Instructions: Summarize one of the articles assigned for the topic of the week using the format below.
- Article reference using APA format (5 points):
- Summarize the purpose of the study (at least 3 sentences – 10 points):
- What/who are the subjects and setting (at least 4 sentences – 10 points):
- What experimental design did the authors use?(at least 2 sentences – 10 points):
- Summarize the results of the study? (at least 4 sentences- 10 points):
- What are your criticisms of the study? What is a possible future direction for the research? In other words, what should come next if you were going to conduct the next study? (at least 5 sentences- 5 points):
ARTICLE ATTATCHED
DUE 10/28/22 11:59 PM EST
JOURNAL OF APPLIED BEHAVIOR ANALYSIS
BEHAVIORAL MOMENTUMJN THE TREATMENT OF NONCOMPLIANCE
F. CHARLES MACE
RUTGERS UNIVERSITY
MICHAEL L. HOCK
UNIVERSITY OF VERMONT
JOSEPH S. LALLI, BARBARA J. WEST, P-Huup BELFIORE, AND
ELIzABETH PINTER
LEHIGH UNIVERSrrY
D. KIRBY BROWN
LANCASTER-LEBANON INTERMEDIATE UNIT
Behavioral momentum refers to the tendency for behavior to persist following a change in environmental conditions. The greater the rate of reinforcement, the greater the behavioral momentum.
The intervention for noncompliance consisted of issuing a sequence of commands with which the
subject was very likely to comply (i.e., high-probability commands) immediately prior to issuing
a low-probability command. In each of five experiments, the high-probability command sequence
resulted in a “momentum” of compliant responding that persisted when a low-probability request
was issued. Results showed the antecedent high-probability command sequence increased compliance
and decreased compliance latency and task duration. “Momentum-like” effects were shown to be
distinct from experimenter attention and to depend on the contiguity between the high-probability
command sequence and the low-probability command.
DESCRIPTORS: behavioral momentum, compliance latency, excessive task duration, noncompliance, high-probability command sequence
Noncompliance is one of the most commonly
reported behavior problems in developmentally disabled populations (Schoen, 1983). In addition to
its prevalence, treatment of noncompliance is important because of its covariation with other aberrant and adaptive behaviors. For example, several
studies have demonstrated that increased compliance often results in collateral reductions in aggression, disruption, self-injury, and tantrums (e.g.,
Cataldo, Ward, Russo, Riordan, & Bennett, 1986;
Parrish, Cataldo, Kolko, Neef, & Egel, 1986; RusThis research was funded in part by a grant from the
Pennsylvania Office of Mental Health/Mental Retardation.
The authors gratefully acknowledge the support of Richard
J. Smith, Timothy Boyer, and Al Deibler of Lehigh County
MH/MR that made this work possible.
Experiment 1 and the concepts of applied behavioral momentum and the high-probability command sequence were
presented at the 12th annual ABA convention (Hock & Mace,
1986). Experiments 1-5 were presented in a symposium at
the 13th annual ABA convention (Mace, 1987).
Reprint requests may be addressed to F. Charles Mace,
Graduate School of Applied and Professional Psychology,
Rutgers University, Box 819, Piscataway, NewJersey 08854.
so, Cataldo, & Cushing, 1981). Conversely, reduced noncompliance has been associated with increased appropriate behavior (Baer, Rowbury, &
Baer, 1973). Thus, intervention to increase compliance appears to be an efficient means of improving a range of socially important behaviors.
A variation of noncompliance is slowness to respond to instructions or complete assigned tasks.
Individuals who are excessively slow at completing
tasks may receive less reinforcement (e.g., income
from vocational tasks) and may incur punitive social
responses from peers or staff.
Considerable research has evaluated procedures
for increasing compliance and, to some extent, for
reducing excessive compliance latency and task duration. However, much of this research has been
conducted with children (Breiner & Beck, 1984;
Fjellstedt & Sulzer-Azaroff, 1973; Forehand &
McMahon, 1981). Procedures commonly used to
increase compliance indude time-out (e.g., Parrish
et al., 1986) and guided compliance (e.g., Neef,
Shafer, Egel, Cataldo, & Parrish, 1983). However,
123
1988,211,123-141 NUMBER2 (summER 1988)
F. CHARLES MACE et al.
a potential liability of these procedures is that they
often require physical contact with a client to achieve
treatment integrity, which for large, uncooperative,
or aggressive clients may be ill-advised. Alternatively, the effectiveness of differential reinforcement
of compliant behavior depends on reinforcement
for compliant responses being rich relative to reinforcement produced by noncompliant or dawdling behavior (cf. Ayllon, Garber, & Pisor, 1976;
Cuvo, 1976; Holt, 1971). Unless a more powerful
reinforcer or richer schedule can be applied to compliant behavior compared to the reinforcer and
schedule maintaining noncompliance, differential
reinforcement may not have the desired effect and
punishment-based alternatives may need to be considered (Myerson & Hale, 1984).
Alternative approaches to increasing compliance
with developmentally disabled adults may be derived from consideration of advances in basic operant research (e.g., Deitz, 1978; Hayes, Rincover,
& Solnick, 1980; Michael, 1980; Pierce & Epling,
1980). For example, Nevin has discussed the relationship between response strength and rate of
reinforcement (see Nevin, 1974, 1979, for reviews). Behavior maintained at steady states by
interval or ratio schedules of reinforcement has been
shown to persist over time following a change in
reinforcement conditions (de Villiers, 1977; Nevin,
1979; Zeiler, 1977). This resistance to change in
the face of altered contingencies has been referred
to as “response strength” (Herrnstein, 1970; Nevin, 1979). Response strength may be relatively low
when response patterns change readily or relatively
high when response rates are slow to change under
modified conditions. In general, behavior controlled
by a multiple schedule will be more resistant to
change during the schedule component that has a
comparatively higher rate of reinforcement. That
is, a relatively higher rate of reinforcement will
result in relatively greater resistance to change or
greater response strength.
Nevin, Mandell, and Atak (1983) suggested a
parallel between a behavior’s resistance to change
and the momentum of objects in motion as described by Newton’s first law of motion. They
argued that it may be worthwhile to consider behavior at possessing the property of momentum.
Accordingly, behavioral momentum can be analyzed in terms analogous to the product of mass
and velocity in classical physics (Nevin et al., 1983,
p. 49). Behavioral mass was considered formally
analogous to response strength and behavioral velocity as corresponding to response rate. Nevin et
al. demonstrated that behavior controlled by a twocomponent multiple schedule procedure was more
resistant to change in the component with a relatively higher rate of reinforcement when reinforcement was provided noncontingently, or when all
reinforcement was discontinued. Thus, factors that
influence rate of reinforcement may be expected to
affect a behavior’s resistance to change.
