This work is based on research supported by the European Union FET Integrated Project PRESENCCIA (Contract number 27731).

<img alt="Figure 1" fo:content-width="4.5in" fo:src="/files/ftp_upload/4375/4375fig1highres.jpg" src="/files/ftp_upload/4375/4375fig1.jpg" /> 
Figure 1. Illustration of the non-linear relationship between the experience of negative and positive affect (valence) and perceived human likeness. The otherwise positive relationship shows a sharp negative peak (i.e. uncanny valley) at the level of realism between the first and second positives peaks of the depicted curve at which subtle differences in the appearance and behavior of a highly realistic yet discernibly unnatural humanlike object is suggested to elicit a sense of strangeness and personal discomfort (i.e. an uncanny feeling). Illustration adapted from 2.
We used different groups of participants for each of the following tasks.
1. Forced Choice Classification Task
1.1 Stimuli
<ol>
	<li>Use avatar and human images as parent faces (i.e. continua endpoints) in the morphing procedure to produce linear morph continua to represent the DHL. We created 32 human-avatar continua using 32 images of human and avatar faces, respectively. Generate avatars using the modeling suite Poser 7 (Smith Micro Software, <a href="http://www.smithmicro.com" target="_blank"> www.smithmicro.com</a>), though other software is available. We generated these morph continua using Funmorpher (Zealsoft Inc., Eden Prairie, MN), but other morphing software may be used.</li>
	<li>Using the morph software, place control points on the corresponding features of the parent faces. For each face, we placed 20 points on the mouth, 18 points on each eye, 20 points on the nose, and 8 points on each eyebrow. We thus used around 100 control points. Try to keep the number of control points constant, but add further points to eliminate any artefacts in the final morphs of the continua.</li>
	<li>Ensure potential confounds are not introduced into the morphing procedure. For example, we used as endpoints of each continuum images of unknown indistinctive male faces with neutral expression, direct gaze and no other salient features such as facial hair or jewelery, and the endpoint images were closely matched for age, configural cues and general facial geometry.</li>
	<li>Use photo editing software to crop external features by using for example a black overlay in elliptic form; we used Adobe; Photoshop; CS3 (<a href="http://www.adobe.com" target="_blank">www.adobe.com</a>). Before morphing, adjust the position of the images to ensure alignment between the endpoint images of configural cues, and adjust contrast levels, overall brightness and skin tone of each pair of endpoint stimuli of each continuum to match.</li>
	<li>Each morph of a DHL continuum represents a difference in physical humanlike similarity at predefined increments. We generated 13 different morphed images and labeled these M0 to M12, that is, the two endpoints and 11 intermediate morphs (Figure 2B).</li>
</ol>
1.2 Stimulus presentation and instructions
<ol>
	<li>Use a two-alternative forced choice classification task to determine which of these morphs are clearly categorized as avatars and as human and to define the position of the category boundary 8.</li>
	<li>Present trials beginning with a fixation point for 500 msec (participants are required to maintain fixation) followed by a morph image for 750 msec. We used Presentation; software (Version 14.1, <a href="http://www.neurobs.com" target="_blank">www.neurobs.com</a>) for stimulus presentation in all tasks in this protocol, but other stimulus presentation platforms may be used.</li>
	<li>Instruct the participant to identify the presented morph stimulus as either an avatar or human as quickly and precisely as possible by pressing one of two response keys.</li>
</ol>
1.3 Data Analysis
Summarize the avatar-human classification data using polynomial regression to describe the shape of the response function. Determine this by fitting logistic function models to the response data of each participant and continuum. First, analyze the individual continua across participants to ensure best fit of logistic functions. Then, test against zero in a one-sample t-test for a step-like shape in the avatar-human category response function across all continua using the parameter estimates derived from the logistic function of each continuum, averaged across participants. Estimate the position of the category boundary along each continuum by submitting the parameter estimates of the logistic function of each continuum to a logit transformation 9. We performed all analyses for the forced choice classification and perceptual discrimination tasks using SPSS Version 16 (<a href="http://www.ibm.com/software/analytics/spss" target="_blank">www.ibm.com/software/analytics/spss</a>).
