Reaction to Owens, McLaughlin, & Sudweeks (2011) by Richard Thripp
EXP 6506 Section 0002: Fall 2015 – UCF, Dr. Joseph Schmidt
September 22, 2015 [Week 5]

Owens, McLaughlin, and Sudweeks (2011) conducted what was, to their knowledge, the first controlled, real-world study regarding text messages and driving (p. 940). They used a closed, 1.4-mile two-lane road, and the actual trials were conducted on straight uphill and downhill sections (I was surprised that no information was given regarding the steepness of these grades). Participants (n = 20) sent and received text messages using their mobile phones (typed) on some trials, and using the in-vehicle Ford SYNC system and selecting from a pre-programmed list of 15 possible text messages on other trials (p. 940). Overall, sending text messages was more dangerous than receiving them, mobile phone use was more dangerous than the in-vehicle system, and texting in general appeared more dangerous for older participants.

I question the generalizability of this experimental study. Why not do a naturalistic study, where drivers agree to have their cars equipped with audiovisual and kinematic sensors that monitor their texting habits in real-world situations? An example of such a study, funded by the same agency (the National Surface Transportation Safety Center for Excellence), is Distraction in commercial trucks and buses: Assessing prevalence and risk in conjunction with crashes and near-crashes (2010). Instead, we get an experimental study that is simplistic in implementation and hampered by safety concerns. Owens, McLaughlin, and Sudweeks (2011) conducted their trials with no other vehicles on the roadway, at a maximum speed of 35 miles per hour, on straightaways! This is not like actual texting while driving, which may involve congestion, traffic signals, curves, higher speeds, pedestrians, and more. They proceed to make inferences that texting by hand results in greatly degraded control of the vehicle, based on steering velocities (p. 945); however, the possibility that participants might text more cautiously (with more frequent steering corrections) on an actual roadway with other drivers is not explored. We are expected to believe that the conditions are valid because participants did not know if a single confederate vehicle might enter the roadway again, after passing them in the opposite direction on the first practice lap (p. 942)—quite a stretch, to say the least.

While mental demand, glances, and steering was measured, there was no consideration of velocity, following distance, weather conditions, or a host of other factors. To be fair, the authors did conduct a naturalistic study for about an hour with each participant, immediately prior to the 40-minute study in question, the results for which were released in 2010 (p. 942). However, both studies are of limited depth and were conducted with an “in-vehicle experimenter” present (p. 942), possibly influencing behavior. Considering that the experimenters had a control tower and many cameras and sensors (pp. 941–942), they could have eliminated the in-vehicle experimenter if they wanted. As a further point to limit generalizability, the system used does not even exist in the real world—the actual Ford SYNC system had to be modified by the manufacturer to allow texting while driving, since it typically disables texting at speeds over 3 miles per hour (pp. 940, 945).

This study was conducted in Virginia, where texting while driving is illegal—therefore, the researchers did not screen participants on their texting while driving habits (p. 940). Had the researchers conducted the research in a state where texting while driving was “legal,” such as Florida, they could have asked these questions and perhaps gained further insights.

The researchers relied on post-hoc tests to investigate interactions (p. 943), including measuring the baseline duration post hoc (p. 942). Post-hoc analysis should be used with caution and may reveal statistically significant patterns that are of no practical significance. They also assumed normality and homogeneity even though there were deviations in the ANOVA residual plots, and did not show us the plots (p. 943).

Importantly, driving while texting was not measured with respect to where the mobile phone was located—it could be better if the phone was in a cradle on the dashboard or mounted to the windshield, since participants would not have to look down (away from the roadway) to use their phones. I did not see any mention of this possibility, though interior glances were timed and counted. Further, the information in this article is already somewhat dated: only 6 of 20 participants had touch-screen phones (p. 940), while in 2015, this proportion would be much higher. 10 of 20 participants had archaic numeric keypads that require much more typing than a full QWERTY keypad (whether it is on a touch-screen or with physical keys). Many phones have fairly reliable voice recognition systems now, which may be less distracting than typing. It is possible that text messaging could be safer for some drivers than inferred from this study: for example, drivers who use a dashboard cradle and primarily text at red lights. Beneficial factors may even exist, such as reduced speed and increased following distance while texting.

References

Distraction in commercial trucks and buses: Assessing prevalence and risk in conjunction with crashes and near-crashes. (2010). Washington, DC: U.S. Dept. of Transportation, Federal Motor Carrier Safety Administration, Office of Analysis, Research and Technology, [2010]. Retrieved from http://ntl.bts.gov/lib/51000/51200/51287/Distraction-in-Commercial-Trucks-and-Buses-report.pdf

Owens, J. M., McLaughlin, S. B., & Sudweeks, J. (2011). Driver performance while text messaging using handheld and in-vehicle systems. Accident Analysis and Prevention, 43, 939–947. doi:10.1016/j.aap.2010.11.019

Note: Per Florida Statute 316.305, texting while driving became illegal on 10/01/2013, but was “legal” at the time of this study (most states already have laws against driving while encumbered, reckless driving, etc., but they typically go unenforced with respect to texting, necessitating the creation of new laws against texting on a state-by-state basis).

