EEG frequency tagging reveals higher order intermodulation components as neural markers of learned holistic shape representations

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EEG frequency tagging reveals higher order intermodulation components as neural markers of learned holistic shape representations ⁎

Mark Vergeera,b, , Naoki Kogoa, Andrey R. Nikolaeva, Nihan Alpa, Veerle Loozena, Brenda Schraepena, Johan Wagemansa a b

Laboratory of Experimental Psychology, Department of Brain & Cognition, KU Leuven, Belgium Department of Psychology, University of Minnesota Twin Cities, USA

A R T I C L E I N F O

A B S T R A C T

No of reviewers = 3

Shape perception is intrinsically holistic: combinations of features give rise to configurations with emergent properties that are different from the sum of the parts. The current study investigated neural markers of holistic shape representations learned by means of categorization training. We used the EEG frequency tagging technique, where two parts of a shape stimulus were 'tagged' by modifying their contrast at different temporal frequencies. Signals from both parts are integrated and, as a result, emergent frequency components (so-called, intermodulation responses, IMs), caused by nonlinear interaction of two frequency signals, are observed in the EEG spectrum. First, participants were trained in 4 sessions to discriminate highly similar, unfamiliar shapes into two categories, defined based on the combination of features. After training, EEG was recorded while frequency-tagged shapes from either the trained or the untrained shape family were presented. For all IMs combined, no learning effects were detected, but post hoc analyses of higher-order IMs revealed stronger occipital and occipito-temporal IMs for both trained and untrained exemplars of the trained shape family as compared to the untrained shape family. In line with recent findings, we suggest that the higher-order IMs may reflect high-level visual computations, like holistic shape categorization, resulting from a cascade of non-linear operations. Higher order frequency responses are relatively low in power, hence results should be interpreted cautiously and future research is needed to confirm these effects. In general, these findings are, to our knowledge, the first to show IMs as a neural correlate of perceptual learning.

Keywords: EEG Frequency tagging SSVEP categorization Shape learning Perceptual organization Gestalt

1. Introduction Human vision involves a multitude of processes of different levels of complexity. Basic features need to be detected and integrated into more complex structures before eventually meaningful object representations can be generated. The notion that complex (mid-level) visual structures generally have a unique perceptual quality that is different from just the sum of the low-level features that they consist of (i.e., “Gestalts”) is at the heart of vision research (see Wagemans and Elder, et al., 2012; Wagemans and Feldman, et al., 2012, for a comprehensive review). According to classical hierarchical frameworks of vision, structural units are defined holistically at higher levels of visual processing, combining the structural units defined at lower levels (e.g., Palmer, 1977). This general idea applies to all kinds of Gestalts with a hierarchical relationship between parts and wholes, from simple dot and line patterns, to shapes, objects, faces and scenes. In this study, we are specifically interested in the neural markers of emerging holistic shape representations. We focus on a particular kind of shape, where the stimulus features defining the differences between exemplars of a

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family of related shapes are usually not perceived as such but as parts of more holistically perceived shapes. The gradual emergence of these holistic shape representations will be investigated through an intensive shape categorization task, while the EEG frequency tagging technique will be applied to investigate neural markers of the emergence of holistic shape representations. To our knowledge, this study is the first to use EEG frequency tagging to reveal changes in neural responses as a function of perceptual learning in general and, more specifically, of the emergence of learned holistic shape representations. Below, some essential background regarding shape perception, categorization, and EEG frequency tagging will be provided to further clarify the purpose of our study and its importance to the field. Shape perception is intrinsically holistic in the sense that combinations of features like dots or short line segments easily give rise to patterns or figures with emergent properties that are different from the sum of the parts (see Pomerantz & Cragin, 2015 for review). Configural superiority effects (Kubilius, Wagemans, & Op de Beeck, 2011; Pomerantz & Portillo, 2011; Pomerantz, Sager, & Stoever, 1977) provide a good case in point. For example, a tilted line segment that bisects

Corresponding author at: Laboratory of Experimental Psychology, Department of Brain & Cognition, KU Leuven, Belgium. E-mail address: [email protected] (M. Vergeer).

https://doi.org/10.1016/j.visres.2018.01.007 Received 31 January 2017; Received in revised form 18 January 2018; Accepted 20 January 2018 0042-6989/ © 2018 Elsevier Ltd. All rights reserved.

Please cite this article as: Vergeer, M., Vision Research (2018), https://doi.org/10.1016/j.visres.2018.01.007