Consideration of Nevin et al.’s (1983) work on
behavioral momentum prompted us to develop a
novel intervention for noncompliance and excessive
compliance latency and task duration. This procedure, referred to as the high-probability command sequence, indirectly manipulates rate of reinforcement to establish what appears to be a
“momentum” of compliant behavior that may persist when subjects are asked to perform a task with
a low probability of compliance. Our objectives in
the following series of experiments were (a) to evaluate the effectiveness of the high-probability command sequence in increasing compliance to “do”
and “don’t” commands (Neef et al., 1983) (Experiment 1), (b) to conduct preliminary investigations regarding the appropriateness of the behavioral momentum analogy (Experiments 2 and
3), and (c) to evaluate the generality of the procedure to reduce excessive compliance latency and
task duration (Experiments 4 and 5).
EXPERIMENT 1
METHOD
Subject and Setting
Bart, a 36-year-old man with severe mental retardation (IQ = 42), served as the subject in this
experiment. Bart had resided in large, state-operated institutions for most of his life and had a long
history of noncompliance and aggression. Bart’s
124
BEHAVIORAL MOMENTUM
large physical stature (height 6’1″, weight 200 lb)
contributed to the severity of his noncompliance
and aggression. In his first community placement,
these behaviors eventually resulted in his recommitment to a private institution.
At the time of the present experiment, Bart had
lived in a university-affiliated group home for approximately 18 months. The program was behavior-analytic in nature and was operated by university graduate students and faculty. Typical staffing
patterns consisted oftwo graduate students working
with six adults with moderate to severe mental
retardation. After 6 months in this program, Bart
became increasingly noncompliant and aggressive.
A structured self-management program consisting
of positive reinforcement for completion of house
jobs and personal hygiene, without aggressive incidents, was effective only for periods of 2 to 3
months.
Sessions were conducted in the living room (5
m by 4 m), family room (3.5 m by 3 m), and
kitchen (5 m by 4 m) of the home. An experimenter, one or two data collectors, and zero to two
other clients were present during these sessions.
Interactions between staff and other dients were
minimal; client-clent interactions were unrestricted. Because of the applied nature of the research,
the subject was allowed free movement in these
rooms to assess experimental effects under natural
conditions.
Response Definitions, Measurement, and
Interobserver Agreement
The principal dependent measure was the percentage of compliance to low-probability (low-p)
“do” and “don’t” commands. In Experiment 1,
low-p commands were instructions or requests issued by the experimenter to the subject with which,
in the experimenter’s experience, the subject was
unlikely to comply. (In the remaining four experiments, the probability value of both low-p and
high-p commands was empirically determined.)
Examples of low-p “do” and “don’t” commands
are “Bart, please put your lunch box away” and
“Bart, please don’t leave your lunch box on the
table.” Commands called for performance of simple tasks that could be completed within 30 to 60
s (i.e., “do” commands) or discontinuation of an
undesirable behavior or condition (i.e., “don’t”
commands). Command compliance was defined as
the subject initiating the response called for by the
command within 10 s of the stated command and
eventually completing the requested response(s).
The independent variable in this experiment was
a sequence of high-probability (high-p) commands
that was issued prior to a low-p command. High-p
commands were instructions or requests with which
the subject had a history of complying. These commands were always stated as a “do” request and
are exemplified by the following: “Give me five,
Bart,” “Come here and give me a hug,” and “Show
me your pipe (or wallet, notebook, etc.), Bart.”
The mean percentage compliance to high-p commands during the entire experiment was 98%.
Two trained observers recorded (a) experimenter
commands or requests directed to the subject for
low-p and high-p behaviors, (b) compliance to “do”
and “don’t” low-p commands, and (c) compliance
to high-p commands. A count of all responses was
made during continuous 10-s intervals. A percentage compliance measure was derived for each
session by dividing the number of compliant responses (of a given dass) by the number of experimenter requests for responses (of the same dass)
and multiplying by 100. Observers stood within
2 to 5 m of the experimenter and subject but did
not speak or make eye contact with the subject.
The second observer independently collected interobserver agreement data from a position no closer
than 2.5 m from the primary observer during an
average of 66% of the sessions across all phases
and conditions of the experiment. For the first three
experiments, total, occurrence, and nonoccurrence
agreement were calculated on a point-by-point basis
within all intervals per session (Page & Iwata, 1986).
Table 1 presents the mean and range of interobserver agreement values for the dependent and independent variables for all experiments.
Procedures
Baseline. During each baseline session, the experimenter stood or sat within 1 to 2 m of the
F. CHARLES MACE et al.
Table 1
Interobserver Agreement: Mean and Range Percentages for Total Agreement (TA), Occurrence Agreement (OA),
Nonoccurrence Agreement (NOA), and Agreement (A) within ±1 s across the Dependent and Independent Variables of
Experiments 1 through 5
Experiment 1 Experiment 2
TA OA NOA TA
Dependent variables
Compliance with “do” commands 99 99 94 99.5
(93-100) (93-100) (63-100) (95-100)
Compliance with “don’t” commands 97 79 96
(92-100) (53-100) (91-100)
Compliance with “do” commands 99.1
(during attention control) (97-100)
Latency to initiate task
Minutes to complete task
Independent variables
Compliance with high-p commands 93 85 89 94.7
(83-98) (71-94) (77-97) (75-100)
Occurrence of high-p 93 85 89 96.4
(83-98) (71-94) (77-97) (87-100)
Occurrence of attention – 98.4
(97-100)
Occurrence of 5-s IPT
Occurrence of 20-s IPT
Occurrence of prompts
Occurrence of contingency statement
Delivery of reinforcement
subject. The primary data collector prompted the
experimenter to issue a command to the subject on
a fixed-time (FI) 1-min schedule. The experimenter made eye contact with the subject and issued
a low-p command or request to Bart in a pleasant
tone of voice. Low-p commands were selected at
random from a pool of 20 low-p commands or, in
the case of many low-p “don’t” commands, were
chosen on the basis of the subject’s behavior (e.g.,
“Bart, don’t put your feet on the coffee table”). If
the subject satisfied the definition of command
compliance, the experimenter provided immediate
descriptive praise (e.g., “That’s good Bart, thanks
for putting your lunch box away”). Descriptive
praise was used as a consequence for compliance
for subjects in all five experiments because, in the
experimenters’ experience, praise appeared to be an
effective reinforcer for these individuals. “Do” and
“don’t” command sessions differed only in the dass
of commands issued to the subject (i.e., either all
“do” or all “don’t” low-p commands).
