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RDATE:20351104T010000 TZOFFSETFROM:-0500 TZOFFSETTO:-0400 TZNAME:America/New_York EDT END:DAYLIGHT BEGIN:DAYLIGHT DTSTART:20360309T030000 RDATE:20361102T010000 TZOFFSETFROM:-0500 TZOFFSETTO:-0400 TZNAME:America/New_York EDT END:DAYLIGHT BEGIN:DAYLIGHT DTSTART:20370308T030000 RDATE:20371101T010000 TZOFFSETFROM:-0500 TZOFFSETTO:-0400 TZNAME:America/New_York EDT END:DAYLIGHT END:VTIMEZONE BEGIN:VEVENT UID:53ca431ba70438cd22a58dc3a209e16e CATEGORIES:RuCCS Colloquia:Spring 2013 CREATED:20151203T141543 SUMMARY:The Perception of Probability (talk recording available) LOCATION:Rutgers University\, Department of Psychology and Center for Cognitive Scie nce DESCRIPTION:When subjects estimate the p parameter of a non-stationary hidden Bernoulli process (e.g., the proportion of green balls in an urn containing red and green balls, with unpredictable silent urn substitutions that change the pr oportion), they do not update their estimate observation by observation (ba ll by observed ball), as extant theories predict they should. Instead, they make stepwise changes. The joint distribution of step widths and step heig hts cannot be explained by a model that assumes a difference threshold on t he output (i.e., subjects don't want to bother to make a change every trial , so they only do so when there has been a nontrivial change in their estim ate). Pace Prospect Theory, their estimates are accurate over the entire ra nge of probabilities and the precision of their estimates, when properly me asured, is the same everywhere. They detect largish changes with a short la tency, a high hit rate, and a low false alarm rate. They have second though ts about some of those changes after seeing more data. I present a simple B ayesian computational model of the perceptual process, with only 2 free par ameters, which reproduces subjects' behavior, including the joint distribut ion of step widths and step heights, the change-detection statistics and th e second thoughts. The model illustrates why and how future information may change our previous representation of a past state of the world. I suggest reasons why similar models may apply to a wide range of percepts.\nTo view a recording of this talk click here (You will need a Rutgers NetID and pas sword) (https://sakai.rutgers.edu/portal/site/876a56f9-2ad8-41da-b3e7-5bbf7 1bc3450/page/0b5b24bb-566f-46db-8259-20ac975ea2d1)\n X-ALT-DESC;FMTTYPE=text/html:
When subjects estimate the p parameter of a non-stationary hidden Bernou lli process (e.g., the proportion of green balls in an urn containing red a nd green balls, with unpredictable silent urn substitutions that change the proportion), they do not update their estimate observation by observation (ball by observed ball), as extant theories predict they should. Instead, t hey make stepwise changes. The joint distribution of step widths and step h eights cannot be explained by a model that assumes a difference threshold o n the output (i.e., subjects don't want to bother to make a change every tr ial, so they only do so when there has been a nontrivial change in their es timate). Pace Prospect Theory, their estimates are accurate over the entire range of probabilities and the precision of their estimates, when properly measured, is the same everywhere. They detect largish changes with a short latency, a high hit rate, and a low false alarm rate. They have second tho ughts about some of those changes after seeing more data. I present a simpl e Bayesian computational model of the perceptual process, with only 2 free parameters, which reproduces subjects' behavior, including the joint distri bution of step widths and step heights, the change-detection statistics and the second thoughts. The model illustrates why and how future information may change our previous representation of a past state of the world. I sugg est reasons why similar models may apply to a wide range of percepts.
To vie w a recording of this talk click here (You will need a Rutgers NetID and pa ssword)
CONTACT:Dr. Charles Randy Gallistel X-EXTRAINFO:Dr. Charles Randy Gallistel (http://ruccs.rutgers.edu/faculty/GnG/gallistel .html) DTSTAMP:20240329T022801 DTSTART;TZID=America/New_York:20130312T130000 DTEND;TZID=America/New_York:20130312T140000 SEQUENCE:0 TRANSP:OPAQUE END:VEVENT END:VCALENDAR