Perceptual Decision Making

Perceptual Decision Making M Castelo-Branco and J Castelhano, IBILI, ICNAS, University of Coimbra, Coimbra, Portugal ã 2015 Elsevier Inc. All rights reserved.

Glossary Binocular rivalry is an example of bistability, whereby conflicting monocular images compete for access to awareness in a dynamic fashion. Multistability is the phenomenal experience of alternating between perceptual representations and has been used

Multistability, Gestalt Formation, and Perceptual Decision Perceptual decision reflects the choice between interpretations of the sensory world and may crucially affect subsequent action selection (Heekeren et al., 2008). Two main types of context challenge perceptual decision: (1) Multiple interpretations are available in the scene (Castelo-Branco et al., 2000, 2002, 2009; Kozak & Castelo-Branco, 2009) leading to rivalrous/conflicting percepts (see Figure 1); (2) holistic integration is required from impoverished sensory content, but local details might not be sufficient for swift gestalt formation. Deficits in gestalt perception may be clinically relevant in autistic spectrum disorders (Bernardino et al., 2012, 2013; Castelo-Branco et al., 2007). The first type of context is instantiated under multistable conditions, whereby the pattern of sensory stimulation in the retina remains constant and still its perceptual appearance can change dramatically over time and switch back and forth. A simple example is bistability, such as binocular rivalry, in which two different images are presented to each eye, which compete for perception. In this paradigm, observers are often requested to press a given button any time the interpretation changes (Fries et al., 2005), as a result of perceptual decision mechanisms. Here, we will address how neuroimaging and related multimodal approaches may contribute to the field of perceptual decision making by elucidating the core and extended neural architecture of decision-making networks and their functional relevance. We will focus on two types of constraints: (1) bistable, rivalrous percepts and (2) holistic integration under impoverished sensory contexts, rendering gestalt perceptual formation difficult under noisy backgrounds.

Neural Correlates of Bistable Perception Neuroimaging and behavioral experiments suggest that binocular rivalry entails competitive interactions at multiple neural sites (Blake et al., 2014; Fries et al., 2005; Tong et al., 2006). Although rivalry depends on low-level inhibitory interactions, high-level excitatory influences may modulate dominance of a stimulus over space and time (Tong et al., 2006). However, although interesting pieces of evidence have been collected suggesting a causal link between fluctuating perceptual states

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experimentally to address the neural correlates of perceptual decision. Perceptual decision represents one of the most fundamental cognitive functions and is defined by the choice between ambiguous alternative interpretations of incoming sensory evidence.

and brain activity measures during rivalry, their generalizability to other perceptual phenomena remains debatable (Blake et al., 2014). This includes the ongoing discussion on the role of the frontal cortex in triggering perceptual decision (Heekeren et al., 2004, 2008; Rebola et al., 2012). Heekeren et al. (2004) had shown that the dorsolateral prefrontal cortex modulates as a function of the amount of evidence and shows greater taskpositive BOLD modulation during perceptual decision. The need to separate distinct cognitive components and their respective roles has been also emphasized by studies showing evidence for separate processing of perceptual and motor decisions (Filimon et al., 2013). The work of Ruff et al. (2010) suggests that this scenario is indeed more complex by showing that systems representing sensory evidence and systems modulating with task difficulty may coexist during perceptual decision making. A recent study showed that when observers passively experienced rivalry without explicitly reporting perceptual alternations, differential neural activity in frontal areas was absent (Fra¨ssle et al., 2014). Occipital and parietal regions showed consistent activation patterns and these results question the notion that frontal areas initiate perceptual alternations, at least in which concerns binocular rivalry. These results are consistent with a recent study demonstrating that retinotopic representations in early visual areas play a role in the dynamics of perceptual alternations (Yamashiro et al., 2014). Studies of a phenomenon termed ‘traveling wave spread’ whereby perceptual transitions are initiated at one location and spread to other parts of the visual field may also help illuminate this controversy. Surface areas of retinotopic areas V1 and V2 but not V3 were recently demonstrated to correlate with wave speed, emphasizing the role of early visual areas in mediating binocular rivalry (Genc¸ et al., 2014). Other striking examples of bistability are plaid patterns (Figure 2(a)). These are formed by superimposed moving gratings that can be perceived as either multiple transparent surfaces or a single coherent surface that results from the perceptual fusion of the former. These overlapping moving gratings can accordingly be perceptually segregated into two independently moving transparent (component) surfaces or as a single (pattern) surface moving in an intermediate direction. Component-motion and pattern-motion representations (perceiving sliding in different directions or a single coherent pattern) are the two alternative perceptual solutions. This notion

http://dx.doi.org/10.1016/B978-0-12-397025-1.00261-X

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Figure 1 Example of a bistable stimulus, allowing for two alternative perceptual interpretations, based on distinct figure versus ground assignments: woman or bird. Courtesy of Otı´lia C. d’ Almeida.

