Chapter 12: We Don’t Constrain Ourselves
Summary:
Objects are created with constraints and affordances that limit or cue proper use. If objects are not properly constrained or given the proper affordances, serious errors can occur. For example, herapin overdoses due to too many similarities in the labels have affected thousands, including Dennis Quaid and his family. The aviation industry is much better at eliminating these types of errors than the medical field because of better naming and differing attitudes towards accepting advice.
Chapter 13: The Grass Does Look Greener
Summary:
Millions of people move out to California every year because they believe they will be happier, but it turns out that the things we focus on tend to not make us as happy as we would expect. Handicapped people are generally very satisified with their life, but most people wouldn't expect that. Finality allows for adaptation, while the option for a "grass is greener" scenario can hamper happiness.
Conclusion:
Summary:
This basically brings all the chapters into one synopsis. The author provides suggestions on how to avoid all the causes of mistakes. Also he discusses the importance of sleep.
Discussion:
The first chapter is very similar to the stuff Norman wrote. I think the concept of constraints and affordances are too closely linked to be labeled differently. For example, I don't think scissors constrain the way we use them, but rather the holes provide cues/affordances on how to use them correctly.
I thought the part about handicapped and disabled people being happier was really eye-opening. I never would have thought that at all. I also liked the bit about how people with the option to do something else generally are less happy, even though I would think it to be the other way around. I don't understand why so many people would want to live in California. But to every man his own I guess.
The conclusion I thought was fitting. I was especially interested in the part about sleep. I liked how he highlighted the important examples in the book, as those were what made the book enjoyable to begin with for me.
Tuesday, April 19, 2011
Paper Reading #23
Comments:
Comment #1
Comment #2
Reference:
Title: Evaluating Automatic Warning Cues for Visual Search in Vascular Images
Author: Boris W. van Schooten, Betsy M.A.G. van Dijk, Anton Nijholt, Johan H.C. Reiber
Venue: IUI 2009/2010
Summary:
This group conducted an experiment using MRA images to analyze the effects of visual searches. Namely, the aim was to determine the level of detriment or effeciency in a false positive, false negative, no suspicious areas, and perfect detection situation. The users were presented with images of vessels, and they were supposed to verify the automatic segmentation. They "compared user time and error performance as well as subjective preference for the following conditions: no warning highlights, only false positives (paranoid), only false negatives (conservative), and perfect highlighting." The results indicated that the users performed significantly faster with the paranoid highlighting than with no highlighting. On top of that, they made less errors as well, although by a relatively insignificant margin. Lastly, the group found that the users preferred the presence of suspicious areas over no suspicious areas, and "appear" to prefer paranoid over conservative highlighting. This is very different than what has been found in other similar studies. Other studies showed detrimental effects for false positive rates approximately the same as in this study, but this experiment indicates some potential positive effects for those false positives. Figure 1 shows an example from the experiment.
Discussion:
This was my first or second short paper, and I basked in all its glory. The experiment was easy to understand, so I think the author did a good job explaining it. I feel sorry for the people who had to participate in the study because this seemed like a pretty boring experiment to take part of. Since visual search is so common, whether it be in airports, medical fields, or even the military, I can see how this type of study could be useful. I would be interested in seeing if other groups could replicate these results on larger scales and with different applications.
Comment #1
Comment #2
Reference:
Title: Evaluating Automatic Warning Cues for Visual Search in Vascular Images
Author: Boris W. van Schooten, Betsy M.A.G. van Dijk, Anton Nijholt, Johan H.C. Reiber
Venue: IUI 2009/2010
Summary:
This group conducted an experiment using MRA images to analyze the effects of visual searches. Namely, the aim was to determine the level of detriment or effeciency in a false positive, false negative, no suspicious areas, and perfect detection situation. The users were presented with images of vessels, and they were supposed to verify the automatic segmentation. They "compared user time and error performance as well as subjective preference for the following conditions: no warning highlights, only false positives (paranoid), only false negatives (conservative), and perfect highlighting." The results indicated that the users performed significantly faster with the paranoid highlighting than with no highlighting. On top of that, they made less errors as well, although by a relatively insignificant margin. Lastly, the group found that the users preferred the presence of suspicious areas over no suspicious areas, and "appear" to prefer paranoid over conservative highlighting. This is very different than what has been found in other similar studies. Other studies showed detrimental effects for false positive rates approximately the same as in this study, but this experiment indicates some potential positive effects for those false positives. Figure 1 shows an example from the experiment.
