Testing the global-slowing hypothesis: Are alcohol's effects on human performance process-specific or task-general?

ELSEVIER Acta Psychologica 92 (1996) 59-78

acta psychologica

Testing the global-slowing hypothesis: Are alcohol's effects on human performance

process-specific or task-general?

Colin Ryan *, Katherine Russo, Janet Greeley Department of Psychology and Sociology, School of Behavioural Sciences, James Cook University,

Townsville, Q4811, Australia

Received 27 June 1994; revised 22 November 1994; accepted 7 December 1994

Abstract

In an interesting recent meta-analysis, Maylor and Rabbitt (1993) suggested that alcohol's effects on human performance may not be process- or stage-specific, but reflect a general, undifferentiated, cognitive slowing. According to this view, performance is globally slowed by a constant multiplicative fraction (b), such that the longer a process takes without alcohol on board (a - ), the more it will be slowed by alcohol (a + ). In summary: RT~+ = b(RTa_). In this sense, the effects of alcohol are determined simply by the duration of a process or stage - not by its function or content - and attempts to map the effects of alcohol to specific cognitive operations are essentially futile. This global-slowing hypothesis entails, then, (i) that the function relating RT a + to RT a_ will be linear and increasing; (ii) that the value of b will be significantly greater than 1.0; and (iii) that all experimental factors which increase the complexity (hence, duration) of a task or stage will interact with alcohol. In this study we tested the global-slowing hypothesis directly using f ixed set, varied set and concurrent sets item-recognition paradigms. All three tasks showed convincing additivity between alcohol and other key experimental factors which affect response latency (e.g., setsize, response type); there was no hint of any of the spectrum of significant interactions predicted by the global-slowing hypothesis. A meta-analysis of varied set latencies, analogous to Maylor and Rabbitt's, yielded a reasonably linear alcohol/no-alcohol function, but with a slope constant (b) less than 1.0. In all, the data provided little support for the global-slowing hypothesis.

* Corresponding author. E-mail: [email protected], Fax: + 61 77 695435, Tel.: + 61 77 815449.

0001-6918/96//$15.00 © 1996 Elsevier Science B.V. All rights reserved SSDI 0001-691 8(94)0005 9-X

60 C. Ryan et al. / Acta Psychologica 92 (1996) 59-78

PsyclNFO classification: 2340

Keywords: Reaction time; Additive factor method; Alcohol; Memory scanning; Global-slowing hypothesis

1. Introduction

Alcohol, given in sufficient doses, has been shown to affect human performance in a wide variety of perceptual-cognitive and motor tasks. Alcohol-induced performance deficits have been demonstrated, for instance, in stability of stance (e.g., Franks et al., 1975, Franks et al., 1976); visual and auditory simple reaction time (e.g., Franks et al., 1975, Franks et al., 1976; Taberner, 1980); visual and auditory choice reaction time tasks (e.g., Rundell and Williams, 1977; Linnoila, 1973; Franks et al., 1975; Attwood, 1978; Landauer and Howat, 1982); visual vigilance (Erwin et al., 1978; Avant, 1990); verbal retention (Jones, 1973); compensatory tracking with and without divided attention (e.g., Moskowitz and Burns, 1981; Landauer and Howat, 1982); character recognition (Huntley, 1972); and simulated and actual driving (e.g., Dott and McKelvey, 1977; Laurel, 1977). 1

At extreme blood alcohol concentrations (BACs), of course, human performance in any and every task will be adversely affected - if only as an individual approaches a comatose state.

Two issues are important in this context. First, alcohol and performance research has tended to be task-predicated rather than process- or operation-predicated. The result is an extensive catalogue of tasks known to be more-or-less affected by alcohol, but relatively little useful generalisation or theoretical integration. A second, related, point is that most of the data are scarcely surprising. They simply document what is known a priori to be the case: alcohol in sufficient doses affects performance. There is a clear need to meaningfully interpret rather than to simply detect alcohol's effects.

One alternative to the atomistic cataloguing of alcohol-affected tasks is to focus on their constituent processes or operations - the task substrate. These underlying operations are, arguably, the building blocks of many complex skills. By establishing which component operations are susceptible to target factors (e.g., alcohol, fatigue, practice) we advance the cause of a more general, task-independent, theory of human performance and of the impact of alcohol upon it. The feasibility of this approach depends, of course, on being able to meaningfully and reliably parse tasks into their constituent operations. The additive factor method of Sternberg (1969) addresses this end. It would appear to promise interesting insights into the nature and locus of alcohol's effects on human performance (to the extent that alcohol is additive or interactive with factors

I For reviews of the effects of alcohol on performance see e.g., Jellinek and McFarland (1940), Carpenter (1962), Wallgren and Barry (1970), Birnbaum and Parker (1977), Howat et al. (1991).

c. Ryan et aL /Acta Psychologica 92 (1996) 59-78 61

known to affect identified stages) and, in turn, to contain (perhaps) the seeds of an integrated task-general theory of those effects. 2