needs to be differentiated between the classifications of ‘pattern’ and ‘component’ motion cells. Pattern cells in primate hMT þ can signal the constituent global motions (regardless of how many surfaces are perceived, e.g., global pattern or global component), unlike component cells (McDonald et al., 2014) that only signal local component motion. These stimuli have been used to investigate with functional magnetic resonance imaging whether the motion-sensitive area hMT þ/V5 is involved in perceptual segmentation and integration of motion signals related to such perceptual surfaces (Figure 2(b)). It was found that motion-sensitive area hMT þ/V5 is involved in mediating the switches between the two percepts (Castelo-Branco et al., 2002, 2009). Activity in hMT þ/V5 correlated with perceptual switches, being higher when the perceptual interpretation was compatible with multiple surfaces, suggesting that these are associated with a reconfiguration of cell assemblies in this area. These data provide evidence on the putative site of perceptual grouping operations underlying the switches between fusion and segregation of moving stimuli. Activity in the hMT þ/V5 complex changes depending on whether subjects integrate all motion signals into the percept of a single surface or whether they segregate signals and perceive two transparent surfaces. Importantly, this was demonstrated using modeldriven (generalized linear models) and data-driven approaches (independent component analysis) (Castelo-Branco et al., 2002).

Can Neuroimaging Test Perceptual Decision Mechanisms at the Columnar Level? One outstanding question is whether one can relate the switches between the two perceptual representations to the

Figure 2 (a) Superimposed moving gratings (plaids) may be perceived as single coherent surface that results from the perceptual fusion of the former. Alternatively, they may also be perceived either as multiple transparent surfaces, one sliding on top of the other. (b) Global motion perception evoked by moving plaids leads to specific modulation of hMT þ complex as observed in statistical map (green colors, statistical map derived from a general linear model, p < 0.001, corrected) superimposed on the cortical surface reconstruction. Posterior colored stripes correspond to the functional delineation of retinotopic regions.

known columnar organization of the mammalian cortex (Schmidt et al., 2006) and more specifically primate (including human) MT. The perception of dual transparent surfaces is probably associated with the formation of two cell assemblies, each of which represents one of the two moving surfaces, whereas the perception of a single fused surface likely requires the formation of only one cell assembly representing a single surface moving in an intermediate direction. Possibly, one can relate each assembly with a columnar representation being active. This is what best explains the data (Castelo-Branco et al., 2002) and will be amenable to experimental testing in the future, now that ultrahigh-field scanners are becoming available. Their use will likely allow the mapping of columns in the human analogue of monkey MT, similar to other regions in the visual cortex (Albright et al., 1984; De Martino et al., 2013). The test of perceptual decision-making mechanisms at the columnar level might provide a tantalizing link between neuroimaging and neurophysiological experiments at the cellular level. The demonstration of a close relation between activity

INTRODUCTION TO COGNITIVE NEUROSCIENCE | Perceptual Decision Making changes in hMT þ/V5 and perceptual switches involving differential binding of stimulus components moving in different directions can therefore be potentially linked to studies on how the brain encodes simultaneously representations of multiple global directions. In sum, visual area MT is a candidate for the interplay between global integration and segmentation mechanisms at the columnar level. Integration implies the existence of only one active neuronal population in the motion map (patternmotion representation). Segmentation seems to be correlated with the coexistence in the same map of several active populations (global component motions). The human MT þ complex is involved in the decision between either single winner-takeall (pattern-motion) or bimodal (component-motion) perceptual solutions, suggesting that its motion map can hold uni- or multimodal active representations. It is also possible that global pattern-motion responses can be observed at the columnar level in early visual cortical areas, due to the interaction of 2-D shape and motion feedback signals. This question can be studied in animals using optical imaging techniques, which have a spatial resolution that allows one to study local responses within attribute maps. In this way, one can analyze the relative responses of populations of neurons responding to contours of different orientations and direction of movement. Pattern-motion selectivity could indeed be detected in population maps of the early cat visual cortex, using different sets of plaid stimuli (Schmidt et al., 2006). For all stimuli, a component-motion map corresponding to the response to local contours was detected. Surprisingly, for the subset of stimuli that were biased for pattern motion, a map was found in which peaks matched the locations predicted by a pattern-motion model. To verify that 2-D intersections critically underlie pattern maps, we studied responses to depth-ordered stimulus configurations, which entail absent 2-D grating intersections. Then, pattern-motion maps became virtually absent, suggesting that 2-D contour integration underlies pattern-motion selectivity. These findings support the notion that global motion responses cannot be detected at unit level in cat visual areas A18 and PMLS but only by patterns of population activity at the columnar level. These results also highlight the potential feasibility of studying perceptual decision making at the columnar level using ultrahighfield scanners in humans.