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Single-cell recordings in macaques have shown that training on a shape categorization task enhances single neuron activity in inferior temporal cortex (IT) for diagnostic stimulus features, relative to features that are non-diagnostic of the different shape categories (Sigala & Logothetis, 2002). Neuroimaging has consistently shown neural selectivity in the lateral occipital complex (LOC) in relation to shape categorization (e.g., Gillebert, Op de Beeck, Panis, & Wagemans, 2009; Jiang et al., 2007; Kourtzi, Betts, Sarkheil, & Welchman, 2005; Panis, Vangeneugden, Op de Beeck, & Wagemans, 2008). In addition, it has been well established that areas V4 (Cadieu et al., 2007; Pasupathy & Connor, 2001; Pasupathy & Connor, 2002) and IT (e.g., Brincat & Connor, 2004; Op de Beeck et al., 2001) play a pivotal role in integrating features into shapes. V4, for instance, is sensitive to largescale properties such as concavities and convexities in a shape (Pasupathy & Connor, 2001), while information about multiple contour elements is integrated in IT (Brincat & Connor, 2004). Although the areas involved in shape processing are by now well documented, much less is still known about the exact mechanisms that are involved in integrating basic features into holistic shape representations. The non-additive nature of Gestalts and holistic processing suggests that they emerge from higher-order interactions in the visual system. Disassociating the neural responses to integrated parts (i.e., wholes) from the neural responses to isolated parts is crucial to capture the non-additive nature of holistic processing. Such dissociation is not possible with fMRI or single/multi-unit recording (Boremanse, Norcia, & Rossion, 2013; Norcia, Appelbaum, Ales, Cottereau, & Rossion, 2015); therefore we applied the frequency tagging technique. With the frequency tagging, different parts of a stimulus are modified in a repetitive cycle at specific temporal frequencies, while steady state visually evoked potentials (SSVEPs; Regan, 1966) are extracted from the recorded EEG signal (for a recent review of SSVEP research, see Norcia et al., 2015). Clear peaks in the frequency spectrum can generally be detected at the tagged frequencies and their harmonics. Importantly, if two (or more) image elements or features are tagged with different frequencies (called “fundamental frequencies”), peaks can also be observed not only at harmonic frequencies but also at socalled “intermodulation” (IM) frequencies, which are sums or differences of integer multiples of the tagged frequencies (e.g., f1 + f2, 2f1 − f2). Responses at these IM frequencies can only arise from neurons that nonlinearly combine the signals of the fundamental frequencies (Regan & Regan, 1988; Zemon & Ratliff, 1984). A number of studies have demonstrated that these nonlinear responses of the visual system reflect processes of perceptual integration (Aissani, Cottereau, Dumas, Paradis, & Lorenceau, 2011; Alp, Kogo, Van Belle, Wagemans, & Rossion, 2016; Alp, Nikolaev, Wagemans, & Kogo, 2017; Appelbaum, Wade, Pettet, Vildavski, & Norcia, 2008; Boremanse et al., 2013; Gundlach & Müller, 2013; Victor & Conte, 2000). For instance, it has been shown that the IM response to Vernier stimuli varies depending on the spatial displacement of the individual line segments, relative to one another (Victor & Conte, 2000), indicating that IM responses reflect neural processes involved in early visual integration. Furthermore, a few more recent studies showed that IM responses can provide a signature for holistic integration of stimulus parts into wholes. Two recent studies applied frequency tagging to illusory shapes (Alp et al., 2016; Gundlach & Müller, 2013). Pacman-like elements (inducers) were tagged at different temporal frequencies. Arrangement of the inducers in such a way that they gave rise to the perception of an illusory surface lead to a significantly larger IM response than in control conditions in which no illusory surface was perceived. Boremanse et al. (2013) applied frequency tagging to face images by sinusoidally modulating the contrast of the left and right half of a face with different temporal frequencies and found that ‘normal’ face alignment leads to stronger IM responses compared to when both halves were either vertically misaligned or when there was a small gap between the two halves. For the right occipito-temporal cortex a significantly larger response was obtained for upright faces than for inverted faces, which is pivotal for the authors’ claim that IMs provide a signature for integrating face parts into holistic face representations. Indeed, face inversion effects