In the past decade or so, a good deal of constructive effort has gone into extending and refining stage and other information-processing models, to take account of performance changes due to, for instance, arousal, ageing, stress, fatigue, emotion, attention, psychopathology, maturation, practice, circadian variations, psychoactive drugs - so- called energetical factors (see e.g., Sanders, 1983, Sanders, 1986, Sanders, 1990; Gopher and Sanders, 1984; Humphreys and Revelle, 1984; Molenaar and Van der Molen, 1986; Miller, 1990; Hockey et al., 1986; Van der Molen et al., 1987) - and the present study should be seen in that context. In essence, there has been a concerted move to extend formal 'd ry ' information-processing models to incorporate biological- motivational, intensive, factors in performance, leading to the development of so-called cognitive energetics (see e.g., Miller, 1990), a resurgence of interest in additive factor logic, and detailed scrutiny of its underpinning assumptions (e.g., Miller, 1988, Miller, 1990; Sanders, 1990; Molenaar and Van der Molen, 1986; Molenaar, 1990). At the heart of the cognit ive-energetic model is the requirement that the effects of so-called suboptimal factors (e.g., barbiturates, amphetamines, sleep loss, alcohol) are selective and stage specific rather than task-general (see e.g., Sanders, 1983; Van der Molen et al., 1987).

While the additive factor approach to understanding alcohol 's effects on performance appears promising in principle, recent meta-analyses by Maylor and Rabbitt (1993) suggest that the search for selective stage-specific alcohol effects is unlikely to be successful in practice since they don ' t exist. The present study experimentally checks that possibility.

Maylor and Rabbitt (1993) argue - by analogy with work on ageing by Cerella ( 1985, Cerella (1990) and child development by Kail (1991) - that alcohol simply slows cognitive processing globally, that specific stages or processes are not uniquely affected by alcohol. It is important to note that their argument is not with stage theories per se, but with the notion of uniquely selective, stage-predicated, alcohol effects. This idea is

2 See Huntley (1972); Tharp et al. (1974) and Oborne and Rogers (1983), who have invoked the additive factor logic with somewhat mixed results. Tharp et al., for example, found that while (auditory) stimulus quality did not interact with alcohol, stimulus-response compatibility did, and interpreted this as evidence for alcohol's effect on response selection-organisation rather than stimulus preprocessing. There are a number of difficulties with the Tharp et al. studies. First, in their Experiment 1, they inappropriately apply Sternberg's additive factor logic to error analyses (they did not measure response latencies). Secondly, in their Experiment 2 - when the logic is correctly applied to RT - there was no significant effect of the drug in the pertinent analysis. In their Experiment 3, where there was a significant drug effect and the logic was correctly applied to RT, they report a highly significant (but, in their terms inexplicable) negative interaction between stimulus discriminability and stimulus-response compatibility which - in the context of their overall analysis - compromises the absence of a significant drug X discriminability interaction on which they rule out an alcohol effect on stimulus preprocessing. While their application of the additive factor logic to this class of problems was less than optimal and their data were equivocal, Tharp et al. correctly identified the potential of the approach.


particularly attractive in that it is - in and of itself - an integrative, task-independent, account of alcohol 's effects.

In essence, the global-s lowing hypothesis posits that alcohol slows human performance by a constant multiplicative factor, such that the longer the duration of a processing stage, the greater the effect of alcohol upon it. Thus:

RTa+ = b (RT~_) ,

where R T a + is mean reaction time with alcohol on board, R T a_ is mean reaction time without alcohol, and b is a multiplicative constant greater than 1.0. Thus, the global- slowing hypothesis emails that R T a + will be an increasing linear function of RTa_. In their meta-analysis, Maylor and Rabbitt plotted RTa+ as a function of R T a_ for a spectrum of 25 conditions from their choice reaction time studies (see Maylor and Rabbitt, 1993, Table 1). The best-fitting linear function:

RTa+ = 1 . 1 2 R T a _ - 17.85

accounted for a compelling 99.7% of the observed variance. The slope constant of 1.12 was significantly greater than 1 and, accordingly, consistent with the global-slowing hypothesis. The zero intercept ( - 1 7 . 8 5 ) , disconcertingly, was significantly less than

3 zero. According to the global-slowing account of alcohol 's effects on performance, the

pivotal question becomes not which stages of processing are perturbed but h o w much

process ing each entails: factor interactions (like setsize X alcohol) - which might be held to index alcohol 's selective effects on a particular processing stage like serial scanning - are attributed to simple global (non-specific, stage-general), alcohol-induced slowing. Thus, increases in setsize increase task complexity (hence duration) and alcohol extends that duration by a constant multiplicative factor. According to this view, stage duration not stage content determines alcohol 's effects on performance. This line of argument is particularly plausible in the domain of ageing where, as Maylor and Rabbitt (1993, p. 302) note, age deficits appear at virtually every stage of processing (see Salthouse, 1985, Salthouse, 1991). This is not to suggest that meta-analyses in this area have been without their critics (see Bashore et al., 1989; Laver and Burke, 1993; Molenaar and Van der Molen, 1994; Bashore, 1994), and we will argue later that alcohol-predicated global-slowing meta-analyses are somewhat problematical as they stand.