Predicting Neural Activity Patterns from Models of Perceptual Inference Mechanistic insights in the cognitive neuroscience of perception require precise models. Two models were proposed to account for the perceived coherent motion of a plaid (Adelson & Movshon, 1982). One model assumes that the true direction of motion of a plaid pattern is given by the intersection of constraints, a two-dimensional velocity space where direction and velocity of the plaid’s components intersect. The intersection of constraints mechanism is thought to require two successive stages of processing. The first evaluates the directions of movements of the local contours, and the second computes the true direction of motion of the composite object by integrating over the output of the local analyzers. The

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fact that neurons computing the second stage have been rarely found in early visual areas might be attributed to their receptive field size not allowing for the integration over larger parts of the composite object. Our fMRI findings do confirm this prediction. A second model proposes that the perceived motion is determined by the motion of the intersections (‘blobs’) and that motion is particularly biased towards the true plaid direction of motion through a low-level ‘blob-tracking mechanism’ (Alais et al., 1994). Our results are less consistent with such an early level blob-tracking mechanism although top-down mechanisms in 2-D intersection detection cannot be excluded (Schmidt et al., 2006). It is possible that the pattern-motion cells that are found in early areas respond more strongly to corners and intersections than component cells. Since early visual areas are very contrast-sensitive, high intersection contrast may increase the saliency of the blobs in a feature-tracking mechanism and bias pattern motion. Evidence for a 2-D contour-tracking mechanism underlying pattern-motion maps in fMRI is quite scant. Top-down effects related to attention to features such as intersections (Nowlan & Sejnowski, 1995) might still occur, but overall, the available evidence suggests that pattern-motion representations are not generated in the early visual cortex.

Inferring Neural Coding Mechanisms and Models from Bistable Perceptual Decision: Linking Levels of Analysis The human visual brain needs to solve disambiguation problems, and this is well instantiated by the question whether motion signals coming from overlapping contours arise from single or multiple surfaces, which was examined using the previously described plaid stimuli. hMT þ not only is a substrate for perceptual decision and seems to represent motion in a manner consistent with the existence of a winner-take-all model but also allows for activity patterns consistent with the simultaneous representation of multiple surfaces. These findings also generate neural coding questions. Indeed, neurons in particular visual cortical areas seem to synchronize their discharges when responding to contours of the same global surface but not when responding to contours belonging to different surfaces (Castelo-Branco et al., 2000). Neurons belonging to the same cortical orientation column respond preferentially to contours of similar orientations. However, these orientations reflect local component motion and not global component surfaces. The notion of temporal codes does therefore still remain quite controversial, and in fact, neural codes just based on the number of action potentials (rate codes) can encode visual information without the need to invoke a temporal code. Accordingly, we have found, using a figure–ground segmentation task, that the early visual cortex uses rate codes to segment figure from ground based on center versus surround contour orientation contrast (Biederlack et al., 2006). However, figure from ground segmentation based on center versus surround contour phase offset contrast seems to require temporal coding mechanisms, through neural synchrony. Although neuroimaging cannot directly assess these neural coding mechanisms, it can help identify and validate the neural regions and columns that

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are critical in such perceptual decision mechanisms. Moreover, the combination of methods such as simultaneous EEG and fMRI or even unit recordings in awake animals is changing this scenario and allowing to have a broader grasp of perceptual decision mechanisms.

Adaptation Mechanisms in Perceptual Decision The role of bottom-up versus top-down mechanisms in perceptual decision remains highly debated (Intaite˙ et al., 2013, 2014). Sensory adaptation or satiety represent a strong mechanism in perceptual decision models. Perceptual motion aftereffects (MAEs) are due to motion adaptation leading to illusory motion perception in direction opposite that of the adapting motion. Classical explanations of MAEs include models that postulate that motion is coded by the ratio between the outputs of detectors tuned to opposite directions (Huk et al., 2001; Kozak & Castelo-Branco, 2009). Alternative distribution-shift models compare between outputs in the whole population of direction detectors, rather than just those tuned to opposite directions (Mather, 1980). Overall, simple neural models based on sensory adaptation may explain strong and compelling perceptual illusions and influence perceptual decision. However, effects such as storage, which leads to their subsequent expression later in time, and their modulation by attention and by the type of inducing stimulus show that they cannot be simply explained by a simple feedforward sensory model and that top-down effects play an important instructive role. Although motion adaptation influences such simple perceptual decisions, such as direction discrimination, it remains to be established how it affects more complex decision making.

