Reaction to “Psychological science can improve diagnostic decisions” by Swets, Dawes, & Monahan (2000)

Reaction to Swets, Dawes, & Monahan (2000) by Richard Thripp
EXP 6506 Section 0002: Fall 2015 – UCF, Dr. Joseph Schmidt
October 7, 2015 [Week 7]

Swets, Dawes, and Monahan (2000) have given us a strong exposition of probability modeling, including engaging and practical applications, with the intention to shift public policy for the better (p. 23). I love this topic, and the need for it is basically summed in this quote: “The distribution of the degrees of evidence produced by the positive condition overlaps the distribution of the degrees produced by the negative condition” (p. 2), meaning that diagnostics is a tradeoff—since there is an overlapping range where scores can indicate both having and not having a condition (such as glaucoma or dangerously cracked airplane wings), whatever decision model is adopted will yield both false positives and false negatives.

I cannot understand why the authors never compare decision making to type I and type II errors from statistical hypothesis testing. For the statistically inclined, this seems an analogy with immense expository power. While the graphs and explanations in the article are helpful and clear, they do become repetitive—9 of 12 figures are receiver operating characteristic (ROC) curves and 2 more are ROC scatterplots. Figures relating to statistical prediction rules (SPRs) would have been welcome, such as a graph showing how reliability increases with number of cases (p. 8).

The possibilities with probability are endless, and while it may initially appear that they are valuable only to highly-educated professionals such as actuaries and medical diagnosticians, they are actually quite relevant even for personal financial literacy. For instance, I was recently tempted by a postcard advertisement to enter a $5,000 sweepstakes that requires calling in and listening to a life insurance pitch. However, after noticing the odds were listed as 1:250,000, I realized that entrants would earn, on average, 2¢. If the phone call takes five minutes of undivided attention, that is 24¢ per hour—a shockingly low return. Would not many of our decisions and practices be changed with a habitualization for seeking solid probabilistic statistics? For example, we might drive far less after realizing that our risk of bodily injury or death is so high—and we would understand a possible reason why auto insurance is expensive.

One grievance with Swets et al. (2000) is that they focused heavily on binary decisions. Diagnosing cancer (pp. 11–15) is a true/false decision, as are the decisions to utilize a particular test. While there may be a choice between tests of varying accuracy and expense, you cannot do a little bit of a biopsy because you are a little bit concerned about breast cancer—you either choose to perform or not perform the test. This might be a criticism of the field in general, since SPRs and ROCs are obviously geared toward binary tests—and a whole bunch of binary tests can approximate a continuous scale. Nonetheless, I would have liked more examples regarding non-binary decisions—for example, deciding what interest rate and credit line to extend to a borrowing customer, or rating the structural integrity of a bridge. We do have the weather forecasting example, but it was only briefly discussed (p. 18).

Screenings with a low frequency of “hits” are an interesting topic (pp. 16, 19). A detector for plastic explosives produces 5 million false positives per true positive (p. 19); 85% of low-risk HIV patients might receive a false positive diagnosis (p. 16). Statistics like these prompt us to question whether we should even bother with tests in low-risk cases? However, airport security is an area where comprehensive screening is required—we cannot simply select every nth passenger because the costs of missing a terrorist are so high and the commonness of terrorists is so low. On the other hand, the U.S. Postal Service does not need to open every package sent via media mail to ensure the contents are eligible—there is no loss of life at stake. Of course, when both false positives and false negatives are costly, such as with detecting cracks in airplane wings (pp. 16–18), detecting HIV, or detecting terrorists, SPRs and ROCs shine. We can then choose how many true positives we want and exactly how far beyond the point of diminishing returns we are willing to go.


Swets, J. A., Dawes, R. M., & Monahan, J. (2000). Psychological science can improve diagnostic decisions. Psychological Science in the Public Interest, 1(1), 1–26.

Reaction to “Signal detection theory and the psychophysics of pain” by Lloyd & Appel (1976)

Reaction to Lloyd & Appel (1976) by Richard Thripp
EXP 6506 Section 0002: Fall 2015 – UCF, Dr. Joseph Schmidt
October 7, 2015 [Week 7]

Lloyd and Appel (1976) review many articles involving signal detection theory and pain research, reaching the conclusion that greater methodological consistency is needed (p 79). This seems a valid point—3 of 9 of the studies from Table 2 (p. 89) did not even report (and may not have used) a zero pain stimulus, so how can they make inferences about pain detection without even knowing what their subjects report at the baseline? The authors rightly criticize the existing literature for other issues such as too few repetitions and lack of consistent standards for SDT measures (p. 88). Further, d′ itself (the sensitivity measure) tends to be higher with a binary measure than a rating system with more categories (p. 90)—to the point that using both types of measures simultaneously has been advocated. Few if any of the available articles considered this.

