Embryologie d un groupement d informa4on et sa rela4on avec l intelligence générale

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1 Embryologie d un groupement d informa4on et sa rela4on avec l intelligence générale Fabien Mathy Collaborateurs : Mustapha Chekaf, Caroline Jacquin, Nicolas Gauvrit, Alessandro Guida 1 MSHS Sud-Est 2014, Symposium du 14 Nov. Extraction de régularités et de connaissances 1

2 Context and outline learning and memory are inextricably intertwined the extraction of regularities domain is twofold: implicit learning (based on exploited statistics; Saffran, Aslin & Newport, 1996), which forms long-term memories vs. explicit learning (based on detected regularities; Cowan, Chen, & Rouder, 2004), which can be immediate and conscious. learning can be manipulated in span tasks to study the STM/WM constructs and their relationships to intelligence. 2

3 Background Individuals have a tendency to group/chunk information (Gobet et al, 2001; Miller, 1956; Simon, 1974) and chunking makes memory more efficient by breaking up long sequences of information (Feigenson & Halberda, 2008; Rabinovitch et al. 2014) E - G - U - S - A- F - R Chunking runs counter a rigorous estimation of the span (Cowan, 2001) 3

4 Background Chunking is generally hindered in memory span tasks by - change detection paradigms, using rapid presentations (Luck & Vogel, 1997) 4

5 Background Chunking is generally hindered in memory span tasks by - change detection paradigms, using rapid presentations (Luck & Vogel, 1997) 4

6 Background Chunking is generally hindered in memory span tasks by - change detection paradigms, using rapid presentations (Luck & Vogel, 1997) 4

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8 - complex span tasks, using concurrent tasks (Baddeley, 1986) 6

9 STM, WM, ChunkingM STM : 7+/- 2 items in simple span tasks (Miller, 1956). WM : 4 +/- 1 items in complex span tasks (Cowan, 2001) and change detection paradigms (Luck & Vogel, 1997)) CM : 4 +/- 1 chunks in chunking span tasks (Mathy & Feldman, 2012), but 4 items or more can be unpacked from the chunks. 4 is the capacity reached AFTER chunking For similar ideas, see Alvarez & Cavanagh (2004) and Brady, Konkle & Alvarez (2009), Exp. 2. 7

10 Digits (2012 study) 8 8

11 In our tasks, we encourage chunking/grouping ON THE FLY, and we predict the expansion of capacity 9 9

12 Result Digits Chunks Prop. Correct Nota Bene: Error bars are +/- 1 SE in all plots 3 chunks 7 digits 9 Mathy & Feldman (2012). Cognition 10

13 Janus 3 chunks 7 digits 9 Mathy & Feldman (2012). Cognition 11

14 Present study - Key idea In simple memory span tasks (e.g., digit span), both storage and processing are uncontrolled In complex memory span tasks (e.g., dual tasks), processing is separated from storage and thus controlled, but processing is not dedicated to the storage process. Working memory does not work on memorizing but works on something else (concurrent task)! In chunking memory span tasks, processing is still controlled while fully supporting storage. This paradigm allows us to study the optimization of storage thanks to... Storage Processing 12

15 Storage Processing 2 slots * 2 items/slot = 4 items 2 slots * 4 items/slot = 8 items 4 slots * 2 items/slot = 8 items 4 slots * 4 items/slot = 16 items etc. 13

16 Compressibility = Algorithmic complexity = Kolmogorov complexity High complexity = noncompressible sequence : PRINT Low complexity = compressible sequence : FOR i=1 TO 200, PRINT 0 6 (Kolmogorov, 1965; Li & Vitányi, 1993, 1997, 2008) 14

17 Compression Lossless versus Lossy Exploits regularity (.png,.gzip) Exploits resolution (.jpeg,.mpeg) 7 15

18 Algorithmic complexity for short strings 16

19 Gauvrit, Zenil, Delahaye, & Soler-Toscano (2014). Beh. Res. Methods Soler-Toscano, Zenil, Delahaye, & Gauvrit (2014). PloS ONE 17

20 Soler-Toscano, Zenil, Delahaye, & Gauvrit (2014). PloS ONE There are * Turing machines with 5 states... * twenty-six trillion five hundred fifty-nine billion nine hundred twenty-two million seven hundred ninety-one thousand four hundred twenty-four ** ** google convert numbers into words 18