Holistic Perception Models: From Core to Extended Architectures of Decision-Making Circuits So far, the understanding of simple perceptual decisions (Castelo-Branco et al., 2002; Heekeren et al., 2008; Rebola et al., 2012) largely remains to be linked to high-level context including motivational top-down decision variables. Recent studies suggest that variables related to sensory context, attentional modulation, and working memory load interact with contingencies of the history of stimulus presentation to shape perceptual decision (Intaite˙ et al., 2014). Multiple levels of decision can be analyzed to unravel the functional architecture of core and extended decision networks using EEG, fMRI, MEG, and EEG/fMRI. Such approaches are helping in defining the multiple levels of decision making, from the perceptual level to the cognitive control and motivational levels (Castelhano et al., 2014). A fundamental question is how these decision-making levels are related, from the most simple perceptual decisions to goaloriented behavior under complex emotional and social contexts. Understanding how people make difficult choices under uncertainty is well modeled by perceptual decision-making paradigms that may serve as basis to study high-level determinants of difficult choice. Aspects related to motivation and cognitive control are important to take into account given the evidence

that neuropsychiatric conditions also involve impaired perceptual decision making (Uhlhaas & Singer, 2012). The study of decision making under uncertainty needs to be generalized to a framework that includes decision under selfrelevant contexts with impact on other motivational/reward variables. This will help generate models of impaired decision making in diseases with both impaired/fragmented perception and behavioral control/motivation such as autism, schizophrenia, and Parkinson’s disease (Bernardino et al., 2013; Uhlhaas & Singer, 2012). In the later, both perceptual decision and reward processing are impaired (Castelo-Branco et al., 2009). The ultimate goal in this field will be the building of a large-scale decision model incorporating perceptual, reward, and cognitive control modules. We argue that parsing the cognitive components of holistic decision making can best be made by combining EEG and fMRI studies (Castelhano et al., 2014). EEG-informed fMRI approaches will be very important for the identification of nonunitary patterns of distinct functional significance. Validation of these models is enhanced by statistical classification approaches that help predict perceptual decision brain states (Castelhano et al., 2013) and decision moments (Pires et al., 2011, 2012; Teixeira et al., 2014), thereby helping validate in a data-driven manner models of brain function. The controversial relation of gamma-band synchrony to holistic perception that concerns the effects of sensory processing, high-level perceptual gestalt formation, motor planning, and response can also be more adequately addressed using multiple approaches. Using dynamic ambiguous stimuli, we found that gamma activity increases well before the report of an emergent holistic percept. Accordingly, gamma-band modulation is increased for ambiguous states prior to a perceptual decision (Castelhano et al., 2013) favoring the hypothesis that brain activity in the gamma frequency range reflects perceptual decision mechanisms. Moreover, data-driven support vector machine time–frequency classification approaches helped distinguish between perceptual and nonperceptual states, based on time–frequency features (Castelhano et al., 2013). This paradigm, which focused on global gestalt mechanisms instead of local processing (the Mooney figures, Figure 3(a)), allowed to show that gamma-band activity and synchrony provide a signature of holistic perceptual states of variable onset, which are separable from sensory and motor processing as shown using fMRI (see Figures 3 and 4 for models of processing and neuroimaging data that may inform EEG data; Figures 5 and 6). Electrophysiological studies often reveal a wide variety of gamma-band patterns and sources for different tasks (Akimoto et al., 2013; Jerbi et al., 2009; Ray & Maunsell, 2011) rendering interpretation of their cognitive significance rather difficult without the combination with fMRI. We have recently addressed holistic perceptual decision making using simultaneous EEG and fMRI to help solve this question (Castelhano et al., 2014). Raising interest in EEG/ERP approaches has been inspired by animal studies (Biederlack et al., 2006; CasteloBranco et al., 1998, 2000) and many cognitive studies (Crone et al., 2011; Fries, 2009; Rodriguez et al., 1999; Tallon-Baudry & Bertrand, 1999) proposing that gamma-band activity patterns may signal emerging object percepts (Gruber et al., 2001; Rodriguez et al., 1999). Unimodal studies have been

