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An Empirical Examination of Secondary Task Reaction Times: Testing What They Really Measure

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Abstract:

This study tests a model of what secondary task reaction times (STRTs) actually measure as outlined in the STRT model proposed by Lang and Basil (1998). Lang and Basil’s (1998) review of the STRT literature found that past literature referred to four theoretical definitions of what STRTs measure: resources required by a message; resources allocated to the message; resources available for processing; and resources remaining in the system. Indeed, they found that individual studies would often fluctuate between conceptualizations. The authors then integrated the limited capacity model of mediated message processing (for a complete discussion of the model, see Lang, 2000) with the four definitions, concluding that a conceptualization of STRTs as an index of available resources at encoding best fit the published STRT results.
STRT methodology requires a subject in an experiment to perform a primary and secondary task at the same time, and it is assumed that the subject cannot concurrently use the same resources for the primary and secondary tasks. As primary tasks require more resources, there are fewer resources left over to conduct the secondary tasks, and STRTs slow down (Lang and Basil, 1998).
Lang and Basil (1998) focused in particular on the counterintuitive results found for STRTs elicited during messages with high and low global video complexity and local video complexity. Theory seemed to predict that global video complex messages would elicit slower STRTs because more resources would be required to process them. But in fact, global video complex messages elicit faster STRTs than do simple video complex messages. Lang and Basil (1998) suggested the reason for this is that more resources are made available during complex messages. This means increasing global complexity leads to more resources being allocated to the message, thus increasing available resources. According to this view, global complexity determines the number of resources allocated to the attention task, and the content/structure of the message determines the number of resources required to process the message (Lang and Basil, 1998). Thus their model suggests that STRT is an indicator of available resources, specifically it indexes those resources made available to the cognitive subprocesses of encoding a message not needed to process the message. In other words, the structural features in globally complex messages elicit a large allocation of resources and the demands for resources made by the ongoing processing and storage of messages to encoding, but because many of those structural features do not require many resources to be processed, an over allocation of resources results (Lang and Basil, 1998, p. 458).
Lang and Basil (1998) define available resources as the resources “allocated to the message (which is increasing) minus the resources required by the message” (p. 460). If the resources required are less than the resources allocated, the available resources in global complex video messages may very well increase. Thus, there would be more resources available during complex messages compared to simple messages, and if this occurs as the reaction times will be faster during global video complex messages.
Studies investigating the effects of global and local video complexity suggest a way to directly test this model. Lang et al. (1999) found that cuts (i.e., change from one scene to an unrelated scene) in television programming automatically allocated resources to encoding. As the number of unrelated cuts increases, more attentional capacity is called to encoding. This resulted in a curvilinear effect on recognition (here an index of encoding) such that recognition improves from slow to medium-paced messages but decreases from medium to fast-paced messages, suggesting an overload.
Conversely, Lang et al. (2000) predicted and found that increasing the number of edits defined as “a change from one camera shot to another within the same visual scene” in a message would show an increase in recognition with no overload (p. 99). This is because the edit adds little new information to be processed, hence resources are allocated but are not needed to process the change.
Thus it is logical to use the number of structural features to manipulate how many resources are allocated to encoding, and the type of structural feature (cut or edit) to manipulate how many resources are needed to process the structural feature. Therefore, fast-paced messages with many edits should leave more resources available than a fast-paced message with many cuts.
Examining the four conceptualizations of what STRTs measure outlined by Lang and Basil (1998), we see that a different pattern of STRT results is predicted for this design by each of the conceptualizations. If STRTs measure resources allocated to the message, then STRTs should slow down as the number of cuts and edits increases and there should be no difference between cuts and edits. If STRTs measure resources remaining in the system, there should again be fewer resources remaining for fast paced messages compared to slow, and if cuts require more resources there should be fewer resources remaining for cuts compared to edits. If STRTs measure resources required by the message, then STRTs should decrease as the number of cuts and edits increases with no difference for the type of structural feature. If STRTs measure resources available, then STRTs should speed up as both edits and cuts increase at a fast pace, and there should be a difference between cuts and edits.
The current study was designed to empirically test this prediction. The design is a 3 (slow, medium, fast pacing) X 2 (edits, cuts) completely within design. STRT, recognition, cued recall, arousal, and attention will be dependent variables.
SAM (Self Assessment Mannequin) and skin conductance (SC) will be used to measure arousal. Heart rate will be gathered to measure attention. To measure recognition we will use four alternated multiple-choice tests, and storage will be measured using cued recall.
Resources will be measured using STRTs. Subjects will watch the tapes (primary task) containing related edits and unrelated cuts. When they hear an audio tone they should push a button (secondary task).
Time line
Stimulus materials already have been prepared. Subjects will be run in January and February. Data analyzed in March and April, and the paper will be ready in May.

