This section outlines an example of perception in driving that illustrates how the theoretical approaches I have described can be applied: the perception of speed. Speed perception is, of course, involved in controlling the vehicle, and in this sense it can be seen as an example of perception for action.
Consider first the visual perception o f speed. In Marr’s computational theoiy o f visual perception, motion is processed by motion detection modules whose output is integrated with the primal sketch to produce the 2Z-D sketch. Motion detectors have been identified in the brain (Newsome, Shadlen, Zohary, Britten, & Movshon, 1995), and are known to process both speed and direction information. Area V5 (or MT+) contains motion sensitive neurons that are tuned to specific speeds (Bom & Bradley, 2005; Groh, Bom, & Newsome, 1997; Lisberger, Morris, & Tychsen, 1987; Liu & Newsome, 2005; Newsome & Pare, 1988; Nichols & Newsome, 2002). Each such neuron has a peak firing rate in response to a particular speed o f motion in its receptive field (area on the retina), and a narrow band-pass so that it does not respond to speeds above or below its tuning peak. Thus the pattern of activation in the neuronal population in MT forms a representation o f the distribution of speed in the image (Price, Ono, Mustari, & Ibbotson, 2005).
Neurons in the visual system show the phenomenon of adaptation, a reduction in firing rate over time in the presence of a constant stimulus. This has been shown for neurons involved in motion detection (Clifford & Ibbotson, 2002). This suggests that a constant speed o f movement in the visual field will be perceived as a gradual slowing, and this has indeed been found in psychophysical tests (Goldstein, 1957; Thompson, 1981). The amount o f the reduction in perceived speed declines exponentially with time (Bex, Bedington, & Hammett, 1999; Clifford & Langley,
1996; Goldstein, 1957; Hammett, Thompson, & Bedingham, 2000). The adaptation appears to be associated with an increase in sensitivity to speed changes (Bex et al., 1999; Clifford & Langley, 1996; Clifford & Wenderoth, 1999; Krekelberg, van Wezel, & Albright, 2006). Heitanen, Crowder, and Ibbotson (2008) found a more complex picture in which, while adaptation at high speeds and testing at low speeds produced a reduction in perceived speeds (relative to veridical speed), adaptation at low speeds and testing at high speeds produced an increase in perceived speeds.
Several authors have developed Bayesian models of visual speed perception that account for some o f these psychophysical results (Ascher & Grzywacz, 2000; Geisler & Kersten, 2003; Stocker & Simoncelli, 2006; Weiss, Simoncelli, & Adelson, 2002). Most of these models assume some form of low-velocity prior, based on the assumption that objects in the environment are more likely to be stationary or moving slowly, than moving quickly. In addition they assume that the perceptual process is noisy. Ascher and Grzywacz’s model, for instance, models the motion detectors in MT as a set of spatial frequency and temporal frequency filters, whose outputs are subject to Gaussian noise and are combined, together with a prior that is biased towards low speeds. Their model predicts the effect that low image contrast causes low speeds to be estimated as lower than veridical, and high speeds to be estimated as higher. Hurlimann, Kiper, and Carandini (2002) found agreement between the Bayesian model of Weiss et al. (2002) and their empirical speed estimates with variable contrast, provided that a non-linear model of image contrast was used during model interpretation. However Hammett, Champion, Thompson, and Morland (2006) found that speed perception at low luminance was not consistent with the current Bayesian models, so these models do not yet provide a comprehensive account.
Other analyses o f visual perception of speed are based on optic flow (Palmer, 1999, p504; Carvallo & Cohen, 2001). Optic flow can be described by a set of velocity vectors associated with each texture element in the visual field. Global movement of texture elements is indicative of self- motion through the environment. Direction of self-movement can be ascertained by the direction of the focus o f expansion of the set of vectors. For instance, in forward motion on the ground, the focus o f expansion is a point on the horizon, and all vectors appear to point away from the focus. Speed information is contained in the magnitude of the vectors. Absolute speed, however, cannot
be estimated directly from optic flow, because o f a fundamental scaling indeterminacy: the same retinal change could result from a large movement relative to a distant texture element or a small movement relative to a close texture element. Additional prior assumptions, or scale information (such as recognition of objects of familiar size, which yields distance estimates) are needed to resolve the scale indeterminacy (Palmer, 1999).
Brandt, Dichgans, and Koenig (1973) showed that human perception o f ego-speed (speed o f self- motion) depends on optic flow in the peripheral visual field rather than the centre: a flow stimulus in the central 30° produces virtually no sensation of speed.
