are you Russian or Ukranian or?
i new a Maxim Smirnov once....he was Russian.........so i am assuming 'Maxim' has that ancestry
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Are you aware that your program is very similar to Target Tracker ( http://www.brainhq.com/why-brainhq/about-the-exercises/attention/target-tracker , also formerly known as "Jewel Diver")?
Incidentally, I can't get yours to play in a browser on a Nexus 7 tablet completely; part of the screen gets cut off.
argumzio
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1. When are GF and WM Isopmorphic and when are they not? (Chuderski, Adam 2013) http://ecfi-group.eu/download/papers/49.pdf
This paper found that relational integration tasks (which the authors classify as a WM task) along with a STM task, were isomorphic with fluid reasoning measures when those tasks were administered under severe time constraints. (A good article in Scientific American about the paper: http://blogs.scientificamerican.com/beautiful-minds/2014/01/22/working-memory-and-fluid-reasoning-same-or-different/)
It seems reasonable to assume that improved performance on these relational integration tasks ( in the dimensions of accuracy, complexity and speed) could transfer to more complicated and novel fluid reasoning tasks (verbal and figural analogies, etc.) if relational integration were a bottleneck for some people. The WM-Gf correlation declined when time was less of a constraint, so it would seem that training relational integration (and improving both speed and the number of objects that can be integrated) could be especially useful if one wanted to improve the ability to reason very quickly on novel complex problems.
2. The relational integration task explains fluid reasoning above and beyond other working memory tasks. (Chuderski, 2013) http://link.springer.com/article/10.3758/s13421-013-0366-x/fulltext.html
This paper explains the relational integration tasks in basic terms and presents new variations of them that could provide options for introducing adaptability into a game version.
Excerpt from the paper:
“In contrast to the aforementioned views about the proper measurement of WMC [the view most of us have been reading about for some time], Oberauer et al. (2007) proposed that the driving force of strong WMC–Gf correlations is neither the sheer storage of information, even in the context of processing, nor the executive control of that storage. In line with analogous theorizing … Oberauer et al. (2007; Oberauer, Süß, Wilhelm, and Wittmann 2008) proposed that the fundamental mechanism that determines both WMC and fluid reasoning is the human capacity to set and maintain the flexible, temporary bindings between chunks held in WM, or between them and their respective positions within some mental structure. For instance, these positions can constitute concrete coordinates like serial positions during recall, or they can be abstract placeholders in some schema required in a reasoning task (so-called role-filler bindings). Due to temporary bindings, a person is able to integrate elementary relations into novel arbitrary relational structures. Creating such structures is the essence of relational thinking—thinking driven by the way objects are assigned to certain roles in situations, and not by objects’ intrinsic features.”
And, getting to the tasks:
“For the purpose of measurement of the effectiveness of relational integration, Oberauer and colleagues (Oberauer, Süß, Schulze, Wilhelm, and Wittmann 2000; Oberauer et al. 2008) have developed versions of a so-called relation-monitoring task (henceforth called the relation integration task). In such a task, a participant observes a constantly changing pattern of stimuli that is available perceptually (no need for storage in WM), and detects stimuli matching a simple rule. For example, the task may consist of the presentation of a three-by-three matrix of words, and may require the pressing of a button if and only if three words in a row, column, or diagonal line rhyme. Other versions require three numbers that end with the same digit to be found, or recognizing four dots that form a square within a pattern of several dots. A few studies showed that the latent variables loaded by the relation integration tasks are at least as strong predictors of fluid reasoning as are complex spans (Buehner, Krumm, and Pick 2005; Buehner, Krumm, Ziegler, and Pluecken 2006; Krumm et al. 2009; Oberauer et al. 2008; Süß, Oberauer, Wittmann, Wilhelm, and Schulze 2002), and much better predictors than both STM and executive control tasks (Chuderski, Taraday, Nęcka, and Smoleń 2012).”
And:
“…though performance in WM may rely on multiple mechanisms and processes (see Conway, Getz, Macnamara, and Engel de Abreu 2011), the link between relatively simple WM tasks and much more complex abstract-reasoning tests may be primarily driven by the relational integration component of WM.”