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Figure 3 (a) Examples of two-tone ambiguous Mooney figures (left, upright configuration, readily favoring holistic perception; right, inverted configuration, rendering holistic perception more difficult). (b) Theoretical predictions of brain activity patterns when an inverted Mooney face configuration is rotated to upright, leading to an eureka-like perceptual decision moment. These predictors are as follows: A, activity reflects merely the processing of low-level features such as black and white blobs (sensory areas that activate tonically); B, activity accumulates until the decision moment (evidence accumulation regions, each color corresponds to a distinct perceptual decision time); C, activity reflects the eureka moment of perceptual decision; D, predictors that truly reflect perceptual decision (top) and pure motor decision (bottom) for conditions where the button press is delayed until the end of the trial. Top: Predictor modulating according to the varying moment of perceptual decision, even when no motor response occurs. Bottom: Predictor modulating only when the motor response occurs at the end. This predictor reflects motor decision but not perceptual decision. Light blue corresponds to unseen objects. Courtesy of Jose´ Rebola.

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Figure 4 Dorsolateral prefrontal region involved in perceptual decision. All activity peaks correspond to a perceptual decision moment irrespective of whether a motor response is present or not. In the response task, the motor report is given at the time of perceptual decision. In the color task, the motor report is given only at the end of the trial (by choosing a color tagging the moment of decision). Truly decision-related areas should peak at the moment of perceptual decision irrespective of when motor reports occur. This is the case for the depicted region in the dorsolateral prefrontal cortex. Red curves: Early eureka moments. Blue curve: Intermediate decision moments. Purple curve: Late perceptual decision moments. Courtesy of J. Rebola.

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Figure 5 Perceptual decision making as fingerprinted with simultaneous EEG and eye tracking. (a) Eye-tracking scan path example for four movie frames; circle sizes indicate group average duration of fixation. (b) Group average time–frequency plot including all the channels (see locations in the right). Data are locked to the response (blue line) and normalized to the baseline (1000 to 700 ms). Note the increased activity for the low-frequency (40 Hz) and high-frequency (60–80 Hz) gamma subbands even before the perceptual decision report (button press).

hampered by the difficulty of parsing and attributing functional meaning for spatially and temporally distinct sources of brain activity (Figure 5). Figure 6 shows that using concurrent EEG/fMRI, one can identify clearly distinct sources of gamma-band patterns with different functional roles, namely, in the visual cortex and the insula (see Castelhano et al., 2014). Clearly, there is not a single gamma activity pattern of a broad frequency band that reflects perception (Gruber et al., 2001; Rodriguez et al., 1999) and decision mechanisms (Guggisberg et al., 2007). We argue that there are separable gamma subbands with distinct spatial sources in the brain to help solve the same perceptual decision task. These include low- and highfrequency oscillatory subbands (Guggisberg et al., 2007; Ray & Maunsell, 2011; Scheeringa et al., 2011). Perceptual decision also involves difficult tasks that require integration of information across visual dorsal and ventral streams (Castelo-Branco et al., 2007; Graewe et al., 2013). We

have also used a 3-D structure-from-motion (SFM) integrative task to characterize the neuronal underpinnings of 3-D perception in Wlliams Syndrome (WS), a neurodevelopmental condition and to probe whether gamma oscillatory patterns reflect changed holistic perception. Coherent faces were parametrically modulated in 3-D depth (three different depth levels) to vary levels of stimulus ambiguity. We have found that low gammaband oscillations (25–40 Hz) induced by this 3-D perceptual integration task were significantly stronger and sustained during the stimulus presentation in WS, whereas high gamma-band oscillations (60–90 Hz) were reduced in this clinical model of impaired visual coherence, as compared to controls. These observations suggest that different perceptual strategies are employed by these patients to reach visual coherence. In sum, two distinct patterns of gamma-band activity occur for the moment a holistic object percept is formed. These are observed both in the Mooney face (ambiguous two-tone figures)

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Figure 6 Perceptual decision-making network as revealed by time–frequency analysis of gamma band, using simultaneous EEG/fMRI during an object categorization task. Brain clusters are shown for two different conditions. Low-frequency (red) and high-frequency (orange) predictors, based on the time–frequency peak analysis of the EEG data, were constructed for the GLM analysis. The anterior insula is activated for the LF conditions (in response to scrambled stimuli, example image in the plot), while early visual regions underlie the HF gamma subband (responses to object stimuli, example image in the plot). The right insula is activated for the LF peaks during object perception. L and R stand for left and right, respectively. A and P stand for anterior and posterior, respectively. TF plots’ color scale represents normalized units.

paradigms requiring difficult holistic integrations (Castelhano et al., 2013) and in perceptual decision paradigms requiring object recognition from 3-D SFM in normal subjects and in Williams syndrome (WS), a clinical model of impaired perceptual coherence (Bernardino et al., 2012, 2013; Castelhano et al., 2014). Low-range gamma (near 40 Hz) was increased in patients, while higher-range gamma (near 60 Hz) was increased in controls, suggesting a critical dissociation exists between low and high gamma oscillations, which may be relevant to understand normal and impaired holistic perception in other conditions such as autism and schizophrenia (Uhlhaas & Singer, 2012). Differential patterning of gamma-band activity during perceptual decision in diseases such as autism and schizophrenia, related with visual processing and general decision mechanisms, should be therefore further investigated in the future, in correlation with neuroimaging data (Castelhano et al, 2014).