Unfortunately, the authors fail to draw sufficient connections between the articles or critically analyze them as a whole, in my opinion. While several connections and comparisons are made, there is no real discussion section to synthesize the research, and the “summary and conclusions” section is less than a single page. While this may be partly forgiven due to a scarcity of SDT research as of 1976, the authors discussed at least 13 articles, which may be enough to attempt such a synthesis. We do have a “Summary of Criticisms” table (p. 89) that helps us identify possible flaws in nine of the articles. Unfortunately, this table is half-baked—all cells are binary “Yes” or “No” data, without any indications of magnitude in columns such as “change in sensitivity” and “change in response bias.” Though yes/no might be a welcome relief from the overwhelming quantity of numbers in recent cognitive psychology literature, the authors could have, at minimum, used superscripts and footnotes to indicate particularly large changes or egregious methodological problems. Further, though an accurate criticism of much of the research, what constitutes “sufficient stimulus presentations” is not specified in the table. Finally, the authors included a table only for the nine modification studies they reviewed (pp. 84–91), neglecting to include a table for the four normative and comparative studies (pp. 91–92).

Lloyd and Appel have produced a strong primer on signal detection theory, complete with copious graphs and figures (pp. 80–84). It would be nice to see some of this explanatory power applied to the literature review, which is devoid of graphs and figures. For instance, they could have provided bar graphs comparing sensitivity and response bias for the different studies by Clark and associates involving diazepam, suggestion, and acupuncture (pp. 85–87). While the authors have explained that comparisons are difficult due to different standards between the studies (p. 88), it seems that studies with the same principal investigator (Clark) should be easier to compare. Nevertheless, the authors have vocalized solid criticisms, and I found the section discussing response biases and placebo effects to be particularly cogent (p. 84).


Lloyd, M. A., & Appel, J. B. (1976). Signal detection theory and the psychophysics of pain: An introduction and review. Psychosomatic Medicine, 38(2), 79–94.

Reaction to “The what, where, and why of priority maps and their interactions with visual working memory” by Zelinsky & Bisley (2015)

Reaction to Zelinsky & Bisley (2015) by Richard Thripp
EXP 6506 Section 0002: Fall 2015 – UCF, Dr. Joseph Schmidt
September 30, 2015 [Week 6]

Zelinsky and Bisley (2015) have presented a literature review regarding visual working memory and priority maps, reaching the conclusion that a vital relationship exists between these concepts, even though researchers often ignore the connection (p. 159). Further, the authors believe priority maps play an integral role in goal-directed behavior, and propose the common source hypothesis: that visual working memory is the foundation for goal prioritization, which is “propagated to all the effector systems” through tailored priority maps for each system, all reaching toward a common goal such as making a cup of tea (pp. 159–160). Priority maps may prevent “interrupts” (distractions) from stealing priority and preventing the goal from being reached.

Zelinsky and Bisley spend a great deal of time talking about the oculomotor system (pp. 156–158). They argue that it and visual working memory provide us with the model for priority maps and that this model generalizes to other visuomotor systems. They discuss evidence of a transformation from retinotopic to motor reference frames as priority maps move from the parietal cortex to the frontal lobe, and predict that a similar transformation will be found for responses in the premotor cortices (pp. 157–158).

The authors seem to have conflated general working memory (“WM”) with visual working memory (“VWM”)—they only refer to WM in regard to the tea-brewing task (p. 160) and argue for the centrality and singular importance of visual working memory throughout their paper. They reach the perhaps regrettable conclusion that all priority maps must have a topographical representation (p. 156). They give agreeable examples involving arm movements (p. 158), saccades (p. 159), and choosing to run right or left (p. 161), while conveniently leaving out discussion of hearing, smell, taste, and touch. Can we not have an auditory or olfactory priority map? Mechanics might listen for particular sounds to diagnose their machines; humans in general may have priority maps for particular smells and tastes to warn them of spoiled or poisonous food. How are these maps topographical? Just because there is a glut of research on vision and visual working memory does not mean that we should simply interpolate such findings to other domains without supporting evidence. Perhaps “priority map” is not the best term, since it is admittedly “by definition, organized into a map of some space” (p. 161). Zelinsky and Bisley seem to want to generalize priority maps to all domains of human attention, and yet the analogy is ill-suited to many of them.

Principally, Baddeley and Hitch’s working memory model and its derivatives focus on the senses that are most salient and important to survival: sight (visuo-spatial sketchpad) and hearing (phonological loop). However, congenitally blind subjects have been found to have significantly better tactile working memory than even semi-blind subjects who were equally fluent in Braille (Cohen, Scherzer, Viau, Voss, & Lepore, 2011). Zelinsky and Bisley (2015) do not even once discuss blindness, nor the possibility of visual working memory’s dominance being experiential in origin. In their defense, congenitally blind subjects have been found to have spatial recognition for Braille reading and to use the same pathways for Braille reading that are typically used for the visual system (Cohen et al., 2010). Nevertheless, the role of experience should always be considered—the priority maps of sighted, congenitally blind, acquired blind, and semi-sighted individuals may have distinct differences. While it is easy to gloss over blind individuals due to their rarity, there may be much to learn from studying blindness. The authors may have benefited from identifying it as an area requiring further research, rather than extrapolating over it.

