Visual search tasks are widely used in the study of visual behaviour, as they give a wealth of data into how different features such as colour and orientation are processed – either independently or in conjunction (McSorley & Findlay, 2001; Wolfe, 1994). In general, a visual search task is when a participant is displayed with a region containing a visual stimulus. The participant is then requested to find a target object within the search area – frequently this is made more complex by the introduction of distractors which may be similar to the target object. By manipulating either the object, or the quantity and similarity of the distractors, it is possible to infer the factors involved in the detection of the target object, and how hard the object is to distinguish from the distractors (Cave & Wolfe, 1990; Treisman, 1982; Treisman & Gelade, 1980; Wolfe, 1994).
In general, observers’ performance at a visual search task with distractors is partitioned into two major categories: serial searches, where the time taken to find the target object is affected (typically linearly) by the number of distractors present; and parallel searches,
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where the time taken to find the target is unaffected by the number of distractors that are present (Treisman & Gelade, 1980). In parallel searches the target is thought to have a ‘pop out’ effect, where the target is clearly different to the distractors and therefore
comparatively trivial to detect, for example a horizontal line in a field of vertical lines. Serial searches are when an increase in the number of distractors increases the time it takes to detect the target. This behaviour is often caused when the target is defined by a conjunction of two other cues (Treisman & Gelade, 1980), for example when the target is a horizontal red line, not a vertical red line or a horizontal green line (Finlayson, Remington, Retell, & Grove, 2013; Nakayama & Silverman, 1986).
Promisingly for the use of binocular vision to break camouflage, there is evidence that depth information can enable the segregation of the scene into planes that can each be rapidly searched (Nakayama & Silverman, 1986). Segregation in depth also improves the detection of objects superimposed on another scene (Harris & Willis, 2001; Harris & Gregory, 1973; Moraglia & Schneider, 1990; Schneider, Moraglia, & Speranza, 1999; Wardle et al., 2010). However, the use of depth for segregation seems to be limited to large disparities (over 6arcmin (de la Rosa, Moraglia, & Schneider, 2008; Mckee et al., 1997)) and sometimes only in certain circumstances e.g. (Finlayson et al., 2013; Steinman, 1987).
Interestingly, studies looking at disparity judgements in visual search found that people were typically faster when the target was located in front of a background, rather than behind (Becker, Bowd, Shorter, King, & Patterson, 1999; Kim, 2013; O’Toole & Walker, 1997). This is the same geometric arrangement as an object in the environment sitting on an opaque background. This perhaps indicates that binocular vision has evolved to assist in detecting environmental objects, an argument further supported by an increased detection time for convex objects (Bertamini & Lawson, 2008). However, these search tasks all use flat planes in their experiments, rather than three dimensional objects that extend over a range of depths like a real world object. Additionally, the task is frequently to identify a collection of small elements rather than large extended objects.
There is some visual search literature using objects: for example we know it is easier to find a 2D object that is closed than an open one, indicating that the visual system is adapted to detecting entire objects (Bertamini & Lawson, 2008; Elder & Zucker, 1993). The closest object based searches come to considering 3D objects is the work considering shadow perception – for example using abstract 2D objects to cast shadows (Rensink & Cavanagh, 2004). There is also some work looking at the perception of photographs of stones with cast and self-shadows (Lovell, Gilchrist, Tolhurst, & Troscianko, 2009). Unfortunately, these studies investigated if shadow processing uses a separate, faster mechanism than object detection rather than considering the effect of any percept of depth caused by the inclusion of shadows.
Unnatural search tasks are a concern, with the majority of visual search tasks being presented on simple backgrounds (Troscianko et al., 2009a; Wolfe, 1994), or on uniform
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grids e.g. (McSorley & Findlay, 2001). Neider et al. did two studies in naturalistic scenes, and found that the effect of the extra information from the naturalistic background could not be solely attributed to the presence of crowding from the extra elements in the scene (Neider, Boot, & Kramer, 2010; Neider, Brotzen, & Zelinsky, 2010). This indicates that it is important to consider the effects of the environment that the target object is located in. Lovell et al. (2015) conducted some naturalistic studies investigating the detection of a snake that was camouflaged via a leaf-like pattern on the snake that matched an artificial leaf background. Interestingly, they managed to make the snake so well camouflaged against an artificial leaf background that they needed no distractors, and could measure how well camouflaged the snake was from detection time alone (Lovell et al., 2015).