Psychotropic intervention-Haldol. On Day 7
of the experiment, Bart’s psychiatrist prescribed 10
mg of Haldol b.i.d. to control his aggressive behavior. This represented a return-to-Haldol intervention, which Bart had experienced during the
past 7 years, after a 6-week period of medication
withdrawal. Baseline procedures remained in effect.
Psychotropic intervention continued during all subsequent phases of the experiment.
High-probability command sequence. This
condition was identical to the baseline procedures
except that each low-p command was preceded by
a sequence of high-probability (high-p) commands.
The high-p command sequence consisted of the
experimenter issuing a series of three or four high-p
commands or requests to the subject immediately
preceding presentation of the low-p command.
126
BEHAVIORAL MOMENTUM
Table 1
(Continued)
127
Experiment 2 Experiment 3 Experiment 4 Experiment 5
OA NOA TA OA NOA TA A ± I s TA A ± I s
96.7 99.6 99 98.7 97 –
(69-100) (96-100) (92-100) (92-100) (80-100)
– – 99 97 97 –
(93-100) (88-100) (82-100)
90.9 99.4 – – 100
(71-100) (97-100)
100
-_- — – – – – 100
95 97.1 95 95 93 100 – 100
(77-100) (86-100) (90-100) (88-100) (79-100)
95.4 97.1 96 96 94 100 100 –
(82-100) (92-100) (91-100) (92-100) (87-100)
94 98.6 100 –
(88-100) (95-100)
– – 99 96 99 –
(98-100) (88-100) (96-100)
99 95 99 – –
(98-100) (82-100) (97-100)
– – – – – – – 100 –
-_— – – 100
100
High-p commands were issued at 10-s intervals
(i.e., the interval between completion of a high-p
task and the next high-p command).
Experimental Design
The experimental conditions described above were
presented to the subject during two 1 5-min sessions
daily that were separated by a 15- to 30-min free
time period. Because “do” and “don’t” commands
have been shown to be members of different stimulus dasses (Neef et al., 1983), sessions with either
all “do” commands or all “don’t” commands were
alternated in a multielement design (Sidman, 1960).
The order in which “do” and “don’t” command
sessions were conducted was determined randomly
each day. In addition, the independent variable was
alternately applied and withdrawn during “do” and
“don’t” command sessions in the context of a reversal design (Sidman, 1960).
REsuLsT
Figure one represents Bart’s percentage of compliance to low-p commands during “do” and
“don’t” command sessions across all phases of the
experiment. During baseline, Bart’s compliance to
low-p requests during “do” sessions averaged 47%
and during “don’t” sessions 53.5%. With the addition of psychotropic medication, mean compliance to “do” commands was 68% versus 53.5%
for “don’t” commands.
During Phase 3, application of the high-p command sequence prior to each “don’t” command
resulted in an increase in mean compliance to 87.5%.
Compliance to “do” commands, which remained
under baseline conditions, averaged only 61%. In
Phase 4, the pattern of compliance reversed with
“do” command sessions increasing to a mean of
90.5% following application of the high-p com-
128 F. CHARLES MACE et al.
High Probability Command Sequence
Preceding
A Low Probability Command
A
00)
100
z
< 80
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a.
>- 60
c –
u K5
w m 40
w 0
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Baseline
“Don’t”
“Do”
5
Psychotropic Applied to Applied to Applied to Applied to
Intervention-Holdol “Don’t” Commonds ‘Do” Commonds ‘Don’t”Commands ‘Do” a”Don’t” Commands
L~~~~ WI
>I KY
10 15 20 25 30 35
SESSIONS
Figure 1. Bart’s percentage compliance to low-probability “do” and “don’t” commands under baseline and psychotropic
intervention conditions, and alternate application and withdrawal of the high-probability command sequence.
mand sequence. “Don’t” command compliance returned to low levels during this period (M = 44%).
In the fifth phase, compliance to “don’t” commands returned to high levels when low-p commands were preceded by a series of compliant responses (M = 91%). Compliance to “do”
commands, which were not preceded by the high-p
command sequence, averaged only 56%. In the
final experimental phase, use of the high-p command sequence resulted in high levels ofcompliance
to both “do” and “don’t” commands. Mean compliance ranged from 87% to 97% for “do” command sessions (M = 93%) and 85% to 97% for
“don’t” command sessions (M = 90%).
DISCUSSION
This experiment demonstrated the effectiveness
of preceding a low-probability command with a
sequence of high-probability commands in the
treatment of noncompliance. Establishing a pattern
of compliant responding by the subject immediately
prior to the issuance of a low-p request resulted in
increases in the subject’s compliance. Our objective
in the second experiment was to assess the subject
generality of the high-p procedure and to examine
possible effects of positive attention alone on compliance.
EXPERIMENT 2
MEHOD
Subject and Setting
The subject of the second experiment was Ned,
a 44-year-old severely retarded (IQ = 21) male
with Down Syndrome. Ned had lived in institutions for most of his life. When asked to perform
a task, Ned typically shook his head “no” and
looked away. Occasionally, he would throw items,
curse, spit, hit others, or lie on the floor when such
commands were issued.
The setting for the study was the same house as
in Experiment 1. During the first four phases of
the experiment, baseline and treatment high-p command sessions were conducted in the kitchen. Attention control sessions were held in the subject’s
second floor bedroom (4 m by 3.5 m). Persons
present and their interactions during the sessions
were similar to those in the first study.
Response Definitions, Measurement, and
Interobserver Agreement
Ned primarily did not comply with “do” requests. The procedure for identifying commands
to which the subject had a low probability of complying consisted of the experimenter approaching
I II
BEHAVIORAL MOMENTUM
Ned and asking him to perform each of 25 tasks
on separate occasions. Ten separate trials were conducted for each of the 25 tasks; those commands
that were complied with four or fewer times in 10
trials were designated as low-p commands. This
procedure resulted in a pool of 15 low-p “do”
commands that were used in the experiment. A
similar procedure used with high-probability commands (i.e., at least 80% compliance) resulted in
the following high-p command sequence: (a) “Ned,
give me five,” (b) “Give me a bump” (i.e., the
experimenter and subject bumped hips in a dancing
motion), and (c) “Ned, show me your radio.”