A substantial virtue of the global-slowing hypothesis is that it makes at least three clear cut predictions: (i) that any factor which significantly affects RT will interact with

3 As Maylor and Rabbitt (1993, p. 303) concede, it is difficult to interpret significantly negative intercepts within the context of the global-slowing hypothesis. They note a similar finding in the Maylor et al. (1992) reanalysis of Huntley's (1972, 1974) data, where the intercept of the alcohol/no-alcohol plot was -91.49 msec. In the same vein, Maylor and Rabbitt (1993, Fig. 1) re-plotted the Haut et al. (1989) data. The intercept in that case was -34.64 msec. For a discussion of the issues here see Maylor and Rabbitt (1993, pp. 311-312).

C. Ryan et al. / Acta Psychologica 92 (1996) 59-78 63

alcohol; (ii) that the function relating RT a + to RTa_ under various conditions will be convincingly linear; and (iii) that function will have a slope constant significantly greater than 1.0. Those predictions were checked directly using three item-recognition paradigms: the fixed-set and varied-set tasks of Sternberg (1966) and the concurrent-sets

task of Forrin and Morin (1969). We sought, also, to check whether additive factor logic would yield a similar (that is, task-general) interpretation in all three paradigms.

2. Method

2.1. Subjects

Twelve male and 12 female subjects, aged 18 to 41 years (Mean = 22.3; SD = 5.8), participated in the experiment in response to notices placed around the University campus. Subjects were excluded from the study if they: (i) had used prescription or non-prescription drugs during the previous month; (ii) were currently undergoing medical or psychological/psychiatric treatment; (iii) had previously sought treatment for alcohol problems; (iv) were smokers; and (v) were heavy caffeine drinkers (5 + cups/day). Additionally, all subjects were required to have previously consumed at least five standard drinks in one hour without becoming ill.

Subjects were asked to have breakfast 2 hours prior to their arrival to the laboratory; and to refrain from: (i) alcohol or drug use for 24 hours prior to testing, and (ii) drinking coffee after breakfast. Subjects were pseudo-randomly assigned to the Alcohol (A) and Placebo (P) groups (6 females and 6 males in each). All subjects expected to receive alcohol. Subjects were paid $35 each for participating in the study.

2.2. Apparatus

Stimulus sequences were generated and displayed, responses were recorded and stored, and reaction times (RTs) were measured in milliseconds (msec) using custom software routines driving 50 Mhz DX-486 PCs. Stimuli were proportional 2 cm high white letters or digits on a blue ground presented on a standard super VGA monitor. All were clearly legible from the viewing distance of approximately 50 cm. Responses were registered via adjacent numberpad keys.

Subjects in the Group A received 1.0 g /kg body weight of alcohol in the form of 37.2% alc/vol vodka diluted with fresh orange juice in the ratio of 3 : 1 by volume. Group P subjects received an equivalent amount of pure fresh orange juice with 5 ml of vodka floated on top immediately prior to serving. Subjects did not observe preparation of the respective beverages.

2.2.1. Testing schedule

Each subject underwent three distinct testing cycles, each consisting of three different item-recognition tasks: (i) a varied-set (VS) task; (ii) a fixed-set (FS) task; and (iii) a concurrent-set (CS) task. Task order was counterbalanced across subjects and held fixed across cycles for a given subject. Within tasks, trials were blocked by setsize, and

64 c. Ryan et al. / Acta Psychologica 92 (1996) 59-78

setsize-order was fully counterbalanced across subjects to obviate confounding setsize- order effects.

2.2.1.1. Varied set task. Positive sets consisted of 2, 4 or 6 items selected at random without replacement from the pool of digits 0-9. There were 60 trials per setsize block [30 positive ( yes ) and 30 negative (no)]. In this task, and the other two, the matching item on positive trials was, within a block, drawn equally often from each serial position of the memory set. On negative trials, probes were selected at random from the residual (non-positive) digits. Thirty complete repetitions of the within-subjects factor combina- tions (Setsize, Response Type) were used in each cycle (3 X 2 X 30 = 180 trials/cycle).

Thus, a 2 X [3 X 2 X 30] design examined the effects of Alcohol, Setsize, Response Type and Repetitions respectively on RT and Errors.

2.2.1.2. Fixed set task. Twenty-four letters of the alphabet were randomly assigned to separate positive and negative pools of 12 items for each subject (the letters O and I were excluded because of their confusability with digits 0 and 1). Nested sets of 2, 4, 6, 8, 10 or 12 letters were constructed from the positive pool. Negative probes were chosen at random, with replacement, from the negative pool. Fifteen repetitions of the within- subjects factor combination were used in each cycle (6 X 2 X 15 = 180 trials/cycle). Thus, a 2 X [6 X 2 X 15] between-within design examined the effects of Alcohol, Setsize, Response Type and Repetitions on RT and Errors.

2.2.1.3. Concurrent sets task. On each trial, subjects were required to decide if a probe item were a member of a newly defined varied set, or a previously learned fixed set. There were 2, 4, or 6 items in the varied set and 2, 4, 6, 8, 10 or 12 items in the (nested) fixed sets. In essence, we combined the FS and VS tasks, outlined above, into a single task. In this case, a 2 X [3 X 6 X 2 X 5] between-within design examined the effects of Group, Varied Setsize, Fixed Setsize, Response Type and Repetitions on RT and Errors respectively.

