Perceptual learning (PL) addresses the ability to improve performance with practice in a perceptual task (Gibson, 1953). Training in a perceptual task enables one to become more efficient at distinguishing subtle differences in sensory information or input after repeated exposure. To perform more efficiently an observer needs the ability to detect or discriminate the difference between the most relevant and least relevant information. In other words, an observer learns to distinguish signal from noise with greater facility. For example, a radiologist can easily detect anomalies in an X-ray whereas a novice cannot. Learned perceptual skills can also be found in other sensory modalities such as taste, touch and hearing (Gibson, 1953). The blind refine their sense of touch to learn Braille and an oenologist refines his/her sense of taste and smell to sample the finest wines. The important element is that learning to perceive subtle differences in stimuli reflects a trainable ability to amplify the most relevant information and reduce the least informative. Moreover, it reflects experience-dependent modifications that persist for extended periods of time, from months to years (Ball & Sekuler, 1987; Fiorentini & Berardi: 1981; Karni & Sagi, 1993).
In its simplest form, the learned ability to perceive differences in visual stimuli with greater accuracy after extended practice is what is known as perceptual learning. The field of perceptual learning, in vision in particular, concerns itself with the conditions for which improvement is observed, its underlying mechanisms, and its generalizability. For perceptual learning to be ultimately valuable, it must generalize to similar tasks and attributes. Generalizability, or transfer, is defined as the ability to use what has been learned and apply it effectively to a new situation (e.g. perceptual task). Conversely, specificity is the extent to which performance improvements in training are independent of performance in a testing phase. The issue of generalizability or transfer is still an open question in the field. In reality, the evidence of transfer can be minimal (See Fahle & Poggio, 2002 for review). The observation of little or no transfer has generated important theoretical claims based on cortical properties in the visual system (e.g. Fiorentini & Berardi, 1980). For the most part, the literature has been dominated by the localization of perceptual learning in early visual cortex based on observations of specificity (Gilbert, Sigman, & Crist, 2001; although see Dosher & Lu, 1998, 1999; Mollon & Danilova, 1996).
The
focus of my research is to understand the contributing factors that influence
the observation of specificity (or, transfer). Characterizing the conditions under which an observer can
optimize performance in training and further generalize to similar tasks with
different dimensions is an important step towards understanding the underlying
mechanisms involved in PL. To do
so, I employ controlled, experimental learning conditions through manipulations
in training and manipulations of stimulus attributes and/or dimensions. I then ask how the experimental
manipulations contribute to specificity (or the lack thereof) in similar tasks
under different conditions.
Characteristics of Perceptual
Learning
Experience-dependent
changes in sensory cortex due to practice are functionally characterized as
perceptual ‘plasticity’. At one
time, not too long ago, it was believed that the ability for early sensory cortex
to physically change its functional response to environmental stimuli was
limited to early childhood development. The classical study by Hubel and Wiesel
(1959) discovered the capacity of the visual system to recover from limited
visual input in early stages of development. This remarkable ability was
restricted to a “critical period”, after which, the visual system was unable to
recover. This suggested that
subsequent cortical changes due to environmental manipulations became less
likely as an observer reached adulthood (Gilbert et al., 2001 for review). This viewpoint changed when it was
reported that practice in a perceptual task could improve performance
substantially in adults indicating experience-dependent plasticity (McKee &
Westheimer, 1978; Ramachandran & Braddick, 1973). The focus in perceptual learning then shifted towards where in the visual system this learning
might occur (Fiorentini & Berardi, 1980). This will be discussed further in
the section on Specificity below.
To
further understand and characterize PL, researchers have used several
experimental methods such as psychophysics, single-cell recording, and
fMRI. Empirically, we use
quantifiable, low-level stimuli to characterize PL through a variety of
perceptual tasks. With practice,
observers can resolve the spatial offset of visual stimuli that differ by a
fraction of a photoreceptor’s diameter, known as hyperacuity (Beard, Levi,
& Reich, 1995; Fahle & Edelman, 1993; Mckee & Westheimer, 1978
Poggio, Fahle, & Edelman, 1992; Westheimer, 1975). Observers can also learn
to discriminate the direction of motion (Ball & Sekuler, 1982; 1987, Liu
& Weinshall, 2000; Lu, Chu, Dosher, & Lee 2005; Matthews & Welch,
1997), and judge the orientation of simple line stimuli (Matthews, Liu, Geesaman,
& Qian,1999; Shiu & Pashler; 1992, Vogels & Orban, 1985) or simple
spatial gratings (Dosher & Lu, 1998, 1999; Petrov, Dosher, & Lu, 2006;
Rentschler, Juttner, & Caelli, 1994).
