Visual attention

To measure the response time: on type of search (feature and conjunctive) and number of distracters.

ABSTRACT:

INTRODUCTION:

Visual attention is an essential part in the mechanism of human cognition. When receiving a barrage of incoming information, individuals rely on visual attention to give order to their environment. After parsing a scene into its distinct components, the visual system directs attention to specific elements for additional processing. This act of selecting Particular items, features, or locations in space can be referred to as visual search, and it is a task that an individual performs perpetually. (Müller & Krummenacher, 2006; Groome, Brace, & Dewart, 2006).

A search likely consists of several uninterrupted distribution of attention until either the target has been found or once the searcher abandons the search. Depending on the properties of the target signal and the surrounding noise in which it is embedded, the process can sometimes be carried out instantaneously and automatically (i.e., a “parallel” search), or it can require an item-by-item, more effortful scan (i.e., a “serial” search) to locate the target. This search process is driven in part by the top-down boosting of stimulus properties relevant to the search (e.g., Melcher, Papathomas, & Vidnyánszky, 2005), a process referred to as “guidance” of attentional selection (Wolfe, 2007), although there is also evidence to suggest that the search process involves the inhibition of irrelevant stimulus attributes (e.g., Most et al., 2001). Visual selective attention is the ability to attend to relevant information and ignore irrelevant stimuli. It is crucial for dealing with the cluttered visual environments of daily life (e.g. Pashler, 1998). It is one of the dominant paradigms used for investigating visual attention. In visual search, the task is to locate a target on the basis of some visual properties. The detection performances in everyday search tasks, Such as, locate a car in a large parking lot or to search for a friend in crowded place. (Groome, Brace, & Dewart, 2006).

In a simplified view of visual processing, the two-stage Feature Integration Theory model predicts two broad categories of searches: parallel and serial. (Triseman and Gelade, 1980) If a target is distinct from distracters along one or more basic feature dimensions (i.e., finding a blue circle amidst green triangles; see Figure 1a), the target can typically be located instantly and automatically (i.e., a “pop-out” effect), with little effect on response time of increasing set size, and such a search is described as a parallel search. Alternately, if a target is specified by the conjunction of multiple features and if some basic features are shared with distracters (i.e., finding a circle amidst green & blue triangles; see Figure 1b), the search requires an item-by-item, serial analysis of each distracter to determine whether it is a target. The time to conduct a serial search is therefore dependent on set size and is a much slower search.

The feature integration theory has been quiet successful in not only predicting but also explaining the results of several experiments. (For example, Treismna & Gelade, 1980).

Main aim of the feature integration theory is that attention must be focused on a single location in order to conjoin the separable feature dimensions present at that location. The empirical distinction between conjunctive and feature search is generally assumed to show that although information concerning the presence of visual features is available pre-attentively, the relations among features can only be recovered with focal attention (Treismna & Gelade, 1980).

In the experiments of Treisman and Gelade (1980), on which these are modeled, the answer seems to be both, depending on the nature of the target and its relation to the background characters. If the target is defined by a single feature, such as shape or color, and none of the background characters have that feature, then the target appears to pop out from the background. That means that the time to search for the target should not depend on the number of background letters. However, when the search is for a conjunction of features, and the background characters have one but not both features, then the characters have to be scanned serially, or one at a time. In this case, search time should increase with the number of background characters. Data such as these support the notion that early in visual perception, stimuli are analyzed into elementary features such as color and shape. Attention is required to "glue" the features together into objects. This reasoning predicts that under rapid presentation, one should sometimes perceive "illusory conjunctions" -- combinations of features that were presented singly but not in combination. Evidence for these illusory conjunctions and for other interesting observable fact in visual perception is also been found. (Riesman, 1986, Cited in Pylyshyn Z. et. al, 1994).