This was my first or second short paper, and I basked in all its glory. The experiment was easy to understand, so I think the author did a good job explaining it. I feel sorry for the people who had to participate in the study because this seemed like a pretty boring experiment to take part of. Since visual search is so common, whether it be in airports, medical fields, or even the military, I can see how this type of study could be useful. I would be interested in seeing if other groups could replicate these results on larger scales and with different applications.
Monday, April 18, 2011
Final Project Proposal
Team Members
Ryan Kerbow
Wesley Konderla
Jeremy Nelissen
Evin Schuchardt
Proposal
We are proposing to create a mobile phone app that will allow poets the ability to use TTS to record poem ideas at any time with little effort. The users will then be able to edit there poems, classify them, and save them to their phones for easy retrieval later. This is an implementation of our Project 3.
Posted by Jeremy Nelissen at 2:19 PM
Ryan Kerbow
Wesley Konderla
Jeremy Nelissen
Evin Schuchardt
Proposal
We are proposing to create a mobile phone app that will allow poets the ability to use TTS to record poem ideas at any time with little effort. The users will then be able to edit there poems, classify them, and save them to their phones for easy retrieval later. This is an implementation of our Project 3.
Posted by Jeremy Nelissen at 2:19 PM
Thursday, April 14, 2011
Book Reading #49 - Why We Make Mistakes
Chapter 10: We All Think We’re Above Average
Summary:
People are incredibly over confident. This is displayed in confidence tests, gym memberships, and other tests of one's skill. Information overload helps confidence, but doesn't affect outcomes.
Chapter 11: We’d Rather Wing It
Summary:
We don't read instructions. We'd rather just do it our own way. Experts are classified as such because they spend thousands upon thousands of hours practicing. Cognitive maps are a key way animals solve problems, as we're always looking for the shortest path to "get the cheese."
Discussion:
I like this book. It uses real world examples, and doesn't speak in overly technical terms to try to sound smart. I thought the over confidence struck home, because I want to do some things after college that I'm probably not qualified for right now. I didn't like the second chapter as much. I think it's funny that people will do things without reading the instructions just to do it their own way. Humans are incredibly stubborn. I admit, I tried the cup pouring example without looking at the answers, and I did it the long way the second time too.
Summary:
People are incredibly over confident. This is displayed in confidence tests, gym memberships, and other tests of one's skill. Information overload helps confidence, but doesn't affect outcomes.
Chapter 11: We’d Rather Wing It
Summary:
We don't read instructions. We'd rather just do it our own way. Experts are classified as such because they spend thousands upon thousands of hours practicing. Cognitive maps are a key way animals solve problems, as we're always looking for the shortest path to "get the cheese."
Discussion:
I like this book. It uses real world examples, and doesn't speak in overly technical terms to try to sound smart. I thought the over confidence struck home, because I want to do some things after college that I'm probably not qualified for right now. I didn't like the second chapter as much. I think it's funny that people will do things without reading the instructions just to do it their own way. Humans are incredibly stubborn. I admit, I tried the cup pouring example without looking at the answers, and I did it the long way the second time too.
Book Reading #48 - Media Equation
Part 1: Machines and Mindlessness: Social Responses to Computers
Summary:
This first paper revolved around how humans treat computers. Even though most people claim that computers do not deserve humane treatment, they still show tendencies to treat the computer with compassion. People still treat the computer as a human, even when they shouldn't.
Part 2: Computers are Social Actors
Summary:
This paper continued the theme from the previous paper by focusing on the social interactions between humans and the computer. It explained 5 experiments that determined the differences between "self" and "other" in people's minds, then asked the people about the computers afterward. The computers would praise, criticize, or tutor the user, among other things, and the group studied the results of each action. Part 3: Can Computer Personalities Be Human Personalities?
Summary:
This last paper tried to delve into creating a personality for a computer. They believed personalities could be programmed into the computer, and demonstrated by the speech/communication habits. People preferred computers with traits close to their own. Discussion:
I thought these papers were pretty interesting. From the first two, I thought it was very interesting how people can treat computers with the social norms you'd expect someone to treat another human. When the computer treated the people well, they tried to reciprocate. This is amazing to me considering it's still a computer. Also, the final bit about personalities is pretty cool too. From the first two papers, it doesn't surprise me at all that people would treat a computer better that had a personality similar to their own. I've never really thought about human computer interaction in this space, so I thought it was pretty interesting.