Concluding Remarks Decision formation occurs as a function of accumulation of sensory evidence (Heekeren et al., 2004; Ploran et al., 2007; Rebola et al., 2012). Formal models inspired from animal research (Shadlen & Kiani, 2013) such as the diffusion model, which assumes that evidence is integrated over time to one of two decision thresholds corresponding to the two choices, will become increasingly relevant. They have inspired previous neuroimaging studies on decision formation (Heekeren et al., 2004) and will certainly be revisited in future investigations of their neural correlates. Current attempts to explain the phenomenology of perceptual decision have provided increasing evidence that these phenomena are shaped by core and extended neural

architectures. Neuroimaging and electrophysiological techniques are likely to provide in the future new insights into mechanisms generating the emergence of perceptual representation. Outstanding questions remain such as the ongoing debate on the role of low-level (sensory) and high-level (topdown) mechanisms in explaining perceptual alternations and the interaction between categorization and value attribution in high-level difficult choice. Finally, future studies should elucidate how perceptual decision can illuminate outstanding issues in decision-making neuroscience (Shadlen & Kiani, 2013). In more general terms, these authors define a decision as a “commitment to a proposition or plan of action based on information and values associated with the possible outcomes.” This requires the elucidation of neural mechanisms that underlie accuracy, speed, and choice confidence. Categorization, the study of generalizable representations (Seger & Peterson, 2013), may well represent an example on how these mechanisms can be integrated into general principles in cognitive neuroscience.

See also: INTRODUCTION TO ANATOMY AND PHYSIOLOGY: Topographic Layout of Monkey Extrastriate Visual Cortex; INTRODUCTION TO COGNITIVE NEUROSCIENCE: Motor Decision-Making; Neuroimaging of Economic Decision-Making; INTRODUCTION TO SOCIAL COGNITIVE NEUROSCIENCE: Social Decision Making.

References Adelson, E. H., & Movshon, J. A. (1982). Phenomenal coherence of moving visual patterns. Nature, 300(5892), 523–525.