a corner formed by a pair of vertical and horizontal line segments has a radically different role than a tilted line segment that connects the end points of the same pair of line segments to form a closed triangle. The perceptual difference between two diagonal line segments oriented left or right is much larger in the context of such arrow and triangle shapes than in isolation. The same is true for contours of more complex shapes formed by combining radial frequency patterns (Wilkinson, Wilson, & Habak, 1998). A series of studies has shown that such shapes are processed globally, based on interactions between different radial frequency mechanisms and multiple curvature mechanisms (e.g., Bell, Wilkinson, Wilson, Loffler, & Badcock, 2009; Dickinson, McGinty, Webster, & Badcock, 2012; Loffler, 2015). Shapes that have a symmetry axis or an axis of elongation are Gestalts in another sense because the local features become parts of an integrated, structural description with the axis providing the reference frame (e.g., Palmer, 1977; Quinlan, 1991). Skeleton representations of shape provide a nice way to capture how local curvature changes along the contour become anchored to local symmetry or elongation axes, that can become branches of a larger axis in a hierarchical representation (e.g., Blum, 1973; Feldman & Singh, 2006). The fact that shapes are spontaneously perceived as integrated wholes or Gestalts corresponds with a holistic view on shape perception, in which shape features are not perceived in isolation and shape dimensions are not perceived analytically. In a more technical sense (Garner, 1974), while color and shape are separable dimensions, the color dimensions of brightness and saturation are integral (i.e., difficult to disentangle perceptually). Although shape dimensions are usually integral as well, the distinction is not clear-cut and fixed; the degree of separability can vary as a function of the specific shapes and shape dimensions, and with experience. For example, the shape dimensions of aspect ratio and axis curvature can be shown to be separable in simple elliptic shapes (Op de Beeck, Wagemans, & Vogels, 2003) and integral in complex shapes (Ons, De Baene, & Wagemans, 2011). In addition, learning and experience have a strong influence on how shapes are represented as well, as will be discussed next. In general, categorization refers to the grouping of related items together into a category, which is usually based on some degree of resemblance between the items. Categorization of objects, for instance, is often based on their resemblance in shape or function, and the refinement with which objects are classified is strongly influenced by experience. For example, biologists will classify animals in a much more refined way than toddlers, who might call all animals with four legs “dog” or “cow”, whichever one they are most familiar with. Shape categorization depends on the perceived similarity between shapes, which implies that learned categorization rules and category boundaries also generalize to new exemplars of a set of related shapes according to their relative similarities and differences (Ashby & Perrin, 1988; Edelman, 1999; Goldstone & Steyvers, 2001). Vice versa, category learning can also affect the perception of shape similarities and differences. Behavioral studies on shape categorization have suggested that the relative distance in the parameter space that captures the shape similarities and differences determines the ease of learning specific categorization rules (e.g., Op de Beeck, Wagemans, & Vogels, 2001; Op de Beeck, Wagemans, & Vogels, 2008). There is also accumulating evidence that categorization training influences the perceived similarity between the trained categories, with exemplars belonging to the same category being perceived as more similar than exemplars belonging to a different category, even when these exemplars are equally distant in the physical parameter space (e.g., Goldstone, Lippa, & Shiffrin, 2001). This means that in the perceptual similarity space, within-category distances are contracted, whereas between-category distances are expanded relative to their physical stimulus differences. In addition, Op de Beeck et al. (2003) have shown that the discriminability of relevant shape dimensions is improved after category learning, relative to irrelevant dimensions, but only when these dimensions are separable. For integral, holistic shape dimensions, in contrast, categorization training does not change the relative sensitivity for relevant versus irrelevant dimensions. 2

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as for the EEG session. Stimuli were constructed in MATLAB (MathWorks Inc., Natick, MA) and tasks were programmed in Python, using PsychoPy libraries (Peirce, 2007; Peirce, 2009).

are often interpreted as a signature of the configural/holistic nature of face perception (e.g., Rossion, 2008). As human beings, we are much more frequently exposed to faces in their upright orientation compared to oblique or inverted orientations. Hence, one could in fact interpret the face inversion effect reported by Boremanse et al. (2013) as an indication that the strength of intermodulation responses might to some extent be linked to our experiences of seeing faces in their upright orientation. Inspired by the previous series of studies linking emergent IM frequency components to holistic perception, the current study investigated explicitly the gradual emergence of holistic shape representations due to shape categorization and established IM responses as a neural signature of the integration of shape features into holistic shape representations after learning. Participants were trained on a shape categorization task for which families of Fourier Boundary Descriptors (FBDs) were created (Cortese & Dyre, 1996; Zahn & Roskies, 1972). As demonstrated by Op de Beeck et al. (2003), these FBDs can only be discriminated and categorized correctly based on holistic representations of the shapes, within which the features are integrated strongly (rather than by analyzing the local features in isolation). The idea is that with extended training, the shape stimuli trigger refined shape representations that might reveal expertise effects akin to those observed much more strongly for face perception (due to its much longer developmental history and much larger behavioral relevance). Therefore, these stimuli are particularly suitable for investigating SSVEP responses to holistic visual integration after learning. As IM responses have previously been shown to be stronger when holistic integration occurs, we predict that the optimization of the participants’ shape representation due to training on shape categorization will lead to an increased IM response to trained shapes and that this learning effect will generalize to novel shapes within the same parameter space. Such generalization is a hallmark of categorization learning instead of learning multiple exemplars. The predicted increased strength of integration would be reflected by a stronger IM response to trained and untrained exemplars of this trained shape family relative to IM responses to exemplars of an untrained family of shapes.

2.3. Stimuli Fourier Boundary Descriptors (FBDs) were created in MATLAB by combining seven sinusoidal radial frequency components (i.e., RFCs). RFCs with different frequencies, amplitudes and phases created smooth deviations in closed contours, their combination resulting in different FBDs. These mathematically defined shapes will not be perceptually decomposed into RFCs when processed in the visual system, and thus they are processed in an integral way (Op de Beeck et al., 2003). The frequencies of the seven sinusoids were 2 to 8 cycles/circle with 1 cycle increment drawn along a circle whose diameter was 7.1° visual angle. Two sets of shapes were created that from here on will be referred to as “shape families”. Within each shape family, the frequencies of the RFCs as well as their phases were equal for all shapes. Different shapes were created though by varying the amplitude of two of the seven RFCs while the amplitudes of the other five RFCs were kept constant. In Family 1, the first variable RFC had a radial frequency of 2 cycles/circle and amplitude that varied from 0.28° to 0.66° of visual angle, with a constant step size between shapes of 0.0635°. The second variable RFC had a radial frequency of 3 cycles/circle, with the same amplitude range as the first variable RFC. In Family 2, the variable RFCs had radial frequencies of 5 and 7 cycles/ circle, respectively. For both frequencies, the amplitude ranged from 0.33° to 0.50°, with a constant step size between shapes of 0.0276°. Again, the parameters of the five remaining RFCs were the same for all exemplars within the family. All parameters are shown in Table 1. In total, each family consisted of 49 exemplars in a two-dimensional (7 × 7) parameter space. Out of these 49 exemplars, only the even numbered ones were used in the experiment (Fig. 1). Consequently, all of the individual exemplars had an equal distance to their adjacent neighbors within the parameter space of the entire family, while no exemplars were situated exactly on the diagonal categorization borders (which will be explained in the next paragraph). Fig. 1 shows the parameter space that was used for both families. Although all exemplars may look very similar to the untrained eye, at the actual size and resolution, and after learning, they could be differentiated reasonable well (see Behavioral results).

