@Article{Turk-Browne-reversal,
  author   = {Nicholas B Turk-Browne and Brian J Scholl},
  journal  = {J Exp Psychol: Hum Perc Perf},
  title    = {Flexible visual statistical learning: Transfer across space and time},
  year     = {2009},
  number   = {1},
  pages    = {195--202},
  volume   = {35},
  abstract = {The environment contains considerable information that is distributed
	across space and time, and the visual system is remarkably sensitive
	to such information via the operation of visual statistical learning
	(VSL). Previous VSL studies have focused on establishing what kinds
	of statistical relationships can be learned but have not fully explored
	how this knowledge is then represented in the mind. These representations
	could faithfully reflect the details of the learning context, but
	they could also be generalized in various ways. This was studied
	by testing how VSL transfers across changes between learning and
	test, and the results revealed a substantial degree of generalization.
	Learning of statistically defined temporal sequences was expressed
	in static spatial configurations, and learning of statistically defined
	spatial configurations facilitated detection performance in temporal
	streams. Learning of temporal sequences even transferred to reversed
	temporal orders during test when accurate performance did not depend
	on order, per se. These types of transfer imply that VSL can result
	in flexible representations, which may in turn allow VSL to function
	in ever-changing natural environments.},
  groups   = {Statistical Learning},
}

{"_id":"6RNBGzGGfQ4nMaDbD","bibbaseid":"turkbrowne-scholl-flexiblevisualstatisticallearningtransferacrossspaceandtime-2009","author_short":["Turk-Browne, N. B","Scholl, B. J"],"bibdata":{"bibtype":"article","type":"article","author":[{"firstnames":["Nicholas","B"],"propositions":[],"lastnames":["Turk-Browne"],"suffixes":[]},{"firstnames":["Brian","J"],"propositions":[],"lastnames":["Scholl"],"suffixes":[]}],"journal":"J Exp Psychol: Hum Perc Perf","title":"Flexible visual statistical learning: Transfer across space and time","year":"2009","number":"1","pages":"195–202","volume":"35","abstract":"The environment contains considerable information that is distributed across space and time, and the visual system is remarkably sensitive to such information via the operation of visual statistical learning (VSL). Previous VSL studies have focused on establishing what kinds of statistical relationships can be learned but have not fully explored how this knowledge is then represented in the mind. These representations could faithfully reflect the details of the learning context, but they could also be generalized in various ways. This was studied by testing how VSL transfers across changes between learning and test, and the results revealed a substantial degree of generalization. Learning of statistically defined temporal sequences was expressed in static spatial configurations, and learning of statistically defined spatial configurations facilitated detection performance in temporal streams. Learning of temporal sequences even transferred to reversed temporal orders during test when accurate performance did not depend on order, per se. These types of transfer imply that VSL can result in flexible representations, which may in turn allow VSL to function in ever-changing natural environments.","groups":"Statistical Learning","bibtex":"@Article{Turk-Browne-reversal,\n author = {Nicholas B Turk-Browne and Brian J Scholl},\n journal = {J Exp Psychol: Hum Perc Perf},\n title = {Flexible visual statistical learning: Transfer across space and time},\n year = {2009},\n number = {1},\n pages = {195--202},\n volume = {35},\n abstract = {The environment contains considerable information that is distributed\n\tacross space and time, and the visual system is remarkably sensitive\n\tto such information via the operation of visual statistical learning\n\t(VSL). Previous VSL studies have focused on establishing what kinds\n\tof statistical relationships can be learned but have not fully explored\n\thow this knowledge is then represented in the mind. These representations\n\tcould faithfully reflect the details of the learning context, but\n\tthey could also be generalized in various ways. This was studied\n\tby testing how VSL transfers across changes between learning and\n\ttest, and the results revealed a substantial degree of generalization.\n\tLearning of statistically defined temporal sequences was expressed\n\tin static spatial configurations, and learning of statistically defined\n\tspatial configurations facilitated detection performance in temporal\n\tstreams. Learning of temporal sequences even transferred to reversed\n\ttemporal orders during test when accurate performance did not depend\n\ton order, per se. These types of transfer imply that VSL can result\n\tin flexible representations, which may in turn allow VSL to function\n\tin ever-changing natural environments.},\n groups = {Statistical Learning},\n}\n\n","author_short":["Turk-Browne, N. B","Scholl, B. J"],"key":"Turk-Browne-reversal","id":"Turk-Browne-reversal","bibbaseid":"turkbrowne-scholl-flexiblevisualstatisticallearningtransferacrossspaceandtime-2009","role":"author","urls":{},"metadata":{"authorlinks":{}}},"bibtype":"article","biburl":"https://endress.org/publications/ansgar.bib","dataSources":["xPGxHAeh3vZpx4yyE","TXa55dQbNoWnaGmMq"],"keywords":[],"search_terms":["flexible","visual","statistical","learning","transfer","space","time","turk-browne","scholl"],"title":"Flexible visual statistical learning: Transfer across space and time","year":2009}