Topics in Cognitive Science 2 (2010) 226–238 Copyright  2009 Cognitive Science Society, Inc. All rights reserved. ISSN: 1756-8757 print / 1756-8765 online DOI: 10.1111/j.1756-8765.2009.01044.x

Shared Mechanisms of Perceptual Learning and Decision Making Chi-Tat Law, Joshua I. Gold Department of Neuroscience, University of Pennsylvania Received 2 March 2009; received in revised form 14 May 2009; accepted 26 May 2009

Abstract Perceptual decisions require the brain to weigh noisy evidence from sensory neurons to form categorical judgments that guide behavior. Here we review behavioral and neurophysiological findings suggesting that at least some forms of perceptual learning do not appear to affect the response properties of neurons that represent the sensory evidence. Instead, improved perceptual performance results from changes in how the sensory evidence is selected and weighed to form the decision. We discuss the implications of this idea for possible sites and mechanisms of training-induced improvements in perceptual processing in the brain. Keywords: Decision making; Plasticity; Attention; Primate; Sensory coding; Parietal cortex

1. Introduction Training can cause substantial, long-lasting improvements in perceptual ability for both children and adults (Fahle & Poggio, 2002; Gibson, 1963; Goldstone, 1998). This phenomenon, known as perceptual learning, implies a persistent capacity for plastic changes in the nervous system (Buonomano & Merzenich, 1998; Sur, Schummers, & Dragoi, 2002). Even the earliest reports of perceptual learning, which involved improved acuity for tactile stimuli after only hours of practice, seemed to imply that the changes did not involve new receptors at the periphery but rather improved sensory processing within the central nervous system (Volkman, 1858). Thus, a long-standing goal of researchers in this field has been to identify how and where in the brain such improvements occur (Gilbert, Sigman, & Crist, 2001). One prevalent hypothesis implicates primary sensory areas of cortex. Perceptual learning is often specific to the stimulus configuration used during training (Fahle, 2005). This specificity helps to distinguish perceptual learning from other forms of learning such as Correspondence should be sent to Joshua I. Gold, Department of Neuroscience, University of Pennsylvania, 116 Johnson Pavilion, 3610 Hamilton Walk, Philadelphia, PA 19104-6074. E-mail: [email protected]

C.-T. Law, J. I. Gold ⁄ Topics in Cognitive Science 2 (2010)


adjustments of strategy that are more likely to generalize. Moreover, this specificity has been used to argue that the underlying changes likely occur in early sensory areas where the specificity of neuronal tuning is comparable with the specificity of learning (Berardi & Fiorentini, 1987; Karni & Sagi, 1991; Poggio, Fahle, & Edelman, 1992; Schoups, Vogels, & Orban, 1995). Accordingly, perceptual learning can correspond to changes in primary auditory, somatosensory, and visual cortical areas in monkeys and humans (Crist, Li, & Gilbert, 2001; Furmanski, Schluppeck, & Engel, 2004; Ghose, Yang, & Maunsell, 2002; Maertens & Pollmann, 2005; Recanzone, Merzenich, Jenkins, Grajski, & Dinse, 1992; Recanzone, Schreiner, & Merzenich, 1993; Schiltz et al., 1999; Schoups, Vogels, Qian, & Orban, 2001; Schwartz, Maquet, & Frith, 2002; Walker, Stickgold, Jolesz, & Yoo, 2005; Watanabe et al., 2002; Yotsumoto, Watanabe, & Sasaki, 2008). However, perceptual learning encompasses a variety of behavioral phenomena that likely reflect an equally diverse set of neural changes, including some that occur beyond primary sensory cortex. Many forms of perceptual learning appear to depend on signals that originate from higher-order areas of cortex, involving attention, feedback, and the ability to generalize under certain circumstances (Ahissar & Hochstein, 2000; Fahle, 2005; Li, Piech, & Gilbert, 2004; Mukai et al., 2007; Seitz & Watanabe, 2005). Changes in these kinds of topdown signals are thought to be responsible for at least some of the training-induced changes found in primary visual cortex (Gilbert & Sigman, 2007; Ito, Westheimer, & Gilbert, 1998; Li, Piech, et al., 2004; Sigman et al., 2005). Moreover, training-induced changes in the tuning properties of individual neurons appear to be larger in a later (V4) than in earlier (V1 and V2) areas of visual cortex under similar conditions, comparable with the effects of attention (Ghose et al., 2002; Mehta, Ulbert, & Schroeder, 2000; Raiguel, Vogels, Mysore, & Orban, 2006; Schoups et al., 2001; Yang & Maunsell, 2004). Finally, even stimulus specificity does not necessarily imply changes in early cortical areas: Higher-order areas can also have narrow tuning (Ito, Tamura, Fujita, & Tanaka, 1995; Kobatake & Tanaka, 1994), and in principle ‘‘the learning may be central and the specificity may lie in what is learnt’’ (Mollon & Danilova, 1996). In this study, we synthesize from recent behavioral and neurophysiological studies a framework for understanding at least some of the neural changes in the more central areas of cortical processing, beyond primary sensory areas, that accompany perceptual learning. According to this framework, improved perceptual performance results from an increasingly selective read-out of the most relevant and sensitive sensory signals in the brain to form decisions that guide behavior (Dosher & Lu, 1999; Jacobs, 2009; Law & Gold, 2008).