2. Methods 2.1. Participants 16 healthy adults took part in the experiment (14 female, age range: 19–35, mean age: 23.6). Participants received course credits or were paid for their participation. All participants gave their informed consent preceding the experiment. The study was approved by the ethical committee of the Faculty of Psychology and Educational Sciences of KU Leuven. This work was carried out in accordance with the Code of Ethics of the World Medical Association (Declaration of Helsinki).

2.4. Task and procedure The entire experiment consisted of five sessions. In the first four sessions, the participants were trained on a categorization task. The EEG recordings, as well as behavioral post-training measurements, took place in the fifth session. All five sessions were held in the same dark, soundproof room. Participants were trained on a categorization task on one of the two families (i.e., the trained family). Trained families were counterbalanced across participants. A diagonal categorization border divided the parameter space of each family into two categories, 12 exemplars in each category (Fig. 1). From each category, half of the exemplars were

2.2. Apparatus Tasks were run on a Windows 7 computer (Dell Precision T1650, Intel core i3). Stimuli were presented on a black LCD screen, with a resolution of 1600x900 pixels and a refresh rate of 60 Hz, at a viewing distance of 57 cm. The same setup was used for the behavioral training

Table 1 RFC parameters for both families of shapes. The values printed in bold indicate the parameters that were variable within a particular family of shapes. These values were varied in steps with size indicated in the most right column of the table. Frequency (cycles/circle)

2

3

4

5

6

7

8

Increment steps

Family 1 Amplitude (deg) Phase (radian)

0.28–0.66 −.94

0.28–0.66 1.53

0.50 −2.2

0.50 −2.37

0.28 −1.54

0.39 −0.33

0.28 2.97

0.0635

Family 2 Amplitude (deg) Phase (radian)

0.44 −1.29

0.44 0.65

0.28 −0.24

0.33–0.50 0.26

0.50 0.6

0.33–0.50 −0.79

0.28 −1.61

0.0276

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Fig. 1. The shape families that were used in the experiment. The diagonal lines separate the two categories within each family. Participants were trained on categorizing the individual shapes (i.e., associating the shapes on each side of the diagonal borderline with the correct response button) for one of the families.

than one of the shapes at the same time. They were instructed to infer the categorization rule through trial and error and to try to keep improving their categorization performance throughout training. Each trial was followed by an inter-trial interval of 300 ms. All four training sessions consisted of five blocks of 120 trials each with a short self-paced break separating the blocks. Each training session lasted approximately 30 min and sessions were spread out over two to four days, with at least four hours in-between each two training sessions. In the fifth session, EEG was recorded and the behavioral posttraining measurement followed immediately after the EEG session. The trial procedure in the post-training session was identical to the one in the training sessions, with the same stimulus and mask duration. The purpose of this final session was to test whether transfer of learning had occurred from the trained exemplars to the untrained exemplars of the trained family, and/or to the untrained family. In the post-training behavioral measurements, the participants performed six blocks in total, each containing 96 trials. In the first two blocks, participants categorized trained exemplars, while in the subsequent two blocks they categorized untrained exemplars of the same family according to the same categorization rule. In the final two blocks, participants had to categorize exemplars from the untrained family. The duration of the post-training test was approximately half an hour. EEG frequency tagging. Shapes from all three conditions (TE, UE, and UF) were used in the EEG session. Again, they were presented on a black background, but now with a blue fixation cross superimposed at the center of the shape (for reasons that will become clear below). However, now frequency tagging was applied to the surface of the shapes to evoke neural responses related to the temporal frequencies that the shapes were tagged with. Tagging involved sinusoidal modulation of the intensity (i.e., the pixel luminance) of the left and right half of the shape’s surface by two different frequencies, between zero (black) and the luminance values of the full range noise pattern. The amplitudes of the intensity modulation decreased linearly towards the midline of the shape, with no modulation at all in the middle. Hence, at the midline the random noise surface was continuously presented at 50% intensity (see Supplemental Movie 1, for an example of a trial). In this way, a gradual “transition region” was established that prevented perceiving a contour between the two halves of the shapes due to the different temporal frequencies of modulation. Several frequency combinations were tested in an EEG piloting study. Eventually the frequencies 7.50 Hz (f1) and 5.45 Hz (f2) were selected as tagging frequencies for the EEG experiment, as they gave the most robust SSVEP responses across participants. Both of these frequencies allowed for an