Summary:
This first paper revolved around how humans treat computers. Even though most people claim that computers do not deserve humane treatment, they still show tendencies to treat the computer with compassion. People still treat the computer as a human, even when they shouldn't.
Part 2: Computers are Social Actors
Summary:
This paper continued the theme from the previous paper by focusing on the social interactions between humans and the computer. It explained 5 experiments that determined the differences between "self" and "other" in people's minds, then asked the people about the computers afterward. The computers would praise, criticize, or tutor the user, among other things, and the group studied the results of each action. Part 3: Can Computer Personalities Be Human Personalities?
Summary:
This last paper tried to delve into creating a personality for a computer. They believed personalities could be programmed into the computer, and demonstrated by the speech/communication habits. People preferred computers with traits close to their own. Discussion:
I thought these papers were pretty interesting. From the first two, I thought it was very interesting how people can treat computers with the social norms you'd expect someone to treat another human. When the computer treated the people well, they tried to reciprocate. This is amazing to me considering it's still a computer. Also, the final bit about personalities is pretty cool too. From the first two papers, it doesn't surprise me at all that people would treat a computer better that had a personality similar to their own. I've never really thought about human computer interaction in this space, so I thought it was pretty interesting.
Wednesday, April 13, 2011
Paper Reading #22
Comments:
Comment #1
Comment #2
Reference:
Title: A POMDP approach to P300-based brain-computer interfaces
Author: Jaeyoung Park, Kee-Eung Kim, Sungho Jo
Venue: IUI 2009/2010
Summary:
Brain Computer Interfaces (BCI) provide a channel of communication for "conveying messages and commands from the brain to external sources." This is significant because by studying BCIs we can improve communication between the brain and the computer. Perhaps the most popular way of measuring BCIs is by the use of EEGs. EEGs are popular because they are non-invasive, cheap, and easy to use. Also, it has a very reliable signal feature called a P300, which "a positive peak in the signal amplitude at about 300ms after a stimulus is given to the user’s attention." One system that has been proposed using the P300 and EEGs is the P300 speller. The P300 speller has a 6x6 matrix of letters, and the letters randomly and systematically flash, so when the letter that the user is looking at flashes, the P300 is received approximately 300 ms later. This is the BCI system that this group used to test their algorithm.
Most BCI work has been focused on the lower-level of the interface, being feature extraction or classification methods. This paper focuses on a higher level problem: the optimal order or sequence of flashes. To do this, the group used a Partially Observable Markov Decision Processes (POMDP) algorithm. Basically, this algorithm uses several variables (8 total) and combined with the given uncertainty to model sequential decision making. To implement this, raw signals are sent to the preprocessor, which is a collection of bandpass and other filters. Then that is classified to determine if a P300 occurred. Then the system is modeled and sent to the POMDP planner.
The baseline for the experiment was BCI algorithms already in use. The group tested 2x2 and 2x3 matrices. The algorithms the group used to compare to the baseline were POMDP with select actions (PWSA) and without select actions (PWOSA). The PWSA improved the bit rates the most, by 242% to 265%. This was 135% to 151% better than the baseline algorithms already being used.
Discussion:
I found this paper mildly enjoyable. I think this is definitely a usable innovation. I admit I don't know much about BCIs, but the stats presented show that this is an obvious improvement. The thing I didn't like about the algorithm was that it can't be used on any data because it is computationally infeasible. However, this is definitely an improvement and in a useful area. The paper used a TON of mathematical notation and explanation. If you've read my blogs before, you know I don't like that in a paper like this. I think that stuff should be left for a follow up, once you've already captured the audience's attention and have them asking for more. The concept is strong, but the paper loses the reader's attention.
Comment #1
Comment #2
Reference:
Title: A POMDP approach to P300-based brain-computer interfaces
Author: Jaeyoung Park, Kee-Eung Kim, Sungho Jo
Venue: IUI 2009/2010
Summary:
Brain Computer Interfaces (BCI) provide a channel of communication for "conveying messages and commands from the brain to external sources." This is significant because by studying BCIs we can improve communication between the brain and the computer. Perhaps the most popular way of measuring BCIs is by the use of EEGs. EEGs are popular because they are non-invasive, cheap, and easy to use. Also, it has a very reliable signal feature called a P300, which "a positive peak in the signal amplitude at about 300ms after a stimulus is given to the user’s attention." One system that has been proposed using the P300 and EEGs is the P300 speller. The P300 speller has a 6x6 matrix of letters, and the letters randomly and systematically flash, so when the letter that the user is looking at flashes, the P300 is received approximately 300 ms later. This is the BCI system that this group used to test their algorithm.