Tasks such as visual search (Ahissar & Hochstein, 1997; Sigman & Gilbert, 2000), texture
discrimination (Karni & Sagi, 1991, 1993), depth perception in random-dot
stereograms (Ramachandran & Braddick, 1973) and discriminating spatial
frequency (Fine & Jacobs, 2000; Fiorentini & Berardi, 1981) are also
often found to show robust learning effects after a period of training.
Perceptual tasks vary in complexity, from the discrimination of simple line gratings (Shiu & Pashler, 1992) to the recognition of 3-D objects (Tanaka, Saito, Fukada, & Moriya, 1991). These tend to exhibit a dynamic range of learning depending on several methodological factors (Fine & Jacobs, 2002). Physical manipulations of the stimuli or psychophysical procedures can influence the extent of the behavioral improvement observed. For example, training for simple line stimuli is more sensitive for cardinal (principal) orientations than for oblique oriented stimuli (Appelle, 1972; Ball & Sekuler, 1982; Mayer, 1983). Performance for oblique stimuli generally starts out worse than that of cardinal orientations, but improves significantly with practice and eventually reaches the same levels of performance as cardinal orientations (Mayer, 1983; Vogels & Orban, 1985). Performance diminishes with presentation of the stimulus in the periphery (eccentricity) relative to foveal presentations (Crist, Kapadia, Westheimer, & Gilbert 1997). Perceptual learning does not require feedback, but progresses at a slower pace than when feedback is provided (Fahle 2004; Fahle & Edelman, 1993; Tsodyks & Gilbert, 2004). Better performance in terms of lower thresholds, higher percent correct, or faster learning rates have been observed for tasks that require “coarse” relative to “finer” angle discriminations, or rather, low precision relative to high precision tasks (Ahissar & Hochstein, 1997; Liu and Weinshall, 2000).
Furthermore, presenting stimuli with and without external noise (masks) has revealed underlying mechanisms that further characterize PL. This describes the inherent internal noise due to processing inefficiencies in the visual system (Dosher & Lu, 1998, 1999). There are two key learning mechanisms identified by Dosher and Lu (1998) that serve to 1) enhance the signal of relevant information and 2) exclude visual noise, elucidating dynamic patterns of learning. Observers demonstrate significant learning in clear displays as well as in noisy displays. However, the magnitude or levels of performance may differ. More importantly, presenting stimuli with and without noise may reveal critical patterns of learning in transfer.
Specificity of Learned Attributes
The lack of transfer of learned attributes, or specificity, has emerged as a key property of perceptual learning,
which has led researchers to further explore the underlying mechanisms of
learned plasticity. Specificity in
a behavioral task reflects the extent to which performance in a transfer task
is independent of performance in a training task. Figure 1 demonstrates generic learning curves where
performance improves (e.g. thresholds decrease) as a function of time. Most perceptual learning studies follow
a standard paradigm where observers first train on a single attribute or task
until performance reaches asymptotic levels, then observers are tested in a
separate task in the transfer stage.
Specificity (S) occurs when
performance returns to baseline in the testing or transfer phase (Figure 1, upper panel). If performance looks like it picks up in the transfer stage,
where it left off in the training stage, it is considered evidence of transfer (T) (Figure 1, lower panel).
Often, reports in the literature display partial transfer, or
partial specificity (Figure 1, middle).
Figure 1 Patterns of Specificity and Transfer
Generic learning curves where
performance improves (e.g. thresholds decrease) are a plotted as a function of
time in a training and transfer phase.