Reaction to Broadbent (1957) by Richard Thripp
EXP 6506 Section 0002: Fall 2015 – UCF, Dr. Joseph Schmidt
September 15, 2015 [Week 4]

Broadbent (1957) presents a model for human attention conceptualized as a Y-shaped tube that receives balls that represent information. A flap divides the Y-connection, and various parallels between what would happen to the actual balls and to human attention and memory are proposed.

Broadbent could instead have used water flowing through a Y-connector as his analogy—the rate or constriction of flow could vary between pipes, for example. There are many analogies that could be used. Whether this is a good analogy is up for debate, but seeing that Broadbent had to attach numerous codicils (p. 206, 208, 210) and discusses many limitations (p. 213) and conceptual problems with his model seems to suggest it is questionable. His modified model in Figure 2 (p. 210) appears as a circuit, which models memory as a recurrent process, but is admittedly an unwieldy and difficult model, given the author’s humorous comments that the apparatus would need to be filled with acid to replicate the disappearance of a memory item by dissolving a ball. This model might be more detrimental than useful as a teaching tool, if it results in profound, lasting misconceptions. The author admits: “Certain properties of the model are likely to be misleading” (p. 213)—no kidding! I can only imagine that getting this published in 1957 was much easier than it would be now.

We are familiar with the idea of “semantically impoverished” stimuli—that stimuli such as colored boxes and abstract shapes are not as salient as real-world stimuli. When Broadbent clarifies that stimuli can bypass the Y tube “if they convey sufficiently little information” (p. 213), one wonders if he considered the distinction between semantically rich and semantically impoverished stimuli? Being that he goes on to discuss reflexes and generalize them to “voluntary” reactions, it appears the distinction was (momentarily) lost on him. Broadbent may have been a visionary if he replaced “convey[ing] sufficiently little information” with something like “requiring sufficiently little processing resources.” The quantity of information is not always the most important part—later on the same page, Broadbent makes the point that decimal digits (base 10) convey far more information than binary digits (base 2), and yet do not require much (or any) extra effort for our brains to remember (p. 213). Therefore, the Y tube model is grossly oversimplified—some balls may in fact be bigger than others, and some may require negligible resources.

Broadbent concedes the Y tube analogy is of “obvious absurdity” if one identifies it with the organism, rather than as a mechanical conceptualization for human attention and immediate memory (p. 213). He proposes the model is primarily for people who find the abstract theory “unintelligible”—and indeed, it may help them. However, individuals who have a rudimentary understanding of attention and memory may be better off skipping Broadbent’s paper, given that it may imbue them with gross simplifications, rather than refining their understanding.

Reference

Broadbent, D. E. (1957). A mechanical model for human attention and immediate memory. Psychological Review, 64(3), 205–215. doi:10.1037/h0047313

Reaction to Wood & Cowan (1995) by Richard Thripp
EXP 6506 Section 0002: Fall 2015 – UCF, Dr. Joseph Schmidt
September 15, 2015 [Week 4]

Wood and Cowan (1995) indicate that they are following up and improving on an old research study that was “conducted rather casually” with a tiny sample size (p. 255). Wood and Cowan’s participants listened to two channels of unrelated, monosyllabic words with stereo headphones, and were asked to attend to and repeat (“shadow”) only the female voice, while being instructed to ignore the male voice in the left earpiece. Unbeknownst to them, the male voice would say their name at either the 4 or 5 minute mark, and the name of another “yoked control participant” at either the 4 or 5 minute mark (p. 256–57). In the experimental condition, 9 of 26 participants noticed their name in the irrelevant channel, and 5 of these 9 participants made a mistake in repeating one or more of the two words before or three words after, compared to a much smaller proportion of errors among the other participants (p. 258). Interestingly, the 9 who noticed had a much higher mean response lag on the second word after their name—approximately 950 ms as compared to 675 ms in the next highest category, possibly indicating distraction (p. 259).