2. Behavior One influential theory describing the role of high-order processing in perceptual learning is known as the ‘‘reverse hierarchy theory’’ (Ahissar & Hochstein, 1997, 2000, 2004). The theory is based on a systematic study of the stimulus specificity of learning on a task requiring detection of an oddly oriented bar among distracters in a briefly presented visual


C.-T. Law, J. I. Gold ⁄ Topics in Cognitive Science 2 (2010)

stimulus. The specificity depended critically on task difficulty. When easier versions of the task were used, for example with longer stimulus presentation times or more easily discriminated distracters, learning was rapid and tended to generalize across target orientations. In contrast, more difficult versions of the task took longer to learn and did not generalize. According to this theory, these effects reflect an attention-driven cascade of processing starting in high-order areas with complex response properties and then feeding back to earlier areas with more narrowly tuned neurons (Desimone & Ungerleider, 1989). Although a mapping of this cascade onto specific brain areas remains elusive, this and similar theories yield valuable insights into the general and flexible role that high-order signals such as error feedback and top-down attention play in perceptual learning (Fahle, 2004; Seitz & Dinse, 2007; Seitz & Watanabe, 2005). Another approach has been to analyze how different stimulus manipulations affect perceptual learning using more specific models of the underlying neural computations. Such models typically make standard assumptions of signal detection theory, namely that the perceptual judgment (e.g., detection of the presence of a dimly flashed light) is based on an internal variable that is a combination of signal and noise and that is applied to a decision rule, like a criterion value (Green & Swets, 1966). Perceptual learning is assumed to arise from changes in the signal-to-noise (SNR) ratio of the internal variable (as opposed to changes in the criterion, although both can occur; Fine & Jacobs, 2002; Maddox, 2002; Wenger & Rasche, 2006). The key to these models is defining internal sources of noise in the brain, then probing how adjusting these noise sources affects behavior by finding the equivalent external noise in the stimulus (Gold, Sekuler, & Bennett, 2004; Lu & Dosher, 2008; Mumford & Schelbe, 1968; Pelli & Farell, 1999). These effects are typically quantified by measuring the relationship between perceptual sensitivity and the level of external noise (Fig. 1). Perceptual sensitivity is assumed to reflect

Fig. 1. External noise method (Gold et al., 2004; Lu & Dosher, 2008; Mumford & Schelbe, 1968; Pelli & Farell, 1999). Discrimination threshold is plotted as a function of external noise added to the stimulus. Empirically, perceptual learning tends to correspond to a downward shift of the entire function (Chung et al., 2005; Dosher & Lu, 1998, 1999; Gold et al., 1999; Li, Levi, et al., 2004). This effect has been interpreted as an improvement in the efficiency with which an internal template filters the incoming sensory data.

C.-T. Law, J. I. Gold ⁄ Topics in Cognitive Science 2 (2010)


the SNR of the internal variable and for discrimination tasks can be measured as the threshold level of stimulus strength needed to obtain a fixed level of performance (Green & Swets, 1966). Normally, discrimination threshold depends little on low levels of external noise, because under these conditions sensitivity is limited primarily by the subject’s internal noise. Accordingly, training-induced improvements in performance on this part of the external-noise curve are thought to reflect either a reduction of internal, additive noise or an increase in signal strength, rather than changes in how the external noise is treated. Threshold then rises roughly linearly as external noise increases to higher levels that dominate perceptual performance. Improvements measured on this part of the curve are therefore thought to reflect better exclusion of external noise. In different studies using a variety of tasks, perceptual learning corresponded to downward shifts (i.e., improved sensitivity) of these curves across all external noise levels (Chung, Levi, & Tjan, 2005; Dosher & Lu, 1998, 1999; Gold, Bennett, & Sekuler, 1999; Li, Levi, & Klein, 2004). This combination of reduced internal noise, improved signal strength, and excluded external noise is thought to reflect an improvement in the efficiency with which an internal template filters the incoming sensory data. One possible implementation of this improvement in efficiency is changes in a weighting function linking sensory inputs to the perceptual decision (Dosher & Lu, 1998, 1999). In particular, improvements in performance appear to reflect an increasingly selective weighing of the most task-relevant sensory inputs to form the perceptual judgment (Li, Levi, et al., 2004; Saarinen, 1996). Like for the analyses of stimulus specificity and attention, inferring the sites of plasticity in the brain corresponding to these computational mechanisms is problematic. In principle, an improved template or filter inferred from behavior could reflect numerous changes in the brain, including how basic stimulus features are represented in the primary visual cortex, how these basic features are combined into complex visual representations in higher visual areas in the extrastriate cortex, and ⁄ or how the visual information is used to form the perceptual decision to guide behavior in sensory–motor and associational areas. Consequently, it is important to measure activity in these brain areas directly to understand the neural mechanisms underlying perceptual learning.