Specificity (S) occurs when
performance returns to baseline in the testing or transfer phase (upper panel). If performance
looks like it picks up in the transfer stage, where it left off in the training
stage, it is considered evidence of transfer
(T) (lower panel). Often, reports in the literature
indicate partial transfer, or partial specificity (middle). (Adapted from Dill,
M. in Fahle & Poggio, 2002).
Groundbreaking work by Hubel and Wiesel (1959, 1962) demonstrating the
orientation and positional selectivity of neurons in visual cortex has been
highly influential in the field of perceptual learning. Specificity for low-level stimulus
attributes such as retinal position, orientation and spatial frequency has been
widely observed for many perceptual tasks (Fiorentini & Berardi, 1981; Schoups, Vogels, & Orban, 1995;
Shiu & Pashler, 1992). Evidence of specificity after training
for a stimulus attribute or task led many to conclude that an orientation or
position-specific result suggested localization of improvement to selective
sites in early visual cortex (Fahle & Poggio 2000 for review; Fiorentini
& Berardi, 1980; Karni & Sagi, 1991). This view, for example, argues for the recruitment of
neurons with relatively small receptive fields located in early areas of visual
cortex (e.g. V1) that respond selectively to simple oriented stimuli at
specific locations in the visual field (Gilbert et al., 2001 for review). This
interpretation maintains that learning can be attributed to changes or
modifications in sensory areas that are selective for these attributes early in
the visual hierarchy (Ahissar & Hochstein, 2004; Karni & Sagi, 1991).
For example, if training occurs at a low-level in the visual hierarchy,
learning will be specific to orientation and location. If learning occurs at
higher levels where broader encoding takes place then learning generalizes (See
Figure 2). Ahissar and Hochstein
(1997) further contend that learning progresses in reverse fashion in their
Reverse Hierarchy Theory. Learning is initiated by ‘high-level’ presentations
of the stimuli, and later becomes more specific as the stimuli demand more
detailed monitoring (Ahissar & Hochstein, 1997, 2004). Therefore, their
theory contends that training at higher levels tends to generalize and training
at lower levels tends to be more specific.
Figure 2 Specificity vs. Transfer in the Visual Hierarchy
If training occurs at a low-level
in the visual hierarchy, learning will be specific to orientation and location.
If learning occurs at higher levels where broader encoding takes place then
learning generalizes. According to
Ahissar & Hochstein, training at higher levels leads to generalization
whereas training at lower levels leads to specificity. (Adapted from Ahissar & Hochstein, 2004)
Dosher
and Lu (1998, 1999, see also Petrov et al., 2006 and Mollon and Danilova, 1996)
advocate an alternative hypothesis that what
is learned may drive specificity more than where
it is learned. The evidence of
specificity often falls short of perfect independence, which in most cases indicates
a degree of partial transfer (e.g. Liu & Weinshall, 2000). An alternative interpretation for
specificity describes mechanisms for perceptual learning that are attributed to
changes of the read - out connections (or weighting structures) from representations
in early visual cortex to decision units, also known as the “re-weighting
hypothesis” (Dosher & Lu, 1998, 1999; Petrov et al., 2006). Here, learning
involves the ongoing selection of orientation and spatial frequency channels
that carry the most relevant information with respect to the task at hand (See
Figure 3). Every channel that is selective for orientation and spatial
frequency in some location in the visual field may be connected to a decision
unit. This view takes into consideration the idea that learning may be
processed at different cortical areas depending on the complexity of the task
and or stimuli, thus activating “weights” associated with the stimulus that are
connected to a shared decision units. As learning continues, only the most
relevant “weights” are activated. When an observer is presented with a transfer
task, the system must recalibrate the most relevant weights to accommodate the
change in stimulus or task attribute. Under this view, specificity for a given
attribute occurs due to the learned weights. In this view, specificity reflects the weighting
of inputs from early representations that are specific to orientation, spatial
frequency, or location but speculates that learning does not imply changes in
these representations.
Figure 3 Schematic of channels in early visual system
Separate visual channels that are
selectively tuned to spatial frequencies and orientations process a Gabor patch
embedded in visual noise. The Gabor patch denotes a representational unit that is
connected to a decision unit via visual channels. Prior to learning, many channels or weights are active. After training, only the most relevant
and informative weights are active, while the least informative are
reduced. (Adapted from Dosher & Lu, 1998)