And, concluding:
“… the study provided new evidence that clearly supports theories … that have proposed that human intelligence may reflect the domain-general ability to construct higher-level relational structures that bind a certain number of more atomic representations (e.g., perceptual or memorial), are extremely flexible, and can be effectively abstracted from any intrinsic features of the low-level representations. This line of research—explaining intelligence as the ability to conduct role-based relational reasoning based on the processing of relational roles explicitly and separately from (perceptual or semantic) features of entities that fill these roles, and that involves coding the bindings of entities to their specific roles—is in a way a revival of Spearman’s (1927) classical idea of the eduction of relations. This relational-reasoning account has recently gained substantial attention within psychology, and seems to be a very fruitful framework for future studies on fluid reasoning.”
It seems that these relational integration tasks developed by Oberauer (see below) and Chuderski could be testing a cognitive capacity (or skill) lying below the level of reasoning - and below the math and logic relational games Maxim recently created - and that these capacities or skills could be bottlenecks for some people in regards to relational/analogical reasoning and fluid intelligence.
3. Which working memory functions predict intelligence? (Oberauer, et al. 2008) http://diyhpl.us/~bryan/papers2/neuro/working-memory/Which%20working%20memory%20functions%20predict%20intelligence%3F.pdf
This paper describes four relational integration tasks in detail that were “all constructed to tap the ability of mentally building and integrating multiple relations between given elements These tasks not only require the detection of pair-wise relations between given elements, but the integration of several relations into the representation of a new configuration.”
1. A verbal task where the relation is rhyming.
2. 2. A numerical task
3. 3. A spatial task: “…flight control, involved monitoring the trajectories of five to nine triangles (representing airplanes) moving in different directions across the screen. Whenever one airplane was about to crash into another plane or a mountain (represented by brown patches) participants were to stop the video and redirect one airplane. Each stop came at a small cost, but each lost airplane incurred a large cost, so that a good score could be obtained by intervening if and only if necessary to prevent crashes.'
4. 4. A second spatial task
As a group, these relational integration tasks correlated with the reasoning measures of the Berlin Intelligence Structure at .94 (and with the Creativity measures at .47). These correlations were greater than any of the more common storage and processing WM measures. (And far greater with regards to creativity).
Actually, there were eight relational integration tasks, as all four were administered in versions that required holding the items to be integrated in memory and also in versions where the items were continually visible and therefore did not require memory to be engaged at all. Surprisingly, the correlations to the reasoning measures were nearly identical in both the memory and no-memory versions! (The no-memory version was the one used in the first study (WM-Gf isomorphic) in this list.)
At this point, you might be wondering whether relational integration tasks are WM tasks at all! This paper’s reviewers wondered the same thing and there is a very interesting part in the discussion where these questions were addressed. For starters, the authors note that the Relational Integration (RI) measures correlated strongly with the more traditional storage and processing (SP) WM measures, so there is that. Additionally:
“These findings have far-reaching implications for our view of working memory and intelligence. The traditional interpretation of the relationship between working memory and fluid intelligence or reasoning is that working memory provides resources for simultaneous storage and processing, that is, the ability to remember information not currently present in the environment, and to manipulate this or other information at the same time.
Both abilities are arguably required in many complex tasks — for example, remembering intermediate results while carrying out further operations in multi-step mental arithmetic tasks (Hitch, 1978). On this account, however, it is hard to understand why relational integration [RI] tasks without any demand on storage should predict reasoning so well. The main difference between specifying the RI factor through memory task versions and specifying it through no-memory task versions was that in the former case, the factor correlated more with SP, confirming that the variation of memory in the RI tasks was effective. This variation had little effect, however, on the RI factor's correlation with reasoning or the other intelligence factors. We conclude that a demand on short-term storage is not a necessary feature of a good measure of WMC [working memory capacity]. Other research (Colom, Rebollo, Abad, & Shih, 2006; Oberauer et al., 2000) has already shown that a processing component is no necessary feature either. Thus, “simultaneous storage and processing” is a good description for one effective and very popular class of tasks used to measure WMC, but it should not be used to define WMC as a construct.
This is not to say that our RI tasks capture all there is to the construct WMC, and that SP tasks are redundant. Rather, we argue that the construct WMC should be conceptualized in a broader way than before, and operationalized by a broader set of tasks. The present RI tasks were intentionally designed to be different from conventional SP tasks, with the goal to establish a separate factor of RI besides SP, and to test the hypothesis that despite their dissimilarity with SP tasks, RI tasks predict reasoning ability. The finding that both our RI tasks and the SP tasks, despite their superficial dissimilarity, share a large amount of variance and account for large amounts of variance in reasoning, raises the need for a conceptualiza- tion of WMC that covers both kinds of tasks.”
