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					Strand 2.3. How does experience shape the moment-to-moment dynamics involved in
perceptual decisions?

We have approached this problem from several angles. Some of the issues discussed
below were originally part of Strand 2.2, before we reorganized this part of the SIP to
better accommodate the large number of projects that fit under what is currently
Strand 2.3. Most of the questions below include new projects that have only been
started in the last few months and should remain active provided renewal of the
center **. The following list is NOT strictly a list of projects but rather of themes that
were (or will be) addressed by groups of projects.


What is the time-course of visual object processing in novices and experts?**
We made important progress. This was an important question for PEN members who had for
years focused mainly on behavioral markers that were fairly coarse in their temporal
definition. In several behavioral studies, we mapped the time-course of perceptual
processing and of post-encoding decisions by manipulating either stimulus presentation or
time allotted to make a response (Curby & Gauthier, 2009; Mack et al., 2008, 2009, in press;
Richler et al., 2009). ERP studies allowed us to look at the time course of familiarity and
expertise effects with faces and non-face objects (D’Lauro et al., 2008; Scott et al., 2008;
Curran & Dolyle, in press). In many cases, differences that appeared qualitative using coarse
measures were revealed to be more graded using a fine timecourse, in some cases forcing
theories to be revised.

Characterize the shift from external to internal error monitoring with the
acquisition of expertise.
We made significant progress. Using ERPs, we mapped this shift by tracking the error-related
negativity (ERN) responses that are elicited by feedback in novices and eventually are
replaced by response ERN when experts are forced to make errors under time pressure
(Krigolson et al, 2009). Our results suggest that the acquisition of perceptual expertise
relies on interactions between the posterior perceptual system and the
reinforcement learning system involving medial-frontal cortex.

How does experience modify visual representations?**
We made important progress. This remains currently one of the most active areas of research
for PEN members. Many studies addressed the special role of individuation in the acquisition
“face-like” expertise (e.g., Wong et al., 2009a). Training studies with objects and with faces
(in particular faces or different races) demonstrated the critical role of individuating objects
during learning, comparing it to various baselines including exposure, categorization at a
more abstract level as well as other training tasks that are at least as difficult as individuation
(Lebrecht et al, 2009; McGugin et al., submitted). We have investigated claims that naming
has a special status as an encoding task and obtained some evidence that naming is not a
critical component of individuation for the acquisition of expertise. We have used both ERPs
and fMRI to demonstrate that how we learn objects has important consequences for their
visual representation (Tanaka & Pierce, in press; Wong et al., 2009b). We have also
demonstrated that expertise training can influence affective responses to objects (Lebrecht
et al, 2009). Another way to study how experience affects our representation of objects has
been to compare expertise across different domains where training goals differ substantially.
We have made significant progress in describing expertise with musical notation and
comparing it to expertise for faces and that for letters (Wong et al., 2009c; Wong, in press;
James et al., 2009).

Time course of expertise training across species.**
We made some progress. We are working towards the development of a training paradigm
that can be used in humans and monkeys to track the changes that are occurring during the
acquisition of expertise. One goal is to integrate recordings from parietal cortex together
with those in inferotemporal cortex. Another goal is to better characterize the neural
differences associated with situations that eventually result in learning compared to those
where the subject never learns.


Stochastic accumulation of evidence for perceptual decision making.**
We made some progress. We aim to ground abstract processing components of theories
that attempt to characterize and explain variability in response times in neurophysiology
(Purcell et al., in press). The goal is to use single-cell recordings in ventral and dorsal areas of
the brain to look at the trial-to-trial dynamics that lead to decisions about visually presented
objects.

How do invariant visual representations develop?**
We made some progress. We are trying to understanding the acquisition of invariant codes for
object recognition (across changes in viewpoint, orientation, size, position, lighting etc.) We
have begun to map the developmental course of these changes in children using fMRI (Scherf
et al., 2007, 2010, submitted). In adults, we are developing paradigms to investigate how the
representations that support object recognition and expert processing generalize (or
alternatively interfere) if we need to acquire the same sort of expertise for objects in different
orientations, or in a more extreme case, different sorts of expertise for the same objects in
different orientations.