used for training, from here on referred to as trained exemplars (TE). The remaining 12 exemplars of the same family, from here on referred to as untrained exemplars (UE), were only used during EEG recording and behavioral post-training measurement to test the generalizability of the learning effects. In addition, half of the exemplars (six from each category) of the untrained family (UF) were used during EEG recording and behavioral post-training baseline measurement. Hence, for each of 3 experimental conditions (TE, UE, and UF) 12 exemplars were used per participant. Trained families, trained exemplars of the trained family and the used exemplars of the untrained family were counterbalanced across participants. Different diagonal categorization rules were applied to the two families. For Family 1, one category was defined by low amplitudes on both variable frequencies (the top left half of exemplars in Fig. 1), while the other category was defined by high amplitudes on both frequencies (the lower right half of exemplars in Fig. 1). For Family 2, one category consisted of the stimuli with high amplitude on one variable frequency and low amplitude on the other variable frequency, and vice versa for the other category. In other words, the boundaries separating the two categories were orthogonal for the two families, as indicated in Fig. 1. The diagonal categorization rules that were applied for both families forced the participants to rely on combinations of features to successfully perform the task. Visual inspection of the behavioral pilot data revealed that the overall task difficulty was similar for the two tested families. Behavioral training. In the training stage, participants trained on categorizing exemplars into two categories. In each trial, one exemplar was displayed on a black background for 500 ms. The surface of each shape was a random noise texture, while a white fixation cross was superimposed on the center of the shape. In addition, the orientation of the shape was jittered at a random angle between −10 and 10 degrees while the presented location was jittered in a random direction from the center of the screen, at a random distance between 0 and 2.76° of visual angle from the center of the screen. The jittering was applied to encourage participants to perceive the figure as a whole instead of using strategies based on local details. Each stimulus presentation was followed by a 32 × 32 square noise mask comprising 11.04° of visual angle, which was presented for 50 ms. Next, participants were asked to indicate with a binary keyboard response to which category they thought the exemplar belonged. A green or a red ring presented after the response for 500 ms indicated whether the response was correct or not, respectively. Participants did not receive any information in advance regarding the parameter space nor regarding the border defining the shape categories. In addition, throughout the experiment participants never saw more 4

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2.5. EEG recordings

integer number of frames per cycle on the 60 Hz refresh rate of the screen. Which side of the stimulus was tagged with which frequency was counterbalanced across trials, within each block.

EEG was recorded using a HydroCel Geodesic Sensor Net with 256 electrodes (Electrical Geodesics Inc, Eugene, OR, USA). Impedance was kept below 50 kΩ. The vertex Cz electrode was used as a reference. The EEG was sampled at 250 Hz. All channels were preprocessed on-line using 0.1 Hz high-pass and 100 Hz low-pass filters. 2.6. EEG analysis EEG data were analyzed with BrainVision Analyzer (Brain Products GmbH, Gilching, Germany) and MATLAB. The EEG was filtered by applying a Butterworth band-pass filter with a low cut-off frequency of 0.53 Hz and a high cut-off frequency of 45 Hz. The EEG was segmented into 11.76-s epochs, starting from 500 ms after trial onset to reduce the influence of the evoked responses to stimulus onset. The epoch length of 11.76 s corresponded to the integer number of cycles for the lowest frequency of interest (0.68 Hz). We excluded epochs if the absolute voltage difference exceeded 50 μV between two neighboring sampling points and if the amplitude difference within an interval of 100 ms was outside ± 100 μV, and if the amplitude was lower than 0.5 μV during more than 100 ms, in any channel. On average, 1.4% of trials per participant was rejected because of artifacts. EEG segments were averaged for each condition and participant separately. We selected three clusters of electrodes based on the visual nature of the task and previous studies which indicated involvement of temporal, parietal and occipital areas in shape categorization and integrating features into shapes (Brincat & Connor, 2004; Cadieu et al., 2007; Gillebert et al., 2009; Jiang et al., 2007; Kourtzi et al., 2005; Op de Beeck et al., 2001; Panis et al., 2008; Pasupathy & Connor, 2001; Pasupathy & Connor, 2002). The three clusters of electrodes were defined around the landmark electrodes of the International 10–20 System of Electrode Placement: occipital (O1, O2), occipito-temporal (P7, P8), and parietal (P3, P4). For each cluster, one central and six surrounding electrodes were combined relative to the left and right landmark electrodes. The Fast Fourier Transform (FFT) was applied to obtain the amplitude spectrum with a resolution of 0.085 (1/11.76) Hz after applying a Hanning window of 10% of the segment length. Next, the amplitude spectra were averaged across channels for each condition and participant separately. This was done separately for three electrode clusters. Fig. 2shows the grand average power spectrum of a representative participant.

Movie 1. During each trial of an EEG recording, the tagged shape was presented for 13 s. The participants’ task was to press the space bar as soon as the fixation cross color changed from blue to red, which occurred between zero and three times per trial for 100 ms before changing back to its original blue color. The goal of this task was to make sure that participants kept eye fixation throughout the recording. Each stimulus presentation was followed by a brief random noise mask for 50 ms and a second shape for 500 ms from the same shape family as the previously presented shape. Participants had to indicate if both shapes were exactly the same or slightly different from each other. The goal of this task was to make sure that participants carefully processed the shapes during the 13-s EEG trial. A green or red ring indicated whether a correct or an incorrect response was given, respectively. An inter-trialinterval of 5.5 s gave participants the opportunity to rest and make eye blinks. An EEG session consisted of two blocks of 36 trials each. In each block, 12 trials from each of the three conditions (TE, UE, and UF) were presented in a randomized order.