Search tasks in the environment using only visual cues are rare, one such study by Foulsham et al (2014) did a search task outside of the lab, using an eye tracker to study the effects of colour on search times (Foulsham et al., 2014). The presence of an eye tracker assisted Foulsham et al. to detect trials when participants looked at the target but did not recognise it.
To us, the most useful naturalistic world search tasks are those that relate to camouflage. Several studies have investigated if visual search can predict participants' performance at a foraging task (active searching of an environment for ‘food’), and have found that if the foraging is mainly visually driven, then visual search is a good analogue to foraging for natural objects (Gilchrist, North, & Hood, 2001; Smith, Hood, & Gilchrist, 2008). Naturally, the effect of the environment is important when considering visual search and camouflage, with reaction times increasing with increased similarity of the target to the background (Neider & Zelinsky, 2006). This is caused by background matching – a form of camouflage. Despite these attempts, there is little literature relating camouflage to complex scenes or with discrete objects, with most inferences about visual camouflage being drawn directly from simpler objects and tasks (see Troscianko et al. (2009a) for a discussion).
In developing the visual search tasks presented in this thesis, we heavily drew inspiration from the more complex camouflage tasks, especially those that were constructed with a real environment in mind (Lovell et al., 2015; Neider, Boot, et al., 2010; Neider, Brotzen, et al., 2010). However, perhaps the most influential were the meta analyses discussing the
advantages and disadvantages of different visual search paradigms. One common paradigm to use is a search task where distractors are always present but with the target only present in some of the trials e.g. (Gilchrist et al., 2001; Neider & Zelinsky, 2006; O’Toole & Walker, 1997; Rensink & Cavanagh, 2004; Wolfe, 1994). The participant’s task is then to identify if the target is present or absent, requiring only a very simple experimental setup. However, this methodology has been found to have serious issues – the prevalence of the target makes a significant difference to both detection rates and reaction times e.g. (Godwin et al., 2014; Schwark et al., 2013) making it hard to analyse the effect of manipulating the target vs the effects of target prevalence.
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Additionally, in camouflage tasks we typically wish to compare the participant’s
performance between several objects to establish if certain attributes cause the target to be better camouflaged. We decided to avoid placing multiple objects in each search task as this introduces problems with interpreting the results. For example, multiple object searches cause an increase in miss rates for the rarer or harder to detect targets – particularly when the participant is tasked to indicate if the object is present or absent (Cain, Adamo, & Mitroff, 2013).
The inclusion of distractors in the visual search task can influence how camouflaged a target object is measured to be. From a study by Neider and Zelinsky (2006) we know that when searching for a camouflaged object, participants will spend time investigating distractors instead of the target similar background. This is a problem, as the location and shape of the distractors will confound our measurements of the target’s camouflage. For example, if we have multiple different targets, then the targets that are most similar to the distractors will take longest to be spotted purely due to our choice of distractors, and not due to any inherent properties of the target. For these reasons, we avoid tasks with distractors and tasks where the target is ever absent. We discuss in depth how we combine these studies into a camouflage visual search task in Section 3.2.2.
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3
Methodology
Outline of stereoscopic stimulus presentation.
Overview of the psychophysical techniques used in the first strand to extract data from the two alternative forced choice experiments.
Overview of the visual search tasks used in the second strand of this thesis.
Participant recruitment.
Figure 3.1: A Dead Leaf Mantis (Deroplatys desiccate, two central brown leaves) displaying mimicry. Animals imitating dead leaves such as this mantis are frequently very thin and flat,
making them appear the right shape for a leaf even to a stereoscopic viewer. Image reproduced with permission, (Pingstone, 2005)
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In this Chapter, we will review the general methodologies used in the thesis. First, we will describe the setup used to display controlled stereoscopic stimuli on a computer screen. The techniques of stereoscopic display will be used in all but the last Experiment (11). We then go on to discuss the methods of data analysis. Due to using different experimental paradigms in the two strands of the thesis, we discuss the data analysis methods in two separate Sections: For the first strand we describe the psychophysical analysis techniques, for the second strand we discuss the data analysis techniques used for visual search tasks. In the final Section of the Chapter, we discuss participant recruitment techniques, which are identical for both strands of the thesis.