Supplement to the article. "Adaptive experiments with a multivariate Elo type algorithm"

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1 Supplement to the article "Adaptive experiments with a multivariate Elo type algorithm" Philipp Doebler Westfälische Wilhelms-Universität Mohsen Alavash and Carsten Giessing Carl von Ossietzky University Author Note Address: Philipp Doebler, Department of Psychology and Sport Sciences, Fliednerstr. 21,D Münster, Germany Mohsen Alavash and Carsten Giessing, Biological Psychology Lab, Department of Psychology,European Medical School, Carl von Ossietzky University, D Oldenburg, Germany doebler@uni-muenster.de mohsen.alavash@uni-oldenburg.de carsten.giessing@unioldenburg.de

2 How to measure neural correlates of conscious perception - an application in neuroscience Previous fmri studies have used different approaches including simple staircase, method of limits or constant stimuli to investigate neural effects of near-threshold stimuli (e.g. Boynton, Demb, Glover & Heeger, 1999; Apkarian, Gelnar, Krauss & Szeverenyi, 2000; Brooks et al., 2011; Brodersen et al., 2012). In many cases these approaches require extensive practice to reach a stable level of performance (Ress et al., 2000; Herrmann, Mountazer-Kouhsari, Carrasco, & Heeger, 2010). Thus new experiments would largely profit from new algorithms that adapt faster to subject-specific abilities. The following section is aimed to provide an example of an application of META to illustrate two points. Frist, we would like to illustrate which research questions can be effectively addressed with META in the field of neuroscience and second, that META can be effectively used to control a specific performance level. The first point is important since adaptive measurements like META are still rarely used in the field of neuroscience and studies often use paradigms with fixed stimuli instead of adaptive procedures. The second point illustrates the robustness of META to reach a stable level of performance also in paradigms where the exact shape of the psychometric function has not been investigated and in which cognitive fatigue might reduce the cognitive resources of test subjects during task performance. An interesting research question in neuroscience is why we consciously perceive identical near-threshold visual stimuli in some situations but not in others? An elegant way to investigate the interaction between conscious perception and other cognitive mechanisms like attention is to use paradigms with individualized near-threshold stimuli. For example, Chica et al. (2012) used a simple one-up-one-down procedure to present near-threshold stimuli and studied the effects of attention on the conscious detection of visual targets. Their study and 1

3 the results from others provide evidence that visual attention and brain regions within a frontoparietal network contribute to the conscious perception of near-threshold visual stimuli (Bartolomeo, Thiebaut de Schotten & Chica, 2012; Chica, Paz-Alonso, Valero-Cabre & Bartolomeo, 2012; Ress et al. 2000; Tong, 2003). Within our experimental approach we used a similar task as Chica et al. (2012), but applied META instead of a simple one-up-onedown procedure to investigate the detection of near-threshold stimuli and to adjust the performance of a single right-handed healthy subject to ~50% in the different experimental conditions. Within an event-related functional magnetic resonance imaging (fmri) experiment, we asked the subject to report the location of small Gabor patches briefly presented either on the left or right visual periphery on a grey background. Meanwhile task-related blood-oxygen-leveldependent (BOLD) activations were recorded by means of a 3-Tesla MRI scanner (Siemens MAGNETOM Verio (Siemens AG, Erlangen, Germany), T2*-weighted gradient echo planar imaging (EPI): Repetition time = 1.5 seconds, echo time = 30 milliseconds, voxel size = millimetres, image size = pixels, number of slices = 27, flip angle = 80 degrees). Each trial started by presenting a fixation cross for 1000 milliseconds. Following the fixation cross a peripheral target at~20 degrees of visual angle appeared for 40 milliseconds. There were also trials on which no target was presented (catch and control trials). Each trial ended by changing the fixation cross into a question mark, asking the subject to report the target location by pressing one of two response buttons. There were 100 trials for each left and right condition, 66 catch trials, and 64 control trials which were later used to define brain regions related to button press. Control and catch trials differed only in the presentation of the fixation cross whose left or right wing turned black on control trials, telling the subject which 2

4 button to press and to control neural activations due to motor responses. The main experiment was 19 minutes in duration. In order to match the subjective performance to ~50%, we incorporated META into the task through adaptive selection of 21 fixed stimuli. Relative contrast of the stimuli against the background was linearly distributed between one (standard deviation of the stimulus pixel intensities SDI = ) and zero (SDI = 0). A non-adaptive pilot measurement with five subjects was conducted to estimate the tuning parameters. The mean detection rates for the left and right conditions were substantially correlated across subjects (rho= 0.94); subjects with high detection rates for the left stimuli also showed high detection rates for the right stimulus. To accommodate for the possibility that the single subject in our current analysis is an unrepresentative sample of the population we set sigma in META to 0.5 so that the estimates of the abilities for the left and right detections could vary more independently. The other parameters were set to,,,,,,. META was implemented in MATLAB version 2012a (MathWorks, Natick, MA), and Cogent 2000 (Functional Imaging Laboratory) was used for control and presentation of trials as well as response collection. The adaptive procedure continued during the whole experiment. As expected, performance converged at a target contrast for each left and right condition, yielding ~50% correctly detected stimuli (left: 55%, right: 45%, catch: 97%; see Fig. 1, A and B). Analysis of the fmri data was conducted using the software package SPM8 ( Frackowiak, Friston, Frith, Dolan, & Mazziotta, 1997). The brain scans were corrected for head motion by spatially realigning them to the first brain volume and then were spatially normalized to the standard stereotaxic MNI space (Montreal 3