Most BCI work has been focused on the lower-level of the interface, being feature extraction or classification methods. This paper focuses on a higher level problem: the optimal order or sequence of flashes. To do this, the group used a Partially Observable Markov Decision Processes (POMDP) algorithm. Basically, this algorithm uses several variables (8 total) and combined with the given uncertainty to model sequential decision making. To implement this, raw signals are sent to the preprocessor, which is a collection of bandpass and other filters. Then that is classified to determine if a P300 occurred. Then the system is modeled and sent to the POMDP planner.
The baseline for the experiment was BCI algorithms already in use. The group tested 2x2 and 2x3 matrices. The algorithms the group used to compare to the baseline were POMDP with select actions (PWSA) and without select actions (PWOSA). The PWSA improved the bit rates the most, by 242% to 265%. This was 135% to 151% better than the baseline algorithms already being used.
Discussion:
I found this paper mildly enjoyable. I think this is definitely a usable innovation. I admit I don't know much about BCIs, but the stats presented show that this is an obvious improvement. The thing I didn't like about the algorithm was that it can't be used on any data because it is computationally infeasible. However, this is definitely an improvement and in a useful area. The paper used a TON of mathematical notation and explanation. If you've read my blogs before, you know I don't like that in a paper like this. I think that stuff should be left for a follow up, once you've already captured the audience's attention and have them asking for more. The concept is strong, but the paper loses the reader's attention.
Tuesday, April 12, 2011
Paper Reading #20
Comments:
Comment #1
Comment #2
Reference:
Title: UIMarks: Quick Graphical Interaction with Speciļ¬c Targets
Author: Olivier Chapuis, Nicolas Roussel
Venue: IUI 2009/2010
Summary:
This paper describes a system designed to allow users to "specify on-screen targets and associated actions by means of a graphical marking language." Essentially, this is a system that allows users to create graphic macros rather than keyboard or typed macros. This allows the user to use recall rather than memorization to specify when to do an action. The types of actions are given by this graphic below:
I thought this was decently interesting. I've read a lot of articles about this type of stuff recently, and throughout the class. I think it's interesting that I read so many articles on this stuff, yet I've never heard of this stuff before this class. I never see it anywhere else or in real world situations. Either that, or I just don't notice them. I wonder who could actually make use of these graphic macros. To me it seems like it would have a small target audience. I thought the paper was well written, not having too much technical information that would bog it down, like other papers do. Overall, I like this system and the paper.
Comment #1
Comment #2
Reference:
Title: UIMarks: Quick Graphical Interaction with Speciļ¬c Targets
Author: Olivier Chapuis, Nicolas Roussel
Venue: IUI 2009/2010
Summary:
This paper describes a system designed to allow users to "specify on-screen targets and associated actions by means of a graphical marking language." Essentially, this is a system that allows users to create graphic macros rather than keyboard or typed macros. This allows the user to use recall rather than memorization to specify when to do an action. The types of actions are given by this graphic below:
As demonstrated by the graphic, there are three types of actions: preceding, primary, and following. Only the primary actions are required, while the other actions provide a for richer choices to be made. To implement this system, the group used a slightly adapted version of the bubble cursor. The marks are made in a specific mode, where a semi-transparent overlay allows differentiation between the marks. They implemented this system on Metisse and OS X. Additionally, UIMarks was only intended to supplement other similar systems, rather than replace them or be a stand alone system. The results of the study showed that UIM had 25-60% better results/times than other systems.
Discussion:I thought this was decently interesting. I've read a lot of articles about this type of stuff recently, and throughout the class. I think it's interesting that I read so many articles on this stuff, yet I've never heard of this stuff before this class. I never see it anywhere else or in real world situations. Either that, or I just don't notice them. I wonder who could actually make use of these graphic macros. To me it seems like it would have a small target audience. I thought the paper was well written, not having too much technical information that would bog it down, like other papers do. Overall, I like this system and the paper.
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