Intrinsic timing models

A relatively recent group of models propose that rather than time being processed by specialised mechanisms, it is in fact an inherent part of sensory neural dynamics. In other words our sense of time is a natural by-product of ongoing neural responses to physical stimuli. This group of models is often referred to as intrinsic timing. In some intrinsic models timing arises within different modalities as groups of neurons respond to a stimulus. For example, the timing of an auditory stimulus would be encoded in auditory regions of the brain, whereas a visual stimulus would have its duration encoded in visual areas (Buonomano, 2000, Burr et al., 2007a). This theory is viable because sensory stimuli cause spatiotemporal patterns of action potentials which are relayed to the central nervous system.

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Figure 32 A simple population clock model consisting of 3 neurons. The neurons display a reliable pattern of firing. The activity of each neuron changes over time. Duration may be coded based on the activity across the neurons at any given point in time. For example, the code representing 3 units of time would be 3, 0, 3. After another unit of time has passed the time signature becomes 2, 2, 0 and so on (Buonomano et al., 2010).

These patterns change over time and so the state of a network of neurons will be different when a stimulus has ceased than it was at stimulus onset. Thus the state of the network provides a representation of the duration of the stimulus. This may be achieved by chain reaction to a stimulus within a population of neurons so that any particular point in time could be represented by the activity of a small group of neurons (Eagleman, 2008) or time could be encoded by a larger group of neurons where it is the unique pattern of firing across the group which enables the passage of time to be measured (Buonomano et al., 2010) (Figure 32).

An alternative theory states that perceived duration could be dependent on the amount of energy expended during the neural response to a stimulus. This theory has been suggested as a cause for the oddball effect (in which a

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“unexpected stimulus” is perceived as being longer than a repeated “expected stimulus” of equal duration), in that when a stimulus is repeated the corresponding neural response is reduced. This phenomenon is known as repetition suppression and leads to less energy expenditure for repeated stimuli. Thus in a train of repeated stimuli the first stimulus is perceived as being longer than the subsequent ones (Rose et al., 1995).

Much of the supporting evidence for intrinsic timing comes from physiological experiments which demonstrate localised, sensory specific timing (Jantzen et al., 2005, Morrone et al., 2005, Bueti et al., 2008b). Examples of these are a transcranial magnetic stimulation experiment in which TMS was applied over V5. Performance in judging a visual stimulus duration was affected, whereas the judgment of a auditory stimulus was left unimpaired (Bueti et al., 2008a) and an fMRI study in which subjects were exposed to an auditory or visual rhythm which they subsequently tapped out (Jantzen et al., 2005). In the visual condition, activity was found to be high in V5 after the initial primer rhythm ceased in line with state dependent models where continuing sensory specific pattern of activity would be expected in order to provide a template for the tapping task. No such activity in V5 was found when the rhythm was primed with an auditory stimulus.

However, a number of studies have shown that if subjects train on discriminating a particular duration, performance improves and is transferred across modalities (Warm et al., 1975, Nagarajan et al., 1998) and from sensory to motor timing (Meegan et al., 2000). It would seem to be problematic to explain this via intrinsic models. Intrinsic models which suggest an early locus for the encoding of time have a further difficulty

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explaining the results of Westheimer (1999). This study had subjects train in temporal discrimination using a stimulus in the left visual field. The gains from this training were found to transfer across to the right visual field, suggesting that time is encoded at higher level visual areas (Westheimer, 1999).

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Chapter 4

Factors influencing time perception

4.1 Stimulus nature

Performance in the judgment of durations has been demonstrated to differ between the senses. A widely reported finding is that auditory duration discrimination thresholds are consistently lower than their visual counterparts. This is the case for both filled and empty intervals (Grondin et al., 1991b, Wearden et al., 1998).

