Strand 2: Visual search

Visual search tasks were used in conjunction with the psychophysics used in this thesis. The psychophysical analysis can give us a quantitative measurement of how changing the

stimulus changes the perception of the stimulus, it cannot tell us how much harder or easier that alteration makes the stimulus to detect. We therefore need to move to visual search experiments in the second strand of the thesis, where we are looking at the interaction of binocular vision with camouflage.

A visual search experiment is one in which the participant’s task is to locate a target in the presented visual scene as quickly as possible. Typically, an object on its own is very easy to spot, so other objects that look like targets are often introduced into the scene – these are called distractors. The participant’s task is to find the target amongst the distractors. There is a wide variety of literature using this paradigm, which was discussed in depth in Section 2.3.

The typical visual search task aims to compare how easy it is to detect the target based on certain properties, e.g. if a red or blue target is easier to spot amongst green distractors. There are various ways to evaluate the participant’s performance – one technique is to evaluate how much longer it takes to spot a target with an increase in the number of

distractors, giving an overall measure of how easy it is to distinguish between the distractors and the target (see Section 2.3) e.g.(Neider & Zelinsky, 2006). In our experiments this

presents a problem as we typically need multiple different shaped targets as we are comparing participants’ performance across many conditions. Specifying exactly what the target looks like to a naïve participant is therefore extremely hard, and it is known that multiple targets can introduce many extra sources of error (Cain et al., 2013) including biases based on the similarity between the distractors and the different targets (Duncan & Humphreys, 1989; Lovell, Gilchrist, Tolhurst, To, & Troscianko, 2008). Additionally, this regime presents issues: the distractors are examined in preference to areas of the

background which may contain an undetected target (Neider & Zelinsky, 2006). Additionally, measuring how much harder distractors make the target to spot is, in camouflage terms,

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more akin to the various forms of mimicry and therefore dependent on the exact properties of the distractors as well as the properties of the target (see Chapter 2.1).

Unfortunately, there are few examples of combining binocular vision and camouflage with visual search tasks. The few studies that do consider depth and visual search typically look at the effects of depth on spotting a 2D target amongst 2D distractors, and investigating how the depth of the target relative to the distractors changes the difficulty of detecting the target (e.g. Finlayson et al., 2013; Kim, 2013; O’Toole & Walker, 1997; see Section 2.3 for a discussion of this literature). While of relevance, this is not the regime we are after, as our targets are aiming to resemble prey items, and therefore must have their own distinct 3D shape. We must therefore turn to a different regime for testing how hard it is to spot a disparity defined 3D object on a featureless background.

To achieve this, we altered a much simpler paradigm used by Lovell et al. (2015) – no distractors with the target always present, and looking at the time it takes to detect the single target object. Across trials, we alter the properties of the object that we are

interested in: the participant does not need to identify each of these objects as a separate target, as we have only instructed them to find an object in the scene. If the target is indeed camouflaged by our experimental manipulation, then it will be harder to spot and the time it takes for the participant to indicate they have spotted the target will increase.

We refer to the time between stimulus onset and the participant’s response as their

reaction time and equate this to the detection time (in the context of camouflage, this is the length of time it takes for a predator to spot their prey). Unfortunately, reaction time also includes the length of time it takes for the participant to make a motor response to indicate that they have spotted the target. However, this should be negligible in comparison to the overall reaction time (measured in seconds) and should be similar for all different stimulus conditions.

Some studies use eye tracking to further break the reaction time down into the time until first fixation on the target, and the length of time spent fixating on the target to positively identify it as the target, called the verification time e.g. (Neider, Brotzen, et al., 2010). This can give us some further information about how the participants are detecting their targets – for example Neider et al. found that verification time increased as the scene searched got more cluttered. In the absence of eye-tracking, we cannot distinguish between the time to first fixation of the target and the time spent verifying that the fixated object is the target. We argue that in a camouflage context the distinction between first fixation and verification time is not essential. This is because in order to react to the presence of a prey item, the prey item must be identified as being the predator’s target. In order to identify the prey, the predator must fixate and verify the target. We consider the length of time it takes to identify the target as a measure of camouflage - time to identification includes both the length of time till first fixation time and the length of time to verification time. Therefore, how well

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camouflaged an object is, is not influenced by the distinction between how much time is spent fixating the target, and how much is spent verifying it.

It is frequent practice in visual search experiments to exclude outliers in reaction time on the grounds that they are typically due to participants not paying attention. However, in our data all the long response times were present in one condition of our experiments

(specifically the smoothest objects in Experiments 9, 10 and 11). This indicates that the long response times are due to the experimental stimuli, not participant inattentiveness and any attempt to remove outliers is invalid. To ensure that outliers do not bias our data however, we will compare the trends in both the mean and the median of the data, as the difference between the mean and median results will inform us about the effect of the spread of the data.