The definition of command compliance and the
data collection procedures for the primary and secondary observers were identical to those in Experiment 1 (see Table 1 for interobserver agreement
values).
Procedures
Baseline. The actions of the experimenter and
data collectors during this condition were virtually
identical to those described for the baseline condition for Experiment 1.
High-probability command sequence. The presentation of the high-p command sequence and the
experimenter’s response to compliance were identical to the procedures used during this condition
in the first experiment.
Attention control. This condition was designed
to provide experimenter attention prior to issuance
of a low-p command without providing specific
discriminative stimuli for behaviors presumably
maintained by high rates of reinforcement. On an
FT 1-min schedule, the experimenter sat or stood
within 1 to 2 m of the subject and directed a
sequence of three or four neutral or positive comments to the subject. Comments were randomly
selected from a pool of 2 5 statements. The interval
between comments ranged from 10 to 15 s. Examples of these comments included “Ned, that’s
a nice shirt you’re wearing,” “We’re going bowling
this afternoon,” and “I’m going to visit my parents
this weekend.” Within 10 s of the last comment,
the experimenter issued a randomly selected low-p
command to the subject. Compliance to low-p commands resulted in descriptive praise on a continuous
reinforcement (CRF) schedule.
Experimental Design
The experimental procedures were presented daily during two 1 5-min sessions separated by a 15-
to 30-min free time period. An ongoing attention
control condition was alternated with either the
baseline or the high-p command sequence condition
in a multielement design. The order in which conditions were conducted was determined randomly
each day. The effects of the high-p command sequence were evaluated with an A-B-A-B reversal
design. In the final phase of the experiment, the
settings in which the high-p command sequence
condition and the attention control condition were
conducted were reversed to control for possible effects of setting-specific commands.
REsuLTs
Figure 2 depicts Ned’s compliance to low-p
commands during all baseline, high-p command
sequence, and attention control conditions. In the
initial baseline phase, issuing low-p commands
without a preceding high-p command sequence resulted in a mean compliance of 26%. When experimenter attention preceded each low-p command, compliant behavior was similar to baseline
(M = 35%). During Phase 2, application of the
high-p command sequence effected an increase in
mean compliant responses to 73%. Compliance
during the attention control sessions remained essentially unchanged from the previous phase (M =
38%).
A return to baseline condition in the third phase
produced an immediate decrease in the subject’s
percentage compliance (M = 39%). Comparable
levels of compliance (M = 43%) continued during
the subsequent attention control condition. In the
fourth phase, high levels of compliance occurred
when the high-p command sequence was reinstated
(M = 84%). Average percentage compliance increased slightly during the ongoing attention control
condition (M = 51%). Finally, the setting reversal
had little effect on the subject’s pattern of compliance during the high-p command sequence (M =
79%) and attention control conditions (M = 47%).
F. CHARLES MACE et al.
No High- P Command
Sequence Preceding A
o CX Low – P Command I .equence Qequec I zeiII
10 I Reversal
z G 800 I
IY3 0 ~ Attention Preceding A
0-
0-ijW/C-40
o Low-P Command
Cr 0 w/o Attention li
W 0 ‘
5 10 15 20 25 30
SESSIONS
Figure 2. Ned’s percentage compliance to low-probability commands during the attention control condition and alternate
application and withdrawal of the high-probability command sequence. In the final experimental phase, the settings in
which the attention control and high-p sequence were conducted were reversed.
DISCUSSION
Results of the second experiment support the
subject generality of the effects produced by the
high-p command sequence inasmuch as the effects
for Ned and Bart were similar. A second important
finding was that experimenter attention was not
itself sufficient to occasion compliance to low-p
requests. That is, experimenter comments presented
in the same manner as high-p commands failed to
influence the probability of subject compliance. This
finding suggests that presentation of discriminative
stimuli for high-probability behaviors is critical to
the momentum-like effects observed.
In the third experiment, we investigated another
parameter of the high-p command procedure that
may determine its effectiveness as an applied procedure and further examines the value of the behavioral momentum analogy. Nevin et al. (1983)
found that resistance to change or behavioral momentum was directly related to rate of reinforcement. The higher the relative rate of reinforcement,
the greater the resistance to change. Therefore, it
may be logical to predict that momentum-like effects will decrease with an increase in the interval
between the last high-p command in the sequence
(or between any high-p commands in the sequence)
and the statement of the low-p command. Increasing this interval presumably has the effect of decreasing rate of reinforcement which, in turn, should
decrease behavioral momentum.
EXPERIMENT 3
METHOD
Subject and Setting
The subject and setting in which experimental
sessions were conducted were identical to those described in Experiment 1. Bart continued to take
10 mg of Haldol b.i.d. for the duration of the
study. This experiment was conducted 1 month
after completion of the first study.
Response Definitions, Measurement, and
Interobserver Agreement
As in Experiment 1, the principal dependent
measure in the third experiment was the percentage
compliance to low-p “do” and “don’t” commands.
The procedure described in Experiment 2 to identify low- and high-probability commands was used
to define a pool of 15 low-p “do” commands, 10
low-p “don’t” commands, and seven high-p “do”
130
BEHAVIORAL MOMENTUM
High Probability Command Sequence
Preceding
A Low Probability Command
A
20-s IPT a”Do” 20-s IPT”Don’t
5-s IPT “Donti 5-s IPT “Do”
to”Don’t”l
‘~~
~~I
5
20-s IPT “Do” 120-sIPT”Don’t”20-s IPT “Do” | 5-s IPT
5-s IPT “Don’t” 5-s IPT “Do” 5-s IPT” Don’t Do a “Don’t f~~~~~~fS~Dot D Dn
I, ,.al. .l I . , , , , . , ,
10 15 20 25 30 35
SESSIONS
Figure 3. Bart’s percentage compliance to low-probability “do” and “don’t” commands during 5-s and 20-s IPT
applications of the high-probability command sequence.
commands. Additional low-p “don’t” commands
were extemporaneously selected during “don’t”
command sessions corresponding to the subject’s
aberrant behavior.
Definitions and procedures used to measure command compliance for “do” and “don’t” low-p
commands and high-p commands were identical to
Experiment 1. The independent variable manipulated in this study was interprompt time (IPT).
IPT was defined as the time interval beginning with
the cessation of the last high-p command in the
high-p command sequence and ending with the
onset of the low-p command. Independent observers measured this interval using a stopwatch. Time
measurements within ±2 s were considered in
agreement. Interobserver agreement measures were
taken for the dependent and independent variables
on an average of 53% of the sessions during all
phases and conditions of the study (see Table 1).