Cohen et al. (2011) present an intriguing possibility: working memory might have a higher capacity when spread over multiple modalities. Could this allow for several simultaneously operating priority maps? Consider a hunter-gatherer exploring a forest—he or she may have multiple priority maps for sight, hearing, smell, and touch (e.g. wind direction and skin temperature), each contributing to finding food and avoiding danger. It is then apparent that a more fundamental analysis, rather than the technical analysis that Zelinsky and Bisley have provided, may be in order. Not only should experiential sensory history and alternate models be considered, but even the possible evolutionary origins of priority maps.


Cohen, H., Scherzer, P., Viau, R., Voss, P., & Lepore, F. (2011). Working memory for braille is shaped by experience. Communicative & Integrative Biology, 4(2), 227–229. doi:10.4161/cib.4.2.14546

Zelinsky, G. J., & Bisley, J. W. (2015). The what, where, and why of priority maps and their interactions with visual working memory. Annals of the New York Academy of Sciences, 1339, 154–164. doi:10.1111/nyas.12606

Reaction to “Different states in visual working memory” by Olivers, Peters, Houtkamp, & Roelfsema (2011)

Reaction to Olivers, Peters, Houtkamp, & Roelfsema (2011) by Richard Thripp
EXP 6506 Section 0002: Fall 2015 – UCF, Dr. Joseph Schmidt
September 30, 2015 [Week 6]

Olivers, Peters, Houtkamp, and Roelfsema (2011) have presented a review of literature regarding interactions between visual working memory and attentional deployment with respect to search tasks. A major focus of their review is on orthogonal coding, where different informational sources are represented by different coding patterns within the same neuronal populations (p. 327). Their literature review concludes, purportedly by convergent evidence, that “only one memory representation can serve as a search template, and this representation blocks attentional guidance by other memory representations” (p. 330).

For me, the idea that only one “search template” can be loaded into visual working memory for active processing, while other templates must be held in abeyance, brings two computing analogies to mind. First, the Microsoft Windows “Clipboard”—a space where text, images, files, or other data may be held, but only one item or set of items can be held at a time—anything from a single character of text to a massive folder with hundreds of files and subfolders. While the virtually unlimited capacity of the Windows clipboard is not analogous, the idea of having to swap things in and is, and becomes particularly salient when you have two types of content that you want to paste into a file in multiple different places, or when you must remember not to accidentally overwrite your clipboard contents. Second, the entire concept reminds me of paging and swap files. Modern computer operating systems exchange information between random access memory or RAM (lower capacity but very fast processing) and hard disk drives or solid-state drives (higher capacity but much slower processing). In this analogy, the active search template is loaded into RAM for efficient processing, while the accessory item(s) are maintained on the HDD or SSD. Swapping search templates is not trivial—RAM is often 1,000 times faster than conventional hard disk drives. While this latency difference is much greater than the sub-5% latency differences shown in typical experiments (p. 329), it represents a conceptually similar process.

If the search target is used repeatedly, the search process is offloaded to “less demanding memory representations” and becomes automated (pp. 328–329), thus freeing up explicit, effortful working memory for a new search template. This is seen in the differences between color search tasks for 1 of 3 colors as compared to 2 of 3 colors—the former is more efficient and neither distractor color captures attention, but the lone distractor color captures attention when looking for 2 of 3 colors (p. 330). The authors ask whether this generalizes to other types of memory, and lament there is a lack of research in this area (p. 332). There is a potential conceptual overlap with other types of memory—for example, one’s name might be an example of an automized search template with respect to auditory cognition and might have explanatory power for the cocktail party phenomenon (Wood & Cowan, 1995). Text search may be another area of interest—for example, say you are searching a printed bank statement for two transactions of different amounts. Should you try to load both search templates at once, or should you make two passes over the statement, looking for only one amount on each pass? How will completion time and error rates vary? While text search involves vision, it is also distinct from colors or objects and involves different considerations such as language, words versus numbers, context, etc. Moreover, implications drawn from visual working memory research might apply in many other areas. At the very least, they can aid in developing research questions.