3. Neurophysiology There are a number of studies that have identified changes in sensory-driven response properties of individual neurons beyond primary sensory areas of cortex as a result of sensory experience. For example, learned associations between a particular sensory input and a behavioral output, rule, or category can give rise to novel visual responses for neurons in high-order visual, associational, or premotor areas of cortex (Freedman & Miller, 2008; Suzuki, 2008; Wise & Murray, 2000). In principle, perceptual learning could also correspond to changes in sensory-driven responses in these areas (Hall, 1991). However, little is known about how such newly emerging sensory responses might contribute to improved sensitivity to the stimulus. In general, a primary challenge to link such changes in


C.-T. Law, J. I. Gold ⁄ Topics in Cognitive Science 2 (2010)

neural activity in high-order cortical areas to improvements in perceptual ability is a limited understanding of the exact role that those areas play in perception. We recently examined physiological correlates of perceptual learning in two brain areas whose relationship to visual perception has been studied extensively (Fig. 2; Law & Gold, 2008). We trained rhesus monkeys on a random-dot motion discrimination task, which requires them to determine the motion direction of a noisy visual stimulus and report the direction decision with an eye movement to an appropriate target. Neurons in the middle temporal (MT) area of extrastriate visual cortex are tuned for the direction of visual motion and represent sensory evidence used to form the direction decision (Britten, Newsome, Shadlen, Celebrini, & Movshon, 1996; Britten, Shadlen, Newsome, & Movshon, 1992; Newsome & Pare, 1988; Salzman, Britten, & Newsome, 1990). Neurons in the lateral

Fig. 2. Training-induced plasticity in decision (area LIP) but not sensory (area MT) neurons (data are from Law & Gold, 2008). (A) Monkeys were trained to decide the direction of random-dot motion and indicate their decision with an eye movement. Acute, extracellular recordings from motion-sensitive neurons in area MT and decision-related neurons in area LIP accompanied training in daily sessions. (B, C) Training had little effect on motion-driven responses in MT but a dramatic effect on both the coherence- and time-dependent responses in LIP and behavioral performance. The lower panels show summary data from a single monkey for the first (B) and last (C) one-third of training sessions. Population MT data are shown as an ideal-observer (ROC) index of the ability to prediction motion direction from the neural responses plotted as a function of viewing time for different motion strengths. Population LIP data are shown as an ideal-observer (ROC) index of the ability to prediction the monkeys’ choices from the neural responses plotted as a function of viewing time for different motion strengths. Behavioral data are shown as discrimination threshold as a function of viewing time. For details, see Law and Gold (2008). These data are consistent with an increasingly selective readout (depicted as thicker lines connecting MT to LIP) of the most informative MT neurons (depicted as darker shades) to form the decision that guides behavior.