Response time (RT) data may also be analysed. In the present analysis, differences in response times depending on morph position are entered in a one factorial ANOVA, with 13 morph positions, using the mean RT of each individual across all continua as dependent variable.
<img alt="Figure 2" fo:content-width="4in" fo:src="/files/ftp_upload/4375/4375fig2highres.jpg" src="/files/ftp_upload/4375/4375fig2.jpg" /> 
Figure 2. Results from the forced choice categorisation task (A) and an example of a morph continuum (B). In panel B, the relative degree of linear physical transition along the 13 morph-continuum between the avatar and human endpoints is shown as a percentage. M0 and M4 were identified as avatars and M8 and M12 as human in the forced choice classification task, as shown in panel A.
2. Perceptual Discrimination Task
2.1 Stimuli
<ol>
	<li>For this version of the same-different perceptual discrimination task 10, select from each morph continuum two morphs categorized in the preceding classification task as avatars (e.g. M0 and M4) and two as human (e.g. M8 and M12). To control for physical differences between morphs, select morphs that represent equivalent increments of physical change along each continuum. We used increments of 33.33% (i.e. M0, M4, M8, M12) (Figure 2B).</li>
</ol>
<img alt="Figure 3" fo:content-width="4in" fo:src="/files/ftp_upload/4375/4375fig3highres.jpg" src="/files/ftp_upload/4375/4375fig3.jpg" /> 
Figure 3. Stimulus conditions for the "same-different" perceptual discrimination task (N = 20). Morphs are selected to form pairs. The morphs of a pair are drawn from within the same category ("within"), are identical ("same"), or they show a change in category between them ("between"). The morphs M0, M4, and M8 are used for avatar trials (A) and M4, M8, and M12 for human trials (B). Note that the first morph of a morph pair in avatar trials is always M4 and in human trials M8 and that avatar and human trials are based on morphs drawn from different continua.
<ol start="2">
	<li>Sort the selected morphs into pairs according to the three experimental morph-pair conditions (Figure 3): "same" (the morphs of a pair are identical, representing no physical or category change), "within" (the morphs of a pair are drawn from within a category), and "between" (the morphs of a pair represent different categories).</li>
	<li>To investigate discrimination performance between the morphs of the morph pairs in relation to the avatar category (these morph pairs are thus termed "avatar trials") ensure that the first morph of each morph pair in the three conditions is always M4 (from the avatar category) (Figure 3A). This results in morph pairs M4 - M4 for the "same", M4 - M0 for the "within" and M4 - M8 for the "between" conditions. The same procedure can be applied for morph pairs in relation to the human category (thus termed "human trials"), ensuring that the first morph is always M8: "same" (M8 - M8), "within" (M8 - M12), and "between" (M8 - M4) (Figure 3B).</li>
	<li>Always ensure that both morphs of a morph pair are drawn from the same continuum in which they were originally morphed. Pseudo-randomize presentation of morph pairs so that no pairs from within the same continuum are shown in close sequence. Presentation of avatar or human trials from a given continuum is random but counterbalanced across all participants to ensure that each participant views either avatar or human trials from any given continuum but not both, and that an equal number of avatar or human trials are viewed.</li>
</ol>
2.2 Presentation and instructions
<ol>
	<li>Present a fixation cross for 500 msec (participants are required to maintain fixation) followed by each face of a face pair for 500 msec with an inter stimulus interval (ISI) of 300 msec between the faces of a pair. We also used an ISI of 75 msec to verify whether different durations of ISI would differentially influence discrimination performance. Present a variable inter-trial interval between trials of morph pairs: we used an interval averaging 2,500 msec.</li>
	<li>Instruct participants to view each trial comprising a morph pair, the morphs being presented successively in the trial, and to indicate by button press as quickly and precisely as possible whether the faces of each face pair are the 'same' or 'different' in appearance.</li>
</ol>
2.3 Data Analysis
Discrimination accuracy is analysed for face pairs that cross the category boundary compared with face pairs from the same side of the boundary. For this, the 'different' responses (indicating that both faces of a pair are of different physical appearance) are computed as proportions of the total number of morph face pairs and subjected to a 2 X 3 factorial ANOVA, with 3 "face-pair trial types" (within, between, same) and 2 "ISI" conditions (75 msec, 300 msec). Greenhouse-Geisser adjustment is used when the assumption of sphericity is violated. The data for avatar trials and human trials are treated separately in analysis.