21 Studies inspired of SIMON Note: The real SIMON game shows a normal distribution around 7 colors (Gendle et Ransom, 2006) Note that our experiment was nonspatial, and that sequences did not resume like in the original game. 19

22 Method - N = 183 young adults aged ~ 20 - Random sequences accross participants; Pace: 1 second per item. - Long task : 50 sequences, 25 minutes total. - Not progressive 20

23 Method (next) - Working Memory Capacity battery (WMCB) (N = 112): - one memory updating task (MU) - two complex span tasks: operation span (OS) and sentence span (SS) - one spatial short-term memory span task (SSTM) - Raven s APM (N = 111) 21

24 Hypothesis Chunking can be used as an estimate of the Storage Processing construct in working memory. Performance at the Simon should best correlate with the memory updating task (MU) and Raven 22

25 Result Performance was related to complexity... Note. The scoring method was based on the all-or-nothing method; 23

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28 Correlations SIMON WM MU OS SS SSTM RAVEN.428**.437**.545**.297**.326**.406** SIMON _.531**.572**.457**.376**.515** WM MU OS Performance at the Simon estimated by a _.630**.767**.824**.630** logistic regression for each subject to find the critical decrease in performance that occurs half-way down the logistic curve (i.e., the inflection point). This simply _ means.499** that participants.466** failed.506** more than 50% of the time on sequences where complexity was above the inflection point. _.651**.374** SS _.345** Note. Memory updating task (MU), operation-span tasks (OS), sentence-span task (SS), spatial short-term memory task (SSTM). **, p <.01; *, p <

29 Correlations SIMON WM MU OS SS SSTM RAVEN.428**.437**.545**.297**.326**.406** SIMON _.531**.572**.457**.376**.515** WM _.630**.767**.824**.630** MU _.499**.466**.506** OS _.651**.374** SS _.345** Note. Memory updating task (MU), operation-span tasks (OS), sentence-span task (SS), spatial short-term memory task (SSTM). **, p <.01; *, p <

30 Resulting component plot in rotated space for Exp. 1 from the exploratory factor analysis using PCA and Oblimin 27

31 Text r =.64, corresponding to 41% of shared variance Chi-square = 2.82 Degrees of freedom = 7 Probability level =.90 28

32 Table 1 Correlations Between WMC and Gf/Reasoning Factors Derived From Confirmatory Factor Analyses of Data From Latent-Variable Studies With Young Adults Study WMC tasks Gf/reasoning tasks r(95% CI) Kyllonen & Christal (1990) Study 2: n 399 ABC numerical assignment, mental arithmetic, alphabet recoding Arithmetic reasoning, AB grammatical reasoning, verbal analogies, arrow grammatical reasoning, number sets Study 3: n 392 Alphabet recoding, ABC21 Arithmetic reasoning, AB grammatical reasoning, ABCD arrow, diagramming relations, following instructions, letter sets, necessary arithmetic operations, nonsense syllogisms Study 4: n 562 Alphabet recoding, mental math Arithmetic reasoning, verbal analogies, number sets, 123 symbol reduction, three term series, calendar test Engle, Tuholski, et al. (1999; N 133) Operation span, reading span, counting span, ABCD, keeping track, secondary memory/ immediate free recall.91 (.89,.93).79 (.75,.82).83 (.80,.85) Raven, Cattell culture fair.60 (.48,.70) Miyake et al. (2001; N 167) Letter rotation, dot matrix Tower of Hanoi, random generation, paper folding, space relations, cards, flags Ackerman et al. (2002; N 135) Conway et al. (2002; N 120) Süß et al. (2002; N 121 a ) ABCD order, alpha span, backward digit span, computation span, figural-spatial span, spatial span, word-sentence span Operation span, reading span, counting span Reading span, computation span, alpha span, backward digit span, math span, verbal span, spatial working memory, spatial shortterm memory, updating numerical, updating spatial, spatial coordination, verbal coordination Ravens, number series, problem solving, necessary facts, paper folding, spatial analogy, cube comparison.64 (.54,.72).66 (.55,.75) Raven, Cattell culture fair.54 (.40,.66) Number sequences, letter sequences, computational reasoning, verbal analogies, fact/opinion, senseless inferences, syllogisms, figural analogies, Charkow, Bongard, figure assembly, surface development.86 (.81,.90) Hambrick (2003; N 171) Computation span, reading span Raven, Cattell culture fair, abstraction, letter sets.71 (.63,.78) Mackintosh & Bennett (2003; Mental counters, reading span, Raven, mental rotations 1.00 N 138 b ) spatial span Colom et al. (2004) Study 1: n 198 Mental counters, sentence Raven, surface development.86 (.82,.89) Study 2: n 203 Study 3: n 193 Kane et al. (2004; N 236) verification, line formation Mental counters, sentence verification, line formation Mental counters, sentence verification, line formation Operation span, reading span, counting span, rotation span, symmetry span, navigation span Surface development, cards, figure classification.73 (.66,.79) Surface development, cards, figure classification.41 (.29,.52) Raven, WASI matrix, BETA III matrix, reading comprehension, verbal analogies, inferences, nonsense syllogisms, remote associates, paper folding, surface development, form board, space relations, rotated blocks.67 (.59,.73) r = ~.70 Kane et al Note. WMC working memory capacity; Gf general fluid intelligence; 95% CI the 95% confidence interval around the correlations; WASI 29