2.3. Procedure

2.3.1. Testing cycles Each of the three testing cycles lasted approximately 90 minutes. Cycle 1 (pretest)

was followed by a 20 minute break, during which subjects began consumption of the appropriate prepared beverage. Consumption of the beverage was completed during breaks in the Cycle 2 testing regime. Consumption took a total of 40 minutes in all. Since BAC was rising for Group A during Cycle 2 it is referred to (for convenience) as the Ascending Cycle. A 45 minute break was given between Cycles 2 and 3, during which a standard light lunch was served. Typically, Group A's BACs were falling during Cycle 3 and this is termed the Descending Cycle.

2.3.2. Cycle 1 and 2 BAC measurements Blood Alcohol Concentration was measured for both groups at the beginning of each

task, and at the end of each setsize block of the VS and FS tasks, using a Drager


Alcotest Type 7410 Breathalyser. In the CS task, BAC was sampled at the end of each FS block. Subjects were asked to estimate their BAC each time it was taken. Those estimates were recorded. Subjects received no feedback as to (i) their actual BAC; or (ii) the accuracy of their BAC estimates.

2.3.3. Testing protocols (a) Varied set task. Memory set items were presented at the rate of 1 item/sec. The

inter-trial interval was 1.5 secs in all three tasks. The end of the positive set was signalled by an asterisk, which served also as a probe-warning. Full RT and error feedback was given on each trial. Subjects rested for two minutes at the end of each block of trials.

(b) Fixed set task. Positive sets were learned to a criterion of three complete repetitions without error or hesitation. Each trial consisted of a 1-sec warning asterisk followed by a single probe letter.

(c) Concurrent Sets Task. A fixed positive set of letters was learned prior to each block. Additionally, on each trial, subjects were presented with a positive varied set of digits. All task parameters were identical to the simple VS task.

2.3.4. Data analysis Response latencies were pooled across repetitions. In the principal analysis, errors

were replaced by the mean RT of the correct responses in the appropriate cell. Response latency and errors were analysed separately. All trend lines were fitted by least-squares estimation. Across all tasks and cycles, subjects responded incorrectly on 11.9% of trials.

3. Results and discussion

We report the results of separate split plot analyses of variance on RTs for each cycle of each task, in turn. Mean RT data for each cycle of each task are summarised in Table 1.

3.1. Varied set task

3.1.1. Cycle 1 - Pretest As might be expected, Group P and Group A were equivalent in this pretest (F < 1).

Mean RT is plotted as a function of Setsize with Response Type (yes/no) as parameter in Fig. 1. The effect of Setsize was significant in all three cycles (Cycle 1: F(2,44) = 34.720; Cycle 2: (F(2 ,44)= 12.630; Cycle 3: F(2,44)=40.249; p <0.001 in all cycles), as was the effect of Response Type (Cycle 1: F(1,22)= 43.788; Cycle 2: F(1,22) = 17.709; Cycle 3: F(1,22) = 59.860; p < 0.001 in all cycles). The convincing linearity of the setsize functions in all cycles (accounting for approximately 98% of the variance in both positive and negative response latencies), and the absence of significant interactions between Setsize X Response Type (Fs < 1 in Cycles 1 and 2; F(2,44)= 3.061; p > 0.05 in Cycle 3), is generally consistent with the serial-exhaustive scanning

66 C. Ryan et al. /Acta Psychologica 92 (1996) 59-78

Table 1 Mean reaction time (msec) for each paradigm, setsize and testing cycle. Latencies are pooled across alcohol and placebo groups in the No-Alcohol Pretest (Cycle 1) where, predictably, there were no significant between-group differences in performance. In the two cycles where half the subjects had alcohol on board (Cycles 2 and 3), alcohol and placebo group latencies are shown with and without parentheses, respectively. CS [VS] refers to the varied-set component of the concurrent-sets task and CS [FS] denotes the fixed-set component of the concurrent-sets task

Paradigm Setsize No-alcohol pretest Ascending BAC Descending BAC Cycle 1 Cycle 2 Cycle 3

VS 2 535 494 (650) 477 (526) 4 620 542 (707) 547 (591) 6 687 608 (787) 595 (660)

FS 2 539 526 (621) 499 (549) 4 587 554 (662) 539 (588) 6 671 620 (753) 563 (666) 8 672 615 (820) 616 (777)

10 727 682 (908) 665 (836) 12 797 713 (968) 651 (792)

CS [VS] 2 833 710 (915) 664 (768) 4 884 750 (903) 665 (802) 6 925 821 (949) 726 (777)

CS [FS] 2 801 694 (859) 649 (848) 4 824 735 (872) 649 (833) 6 858 743 (921) 697 (840) 8 911 778 (965) 712 (894)

10 909 768 (898) 697 (931) 12 980 846 (1018) 707 (1048)

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C. Ryan et a l . / Acta Psychologica 92 (1996) 59-78 67

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model of Sternberg (1966). 4 The mean scanning rate of 33.3 msec per item over the three cycles is virtually identical to that reported by Sternberg (1966). No other interactions approached significance (F < 1). In effect, then, Sternberg's seminal findings were nicely replicated in all three cycles.