Individual accuracy scores may also be determined using the A' statistic 47,79 (for Signal Detection Theory, see, e.g. 45, 46, 47). A' provides a measure of discrimination sensitivity that is independent of response bias. It varies between 0.5 (chance) and 1 (perfect discrimination). Various software packages may be used to compute A' and other measures of discrimination sensitivity (and bias) 46, 47, 48 49, 50. We analysed discrimination sensitivity using a 2 X 2 repeated measures ANOVA, with 2 "face-pair trial types" (within, between) and "ISI" conditions (75 msec, 300 msec), with separate analyses for avatar trials and human trials, and A' as the dependent variable. Response bias is not often generally reported, but see 38. For response bias, we used the &beta;"D statistic 47 as the dependent variable in a separate analysis using otherwise the same 2 X 2 ANOVA design.
RT data may also be analysed for "different", "same" and "between" responses. In this example, we compare the "different", "same" and "between" conditions for avatar and human trails in one analysis to gain a summary view of RT across all conditions. For this, we performed a 3 X 2 X 2 ANOVA with the factors "face-pair trial types" (different, same, between), "category" (avatar, human) and "ISI" (75 msec, 300 msec), using the mean RT of correct responses of each individual across all continua as the dependent variable.
3. fMRI Task
3.1 Stimuli
The stimulus conditions, i.e. the morph stimuli for the face pairs in the within, same and between conditions in the avatar and human trials, are the same as described in the preceding perceptual discrimination task.
3.2 Presentation and instructions
<ol>
	<li>Use a target monitoring task to examine implicit processing of physical and category-related change along the DHL while maintaining participants' attention to the stimuli of interest.</li>
	<li>Instruct participants to press a response button upon detection of a rare target. We presented 15% of all morph pairs as targets, the faces being shown upside-down. Use as targets one of four possible morphs (M0, M4, M8, or M12) selected at random from a morph continuum not used for stimulus presentation otherwise. Ensure that the target morph is presented as the first or second morph of a morph pair to avoid differential attention during monitoring for targets to the first or second morph of the morph pairs.</li>
	<li>Each scanning session consists of two experimental runs of stimulus presentation counterbalanced in order across participants. The break between runs allows the participants a brief rest. Participants fixate a cross at the beginning of each run to establish a steady state in the MR signal.</li>
</ol>
3.3 Preparing the Subject for the Scan
<ol>
	<li>All participants provide written informed consent before the experimental protocol is conducted. The protocol, all procedures and consent forms are approved by the local Ethics Committee. Avoid confounds in the lateralization of brain activations by scanning right-handed participants. Control for the potential impact of previous experience with avatars.</li>
	<li>Before scanning, participants are familiarized with the laboratory, informed about the scanning procedures, given clear instructions as to the target monitoring task, total scanning time and how to alert staff if required.</li>
	<li>For scanning, the participant lies supine on the scanning table. Head cushions are used to ensure comfort and minimize head movement during scanning. Participants are given earplugs and headphones to reduce the impact of scanner noise and to enable communication with the experimenter.</li>
	<li>The participants' right hand is positioned over the response panel for the target monitoring task. The left hand is placed next to the emergency stop button should the participant want to stop scanning.</li>