Lang, A. (2000). The limited capacity model of mediated message processing. Journal of Communication, 50(1), 46-70.

Lang, A. & Basil, M.D. (1998). Attention, resource allocation, and communication research: What do secondary task reaction times measure, anyway? In M. Roloff (Ed.). Mass Communication Yearbook, 21, 443-473. Sage: Beverly Hills, CA.

Lang, A., Bolls, P., Potter, R.F., & Kawahara, K. (1999). The effects of production pacing and arousing content on the information processing of television messages. Journal of Broadcasting and Electronic Media, 43(4), 451-475.

Lang, A., Geiger, S., Strickwerda, M., & Summer, J. (1993). The effects of related and unrelated cuts on viewers’ memory for television: A limited capacity theory of television viewing. Communication Research, 20, 4-29.

Lang, A. Zhou, S., Schwartz, N., Bolls, P.D., & Potter, R. F. (2000). The effects of edits on arousal, attention, and memory for television messages: When is an edit is an edit can an edit be too much? Journal of Broadcasting and Electronic Media, 44(1), 94-109.

Most Common Document Word Stems:

resourc (42), messag (32), strts (19), lang (17), edit (17), measur (17), cut (15), complex (13), process (12), alloc (12), avail (12), requir (10), 1998 (10), task (10), basil (10), increas (10), global (9), video (8), strt (8), pace (8), fast (7),

Author's Keywords:

secondary task reacton time, Limited Capacity Model, cognitive capacity, structural features
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MLA Citation:

Bradley, Samuel., Lang, Annie., Haverhals, Leah. and Shin, Mija. "An Empirical Examination of Secondary Task Reaction Times: Testing What They Really Measure" Paper presented at the annual meeting of the International Communication Association, Marriott Hotel, San Diego, CA, May 27, 2003 <Not Available>. 2009-05-26 <http://www.allacademic.com/meta/p111681_index.html>

APA Citation:

Bradley, S. D., Lang, A. , Haverhals, L. M. and Shin, M. , 2003-05-27 "An Empirical Examination of Secondary Task Reaction Times: Testing What They Really Measure" Paper presented at the annual meeting of the International Communication Association, Marriott Hotel, San Diego, CA Online <.PDF>. 2009-05-26 from http://www.allacademic.com/meta/p111681_index.html