5 Figure 1. Application of META to visual stimuli within an event-related fmri experiment. (A, B) The algorithm converged at a near-threshold contrast level through an adaptive selection of stimuli yielding approximately 50 percent performance (C) fmri activation map for contrasts correct discrimination>misses and misses>correct discrimination (p< uncorrected, extend threshold: 21 voxels, each masked with the contrast control >catch, p<0.05) in a single-subject (SOG: Superior occipital gyrus, MFG: Middle frontal gyrus, SPL: Superior parietal lobule, FEF: Fronal eye field, SFG: Superior frontal gyrus). Neurological Institute; Finally data were spatially smoothed with a Gaussian kernel of 8 mm full width at half maximum to increase the signal to noise ratio. For the statistical analysis, a general linear model was used in which the fmri data were predicted by the different task conditions. In order not to artificially influence our results by differences in stimulus intensity, the mean BOLD activation during correct and incorrect trials were compared during trails with two contrast levels (one contrast level for right and one for left target presentations). These contrast levels (right: contrast level 0.7; left contrast level: 0.15) were most often presented and their presentations resulted in both, a high number of correct and incorrect responses (see Fig. 1, A and B). The activation maps are presented based 4

6 on a significance level of p< (uncorrected for multiple comparisons) and only clusters of activations with more than 20 continuous voxels are reported (Fig. 1, C). In our analysis we investigated in which brain areas the activation is increased when targets are detected in contrast to the trials when the targets are missed. Our results revealed increased activation in left and right superior frontal gyri (SFG), brain regions which had previously been found to be associated with shifting of visuospatial attention (Hopfinger, Buonocore, & Mangun, 2000;). During misses (in comparison to correctly detected trials) we found increased activation within the right superior occipital gyrus (SOG), right middle frontal gyrus (MFG), left and right superior parietal lobules (SPLs), along with left and right frontal eye fields (FEFs). These brain regions have been related to control of spatial attention (Chica et al. 2012; Hung, Driver, & Walsh, 2011; Perry & Zeki, 2000; Ronconi, Basso, Gori, & Facoetti, 2012) and online storage of spatial information (Leung, Gore, & Goldman-Rakic, 2002). The above observations are consistent with the involvement of a widespread frontoparietal activation pattern in visual consciousness as proposed in recent years (Chica et al., 2012). Our analysis might help future fmri studies in clinical neuroscience where efficient control of the task performance is important. Within this context, META could serve the design of tasks patients can perform (Price & Friston, 1999; Price et al. 2006). Many groups of psychiatric patients show declines in perceptual performance. As a result, between group differences in visual perception is often confounded by differences in task demands which makes it difficult to interpret between group differences in brain activations (see Snyder et al., 2011). In the extreme case, the underlying neural mechanisms of a central behavioural symptom cannot be investigated since patients do not perceive the presented visual stimuli 5

7 during the experiment. Thus, META can be used as an effective tool to control performance levels in clinical neuroscience. In this regard one should consider the potential effect of differences in stimulus intensities on BOLD signal. Previous studies have tried to control differences in performance by ANCOVA approaches (Murphya & Garavan, 2004). In contrast, in clinical situations with large differences in performance it might be better to use adaptive procedures to adjust similar performance levels and to control differences in stimulus intensities with statistical approaches. Incorporating META to fmri experiments can also be beneficial for other reasons as summarized in the next section. Concluding remarks In practice, META has its potential usefulness in neurocognitive experiments, in particular fmri studies, in the following senses: META can be used for an adaptive threshold measurement of multivariate traits. META allows to control the perceptual load or difficulty of experimental tasks. Due to its capability to estimate a subject s latent ability as reflected in the person parameter, the algorithm provides a convenient setting to make both within-subject and between-subject behavioural comparisons. Due to shorter test lengths the effects of possible confounding factors such as practice or fatigue are reduced. 6

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9 Herrmann, K., Mountazer-Kouhsari, L., Carrasco, M., & Heeger, D. J. (2010). When size matters: attention affects performance by contrast or response gain. Nat Neurosci 13(12): Hopfinger, J. B., Buonocore, M. H., & Mangun, G. R. (2000). The neural mechanisms of topdown attentional control. Nat Neurosci 3(3): Hung, J., Driver, J., Walsh, V. (2011). Visual selection and the human frontal eye fields: effects of frontal transcranial magnetic stimulation on partial report analyzed by Bundesen's theory of visual attention. J Neurosci 31(44): Leung, H.-C., Gore, G. C., & Goldman-Rakic, P. S (2002). Sustained mnemonic response in the human middle frontal gyrus during on-line storage of spatial memoranda. Journal of Cognitive Neuroscience 14(4): Murphy, K., Garavan, H. (2004). Artifactual fmri group and condition differences driven by performance confounds. Neuroimage 21(1): Perry, R. J., Zeki, S. (2000). The neurology of saccades and covert shifts in spatial attention. Brain (123): Price, C. J., Crinion, J., Friston, K. J. (2006). Design and analysis of fmri studies with neurologically impaired patients. Journal of Magnetic Resonance Imaging 23: Price, C. J., Friston, K. J. (1999). Scanning patients with tasks they can perform. Human Brain Mapping 8: Ress, D., Backus, B. T., & Heege, D. J. (2000). Activity in primary visual cortex predicts performance in a visual detection task. Nature Neuroscience 3(9):

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