Mobile Edition
Print
Get the Mag
Weekly UpdatesA limited capacity approach predicts this phenomenon. It suggests that both the related and the unrelated cut will elicit orienting responses which result in more resources being allocated to encoding the message. However, the unrelated cut by definition is associated with the introduction of new (unrelated) information. As a result--the additional resources allocated in response to the cut are in fact needed to encode new and unrelated information introduced by the cut. Therefore, the viewer's processing capacity is briefly overloaded, due to the new and unrelated information, which results in a momentary decrease in memory for information occurring at the point of transition marked by the cut.
Related cuts, on the other hand, are associated with less new information and information after the cut which is related to previous information. Typically, related cuts provide either a different view of the same scene, or a new scene introduced and therefore expected in the context of the message. Thus, at least some of the additional resources allocated to encoding as a result of the orienting response may not be needed. This super abundance in resources may result in more information being encoded than is absolutely necessary to "keep up" with the meaning of the message, resulting in an increase in memory for information occurring right at the point of the edit.
A recent study (Lang et al., 1999) extended this work by examining the effects of the number of cuts in a message on overall memory for the message. In this study, cuts were defined as a shift from one visual scene to a completely new visual scene within coherent 30 second messages. All of the cuts in this study were semantically related (that is, they were part of a naturally occurring 30-second segment), but a cut always introduced a completely new visual scene. Thus, a cut always added new information in the form of a new visual scene. The number of cuts in 30-second messages was varied, and memory for the messages was measured. The limited capacity approach predicts an inverted U-shaped relationship between increase in cuts and memory, since initially cuts will help viewers to process the message by increasing the processing resources allocated to the message. However, as cuts continue to increase in number, the viewer will be unable to keep up with the demand for processing resources, and memory will suffer. Results showed that as the number of cuts increased from slow (0-1 cuts in 30 seconds) to medium (5-6 cuts in 30 seconds), viewer memory for the messages increased. However, as predicted, memory for fast messages (those with 10 or more cuts in 30 seconds) was lower than memory for medium messages.
Both Lang and Basil (1998) and Lang et al. (1999) suggest that a different story may be told, if one manipulated the amount of new information introduced by the cut. Cuts which introduce very little new information into a message should elicit orienting responses, and therefore elicit an increase in processing resources allocated to encoding, but all of the additional resources allocated to encoding the cut, may not be needed, since there is little new information to be encoded. It is logical to suggest that increasing the number of cuts which add little "new" information to a message may not overload a viewer' s processing system as quickly as increasing the rate of cuts which do add new information. Conceivably, cuts that introduce almost no new information might never overload the processing system.
This possibility is examined in the present study. Cuts are defined as a change from one visual scene to another within a coherent (semantically related) 30 second message (the same definition used in Lang et al., 1999). An edit, on the other hand, is defined as a change from one camera shot to another within the same visual scene. In other words, edits are camera changes within the context of a single location. For example, alternating speakers' faces during a conversation would be described as edits. Unlike a cut, which takes the viewer to a completely new environment and therefore adds a considerable amount of new information to the message, an edit should elicit orienting responses but introduce much less new information, and therefore require less effort to process(2). As a result, increasing the number of edits in a message should increase the number of orienting responses elicited, which, in turn, should increase viewer attention to the message. This leads to hypothesis 1:
[H.sub.1]: As the number of edits in a message increases, attention to the message will increase.
At the same time, because all of these additional resources may not be needed to process new information, memory for the messages should increase. This leads to hypothesis 2:
[H.sub.2]: As the number of edits in a television message increases, memory for the content of the message will increase.