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intraparietal area (LIP) have a variety of sensory, motor, and cognitive properties and for the motion discrimination task represent the conversion of sensory evidence into the categorical direction judgment that instructs the eye-movement response (Hanks, Ditterich, & Shadlen, 2006; Huk & Shadlen, 2005; Roitman & Shadlen, 2002; Shadlen & Newsome, 2001). We recorded the activity of individual neurons in areas MT and LIP of monkeys learning the task to identify neural changes that accompanied their performance gains. We found that training had little effect on motion-driven responses in MT. Specifically, the ability of an ideal observer to determine the direction of motion based only on the responses of an individual MT neuron varied considerably from neuron to neuron. However, this ability did not change systematically across sessions either before or during training. Thus, perceptual learning for this task did not appear to involve plasticity at the level of MT or its ascending sources of motion selectivity, including primary visual cortex (Movshon & Newsome, 1996). Although training did not substantially alter the sensitivity of MT neurons to the motion stimulus, it did affect the relationship between MT activity and choice. This relationship was measured using a quantity called choice probability, which compares trialby-trial fluctuations in the activity of individual MT neurons and behavioral choices on trials using ambiguous stimuli (Parker & Newsome, 1998). At the beginning of training, MT choice probability was near chance on average, implying that MT activity had little or no relationship with behavioral choices. In contrast, at the end of training, choice probability was slightly but reliably above chance on average, consistent with the idea that MT provided noisy evidence for the decision about motion direction that guided the behavioral response (Britten et al., 1996; Shadlen, Britten, Newsome, & Movshon, 1996). However, the training-induced increases in choice probability were not uniform across the population of MT neurons but rather increased selectively for the neurons most sensitive to the motion stimulus. This result implies that perceptual learning corresponded to an increasingly strong relationship between the most sensitive MT neurons and choice (Law & Gold, 2008). The changes in MT choice probability were accompanied by dramatic changes in motion-driven responses in LIP. At the beginning of training, individual LIP neurons were selective for the monkey’s eye movement response but were relatively insensitive to the motion stimulus. As training progressed and the monkeys’ perceptual sensitivity improved, LIP neurons became increasingly sensitive to the strength and duration of the motion stimulus. The time course and magnitude of changes in LIP matched the behavioral improvements. Moreover, like behavioral performance, these changes in LIP tended to be somewhat specific to the stimulus configurations used during training, with slightly higher sensitivity for motion directions used in previous training sessions (Law & Gold, 2008). These changes in sensory-motor processing in LIP suggest that training can affect mechanisms of saccadic decision making, an idea supported by several other, related findings. First, using a microstimulation technique used previously to identify correlates of perceptual decision making in developing oculomotor commands in trained monkeys, our laboratory found systematic changes in the strength of the sensory evidence represented in those


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commands over the course of training (Connolly, Bennur, & Gold, 2009; Gold & Shadlen, 2000, 2003). These changes, which were comparable to the improvements in perceptual sensitivity, suggest that mechanisms of improved perceptual processing for this task are closely tied to mechanisms that select the appropriate behavioral response. Second, saccadic choice biases also changed systematically with training (Gold, Law, Connolly, & Bennur, 2008). Early in training, the monkeys’ choices tended to reflect not just the sensory stimulus but also the pattern of recent choices. This behavior was suboptimal because, in our task, the motion direction was chosen randomly for each trial. Accordingly, further training reduced the influence of prior choices on behavior as perceptual sensitivity and overall performance improved. Together, these results indicate that training can shape how the brain uses both sensory and nonsensory signals to form the decision that guides behavior, even for a simple perceptual task.

4. Synthesis The physiological results described above appear to be consistent with the idea that perceptual learning can involve changes in how the brain weighs sensory evidence used to form decisions that guide behavior (Jacobs, 2009; Law & Gold, 2009; Petrov, Dosher, & Lu, 2005). According to this idea, MT provides sensory evidence to a decision process, represented in LIP, that selects the appropriate behavioral response. Motion-driven responses of individual MT neurons do not change with training. However, these responses vary considerably across the population of MT neurons, possibly reflecting different tuning properties for task-irrelevant visual features such as disparity (DeAngelis & Newsome, 1999). Improving the sensitivity of the decision process involves learning to read out more selectively the activity of the most informative sensory neurons for the particular task (Law & Gold, 2008, 2009). This conceptual scheme, involving training-induced changes in how the brain interprets sensory activity to form perceptual decisions, is not likely to be limited to motion perception and the MT–LIP pathway. Similar schemes have been inferred from computational and behavioral studies of learning for other visual discrimination tasks, including those involving orientation and slant (Dosher & Lu, 1998, 1999; Jacobs, 2009; Petrov et al., 2005). However, further neurophysiological studies are needed to test the validity and neural underpinnings of this scheme for these different tasks. Further work is also needed to understand how well this scheme generalizes to other details of the task design including attentional demands and feedback that are thought to invoke different mechanisms of plasticity (Ahissar & Hochstein, 2004; Seitz & Dinse, 2007; Seitz & Watanabe, 2005). For example, short-term (within a single session) improvements in performance on the coarse direction discrimination task described above correspond to changes in the sensitivity of individual MT neurons to the motion stimulus, probably reflecting changes in attention (Zohary, Celebrini, Britten, & Newsome, 1994). Likewise, learning for slightly different tasks with passive viewing or finer discriminations are thought to invoke context-dependent changes in early visual