Fig. 2. Power spectrum of a representative subject after averaging across all trials. The predominant responses are labeled with the frequency combinations they represent, with different colors for different frequency types.

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Signal-to-noise ratios (SNRs) were computed by dividing the amplitude spectrum value at each bin by the average amplitude value of five neighboring bins on both sides, excluding the immediately neighboring bins (i.e., 5 + 5 − 2 = 8 bins in total) (Srinivasan, Russell, Edelman, & Tononi, 1999). Similarly, we computed z-scores by taking the difference between the amplitude of the FFT value at a certain bin and the mean amplitude of five frequency bins on each side (skipping one bin adjacent to the bin of interest) and then dividing this difference by the standard deviation of the same surrounding bins. In further analyses, we considered only frequencies that passed a threshold of 99% on z-scores for the averaged FFT of all trials across conditions and participants (Z = 2.57, p < .01, one-tailed: signal > noise). Statistical analyses were performed on SNR values computed separately for each participant, condition, and frequency of interest (i.e., the tagged, fundamental frequencies, their harmonics, and IMs). Pairwise comparisons were done between conditions (TE, UE, and UF) for different frequency types (fundamentals, harmonics, and IMs), for different cortical regions (occipital, occipito-temporal and parietal).

Fig. 4. Mean performance in the behavioral post-training test for trained exemplars (TE), untrained exemplars (UE), and untrained family (UF). Bars represent the mean performance on 2 blocks across participants, whereas the dashed lines indicate pre- and posttraining baselines, measured as mean performance on the first 2 blocks and on the last 2 blocks of training, respectively.

3. Results 3.1. Behavioral results Behavioral performance on the categorization task indicated that training yielded improved performance on the task (Fig. 3). Linear regression slopes were computed for each participant. Single sample ttests on these regression slopes show that categorization performance was significantly improved after training (t(15) = 5.2, p < .001).

categorization rule (i.e., the separation of two subsets of similar shapes) for the full shape parameter space of the trained family (i.e., the complete set of relevant shape differences and similarities) instead of having just extracted the proper mapping rule for the trained exemplars of the trained family. The lack of transfer to the untrained family shows that the learning effect is specific to the trained shape family and does not simply reflect task learning.

3.2. Behavioral post-training results After EEG recording we measured categorization performance for each of the 3 conditions. Mean categorization performance of 2 blocks for each condition is plotted in Fig. 4. Performance on the trained exemplars was similar to performance during the final 2 blocks of training (81.5 vs. 80.0% correct) and did not differ significantly. For the untrained exemplars of the trained family we computed the transfer index as [(post EEG performance UE – pre-training performance)/(post EEG performance TE – pre-training performance)], whereas for the untrained family we computed this index as [(post EEG performance UF – pre-training performance)/(post EEG performance TE – pre-training performance)]. There was a significant transfer of categorization learning from the TE to the UE condition of 71% (t(15) = 6.08, p < .0001), while the transfer to the UF condition of 15.2% was not significant. The transfer of the learning effect to the untrained exemplars of the trained family indicates that participants had learned the

3.3. EEG results We hypothesized that training of novel holistic shape representations would be reflected by an increased SNR at intermodulation frequencies (IMs). In other words, we expected higher signal-to-noise ratios at IMs for Trained and Untrained Exemplars of the Trained Family than for the Untrained Family. Fig. 5 shows the average SNR at all frequencies that had survived thresholding based on their z-scores. For occipital and for occipito-temporal channels, the same frequencies survived thresholding for the different types of frequency (fundamentals: 5.45 and 7.5 Hz; harmonics: 10.91, 15, 16.36, 21.81, 22.5, 27.27, and 30 Hz; IMs: 2.05, 3.41, 4.10, 8.18, 8.86, 9.55, 11.59, 12.95, 14.32, 17.05, 17.72, 18.41, 19.09, 19.77, 20.45, 23.86, 24.54, 25.23, 25.91, and 27.95 Hz). For the parietal channels, fewer frequencies survived thresholding (fundamentals: 5.45 and 7.5 Hz; harmonics: 10.91, 15, 16.36, 22.5, and 30 Hz; IMs: 2.05, 3.41, 4.09, 9.55, 12.95, 17.05, 18.41, and 20.45 Hz). For each type of frequency (fundamentals, harmonics, and IMs) we compared the differences in SNR (1) between trained exemplars (TE) and exemplars of the untrained family (UF), (2) between trained exemplars (TE) and untrained exemplars of the trained family (UE), and (3) between untrained exemplars of the trained family (UE) and exemplars of the untrained family (UF). For none of the selected clusters (occipital, occipito-temporal, and parietal) these comparisons revealed significant differences in SNRs, for any of the frequency types. The results of a recent study of our group has suggested that different orders of IMs might be associated with different levels and complexity of the interactions taking place during information processing along the visual system hierarchy (Alp et al., 2017). As categorization is a higher-level visual task, in the following post hoc analysis we separated IMs into three groups basing on the order of interaction between the fundamental frequencies: 2nd order (f1 − f2, f1 + f2), 3rd order (2f2 − f1, 2f1 − f2, f1 + 2f2, 2f1 + f2), and higher-order (4th, 5th, 6th, 7th, and 8th order; 3f2 − f1, f1 + 3f2, 2f1 + 2f2, 2f1 − 2f2, 3f1 − f2,

Fig. 3. Behavioral training results on the categorization task averaged across all 16 participants. Data points and error bars represent mean ± SEM. The dashed line indicates baseline performance measured by averaging performance in the first 2 blocks.