While this may be a compact and nicely structured study, the generalizability is limited—it is not similar to the “cocktail party” analogy at all. All words used were monosyllabic, and participants were specifically selected who had monosyllabic names—a highly unrealistic scenario, given the plethora of common disyllabic names. The attended channel was always in a female voice and the irrelevant channel in a male voice—given that higher pitched voices may be easier to hear, it would have been interesting to see the authors switch this up. Both channels played words simultaneously and at a rate of exactly one word per second (p. 257), which is not generalizable to a cocktail party, nor even most human conversation. I was surprised that while the authors were careful to play half of participants’ names at the 4 minute mark and others at the 5 minute mark, they did not try switching the ears (the attended channel was always the right earpiece). Furthermore, the entire experiment was only 5 ½ minutes—placing the stimulus so late in playback could produce different results from placing it toward the middle or beginning, although a Fisher’s exact test indicated no difference between the 4 and 5 minute conditions.

I cannot understand why 5 participants were rejected due to not having yoked control participants (p. 256). Why not just select monosyllabic names at random from a list of common names? In fact, I am not sure of the necessity of having yoked participants at all—selecting names randomly could have worked for all participants, freeing the authors up to manipulate some other variable. The authors’ admit that “the order of words was otherwise [besides the insertion of two names] identical across participants” (p. 257), but this could have been varied by experimental condition. There is also a gender bias that is not addressed: 25 (73.5%) of participants are male and only 9 (26.5%) are female. The authors could have selected equal numbers per gender, and could have broken out the results by gender.

I would like to have seen more discussion regarding the fact that none of the 26 experimental participants recalled hearing the yoked control participant’s name. This may be an indicator that monosyllabic names and words are not differentiated by our brains like our name is, but this possibility was not explored. It would even be interesting to conduct an experiment where the irrelevant channel consisted primarily or completely of monosyllabic names, to see whether this is noticed and whether a similar proportion of participants notice their names. Notice that 85% of participants were not even able to recall a specific word from the irrelevant channel, and 62% did not volunteer that it was in a male voice, even though all were asked for information about the channel’s content (p. 257). Were some participants just listening better than others, or not following the directions precisely? Participants who noticed their names made fewer errors on average: 17.0 versus 20.5 (p. 257). While this difference was not significant, recall that participants who noticed their names made more errors in the three words immediately after their names (p. 258–59). Perhaps if the errors immediately after their names were partialed out, a significant difference would have been found? We may never know.

There are many other conditions the researchers could have tried. While a sample of 34 is generally sufficient for a cognitive experiment, this was a very short and simple experiment that required little time or energy from participants. It would be nice to see the authors use a larger sample size and try more interesting experimental conditions, rather than rejecting 6 participants (p. 256) due to a shortage of names (n = 5) and due to an experimenter mistakenly letting the cat out of the bag (n = 1), albeit the latter is my speculation.

Reference

Wood, N., & Cowan, N. (1995). The cocktail party phenomenon revisited: How frequent are attention shifts to one’s name in an irrelevant auditory channel? Journal of Experimental Psychology, 21(1), 255–260.

Reaction to Brady, Konkle, & Alvarez (2011) by Richard Thripp
EXP 6506 Section 0002: Fall 2015 – UCF, Dr. Joseph Schmidt
September 7, 2015 [Week 3]

Brady, Konkle, and Alvarez (2011) provide a thorough, though not exhaustive, review of visual memory research, broken down into convenient sections and subsections. Overall, two main sections regarding visual working memory and visual long-term memory investigate the ideas and research about various aspects of these systems, such as memory fidelity (p. 2–5, 13–15), basic units of representation (p. 5–7), interactions (p. 7–10, 23–25), and the effects of stored knowledge (p. 10–12, 15–19). Generally, these topics were treated separately with regard to working memory and long-term memory, which is a traditional distinction that is advantageous for conceptualization, but is of uncertain validity (p. 23–25). Based on an abundance of cited research, several themes emerged. With respect to working memory, capacity may be issue of both quality (fidelity) of memory and quantity of items remembered (p. 12). Structured representations and ensemble effects should be considered, meaning that information is stored in multiple and interacting layers (p. 10, 12). Long-term memory is surprisingly robust, especially with stimuli that are both semantically rich and real-world, supposedly because real-world scenery allows us to employ “passive episodic retrieval”, unlike “semantically impoverished stimuli” such as colored squares (p. 24). Overall, both working visual memory and long-term visual memory are more intricate and inter-dependent than once thought, which makes compartmentalizing any one subcomponent, and experimental research in general, highly difficult.

I found the results regarding long-term memory experiments interesting—I did not recall that our long-term memory is near-perfect for 10,000 items, as long as those items are unique and meaningful (p. 22). The authors did a good job of organizing the subjects and materials into numerous headings and subheadings, which made this review feel less onerous than many experimental research articles regarding cognition.