Publication Type: Conference Paper/Unpublished Manuscript
Review Method: Peer Reviewed
Abstract: This study tests a model of what secondary task reaction times (STRTs) actually measure as outlined in the STRT model proposed by Lang and Basil (1998). Lang and Basil’s (1998) review of the STRT literature found that past literature referred to four theoretical definitions of what STRTs measure: resources required by a message; resources allocated to the message; resources available for processing; and resources remaining in the system. Indeed, they found that individual studies would often fluctuate between conceptualizations. The authors then integrated the limited capacity model of mediated message processing (for a complete discussion of the model, see Lang, 2000) with the four definitions, concluding that a conceptualization of STRTs as an index of available resources at encoding best fit the published STRT results.
STRT methodology requires a subject in an experiment to perform a primary and secondary task at the same time, and it is assumed that the subject cannot concurrently use the same resources for the primary and secondary tasks. As primary tasks require more resources, there are fewer resources left over to conduct the secondary tasks, and STRTs slow down (Lang and Basil, 1998).
Lang and Basil (1998) focused in particular on the counterintuitive results found for STRTs elicited during messages with high and low global video complexity and local video complexity. Theory seemed to predict that global video complex messages would elicit slower STRTs because more resources would be required to process them. But in fact, global video complex messages elicit faster STRTs than do simple video complex messages. Lang and Basil (1998) suggested the reason for this is that more resources are made available during complex messages. This means increasing global complexity leads to more resources being allocated to the message, thus increasing available resources. According to this view, global complexity determines the number of resources allocated to the attention task, and the content/structure of the message determines the number of resources required to process the message (Lang and Basil, 1998). Thus their model suggests that STRT is an indicator of available resources, specifically it indexes those resources made available to the cognitive subprocesses of encoding a message not needed to process the message. In other words, the structural features in globally complex messages elicit a large allocation of resources and the demands for resources made by the ongoing processing and storage of messages to encoding, but because many of those structural features do not require many resources to be processed, an over allocation of resources results (Lang and Basil, 1998, p. 458).
Lang and Basil (1998) define available resources as the resources “allocated to the message (which is increasing) minus the resources required by the message” (p. 460). If the resources required are less than the resources allocated, the available resources in global complex video messages may very well increase. Thus, there would be more resources available during complex messages compared to simple messages, and if this occurs as the reaction times will be faster during global video complex messages.
Studies investigating the effects of global and local video complexity suggest a way to directly test this model. Lang et al. (1999) found that cuts (i.e., change from one scene to an unrelated scene) in television programming automatically allocated resources to encoding. As the number of unrelated cuts increases, more attentional capacity is called to encoding. This resulted in a curvilinear effect on recognition (here an index of encoding) such that recognition improves from slow to medium-paced messages but decreases from medium to fast-paced messages, suggesting an overload.
Conversely, Lang et al. (2000) predicted and found that increasing the number of edits defined as “a change from one camera shot to another within the same visual scene” in a message would show an increase in recognition with no overload (p. 99). This is because the edit adds little new information to be processed, hence resources are allocated but are not needed to process the change.
Thus it is logical to use the number of structural features to manipulate how many resources are allocated to encoding, and the type of structural feature (cut or edit) to manipulate how many resources are needed to process the structural feature. Therefore, fast-paced messages with many edits should leave more resources available than a fast-paced message with many cuts.
Examining the four conceptualizations of what STRTs measure outlined by Lang and Basil (1998), we see that a different pattern of STRT results is predicted for this design by each of the conceptualizations. If STRTs measure resources allocated to the message, then STRTs should slow down as the number of cuts and edits increases and there should be no difference between cuts and edits. If STRTs measure resources remaining in the system, there should again be fewer resources remaining for fast paced messages compared to slow, and if cuts require more resources there should be fewer resources remaining for cuts compared to edits. If STRTs measure resources required by the message, then STRTs should decrease as the number of cuts and edits increases with no difference for the type of structural feature. If STRTs measure resources available, then STRTs should speed up as both edits and cuts increase at a fast pace, and there should be a difference between cuts and edits.
The current study was designed to empirically test this prediction. The design is a 3 (slow, medium, fast pacing) X 2 (edits, cuts) completely within design. STRT, recognition, cued recall, arousal, and attention will be dependent variables.
SAM (Self Assessment Mannequin) and skin conductance (SC) will be used to measure arousal. Heart rate will be gathered to measure attention. To measure recognition we will use four alternated multiple-choice tests, and storage will be measured using cued recall.
Resources will be measured using STRTs. Subjects will watch the tapes (primary task) containing related edits and unrelated cuts. When they hear an audio tone they should push a button (secondary task).
Time line
Stimulus materials already have been prepared. Subjects will be run in January and February. Data analyzed in March and April, and the paper will be ready in May.

Lang, A. (2000). The limited capacity model of mediated message processing. Journal of Communication, 50(1), 46-70.

Lang, A. & Basil, M.D. (1998). Attention, resource allocation, and communication research: What do secondary task reaction times measure, anyway? In M. Roloff (Ed.). Mass Communication Yearbook, 21, 443-473. Sage: Beverly Hills, CA.

Lang, A., Bolls, P., Potter, R.F., & Kawahara, K. (1999). The effects of production pacing and arousing content on the information processing of television messages. Journal of Broadcasting and Electronic Media, 43(4), 451-475.

Lang, A., Geiger, S., Strickwerda, M., & Summer, J. (1993). The effects of related and unrelated cuts on viewers’ memory for television: A limited capacity theory of television viewing. Communication Research, 20, 4-29.

Lang, A. Zhou, S., Schwartz, N., Bolls, P.D., & Potter, R. F. (2000). The effects of edits on arousal, attention, and memory for television messages: When is an edit is an edit can an edit be too much? Journal of Broadcasting and Electronic Media, 44(1), 94-109.

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An Empirical Examination of the Secondary Task Reaction Times: Testing What They Really Measure This study tests a model of what secondary task reaction times (STRTs) actually measure as outlined in the STRT model proposed by Lang and Basil (1998). Lang and Basil’s (1998) review of the STRT literature found that past literature referred to four theoretical definitions of what STRTs measure: resources required by a message; resources allocated to the message; resources available for processing; and resources remaining
21 443-473. Sage: Beverly Hills CA. Lang A. Bolls P. Potter R.F. & Kawahara K. (1999). The effects of production pacing and arousing content on the information processing of television messages. Journal of Broadcasting and Electronic Media 43(4) 451-475. Lang A. Geiger S. Strickwerda M. & Summer J. (1993). The effects of related and unrelated cuts on viewers’ memory for television: A limited capacity theory of television viewing. Communication Research 20 4-29. Lang A. Zhou S. Schwartz N. Bolls


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