In addition, for both filled and empty stimuli, perceived auditory durations are typically longer than the perceived duration of physically identical visual durations (Behar et al., 1961). This has been found for a wide range of durations (Goldstone et al., 1974, Wearden et al., 1998, Wearden et al., 2006). These findings may be explained in terms of the pacemaker accumulator hypothesis if we consider audition as causing an increase in the rate of pulses produced by a pacemaker. If this were the case then more accumulated pulses would equate to a greater perceived duration. Also, because each pulse produced by a faster pulse rate demarks a shorter period of time, finer distinctions may be made, resulting in the smaller JNDs found in the literature. In addition it has also been found that low level visual stimulus characteristics influence perceived duration and that under certain

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circumstances perceived visual duration may exceed that of an auditory stimulus (see Chapter 8.10).

Filled intervals have been found to be judged as being longer than empty ones of the same duration (Allan, 1979). Filled intervals have also been shown to be judged with greater sensitivity than empty ones (Rammsayer et al., 1998), although there are some conditions in which this is not the case (Grondin et al., 1998). Also, an “empty” interval punctuated with flashes or beeps has been found to be perceived as being longer than a completely empty one (Goldstone et al., 1976, Allan, 1979).

With regard to inter-sensory bias, it has been shown that if a duration is demarked by transient bimodal (visual and auditory of physically equal duration), the duration is perceived as being the same or very similar to when it is marked by audition alone (Walker et al., 1981b). Welch and Warren (1980) propose that when faced with conflicting information from the senses, we give priority to the most appropriate sense for the task in hand. In this case, audition is more sensitive in temporal perception hence its dominance over vision (Welch et al., 1980a). When judging empty durations which have cross modal markers, performance has been shown to drop significantly (Rousseau et al., 1983, Westheimer, 1999, Rousseau et al., 1973). It was originally proposed that this points towards different timing mechanisms for intra modal and cross modal timing (Rousseau et al., 1973). However, a later study concluded that the differences found in discrimination thresholds were due to noise caused by the attentional shift between visual and auditory modalities required for the cross modal task. Therefore the findings are not in conflict with a supra modal timing hypothesis (Rousseau et al., 1983).

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Westheimer (1999) finds that subjects’ discrimination thresholds may be lowered with training and that this effect transfers between visual hemispheres and is therefore located beyond areas of retinotopic representation putting the locus of timing at late processing stage. Westheimer also suggests that the increase in thresholds found with cross- modal stimuli is due to difficulties in collating signals from different sensory areas with differing neural firing patterns and different sources of background noise (Westheimer, 1999).

Woodrow (1928) found that in the case of empty auditory intervals, the markers used can influence perceived duration of the silence between the sounds. Making the markers longer resulted in an increase in the judged interval (Woodrow, 1928). This effect was found to be more pronounced as the onset marker was lengthened.

A study using a pattern of eight flashing lights to mark an interval in a reproduction task found that the more ordered and simple the flashing pattern, the shorter the duration reproduced. This effect was found to be more pronounced with shorter durations (Bobko et al., 1977a).

A study using a variable number of box-like stimuli moving along an invisible predetermined pathway found that increasing the speed of the stimuli lengthens the perception of its duration but varying the number of stimuli had little effect (Brown, 1995). A subsequent paper (Kanai et al., 2006) using a variety of stimuli, found that when randomly moving black dots were used as a stimulus, the speed of movement had a direct effect on perceived duration. The same paper describes an experiment involving the use of an expanding

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concentric grating in which temporal frequency was manipulated to see whether temporal frequency or speed produced the greater time dilation. The authors concluded that speed had little effect and that temporal frequency was the significant factor. However, a more recent paper used a vertical grating and concluded that the opposite was in fact the case (Kaneko et al., 2009). The authors suggest that the differing results were due to the different stimuli used in each study and that the use of concentric rings produced a variance in luminance over the interval being judged. This variance produced a flicker effect which could have produced the time dilations found rather than them being as a result of temporal frequency. If either speed or temporal frequency can be shown to be totally responsible for the time dilation found by these studies, then this may have implications for the locus of the effect. If speed is totally responsible, this would place the locus for the effect at a later stage than if the effect is due to temporal frequency. Neurons tuned to speed independent of spatial frequency have been found in the macaque MT. In contrast to this, neurons in V1 prefer temporal frequency independent of spatial frequency (Priebe et al., 2006) .