	<li>The visual stimuli may be presented on a projection screen placed in front or at the rear of the MRI scanner. We used an MRI-compatible head-mounted display ("VisuaStim - Digital", Resonance Technology Inc.). This has the advantage of excluding from sight all visual input other than the intended stimuli.</li>
	<li>Before commencing data collection, ensure that stimulus presentation, the response panel and the emergency stop button are operating properly.</li>
</ol>
3.4 Data Recording and Scanning Parameters
We acquired structural and functional images of the entire brain using a 3-T whole-body MR unit (Philips Medical Systems, Best, The Netherlands). Structural images were registered using a T1-weighted 3D, spoiled gradient echo pulse sequence (180 slices, TR = 20 msec, TE = 2.3 msec, flip angle = 20&deg;, FOV = 220 mm &times; 220 mm &times; 135 mm, matrix size = 224 &times; 187, voxel size = 0.98 mm &times; 1.18 mm &times; 0.75 mm, resliced to 0.86 mm &times; 0.86 mm &times; 0.75 mm). Functional images were acquired from 225 whole-head scans per run using a single-shot echo planar sequence (repetition time, TR = 2.6 sec; echo time, TE = 35 msec; field of view = 220 mm &times; 220 mm &times; 132 mm; flip angle = 78&deg;; matrix size = 80 &times; 80; voxel size = 2.75 mm &times; 2.75 mm &times; 4 mm, resliced to 1.72 mm &times; 1.72 mm &times; 4 mm).
3.5 Data Analysis
<ol>
	<li>We used MATLAB 2006b (Mathworks Inc., Natick, MA, USA) and the SPM5 software package (<a href="http://www.fil.ion.ucl.ac.uk/spm" target="_blank">http://fil.ion.ucl.ac.uk/spm</a>) for preprocessing and MRI data analysis. Preprocessing typically entails alignment of images to the first recorded volume, motion correction, normalization into standard stereotactical space, and smoothing (e.g. 6 mm3 Kernel).</li>
	<li>The fMRI data analysis makes use of a phenomenon referred to as repetition suppression (RS) (11, 13, 14, for reviews, see 15, 16). Considered in the context of the DHL, the morphs of a morph pair are presented in rapid succession. Repetition in the second morph of the stimulus or stimulus attributes presented in the first morph results in a decrease in activation (i.e. RS) in brain region's sensitive to that specific stimulus or its attributes (e.g. physical or category-related attributes). In this protocol, repetition of the stimulus or stimulus attributes between the first and second morph is manipulated in the "within", "between", and "same" conditions in terms of similarity or dissimilarity of physical and category-related attributes of the DHL. By contrasting these conditions, the fMRI data analysis identifies brain regions engaged in processing a particular stimulus or physical or category-related stimulus attribute on the basis of the extent of relative differences in signal decrease following stimulus repetition 17, 18, 19, 20.</li>
	<li>Identify brain regions responsive to physical and to category-related change along the DHL using the following contrast of the stimulus conditions (within, between, and same). These contrasts are defined in terms of the morph used as the second face in the three face-pair conditions (note that the first morph is the same in the avatar and human trials, respectively). To detect sensitivity to physical change for the avatar trials, use the contrast M0 plus M8 &gt; M4, and use M12 plus M4 &gt; M8 for the human trials. To detect brain regions selectively responsive to category change across the boundary in the direction avatar to human (i.e. avatar trials), use the contrast M8 &gt; M4 plus M0. For the direction human to avatar, use the contrast M4 &gt; M8 plus M12.</li>
	<li>For individual level analyses, the fMRI responses of each subject to the second morph of each morph pair in each of the six morph pair conditions (i.e. within, same, and between for avatar and human trials) may be used to contrast brain activity between these conditions. These individual contrasts are then entered into group level analyses for inferential purposes.</li>
</ol>

No conflicts of interest declared.