Palmer and his associates (Palmer et al, 1993; Palmer, 1994)developed these models for performance measure - target-distracter difference Threshold, and demonstrated that set-size effects in visual search for many simple features (line length, orientation, brightness, aspect ratio of rectangle, colour) are in accordance with the predictions of unlimited capacity signal detection theory model (at least for set-sizes 1-8). Palmer (1994) tested some more complex stimuli (rotated Ts and Ls, objects consisting of pairs of black and white dots) and obtained results intermediate between unlimited and limited capacity models. Moreover, he found that non-attention “sensory” interactions were larger with these complex stimuli that complicated the measurement of “pure” set-size effect related to central capacity limitations. Thus, the capacity limitations were not convincingly demonstrated within signal detection theory paradigm of visual search, and the roles of central (attention) and peripheral (sensory) factors were far from being determined.

Why bother doing this replication now….

Many authors (for e.g., Duncan & Humphreys, 1989; Wolfe, et.al, 1989; found in Wolfe, 1998) have proposed alternatives to the all-or-none interpretation of visual search findings. But this experiment is a replication of the original Treisman & Gelade study done in 1980.The visual search task is commonly used to investigate the allocation of spatial attention. In this task, a target item is placed among a varying number of distracters, and its presence, location, or identity must be reported; this allows one to measure the effects of increasing the number of distracter items in the display. If finding a particular target does not require focal attention, its presence will be reported with the same ease, regardless of the number of distracters surrounding it. Such a target is said to “pop out” of the display (i.e., to be processed without attention, in parallel with distracter items, or pre-attentively) (see, e.g., Treisman & Gelade, 1980).

METHOD:

Participants:

Thirty-Three students participated in this study. The study was done on 2nd year psychology undergraduate students of both genders, studying module PSY2004, an in -class experiment for psychology research and methods lab, at the Middlesex University campus in Dubai.

Design:

The experiment was a repeated measures design (within subjects). There are two independent variable of search (2 levels, feature and conjunctive) and of distracters (three levels, 4, 16 & 64.The dependent variable is response time.

Materials:

Superlab version 4.0 was used to design the experimental trials and present it to the participants with the use of a 19' inch Computer screen. A set of instructions outlined the appropriate keys that had to be pressed for a yes “/” or no “z” response in the keyboard. The results were generated through SPSS.

Procedure:

Participants were seated in front of individual 19' inch Computer screen. They were asked to open files in Superlab version 4.0 and read the instructions that followed carefully. The screen was 4.5 inch in superlab throughout the experimental trials. The instruction asked the participants to search for a blue circle against either green triangle distracters or a mixture of green and blue triangle distracters. Participants had to answer as quickly as possible and outlined the keys to be used to answer the trials. A “/” key to locate the circle in the trials and “Z” key if the participants did not locate the circle. There were 8 trials with 4 distracters, 8 with 16 distracters and 8 with 64 distracters, giving a total of 96 trials. On each trial, the participant was presented with: a blank screen for 1 second, and then the stimulus appeared. The stimulus remains on the screen until the participant responded by pressing the allotted key and also to initiate the next trial. On completing the experiment participants were asked to save their response sheets on the desktop but no feedback was given to them on their performance. Data collected from the response sheets was used to find out the response time for each of the participant. The saved file on the desktop was opened via SPSS to read individual participants response. Each participant were asked to Write, number of correct and errors response adding up to 96, along with yes responses of the type of search (feature and conjunctive) with the distracter 4, 16 and 64 in a piece of paper which was handed to the tutor at the end of the class. Results were generated using Two-Way ANOVA in SPSS.

RESULT:

Table 1: Mean and standard deviation of median response time of participants.

DISCUSSION:

Limitations:

The participants would not have read the given instruction in superlab carefully.

The second limitation is as mention above that the screen was 4.5 inches for some participants it could have been unsuitable size, which in return did not allow them to view clear slide and give a correct response.

Future findings:

Lowercase English alphabets “b”, “d” and “p” as the alphabets are similar, and help in measuring the response time of the distracters

However further research has to be carried out since the present body of research fails to establish a correct and universal model for visual word recognition.

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