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Fig. 5. Mean signal-to-noise ratios (SNRs) for three learning conditions (TE, UE, and UF) at fundamental frequencies (F), harmonics (H) and intermodulation frequencies (IM) that survived the z-score thresholding. Means are plotted for (A) occipital channels; (B) occipito-temporal channels; (C) parietal channels. Bars and error bars represent means ± SEM.

3f1 + f2, 3f1 − 2f2, 4f2 − f1, 4f1 − f2, 4f1 − 2f2, 5f2 − f1, 6f2 − f1, 4f1 − 4f2, 6f2 − 2f1). Again, SNRs were compared (1) between trained exemplars (TE) and exemplars of the untrained family (UF), (2) between trained exemplars (TE) and untrained exemplars of the trained family (UE), and (3) between untrained exemplars of the trained family (UE) and exemplars of the untrained family (UF). For the occipital cluster, SNR comparisons for 2nd and 3rd order IMs did not reveal any significant differences. However, significant training effects were found on SNRs for the higher-order IMs for both the trained exemplars (t (15) = 3.44, p < .005), as for the untrained exemplars of the trained family (t(15) = 2.57, p < .05), both compared with the untrained family (Fig. 6A). For the occipito-temporal cluster, the SNR comparisons at different orders of IMs gave similar results as for occipital channels. SNR comparisons for 2nd and 3rd order IMs did not reveal any significant differences, but again significant training effects were found on

SNRs for the higher-order IMs for both the trained exemplars (t (15) = 2.15, p < .05), and for the untrained exemplars of the trained family (t(15) = 2.43, p < .05), both compared with the untrained family (Fig. 6B). A similar analysis for parietal channels did not reveal significant differences between conditions. To further explore the effects of categorization training on SSVEP responses, we have plotted scalp topographies of SNR for the different frequency types averaged across conditions (Fig. 7). As shown in Panel A, all frequency types show the strongest response over the occipital regions. Interestingly, for IM frequencies, the region of maximum SNR narrows from lower to higher order IMs. Note that the narrowing of the maximum SNR regions with IM order cannot be explained by the simple decrease of SNR/power with IM order because Fig. 7A indicates that: 1) SNR for Fundamentals is more than two times higher than for the 2nd order IMs but the region is larger for the 2nd order IMs than for

Fig. 6. SNRs for different orders of intermodulation for trained exemplars (TE), untrained exemplars of the trained family (UE) and exemplars of the untrained family (UF) for occipital channels (A) and occipito-temporal channels (B). Bars and error bars represent means ± SEM. The asterisks indicate conditions with significantly higher SNRs than the untrained family (**p < .005, *p < .05).

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Fig. 7. Scalp topographies. A) SNR averaged across observers and conditions for each frequency type. Note that scaling varies between frequency types. B) Normalized SNR at higher order IMs for trained exemplars and untrained exemplars of the trained family, and for the untrained family. Normalization was done by dividing the SNR at an electrode by the mean SNR across electrodes and conditions.

results indicates categorization learning that is sufficiently refined to make possible the detection of the similarities and differences within a highly familiar set of shapes, while at the same time being specific to the set of shape parameters within which categorization was trained. The stimuli and task design forced the participants to rely on combinations of features for optimal performance. Therefore, the behavioral results indicate that categorization training leads to improved configural or holistic representations of the shapes, with the features being both better integrated and more refined. In addition, categorization training may have led to a better representation of the perceptual similarities and differences within the shape space (i.e., of the family structure), and also to a better mapping of exemplars to categories at the response level. The EEG frequency tagging paradigm aimed at investigating neural markers of the emergence of holistic shape representations. Several recent studies have all in their own specific way linked the strength of perceptual integration with larger intermodulation (IM) responses (Aissani et al., 2011; Alp et al., 2016; Alp et al., 2017; Appelbaum et al., 2008; Boremanse et al., 2013; Gundlach & Müller, 2013). These observations indicate that the IM response can reflect the degree to which the tagged stimulus is processed holistically. Based on these findings we hypothesized that improving holistic processing of shapes through categorization training would be reflected by stronger IM responses. However, the analysis of IM responses averaged across all frequencies did not confirm this hypothesis: no significant differences were found between IM responses for exemplars of the trained family compared to members of the untrained family, neither for occipital nor for parietal channels. Whereas previous studies generally did not consider IM responses of different order separately, such a separation could provide valuable indicators of underlying neural operations. In particular, results from a recent study of our group suggest that the non-linearity of neural interactions may increase with propagation of neural signals through the visual system’s hierarchy (Alp et al., 2017). It was found that 2nd order IM components reflect the mid-level factor of motion synchrony of multiple point-light walkers, whereas 3rd order IM components are sensitive to the high-level factor of human quality of the same point-