3.1.2. Cycle 2 - Ascending BAC

Mean response time was significantly slower in the Alcohol group (F(1,22) = 17.43; p < 0.001) than the Placebo group. Mean RT is plotted as a function of Setsize, with Alcohol (alcohol/placebo) as parameter in Fig. 2. s

There was no hint of a significant interaction of Setsize and/or Response Type with Alcohol (Fs < 1); alcohol had no effect on scanning rate or response selection per se. The 123.7 ms intercept shift for Group A indicates (but does not conclusively demonstrate) that the locus of alcohol's effects was most likely the remaining non-scanning components of the task, (arguably) stimulus preprocessing and/or response execution.

The additivity of the effects of Alcohol, Response Type and Setsize in this context poses substantial problems for the global-slowing hypothesis.

4 Several researchers have questioned whether the Sternberg paradigms indeed entail a serial-exhaustive scan of the memory set. The issue is not pivotal here: our principal concern is whether alcohol's effects are stage-specific, not with the precise specification of the processes corresponding to each of the constituent stages per se. For reviews of the issue see, e.g., Townsend and Ashby (1983) and Logan and Stadler (1991).

5 Alcohol's effects are demonstrated, here, via between-groups contrasts for each cycle separately. A reviewer suggested that, strictly, Cycle 1 (no-alcohol) and Cycle 2 (alcohol) should be formally contrasted, to show that the between groups differences are reliably larger after alcohol than before. When reanalysed in this way, both VS and FS data showed significant Cycles X Alcohol interactions (VS: F(1,22)=9.432; FS: F(1,22)=4.982; ps < 0.05 in both cases), attesting to the fact that the between groups differences in performance were indeed reliably greater in Cycle 2.


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Fig. 3. F8 Cycle 1 (Pretest). Mean Reaction Time as a function of Setsize with Response Type as parameter. Errors Percent is plotted as a function of Setsize with Error Type as parameter in the lower portion of the figure.

3.1.3. Cycle 3 - Descending BAC

As mean BAC diminished, the effect of Alcohol on performance became marginal ( p = 0.10), with the intercept difference between the i tem-recognit ion functions for the two groups reduced to approximately 37 ms. Again, there was no hint of an Alcohol X Setsize interaction, or an Alcohol X Response Type interaction ( F s < 1).

3.1.4. Summary o f VS data

The data provide strong evidence against the global-slowing hypothesis: Setsize and Response Type significantly affect response latency, but do not interact with Alcohol. The effects of alcohol appear, then, to be stage-specific.

3.2. Fixed set task

3.2.1. Cycle 1 - Pretest

As expected, neither the effect of Alcohol nor any of its interactions approached significance in this baseline phase ( F < 1). As in the VS task, there were significant effects of both Setsize and Response Type across all three cycles of the FS task ( p < 0.01 in all cases) and no hint of an interaction between them ( F s < 1). So, as well as being convincingly linear, the functions were statistically parallel throughout - consistent with a serial-exhaustive scan. Mean RT is plotted as a function of Setsize in Fig. 3 with Response Type as parameter. Positive and negative latencies are well described by linear functions in all three cycles, on average accounting for about 95% of the observed variance in RT.

3.2.2. Cycle 2 - Ascending BAC Alcohol significantly slowed responding ( F ( 1 , 2 2 ) = 10.09; p < 0.01). Most impor-

tant, none of the factor interactions with Alcohol approached significance ( p > 0.05).

c. Ryan et al. / Acta Psychologica 92 (1996) 59-78 69

3.2.3. Cycle 3 - Descending BAC While performance was still significantly affected by Alcohol during Cycle 3

(F(1,22) = 8.190; p < 0.01), none of the factor interactions with Alcohol was significant ( p > 0.05).

3.2.4. Summary o f FS data In general, the data are consistent with the conclusion reached with the VS task:

alcohol has no effect on scanning or response selection stages in FS item-recognition. Again, the consistent additivity of Alcohol and Setsize and Alcohol and Response Type is evidence against the global-slowing hypothesis.

3.3. Concurrent sets task

3.3.1. Cycle 1 - Pretest Predictably, no difference in performance was found between the groups in this

pretest phase ( F < 1). Fixed Setsize ( F ( 5 , 1 1 0 ) = 5.31; p < 0.001) and Varied Setsize (F(2,44) = 6.41; p < 0.01) significantly affected response latencies, however there was no hint of interaction between these two factors ( F < 1). This suggests that the tasks run off concurrently and independently - without interference. 6 Neither Setsize factor interacted with Response Type (Fs < 1), evidence again for independent serial exhaustive scans of both sets - even when they are concurrently relevant and simultaneously

probed. While there was no hint of interaction between the concurrent scans, there was,

however, a marked cost of concurrency per se. Those costs are summarised in Fig. 4a and Fig. 4b, where the baseline (single-task) VS and FS data are contrasted with the concurrent FS and VS conditions respectively.