As a person with some computing knowledge, I liked the analogy of memory to a USB drive (p. 2), but found it constrained by technical inaccuracies. Saying that “the number of files that can be stored is limited only by the size of those files” with respect to a USB drive (p. 2) is inaccurate, given that it is constrained by the cluster size of the file system. USB flash drives typically use the FAT or FAT32 file system with cluster sizes of 4096, 8192, or 16,384 bytes (depending on drive capacity). Any file occupies the nearest higher discrete number of clusters—the author’s example of a 16 × 16 pixel image would typically take up at least 4096 bytes, even though the actual file would be 768 bytes or less. Conceptually, this adds a more complicated layer to the analogy that might actually be relevant to visual working memory—perhaps there is a lower bound on the space an item occupies, regardless of further reductions in fidelity? This characterization offers an appealing middle ground between continuous and discrete visual working memory models, which was not directly addressed by the authors (and may not yet be addressed in the literature).

Expanding on computing analogies, the authors missed a great chance to compare the concept of structured representations to progressive image rendering. Progressive JPEG images look blocky at first, and then gradually appear clearer as more data is received and decoded. This is similar to the idea of a “hierarchically structured feature bundle” (p. 7) where low-level features at the bottom level coalesce into a complete object representation through multiple, structured levels of data. Progressive image rendering shares remarkable commonalities with structured memory models (p. 10), and may even provide a conceptual framework to explore and develop visual working memory models.

Reference

Brady, T., Konkle, T., & Alvarez, G. A. (2011). A review of visual memory capacity: Beyond individual items and toward structured representations. Journal of Vision, 11(5), 1–34. doi:10.1167/11.5.4

Reaction to Baddeley (2004) by Richard Thripp
EXP 6506 Section 0002: Fall 2015 – UCF, Dr. Joseph Schmidt
September 2, 2015 [Week 2]

Baddeley (2004) discusses the contemporary research and competing models on how various aspects of human memory operate. Based on research, a general model dividing declarative (explicit) and nondeclarative (implicit) memory has achieved broad acceptance (p. 6)—however, the details remain up for field testing and debate, such as which distinct types of memory exist, how they overlap, what category or categories they fit into, and how these types of memory relate to everyday life. Intense inquiry, including studying patients with brain damage, memory deficits, and amnesia, has greatly refined the psychology of memory; it is now regarded as a complex and nuanced system that interacts, both within its components (short-term memory, long-term memory, and their subtypes) and with the external environment. We have progressed greatly in the past century—we no longer regard memory as a monolithic faculty, nor do we take semantic memory for granted as psychologists did prior to the 1960s (p. 6).

Baddeley has produced a literature review that is engaging and highly readable. He has done a great deal of research in this area—he references 15 articles for which he was the primary author, and seven more articles that he co-authored. His scientific humility is shown in areas where he presents competing viewpoints or suggests reading other authors who have expanded and refined his works, such as the expansions by Vallar & Papagno (2002) and Della Sala & Logie (2002) on the Baddeley & Hitch (1974) model of working memory (Baddeley, 2004, p. 3-4). He is cautious to not pick sides or make definitive judgments—this can be seen in phrasing such as “among the strongest arguments” (p. 1), “it is generally accepted” (p. 6), and “one view is that” (p. 8). This concern for impartiality, rigor, and detail endears Baddeley to the reader and shows him leading by example, encouraging the reader to consider all the evidence and potential unknowns.

Baddeley presents the viewpoint of Squire (1992), that semantic memory is simply the result of episodic memories for which the brain has lost context (p. 6). Similarly, in a lecture on April 21, 2015, to a Developmental Psychology graduate class, Professor Sims proposed the argument that “wisdom” might be characterized as knowledge without context, where the source of the knowledge has simply been forgotten, while the knowledge remains. Forgetting where, how, or from whom you learned something does not mean the episode or source does not exist, but it does mean it may be, for practical purposes, irretrievable. Alternately, the acquisition may have been spread out over a long period of time, making it hard to quantify. However, it is apparent why we may want to attribute this to experience or wisdom rather than memory loss—it is a much more palatable and polite designation. Squire’s characterization of semantic memory provides a potential explanation for how we learn language, culture, and habits—not in a singular episode, but slowly, over time, and typically without conscious consideration.

I was delighted by the discourse on prospective memory, which is an area where the elderly are paradoxically better than young people (Baddeley, 2004, p. 9), perhaps because they are more cautious about writing things down, keeping a schedule, setting alarms, and recognizing that their memory is highly fallible. On the other hand, young people are often overly trusting of their own ability to remember, to hilarious or disastrous consequences, such as showing up to class on Labor Day, or forgetting the due date for a course project. These and other “everyday” problems are more interesting to laypersons than laboratory settings, and for this reason, naturalistic materials are even being adopted in controlled settings (p. 11).

Reference

Baddeley, A. D. (2004). “Chapter 1: The psychology of memory.” In A. D. Baddeley et al. (Ed.), The essential handbook of memory disorders for clinicians. Chichester, England: John Wiley & Sons.