The above discussion has outlined visual speed perception in generic visual environments. However, because of its importance in road safety, there have also been a number o f applied studies o f speed perception specifically in driving. Many of these draw on or illustrate the themes outlined above. For example, Godley, Triggs, and Fildes (2004) attributed reductions in speed by drivers in a driving simulator, in the presence of a hatched marking in the centre o f the road, to the markings causing enhanced peripheral visual speed perception.
Different classes of texture elements in the optic flow have been found to produce different perceptions of speed during driving. Denton (1980) projected either textured patterns or transverse strips onto the roadway in a simulator study. Drivers were asked to reduce their speeds by half on entering the test zone; transverse strips produced greater reductions than textured patterns. Such research led to the introduction o f transverse strips on approaches to roundabouts in the UK (Denton, 1980) and on highway exits in France (Malterre, 1977). Vertical features also affect speed perception: Manser and Hancock (2007) found that drivers in a simulated tunnel decreased speed in response to a pattern of decreasing-width stripes on the walls, and increased speed in response a pattern of increasing-width stripes. Transverse and vertical visual features are now part o f the repertoire of “psychological traffic calming” measures (Kennedy, Gorell, Crinson, Wheeler, & Elliott, 2005). However there is also evidence that drivers who regularly use a particular road section, gradually adapt to the presence of such markings, so that after an initial reduction in mean speeds on introduction, drivers who become familiar with them gradually increase their speeds on subsequent drives (Shinar, Rockwell, & Maleki, 1980).
There is also evidence that drivers adapt to speed itself, in such a way that the sensation of speed reduces with time during extended intervals of constant-speed driving (Irving, 1973; Schmidt & Tiffin, 1967). Recarte and Nunes (1996) found that previous acceleration or deceleration influenced verbal estimates of current speed. However experimental data on both the magnitude of the adaptation and the time for the adaptation to take effect have been inconsistent. For instance, adaptation time was approximately 1 minute in Irving’s (1973) study, but over 30 minutes in that by Schmidt and Tiffin (1967).
Researchers have often used a paradigm in which drivers are asked to produce a speed, rather than verbally estimate it: for instance, doubling or halving their current speed (Groeger, Carsten, Blana, & Jamson, 1999; Recarte & Nunes, 1996). In this paradigm speed perception is specifically used to direct action. Typically when drivers are asked to halve their current speed, they do not reduce it sufficiently; whereas when drivers are asked to double their current speed, they do not increase it enough. These results have been interpreted in terms of speed adaptation, which causes the initial speed to be perceived as lower, so that smaller increments of change are required to halve or double it. On a cautionary note Borg (1961) pointed out that in halving and doubling experiments, the time taken to execute a halving or doubling o f speed also varies systematically with initial speed, and this may confound these findings. However, the phenomenon of speed adaptation in driving agrees well with the adaptation of neuronal signals outlined above.
Speed perception is also influenced by other, non-visual perceptual cues. In a frequently cited study, Evans (1970) asked participants in the passenger seat of a car to estimate the speed of the car (driven at a range of actual speeds) in normal conditions, or blindfolded, or wearing ear defenders to impair hearing, or both. Speed estimates were higher when blindfolded, but lower when hearing was impaired. These results suggested that sound is used as an additional cue in estimating the speed of self-motion in a car, and in its absence speed was consistently under-estimated. Other authors (Horswill & McKenna, 1999; McLane & Wierwille, 1979; Matthews & Cousins, 1980) have found inconsistent results when asking drivers to make verbal speed estimates with and without auditory attenuation. Triggs and Berenyi (1982) found that auditory masking with lOOdB white noise, rather than auditoiy attenuation, also reduced estimated speeds by lOkph. Horswill and
Plooy (2008a) confirmed the under-estimation effect of attenuation using a robust paired comparison method.
These findings can be interpreted using a Bayesian model of speed perception that includes a prior distribution for speed, which peaks at zero. The prior represents the assumption that in the absence of perceptual information to the contrary, the most likely speed through the environment is zero. In the presence of consistent visual and auditory cues to speed, the prior has relatively little weight. When the auditory cue is attenuated, the reduced loudness represents a sensory cue for lower speed, which will reduce the weighted average. In addition, the reliability o f the auditory loudness reduces, so the relative weight of the prior increases, which reduces the overall speed estimate (Horswill & Plooy, 2008a). An interesting parallel to this is the experiment by Horswill and Plooy (2008b) in which speed estimates were lower when image contrast was reduced. The Bayesian interpretation of this finding is that the reduced contrast decreases the reliability o f the visual cues, reducing their weight relative to the prior, resulting in under-estimation of speed.