Procedures
High-probability command sequence-20-s
IPT. All procedures in this condition were identical
to those described for the high-p command sequence in Experiment 1 with one exception. After
the last high-p command in the high-p command
sequence was issued, the experimenter paused 20
s without speaking to the subject, and then stated
a randomly selected low-p “do” command or a
low-p “don’t” command that corresponded to the
subject’s inappropriate behavior. The primary data
collector timed the IPT interval and nonvocally
cued the experimenter to deliver the low-p command.
High-probability command sequence-5-s
IPT. This condition consisted of the same procedures described for the high-p command sequence
in the first study except that the 5-s IPT interval
was timed by the primary data collector.
Experimental Design
The high-p command sequence preceded each
low-p command in all sessions of the experiment.
“Do” command sessions and “don’t” command
sessions were alternated in a random order daily
according to a multielement design. The effects of
5-s and 20-s IPTs were compared by alternately
applying each IPT condition to “do” and “don’t”
command sessions across successive phases of the
study in the context of a reversal design.
RESULTS
Bart’s compliance to low-p “do” and “don’t”
commands under 5-s and 20-s IPT conditions is
presented in Figure 3. During Phase 1, application
of the high-p command sequence with a 5-s IPT
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80
60
40
20
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F. CHARLES MACE et al.
to “don’t” commands resulted in consistently higher
compliance (M = 83%) than the high-p sequence
with a 20-s IPT applied to “do” commands (M =
53%). When IPT conditions were reversed in the
second phase, mean compliance to “do” commands
using a 5-s IPT increased to 89%, whereas compliance to “don’t” requests dropped with the use
of a 20-s IPT to an average of 27%. The reversal
pattern continued during Phases 3 through 5. Mean
percentage compliance with the 5-s IPT was 80%,
86%, and 78% for Phases 3, 4, and 5, respectively.
By contrast, the high-p command sequence with a
20-s IPT resulted in low levels of compliance. Percentage compliance averaged 22%, 29%, and 37%
during the third through fifth phases of the experiment, respectively. In the final phase, compliance averaged 91% for “do” commands and 78%
for “don’t” commands when the high-p procedure
was used with the 5-s IPT for both stimulus classes.
DISCUSSION
Two important findings may be gleaned from
the third experiment. First, the momentum-like
effects produced by the high-p command sequence
appear to depend on the temporal contiguity between the high-p command sequence and the low-p
command. The relatively longer IPT interval failed
to elevate compliance levels above those achieved
by this subject during the baseline phase of Experiment 1. Thus, on the basis of this subject’s
data, it appears that practitioners must ensure that
low-p commands are issued immediately after the
high-p sequence. Extensions of the IPT interval
appear to negate the controlling effect that high-p
commands have on compliant behavior. Second,
these findings may have been due to differences in
the rate of reinforcement between the 5-s and 20-s
IPT condition. Issuing three high-p commands at
10-s intervals followed by a low-p command at an
IPT of 20 s results in a reinforcement rate that is
approximately half the rate using a 5-s IPT. Thus,
the results of Experiment 3 are predicted by the
behavioral momentum analogy, if rate of reinforcement is analogous to behavioral mass as Nevin et
al. (1983) have argued.
The fourth and fifth experiments examined the
application of the high-p command procedure to
a problem related to noncompliance, excessive response latencies. These studies investigated whether
the high-p command procedure could reduce subjects’ latency to respond to experimenter commands
or requests to perform tasks.
EXPERIMENT 4
METHOD
Subjects and Setting
Two adult men with moderate mental retardation served as subjects. Tim was a 34-year-old male
with Down Syndrome (IQ = 53) who lived with
his parents until age 33. He performed most selfcare skills and household tasks independently.
However, the speed with which he responded to
staffrequests was extremely slow. During the period
following staff instructions, Tim would typically
engage in various forms of stereotypy or stare into
space and move very slowly toward initiation of
the task.
The second subject, Mitch, was 45 years old
(IQ = 47) and had lived most of his life in stateoperated institutions. He had grand mal seizures
that were controlled by 500 mg of Tegretol and
250 mg of Mysoline per day. His psychiatrist also
prescribed 100 mg of Mellaril per day to control
his “psychotic” behavior, which consisted of talking
to himself or talking out of context. Mitch was
skilled at most self-care and household tasks; however, he was sometimes very slow to respond to
staff requests or spent excessive periods of time
performing tasks such as showering, making his
bed, or preparing his lunch. When off-task, Mitch
would typically stare into space or talk to himself.
Both subjects lived in the community group home
described in Experiment 1. All sessions were conducted in the kitchen.
Response Definitions, Measurement, and
Interobserver Agreement
The dependent measure for both subjects was
compliance latency defined as the interval beginning
with the completion of an experimenter’s instruc132
BEHAVIORAL MOMENTUM
tion and ending with initiation of the specified task.
Task initiation for Tim was defined as lifting his
plate or glass from the dining table. For Mitch,
task initiation entailed performing one of the following depending on the task selected: (a) lifting
the kitchen trash container, (b) lifting a broom, or
(c) touching the mirror-cleaning materials. Compliance latency was measured in seconds by the
experimenter using a stopwatch. A trained independent observer collected interobserver agreement
measures for the dependent and independent variables on an average of 52% and 40% of the sessions
across conditions of the study for Tim and Mitch,
respectively. All interobserver latency measures
agreed to within ± 1 s (see Table 1).
Measures of the integrity of the independent
variables were obtained for all sessions. Event records were collected for the following variables during their respective experimental sessions: (a) occurrence of high-p commands, (b) compliance with
high-p commands, and (c) occurrence of attention
statements. The integrity measures indicated that
the experimenter issued high-p or attention statements according to the procedures on 100% of the
compliance trials. Compliance to high-p commands
was 100% for both subjects. Interobserver agreement calculated on a trial-by-trial basis was 100%
for all independent variables (see Table 1) (Page
& Iwata, 1986).
Procedures
Baseline: No high-probability command sequence. For Tim this condition was conducted immediately after he finished eating his breakfast,
lunch, or dinner. Tim was seated along one side of
an oblong dining table and the experimenter was
seated across from him. Within 5 s of the subject
placing his napkin on his plate indicating the end
of the meal, the experimenter made eye contact
with Tim and issued the following instruction:
“Tim, please dear your place at the table.” The
experimenter remained seated and directed no other
comments to Tim until he complied with the task
request (i.e., rinsed his plate and glass and placed
them in the dishwasher). Descriptive praise was
provided immediately after Tim performed the task.