Olivers, C. N. L., Peters, J., Houtkamp, R., & Roelfsema, P. R. (2011). Different states in visual working memory: When it guides attention and when it does not. Trends in Cognitive Sciences, 15(7), 327–334. doi:10.1016/j.tics.2011.05.004

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 “Visual attention within and around the field of focal attention: A zoom lens model” by Eriksen & St. James (1986)

Reaction to Eriksen & St. James (1986) by Richard Thripp
EXP 6506 Section 0002: Fall 2015 – UCF, Dr. Joseph Schmidt
September 24, 2015 [Week 5]

Eriksen and St. James (1986) believe their experiments support the zoom lens model, which differs from the spotlight model in that it proposes we can vary our attentional distribution on a continuum from a wide field of view to a fraction of a degree (pp. 226–227). The spotlight model typically involves a discrete or even binary dichotomy where we can only have a broadly or narrowly focused attentional field, but with restricted or nonexistent choices in between these two poles (p. 226).

As a photographer, I could not help but thinking of analogies to camera lenses and digital processing chips while reading this article, particularly since that is the crux of the authors’ analogy. The authors indicate in the discussion for experiment 1 that a 50 ms stimulus onset asynchrony (SOA) does not allow time for the attentional field to “zoom in,” so to speak, so an incompatible noise letter three positions away from the cued area delays the subject’s response time, but if given 100 ms, delayed reaction time is not observed, which may indicate the noise letter is now excluded or “cropped out,” so to speak (p. 233). This reminds me of the autofocus delay on cameras, which often measures in hundreds of milliseconds and can prevent the photographer from capturing desired moments.

Regarding the displays in experiment 1 where 3 of 8 letters were cued, reaction times paradoxically increased in the 200-ms SOA condition as compared to the 100 ms or even 50 ms displays. As an explanation, the authors present the possibility that with 3 of 8 letters cued, attending to the whole display may be nearly as efficient as attending to the cued area; thus, subjects may have elected to attend to the whole field in some displays, increasing reaction time (p. 234). Unfortunately, this is a post-hoc explanation and the experiments did not entail the collection of data to support this possibility. While the authors believe experiment 2 verified this explanation, it also had a very small sample size (n = 6), fewer trials, and an incompatible noise letter that was comparatively ineffective (p. 239). Fortunately, the authors seem to have produced a stronger argument that the cued letters are searched simultaneously rather than serially—specifically, that reaction times between 1, 2, and 3 cued positons increased far less than it should have if the positions were searched serially (p. 234).

In both experiments, multiple cued letters were always adjacent to each other in the circle. It would be interesting to see 2 cued locations not adjacent to each other—would the subject revert to processing the whole display, or somehow divide attention between the non-adjacent cued locations? How would this fit into the zoom lens model? Also: the authors assume that with no precues, all display elements are processed in parallel (pp. 232–233). In experiment 2, they include displays where all 8 letters are precued (p. 237). It would be nice to see if there are any implications of precueing all the positions versus none of them. When all the letters are underlined, does the underlining have any effects on reaction time? In neither experiment were there any conditions that had no cued or precued letters.

The authors’ ANOVA results have impressive statistical significance and they have purposely used methodology similar to past research in the hopes of allowing compatible comparisons (p. 229). However, I have lingering doubts about unspecified variables. We are given very little detail about the subjects—only that they are right-handed University of Illinois students who self-reported having normal or corrected vision (pp. 229, 237). Who is to say these self-reports were accurate? Why did the authors not bother with a visual acuity test? What were the ages of the participants? Did they have any other visual or attentional problems? The sample sizes of 8 and 6 are fairly small, meaning that a smaller number of non-equivalent participants could have thrown off the results. This research was published in 1986 and used a tachistoscope with individually constructed slides with affixed letters, rather than computer displays (pp. 229–230). The care and uniformity with which these slides were constructed is not specified. We are told subjects received reaction time feedback after each trial (p. 230), but not how this feedback was structured or conveyed. Whether the feedback was spoken by the researchers or conveyed in text or graphs may have implications. Further, encouraging participants to keep their error rate below 10% (p. 230) could have been a factor in the unusual reaction time pattern shown in figure 4 (p. 232)—perhaps this pattern is not indicative of parallel processing, but rather error avoidance? Despite the statistical power of their results, the authors may be overreaching, based on the assuredness of their conclusions.


Eriksen, C., & St. James, J. (1986). Visual attention within and around the field of focal attention: A zoom lens model. Perception & Psychophysics, 40(4), 225–240. doi:10.3758/BF03211502

Reaction to “Driver performance while text messaging using handheld and in-vehicle systems” by Owens, McLaughlin, & Sudweeks (2011)

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.


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

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 “A mechanical model for human attention and immediate memory” by Broadbent (1957)

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.


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 “The cocktail party phenomenon revisited” by Wood & Cowan (1995)

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.


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 “A review of visual memory capacity” by Brady, Konkle, & Alvarez (2011)

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.


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 “The psychology of memory” by Baddeley (2004)

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).


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.

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