    References for section 2.3 (I left them in order that I cited them so you can find your way
    around them more easily)

   Curby, K.M., Gauthier, I. (2009). The temporal advantage for encoding objects of
          expertise, Journal of Vision, 9(6):7.1-13.
   Mack, M., Gauthier, I., Sadr, J., & Palmeri, T.J. (2008). Object detection and basic-level
          categorization: Sometimes you know it is there before you know what it
          is. Psychonomic Bulletin & Review, 15(1), 28-35
   Mack, M.L., Wong, A.C.-N., Gauthier, I., Tanaka, J.W., & Palmeri, T.J. (2009). Time-course of
          visual object categorization: Fastest does not necessarily mean first. Vision
          Research, 49, 1961-1968.
Mack, M.L., & Palmeri, T.J. (in press). Decoupling object detection and
        categorization. Journal of Experimental Psychology: Human Perception and
        Performance.
Richler, J.J., Mack, M.L., Gauthier, I., & Palmeri, T.J. (2009). Holistic processing of faces at a
        glance. Vision Research, 49, 2856-2861.

Curran, T., & Doyle, J. (in press). Picture superiority doubly dissociates the ERP correlates
         of recollection and familiarity. Journal of Cognitive Neuroscience.
D'Lauro, C., Tanaka, J. W., & Curran, T. (2008). The preferred level of face categorization
         depends on discriminability. Psychonomic Bulletin & Review, 15, 623-629.
Scott, L. S., Tanaka, J. W., Sheinberg, D. L., & Curran, T. (2008). The role of category
         learning in the acquisition and retention of perceptual expertise: A behavioral
         and neurophysiological study. Brain Research, 1210, 204-215.
Krigolson, O.E., Pierce, L.J., Holroyd, C.B., Tanaka, J.W. (2009), Learning ot become an
         expert: reinforcement learning and the acquisition of perceptual expertise. J. Cog.
         Neurosc., 21(9): 1834-41.
Wong, A.C.-N., Palmeri, T.J., & Gauthier I. (2009a). Conditions for face-like expertise with
         objects: Becoming a Ziggerin expert – but which type? Psychological Science, 20,
         1108-1117.
Wong, A.C.-N., Palmeri, T.J., Rogers, B.P., Gore, J.C., & Gauthier, I. (2009b). Beyond shape:
         Experience can determine patterns of category selectivity in the visual system.
         PLoS One, 4(12), e8405.
Lebrecht S., Pierce, L.J., Tarr, M.J., & Tanaka, J.W., (2009). Perceptual other-race training
         reduces implicit racial bias, PLoS One, 4(1): e4215
Tanaka, J.W. & Pierce, L.J. (in press). The neural plasticity of other-race face
         recognition. Cognitive, Affective and Behavioral Neuroscience.
McGugin, R.W., Tanaka, J.W., Lebrecht, S., Tarr, M.J. & Gauthier, I. (submitted). Race-
         specific perceptual discrimination improvement following short individuation
         training with faces.
Wong, A.C.-N., Jobard, G., James, K.H., James, T.W., & Gauthier, I. (2009c). Expertise with
         characters in alphabetic and non-alphabetic writing systems engage overlapping
         occipito-temporal areas. Cognitive Neuropsychology, 26(1): 111:127.
Wong, Y.K. Gauthier, I. (in press). A multimodal neural network recruited by expertise
         with musical notation, Journal of Cognitive Neuroscience.
James, K.H., Gauthier, I. (2009). When writing impairs reading: Letter perception’s
         susceptibility to motor interference. Journal of Experimental Psychology: General,
         138(3):416-31.
Purcell, B.A., Heitz, R.P., Cohen, J.Y., Schall, J.D., Logan, G.D., & Palmeri, T.J. (in press).
         Neurally-constrained modeling of perceptual decision making. Psychological
         Review.
Scherf, K.S., Behrmann, M., Humphreys, K & Luna, B. (2007). Visual Category-selectivity
         for faces, places and objects emerges along different developmental trajectories.
         Dev. Sci. 10(4): 15-30.
Scherf, K. S., Luna, B., Minshew, N. and Behrmann, M. (2010). Atypical Development of
         Face-Related Activation in Autism. Frontiers in Neuroscience, in press.
Scherf, K. S., Luna, B., Avidan, G. and Behrmann, M. Adolescence: A new critical period in
         the development of face recognition, submitted manuscript.

				
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