During baseline for Mitch, the experimenter took
Mitch into the kitchen where all task materials were
located and issued one of the following five randomly selected task commands: (a) “Mitch, please
empty the trash,” (b) “. . . sweep the downstairs
(or upstairs) bathroom floor,” or (c) “… clean the
downstairs (or upstairs) bathroom mirror.” Procedures for descriptive praise were identical to those
described for Tim.
High-probability command sequence. Procedures in this condition were identical to baseline,
except preceding the statement of each task request,
the experimenter delivered the following sequence
of high-p commands in a manner identical to that
described in Experiments 1 through 3: “Tim (or
Mitch), shake my hand,” “Tim (or Mitch), give
me five,’ and “Tim (or Mitch), give me a hug.’
Within 10 s of the subject’s compliance to the last
high-p command in the sequence, the experimenter
issued the task request described for each subject
during baseline.
The probability value of each high-p command
was determined empirically prior to the experiment.
Ten separate trials for each of the high-p requests
were conducted for both subjects. Trials were separated by at least 15 min. Both subjects complied
with all high-p requests 100% of the time during
this preliminary assessment.
Attention control. Procedures in this condition
were the same as those described in Experiment 2.
Experimental Design
Experimental conditions were presented in the
context of a multielement design. During the first
9 days of Tim’s study, the baseline, high-p command sequence, and attention control conditions
were administered one per day in a random order
across days without balancing the number of times
each condition was conducted. On Days 10 through
2 7 these conditions were administered in a random
and balanced order.
Six sessions were conducted for Mitch each day,
with two of each of the three types of tasks represented (i.e., empty trash, sweep floor, clean mirror). Each day of the experiment a baseline and
high-p command sequence condition were con133
F. CHARLES MACE et al.
TI
High-P Command Sequence
5 10 15 20 25
Sessions
Figure 4. Latency in seconds to initiate task following staff instruction during baseline (no high-p) and high-p command
sequence condition (Tim and Mitch), and attention control condition (Tim). Different data symbols represent different tasks
for Mitch.
ducted for each of the three task pairs. The order
in which these six sessions were conducted was
determined randomly on a daily basis.
RESULTS
Figure 4 represents the subjects’ latency to initiate each task following a staff instruction during
the different experimental conditions. During the
baseline or no high-p command sequence condition,
Tim’s compliance latency varied greatly from 12 s
to 848 s (M = 156 s). Experimenter comments
prior to the task command during the attention
control condition produced results similar to baseline. Mean compliance latency was 117 s with a
range of 16 s to 416 s. By contrast, Tim consistently
responded quickly to experimenter instructions that
134
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BEHAVIORAL MOMENTUM
were preceded by the high-p command sequence.
His average latency to compliance was 17 s, with
a narrow range of 11 to 25 s.
Similar results were obtained for Mitch. Without
the preceding high-p command sequence, compliance latency was quite variable and often lengthy.
During baseline, the average latency to compliance
across all three task types was 151 s (range, 5 s to
377 s). Use ofthe high-p command sequence sharply
reduced the subject’s latency to comply. Mean compliance latency with the high-p procedure was 10
s across all task types. Momentum-like effects were
also usually consistent across the three types of tasks
used in the experiment. With the high-p command
sequence, average latencies to initiate emptying trash,
sweeping floors, and cleaning mirrors were 5 s, 17
s, and 8 s, respectively. Without the high-p procedure, compliance latencies averaged 98 s, 160 s,
and 194 s for the three tasks, respectively.
The fifth experiment extended the application
of the high-p command sequence to reduce the
time a subject spent performing an entire task.
When applied to reduce task duration, the high-p
command procedure was presented when off-task
behavior or dawdling occurred in the course of
performing the task. Because the high-p command
sequence requires continual supervision of the task
and is more complex to administer than simple
prompts to resume task-related behavior, the applied value of the high-p procedure for reducing
excessive task duration depends on it being highly
effective. For this reason, the fifth study compared
the effectiveness of the high-p command sequence
with simple prompts and a contingency management procedure.
EXPERIMENT 5
METHOD
Subject and Setting
Mitch (of Experiment 4) served as the subject
in this study. The target behavior of interest was
the excessive amount of time Mitch spent taking
a shower.
The study was conducted in the group home’s
second floor bathroom (2.5 m by 3.5 m) and Mitch’s
bedroom (5 m by 5 m). The bathroom was equipped
with a tub, shower head, and plastic shower curtain,
and was located 6 m down the hallway from Mitch’s
bedroom. In general, only the experimenter and
secondary observer were present during experimental sessions.
Response Definitions, Measurement, and
Interobserver Agreement
Showering sessions were divided into three task
segments which Mitch, on average, spent comparable amounts of time performing. The dependent measure was the time elapsed to complete
each of the three task segments. Task Segment 1
was shower preparation and was defined as the
period beginning with the experimenter’s instruction “Mitch, it’s time to take your shower” and
ending with the subject entering the bathroom
wearing his bathrobe and slippers and carrying a
towel and washcloth. During this segment the subject undressed in his room, put his clothes away,
dressed in his bathrobe and slippers, and obtained
a towel and washdoth from his drawer. The second
task segment was showering, which began with the
end of Task Segment 1 and ended when the subject
turned off the shower water. During this period,
Mitch undressed, washed most body parts, and
shampooed his hair. Task Segment 3 began with
the end of Task Segment 2 and ended when the
subject was dressed in his pajamas and slippers.
This task had become very routine for Mitch, and
no steps were omitted during any session of the
experiment.
Task segment durations were measured by the
experimenter using a stopwatch. During baseline,
the experimenter assumed a position in the hallway
that would permit observation of Mitch’s bedroom
and the bathroom. During intervention phases,
timing took place in the room in which the subject
was located. Interobserver agreement measures for
the dependent and independent variables were collected in no less than 29% of the sessions across
all conditions and phases of the study. All inter135
F. CHARLES MACE et al.
observer duration measures were within ± 1 s (see
Table 1).
Event recording was used to measure the integrity of the independent variables during all sessions.
The following variables were measured during the
conditions in which they occurred: (a) occurrence
of vocal prompts, (b) occurrence of contingency
statement, (c) occurrence of high-p commands, (d)
compliance with high-p commands, and (e) delivery of contingent reinforcement. The integrity measures indicated that (a), (b), (c), and (e) were administered the number of times described in the
procedure section on 100% of the sessions. Percentage compliance to high-p commands (d) was
96% for Mitch. Interobserver agreement computed
on a trial-by-trial basis was 100% for all independent variables (see Table 1).
