Mechanisms of human motion perception: combining evidence from evoked potentials, behavioural performance and computational modelling

Abstract

Based on single cell recordings in monkey, it has been suggested that neural activity can be related directly to psychophysically measured threshold behaviour. Here, we investigated in humans whether evoked potentials correlate with behavioural measurements like discrimination thresholds and reaction time. Subjects were asked to report the perceived direction of object motion stimuli which contained variable amounts of coherent motion. Simultaneously, we recorded evoked potentials with a multielectrode array, or measured the reaction time. We show here that motion coherence had a strong influence on both amplitude and latency of the evoked potential. Stronger motion signals evoked stronger and faster cortical responses. The latency reduction of the motion onset response with increasing coherence correlated very well with the concurrent decrease in reaction time. Taken together, these results suggest that temporal integration is an important step in analysing motion signals to generate a reliable behavioural response. We stimulated a two-dimensional array of correlation-type motion detectors with the same motion sequences, and analysed the distribution of local motion signals according to signal detection theory. Performance resembled that of human subjects when the decision strategy was optimized so as to exclude small signals and, in particular, when the ideal observer had some knowledge about a region of interest in which the object was to be expected.

Estimation of the Timing of Human Visual Perception from Magnetoencephalography

“…The delay value was estimated as the difference between the actual RT and the detection latency estimated by each model, averaged over all thee coherence levels. For the peak detector model, the slopes were much smaller than 1 (0.190 Ϯ 0.092), which is in agreement with previous reports (Osaka and Yamamoto, 1978;Musselwhite and Jeffreys, 1985;Mihaylova et al, 1999;Patzwahl and Zanker, 2000;Kawakami et al, 2002;Vassilev et al, 2002). For the level detector model, the slopes were increased but were not sufficiently close to 1 (0.810 Ϯ 0.122).…”

“…Although some EEG studies have suggested that the latency of the peak response changes in parallel with the manual RT (Vaughan et al, 1966;Jaskowski et al, 1990), most EEG/MEG studies (Osaka and Yamamoto, 1978;Musselwhite and Jeffreys, 1985;Mihaylova et al, 1999;Patzwahl and Zanker, 2000;Kawakami et al, 2002;Vassilev et al, 2002), including the present one (Figs. 2 and 6), indicate that the change in the peak latency is too small to account for the change in RT (peak detector model).…”

“…Previous attempts to relate RT with EEG or MEG responses generally failed in quantitatively predicting RT from the peak latency of the evoked response (Osaka and Yamamoto, 1978;Musselwhite and Jeffreys, 1985;Mihaylova et al, 1999;Patzwahl and Zanker, 2000;Kawakami et al, 2002;Vassilev et al, 2002). These results are not disappointing, however, given that there is no theoretical reason to expect the peak latency to correspond to the completion of a perceptual process.…”

Cortical representations of confidence in a visual perceptual decision

“…The cortical activity that gave rise to this effect and linearly increased with motion coherence is likely to reflect compound activitiy of large parts of motion sensitive visual cortex including, among others, areas MT/MST, V3A, and also parietal cortex (see for example, Figs 2,4 and 5). Neurons in area MT have been shown to linearly increase firing rate with motion coherence in various single-cell recording studies 17,18 and the strength of visual motion is reflected in human area MT þ activity in fMRI [19][20][21] , MEG 13 and EEG data 22 . Interestingly, the evoked activity following invalid postcues in our Experiment 1 also varied with attributes of the visual stimulus, that is, motion coherence (Fig.…”

An Analysis of the Motion Signal Distributions Emerging from Locomotion through a Natural Environment

“…The apparatus also allows to grab single images could at sequences of predefined positions, in order to generate more complex trajectories. Image sequences were analysed with a two-dimensional, correlation-type motion detector model (2DMD) which has been used previously to identify the processing requirements faced by fiddler crabs in detecting and recognising species-specific movement signals , to study the characteristic patterns of image motion produced by wasps during their learning flights (Zeil et al, 1998), and to simulate a wide range of psychophysical phenomena (e.g., Zanker et al, 1997;Patzwahl and Zanker, 2000;Zanker, 2001). The basic building block is tha elementary motion detector (EMD) of the correlation type which has been shown in many behavioural and physiological studies to be a very good candidate for the biological implementation of motion processing (for review, see Reichardt, 1987;Borst and Egelhaaf, 1989).…”