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Akimoto, Y., Kanno, A., Kambara, T., et al. (2013). Spatiotemporal dynamics of highgamma activities during a 3-stimulus visual oddball task. PLoS ONE, 8(3), e59969. Alais, D., Wenderoth, P., & Burke, D. (1994). The contribution of one-dimensional motion mechanisms to the perceived direction of drifting plaids and their after effects. Vision Research, 34(14), 1823–1834. Albright, T. D., Desimone, R., & Gross, C. G. (1984). Columnar organization of directionally selective cells in visual area MT of the macaque. Journal of Neurophysiology, 51(1), 16–31. Bernardino, I., Castelhano, J., Farivar, R., et al. (2013). Neural correlates of visual integration in Williams syndrome: Gamma oscillation patterns in a model of impaired coherence. Neuropsychologia, 51(7), 1287–1295. Bernardino, I., Mouga, S., Almeida, J., et al. (2012). A direct comparison of local-global integration in autism and other developmental disorders: Implications for the central coherence hypothesis. PLoS ONE, 7(6), e39351. Biederlack, J., Castelo-Branco, M., Neuenschwander, S., et al. (2006). Brightness induction: Rate enhancement and neuronal synchronization as complementary codes. Neuron, 52(6), 1073–1083. Blake, R., Brascamp, J., & Heeger, D. J. (2014). Can binocular rivalry reveal neural correlates of consciousness? Philosophical Transactions of the Royal Society of London. Series B, Biological Sciences, 369(1641), 20130211. Castelhano, J., Duarte, I. C., Wibral, M., Rodriguez, E., & Castelo-Branco, M. (2014). The dual facet of gamma oscillations: Separate visual and decision making circuits as revealed by simultaneous EEG/fMRI. Human Brain Mapping, 35(10), 5219–5235. Castelhano, J., Rebola, J., Leita˜o, B., Rodriguez, E., & Castelo-Branco, M. (2013). To perceive or not perceive: The role of gamma-band activity in signaling object percepts. PLoS ONE, 8(6), e66363. Castelo-Branco, M., Formisano, E., Backes, W., et al. (2002). Activity patterns in human motion-sensitive areas depend on the interpretation of global motion. Proceedings of the National Academy of Sciences of the United States of America, 99(21), 13914–13919. Castelo-Branco, M., Goebel, R., Neuenschwander, S., & Singer, W. (2000). Neural synchrony correlates with surface segregation rules. Nature, 405(6787), 685–689. Castelo-Branco, M., Kozak, L. R., Formisano, E., et al. (2009). Type of featural attention differentially modulates hMT þ responses to illusory motion aftereffects. Journal of Neurophysiology, 102(5), 3016–3025. Castelo-Branco, M., Mendes, M., Sebastia˜o, A. R., et al. (2007). Visual phenotype in Williams-Beuren syndrome challenges magnocellular theories explaining human neurodevelopmental visual cortical disorders. The Journal of Clinical Investigation, 117(12), 3720–3729. Castelo-Branco, M., Neuenschwander, S., & Singer, W. (1998). Synchronization of visual responses between the cortex, lateral geniculate nucleus, and retina in the anesthetized cat. The Journal of Neuroscience: The Official Journal of the Society for Neuroscience, 18(16), 6395–6410. Crone, N. E., Korzeniewska, A., & Franaszczuk, P. J. (2011). Cortical g responses: Searching high and low. The International Journal of Psychophysiology is the official journal of the International Organization of Psychophysiology, 79(1), 9–15. De Martino, F., Zimmermann, J., Muckli, L., et al. (2013). Cortical depth dependent functional responses in humans at 7 T: Improved specificity with 3D GRASE. PLoS ONE, 8(3), e60514. Filimon, F., Philiastides, M. G., Nelson, J. D., Kloosterman, N. A., & Heekeren, H. R. (2013). How embodied is perceptual decision making? Evidence for separate processing of perceptual and motor decisions. The Journal of Neuroscience: The Official Journal of the Society for Neuroscience, 33(5), 2121–2136. Fra¨ssle, S., Sommer, J., Jansen, A., Naber, M., & Einha¨user, W. (2014). Binocular rivalry: frontal activity relates to introspection and action but not to perception. The Journal of Neuroscience: The Official Journal of the Society for Neuroscience, 34(5), 1738–1747. Fries, P. (2009). Neuronal gamma-band synchronization as a fundamental process in cortical computation. Annual Review of Neuroscience, 32, 209–224. Fries, P., Castelo-Branco, M., Engel, A., & Singer, W. (2005). The functional role of oscillatory neuronal synchronization for perceptual organization and selection. In Binocular rivalry and perceptual ambiguity. Cambridge: MIT Press. Genc¸, E., Bergmann, J., Singer, W., & Kohler, A. (2014). Surface area of early visual cortex predicts individual speed of traveling waves during binocular rivalry. Cerebral Cortex http://dx.doi.org/10.1093/cercor/bht342 [Epub ahead of print]. Graewe, B., Lemos, R., Ferreira, C., et al. (2013). Impaired processing of 3D motiondefined faces in mild cognitive impairment and healthy aging: An fMRI study. Cerebral Cortex, 23(10), 2489–2499. Gruber, T., Keil, a, & Mu¨ller, M. M. (2001). Modulation of induced gamma band responses and phase synchrony in a paired associate learning task in the human EEG. Neuroscience Letters, 316(1), 29–32. Guggisberg, A. G., Dalal, S. S., Findlay, A. M., & Nagarajan, S. S. (2007). Highfrequency oscillations in distributed neural networks reveal the dynamics of human decision making. Frontiers in Human Neuroscience, 1(March), 14.