<table border="1">
 <tbody>
 <tr>
 <td>Funmorph</td>
 <td>Zealsoft Inc.</td>
 <td></td>
 <td></td>
 </tr>
 <tr>
 <td>Poser 7</td>
 <td>Smith Micro Software</td>
 <td></td>
 <td><a href="http://www.smithmicro.com" target="_blank">www.smithmicro.com</a></td>
 </tr>
 <tr>
 <td>Adobe; Photoshop; CS3</td>
 <td>Adobe</td>
 <td></td>
 <td><a href="http://www.adobe.com" target="_blank">www.adobe.com</a></td>
 </tr>
 <tr>
 <td>Presentation; software </td>
 <td></td>
 <td></td>
 <td>Version 14.1, <a href="http://www.neurobs.com" target="_blank">www.neurobs.com</a> </td>
 </tr>
 <tr>
 <td>SPSS Version 16 </td>
 <td></td>
 <td></td>
 <td><a href="http://www.ibm.com/software/analytics/spss" target="_blank">www.ibm.com/software/analytics/spss</a> </td>
 </tr>
 <tr>
 <td>MRI-compatible head-mounted display</td>
 <td>Resonance Technology Inc.</td>
 <td></td>
 <td>"VisuaStim - Digital"</td>
 </tr>
 <tr>
 <td>3-T whole-body MR unit</td>
 <td>Philips Medical Systems</td>
 <td></td>
 <td></td>
 </tr>
 <tr>
 <td>MATLAB 2006b</td>
 <td>Mathworks Inc.</td>
 <td></td>
 <td></td>
 </tr>
 <tr>
 <td>SPM5 software package</td>
 <td></td>
 <td></td>
 <td><a href="http://fil.ion.ucl.ac.uk/spm" target="_blank">http://fil.ion.ucl.ac.uk/spm</a></td>
 </tr>
 </tbody>
</table>

perceptual and category processing of the uncanny valley hypothesis' dimension of human likeness: some methodological issues

Mori's Uncanny Valley Hypothesis1,2 proposes that the perception of humanlike characters such as robots and, by extension, avatars (computer-generated characters) can evoke negative or positive affect (valence) depending on the object's degree of visual and behavioral realism along a dimension of human likeness (DHL) (Figure 1). But studies of affective valence of subjective responses to variously realistic non-human characters have produced inconsistent findings 3, 4, 5, 6. One of a number of reasons for this is that human likeness is not perceived as the hypothesis assumes. While the DHL can be defined following Mori's description as a smooth linear change in the degree of physical humanlike similarity, subjective perception of objects along the DHL can be understood in terms of the psychological effects of categorical perception (CP) 7. Further behavioral and neuroimaging investigations of category processing and CP along the DHL and of the potential influence of the dimension's underlying category structure on affective experience are needed. This protocol therefore focuses on the DHL and allows examination of CP. Based on the protocol presented in the video as an example, issues surrounding the methodology in the protocol and the use in "uncanny" research of stimuli drawn from morph continua to represent the DHL are discussed in the article that accompanies the video. The use of neuroimaging and morph stimuli to represent the DHL in order to disentangle brain regions neurally responsive to physical human-like similarity from those responsive to category change and category processing is briefly illustrated.

1. Forced choice classification task
Analysis of the response data of N = 25 participants was already reported in 7. This confirmed that the slope of the fitted regression curve of each individual continuum and across all continua has a logistic profile (Figure 2A). This slope reflects a sigmoid step-like function consistent with the presence along the DHL of a categorical component in the responses of the participants to the morph faces of the continua. The slope of the curve is thus characterized by lower and upper asymptotes of avatar or human categorization responses which approach 100% for avatars and 100% for humans. In contrast, the estimate of the mean category boundary value derived from the fitted logistic curve and the ordinate midpoint between the lower and upper asymptotes of the categorization responses indicates that the maximum uncertainty of 50% in categorization judgments is associated with the morph M6.
Analysis of RT data was reported also in 7. The RT analysis of all morphs (see Figure 4) showed shortest RTs for the avatar and human ends of the continua, increasing RT with greater morph distance from the avatar and human ends of the continua, and longest RTs at M6 at which there is maximum uncertainty in the category decision responses, as can be seen in Figure 2B. To verify the latter finding more clearly, the mean RT values at M6 can be compared with the mean RT values at all other morph positions. A one-way RM-ANOVA analysis with morph position (two levels: M6 versus all other morphs) and RT as dependent variable collapsed across continua showed that RT for M6 (M = 1.42, SD = 0.26) differed highly significantly from RT for the other morph positions (M = 0.99, SD = 0.46), F(1,24) = 62.04, p &lt; 0.001.