There was an intercept shift of 326.7 msec in the VS item-recognition function when subjects had to keep a concurrently relevant fixed set in mind (Fig. 4a) and a 265.8 msec increase in the intercept of the FS function (Fig. 4b). Both the FS and VS functions remained convincingly linear in this CS paradigm, accounting for better than 95% of the observed variance in RT in both cases.

If the concurrent scans of the fixed and varied set are indeed independent (non-inter- feting) - which is the commentary provided by the absence of any interactions between fixed and varied setsize - there is no reason to expect that the slopes of the single-task and concurrent-task functions are statistically different. This was confirmed by split-plot anovas checking the effects of Alcohol, Task (concurrent-task vs. single-task), Setsize and Response Type for fixed sets and varied sets separately. The Task main effect was significant for both the varied-set (F(1,22) = 65.639; p < 0.001) and fixed-set (F(1,22) = 66.518; p < 0.001) comparisons. Moreover, there was no hint of a Task X Setsize interaction in either analysis ( F s < 1), consistent with the view that set-concurrency

6 Corballis (1986) has questioned whether the memory searches are indeed concurrent, rather than serial. More recently, Corballis et al. (1989) concede the plausibility of Ryan's concurrent-scans hypothesis.


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Fig. 4. (a) CS Task Cycle 1 (Pretest). Mean Reaction Time as a function of the number of items in the varied set in the single-set (VS) and the concurrent-sets (CS) paradigms. Errors Percent is plotted as a function of Setsize with Task (VS/CS) as parameter in the lower part of the figure. (b) CS Task Cycle 1 (Pretest). Mean Reaction Time as a function of the number of items in the fixed set in the single-set (FS) and the concurrent-sets (CS) paradigms. Errors Percent is plotted as a function of Setsize with Task (VS/CS) as parameter in the lower part of the figure.

precipi ta ted intercept shifts but had no impac t on the rate o f scanning through ei ther

m e m o r y set ( for d iscuss ion o f other possible interpretat ions o f concurrent-sets data o f

this k ind see Corball is , 1986; Corbal l is et al., 1989).

3.3.2. Cyc le 2 - A s c e n d i n g B A C

Alcoho l s ignif icant ly impai red pe r fo rmance ( F ( 1 , 2 2 ) = 7.36; p < 0.05). Whi le the

effects o f Var ied Sets ize ( F ( 2 , 4 4 ) = 9.99; p < 0.001), F ixed Sets ize ( F ( 5 , 1 1 0 ) = 5.82;

c. Ryan et aL / Acta Psychologica 92 (1996) 59-78 71

p < 0.001) and Response Type (F(1,22) = 8.782; p < 0.01) on RT were all significant, none of those factors significantly interacted with Alcohol and none of the higher-order Alcohol interactions was significant. This absence of factor interactions with Alcohol is strong evidence against the global-slowing hypothesis.

Varied Setsize and Fixed Setsize did not interact, and neither factor interacted significantly with Response Type (F < 1 in all cases): processing of the concurrent memory sets remained effectively independent, and both scans were apparently serial and exhaustive, even when subjects had received a substantial dose of alcohol. Again, in this quite complex variant of the item-recognition paradigm, there is evidence that alcohol does not affect scanning or response selection. The data are generally consistent with the notion that alcohol affects stimulus preprocessing and/or response execution (as indexed by the zero intercept shifts) but, since we did not manipulate factors known to affect those stages (stimulus quality, S-R probability) positive identification of the precise locus of alcohol's effects was precluded. All three tasks yield similar commen- taries, providing some encouragement for the notion, advanced earlier, that additive factor logic may be useful in trying to frame less task-specific accounts of the effects of alcohol on performance.

3.3.3. Cycle 3 - Descending BAC Alcohol continued to significantly slow performance as BACs fell (F(1 ,22)= 8.44;

p < 0.01). However, none of the first- or higher-order interactions with Alcohol was significant, a finding generally consistent with selective stage-predicated effects of alcohol on performance and at odds with global-slowing.

3.3.4. Summary of CS data The additivity of Alcohol with all other experimental factors in Cycles 2 and 3 is

good evidence against global slowing.

3.4. Error analysis

As might be expected, the was no hint of an effect of Alcohol on errors in the Pretest phase of any of the three tasks (F < 1). Errors increased significantly with Setsize in all three cycles of both the VS (Cycle 1: F(2,44) = 32.168; Cycle 2: F(2,44) = 51.197; Cycle 3: F(2,44) = 28.438; p < 0.001 in all cycles) and FS tasks (Cycle 1:F(5,110) = 7.046; Cycle 2: F(5,110)= 14.169; Cycle 3: F(5,110)= 14.045; p <0 .0 0 1 in all cycles), and as a function of Fixed Setsize in all three cycles of CS item-recognition (Cycle 1: F(5,110)=2.498; Cycle 2: F(5,110)=5.691; Cycle 3: F(5,110)=5.737; p < 0.05 in all cycles). Varied Setsize did not affect error rates in Cycles 1 or 2 of the CS task (Fs < 1), but errors increased with Varied Setsize in Cycle 3 (F(2,44) = 5.462; p < 0.01).