Procedures
Baseline. Sessions were begun at approximately
8:00 p.m. each evening. The experimenter approached Mitch, made eye contact, and provided
the instruction to take a shower. No other instructions or contingencies were announced. The experimenter followed the subject upstairs and continued
timing task duration from the hallway position.
When Task Segment 3 was completed and the
subject exited his bedroom, the experimenter said
“Mitch, I’m glad to see you finished your shower.”
Contingency management. Procedures in this
condition were identical to baseline with the following exceptions. The experimenter (and secondary observer) stood approximately 1 to 3 m from
the subject. Contingent on the first occurrence of
off-task behavior the experimenter showed Mitch
two cupcakes, two quarters, and one of his favorite
books and said “Mitch, if you finish (last step in
the task segment) by the time the buzzer sounds
you can have your choice when you’re done with
your shower.” “Off-task” was defined as 15 continuous seconds of any behavior that was unrelated
to completion of the task. Examples included (a)
repetitive motor movements such as removing or
replacing his watch, wallet, comb, etc., (b) rearranging items on his dresser, (c) talking to himself
or out of context without working on the task, and
(d) staring into space. After stating the contingency,
the experimenter set a kitchen timer for 16 min,
positioned the timer within Mitch’s view, and left
the room. The 16-min criterion was 2 min lower
than the subject’s lowest baseline data point. When
the timer sounded, the experimenter entered the
room, told the subject whether or not the reinforcer
had been earned, and praised successful task completion. On 85% of the sessions, the experimenter
stated the contingency within 60 s of the onset of
the task segment and between 60 s and 120 s
during the remaining sessions.
Prompts. These procedures paralleled baseline
except that the experimenter stood within 1 to 3
m of the subject and provided a combination vocal
and gestural prompt to resume the task contingent
on each occurrence of off-task behavior. The prompt
was repeated every 15 s until the subject resumed
on-task behavior. Descriptive praise was delivered
for compliance with the prompts. An average of
4.7 prompts per session were required to sustain
Mitch’s involvement in the task (range, 1 to 13).
High-p command sequence. The procedures in
this condition were the same as the prompt condition except that the high-p command sequence
was applied instead of a prompt, contingent on
each instance of off-task behavior. The high-p commands, timing of high-p commands, and descriptive praise were identical to Experiment 4. Durations of each high-p command sequence were
induded in the measures of task duration. The
mean number of high-p command sequences administered per session was 1.8 and 1.5 during Phases 2 and 4, respectively.
Experimental Design
Experimental conditions were administered in
the context of a four-phase multielement design.
Baseline conditions were in effect during all task
segments for the first and third phases of the experiment. Phase 2 randomly assigned the contingency management, vocal prompts, and the high-p
command sequence conditions to Task Segments 1
through 3 for each day of the experiment. In the
fourth phase, the high-p command sequence was
applied during all three task segments per session.
136
BEHAVIORAL MOMENTUM
Comparison of High- P Command Sequence,
Prompts, ed Cestingemey Msongement
It!
o~ . .1. ……… 1|
U)5401 0 2 W3 04
30 –
a
Yolk SegomS
I
5 tO iS 20 25 30 38 40 45
Non – Consecutive Days
Figure 5. Minutes to complete each of three showering task segments during baseline, alternating treatments, and
application of the most effective treatment during the entire task. Different data symbols correspond to different task segments.
REsuLTs
Durations for each of the three task segments
during all experimental conditions are presented in
Figure 5. In the first baseline phase, durations were
similar although quite variable across the three task
segments. The average time spent performing Task
Segment 1 was 35 min. Mean durations for the
second and third task segments were 31.8 min and
33.9 min, respectively.
All three interventions resulted in faster performance of task segments compared to baseline. The
most effective procedure was the high-p command
sequence, which reduced task durations to a mean
duration of 10.3 min per task segment (range, 4.3
min to 15.2 min). Prompts were the next most
effective, reducing task duration to approximately
one half of baseline. With prompts, task segment
durations averaged 16.7 min. Least effective of the
three interventions was the contingency management procedure. Reinforcement of short task durations resulted in an average of 18.4 min per task
segment. Mitch met the criteria for reinforcement
on 57% of the sessions during this condition.
The return to baseline condition in the third
phase of the study again resulted in longer task
durations. However, unlike the first baseline phase,
Mitch spent considerably more time performing
Task Segment 1 than Segments 2 and 3. Average
duration for the first task segment was 40.7 min
compared to 21.6 min and 28.7 min to complete
Task Segments 2 and 3, respectively. Although
performance in the second baseline was differentiated on the basis of task segment, the overall time
required to complete the three task segments was
similar for both baseline phases. Mean overall task
duration was 100.7 min for Baseline 1 and 91 min
for Baseline 2.
In the final phase of the study, the most effective
intervention was applied during all task segments.
Administration of the high-p command sequence
during the entire task resulted in uniformly short
task segment durations. The mean durations for
Task Segments 1 through 3 were 9.7 min, 12.2
min, and 12.1 min, respectively. This resulted in
an overall task duration mean of 33.9 min, which
again was approximately one third of the baseline
level.
GENERAL DISCUSSION
Concepts and findings from the basic behavior
analysis literature stimulated the development of
an innovative intervention for adult noncompliance.
A nonhuman model of behavioral momentum
(Nevin et al., 1983) was useful to predict how
persons with severe developmental disabilities would
respond to low-probability commands under dif137
F. CHARLES MACE et al.
ferent antecedent conditions. Presentation of a sequence of high-probability commands immediately
prior to issuance of a task request increased the
probability of compliance for some subjects and
reduced compliance latency and task duration for
other subjects. The precision of our analogy with
Nevin’s behavioral momentum, as well as the fit
between behavioral and physical momentum, may
at some point prove to be less than perfect. However, there may be applied and theoretical value in
viewing behavioral momentum as a distinct phenomenon.
The applied value of the analogy lies in its inspiration of innovative intervention procedures. The
high-probability command sequence used in the
present research seemed to establish a series of responses with high behavioral mass. Commands that
have a high probability of occasioning compliant
responses are, we assume, discriminative stimuli for
behavior that has produced reinforcement in the
past. Although the exact reinforcers and their schedules were not analyzed in this research, the subjects
quickly and reliably responded to the high-p requests and, anecdotally, seemed to enjoy doing so.
