國立虎尾科技大學 |

Decoding Three Categories of Conventional Wisdom in Pattern Similarity Analyses.

紀錄類型:	書目-語言資料,手稿 : Monograph/item
正題名/作者:	Decoding Three Categories of Conventional Wisdom in Pattern Similarity Analyses./
作者:	Chow, Michael Anthony.
面頁冊數:	1 online resource (158 pages)
附註:	Source: Dissertation Abstracts International, Volume: 78-11(E), Section: B.
標題:	Cognitive psychology. -
電子資源:	click for full text (PQDT)
ISBN:	9780355041873

Decoding Three Categories of Conventional Wisdom in Pattern Similarity Analyses.
Chow, Michael Anthony.

Decoding Three Categories of Conventional Wisdom in Pattern Similarity Analyses. - 1 online resource (158 pages)

Source: Dissertation Abstracts International, Volume: 78-11(E), Section: B.

Thesis (Ph.D.)--Princeton University, 2017.

Includes bibliographical references

Many methods in neuroscience characterize shared patterns of activity through the analysis of covariance matrices. Depending on the experimental designs and dimensions of interest, the domains of these analyses are sometimes referred to as functional connectivity, pattern similarity analysis, or inter-subject correlation. Progress in these domains has led to a richer understanding of the brain. However, unexpected properties of commonly employed covariance methods within these domains calls into question conventional wisdom that sometimes motivates analyses. While the common practice of averaging pairwise correlations within- and across-groups of observations may appear straightforward, and comparing these within- and across-group averages is simple to perform, researchers often rely on verbal explanations of what such a contrast might mean.

Electronic reproduction.
Ann Arbor, Mich. :
ProQuest,
2018

Mode of access: World Wide Web

ISBN: 9780355041873Subjects--Topical Terms:

556029
Cognitive psychology.
Index Terms--Genre/Form:

554714
Electronic books.

Decoding Three Categories of Conventional Wisdom in Pattern Similarity Analyses.
LDR:03455ntm a2200373K 4500 001 913677
005 20180622095235.5
006 m o u
007 cr mn||||a|a||
008 190606s2017 xx obm 000 0 eng d
020 $a 9780355041873
035 $a (MiAaPQ)AAI10285622
035 $a (MiAaPQ)princeton:12204
035 $a AAI10285622
040 $a MiAaPQ $b eng $c MiAaPQ
100 1 $a Chow, Michael Anthony. $3 1186609
245 1 0 $a Decoding Three Categories of Conventional Wisdom in Pattern Similarity Analyses.
264 0 $c 2017
300 $a 1 online resource (158 pages)
336 $a text $b txt $2 rdacontent
337 $a computer $b c $2 rdamedia
338 $a online resource $b cr $2 rdacarrier
500 $a Source: Dissertation Abstracts International, Volume: 78-11(E), Section: B.
500 $a Advisers: Uri Hasson; Andrew R.A. Conway.
502 $a Thesis (Ph.D.)--Princeton University, 2017.
504 $a Includes bibliographical references
520 $a Many methods in neuroscience characterize shared patterns of activity through the analysis of covariance matrices. Depending on the experimental designs and dimensions of interest, the domains of these analyses are sometimes referred to as functional connectivity, pattern similarity analysis, or inter-subject correlation. Progress in these domains has led to a richer understanding of the brain. However, unexpected properties of commonly employed covariance methods within these domains calls into question conventional wisdom that sometimes motivates analyses. While the common practice of averaging pairwise correlations within- and across-groups of observations may appear straightforward, and comparing these within- and across-group averages is simple to perform, researchers often rely on verbal explanations of what such a contrast might mean.
520 $a In this dissertation, we show that these verbal explanations rest on stringent assumptions, and as such, do not hold without qualification. Procedures which selectively average groups prior to correlating, or use elaborate forms of parceling the data and correlating across parcels, rely on the same---often implicit---assumptions. Throughout this paper we demonstrate that these analyses can be viewed as special cases of a latent factor model, in which all of the variances of the latent factors are assumed to be equal. Moreover, this model is not only able to explain such procedures, but has been studied and extended across the social, health, and physical sciences. As a result, we draw connections to analogous issues discussed in the behavioral psychology and psychometric literatures. The first section of this dissertation is empirical research on individual differences in working memory and intelligence, that uses a latent factor model common to individual differences psychology. The second section of this dissertation uses this model to evaluate the three aforementioned procedures in neuroscience.
520 $a Such a perspective is valuable because it creates a clear mapping between methodological issues that were (and often continue to be) widespread in covariance analyses from behavioral psychology and their neuroscience counterparts.
533 $a Electronic reproduction. $b Ann Arbor, Mich. : $c ProQuest, $d 2018
538 $a Mode of access: World Wide Web
650 4 $a Cognitive psychology. $3 556029
650 4 $a Neurosciences. $3 593561
650 4 $a Statistics. $3 556824
650 4 $a Psychology. $3 555998
655 7 $a Electronic books. $2 local $3 554714
690 $a 0633
690 $a 0317
690 $a 0463
690 $a 0621
710 2 $a ProQuest Information and Learning Co. $3 1178819
710 2 $a Princeton University. $b Psychology. $3 1186553
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=10285622 $z click for full text (PQDT)