Taken together, the categorization response data confirm that the first criterion for the presence of CP is fulfilled, namely that there is a category boundary (for all criteria, see e.g. 11), and the response times for the category decisions are consistent with the response data in that they show longer response times with increasing categorization uncertainty.
<img alt="Figure 4" fo:content-width="5in" fo:src="/files/ftp_upload/4375/4375fig4highres.jpg" src="/files/ftp_upload/4375/4375fig4.jpg" /> 
Figure 4. Reaction time results of the forced choice categorization task, showing longest mean response latency for categorization judgments for stimuli at morph position M6 at which categorization ambiguity is greatest. Error bars show &plusmn;1 standard error.
2. Perceptual discrimination task
The data analyses of N = 20 participants was already reported in 7. Using as an example the data for avatar trials from that study (Figure 5), the analysis showed enhanced discrimination accuracy for face pairs that cross the category boundary in the between condition compared with attenuated discrimination accuracy for face pairs in the within condition. This is consistent with CP. The data show also that there is a significant difference in discrimination accuracy within the category in that there is greater discrimination accuracy for face pairs in the within condition than in the same condition. The variation in ISI of 75 and 300 msec differentially affected participants' responses, but not in the human trials.
<img alt="Figure 5" fo:content-width="3in" fo:src="/files/ftp_upload/4375/4375fig5highres.jpg" src="/files/ftp_upload/4375/4375fig5.jpg" /> 
Figure 5. Results of the "same-different" perceptual discrimination task for avatar trials. Participants (N = 20). judged whether the morphs of a morph pair were the same or different in physical appearance. Controlling for relative distance of morphs along the continua, results show better discrimination accuracy for face pairs that crossed the category boundary (that was determined in the forced choice classification task) than for pairs drawn from the same (i.e. avatar or human) side of the boundary, thus demonstrating categorical perception along the continua of human likeness. The impact of a shorter and longer ISI of 75 msec and 300 msec was also tested and found to influence discrimination performance for avatar trials only. Error bars show &plusmn;1 standard error.
Using the A' statistic as a measure of discrimination performance independent of response bias, there was in the avatar trials a significant main effect on discrimination sensitivity of face-pair trial types (i.e. (within and between), F(2,38) = 107.11, p &lt; 0.001, with greater discrimination sensitivity for cross-category (A' = 0.89, SD = 0.07) than for within-category pairs (A' = 0.55, SD = 0.17) (Figure 6). Similarly, there was significantly greater discrimination sensitivity for cross-category (A' = 0.94, SD = 0.1) than for within-category pairs (A' = 0.56, SD = 0.22) in the human trails, F(2,38) = 107.11, p &lt; 0.001. There was no effect of face-pair trial types on ISI. Using the &beta;"D statistic as a measure of response bias, there was a significant main effect on bias of face-pair trial types [F(2,38) = 70.53, p &lt; 0.001], with participants showing a strong tendency to judge within-category pairs as different (&beta;"D = 0.81, SD = 0.23) compared with the response to cross-category pairs (&beta;"D = -0.18, SD = 0.59). This is consistent with the idea that participants tend to favor "different" decisions in this particular task when the same-different decision is more difficult for within-category pairs.
<img alt="Figure 6" fo:content-width="3in" fo:src="/files/ftp_upload/4375/4375fig6highres.jpg" src="/files/ftp_upload/4375/4375fig6.jpg" /> 
Figure 6. Using the A' statistic as a measure of discrimination performance independent of response bias (N = 20), discrimination sensitivity was greater for cross-category than for within-category pairs in both avatar and human trials. Error bars show &plusmn;1 standard error.
