solving the problem. If minimum sufficiency is met, the job can be considered complete. You can always walk the track for an even easier solution. For a long track the applied automation of a vehicle may be an improvement on efficiency. Especially, if time is a consideration in the problem set. It all depends on the problem to be solved.

Perhaps we operate within this same framework of practical minimum sufficiency as a lower threshold for our performance similar to a “best evidence rule” using the evidence available. Further investigation may discover better “best evidence” yet, it would be a slightly different problem with this new information. We would prefer to not have to try something over and over (practice) until we find a solution. Simple problems should be easier than that. Most problems in life are indeed simple. So simple we can do them automatically, such as walking down the sidewalk. Falling off the curb is a crash, it is a failure to solve the problem of walking (on the sidewalk) to the next block. Chewing gum and biting your tongue. Failure.

What about making a hypothesis of individualization? Did you use all the information available? A latent match with 29 minutiae has 406 level two spatial relationships, plus levels one and three. Did we really compare and analyze all that information? Even with a binary fingerprint image without level three, did we still compare everything? Or did we find some particular level, well above “minimum sufficiency” in our formal comparison process and scan the rest? Perhaps, we were satisfied with a level that got the job done, yet with a good measure of professional confidence that greatly exceeds what would be considered routine, average, or simply minimally sufficient.

It can be debated that practical recognition is not a subset of formal individualization and it can be argued that non-specificity and minimal sufficiency are not real. However, I see so many holes in our current logic that it would seem prudent to investigate further. Perhaps we have worked ourselves into a corner and need a new perspective. I have been trying to step back a bit to get a wider perspective to see what we are really doing. Should we always be focused on a tree we may miss the fact we are in a forest. Perhaps we can get a better understanding on our science by understanding it within our normal problem solving processes.