Fundamentals; 2) the regions for Harmonics and the higher-order IMs are about the same size but the SNR for Harmonics is five times higher than for the higher-order IMs. Panel B looks at SNR responses at higher order IMs for the three experimental conditions. Normalized SNRs at higher order IMs were computed by dividing the SNR at a channel for a certain condition by the mean SNR across channels and conditions. These topographies show increased occipital response, most visible for the trained exemplars, followed by the untrained exemplars of the trained family. For the untrained family only a weak IM response can be observed, consistent with our result that SNR at higher order IMs is larger for both trained and untrained exemplars of the trained family than for exemplars of the untrained family. Next, we computed correlations between the behavioral learning effects and the higher order IM learning effects at both occipital and occipito-temporal areas. The latter were computed by dividing the SNR for the trained family by the SNR for the untrained family, separately for trained and untrained exemplars of the trained family. None of the performed correlations between behavioral and EEG learning effects turned out significant. Higher order IM effects of training (trained family vs. untrained family) at occipital areas were correlated with higher order IM effects of training at occipito-temporal areas, revealing a moderate positive correlation of .46 for trained exemplars (t (14) = 1.92, p = .08) and a strong positive correlation of .68 for the untrained exemplars of the trained family (t(14) = 3.50, p < .005). 4. Discussion The purpose of this study was to investigate neural markers of the emergence of holistic shape representations using EEG frequency tagging. The behavioral data show that performance on the shape categorization task gradually improved over 20 blocks of training. This result is in line with what is generally found in shape categorization studies (e.g., Op de Beeck et al., 2003; Op de Beeck et al., 2008). In addition, behavioral post training data show that the improved categorization performance generalizes to untrained exemplars of the trained shape family, whereas no transfer was found to categorization performance for exemplars of the untrained family. This pattern of 8

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light walkers. These findings suggest that there might be a relation between the order of IM responses and the level of visual processing within the cortical hierarchy. IM responses result from non-linear neural interactions (Ratliff & Zemon, 1982; Zemon & Ratliff, 1982), so the neural interactions at the higher levels of the visual system’s hierarchy are probably reflected in higher-order IM responses than the neuronal interactions at lower levels, which are probably reflected in lower-order IM responses. In other words, a cascade of non-linear operations may result in the emergence of higher-order visual processes and correspondingly higher-order IM responses. Therefore, considering the higher-level nature of holistic shape integration and categorization, in the current study we decided to look at 2nd and 3rd, and higherorder (4th order and higher) IMs separately. Whereas for 2nd and 3rd order IMs no significant differences between conditions were found, for the higher-order IMs, SNRs were significantly larger for trained exemplars than for the untrained family, and for the untrained exemplars of the trained family compared to the untrained family, for occipital channels as well as for occipito-temporal channels. The learning effects at higher order IM frequencies at occipital and occipito-temporal channels correlated with one another, which suggests that the EEG learning effects obtained at both regions indicate the same or related underlying cortical processes. Although no significant correlations were found between the behavioral and the EEG learning effects, this pattern of results (Fig. 6, right) nicely matches the behavioral effects (Fig. 4). The scalp topographies show a clear central occipital origin for the effects we report here: Trained exemplars show the strongest higher order IM response, it is smaller for untrained exemplars, and is almost absent for the untrained family (Fig. 7B). In addition, the mean SNR across conditions shows a narrowing of response tuning from lower to higher order IMs (Fig. 7A). This observation is in line with the idea that increased specialization leads to narrower neural tuning (e.g., Johnson, 2011). The modulation of the higher-order IM responses that we report here may reflect the complexity of shape integration required to successfully perform the categorization task involving high-level visual computations. However, as these results were obtained with post hoc analyses, and as the relation between the order of visual computation and the order of IM responses is not yet well established in the literature, we maintain the appropriate reservations with regard to this conclusion. Future research should further confirm the relation between the order of cortical processing and the order of IM responses, for instance, by systematically contrasting IM effects on different levels of visual computations (as established by other methods). This approach can then prove to become essential in developing both general computational models and theories on vision. In sum, the findings presented here are, to our knowledge, the first to show IMs as a neural correlate of perceptual learning in general and, more specifically, of the emergence of holistic shape representations after category learning. Increased IM responses to trained shape categories were only found for higher-order IM frequencies, suggesting a relation between the level of visual processing and the order of IMs, where higher-order IMs reflect the outcome of consecutive non-linear operations in visual cortical hierarchy. Future research will have to provide additional evidence for and further insights into this suggested relation.

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Acknowledgments This research was funded by the Flemish government (FWO Pegasus Marie Curie Fellowship 1212513N, by the Flemish government and the European Union, awarded to MV, FWO post-doc fellowship 12L5112L awarded to NK, FWO PhD fellowship 11Q7314N, awarded to NA, and long-term structural funding (METH/14/02) awarded to JW). AN was supported by an Odysseus grant from FWO, awarded to Cees van Leeuwen. In addition, we thank Bart Machilsen for his help in generating the stimuli. 9

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