The analysis of RT data showed no differences between avatar and human trials and between short and long ISI. There was as expected a main significant effect for RT between the three stimulus pair conditions (see Figure 7), F(2,38) = 34.55, p &lt; 0.001. Pre-planned tests of within-subject contrasts showed that RT for cross-category faces (i.e. 'between' face-pair trial type) were significantly faster (M = 0.79, SE = 0.05) than RT for face pairs from within a category ('within' trial type) (M = 1.26, SE = 0.09) [F(1,19) = 60.09, p &lt; 0.001] and face pairs in the same face pair condition (M = 0.88, SE = 0.08), F(1,19) = 43.1, p &lt; 0.001.
<img alt="Figure 7" fo:content-width="4.5in" fo:src="/files/ftp_upload/4375/4375fig7highres.jpg" src="/files/ftp_upload/4375/4375fig7.jpg" /> 
Figure 7. Reaction time (RT) results of the "same-different" perceptual discrimination task for avatar and human trials (N = 20). The graph shows that RT for stimulus pairs that cross the category boundary (i.e. in the between condition) were shorter than the RT for faces from within a category. Error bars show &plusmn;1 standard error.
The categorization response data thus confirm the second criterion for the presence of CP in that there is better discrimination accuracy for pairs that cross the category boundary than for equidistant pairs drawn from within a category. This demonstrates that there is a so-called discrimination boundary with enhanced sensitivity for the physical stimulus features close to the category boundary. The RT data support this in showing shorter response latencies for cross-category compared with with-category face pairs.
This particular perceptual discrimination task does not define the specific point of the discrimination boundary along the DHL. A much smaller morph distance between pairs of presented morphs could be used to resolve this. Here we show an example using a traditional ABX discrimination task 12, 13. ABX discrimination entails sequential presentation of different face stimuli (e.g. Morph A and Morph B) followed by a second presentation of either A or B as the target stimulus X. After viewing images A, B and X, participants are required to indicate whether A or B is identical to X. In this example, a 2-step discrimination procedure between morphs (i.e. 1-3, 2-4, 3-5, etc.) is presented (Figure 8B). Analyses are described in 8. For the purpose of illustration, the ABX discrimination task was performed on 24 participants using 4 morph continua, each with 11 morphs, using endpoint stimuli drawn from the study of Cheetham et al. 7. Following the ABX discrimination task, a forced choice categorisation task was performed with the same participants. This sequence of task presentation is thought to minimize the influence of explicit category decision making on the ABX discrimination task. Figure 8B indicates clearly that there is a peak in perceptual discrimination sensitivity at the morph position predicted by and aligned with the category boundary (see Figure 8A). Using the 2-step distance between morphs, the peak in discrimination performance can be clearly identified in the interval between morph pair M5-M7. See 8 for findings using the ABX paradigm and morph stimuli drawn from dimensions of human likeness with monkey, cow and human faces as the endpoints of the continua.
<img alt="Figure 8" fo:content-width="3.5in" fo:src="/files/ftp_upload/4375/4375fig8highres.jpg" src="/files/ftp_upload/4375/4375fig8.jpg" /> 
Figure 8. Representative results of the ABX perceptual discrimination and forced choice categorization tasks. The 2-step discrimination procedure (i.e. 1-3, 2-4, 3-5, etc.) in the ABX perceptual discrimination task in panel B shows that the peak in perceptual discrimination sensitivity is predicted by the category boundary determined in the forced choice categorization task shown in panel A. Panel A shows the logistic profile of the fitted regression curves of the four continua. Maximum uncertainty of 50% in categorization judgments of morphed faces as human is associated with morph M6.
The same-different discrimination task confirms that the third criterion for the presence of CP in showing that the discrimination boundary is aligned with the category boundary. In other words, the position of the category boundary predicts the position of the discrimination boundary.
The fourth criterion, which is not always applied in studies of CP 13, 14 is that discrimination is at chance within the categories. The data of the illustrative example using the ABX design would suggest that discrimination is slightly above chance for those morphs located between the continua endpoints and the category boundary.