Thus, it appears that by manipulating the type of
command issued it is possible to reliably evoke
behavior that effects reinforcement and, accordingly, establish a pattern of responding that has a
relatively high behavioral mass. Interpreted from a
behavioral momentum framework, increased compliance to low-p commands following the high-p
sequence may illustrate resistance to change in the
face of altered environmental conditions (i.e., when
a low-p command is presented).
The results of Experiments 2, 3, and 4 offer
some preliminary support for the appropriateness
of the behavioral momentum analogy. First, Experiment 3 illustrated that when reinforcement rate
was reduced by increasing the interval between the
high-p sequence and low-p command, compliance
to low-p commands decreased. This effect is predicted by the behavioral momentum analogy because decreases in reinforcement rate should produce corresponding decreases in resistance to change
or behavioral momentum. Second, in the attention
control conditions of Experiments 2 and 4, pleasant,
neutral statements delivered to subjects on the same
schedule as the high-p commands failed to alter
compliance to low-p requests. This suggests the
important role of the high-p command, which presumably serves as a discriminative stimulus for behavior maintained by high rates of reinforcement.
We should emphasize, however, that these analyses
are preliminary. Further research should directly
manipulate reinforcement rates and intervals between high-p commands and compare reinforcement associated with neutral statements versus
high-p commands.
Several dimensions of the present experiments
differed from the basic work of Nevin et al. (1983).
First, Nevin et al. directly manipulated subjects’
access and rate of reinforcement. By contrast, we
manipulated discriminative stimuli assumed to be
correlated with reinforcement (i.e., high-p commands). Thus, without direct manipulation of reinforcement rates we must be cautious in our condusion that the high-p procedure produced a
relatively high behavioral mass. Second, if we can
assume that reinforcement rates were manipulated
indirectly with the high-p commands, the reinforcement schedule for compliance to high-p commands approximated a CRF schedule. This differed
from Nevin et al.’s work in which resistance to
change was examined under different, and highly
intermittent, variable-interval (VI) schedules of reinforcement. Finally, Nevin et al. used a two-component, multiple-schedule procedure in which each
component (i.e., reinforcement schedule) was correlated with a different discriminative stimulus (i.e.,
a red or green response key). Subjects’ rate of responding was controlled by the discriminative stimuli only via their associated reinforcement schedule.
In the present experiments, the rate of compliant
responding was controlled directly by the number
of high-p and low-p commands issued.
Given the differences between Nevin et al.’s
(1983) basic research and the present attempt to
apply these concepts in the high-p command procedure, alternative explanations for the results of
the present research merit discussion. One plausible
account may be stimulus generalization, which refers to the spread of the effects of reinforcement to
138
BEHAVIORAL MOMENTUM
stimulus conditions that have not been associated
with reinforcement (Catania, 1984). Thus, stimulus generalization indicates a lack of stimulus control. When the subjects in the present experiments
complied with low-p commands following the
high-p command sequence, it could be said that
compliance to high-p commands generalized to
low-p commands and that the stimulus control of
high-p commands was weak. However, as Nevin
(1974) noted, stimulus generalization appears to
be an instance of resistance to change rather than
an alternative to it. During extinction, resistance to
change is greatest at the training stimulus and decreases as the test stimulus departs from the training
stimulus (Nevin, 1974, p. 406). Thus, the antecedent presentation of high-p commands may
weaken the distinction between high- and lowprobability commands, thereby increasing resistance to change and inducing stimulus generalization.
Our results also bear some resemblance to the
effects reported in the generalized imitation literature. Several studies have shown that, following
imitation training, subjects made imitative responses to unreinforced models (e.g., Baer & Sherman, 1964; Brigham & Sherman, 1968). Further,
the probability of imitation to unreinforced models
increased when unreinforced models were interspersed among models that were reinforced (Peterson, 1968) and decreased when discrimination
between reinforced and unreinforced models was
facilitated (Burgess, Burgess, & Esveldt, 1970). It
may be possible to view the present findings in this
context. The dose temporal contiguity (5 s) between the high-p commands and the low-p command (i.e., interspersal) may have facilitated compliance to low-p commands whose historical
association was presumably with relatively weak
reinforcement. In Experiment 3, extending the IPT
interval to 20 s may have induced discrimination
between high-p and low-p commands, resulting in
lower percentages of compliance to low-p commands. These speculations could be tested by randomly interjecting a low-p command in the high-p
sequence and introducing stimuli antecedent to the
low-p command that may enhance its discrimination (e.g., verbal statements or different experimenters correlated with different command types).
Future investigations of the high-p command
sequence and/or applications of behavioral momentum could improve on some aspects of the
methodology used in these experiments. First, all
sessions were conducted by an experimenter who
was aware of the experimental hypotheses. Where
possible, staff who are uninformed of the experimental hypotheses should conduct sessions to avoid
possible expectation effects and to assess the practical value of the procedures for applied settings.
Second, as a novel intervention, the acceptability
of the high-p command procedure should be assessed by those who use it and observe its use. The
topography of high-p requests may need to be
altered to be consistent with the subject’s age and
functioning level to gain widespread acceptance of
the procedure. Finally, general conclusions regarding the comparative efficacy of the high-p procedure
and the contingency management intervention (Experiment 5) should be made with caution. The
degree of effectiveness of the contingency management procedure may have been influenced by the
level at which the criterion was set. Perhaps a lower
criterion would have resulted in shorter task durations and represented an optimally effective representation of contingency management (Van Houten, 1987).
Finally, we hope that the present findings will
stimulate additional research on the use of high-p
command sequences as well as investigations of
behavioral momentum in applied settings. Conceivably, modifications could be made to the high-p
command procedure that would make it applicable
to a range of target behaviors and populations. In
addition to studies with an applied focus, more
research is needed to establish the appropriateness
of the behavioral momentum analogy. Specifically,
more experiments are needed that directly manipulate variables affecting behavioral mass and examine their relationship to the degree of persistent
responding in applied settings. Enthusiasm for the
applied value of the behavioral momentum analogy
must await the outcome of these studies. However,
at the very least, we must credit the heuristic value
139
140 F. CHARLES MACE et al.
of Nevin et al.’s (1983) basic research in stimulating the development of an innovative treatment
for noncompliance.
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ReceivedJune 10, 1987
Initial editorial decision August 25, 1987
Revisions received November 24, 1987; January 8, 1988
Final acceptance March 8, 1988
Action Editor, Brandon F. Greene