The authors go on to defend against some specific arguments that RI should be considered a component of reasoning itself, rather than WM. I’m not entirely convinced by their arguments, but whether RI should be classified as a component of WM or of reasoning (or both:) may not be important to the question of whether training such skills, which seem to underpin much more complex reasoning (figural analogy tests, etc.), may be worthwhile. We get into questions of near vs. far transfer, of course, but personally I’m not opposed to the notion of training basic underlying skills and capacities that might make me better at reasoning, even if that improvement represents only near transfer.
However, I’ll add the following because it helps clarify exactly what these relational integration tasks are meant to be testing (and what, converted into adaptive games, they might be used to train):
“It can be argued that the relational processing involved in our RI tasks is very similar to the relational processing necessary in analogical reasoning tasks such as those used by Sternberg (1985) to measure the “inference” component (i.e., discovering the relationship between the first 2 terms of an analogy)… In response … we fully agree that discovering the relation between two terms in an analogy is the same kind of relational processing as is measured in our RI tasks. Yet we insist that WMC is not a limit on processing individual relations but on integrating relations, and this is what is measured by the RI tasks. Analogy tasks also require integrating relations, but this is necessary only in the next step, where the relation between the first two terms is applied to the third term to complete the analogy — Sternbergs components “mapping” and “application”. This is why, in our view, analogy tasks correlate with WMC.”
4. What is working memory, and how can we measure it? (Wilhelm, Hildebrandt, Oberauer 2013) http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3721021/pdf/fpsyg-04-00433.pdf
A number of novel working memory binding tests are described in this paper that could be made into adaptive tasks to train the building and maintenance of bindings (but stopping short of integrating bindings as in the relational integration tasks) in WM. Chuderski suggests that these tests could be tapping a lower-level capacity upon which relational integration relies.
From the abstract: “The findings support the hypothesis that individual differences in WMC reflect the ability to build, maintain and update arbitrary bindings.”
From the paper:
“In our own view, working memory is a system for building, maintaining and rapidly updating arbitrary bindings. For instance, items in a list are bound to list positions, objects are bound to locations in space, and concepts are bound to roles in propositional schemata. The capability for rapid formation of temporary bindings enables the system to construct and maintain new structures, such as random lists, spatial arrays, or mental models. Working memory is important for reasoning because reasoning requires the construction and manipulation of representations of novel structures. The limited capacity of working memory arises from interference between bindings, which effectively limits the complexity of new structural representations, and thereby constrains reasoning ability .”
A surprising hypothesis from the conclusion of the paper:
“According to the binding hypothesis, high WMC reflects the ability to establish robust bindings in working memory, which in turn support encoding of those bindings into SM. Therefore, high [working memory capacity] might be a cause, not a consequence, of a well-functioning [short-term memory].
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After blazing passed level 30 in the Math Relational Ability game, I am beginning to think it is either too predictible or there is indeed a higher level pattern to it, such that difficulty follows a U-curve (seemingly centered around level 8) instead of an upward slope. In either case, it may be a good idea to mix things up... if only to prevent me from being able to do this. ;)
argumzio
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I've recently made a game that improves object individuation ability, which I believe is an important component of non verbal working memory that is an untapped opportunity to improve. Game and scientific support here:http://twitchmath.com/objectIndividuation.htmlThanks to those who provided feedback so far :)I'm also planning on making some additional games relating to both non verbal working memory and relational ability, and I wanted to check in if there was a game that a lot of people wanted to see made.If no one makes any other suggestions I'll first make a game that stresses non verbal working memory in a format similar to n back but probably closest to lumosity's follow that frog. I'll also probably add some features like variable timing. I believe the follow that frog type format where the possible position are irregular and distributed differently every time you play MAY be better than n back because n back may begin to rely a bit on crystallized intelligence as you develop familiarity because the grid is the exact same every time.That said I think follow that frog is fairly limited because it takes about a minute for the game to start every time you play because it completely halts and provides the steps numbered for you every time the path increases by one, also you can pause whenever you want, which I don't think provides the best training.