33 Chi-square = 1.22 Degrees of freedom = 3 Probability level =.75 30

34 2 nd Study : SIMON 400 ms 600 ms 400 ms Time 600 ms 400 ms 600 ms 31

35 Method - N = Same sequences accross participants; Pace: 1second per item. Quick task : 5 minutes total. - progressive difficulty: 2 colors, 3 colors, trials per length until failing - Two conditions counterbalanced : moderately easy (thus chunkable) vs hard (nonchunkable) based on the algorithmic complexity metric 32

36 Method (next) - WAIS-IV: digit span subtests (N = 107): - Digit Span Forward: DSF - Digit Span Forward: DSB - Digit Span Sequencing: DSS - Raven s APM (N = 95) 33

37 Hypotheses - The simple task estimates Storage Processing The difficult task estimates Storage - The simple Simon task (allowing more chunking) better predicts the Raven than the difficult Simon task. - We can estimate: processing = (Storage Processing) / Storage 34

38 Result Again, performance was related to complexity. Note. The scoring method was based on the all-or-nothing method; 35

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40 COMPL DSF DSB DSS RAV SIMPL.422**.294**.337** ** COMPL.229*.353**.310**.385** DSF.473**.273**.290** DSB.476**.446** DSS.297** Correlation RAVEN-Processing = -.04! Correlation Compl-Processing = -.59! 37

41 S S*P P S*P P 4 7 1,8 S ,2 S*P , ,0 38

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43 Chi-square = 3.2 Degrees of freedom = 7 Probability level =.87 40

44 Conclusion - Chunking opportunity is favored by the compressibility of a set of objects. (different from Luck & Vogel, 1997 Brady, Konkle, & Alvarez, 2009, in which grouping occurs within objects; closer to Brady, Konkle, & Alvarez, 2009, in their Exp 2, who also suggest that chunking can be used as an approximation of psychological compression) -Chunking performance can be used as an estimate of the processing component in working memory, in situations where processing directly supports storage. -Originality : processing demand not linearly dependant on the number of items to be stored - Take-away message : The birth of a chunk can take place in working memory. 41

45 Main reference Chekaf, M., Gauvrit, N., Guida, A. & Mathy, F. (in prep.). The capacity of memory span while processing is fully dedicated to storage. 4 Other references on chunking processes Chekaf, M., & Mathy, F. (submitted). Chunking of categorizable objects on the fly. Haladjian, H. H., & Mathy, F. (in revision). Snapshot encoding of spatial information: Location memory for visual-short-term- and short-termmemory exposures. Mathy, F., & Varré, J. S.. (2013). Retention-error patterns in complex alphanumeric serial-recall tasks. Memory, 21, Mathy, F., & Feldman, J. (2012). What s magic about magic numbers? Chunking and data compression in short-term memory. Cognition, 122,

46 Merci! 43

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