Previous research on the rates of cuts and edits suggests that fast paced messages (those with many structural features) elicit, in addition to increased attention, increased arousal in viewers (Gunter, 1987; Hitchon, Thorson, & Duckier, 1994; Reeves, Thorson, & Schleuder, 1986; Watt & Krull, 1977; Yoon et al., 1997; Yoon et al., 1998). Because arousal is often associated with an increase in memory and liking for messages (Lang, Dhillon, & Dong, 1995; Lang, Greenwald, Bradley, & Hamm, 1993) arousal is an important variable to examine(3). Increasing the number of cuts in a message was clearly shown to increase both viewers self-reports of arousal and their autonomic arousal (measured by skin conductance) in the study reported above (Lang, et al., 1999). It is expected that increasing edits will similarly increase viewers' levels of autonomic nervous system activation-or arousal. Hence:
[H.sub.3]: As the level of edits in a television message increases, viewer arousal will increase.
Method
This overall experiment is a mixed 4 (Order of Presentation) X 4 (Edits) X 5 (Message) design. To construct the stimulus tapes, 20 coherent messages, each one-minute long, were chosen from a pool of television programs, advertisements, and feature movies. The messages chosen included dramas (4), comedies (1), science fiction (1), cop shows (1), cartoons (2), sports (4), commercials (2), information shows (2), self-help shows (2), and talk shows (1). No one genre appears more than once in any level of rate of edits. Messages were chosen to create four levels of edits (slow, medium, fast and very fast). Four random experimental orders were constructed. Order of presentation was the only between subject variable.
Edits were operationalized as change from one camera shot to another within one visual scene. A visual scene was defined by an establishing shot. Cuts to anything in the establishing shot were defined as edits. Cuts to things not present in the establishing shot were defined as cuts. Thus, if the inside of a football stadium is an establishing shot, all shots taken within the stadium are then defined as edits. But a scene change to something outside the stadium is a cut. Clearly, using this operational definition, all edits do not introduce the same amount of new information, but one can be reasonably confident that the edits (as operationally defined here) introduce less new information than do cuts. The four levels of edits were defined according to the following criteria. 0-7 edits within the one-minute message is slow, 8-15 is medium, 16-23 is fast, more than 24 is very fast paced. All the messages contained fewer than 3 cuts (a level defined as "slow" in previous research)(4).
Subjects
Thirty nine communication majors participated in the experiment for class credit. Visual recognition, heart-rate, and skin conductance were collected from all 39 subjects.
Dependent Variables
Memory was measured using a visual recognition task. Brief video scenes were shown to the subjects who pushed the buttons on a joystick to indicate whether they had seen the scene before. Each scene was 6 frames long. Subjects viewed 120 scenes separated by 2 seconds of black. Sixty of the scenes came from messages the subjects had seen before, while the other half, foils, came from messages they had not seen before. Both the correctness and the speed of their responses were measured. The visual recognition task took 4.2 minutes.
Recognition hypotheses were tested using a signal detection analysis of the recognition data. Signal detection theory is discussed at length and applied to communication research in Shapiro, 1994. Signal detection analysis is based on the theory that finding a memory in your memory is like detecting a weak signal in the environment. Two major components affect a person's decision as to whether or not a signal (or memory) has been detected. One dimension of that decision is how sensitive one's senses are to changes in the environment. This is called sensitivity or d prime. The second dimension of that decision relates to how willing a person is to guess, that is how liberal or how conservative their decision-making strategy is. This is called the criterion bias.
To perform a signal detection analysis four values are computed for each subject: (1) the percentage of hits-that is the percentage of items subjects say they have seen before which they have in fact seen before; (2) the percentage of misses-that is, the percentage of items they said they had not seen before which they had seen before; (3) the percentage of correct rejections-that is the percentage of items they said they had not seen before that, they had not seen before, and; (4) their percentage of false alarms-that is, items they said they had seen before that they had not seen before. These values are combined to compute a subject's sensitivity (called d prime) and the subject's criterion bias. The greater a subject's sensitivity - the more accurate their memory is, both in terms of hits and correct rejections. Criterion bias is determined by the number of false alarms and misses and is interpreted as how confident a subject needs to feel about having seen an item before he or she is willing to say the item was seen before. Using signal detection analysis allows one to attribute an increase in percent correct to either improved sensitivity or a shift in criterion bias.
FEED