Heekeren, H. R., Marrett, S., Bandettini, P.a., & Ungerleider, L. G. (2004). A general mechanism for perceptual decision-making in the human brain. Nature, 431(7010), 859–862. Heekeren, H. R., Marrett, S., & Ungerleider, L. G. (2008). The neural systems that mediate human perceptual decision making. Nature Reviews. Neuroscience, 9(6), 467–479. Huk, A. C., Ress, D., & Heeger, D. J. (2001). Neuronal basis of the motion aftereffect reconsidered. Neuron, 32(1), 161–172. Intaite˙, M., Koivisto, M., & Castelo-Branco, M. (2014). The linear impact of concurrent working memory load on dynamics of Necker cube perceptual reversals. Journal of Vision, 14(1), 1–9. Intaite˙, M., Koivisto, M., & Revonsuo, A. (2013). Perceptual reversals of Necker stimuli during intermittent presentation with limited attentional resources. Psychophysiology, 50(1), 82–96. Jerbi, K., Ossando´n, T., Hamame´, C. M., et al. (2009). Task-related gamma-band dynamics from an intracerebral perspective: Review and implications for surface EEG and MEG. Human Brain Mapping, 30(6), 1758–1771. Kozak, L. R., & Castelo-Branco, M. (2009). Peripheral influences on motion integration in foveal vision are modulated by central local ambiguity and center-surround congruence. Investigative Ophthalmology & Visual Science, 50(2), 980–988. Mather, G. (1980). The movement aftereffect and a distribution-shift model for coding the direction of visual movement. Perception, 9(4), 379–392. McDonald, J. S., Clifford, C. W.G, Solomon, S. S., Chen, S. C., & Solomon, S. G. (2014). Integration and segregation of multiple motion signals by neurons in area MT of primate. Journal of Neurophysiology, 111(2), 369–378. Nowlan, S. J., & Sejnowski, T. J. (1995). A selection model for motion processing in area MT of primates. The Journal of Neuroscience: The Official Journal of the Society for Neuroscience, 15(2), 1195–1214. Pires, G., Nunes, U., & Castelo-Branco, M. (2011). Statistical spatial filtering for a P300-based BCI: Tests in able-bodied, and patients with cerebral palsy and amyotrophic lateral sclerosis. Journal of Neuroscience Methods, 195(2), 270–281. Pires, G., Nunes, U., & Castelo-Branco, M. (2012). Comparison of a row-column speller vs. a novel lateral single-character speller: Assessment of BCI for severe motor disabled patients. Clinical Neurophysiology: Official Journal of the International Federation of Clinical Neurophysiology, 123(6), 1168–1181. Ploran, E. J., Nelson, S. M., Velanova, K., et al. (2007). Evidence accumulation and the moment of recognition: Dissociating perceptual recognition processes using fMRI. The Journal of Neuroscience: The Official Journal of the Society for Neuroscience, 27(44), 11912–11924. Ray, S., & Maunsell, J. H.R (2011). Different origins of gamma rhythm and high-gamma activity in macaque visual cortex. PLoS Biology, 9(4), e1000610. Rebola, J., Castelhano, J., Ferreira, C., & Castelo-Branco, M. (2012). Functional parcellation of the operculo-insular cortex in perceptual decision making: An fMRI study. Neuropsychologia, 50(14), 3693–3701. Rodriguez, E., George, N., Lachaux, J. P., et al. (1999). Perception’s shadow: Long-distance synchronization of human brain activity. Nature, 397(6718), 430–433. Ruff, D. A., Marrett, S., Heekeren, H. R., Bandettini, P. A., & Ungerleider, L. G. (2010). Complementary roles of systems representing sensory evidence and systems detecting task difficulty during perceptual decision making. Frontiers in Neuroscience, 4, 190. Scheeringa, R., Fries, P., Petersson, K.-M., et al. (2011). Neuronal dynamics underlying high- and low-frequency EEG oscillations contribute independently to the human BOLD signal. Neuron, 69(3), 572–583. Schmidt, K. E., Castelo-Branco, M., Goebel, R., et al. (2006). Pattern motion selectivity in population responses of area 18. The European Journal of Neuroscience, 24(8), 2363–2374. Seger, C. A., & Peterson, E. J. (2013). Categorization ¼ decision making þ generalization. Neuroscience and Biobehavioral Reviews, 37(7), 1187–1200. Shadlen, M. N., & Kiani, R. (2013). Decision making as a window on cognition. Neuron, 80(3), 791–806. Tallon-Baudry, C., & Bertrand, O. (1999). Oscillatory gamma activity in humans and its role in object representation. Trends in Cognitive Sciences, 3(4), 151–162. Teixeira, M., Pires, G., Raimundo, M., et al. (2014). Robust single trial identification of conscious percepts triggered by sensory events of variable saliency. PLoS ONE, 9(1), e86201. Tong, F., Meng, M., & Blake, R. (2006). Neural bases of binocular rivalry. Trends in Cognitive Sciences, 10(11), 502–511. Uhlhaas, P. J., & Singer, W. (2012). Neuronal dynamics and neuropsychiatric disorders: Toward a translational paradigm for dysfunctional large-scale networks. Neuron, 75(6), 963–980. Yamashiro, H., Yamamoto, H., Mano, H., et al. (2014). Activity in early visual areas predicts interindividual differences in binocular rivalry dynamics. Journal of Neurophysiology, 111(6), 1190–1202.