C.-T. Law, J. I. Gold ⁄ Topics in Cognitive Science 2 (2010)


areas, such as V1 (Li, Piech, & Gilbert, 2008; Li, Piech, et al., 2004; Schoups et al., 2001; Sigman et al., 2005; Watanabe et al., 2002) or V4 (Raiguel et al., 2006; Yang & Maunsell, 2004). It is not currently known the exact conditions that give rise to these kinds of changes in early visual processing or whether these changes occur alone or in tandem with changes in decision processing to drive the accompanying perceptual improvements. Despite these caveats, the idea that perceptual learning involves changes in decision processing has several interesting implications. The first is that this scheme appears to have a lot in common with some mechanisms of selective attention, which can help to control the sensitivity by routing appropriate, incoming information to working memory and decision processes (Dayan, Kakade, & Montague, 2000; Knudsen, 2007; Maunsell & Treue, 2006). Our results suggest that training might help to identify which neurons contain the most informative signals for a given task, thereby establishing where and when top-down attentional mechanisms should be directed. This idea implies that attentional modulation of sensory processing should increase as perceptual sensitivity improves, a widely reported phenomenon (Ahissar & Hochstein, 2000; Crist et al., 2001; Ito et al., 1998; Li, Piech, et al., 2004). Likewise, a recent fMRI study reported a relationship between perceptual learning and changes in BOLD activation in a network of frontal and parietal areas implicated in attention (Mukai et al., 2007). A second implication is a close relationship between mechanisms of perceptual and associative learning (Hall, 1991). Associations can be formed by establishing functional connectivity from sensory neurons representing a particular visual feature to decision neurons that generate a behavior based on that feature. The results presented here suggest that perceptual learning can involve refining this connectivity so that the readout scheme is more appropriate for the particular task demands, allowing the decision process to more effectively distinguish between the alternatives (Geisler & Albrecht, 1997; Pouget, Dayan, & Zemel, 2003; Seung & Sompolinsky, 1993). This link between associative and perceptual learning suggests that they might also share mechanisms that guide the underlying changes in neural connectivity. One potential mechanism is based on feedback reinforcement, possibly involving reward prediction errors thought by some to be encoded by the dopaminergic system in the brainstem (Schultz, 2002; Sutton & Barto, 1998). Models of plasticity based on these signals are consistent with plausible synaptic mechanisms and can account for many forms of associative learning (Dayan & Abbott, 2001; Fusi, Asaad, Miller, & Wang, 2007; Mazzoni, Andersen, & Jordan, 1991a, 1991b; Seung, 2003). In addition, dopamine has been shown to drive changes in primary auditory cortex that could, in principle, improve perceptual sensitivity to auditory tones, and the dopaminergic system has been proposed to play a general role in visual perceptual learning (Bao, Chan, & Merzenich, 2001; Seitz & Dinse, 2007). We recently showed that a reinforcement learning model that shapes connectivity between pools of MT and LIP neurons can account for a range of behavioral and neural changes related to associative and perceptual learning for our task (Law & Gold, 2009). However, more research is needed to establish the validity of this model and understand how mechanisms of reinforcement learning can shape perceptual ability.


C.-T. Law, J. I. Gold ⁄ Topics in Cognitive Science 2 (2010)

5. Conclusions The prevalence of perceptual learning implies a widespread capacity for experiencedependent plasticity in the brain, even for adults. How and where in the brain this plasticity occurs remains an active and unresolved area of research. One clear complication is that perceptual learning encompasses a wide range of phenomena that can differ in terms of the role of attention, feedback, difficulty, and other factors. Thus, there is likely to be an equally diverse set of neural mechanisms that make different contributions to perceptual learning depending on the particular context. Nevertheless, at least some forms of perceptual learning appear to share some common mechanisms. Here we focused on those forms that involve top-down control of task-specific attention and feedback. In these cases, there is a growing body of evidence to suggest that experience helps to shape the selectivity of the attention process, so that only the most appropriate signals are used to guide perception. In neural terms, this process appears to involve an increasingly selective readout of the most sensitive and relevant sensory neurons to form the perceptual judgment. This selection process might in principle be driven by the same kinds of mechanisms that underlie associative learning, suggesting strong links between reinforcement, perceptual, and associative learning and perceptual decision making in the brain.

Acknowledgments We thank L. Ding and R. Kalwani for helpful comments on this manuscript. Supported by the Sloan Foundation, the McKnight Endowment Fund for Neuroscience, the BurroughsWellcome Fund, and NIH R01-EY015260, P50-MH062196, and T32-EY007035.

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Shared mechanisms of perceptual learning and decision making.

Perceptual decisions require the brain to weigh noisy evidence from sensory neurons to form categorical judgments that guide behavior. Here we review ...
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