Alcohol significantly increased error rates in the ascending BAC cycle of all three tasks, but only the CS paradigm (where processing load was maximal) showed a significant effect of Alcohol on Errors in Cycle 3 (p < 0.05). In general, Alcohol increased Misses (incorrect negatives), while the False Alarm (false positives) rate remained effectively invariant. The key Alcohol x Error Type interactions were signifi-

72 C. Ryan et a l . / Acta Psychologica 92 (1996) 59-78

cant in the Ascending BAC cycle of all three paradigms and in Cycle 3 of the CS task (ps < 0.05). This increase in Misses (without a corresponding reduction in the FA rate) suggested a progressive, alcohol-related failure to retain memory set items, particularly at larger setsizes. Errors Percent is plotted in the lower portions of all figures.

3.4.1. Variability in response times Separate analyses of variance were carried out on the standard deviations of all

response times for the ascending and descending BAC cycles of each of the three tasks. The performance of subjects with alcohol on board was significantly more variable than that of the placebo group (p < 0.01 in all three tasks). While negative responses were significantly more variable than positive responses in all three tasks (ps < 0.001 in the FS and VS tasks and p0.05 in the CS task), there was no significant interaction with Alcohol. Similarly, variability in response latency increased with Setsize in all three tasks (p's < 0.001 in all cases), but the effects of Setsize and Response Type did not interact significantly with Alcohol. These data provide an interesting confirmation of the commentary provided by the additive factor logic of the RT analysis, being consistent with the view that neither the serial scanning stage nor the response selection stage is affected by alcohol.

It is usual in choice reaction time tasks for there to be a monotonic relationship between mean correct RT and the variability of correct responses. Since alcohol increased mean RT, there was a good chance that it also increased variability. But was that increase greater than the increase in variability to be expected from the increase in the means per se? Did alcohol uniquely increase variability? Maylor and Rabbitt (1987; see also Maylor et al., 1987) suggested that it is useful, in this context, to perform linear regressions of mean RT against standard deviations for the alcohol and no-alcohol conditions, contrasting slope and intercept parameters. The studies of Maylor, Rabbitt and colleagues strongly suggested that alcohol did not uniquely affect RT variability over and above the effect predictable from changes in mean. We explored our data in an analogous manner.

As a baseline, we regressed mean RTs for each subject against standard deviations in the Pretest cycle of both the simple VS and FS tasks (neither group had alcohol at pretest). This yielded an average correlation coefficient of 0.733, confirming the tendency for variability to increase with mean response time (cf. Maylor and Rabbitt, 1987: r = 0.814). Regression analyses were carried out on the Group A and Group P data separately for both the Ascending and Descending alcohol cycles of both tasks. Some interesting alcohol-related differences emerged. Somewhat surprisingly, while the Group A correlation was substantially unchanged with alcohol on board in the Ascend- ing and Descending cycles, the placebo group correlation coefficient reduced substantially by comparison with pretest (Group A: mean r = 0.697 overall; Group P: r = 0.357), and the mean slope of the best-fitting linear function approached zero (0.151). In the Placebo group, means and standard deviations became progressively uncorrelated with extended practice. Given immediate feedback on RT and errors after each response, subjects without alcohol on board appeared to control variability within set bounds irrespective of their actual mean response times. Alcohol brought about a marked decrease in the mean intercept of the regression line across both simple tasks, by


comparison with pretest estimates (Pretest: -231.98 ms; Ascending and Descending Cycles: - 436.6).

3.4.2. Comparisons with Maylor and Rabbitt's meta-analysis A key feature of the global slowing hypothesis is the notion that the function relating

RT~ + to RT,_ should be convincingly linear and have a slope constant greater than 1.0. This should be the case both across tasks and across conditions within a task (see Maylor and Rabbitt, 1993, p. 308-309). Given the fact that Alcohol and Setsize and Response Type were additive in the VS task, for example, it was rather implausible that these conditions might be satisfied. In fact they were not. The least-squares function fit to mean RTs for the appropriate Cycle 1 and Cycle 2 conditions accounted for 86% of the observed variance. While the linear fit was reasonable, the slope constant was only 0.83 (that is, less than 1.0 and, accordingly highly problematic for the global-slowing hypothesis). Moreover, the intercept of that function (127.6 msec) was substantially greater than the zero predicted by the global slowing hypothesis. This simply expresses of course, the already demonstrated fact that alcohol increased mean RT by an additi~'e constant in the VS task, not a multiplicative constant.