3. fMRI task
4.3.1 Sensitivity to physical change
By comparing the conditions in which there is a physical change between the first and second morph with the condition in which there is no such change, a brain region in the fusiform gyrus (Figure 9A) is shown to be sensitive to the presentation of fine-grained change along the DHL in the physical appearance of face morphs in the avatar trials. A similar result for human trials is not shown in the figure. This region has been referred to as the fusiform face area because of its role as part of the visual system in processing facial information. Together with the human trials, this finding is consistent with the reported response of fusiform areas to differences in facial physical attributes 23, facial geometry 16, 21, 24 and facial texture 21.
4.3.2 Sensitivity to category change
Figure 9B shows, using the example of avatar trials, brain regions sensitive to category change along the DHL. This was achieved by comparing the conditions in which there is a category change between the first and second morph with the condition in which there is no such change. The imaging data show that category change in avatar trials (i.e. a change from avatar-to-human direction along the DHL) revealed responsiveness of the hippocampus, amygdala, and insula. The role of these regions needs to be interpreted in the context of the paradigm used and categorization and has already been described 7. Generally, the amygdala is responsive to faces, affective valence, novelty, and uncertainty 55, 56, 57, 58, 59. The amygdala is suggested to influence processing of other brain regions involved in categorization depending on the affective meaning of a situation 60. The insula is consistently reported in association with category processing and processing under conditions of uncertainty 61, 62, 63. In the context of the paradigm used, this region might contribute to enhancing attentional resources for categorization processing 63. The specific region of activation could also be associated with signaling the presence of uncertainty, threat, or potential threat 64, 65. The hippocampus is involved in visual categorization and perceptual learning 66. The category change in human trials (i.e. a change in the human-to-avatar direction along the DHL) revealed that the putamen, head of caudate, and thalamus, are responsive to this condition. Generally, these regions are associated with learning stimulus-category associations, signaling category membership, decision uncertainty during categorization, switching between potential category rules used to establish category membership and adjustment of the represented categorical boundary in order to minimize errors 67, 68, 69, 70.
Interpretation of these results at a broad level and within the context of the experimental paradigm used suggests that avatar and human faces represent different categorization problems depending on the degree of previous categorization experience with a given category (e.g. 25); the participants are expert in human face processing but were especially selected on the basis that they report no explicit knowledge of previous experience with avatar faces (e.g. in video games, movies, second life) and, as confirmed at debriefing, had never previously seen faces of the kind we presented.
<img alt="Figure 9" fo:content-width="6in" fo:src="/files/ftp_upload/4375/4375fig9highres.jpg" src="/files/ftp_upload/4375/4375fig9.jpg" /> 
Figure 9. Neural correlates of physical and of category change along the DHL in avatar trials. The activation maps are superimposed on the coronal (A), transversal (B) and sagittal (C) views of a single subject. The color bars signify the gradient of t values of the activation maps (p &lt; 0.005, 20 contiguous voxels).

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Perceptual and Category Processing of the Uncanny Valley Hypothesis&#039; Dimension of Human Likeness: Some Methodological Issues

Investigation of the Uncanny Valley Hypothesis and affective experience requires an understanding of the hypothesis' dimension of human likeness (DHL). This protocol allows representation of the DHL and examination of categorical perception. Use of the same stimuli and fMRI to distinguish brain regions responsive to physical and category change is illustrated.

Perceptual and Category Processing of the Uncanny Valley Hypothesis' Dimension of Human Likeness: Some Methodological Issues

Mori's Uncanny Valley Hypothesis1,2 proposes that the perception of humanlike characters such as robots and, by extension, avatars (computer-generated ...

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17.2K Views.  University of Zurich. Investigation of the Uncanny Valley Hypothesis and affective experience requires an understanding of the hypothesis' dimension of human likeness (DHL). This protocol allows representation of the DHL and examination of categorical perception. Use of the same stimuli and fMRI to distinguish brain regions responsive to physical and category change is illustrated.