So, how might one account for the differences between Maylor and Rabbitt's account and our data? First, we should point out that their RT meta-analysis was based on choice reaction time (CRT) tasks, rather than item-recognition tasks per se. Mean RT typically increases with the log of the number of alternatives in consistent-mapping CRT paradigms (see Briggs, 1974, for a review). Ryan (1981, Ryan (1983) has argued that these approximately logarithmic latency functions are more appropriately described as bi-linear or piecewise-linear, indexing the first-finishing of two concurrent operations (arguably, in the case of consistent-mapping FS item-recognition, a serial scan at small setsizes (Sternberg, 1966), and categorical classification of the probe on the basis of its familiarity at larger setsizes (Atkinson and Juola, 1973; Juola et al., 1971)). Ryan (1981) showed, in this context, (i) how two processes operating in tandem might generate interactions they don't severally show - for instance, if alcohol affected scanning and probe categorisation differently, this could result in significant Alcohol X Setsize interactions, when neither component operation, in isolation, showed any hint of one; and (ii) how other classes of interaction can be generated when the relative efficiency (speed) of component operations changes, for instance, in the course of practice. One consequence of this view is that great care should be exercised when interpreting patterns of factor interaction if the underlying setsize function is negatively accelerated (or if setsize is not varied and its form is not known). All the item-recognition functions we report are compellingly linear, arguably indexing a single serial scan, and there was no hint of any factor interactions with alcohol. The clear challenge to proponents of global slowing would be to demonstrate Alcohol X Setsize and Alcohol X Response Type interactions in conjunction with linear item-recognition (setsize) functions, and linear regressions of RTa+ against RT a_ with slope constants significantly greater than 1 and (at least) non-negative intercepts.

3.4.3. Estimated and actual BAC We were interested a priori to see which of estimated and actual BAC might be a

better predictor of response latency. It turned out that they were highly correlated with


<

0.1

0.08

0.06

0.04

0.02

I I I I I I

CYCLE TWO CYCLE THREE

Blocks of Training

Fig. 5. BAC and Estimated BAC as a function of Blocks of Trials for Cycles 2 (Ascending BAC) and 3 (Descending BAC) of Testing.

each other (r z = 0.979) and, not surprisingly, equally good predictors. The two dose response-curves (RT vs. BAC and RT vs. Estimated BAC) differ only by a linear transform. Estimated and Actual BAC are plotted as a function of Blocks of Training (A, B, C) for Cycles 2 and 3 in Fig. 5.

4. General discussion

The present data provide good evidence for stage-specific localisation of the effects of alcohol on performance in three separate item-recognition paradigms. There are several examples of additivity between alcohol and other experimental factors which significantly affect RT in all three paradigms and, to that extent, the data are clearly at odds with Maylor and Rabbitrs global-slowing hypothesis. Alcohol's effects in the FS and VS tasks, for example, are independent of Setsize. If, as Maylor and Rabbitt argue, increases in setsize increase task complexity (hence duration), their account would predict significant Alcohol × Setsize interactions in both tasks. The present FS and VS data aside, we know of no single-task item recognition studies which have produced reliable interactions between these factors.

In a similar vein, both the FS and VS tasks show no significant Alcohol × Response Type interaction, despite the fact that the Response Type (yes/no) main effect is highly significant. Again, these data are good evidence against the global-slowing hypothesis. They are generally consistent with selective stage-specific effects of alcohol on item recognition performance.


A cautionary note should be added here, however. The present evidence against the global-slowing hypothesis is based, in part, on acceptance of the null hypothesis: stage-specificity is inferred from the absence of significant Alcohol interactions. This difficulty is exacerbated somewhat by the fact that Alcohol is a between-groups factor in the present study. It is unlikely, however, that theoretically interesting Alcohol interactions have been obscured. The bulk of the targeted Alcohol interactions returned F values less than 1. We note here, also, that the error terms used to test the key (non-significant) interactions with Alcohol were the same as used to test the highly significant main effects of Setsize and Response Type. Moreover, there is no support at all for the global-slowing hypothesis in the slope and intercept parameters of the VS function relating latencies with and without alcohol, detailed at the end of Section 3.

As noted above, the lack of significant Alcohol × Response Type interactions in Cycles 2 and 3 of the VS, FS and CS tasks is good evidence against response selection being the locus of the observed alcohol effects. This consistency across paradigms is encouraging for the idea that additive factor logic might be useful in developing task-general accounts of alcohol's effects on performance. On the face of it, the present data are at odds, however, with the conclusion by Huntley (1972, Huntley (1974) that alcohol specifically impairs the response selection stage of information processing. Huntley (1974), for example, had subjects complete a 1 : 1 mapping version of Stern- berg's FS task. It is usual in such tasks (and in the more usual many-to-one version of the task, under certain conditions) for RT to increase with log 2 of setsize. As noted above, there is good evidence, that two distinct concurrent and independent processes are implicated in such tasks, not one (see e.g., Ellis and Chase, 1971; Ryan, 1981, Ryan, 1983) and, accordingly, any one-process interpretation of such logarithmic search functions is likely to be highly problematical.

In summary, then, the present data yield little support for the notion that alcohol simply causes global slowing of cognitive processes. Alcohol-induced performance deficits appear to be stage-specific, in the present tasks at least: stimulus preprocessing and/or response execution seem to be implicated. The consistency of our findings across three item-recognition paradigms provides some encouragement for the idea that additive factor logic may be useful in developing a task-independent model of alcohol's effect on performance.

Acknowledgements

We are grateful to Dr Michael Smithson for his insightful observations on various aspects of this study. Revision of the paper was greatly assisted by the anonymous reviewers and the very constructive guidance of the editor.

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Testing the global-slowing hypothesis: Are alcohol's effects on human performance process-specific or task-general?